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Rulla Alsaedi , Kimberly McKeirnan; Literature Review of Type 2 Diabetes Management and Health Literacy. Diabetes Spectr 1 November 2021; 34 (4): 399–406. https://doi.org/10.2337/ds21-0014

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The purpose of this literature review was to identify educational approaches addressing low health literacy for people with type 2 diabetes. Low health literacy can lead to poor management of diabetes, low engagement with health care providers, increased hospitalization rates, and higher health care costs. These challenges can be even more profound among minority populations and non-English speakers in the United States.

A literature search and standard data extraction were performed using PubMed, Medline, and EMBASE databases. A total of 1,914 articles were identified, of which 1,858 were excluded based on the inclusion criteria, and 46 were excluded because of a lack of relevance to both diabetes management and health literacy. The remaining 10 articles were reviewed in detail.

Patients, including ethnic minorities and non-English speakers, who are engaged in diabetes education and health literacy improvement initiatives and ongoing follow-up showed significant improvement in A1C, medication adherence, medication knowledge, and treatment satisfaction. Clinicians considering implementing new interventions to address diabetes care for patients with low health literacy can use culturally tailored approaches, consider ways to create materials for different learning styles and in different languages, engage community health workers and pharmacists to help with patient education, use patient-centered medication labels, and engage instructors who share cultural and linguistic similarities with patients to provide educational sessions.

This literature review identified a variety of interventions that had a positive impact on provider-patient communication, medication adherence, and glycemic control by promoting diabetes self-management through educational efforts to address low health literacy.

Diabetes is the seventh leading cause of death in the United States, and 30.3 million Americans, or 9.4% of the U.S. population, are living with diabetes ( 1 , 2 ). For successful management of a complicated condition such as diabetes, health literacy may play an important role. Low health literacy is a well-documented barrier to diabetes management and can lead to poor management of medical conditions, low engagement with health care providers (HCPs), increased hospitalizations, and, consequently, higher health care costs ( 3 – 5 ).

The Healthy People 2010 report ( 6 ) defined health literacy as the “degree to which individuals have the capacity to obtain, process, and understand basic health information and services needed to make appropriate health decisions.” Diabetes health literacy also encompasses a wide range of skills, including basic knowledge of the disease state, self-efficacy, glycemic control, and self-care behaviors, which are all important components of diabetes management ( 3 – 5 , 7 ). According to the Institute of Medicine’s Committee on Health Literacy, patients with poor health literacy are twice as likely to have poor glycemic control and were found to be twice as likely to be hospitalized as those with adequate health literacy ( 8 ). Associations between health literacy and health outcomes have been reported in many studies, the first of which was conducted in 1995 in two public hospitals and found that many patients had inadequate health literacy and could not perform the basic reading tasks necessary to understand their treatments and diagnoses ( 9 ).

Evaluation of health literacy is vital to the management and understanding of diabetes. Several tools for assessing health literacy have been evaluated, and the choice of which to use depends on the length of the patient encounter and the desired depth of the assessment. One widely used literacy assessment tool, the Test of Functional Health Literacy in Adults (TOFHLA), consists of 36 comprehension questions and four numeric calculations ( 10 ). Additional tools that assess patients’ reading ability include the Rapid Estimate of Adult Literacy in Medicine (REALM) and the Literacy Assessment for Diabetes. Tests that assess diabetes numeracy skills include the Diabetes Numeracy Test, the Newest Vital Sign (NVS), and the Single-Item Literacy Screener (SILS) ( 11 ).

Rates of both diabetes and low health literacy are higher in populations from low socioeconomic backgrounds ( 5 , 7 , 12 ). People living in disadvantaged communities face many barriers when seeking health care, including inconsistent housing, lack of transportation, financial difficulties, differing cultural beliefs about health care, and mistrust of the medical professions ( 13 , 14 ). People with high rates of medical mistrust tend to be less engaged in their care and to have poor communication with HCPs, which is another factor HCPs need to address when working with their patients with diabetes ( 15 ).

The cost of medical care for people with diabetes was $327 billion in 2017, a 26% increase since 2012 ( 1 , 16 ). Many of these medical expenditures are related to hospitalization and inpatient care, which accounts for 30% of total medical costs for people with diabetes ( 16 ).

People with diabetes also may neglect self-management tasks for various reasons, including low health literacy, lack of diabetes knowledge, and mistrust between patients and HCPs ( 7 , 15 ).

These challenges can be even more pronounced in vulnerable populations because of language barriers and patient-provider mistrust ( 17 – 19 ). Rates of diabetes are higher among racial and ethnic minority groups; 15.1% of American Indians and Alaskan Natives, 12.7% of Non-Hispanic Blacks, 12.1% of Hispanics, and 8% of Asian Americans have diagnosed diabetes, compared with 7.4% of non-Hispanic Whites ( 1 ). Additionally, patient-provider relationship deficits can be attributed to challenges with communication, including HCPs’ lack of attention to speaking slowly and clearly and checking for patients’ understanding when providing education or gathering information from people who speak English as a second language ( 15 ). White et al. ( 15 ) demonstrated that patients with higher provider mistrust felt that their provider’s communication style was less interpersonal and did not feel welcome as part of the decision-making process.

To the authors’ knowledge, there is no current literature review evaluating interventions focused on health literacy and diabetes management. There is a pressing need for such a comprehensive review to provide a framework for future intervention design. The objective of this literature review was to gather and summarize studies of health literacy–based diabetes management interventions and their effects on overall diabetes management. Medication adherence and glycemic control were considered secondary outcomes.

Search Strategy

A literature review was conducted using the PubMed, Medline, and EMBASE databases. Search criteria included articles published between 2015 and 2020 to identify the most recent studies on this topic. The search included the phrases “diabetes” and “health literacy” to specifically focus on health literacy and diabetes management interventions and was limited to original research conducted in humans and published in English within the defined 5-year period. Search results were exported to Microsoft Excel for evaluation.

Study Selection

Initial screening of the articles’ abstracts was conducted using the selection criteria to determine which articles to include or exclude ( Figure 1 ). The initial search results were reviewed for the following inclusion criteria: original research (clinical trials, cohort studies, and cross-sectional studies) conducted in human subjects with type 2 diabetes in the United States, and published in English between 2015 and 2020. Articles were considered to be relevant if diabetes was included as a medical condition in the study and an intervention was made to assess or improve health literacy. Studies involving type 1 diabetes or gestational diabetes and articles that were viewpoints, population surveys, commentaries, case reports, reviews, or reports of interventions conducted outside of the United States were excluded from further review. The criteria requiring articles to be from the past 5 years and from the United States were used because of the unique and quickly evolving nature of the U.S. health care system. Articles published more than 5 years ago or from other health care systems may have contributed information that was not applicable to or no longer relevant for HCPs in the United States. Articles were screened and reviewed independently by both authors. Disagreements were resolved through discussion to create the final list of articles for inclusion.

PRISMA diagram of the article selection process.

Data Extraction

A standard data extraction was performed for each included article to obtain information including author names, year of publication, journal, study design, type of intervention, primary outcome, tools used to assess health literacy or type 2 diabetes knowledge, and effects of intervention on overall diabetes management, glycemic control, and medication adherence.

A total of 1,914 articles were collected from a search of the PubMed, MEDLINE, and EMBASE databases, of which 1,858 were excluded based on the inclusion and exclusion criteria. Of the 56 articles that met criteria for abstract review, 46 were excluded because of a lack of relevance to both diabetes management and health literacy. The remaining 10 studies identified various diabetes management interventions, including diabetes education tools such as electronic medication instructions and text message–based interventions, technology-based education videos, enhanced prescription labels, learner-based education materials, and culturally tailored interventions ( 15 , 20 – 28 ). Figure 1 shows the PRISMA diagram of the article selection process, and Table 1 summarizes the findings of the article reviews ( 15 , 20 – 28 ).

Findings of the Article Reviews (15,20–28)

SAHLSA, Short Assessment of Health Literacy for Spanish Adults.

Medical mistrust and poor communication are challenging variables in diabetes education. White et al. ( 15 ) examined the association between communication quality and medical mistrust in patients with type 2 diabetes. HCPs at five health department clinics received training in effective health communication and use of the PRIDE (Partnership to Improve Diabetes Education) toolkit in both English and Spanish, whereas control sites were only exposed to National Diabetes Education Program materials without training in effective communication. The study evaluated participant communication using several tools, including the Communication Assessment Tool (CAT), Interpersonal Processes of Care (IPC-18), and the Short Test of Functional Health Literacy in Adults (s-TOFHLA). The authors found that higher levels of mistrust were associated with lower CAT and IPC-18 scores.

Patients with type 2 diabetes are also likely to benefit from personalized education delivery tools such as patient-centered labeling (PCL) of prescription drugs, learning style–based education materials, and tailored text messages ( 24 , 25 , 27 ). Wolf et al. ( 27 ) investigated the use of PCL in patients with type 2 diabetes and found that patients with low health literacy who take medication two or more times per day have higher rates of proper medication use when using PCL (85.9 vs. 77.4%, P = 0.03). The objective of the PCL intervention was to make medication instructions and other information on the labels easier to read to improve medication use and adherence rates. The labels incorporated best-practice strategies introduced by the Institute of Medicine for the Universal Medication Schedule. These strategies prioritize medication information, use of larger font sizes, and increased white space. Of note, the benefits of PCL were largely seen with English speakers. Spanish speakers did not have substantial improvement in medication use or adherence, which could be attributed to language barriers ( 27 ).

Nelson et al. ( 25 ) analyzed patients’ engagement with an automated text message approach to supporting diabetes self-care activities in a 12-month randomized controlled trial (RCT) called REACH (Rapid Education/Encouragement and Communications for Health) ( 25 ). Messages were tailored based on patients’ medication adherence, the Information-Motivation-Behavioral Skills model of health behavior change, and self-care behaviors such as diet, exercise, and self-monitoring of blood glucose. Patients in this trial were native English speakers, so further research to evaluate the impact of the text message intervention in patients with limited English language skills is still needed. However, participants in the intervention group reported higher engagement with the text messages over the 12-month period ( 25 ).

Patients who receive educational materials based on their learning style also show significant improvement in their diabetes knowledge and health literacy. Koonce et al. ( 24 ) developed and evaluated educational materials based on patients’ learning style to improve health literacy in both English and Spanish languages. The materials were made available in multiple formats to target four different learning styles, including materials for visual learners, read/write learners, auditory learners, and kinesthetic learners. Spanish-language versions were also available. Researchers were primarily interested in measuring patients’ health literacy and knowledge of diabetes. The intervention group received materials in their preferred learning style and language, whereas the control group received standard of care education materials. The intervention group showed significant improvement in diabetes knowledge and health literacy, as indicated by Diabetes Knowledge Test (DKT) scores. More participants in the intervention group reported looking up information about their condition during week 2 of the intervention and showed an overall improvement in understanding symptoms of nerve damage and types of food used to treat hypoglycemic events. However, the study had limited enrollment of Spanish speakers, making the applicability of the results to Spanish-speaking patients highly variable.

Additionally, findings by Hofer et al. ( 22 ) suggest that patients with high A1C levels may benefit from interventions led by community health workers (CHWs) to bridge gaps in health literacy and equip patients with the tools to make health decisions. In this study, Hispanic and African American patients with low health literacy and diabetes not controlled by oral therapy benefited from education sessions led by CHWs. The CHWs led culturally tailored support groups to compare the effects of educational materials provided in an electronic format (via iDecide) and printed format on medication adherence and self-efficacy. The study found increased adherence with both formats, and women, specifically, had a significant increase in medication adherence and self-efficacy. One of the important aspects of this study was that the CHWs shared cultural and linguistic characteristics with the patients and HCPs, leading to increased trust and satisfaction with the information presented ( 22 ).

Kim et al. ( 23 ) found that Korean-American participants benefited greatly from group education sessions that provided integrated counseling led by a team of nurses and CHW educators. The intervention also had a health literacy component that focused on enhancing skills such as reading food package labels, understanding medical terminology, and accessing health care services. This intervention led to a significant reduction of 1–1.3% in A1C levels in the intervention group. The intervention established the value of collaboration between CHW educators and nurses to improve health information delivery and disease management.

A collaboration between CHW educators and pharmacists was also shown to reinforce diabetes knowledge and improve health literacy. Sharp et al. ( 26 ) conducted a cross-over study in four primary care ambulatory clinics that provided care for low-income patients. The study found that patients with low health literacy had more visits with pharmacists and CHWs than those with high health literacy. The CHWs provided individualized support to reinforce diabetes self-management education and referrals to resources such as food, shelter, and translation services. The translation services in this study were especially important for building trust with non-English speakers and helping patients understand their therapy. Similar to other studies, the CHWs shared cultural and linguistic characteristics with their populations, which helped to overcome communication-related and cultural barriers ( 23 , 26 ).

The use of electronic tools or educational videos yielded inconclusive results with regard to medication adherence. Graumlich et al. ( 20 ) implemented a new medication planning tool called Medtable within an electronic medical record system in several outpatient clinics serving patients with type 2 diabetes. The tool was designed to organize medication review and patient education. Providers can use this tool to search for medication instructions and actionable language that are appropriate for each patient’s health literacy level. The authors found no changes in medication knowledge or adherence, but the intervention group reported higher satisfaction. On the other hand, Yeung et al. ( 28 ) showed that pharmacist-led online education videos accessed using QR codes affixed to the patients’ medication bottles and health literacy flashcards increased patients’ medication adherence in an academic medical hospital.

Goessl et al. ( 21 ) found that patients with low health literacy had significantly higher retention of information when receiving evidence-based diabetes education through a DVD recording than through an in-person group class. This 18-month RCT randomized participants to either the DVD or in-person group education and assessed their information retention through a teach-back strategy. The curriculum consisted of diabetes prevention topics such as physical exercise, food portions, and food choices. Participants in the DVD group had significantly higher retention of information than those in the control (in-person) group. The authors suggested this may have been because participants in the DVD group have multiple opportunities to review the education material.

Management of type 2 diabetes remains a challenge for HCPs and patients, in part because of the challenges discussed in this review, including communication barriers between patients and HCPs and knowledge deficits about medications and disease states ( 29 ). HCPs can have a positive impact on the health outcomes of their patients with diabetes by improving patients’ disease state and medication knowledge.

One of the common themes identified in this literature review was the prevalence of culturally tailored diabetes education interventions. This is an important strategy that could improve diabetes outcomes and provide an alternative approach to diabetes self-management education when working with patients from culturally diverse backgrounds. HCPs might benefit from using culturally tailored educational approaches to improve communication with patients and overcome the medical mistrust many patients feel. Although such mistrust was not directly correlated with diabetes management, it was noted that patients who feel mistrustful tend to have poor communication with HCPs ( 20 ). Additionally, Latino/Hispanic patients who have language barriers tend to have poor glycemic control ( 19 ). Having CHWs work with HCPs might mitigate some patient-provider communication barriers. As noted earlier, CHWs who share cultural and linguistic characteristics with their patient populations have ongoing interactions and more frequent one-on-one encounters ( 12 ).

Medication adherence and glycemic control are important components of diabetes self-management, and we noted that the integration of CHWs into the diabetes health care team and the use of simplified medication label interventions were both successful in improving medication adherence ( 23 , 24 ). The use of culturally tailored education sessions and the integration of pharmacists and CHWs into the management of diabetes appear to be successful in reducing A1C levels ( 12 , 26 ). Electronic education tools and educational videos alone did not have an impact on medication knowledge or information retention in patients with low health literacy, but a combination of education tools and individualized sessions has the potential to improve diabetes medication knowledge and overall self-management ( 20 , 22 , 30 ).

There were several limitations to our literature review. We restricted our search criteria to articles published in English and studies conducted within the United States to ensure that the results would be relevant to U.S. HCPs. However, these limitations may have excluded important work on this topic. Additional research expanding this search beyond the United States and including articles published in other languages may demonstrate different outcomes. Additionally, this literature review did not focus on A1C as the primary outcome, although A1C is an important indicator of diabetes self-management. A1C was chosen as the method of evaluating the impact of health literacy interventions in patients with diabetes, but other considerations such as medication adherence, impact on comorbid conditions, and quality of life are also important factors.

The results of this work show that implementing health literacy interventions to help patients manage type 2 diabetes can have beneficial results. However, such interventions can have significant time and monetary costs. The potential financial and time costs of diabetes education interventions were not evaluated in this review and should be taken into account when designing interventions. The American Diabetes Association estimated the cost of medical care for people with diabetes to be $327 billion in 2017, with the majority of the expenditure related to hospitalizations and nursing home facilities ( 16 ). Another substantial cost of diabetes that can be difficult to measure is treatment for comorbid conditions and complications such as cardiovascular and renal diseases.

Interventions designed to address low health literacy and provide education about type 2 diabetes could be a valuable asset in preventing complications and reducing medical expenditures. Results of this work show that clinicians who are considering implementing new interventions may benefit from the following strategies: using culturally tailored approaches, creating materials for different learning styles and in patients’ languages, engaging CHWs and pharmacists to help with patient education, using PCLs for medications, and engaging education session instructors who share patients’ cultural and linguistic characteristics.

Diabetes self-management is crucial to improving health outcomes and reducing medical costs. This literature review identified interventions that had a positive impact on provider-patient communication, medication adherence, and glycemic control by promoting diabetes self-management through educational efforts to address low health literacy. Clinicians seeking to implement diabetes care and education interventions for patients with low health literacy may want to consider drawing on the strategies described in this article. Providing culturally sensitive education that is tailored to patients’ individual learning styles, spoken language, and individual needs can improve patient outcomes and build patients’ trust.

Duality of Interest

No potential conflicts of interest relevant to this article were reported.

Author Contributions

Both authors conceptualized the literature review, developed the methodology, analyzed the data, and wrote, reviewed, and edited the manuscript. R.A. collected the data. K.M. supervised the review. K.M. is the guarantor of this work and, as such, has full access to all the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.

Prior Presentation

Portions of this research were presented at the Washington State University College of Pharmacy and Pharmaceutical Sciences Honors Research Day in April 2019.

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• Review Article
• Published: 06 June 2022

The burden and risks of emerging complications of diabetes mellitus

• Dunya Tomic   ORCID: orcid.org/0000-0003-2471-2523 1 , 2 ,
• Jonathan E. Shaw   ORCID: orcid.org/0000-0002-6187-2203 1 , 2   na1 &
• Dianna J. Magliano   ORCID: orcid.org/0000-0002-9507-6096 1 , 2   na1  

Nature Reviews Endocrinology volume  18 ,  pages 525–539 ( 2022 ) Cite this article

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• Diabetes complications
• Type 1 diabetes
• Type 2 diabetes

The traditional complications of diabetes mellitus are well known and continue to pose a considerable burden on millions of people living with diabetes mellitus. However, advances in the management of diabetes mellitus and, consequently, longer life expectancies, have resulted in the emergence of evidence of the existence of a different set of lesser-acknowledged diabetes mellitus complications. With declining mortality from vascular disease, which once accounted for more than 50% of deaths amongst people with diabetes mellitus, cancer and dementia now comprise the leading causes of death in people with diabetes mellitus in some countries or regions. Additionally, studies have demonstrated notable links between diabetes mellitus and a broad range of comorbidities, including cognitive decline, functional disability, affective disorders, obstructive sleep apnoea and liver disease, and have refined our understanding of the association between diabetes mellitus and infection. However, no published review currently synthesizes this evidence to provide an in-depth discussion of the burden and risks of these emerging complications. This Review summarizes information from systematic reviews and major cohort studies regarding emerging complications of type 1 and type 2 diabetes mellitus to identify and quantify associations, highlight gaps and discrepancies in the evidence, and consider implications for the future management of diabetes mellitus.

With advances in the management of diabetes mellitus, evidence is emerging of an increased risk and burden of a different set of lesser-known complications of diabetes mellitus.

As mortality from vascular diseases has declined, cancer and dementia have become leading causes of death amongst people with diabetes mellitus.

Diabetes mellitus is associated with an increased risk of various cancers, especially gastrointestinal cancers and female-specific cancers.

Hospitalization and mortality from various infections, including COVID-19, pneumonia, foot and kidney infections, are increased in people with diabetes mellitus.

Cognitive and functional disability, nonalcoholic fatty liver disease, obstructive sleep apnoea and depression are also common in people with diabetes mellitus.

As new complications of diabetes mellitus continue to emerge, the management of this disorder should be viewed holistically, and screening guidelines should consider conditions such as cancer, liver disease and depression.

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Introduction.

Diabetes mellitus is a common, albeit potentially devastating, medical condition that has increased in prevalence over the past few decades to constitute a major public health challenge of the twenty-first century 1 . Complications that have traditionally been associated with diabetes mellitus include macrovascular conditions, such as coronary heart disease, stroke and peripheral arterial disease, and microvascular conditions, including diabetic kidney disease, retinopathy and peripheral neuropathy 2 (Fig.  1 ). Heart failure is also a common initial manifestation of cardiovascular disease in patients with type 2 diabetes mellitus (T2DM) 3 and confers a high risk of mortality in those with T1DM or T2DM 4 . Although a great burden of disease associated with these traditional complications of diabetes mellitus still exists, rates of these conditions are declining with improvements in the management of diabetes mellitus 5 . Instead, as people with diabetes mellitus are living longer, they are becoming susceptible to a different set of complications 6 . Population-based studies 7 , 8 , 9 show that vascular disease no longer accounts for most deaths among people with diabetes mellitus, as was previously the case 10 . Cancer is now the leading cause of death in people with diabetes mellitus in some countries or regions (hereafter ‘countries/regions’) 9 , and the proportion of deaths due to dementia has risen since the turn of the century 11 . In England, traditional complications accounted for more than 50% of hospitalizations in people with diabetes mellitus in 2003, but for only 30% in 2018, highlighting the shift in the nature of complications of this disorder over this corresponding period 12 .

The traditional complications of diabetes mellitus include stroke, coronary heart disease and heart failure, peripheral neuropathy, retinopathy, diabetic kidney disease and peripheral vascular disease, as represented on the left-hand side of the diagram. With advances in the management of diabetes mellitus, associations between diabetes mellitus and cancer, infections, functional and cognitive disability, liver disease and affective disorders are instead emerging, as depicted in the right-hand side of the diagram. This is not an exhaustive list of complications associated with diabetes mellitus.

Cohort studies have reported associations of diabetes mellitus with various cancers, functional and cognitive disability, liver disease, affective disorders and sleep disturbance, and have provided new insights into infection-related complications of diabetes mellitus 13 , 14 , 15 , 16 , 17 . Although emerging complications have been briefly acknowledged in reviews of diabetes mellitus morbidity and mortality 11 , 17 , no comprehensive review currently specifically provides an analysis of the evidence for the association of these complications with diabetes mellitus. In this Review, we synthesize information published since the year 2000 on the risks and burden of emerging complications associated with T1DM and T2DM.

Diabetes mellitus and cancer

The burden of cancer mortality.

With the rates of cardiovascular mortality declining amongst people with diabetes mellitus, cancer deaths now constitute a larger proportion of deaths among this population in some countries/regions 8 , 9 . Although the proportion of deaths due to cancer appears to be stable, at around 16–20%, in the population with diabetes mellitus in the USA 7 , in England it increased from 22% to 28% between 2001 and 2018 (ref. 9 ), with a similar increase reported in Australia 8 . Notably, in England, cancer has overtaken vascular disease as the leading cause of death in people with diabetes mellitus and it is the leading contributor to excess mortality in those with diabetes mellitus compared with those without 9 . These findings are likely to be due to a substantial decline in the proportion of deaths from vascular diseases, from 44% to 24% between 2001 and 2018, which is thought to reflect the targeting of prevention measures in people with diabetes mellitus 18 . Over the same time period, cancer mortality rates fell by much less in the population with diabetes mellitus than in that without diabetes 9 , suggesting that clinical approaches for diabetes mellitus might focus too narrowly on vascular complications and might require revision 19 . In addition, several studies have reported that female patients with diabetes mellitus receive less-aggressive treatment for breast cancer compared with patients without diabetes mellitus, particularly with regard to chemotherapy 20 , 21 , 22 , suggesting that this treatment approach might result in increased cancer mortality rates in women with diabetes mellitus compared with those without diabetes mellitus. Although substantial investigation of cancer mortality in people with diabetes mellitus has been undertaken in high-income countries/regions, there is a paucity of evidence from low-income and middle-income countries/regions. It is important to understand the potential effect of diabetes mellitus on cancer mortality in these countries/regions owing to the reduced capacity of health-care systems in these countries/regions to cope with the combination of a rising prevalence of diabetes mellitus and rising cancer mortality rates in those with diabetes mellitus. One study in Mauritius showed a significantly increased risk of all-cause cancer mortality in patients with T2DM 23 , but this study has yet to be replicated in other low-income and middle-income countries/regions.

Gastrointestinal cancers

Of the reported associations between diabetes mellitus and cancer (Table  1 ), some of the strongest have been demonstrated for gastrointestinal cancers.

Hepatocellular carcinoma

In the case of hepatocellular carcinoma, the most rigorous systematic review on the topic — comprising 18 cohort studies with a combined total of more than 3.5 million individuals — reported a summary relative risk (SRR) of 2.01 (95% confidence interval (CI) 1.61–2.51) for an association with diabetes mellitus 24 . This increased risk of hepatocellular carcinoma with diabetes mellitus is supported by the results of another systematic review that included case–control studies 25 . Another review also found that diabetes mellitus independently increased the risk of hepatocellular carcinoma in the setting of hepatitis C virus infection 26 .

Pancreatic cancer

The risk of pancreatic cancer appears to be approximately doubled in patients with T2DM compared with patients without T2DM. A meta-analysis of 36 studies found an adjusted odds ratio (OR) of 1.82 (95% CI 1.66–1.89) for pancreatic cancer among people with T2DM compared with patients without T2DM 27 (Table  1 ). However, it is possible that these findings are influenced by reverse causality — in this scenario, diabetes mellitus is triggered by undiagnosed pancreatic cancer 28 , with pancreatic cancer subsequently being clinically diagnosed only after the diagnosis of diabetes mellitus. Nevertheless, although the greatest risk (OR 2.05, 95% CI 1.87–2.25) of pancreatic cancer was seen in people diagnosed with T2DM 1–4 years previously compared with people without T2DM, those with a diagnosis of T2DM of more than 10 years remained at increased risk of pancreatic cancer (OR 1.51, 95% CI 1.16–1.96) 27 , suggesting that reverse causality can explain only part of the association between T2DM and pancreatic cancer. Although T2DM accounts for ~90% of all cases of diabetes mellitus 29 , a study incorporating data from five nationwide diabetes registries also reported an increased risk of pancreatic cancer amongst both male patients (HR 1.53, 95% CI 1.30–1.79) and female patients (HR 1.25, 95% CI 1.02–1.53) with T1DM 30 .

Colorectal cancer

For colorectal cancer, three systematic reviews have shown a consistent 20–30% increased risk associated with diabetes mellitus 31 , 32 , 33 . One systematic review, which included more than eight million people across 30 cohort studies, reported an incidence SRR of 1.27 (95% CI 1.21–1.34) of colorectal cancer 31 , independent of sex and family history (Table  1 ). Similar increases in colorectal cancer incidence in patients with diabetes mellitus were reported in a meta-analysis of randomized controlled trials (RCTs) and cohort studies 32 and in a systematic review that included cross-sectional studies 33 .

Female-specific cancers

Endometrial, breast and ovarian cancers all occur more frequently in women with diabetes mellitus than in women without diabetes mellitus.

Endometrial cancer

For endometrial cancer, one systematic review of 29 cohort studies and a combined total of 5,302,259 women reported a SRR of 1.89 (95% CI 1.46–2.45) and summary incidence rate ratio (IRR) of 1.61 (95% CI 1.51–1.71) 34 (Table  1 ). Similar increased risks were found in two systematic reviews incorporating cross-sectional studies 35 , 36 , one of which found a particularly strong association of T1DM (relative risk (RR) 3.15, 95% CI 1.07–9.29) with endometrial cancer.

Breast cancer

The best evidence for a link between diabetes mellitus and breast cancer comes from a systematic review of six prospective cohort studies and more than 150,000 women, in which the hazard ratio (HR) for the incidence of breast cancer in women with diabetes mellitus compared with women without diabetes mellitus was 1.23 (95% CI 1.12–1.34) 32 (Table  1 ). Two further systematic reviews have also shown this increased association 37 , 38 .

The association of diabetes mellitus with breast cancer appears to vary according to menopausal status. In a meta-analysis of studies of premenopausal women with diabetes mellitus, no significant association with breast cancer was found 39 , whereas in 11 studies that included only postmenopausal women, the SRR was 1.15 (95% CI 1.07–1.24). The difference in breast cancer risk between premenopausal and postmenopausal women with diabetes mellitus was statistically significant. The increased risk of breast cancer after menopause in women with diabetes mellitus compared with women without diabetes mellitus might result from the elevated concentrations and increased bioavailability of oestrogen that are associated with adiposity 40 , which is a common comorbidity in those with T2DM; oestrogen synthesis occurs in adipose tissue in postmenopausal women, while it is primarily gonadal in premenopausal women 41 . Notably, however, there is evidence that hormone-receptor-negative breast cancers, which typically carry a poor prognosis, occur more frequently in women with breast cancer and diabetes mellitus than in women with breast cancer and no diabetes mellitus 42 , indicating that non-hormonal mechanisms also occur.

Ovarian cancer

Diabetes mellitus also appears to increase the risk of ovarian cancer, with consistent results from across four systematic reviews. A pooled RR of 1.32 (95% CI 1.14–1.52) was reported across 15 cohort studies and a total of more than 2.3 million women 43 (Table  1 ). A SRR of 1.19 (95% CI 1.06–1.34) was found across 14 cohort studies and 3,708,313 women 44 . Similar risks were reported in meta-analyses that included cross-sectional studies 45 , 46 .

Male-specific cancers: prostate cancer

An inverse association between diabetes mellitus and prostate cancer has been observed in a systematic review (RR 0.91, 95% CI 0.86–0.96) 47 , and is probably due to reduced testosterone levels that occur secondary to the low levels of sex hormone-binding globulin that are commonly seen in men with T2DM and obesity 48 . Notably, however, the systematic review that showed the inverse association involved mostly white men (Table  1 ), whereas a systematic review of more than 1.7 million men from Taiwan, Japan, South Korea and India found that diabetes mellitus increased prostate cancer risk 49 , suggesting that ethnicity might be an effect modifier of the diabetes mellitus–prostate cancer relationship. The mechanisms behind this increased risk in men in regions of Asia such as Taiwan and Japan, where most study participants came from, remain unclear. Perhaps, as Asian men develop diabetes mellitus at lower levels of total adiposity than do white men 50 , the adiposity associated with diabetes mellitus in Asian men might have a lesser impact on sex hormone-binding globulin and testosterone than it does in white men. Despite the reported inverse association between diabetes mellitus and prostate cancer in white men, however, evidence suggests that prostate cancers that do develop in men with T2DM are typically more aggressive, conferring higher rates of disease-specific mortality than prostate cancers in men without diabetes mellitus 51 .

An assessment of cancer associations

As outlined above, a wealth of data has shown that diabetes mellitus is associated with an increased risk of various cancers. It has been argued, however, that some of these associations could be due to detection bias resulting from increased surveillance of people with diabetes mellitus in the immediate period after diagnosis 52 , or reverse causality, particularly in the case of pancreatic cancer 53 . However, neither phenomenon can account for the excess risks seen in the longer term. An Australian study exploring detection bias and reverse causality found that standardized mortality ratios (SMRs) for several cancer types in people with diabetes mellitus compared with the general population fell over time, but remained elevated beyond 2 years for pancreatic and liver cancers 54 , suggesting that diabetes mellitus is a genuine risk factor for these cancer types.

A limitation of the evidence that surrounds diabetes mellitus and cancer risk is high clinical and methodological heterogeneity across several of the large systematic reviews, which makes it difficult to be certain of the effect size in different demographic groups. Additionally, many of the studies exploring a potential association between diabetes mellitus and cancer were unable to adjust for BMI, which is a major confounder. However, a modelling study that accounted for BMI found that although 2.1% of cancers worldwide in 2012 were attributable to diabetes mellitus as an independent risk factor, twice as many cancers were attributable to high BMI 55 , so it is likely that effect sizes for cancer risk associated with diabetes mellitus would be attenuated after adjustment for BMI. Notably, however, low-income and middle-income countries/regions had the largest increase in the numbers of cases of cancer attributable to diabetes mellitus both alone and in combination with BMI 55 , highlighting the need for public health intervention, given that these countries/regions are less equipped than high-income countries/regions to manage a growing burden of cancer.

As well as the cancer types outlined above, diabetes mellitus has also been linked to various other types of cancer, including kidney cancer 56 , bladder cancer 57 and haematological malignancies; however, the evidence for these associations is not as strong as for the cancers discussed above 58 . Diabetes mellitus might also be associated with other cancer types such as small intestine cancer, but the rarity of some of these types makes it difficult to obtain sufficient statistical power in analyses of any potential association.

Potential aetiological mechanisms

Several aetiological mechanisms that might be involved in linking diabetes mellitus to cancer have been proposed, including hyperinsulinaemia, hyperglycaemia, inflammation and cellular signalling mechanisms.

Hyperinsulinaemia

Most cancer cells express insulin receptors, through which hyperinsulinaemia is thought to stimulate cancer cell proliferation and metastasis 59 . Hyperinsulinaemia might also promote carcinogenesis through increased local levels of insulin-like growth factor 1 (IGF1), which has potent mitogenic and anti-apoptotic activities 60 , owing to decreased levels of insulin-like growth factor binding proteins. As outlined above, people with diabetes mellitus show a strong risk of pancreatic and liver cancers; this increased risk might occur because insulin is produced by pancreatic β-cells and transported to the liver via the portal vein 61 , thereby exposing the liver and pancreas to high levels of endogenous insulin 59 .

Hyperglycaemia and inflammation

Hyperglycaemia can induce DNA damage 62 , increase the generation of reactive oxygen species 63 and downregulate antioxidant expression 64 , all of which are associated with cancer development. Inflammatory markers, including cytokines such as IL-6, appear to have an important role in the association between diabetes and cancer 65 .

Cellular signalling mechanisms

Several cellular signalling components are common to the pathogenesis of T2DM and cancer. These include the mechanistic target of rapamycin (mTOR), a central controller of cell growth and proliferation; AMP-activated protein kinase, a cellular energy sensor and signal transducer 66 ; and the phosphatidylinositol 3-kinase (PI3K)–AKT pathway, which transduces growth factor signals during organismal growth, glucose homeostasis and cell proliferation 67 . Dysregulation of any of these cellular signalling components or pathways could contribute to the development of cancer and metabolic disorders, including T2DM, and glucose-lowering drugs such as metformin have been associated with a reduction in cancer cell proliferation through effective inhibition of some of these components 68 .

Diabetes mellitus and infections

Infection-related complications.

Although infection has long been recognized as a complication of diabetes mellitus, an association between diabetes mellitus and infection has not been well documented in epidemiological studies 69 . Only in the past decade have major studies quantified the burden of infection-related complications in people with diabetes mellitus and explored the specific infections accounting for this burden. In a US cohort of 12,379 participants, diabetes mellitus conferred a significant risk of infection-related hospitalization, with an adjusted HR of 1.67 (95% CI 1.52–1.83) compared with people without diabetes mellitus 70 (Table  2 ). The association was most pronounced for foot infections (HR 5.99, 95% CI 4.38–8.19), with significant associations also observed for respiratory infection, urinary tract infection, sepsis and post-operative infection, but not for gastrointestinal infection, a category that included appendicitis and gastrointestinal abscesses but not viral or bacterial gastroenteritis. Interestingly, a report from Taiwan demonstrated an association between the use of metformin and a lower risk of appendicitis 71 .

In an analysis of the entire Hong Kong population over the period 2001–2016, rates of hospitalization for all types of infection remained consistently higher in people with diabetes mellitus than in those without diabetes mellitus 72 . The strongest association was seen for hospitalization due to kidney infections, for which the adjusted RR was 4.9 (95% CI 3.9–6.2) in men and 3.2 (95% CI 2.8–3.7) in women with diabetes mellitus compared with those without diabetes mellitus in 2016 (Table  2 ). Diabetes mellitus roughly doubled the risk of hospitalization from tuberculosis or sepsis. The most common cause of infection-related hospitalization was pneumonia, which accounted for 39% of infections across the study period, while no other single cause accounted for more than 25% of infections across the same period. Pneumonia-related hospitalization rates increased substantially from 2001 to 2005, probably as a result of the 2003 severe acute respiratory syndrome (SARS) epidemic and the decreased threshold for pneumonia hospitalization in the immediate post-epidemic period. Rates for hospitalization for influenza increased from 2002 to 2016, possibly because of changes in the virus and increased testing for influenza. Declining rates of hospitalization for tuberculosis, urinary tract infections, foot infections and sepsis could be due to improvements in the management of diabetes mellitus.

Infection-related mortality rates were found to be significantly elevated among 1,108,982 Australians with diabetes mellitus studied over the period 2000–2010 compared with rates in people without diabetes mellitus 73 . For overall infection-related mortality, SMRs were 4.42 (95% CI 3.68–5.34) for T1DM and 1.47 (95% CI 1.42–1.53) for people with T2DM compared with those without diabetes mellitus (Table  2 ). Substantially higher infection-related mortality rates were seen in people with T1DM compared with those with T2DM for all infection types, even after accounting for age. Hyperglycaemia is thought to be a driver of infection amongst people with diabetes mellitus (see below) 73 , which might explain the higher SMRs amongst people with T1DM, in whom hyperglycaemia is typically more severe, than in those with T2DM. The highest SMRs were seen for osteomyelitis, and SMRs for septicaemia and pneumonia were also greater than 1.0 for both types of diabetes mellitus compared with those without diabetes mellitus.

Post-operative infection

Post-operative infection is also an important complication of diabetes mellitus. In a meta-analysis, diabetes mellitus was found to be associated with an OR of 1.77 (95% CI 1.13–2.78) for surgical site infection across studies that adjusted for confounding factors 74 (Table  2 ). The effect size appears to be greatest after cardiac procedures, and one US study of patients undergoing coronary artery bypass grafting found diabetes mellitus to be an independent predictor of surgical site infection, with an OR of 4.71 (95% CI 2.39–9.28) compared with those without diabetes mellitus 75 . Risks of infection of more than threefold were reported in some studies of gynaecological 76 and spinal surgery 77 in people with diabetes mellitus compared with those without diabetes mellitus. Increased risks of infection among people with diabetes mellitus were also observed in studies of colorectal and breast surgery and arthroplasty, suggesting that the association between diabetes mellitus and post-operative infection is present across a wide range of types of surgery 74 .

Respiratory infections

The incidence of hospitalizations due to respiratory infections among people with diabetes mellitus was increasing substantially even before the onset of the coronavirus disease 2019 (COVID-19) pandemic, probably owing to increased life expectancy in these patients as well as an increased likelihood of them being hospitalized for conditions such as respiratory infections, which occur mostly in older age 12 . This rising burden of respiratory infection, in combination with the rising prevalence of diabetes mellitus, highlights the importance of addressing the emerging complications of diabetes mellitus to minimize impacts on health-care systems in current and future global epidemics.

Although diabetes mellitus does not appear to increase the risk of becoming infected with COVID-19 (ref. 78 ), various population-based studies have reported increased risks of COVID-19 complications among people with diabetes mellitus. In a study of the total Scottish population, people with diabetes mellitus were found to have an increased risk of fatal or critical care unit-treated COVID-19, with an adjusted OR of 1.40 (95% CI 1.30–1.50) compared with those without diabetes mellitus 79 (Table  2 ). The risk was particularly high for those with T1DM (OR 2.40, 95% CI 1.82–3.16) 79 . Both T1DM and T2DM have been linked to a more than twofold increased risk of hospitalization with COVID-19 in a large Swedish cohort study 80 . In South Korean studies, T2DM was linked to intensive care unit admission among patients with COVID-19 infection 81 , and diabetes mellitus (either T1DM or T2DM) was linked to a requirement for ventilation and oxygen therapy 82 in patients with COVID-19. Diabetes mellitus appears to be the primary predisposing factor for opportunistic infection with mucormycosis in individuals with COVID-19 (ref. 83 ). The evidence for diabetes mellitus as a risk factor for post-COVID-19 syndrome is inconclusive 84 , 85 . Interestingly, an increase in the incidence of T1DM during the COVID-19 pandemic has been reported in several countries/regions 86 , and some data suggest an increased risk of T1DM after COVID-19 infection 87 , but the evidence regarding a causal effect is inconclusive.

Pneumonia, MERS, SARS and H1N1 influenza

The data regarding diabetes mellitus and COVID-19 are consistent with the published literature regarding other respiratory infections, such as pneumonia, for which diabetes mellitus has been shown to increase the risk of hospitalization 88 and mortality 88 , with similar effect sizes to those seen for COVID-19, compared with no diabetes mellitus. Diabetes mellitus has also been also linked to adverse outcomes in people with Middle East respiratory syndrome (MERS), SARS and H1N1 influenza 89 , 90 , 91 , 92 , suggesting that mechanisms specific to COVID-19 are unlikely to be responsible for the relationship between diabetes mellitus and COVID-19. Unlike the case for COVID-19, there is evidence that people with diabetes mellitus are at increased risk of developing certain other respiratory infections, namely pneumonia 93 and possibly also MERS 94 .

The mechanisms that might link diabetes mellitus and infection include a reduced T cell response, reduced neutrophil function and disorders of humoral immunity.

Mononuclear cells and monocytes of individuals with diabetes mellitus secrete less IL-1 and IL-6 than the same cells from people without diabetes mellitus 95 . The release of IL-1 and IL-6 by T cells and other cell types in response to infection has been implicated in the response to several viral infections 96 . Thus, the reduced secretion of these cytokines in patients with diabetes mellitus might be associated with the poorer responses to infection observed among these patients compared with people without diabetes mellitus.

In the context of neutrophil function, hyperglycaemic states might give rise to reductions in the mobilization of polymorphonuclear leukocytes, phagocytic activity and chemotaxis 97 , resulting in a decreased immune response to infection. Additionally, increased levels of glucose in monocytes isolated from patients with obesity and/or diabetes mellitus have been found to promote viral replication in these cells, as well as to enhance the expression of several cytokines, including pro-inflammatory cytokines that are associated with the COVID-19 ‘cytokine storm’; furthermore, glycolysis was found to sustain the SARS coronavirus 2 (SARS-CoV-2)-induced monocyte response and viral replication 98 .

Elevated glucose levels in people with diabetes mellitus are also associated with an increase in glycation, which, by promoting a change in the structure and/or function of several proteins and lipids, is responsible for many of the complications of diabetes mellitus 99 . In people with diabetes mellitus, antibodies can become glycated, a process that is thought to impair their biological function 100 . Although the clinical relevance of this impairment is not clear, it could potentially explain the results of an Israeli study that reported reduced COVID-19 vaccine effectiveness among people with T2DM compared with those without T2DM 101 .

Diabetes mellitus and liver disease

Nonalcoholic fatty liver disease.

The consequences of nonalcoholic fatty liver disease (NAFLD) make it important to recognize the burden of this disease among people with diabetes mellitus. NAFLD and nonalcoholic steatohepatitis (NASH; an advanced form of NAFLD) are major causes of liver transplantation in the general population. In the USA, NASH accounted for 19% of liver transplantations in 2016 — second only to alcoholic liver disease, which was the cause of 24% of transplantations 102 . In Australia and New Zealand, NAFLD was the primary diagnosis in 9% of liver transplant recipients in 2019, only slightly below the figure for alcoholic cirrhosis of 13% 103 . In Europe, NASH increased as the reason for transplantations from 1% in 2002 to more than 8% in 2016, in parallel with the rising prevalence of diabetes mellitus 104 .

NAFLD is highly prevalent among people with T2DM. In a systematic review of 80 studies across 20 countries/regions, the prevalence of NAFLD among 49,419 people with T2DM was 56% 105 , while the global prevalence of NAFLD in the general population is estimated to be 25% 106 . In a Chinese cohort study of 512,891 adults, diabetes mellitus was associated with an adjusted HR of 1.76 (95% CI 1.47–2.16) for NAFLD compared with no diabetes mellitus 107 (Table  3 ). Another smaller longitudinal Chinese study also reported an increased risk of developing NAFLD among those with T2DM compared with those without T2DM 108 . However, most evidence regarding the association between NAFLD and diabetes mellitus is from cross-sectional studies 109 , 110 , 111 .

NASH and fibrosis

Diabetes mellitus appears to enhance the risk of NAFLD complications, including NASH and fibrosis. An analysis of 892 people with NAFLD and T2DM across 10 studies showed that the prevalence of NASH was 37% (ref. 105 ); figures for the prevalence of NASH in the general population with NAFLD vary greatly across different study populations, ranging from 16% to 68% 112 . Amongst 439 people with T2DM and NAFLD in seven studies, 17% had advanced fibrosis 105 . An analysis of 1,069 people with NAFLD in a US study found that diabetes mellitus was an independent predictor for NASH (OR 1.93, 95% CI 1.37–2.73) and fibrosis (3.31, 95% CI 2.26–4.85) 113 .

Bidirectional relationship between diabetes mellitus and liver disease

The relationship between diabetes mellitus and NAFLD is bidirectional, as NAFLD is associated with an increased risk of developing T2DM 114 . There is also a notable bidirectional relationship between diabetes mellitus and liver cirrhosis. The prevalence of diabetes mellitus in people with liver cirrhosis has been reported as 20–63%, depending on the severity of liver damage, aetiology and diagnostic criteria 115 . In an Italian study of 401 participants with cirrhosis, 63% of those with decompensated liver disease had diabetes mellitus compared with 10% of those with well-compensated liver disease 116 , suggesting that diabetes mellitus is more common in severe cases of liver damage. The association between diabetes mellitus and cirrhosis also varies according to the cause of liver disease. In a US study of 204 people with cirrhosis, the prevalence of diabetes mellitus was 25% among those with cirrhosis caused by hepatitis C virus, 19% among those with cirrhosis from alcoholic liver disease and only 1% among those with cirrhosis due to cholestatic liver disease 117 . Among the causes of cirrhosis, haemochromatosis has the strongest association with diabetes mellitus, with diabetes mellitus mainly resulting from the iron deposition that is characteristic of haemochromatosis 118 .

Several factors have been implicated in the aetiology of liver disease in people with diabetes mellitus, with insulin resistance being the most notable 119 .

Insulin resistance

Insulin resistance causes lipolysis, thereby increasing the circulating levels of free fatty acids, which are then taken up by the liver as an energy source 120 . These fatty acids overload the mitochondrial β-oxidation system in the liver, resulting in the accumulation of fatty acids and, consequently, NAFLD 121 . Of those individuals with NAFLD, 2–3% develop hepatic inflammation, necrosis and fibrosis, which are the hallmarks of NASH 122 . The exact mechanisms leading to steatohepatitis are unclear, although dysregulated peripheral lipid metabolism appears to be important 14 .

Ectopic adipose deposition

Excessive or ectopic deposition of adipose tissue around the viscera and in the liver might be an important mechanism underlying both T2DM and liver disease, particularly NAFLD 123 . Dysfunction of long-term adipose storage in white adipose tissue is known to lead to ectopic adipose deposition in the liver. In this state, increased levels of fatty acyl-coenzyme As, the activated form of fatty acids, might lead to organ dysfunction, including NAFLD 124 . Ectopic adipose deposition leading to organ-specific insulin resistance has emerged as a major hypothesis for the pathophysiological basis of T2DM, and ectopic adipose in the pancreas could contribute to β-cell dysfunction and, thus, the development of T2DM 125 .

Diabetes mellitus and affective disorders

The prevalence of depression appears to be high among people with diabetes mellitus. The strongest evidence for an association comes from a systematic review of 147 studies among people with T2DM, which revealed a mean prevalence of depression of 28% 126 , while the global prevalence of depression in the general population is estimated at around 13% 127 . For T1DM, a systematic review reported a pooled prevalence of depression of 12% compared with only 3% in those without T1DM 128 . The risk of depression among people with diabetes mellitus appears to be roughly 25% greater than the risk in the general population, with consistent findings across several meta-analyses (Table  4 ). A 2013 study found an adjusted RR of 1.25 (95% CI 1.10–1.44) for incident depression among people with diabetes mellitus compared with those without diabetes mellitus 129 . Another systematic review of people with T2DM reported a near identical effect size 130 .

Anxiety and eating disorders

Evidence exists for an association of diabetes mellitus with anxiety, and of T1DM with eating disorders. In a systematic review involving 2,584 individuals with diabetes mellitus, a prevalence of 14% was found for generalized anxiety disorder and 40% for anxiety symptoms, whereas the prevalence of generalized anxiety disorder in the general population is estimated as only 3–4% 131 . People with diabetes mellitus had an increased risk of anxiety disorders (OR 1.20, 95% CI 1.10–1.31) and anxiety symptoms (OR 1.48, 95% CI 1.02–1.93) compared with those without diabetes mellitus in a meta-analysis 132 (Table  4 ), although these findings were based on cross-sectional data. Across 13 studies, 7% of adolescents with T1DM were found to have eating disorders, compared with 3% of peers without diabetes mellitus 133 .

Broader psychological impacts

There is a substantial literature on a broad range of psychological impacts of diabetes mellitus. Social stigma 134 can have profound impacts on the quality of life of not only people with diabetes mellitus, but their families and carers, too 135 . In a systematic review, diabetes mellitus distress was found to affect around one-third of adolescents with T1DM, which was consistent with the results of studies of adults with diabetes mellitus 136 . Diabetes mellitus burnout appears to be a distinct concept, and is characterized by exhaustion and detachment, accompanied by the experience of a loss of control over diabetes mellitus 137 .

Diabetes mellitus and depression appear to have common biological origins. Activation of the innate immune system and acute-phase inflammation contribute to the pathogenesis of T2DM — increased levels of inflammatory cytokines predict the onset of T2DM 138 — and there is growing evidence implicating cytokine-mediated inflammation in people with depression in the absence of diabetes mellitus 139 . Dysregulation of the hypothalamic–pituitary–adrenal axis is another potential biological mechanism linking depression and diabetes mellitus 140 . There have been numerous reports of hippocampal atrophy, which might contribute to chronic activation of the hypothalamic–pituitary–adrenal axis, in individuals with T2DM as well as those with depression 141 , 142 . A meta-analysis found that, although hypertension modified global cerebral atrophy in those with T2DM, it had no effect on hippocampal atrophy 143 . This suggests that, although global cerebral atrophy in individuals with T2DM might be driven by atherosclerotic disease, hippocampal atrophy is an independent effect that provides a common neuropathological aetiology for the comorbidity of T2DM with depression. There is a lack of relevant information regarding the potential aetiological mechanisms that link diabetes to other affective disorders.

Diabetes mellitus and sleep disturbance

Obstructive sleep apnoea.

Obstructive sleep apnoea (OSA) is highly prevalent among people with diabetes mellitus. In a systematic review of 41 studies of adults with diabetes mellitus, the prevalence of OSA was found to be 60% 144 , whereas reports for OSA prevalence in the general population range from 9% to 38% 145 . In a UK study of 1,656,739 participants, T2DM was associated with an IRR for OSA of 1.48 (95% CI 1.42–1.55) compared with no T2DM 146 . A population-based US study reported a HR of 1.53 (95% CI 1.32–1.77) for OSA in people with T2DM compared with those without diabetes mellitus 147 . However, the association in this latter report was attenuated after adjustment for BMI and waist circumference (1.08, 95% CI 1.00–1.16), suggesting that the excess risk of OSA among people with diabetes mellitus might be mainly explained by the comorbidity of obesity. Although most studies on OSA have focused on T2DM, a meta-analysis of people with T1DM revealed a similar prevalence of 52% 148 ; however, this meta-analysis was limited to small studies. The association between T2DM and OSA is bidirectional: the severity of OSA was shown to be positively associated with the incidence of T2DM, independent of adiposity, in a large US cohort study 149 .

The mechanism by which T2DM might increase the risk of developing OSA is thought to involve dysregulation of the autonomic nervous system leading to sleep-disordered breathing 150 . Conversely, the specific mechanism behind OSA as a causative factor for T2DM remains poorly understood. It has been suggested that OSA is able to induce insulin resistance 151 , 152 and is a risk factor for the development of glucose intolerance 152 . However, once T2DM has developed, there is no clear evidence that OSA worsens glycaemic control, as an RCT of people with T2DM found that treating OSA had no effect on glycaemic control 153 .

Diabetes mellitus and cognitive disability

Dementia and cognitive impairment.

Dementia is emerging as a major cause of mortality in both individuals with diabetes mellitus and the general population, and is now the leading cause of death in some countries/regions 9 . However, compared with the general population, diabetes mellitus increases the risk of dementia, particularly vascular dementia. The association is supported by several systematic reviews, including one of eight population-based studies with more than 23,000 people, which found SRRs of 2.38 (95% CI 1.79–3.18) for vascular dementia and 1.39 (95% CI 1.16–1.66) for Alzheimer disease comparing people with diabetes mellitus with those without diabetes mellitus 154 (Table  4 ). Similar results, as well as a RR of 1.21 (95% CI 1.02–1.45) for mild cognitive impairment (MCI), were reported across 19 population-based studies of 44,714 people, 6,184 of whom had diabetes mellitus 155 . Two meta-analyses of prospective cohort studies have shown increased risks of all-cause dementia in people with diabetes mellitus compared with those without diabetes mellitus 156 , 157 , and T2DM has been shown to increase progression to dementia in people with MCI 158 .

The boundaries between Alzheimer disease and vascular dementia remain controversial, and these conditions are often difficult to differentiate clinically 159 . Consequently, vascular dementia might have been misdiagnosed as Alzheimer disease in some studies investigating diabetes mellitus and dementia, resulting in an overestimation of the effect size of the association between diabetes mellitus and Alzheimer disease. Although a cohort study found a significant association between diabetes mellitus and Alzheimer disease using imaging 160 , autopsy studies have failed to uncover an association between diabetes mellitus and Alzheimer disease pathology 161 , 162 , suggesting that vascular mechanisms are the key driver of cognitive decline in people with diabetes mellitus.

Another important finding is a 45% prevalence of MCI among people with T2DM in a meta-analysis, compared with a prevalence of 3–22% reported for the general population 163 . Notably, however, the prevalence of MCI in individuals with T2DM was similar in people younger than 60 years (46%) and those older than 60 years (44%), which is at odds with previous research suggesting that MCI is most common in older people, particularly those aged more than 65 years 164 However, another meta-analysis found cognitive decline in people with T2DM who are younger than 65 years 165 , suggesting that a burden of cognitive disease exists among younger people with diabetes mellitus.

Although there is solid evidence that links diabetes mellitus to cognitive disability, our understanding of the underlying mechanisms is incomplete. Mouse models suggest a strong association between hyperglycaemia, the advanced glycation end products glyoxal and methylglyoxal, enhanced blood–brain barrier (BBB) permeability and cognitive dysfunction in both T1DM and T2DM 166 . The BBB reduces the access of neurotoxic compounds and pathogens to the brain and sustains brain homeostasis, so disruption to the BBB can result in cognitive dysfunction through dysregulation of transport of molecules between the peripheral circulation and the brain 167 . There appears to be a continuous relationship between glycaemia and cognition, with associations found between even high-normal blood levels of glucose and cognitive decline 168 . Another hypothetical mechanism involves a key role for impaired insulin signalling in the pathogenesis of Alzheimer disease. Brain tissue obtained post mortem from individuals with Alzheimer disease showed extensive abnormalities in insulin and insulin-like growth factor signalling mechanisms compared with control brain tissue 169 . Although the synthesis of insulin-like growth factors occurred normally in people with Alzheimer disease, their expression levels were markedly reduced, which led to the subsequent proposal of the term ‘type 3 diabetes’ to characterize Alzheimer disease.

Diabetes mellitus and disability

Functional disability.

Disability (defined as a difficulty in functioning in one or more life domains as experienced by an individual with a health condition in interaction with contextual factors) 170 is highly prevalent in people with diabetes mellitus. In a systematic review, lower-body functional limitation was found to be the most prevalent disability (47–84%) among people with diabetes mellitus 171 The prevalence of difficulties with activities of daily living among people with diabetes mellitus ranged from 12% to 55%, although most studies were conducted exclusively in individuals aged 60 years and above, so the results are not generalizable to younger age groups. A systematic review showed a significant association between diabetes mellitus and falls in adults aged 60 years and above 172 . A 2013 meta-analysis 173 showed an increased risk of mobility disability, activities of daily living disability and independent activities of daily living disability among people with diabetes mellitus compared with those without diabetes mellitus (Table  4 ). Although this analysis included cross-sectional data, results were consistent across longitudinal and cross-sectional studies, suggesting little effect of reverse causality. However, people with functional disabilities that limit mobility (for example, people with osteoarthritis or who have had a stroke) might be more prone to developing diabetes mellitus owing to physical inactivity 174 .

Workplace productivity

Decreased productivity while at work, increased time off work and early dropout from the workforce 175 are all associated with diabetes mellitus, probably partly due to functional disability, and possibly also to comorbidities such as obesity and physical inactivity 176 . Given that young-onset diabetes is becoming more common, and most people with diabetes mellitus in middle-income countries/regions are less than 65 years old 177 , a pandemic of diabetes mellitus-related work disability among a middle-aged population does not bode well for the economies of these regions.

The mechanisms by which diabetes mellitus leads to functional disability remain unclear. One suggestion is that hyperglycaemia leads to systemic inflammation, which is one component of a multifactorial process that results in disability 154 . The rapid loss of skeletal muscle strength and quality seen among people with diabetes mellitus might be another cause of functional disability 178 (Box  1 ). In addition, complications of diabetes mellitus, including stroke, peripheral neuropathy and cardiac dysfunction, can obviously directly cause disability 179 .

Box 1 Diabetes mellitus and skeletal muscle atrophy

Individuals with diabetes mellitus exhibit skeletal muscle atrophy that is typically mild in middle age and becomes more substantial with increasing age.

This muscle loss leads to reduced strength and functional capacity and, ultimately, increased mortality.

Skeletal muscle atrophy results from a negative balance between the rate of synthesis and degradation of contractile proteins, which occurs in response to disuse, ageing and chronic diseases such as diabetes mellitus.

Degradation of muscle proteins is more rapid in diabetes mellitus, and muscle protein synthesis has also been reported to be decreased.

Proposed mechanisms underlying skeletal muscle atrophy include systemic inflammation (affecting both protein synthesis and degradation), dysregulation of muscle protein anabolism and lipotoxicity.

Mouse models have also revealed a key role for the WWP1/KLF15 pathway, mediated by hyperglycaemia, in the pathogenesis of muscle atrophy.

See refs 195 , 196 , 197 , 198 .

Diabetes management and control

Although a detailed discussion of the impacts of anti-diabetes mellitus medications and glucose control on emerging complications is beyond the scope of this Review, their potential effect on these complications must be acknowledged.

Medications

Anti-diabetes mellitus medications and cancer.

In the case of cancer as an emerging complication, the use of medications for diabetes mellitus was not controlled for in most studies of diabetes mellitus and cancer and might therefore be a confounding factor. People taking metformin have a lower cancer risk than those not taking metformin 180 . However, this association is mainly accounted for by other factors. For example, metformin is less likely to be administered to people with diabetes mellitus who have kidney disease 181 , who typically have longer duration diabetes mellitus, which increases cancer risk. A review of observational studies into the association between metformin and cancer found that many studies reporting significant reductions in cancer incidence or mortality associated with metformin were affected by immortal time bias and other time-related biases, casting doubt on the ability of metformin to reduce cancer mortality 182 . Notably, the use of insulin was associated with an increased risk of several cancers in a meta-analysis 183 . However, in an RCT of more than 12,000 people with dysglycaemia, randomization to insulin glargine (compared with standard care) did not increase cancer incidence 184 . Furthermore, cancer rates in people with T1DM and T2DM do not appear to vary greatly, despite substantial differences in insulin use between people with these types of diabetes mellitus.

Anti-diabetes mellitus medications and other emerging complications

Anti-diabetes medications appear to affect the onset and development of some other emerging complications of diabetes mellitus. Results from RCTs suggest that metformin might confer therapeutic effects against depression 185 , and its use was associated with reduced dementia incidence in a systematic review 186 . In an RCT investigating a potential association between metformin and NAFLD, no improvement in NAFLD histology was found among people using metformin compared with those given placebo 187 . An RCT reported benefits of treatment with the glucagon-like peptide 1 receptor agonist dulaglutide on cognitive function in a post hoc analysis 188 , suggesting that trials designed specifically to test the effects of dulaglutide on cognitive function should be undertaken.

Glucose control

Another important consideration is glycaemic control, which appears to have variable effects on emerging complications. A meta-analysis found no association of glycaemic control with cancer risk among those with diabetes mellitus 189 , and an RCT found no effect of intensive glucose lowering on cognitive function in people with T2DM 190 . However, glycaemic control has been associated with improved physical function 191 , decreased COVID-19 mortality 192 and a decreased risk of NAFLD 193 in observational studies of patients with diabetes mellitus; notably, no RCTs have yet confirmed these associations.

Conclusions

With advances in the management of diabetes mellitus and associated increased life expectancy, the face of diabetes mellitus complications is changing. As the management of glycaemia and traditional complications of diabetes mellitus is optimized, we are beginning instead to see deleterious effects of diabetes mellitus on the liver, brain and other organs. Given the substantial burden and risk of these emerging complications, future clinical and public health strategies should be updated accordingly. There is a need to increase the awareness of emerging complications among primary care physicians at the frontline of diabetes mellitus care, and a place for screening for conditions such as depression, liver disease and cancers in diabetes mellitus guidelines should be considered. Clinical care for older people with diabetes mellitus should target physical activity, particularly strength-based activity, to reduce the risk of functional disability in ageing populations. Ongoing high-quality surveillance of diabetes mellitus outcomes is imperative to ensure we know where the main burdens lie. Given the growing burden of these emerging complications, the traditional management of diabetes mellitus might need to broaden its horizons.

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Acknowledgements

D.T. is supported by an Australian Government Research Training Program (RTP) Scholarship and Monash Graduate Excellence Scholarship. J.E.S. is supported by a National Health and Medical Research Council Investigator Grant. D.J.M. is supported by a National Health and Medical Research Council Senior Research Fellowship.

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Dunya Tomic, Jonathan E. Shaw & Dianna J. Magliano

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D.T. researched data for the article and wrote the article. J.E.S and D.J.M. contributed substantially to discussion of the content. D.T., J.E.S. and D.J.M reviewed and/or edited the manuscript before submission.

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Tomic, D., Shaw, J.E. & Magliano, D.J. The burden and risks of emerging complications of diabetes mellitus. Nat Rev Endocrinol 18 , 525–539 (2022). https://doi.org/10.1038/s41574-022-00690-7

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• Published: 08 November 2019

Type 2 diabetes and pre-diabetes mellitus: a systematic review and meta-analysis of prevalence studies in women of childbearing age in the Middle East and North Africa, 2000–2018

• Rami H. Al-Rifai   ORCID: orcid.org/0000-0001-6102-0353 1 ,
• Maria Majeed 1 ,
• Maryam A. Qambar 2 ,
• Ayesha Ibrahim 2 ,
• Khawla M. AlYammahi 2 &
• Faisal Aziz 1  

Systematic Reviews volume  8 , Article number:  268 ( 2019 ) Cite this article

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Investing in women’s health is an inevitable investment in our future. We systematically reviewed the available evidence and summarized the weighted prevalence of type 2 diabetes (T2DM) and pre-diabetes mellitus (pre-DM) in women of childbearing age (15–49 years) in the Middle East and North African (MENA) region.

We comprehensively searched six electronic databases to retrieve published literature and prevalence studies on T2DM and pre-DM in women of childbearing age in the MENA. Retrieved citations were screened and data were extracted by at least two independent reviewers. Weighted T2DM and pre-DM prevalence was estimated using the random-effects model.

Of the 10,010 screened citations, 48 research reports were eligible. Respectively, 46 and 24 research reports on T2DM and pre-DM prevalence estimates, from 14 and 10 countries, were included. Overall, the weighted T2DM and pre-DM prevalence in 14 and 10 MENA countries, respectively, were 7.5% (95% confidence interval [CI], 6.1–9.0) and 7.6% (95% CI, 5.2–10.4). In women sampled from general populations, T2DM prevalence ranged from 0.0 to 35.2% (pooled, 7.7%; 95% CI, 6.1–9.4%) and pre-DM prevalence ranged from 0.0 to 40.0% (pooled, 7.9%; 95% CI, 5.3–11.0%). T2DM was more common in the Fertile Crescent countries (10.7%, 95% CI, 5.2–17.7%), followed by the Arab Peninsula countries (7.6%, 95% CI, 5.9–9.5%) and North African countries and Iran (6.5%, 95% CI, 4.3–9.1%). Pre-DM prevalence was highest in the Fertile Crescent countries (22.7%, 95% CI, 14.2–32.4%), followed by the Arab Peninsula countries (8.6%, 95% CI, 5.5–12.1%) and North Africa and Iran (3.3%, 95% CI, 1.0–6.7%).

Conclusions

T2DM and pre-DM are common in women of childbearing age in MENA countries. The high DM burden in this vital population group could lead to adverse pregnancy outcomes and acceleration of the intergenerational risk of DM. Our review presented data and highlighted gaps in the evidence of the DM burden in women of childbearing age, to inform policy-makers and researchers.

Systematic review registration

PROSPERO CRD42017069231

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The global burden of type 2 diabetes mellitus (T2DM) is rapidly increasing, affecting individuals of all ages. The global T2DM prevalence nearly doubled in the adult population over the past decade from 4.7% in 1980 to 8.5% in 2014 [ 1 ]. The global burden of T2DM in people 20–79 years is further projected to increase to 629 million in 2045 compared to 425 million in 2017 [ 1 ]. Low- and middle-income countries will be the most affected with the rise in the burden of T2DM. For the period between 2017 and 2045, the projected increase in the prevalence of T2DM in the Middle East and North Africa (MENA) region is 110% compared to 16% in Europe, 35% in North Africa and the Caribbean, and 62% in South and Central America [ 1 ]. Pre-diabetes (pre-DM) or intermediate hyperglycaemia is defined as blood glucose levels above the normal range, but lower than DM thresholds [ 1 ]. The burden of pre-DM is increasing worldwide. By 2045, the number of people aged between 20 and 79 years old with pre-DM is projected to increase to 587 million (8.3% of the adult population) compared to 352.1 million people worldwide in 2017 (i.e., 7.3% of the adult population of adults aged 20 to 79 years) [ 1 ]. About three quarters (72.3%) of people with pre-DM live in low- and middle-income countries [ 1 ].

Pre-DM or T2DM are associated with various unfavorable health outcomes. People with pre-DM are at high risk of developing T2DM [ 1 ]. Annually, it is estimated that 5–10% of people with pre-DM will develop T2DM [ 2 , 3 ]. Pre-DM and T2DM are also associated with early onset of nephropathy and chronic kidney disease [ 4 , 5 , 6 , 7 ], diabetic retinopathy [ 6 , 8 , 9 ], and increased risk of macrovascular disease [ 10 , 11 ]. T2DM is also reported to increase the risk of developing active [ 12 ] and latent tuberculosis [ 13 ]. The rising levels of different modifiable key risk factors, mainly body overweight and obesity, driven by key changes in lifestyle, are the attributes behind the continued burgeoning epidemics of pre-DM and T2DM [ 14 , 15 , 16 ]. Women of childbearing age (15–49 years) [ 17 ] are also affected by the global rise in pre-DM and T2DM epidemics. Rising blood glucose levels in women of childbearing age has pre-gestational, gestational, and postpartum consequences, including increased intergenerational risk of DM [ 18 ].

The total population in 20 countries (Algeria, Bahrain, Djibouti, Egypt, Iran, Iraq, Jordan, Kuwait, Lebanon, Libya, Malta, Morocco, Oman, Palestine, Qatar, Saudi Arabia, Syria, Tunisia, the United Arab Emirates, and Yemen) in the Middle East and North Africa region comprises almost 6.7% (~ 421 million people) of the world’s population, with about 200 million females as of July 1, 2015 [ 19 ]. In adults ≥ 18 years, T2DM prevalence rose sharply by 2.3 times in each of the Eastern Mediterranean regions and the African region, between 1980 and 2014 [ 20 ]. This sharp increase in these two regions is higher than that reported in the region of the Americas (1.7 times), the European region (1.4 times), and the Western Pacific Region (1.9 times) [ 20 ].

Key pre-DM and T2DM risk factors, body overweight and obesity, are highly prevalent in people in the MENA countries. In 2013, the age-standardized prevalence of overweight and obesity among women ≥ 20 years was 65.5% (obese 33.9%) [ 21 ]. The high burden of overweight and obesity in several MENA countries attributed to the interrelated economic, dietary, lifestyle behavioral factors. The nutrition transitions and changes in the food consumption habits were supported by the witnessed economic development in most of the MENA countries. For instance, in the past five decades, the economic development in the Arab Gulf countries linked to the discovery of oil and gas reserves led to changes in eating habits towards the consumption of foods rich in fat and calories as well as increasing behavioral habits towards a sedentary lifestyle [ 22 , 23 ]. This is particularly true with the significant shift from the consumption of traditional low-fat food to fat-rich foods, as well as with a major change from an agricultural lifestyle to an urbanized lifestyle that is often accompanied by decreased levels of physical activity. The urbanized lifestyle increases exposure to fast foods through the high penetration of fast food restaurants serving fat-rich foods, the reliance on automobiles for transport, and the increasing penetration of cell phones, all of which facilitate low levels of physical activity. Globally, physical inactivity is estimated to cause around 27% of diabetes cases [ 24 ]. In eight Arab countries, based on national samples, low levels of physical activity in adults ranged from 32.1% of the population in Egypt in 2011–2012 to as high as 67% of the population in Saudi Arabia in 2005 [ 25 ]. Furthermore, fruit and vegetable consumption is inversely associated with weight gain [ 26 ]. Studies indicated a low intake of fruit and vegetables in some of the MENA countries [ 27 , 28 ]. The growing burden of the possible risk factors of body overweight and obesity in women may further affect and exacerbate the burden of DM and its associated complications in the MENA countries.

To develop effective prevention and control interventions, there is a need for understanding the actual burden of pre-DM and T2DM epidemics in vital population groups, such as women of childbearing age (15–49 years), in the MENA region. Thus, individual studies need to be compiled and summarized. According to our previously published protocol (with a slight deviation) [ 29 ], here, we present the results of the systematically reviewed published quantitative literature (systematic review “1”), to assess the burden (prevalence) of T2DM and pre-DM in women of childbearing age in the MENA region, from 2000 to 2018.

Investing in women’s health paves the way for healthier families and stronger economies. Societies that prioritize women’s health are likely to have better population health overall and to remain more productive for generations to come [ 30 ]. Against this background, our review was aimed at characterizing the epidemiology of T2DM and pre-DM in population groups of women of childbearing age in the MENA through (1) systematically reviewing and synthesizing all available published records of T2DM and pre-DM and (2) estimating the mean T2DM and pre-DM prevalence at national, sub-regional, and regional levels, from January 2000 to July 2018. The findings of the review fill an evidence gap to inform policy-makers on the epidemiologic burden of T2DM and pre-DM in women of childbearing age.

Following our published protocol [ 29 ] that is registered with the International Prospective Registry of Systematic Reviews (PROSPERO registration number “CRD42017069231” dated 12/06/2017), we reported here systematic review “1”. This review adheres to the Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA) 2009 guidelines [ 31 , 32 , 33 ]. The PRISMA checklist is provided in the Additional file  1 .

Data source and search strategy

To identify eligible studies on T2DM and pre-DM prevalence measures in MENA countries, we implemented a comprehensive computerized search of six electronic databases (MEDLINE, EMBASE, Web of Science, SCOPUS, Cochrane library, and Academic Search Complete) from January 1, 2000, to July 12, 2018, using variant Medical Subject Headings (MeSH) and free-text (Text) terms. The detailed search strategy is presented in an additional box file (see Additional file  2 ). We also hand-searched the reference lists of eligible studies for further studies that might have been missed.

We defined the participants, exposure, comparator, outcome(s), and type of study “PECO(T)”. The PECO(T) statement provides the framework for the identification and selection of studies for inclusion [ 34 ]. As we were looking for prevalence studies, we only considered participants and the outcomes.

Inclusion and exclusion criteria

Participants : Women of childbearing age were defined according to the World Health Organization (WHO) as women aged between 15 and 49 years (thereafter, women of childbearing age) [ 35 ]. Pregnant women were also considered in this review as long as they were tested for T2DM and/or pre-DM according to what was reported in the individual studies.

Outcomes : T2DM and pre-DM. The included studies should have reported quantitative or calculable pre-DM or T2DM prevalence estimate(s) in women of childbearing age regardless of the sample size, pregnancy status, or pre-DM/T2DM ascertainment methodology, in any of the 20 MENA region countries [ 36 ]. We excluded studies of self-reported pre-DM/T2DM not supported with either anti-DM medications or a documented diagnosis. We also excluded studies on metabolic syndrome as long as there was no clear information on the proportion of women of childbearing age with pre-DM or T2DM. Studies were also excluded if they pooled women of childbearing age with pre-DM/T2DM with other non-communicable diseases in the same category, or together with males, or for each gender separately but without age stratification. We excluded studies with incalculable pre-DM/T2DM prevalence after attempting to contact the authors at least twice with no response.

Types of studies : We included observational studies if they were cross-sectional, comparative cross-sectional, case-control (not comparing T2DM/pre-DM vs. no T2DM/pre-DM), or cohort study designs. We excluded observational studies of other study designs.

Detailed eligibility criteria are available in the published protocol [ 29 ]. The PRISMA flow chart for the selection of studies is shown in Fig.  1 .

PRISMA flow chart

Identifying eligible studies

Titles and abstracts of the remaining citations were screened independently by four reviewers (AI, KA, MM, and MQ) for any potential study on pre-DM/T2DM in childbearing age women. Full-texts of the identified potentially eligible studies were thoroughly screened and independently assessed by the four reviewers. The qualities of the extracted studies were independently assessed by two other reviewers (RHA and FA). Discrepancies in data extraction were discussed and resolved.

Data extraction

Data from fully eligible studies were extracted into a pre-defined data extraction excel file using a pre-defined list of variables [ 29 ]. Our outcome of interest was the national/regional weighted pooled prevalence of T2DM and pre-DM in women of childbearing age in the MENA. We extracted the following data on the baseline characteristics of the eligible research reports (author names, year of publication, country, city, and study setting), study methodology (design, time period, sampling strategy, and T2DM/pre-DM ascertainment methodology), and study population (age, pregnancy status, co-morbidity, and number of women with the outcomes of interest).

In research reports which provided stratified T2DM/pre-DM prevalence estimates, the prevalence of the total sample was replaced with the stratified estimates keeping the rule of having at least 10 tested subjects per strata, otherwise we extracted information on the whole tested sample. We followed a pre-defined sequential order when extracting stratified prevalence estimates. Outcome measures stratified according to body mass index (BMI) were prioritized, followed by age and year. This prioritization scheme was used to identify the strata with more information on the tested women. When the strata were not prioritized, the overall outcome prevalence measured was extracted. For a research report that stratified the prevalence of the outcome of interest at these different levels (i.e., age and BMI), one stratum per research report was considered and included to avoid double counting. If the outcome measure was ascertained by more than one ascertainment guideline, we extracted relevant information based on the most sensitive and reliable ascertainment assay (i.e., prioritizing fasting blood glucose “FBG” over self-reported DM status), or the most recent and updated criteria (i.e., prioritizing WHO 2006 over WHO 1999 criteria).

We generated a funnel plot to explore the small-study effect on the pooled prevalence estimates. The funnel plot was created by plotting each prevalence measure against its standard error. The asymmetry of the funnel plot was tested using the Egger’s test [ 37 ] (see Additional files  3 and 4 ).

Quality appraisal and risk of bias

We assessed the methodological quality and risk of bias (ROB) of the studies on T2DM or pre-DM prevalence measures using six-quality items adapted from the National Heart, Lung, and Blood Institute (NIH) tool [ 38 ]. Of the 14 items proposed for observational studies on the NIH tool, eight items were not used as they are relevant only for cohort studies assessing the relationship between an exposure and an outcome [ 38 ]. We also assessed the robustness of the implemented sampling methodology and the ascertainment methodology of the measured outcome(s) using three additional quality criteria (sampling methodology, ascertainment methodology, and precision of the estimate). Studies were considered as having “high” precision if at least 100 women tested for T2DM/pre-DM; a reasonable precision, given a pooled prevalence of 7.2% for T2DM or 7.6% for pre-DM estimated in this study, was obtained. We computed the overall proportion of research reports with potentially low risk of bias across each of the nine quality criteria. We also computed the proportion (out of nine) of quality items with potentially a low risk of bias for each of the included research reports.

Quantitative synthesis: meta-analysis

Meta-analyses of the extracted data to estimate the weighted pooled prevalence of T2DM and pre-DM and the corresponding 95% confidence interval (CI) were executed. The variances of prevalence measures were stabilized by the Freeman-Tukey double arcsine transformation method [ 39 , 40 ]. The estimated pooled prevalence measures were weighted using the inverse variance method [ 40 ], and an overall pooled prevalence estimate was generated using a Dersimonian–Laird random-effects model [ 41 ]. Heterogeneity measures were also calculated using the Cochran’s Q statistic and the inconsistency index; I –squared ( I 2 ) [ 42 ]. In addition to the pooled estimates, the prevalence measures were summarized using ranges and medians. The prediction interval, which estimated the 95% interval in which the true effect size in a new prevalence study will lie, was also reported [ 42 , 43 ].

Country-level pooled estimates were generated according to the population group of tested women (general population, pregnant, non-pregnant with history of gestational DM (GDM), and patients with co-morbidity), and the overall country-level pooled prevalence, regardless of the tested population and study period. To assess if the prevalence of T2DM and pre-DM is changing over time, we stratified studies into two time periods: 2000–2009 and 2010–2018. In order not to miss any important data when estimating country-level, sub-regional, and regional prevalence, the period for studies that overlapped these two periods was defined as “overlapping”. In studies with an unclear data collection period, we used the median (~ 2 years) that was obtained from subtracting the year of publication from the year of data collection to estimate the year of data collection in those studies. The “patients with co-morbidity” included women of childbearing age with organ transplant, kidney dialysis, cancer, HIV, chronic obstructive pulmonary disease, polycystic ovarian syndrome (PCOS), or schizophrenia. Categorization of the study period was arbitrary with an aim to estimate the change in T2DM and pre-DM at the country-level and overall, over time.

We also estimated the weighted pooled prevalence, regardless of country, according to the tested women’s population group, study period, T2DM/pre-DM ascertainment guidelines (WHO guidelines, American DM Association (ADA) guidelines, International DM Association (IDF) guidelines, or medical records/anti-DM medications/self-reported), and sample size (< 100 or ≥ 100). The overall weighted pooled prevalence of T2DM and pre-DM regardless of the country, tested population, study period, ascertainment guidelines, and sample size was also generated. Providing pooled estimates regardless of the ascertainment guidelines was justified by the fact that the subject women were defined and treated as T2DM or pre-DM patients following each specific ascertainment guidelines.

To provide prevalence estimates at a more sub-regional level, countries in the MENA region were re-grouped into three sub-regions, namely, “Arab Peninsula, Fertile crescent, and North Africa and Iran.” The pooled prevalence in these three sub-regions was estimated according to the tested population group, study period, ascertainment guidelines, and sample size, as well as overall for each sub-region.

We also estimated the weighted pooled prevalence of T2DM and pre-DM according to age group. We categorized women of childbearing age into three age groups (15–29 years, 30–49 years) and not specified/overlapping. The “not specified/overlapping” category covers women who did fell in the other two age groups. For example, women with an age range of 25–34 years or 18–40 years. The age group weighted pooled prevalence produced regardless of the country, sub-region, and tested population as well as study period.

All meta-analyses were performed using the metaprop package [ 33 ] in Stata/SE v15 [ 44 ].

Sources of heterogeneity: meta-regression

Random-effects univariate and multivariable meta-regression models were implemented to identify sources of between-study heterogeneity and to quantify their contribution to variability in the T2DM and pre-DM prevalence. In univariate meta-regression models, analysis was performed by country, tested population, study period, ascertainment guidelines, and sample size. All variables with a p  < 0.1, in the univariate models, were included in the multivariable model. In the final multivariable model, a p value ≤ 0.05 was considered statistically significant, contributing to heterogeneity in prevalence estimates.

All meta-regression analyses were performed using the metareg package in Stata/SE v15 [ 44 ].

Search and eligible research reports

Of the 12,825 citations retrieved from the six databases, 48 research reports were found eligible (Fig. 1 ); 46 reported T2DM prevalence [ 45 , 46 , 47 , 48 , 49 , 50 , 51 , 52 , 53 , 54 , 55 , 56 , 57 , 58 , 59 , 60 , 61 , 62 , 63 , 64 , 65 , 66 , 67 , 68 , 69 , 70 , 71 , 72 , 73 , 74 , 75 , 76 , 77 , 78 , 79 , 80 , 81 , 82 , 83 , 84 , 85 , 86 , 87 , 88 , 89 , 90 ] while 24 reported pre-DM prevalence [ 48 , 49 , 51 , 52 , 53 , 54 , 55 , 56 , 57 , 60 , 62 , 63 , 66 , 67 , 70 , 73 , 75 , 81 , 85 , 88 , 89 , 90 ].

Scope of reviewed T2DM reports

The 46 research reports on T2DM prevalence yielded 102 T2DM prevalence studies. The 46 reports were from 14 countries (Algeria, Egypt, Iran, Iraq, Jordan, Kuwait, Lebanon, Morocco, Oman, Qatar, Saudi Arabia, Tunisia, the United Arab Emirates [UAE], and Yemen); ranging by year between 2000 in Saudi Arabia [ 79 ] and 2018 in UAE [ 81 ]. Sixteen (34.9%) research reports were reported in Saudi Arabia [ 64 , 65 , 66 , 67 , 68 , 69 , 70 , 71 , 72 , 73 , 74 , 75 , 76 , 77 , 78 , 79 ], followed by 19.6% in the UAE [ 81 , 82 , 83 , 84 , 85 , 86 , 87 , 88 , 89 ], and 15.2% in Iran [ 47 , 48 , 49 , 50 , 51 , 52 , 53 ]. Over one third (37.3%) of the yielded 102 T2DM prevalence studies were in Saudi Arabia. Of the 102 T2DM prevalence studies, 79.4% were in women sampled from general populations and 11.8% in pregnant women. Over two thirds (69.6%) of the T2DM prevalence studies were in or before 2009 and 82.4% tested ≥ 100 women (Table  1 ).

Pooled T2DM prevalence

In the 14 countries, the weighted T2DM prevalence in women of childbearing age estimated at 7.5% (95% CI, 6.1–9.0%, I 2 , 98.2%) (Table  2 , Fig.  2 ). The weighted T2DM prevalence was not significantly different ( p  = 0.4) in studies reported between 2000 and 2009 (7.9%, 95% CI, 6.2–9.7%, I 2 , 97.9%) and studies reported between 2010 and 2018 (5.8%, 95% CI, 3.4–8.7%, I 2 , 95.4%) (Table 2 ). The weighted T2DM prevalence was higher in women with an age range of 15–19 years (10.9%, 95% CI, 8.8–13.3%, I 2 , 97.9%) than women with an age range of 30–49 years (2.5%, 95% CI, 1.8–3.2%, I 2 , 83.6%) (see Additional file  5 ).

Forest plot of the meta-analyses for the 14 MENA countries’ studies on T2DM

Pooled findings of 102 T2DM prevalence estimates reported in 14 countries in the MENA region. The individual 102 estimates and their 95% confidence interval (CI) omitted to fit the plot. The diamond is centered on the summary effect estimate, and the width indicates the corresponding 95% CI. UAE, United Arab Emirates; T2DM, type 2 diabetes mellitus; MENA, Middle East and Northern Africa

The highest two weighted T2DM estimates were observed in infertile women of childbearing age in Egypt (28.2%, 95% CI, 17.4–40.3%) and in non-pregnant women with a history of GDM in Iran (24.7%, 95% CI, 18.5–31.5%) (Table 2 ). In general populations, the weighted T2DM prevalence ranged between 1.3% (95% CI, 0.0–4.7%) in 2001–2002 in Morocco [ 60 ] and 16.4% (95% CI, 6.5–29.8%, I 2 , 96.5%) in Iraq in 2007 [ 55 ] and in 2011–2012 [ 54 ]. In Saudi Arabia, in women of childbearing age sampled from general populations, the pooled T2DM prevalence estimated at 8.0% (95% CI, 5.3–11.3%, I 2 , 96.5%) (Table 1 ). In Saudi Arabia, the weighted T2DM prevalence in women of childbearing age, regardless of source of population and timeline, estimated at 7.2% (95% CI, 4.6–10.2%, I 2 , 98.6%) (Table 2 ). In Oman, the weighted T2DM prevalence in women of childbearing age sampled from general populations estimated at 8.0% (95% CI, 2.9–15.4%, I 2 , 95.9%) in 2000. In Qatar, the weighted T2DM was prevalence in women of childbearing age sampled from general populations 10.7% (95% CI, 2.2–24.4%, I 2 , 93.7%) between 2007 and 2008. In the UAE, in women of childbearing age sampled from general populations, the pooled T2DM prevalence estimated at 8.0% (95% CI, 4.8–11.9%, I 2 , 98.9%) that declined from 9.4% (95% CI, 5.6–14.1%, I 2 , 95.1%) between 2000 and 2009 to 6.0% (95% CI, 3.3–6.5%, I 2 , 90.5%) between 2010 and 2018 (Table 2 ).

Sub-regional pooled T2DM prevalence

The pooled T2DM prevalence measures estimated at 6.5% (95% CI, 4.3–9.1%, I 2 , 96.0%) in North African countries including Iran, 10.7% (95% CI 5.2–17.7%, I 2 , 90.7%) in the Fertile Crescent countries, and 7.6% (95% CI, 5.9–9.5%, I 2 , 98.5%) in the Arabian Peninsula countries (see Additional file  6 ).

Additional file  7 shows figures presenting the sub-regional-weighted prevalence of T2DM (Fig. 1 ) in women of childbearing age from 2000 to 2009 and from 2010 to 2018. Additional file  8 shows figures presenting timeline view of the weighted prevalence of T2DM (Fig. 1 ) by publication year.

Meta-bias in T2DM prevalence

The asymmetry in the funnel plot examining the small-study effects on the pooled T2DM prevalence among women of childbearing age indicates evidence for the presence of a small-study effect (Egger’s test p  < 0.0001). The funnel plot is presented in an additional figure file (see Additional file  3 ).

Predictors of heterogeneity in T2DM prevalence

In the univariate meta-regression models, all variables except study period, T2DM ascertainment criteria, and sample size were associated with T2DM prevalence at p value < 0.1. In the adjusted meta-regression model, none of the included variables was significantly associated with T2DM prevalence at p value < 0.05. In two studies in infertile women of childbearing age in Egypt, the T2DM prevalence was higher (adjusted odds ratio (aOR), 5.26, 95% CI, 0.87–32.1) compared to women of childbearing age in Saudi Arabia. Overall, compared to women of childbearing age sampled from general populations, T2DM prevalence in non-pregnant women of childbearing age with a history of GDM was 234% higher (aOR, 3.34%, 95% CI, 0.90–12.41) (see Additional file  9 ).

Scope of reviewed pre-DM reports

The 24 research reports on pre-DM prevalence yielded 52 pre-DM prevalence studies and were from 10 countries (Iran, Iraq, Jordan, Kuwait, Morocco, Oman, Qatar, Saudi Arabia, UAE, and Yemen); ranging by year between 2002 in Oman [ 62 ] and 2018 in Saudi Arabia [ 81 ]. Thirteen (25.0%), 11 (21.2%), and 11 (21.2%) of the pre-DM prevalence studies were from Iran, Saudi Arabia, and UAE, respectively. Approximately 87.0% of the pre-DM prevalence studies tested women of childbearing age sampled from general populations. The pre-DM prevalence estimates ranged from 0.0% in various age groups in multiple countries [ 51 , 60 , 70 ] to 40.0% in Iraq in women aged 20–39 years, recruited from the general population [ 55 ] (Table 1 ).

Pooled pre-DM prevalence

In the 10 countries, the weighted pre-DM prevalence in women of childbearing age was estimated at 7.6% (95% CI, 5.2–10.4%, I 2 , 99.0%) (Table  3 , Fig.  3 ). The weighted pre-DM prevalence in studies reported between 2000 and 2009 (4.8%, 95% CI 4.0–7.8%, I 2 , 97.1%) was significantly lower ( p  < 0.001) than the weighted prevalence estimated in studies reported between 2010 and 2018 (9.3%, 95%, 4.7–15.2%, I 2 , 93.9%) (Table 3 ). Weighted pre-DM prevalence was 1.70 times higher in women with an age range of 15–19 years (9.0%, 95% CI, 4.9–14.1%, I 2 , 99.2%) than women with an age range of 30–49 years (5.3%, 95% CI, 1.8–10.3%, I 2 , 99.0%) (see Additional file 5 ).

Forest plot of the meta-analyses for the 10 MENA countries’ studies on pre-DM pooled findings of 52 pre-DM prevalence estimates reported in 10 countries in the MENA region. The individual 52 estimates and their 95% confidence interval (CI) omitted to fit the plot. The diamond is centered on the summary effect estimate, and the width indicates the corresponding 95% CI. UAE, United Arab Emirates; pre-DM, pre-diabetes mellitus; MENA, Middle East and Northern Africa

In general populations, the highest three weighted pre-DM prevalence estimates were observed in women of childbearing age in Iraq (25.5%, 95% CI, 15.4–37.1%, I 2 , 92.2%), followed by UAE (15.5%, 95% CI, 10.5–21.2%, I 2 , 99.0%), and Kuwait (13.8%, 95% CI, 7.7–21.4%, I 2 , 96.8%) (Table 3 ). In 13 studies in Iran (7 from the general population), the prevalence of pre-DM ranged from 0.0 to 21.4% with an overall weighted prevalence of 3.8% (95% CI, 1.2–7.6%, I 2 , 98.3%). The 11 pre-DM studies in Saudi Arabia were in women of childbearing age sampled from the general population, with an overall weighted pre-DM prevalence of 6.6% (95% CI, 3.7–10.3%, I 2 , 93.5%) (2000–2009: 9.4% vs. 2010–2018: 4.4%). Regardless of the tested population in UAE, the weighted pre-DM prevalence was 6.6% (95% CI, 5.1–8.3%, I 2 , 65.6%) in studies reported between 2000 and 2009, and 12.0% (95% CI, 8.9–15.5%) in studies reported between 2010 and 2018 with an overall pre-DM prevalence of 14.4% (95% CI, 9.5–20.0%, I 2 , 99.1%) (Table 3 ).

Sub-regional pooled pre-DM prevalence

The pooled pre-DM prevalence estimated at 3.3% (95% CI, 1.0–6.7%, I 2 , 98.1%) in North African countries including Iran, 22.7% (95% CI, 14.2–32.4%, I 2 , 90.0%) in the Fertile crescent countries, and 8.6% (95% CI, 5.5–12.1%, I 2 , 99.1%) in the Arabian Peninsula countries (see Additional files  10 ). Additional file 7 shows figures presenting the sub-regional weighted prevalence of pre-DM (Fig. 2 ) in women of childbearing age from 2000 to 2009 and from 2010 to 2018. Additional file 8 shows figures presenting timeline view of the weighted prevalence of pre-DM (Fig. 2 ) by publication year.

Meta-bias in pre-DM prevalence measures

The asymmetry in the funnel plot examining the small-study effects on the pooled pre-DM prevalence among women of childbearing age indicates evidence for the presence of a small-study effect (Egger’s test p  < 0.0001). The funnel plot is presented in an additional figure file (Additional file  4 ).

Predictors of heterogeneity in pre-DM prevalence

Country, study period, and pre-DM ascertainment criteria were associated with a difference in the pre-DM prevalence in the univariate meta-regression models at p value < 0.1. In the univariate meta-regression models, pre-DM prevalence in women of childbearing age in Iraq was 424% higher compared to such women in Saudi Arabia (OR, 5.24, 95% CI, 1.45–18.94%). This significant association turned insignificant in the multivariable model (aOR, 2.20, 95% CI, 0.52–10.82%). In the multivariable model, compared to Saudi Arabia, pre-DM prevalence in women of childbearing age was 70% lower in Iran (aOR, 0.30, 95% CI, 0.11–0.79%) and 88% lower in Morocco (aOR, 0.12, 95% CI, 0.01–0.91%) (see Additional file  11 ).

Quality assessment of the T2DM/pre-DM research reports

Findings of our summarized and research report-specific quality assessments for relevant DM prevalence studies can be found in Additional file  12 . Briefly, all the 48 research reports clearly stated their research questions or objectives, clearly specified and defined their study populations, and selected or recruited the study subjects from the same or similar populations. There was a clear gap in the reporting or justifying of the sample size calculation in 79.2% of the research reports. The majority (87.5%) of the research reports tested ≥ 100 women of childbearing age, and they were classified as having high precision.

Overall, the 48 research reports were of reasonable quality with potentially low ROB in an average of 7.2 items (range, 6–9). Four (8.3%) of the 48 reports had potentially low ROB in all the measured nine quality items [ 66 , 82 , 83 , 86 ] (see Additional file  12 ).

We provided, to our knowledge, the first regional study that comprehensively reviewed and estimated the regional, sub-regional, and country-level burden of T2DM and pre-DM in various populations of women of childbearing age in the MENA. Based on the available data from 14 and 10 studies in MENA countries, the present findings document the comparable burden of T2DM (7.5%, 95% CI 6.9–9.0%) and pre-DM (7.6%, 95% CI 5.2–10.4%) in women of childbearing age. The estimated prevalence of T2DM and pre-DM in 14 countries in the MENA is similar to the estimated worldwide crude diabetes prevalence of 8.2% (95% credible interval (CI) 6.6–9.9%) in adult women in 2014 (age-standardized 7.9%, 95% CI 6.4–9.7%) [ 91 ]. The T2DM and pre-DM prevalence in women of childbearing age varied across the three sub-regions in the MENA, by population group, time period, DM ascertainment criteria, and sample size. The obvious common prevalence of T2DM and pre-DM in women of childbearing age in the MENA countries reflects the highest prevalence of adult diabetes estimated for the MENA [ 91 ]. In this region, the crude diabetes prevalence in adult women increased from 5.0% in 1980 to 9.0% in 2014 [ 91 ]. This increase in diabetes prevalence among adult populations in the MENA over time is higher than many other regions including Europe and Central and West Africa [ 91 ]. The highest national adult diabetes prevalence estimates documented in the MENA is 5–10 times greater than the lowest national prevalence estimates documented in Western European countries [ 91 ].

T2DM is a significant public health problem in both developed and developing countries that can lead to various health complications including increased overall risk of dying prematurely [ 20 ]. The common burden of T2DM and pre-DM in women of childbearing age, which is reflected in the high burden of adult diabetes in this region [ 91 ], might be mainly driven by the sociodemographic changes in this region. In recent decades, there was an increase in median age, sedentary lifestyle, and physical inactivity in the MENA [ 92 ]. These lifestyle changes are linked to an increase in the burden of body overweight and obesity that are shared predisposing factors for pre-DM and T2DM [ 20 ]. At the population level, physical inactivity was very common in many MENA countries (Saudi Arabia 67.6% in 2005; Kuwait 62.6% in 2014; Qatar 45.9% in 2012; Egypt 32.1% in 2011–2012; Iraq 47.0% in 2015) [ 25 ]. The burden of body overweight and obesity is higher in many low-income and middle-income countries in the MENA than in Europe and Asia Pacific countries [ 93 ]. Obesity in women in several Middle Eastern countries was 40–50% [ 93 ]. The age-standardized prevalence of obesity was 32.0% in Egypt, 35.5% in Jordan, 30.4% in Iraq, 32.5% in Libya, and 35.4% in Saudi Arabia [ 94 ]. In Tunisia, 43.7% and 24.1% of 35–70-year-old females in urban and rural areas, respectively, were obese [ 95 ]. In 2016, in almost all of the countries in MENA, the mean BMI for people aged ≥ 18 years was ≥ 25.0 [ 96 ].

To curb the burden of DM and its associated complications in women of childbearing age in the MENA countries, our results suggest three main implications for care. First, based on the estimated 5–10% progression rate from pre-DM to T2DM [ 3 , 10 ], out of the 47,958 tested women of childbearing age for pre-DM (Table 3 ), we estimate that 2398 to 4796 women are expected to progress to T2DM. This risk of progression to T2DM could be reduced through lifestyle and drug-based interventions as it was reported elsewhere [ 97 , 98 , 99 ]. In England, 55–80% of participants with hyperglycemia at baseline had normal glycaemia at 10 year follow-up [ 3 ]. The high burden of DM along with pre-DM in women of childbearing age could accelerate maternal complications including GDM leading to increased intergenerational risk of DM. Programs to halt the growing epidemic of DM among different population groups could start by addressing the key risk factors including sedentary lifestyle and increased body weight. Addressing this problem would require social and public policies and efforts to reduce the national and regional burden of increased body weight and obesity through enhancing healthy eating behaviors and physical activity. Second, there is a critical need for strengthened surveillance systems that match the scale and nature of the DM epidemic in women of childbearing age in the MENA. Enhancing early detection and management of high-risk individuals requires accessible and affordable health care systems, outreach campaigns to raise public awareness, and social and medical support to induce and maintain a healthy lifestyle. Adult people at increased risk of T2DM and pre-DM can be predicted based on good screening tools from the Centers for Disease Control and Prevention (CDC) [ 100 ] and the American Diabetes Association (T2DM Risk Test) [ 101 ]. Early screening and detection will require government-funded prevention programs. Third, controlling the burden of T2DM and pre-DM in MENA countries requires strong and successful partnerships between public health and clinical departments. Physicians have a fundamental role in the care of individual patients to screen, diagnose, and treat both pre-DM and T2DM in clinical settings. In addition, physicians have a fundamental role in working to raise awareness and participating in developing prevention programs and engaging communities. Concerted efforts and partnership between physicians, health departments, and community agencies are needed to strengthen health care services, encouraging and facilitating early screening and detection, and promoting healthy diets and physical activity.

Providing summary estimates and up-to-date mapping gaps-in-evidence of T2DM and pre-DM prevalence in women of childbearing age in different MENA countries provides the opportunities for future public health interventions and research to better characterize the T2DM and pre-DM epidemiology nationally and regionally. Nevertheless, present review findings suggest that the DM burden in women of childbearing age in MENA countries is capturing only the tip of the iceberg. Identifying gaps-in-evidence through systematically reviewing and summarizing the literature has public health research implications. Our review shows that in many countries, the estimation of the burden of T2DM or pre-DM in women of childbearing age in general populations occurred more than a decade ago (Table 1 ). Additionally, the review shows that there was no data on the burden of T2DM and pre-DM in women of childbearing age in several countries in the MENA region. This lack of evidence on a key public heath outcome requires a strongly resourced research capacity and research funding schemes. There is evidence that federally funded research can impact important health issues that affect a large segment of the population [ 102 ].

This robust approach to the literature search and review as well as in retrieving and extracting relevant data from the published literature allowed us to provide summary estimates on the burden of T2DM and pre-DM in women of childbearing age from the 14 and 10 countries in the MENA, respectively. Once the diagnosis was established, regardless of the ascertainment criteria, patients were treated as having diabetes or pre-diabetes. Thus, generating pooled estimates, regardless of the DM ascertainment criteria, stratified according to various population groups, provided more insights into the actual burden of T2DM and pre-DM in various populations of women of childbearing age. The meta-regression analysis identified sources of variations in T2DM and pre-DM prevalence and sources of between-study heterogeneity in prevalence estimates. (Additional files 9 and 11 show these in more detail). The country-stratified and population-stratified T2DM and pre-DM prevalence reports revealed gaps in evidence that can help strengthen research and DM control programs in the most affected countries and populations. The use of probability sampling was very common in the studies included, which may provide broader insights on the representation of our findings to the general or specific group of women of childbearing age at the national, but not at the regional, level.

Limitations

There are important but unavoidable limitations when interpreting the results of our review. Despite the estimated DM prevalence, the actual DM burden could have been underestimated, at country, sub-regional, or regional level, due to several reasons. The inaccessibility of data on pre-DM or T2DM in women of childbearing age from several countries in the MENA may not necessarily mean an actual lack of data. To meet the aim of our review of estimating the burden of pre-DM and T2DM in women of childbearing age, in several published studies reviewed, women of childbearing age were found to have been combined with those of other age groups or with men. The presented overall pooled estimates, regardless of the tested population group, should not be interpreted as the total burden of the outcome at the population level. Utilizing data on T2DM and pre-DM from only 14 and 10 countries may limit the findings from being generalizable to the entire MENA region. Although we followed a thorough and well-defined search strategy, there is a potential of publication bias as shown in funnel plots (Additional files 3 and 4 ). The estimated T2DM and pre-DM prevalence suggest that only the tip of the iceberg was captured. The presented estimates may not be representative of the true prevalence for each population. This underestimation may be particularly true in low-resource settings where necessary resources and capacity in investigating pre-DM at the community level are lacking. The wide array of blood glucose cut-off points and criteria used for T2DM and pre-DM ascertainment also suggests that overestimation and underestimation bias cannot be excluded. Unless estimated from individual population-based studies only, the presented weighted pooled estimates at the country, sub-regional, or regional level should not be interpreted as the burden of the measured outcomes at the population level. Also, the presented pooled estimates according to the two time periods, from 2000 to 2009 and from 2010 to 2018, should not be interpreted as an over-time change in the burden of the measured outcomes. While our meta-analyses revealed substantial heterogeneity across studies, the meta-regression analyses identified the potential sources of between-study heterogeneity within the framework of the present study and the level of detail that can be used in describing these sources (Tables  1 and 2 ). Thus, much of the variability in T2DM and pre-DM prevalence across studies might remain unexplained.

Despite these potential limitations, our study provided a characterization of the scale of T2DM and pre-DM among women of childbearing age in several MENA countries based on the best available evidence. Data presented in this review can be used to (a) understand the burden of T2DM and pre-DM among a vital population group and to identify at high-risk populations within this specific population group; (b) guide the planning, implementation, and evaluation of programs to prevent and control DM; (c) implement immediate public health actions to prioritize the allocation of public health resources; and (d) formulate research hypotheses and provide a basis for epidemiologic studies. Future research opportunities should prioritize large country-level and multicenter comparable studies, to determine the prevalence of T2DM and pre-DM in various population groups of women of childbearing age. A definitive characterization of the burden of DM in women of childbearing age at the regional and sub-regional level would require comparable and empirical studies using standardized methodology and comparable DM ascertainment assays.

In conclusion, women of childbearing age in the MENA region bear an appreciable burden of T2DM and pre-DM. The estimated burden of T2DM and pre-DM was higher in the Arabian Peninsula and Fertile Crescent countries compared to the rest of the MENA countries identified with prevalence estimates in this review. Although both T2DM (7.5%) and pre-DM (7.6%) had similar overall estimated prevalence, there is need for a more focused attention on early detection and control by public health authorities to avoid DM-associated pre-gestational, gestational, and post-gestational complications. Country-level early DM detection and control programs should consider the key risk factors of DM, mainly the growing burden of body overweight and obesity. Furthermore, facilitating high-quality research and surveillance programs in countries with limited data on DM prevalence and reporting of DM prevalence estimates in women of childbearing age warrant focus.

Availability of data and materials

The datasets used and/or analyzed during the current study and its supplementary information files are available from the corresponding author on reasonable request.

Abbreviations

American DM association

Adjusted odds ratio

Confidence interval

Diabetes mellitus

Gestational diabetes mellitus

International Diabetes Mellitus Association

Middle East and North Africa

Medical Subject Headings

National Heart, Lung, and Blood Institute

Participants, exposure, comparator, and outcome

• Pre-diabetes mellitus

Preferred Reporting Items for Systematic Review and Meta-Analysis

Risk of bias

• Type 2 diabetes

United Arab Emirates

World Health Organization

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Acknowledgments

Authors are grateful to the Institute of Public Health, College of Medicine and Health Sciences at the United Arab Emirates University for the infrastructure provided.

This systematic review was funded by the Summer Undergraduate Research Experience (SURE) PLUS-Grant of the United Arab Emirates University, 2017 (Research grant: 31M348). The funder had no role in the study design, collection, analysis, or interpretation of the data, nor in writing and the decision to submit this article for publication.

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RHA conceptualized and designed the study. AI, MM, MQ, KA, and FA assessed the eligibility of the retrieved citations in the titles/abstracts and full-text screening phases. RHA, MM, and FA critically assessed the eligible studies and extracted data. RHA analyzed and interpreted the data. RHA drafted the manuscript. All authors critically reviewed the manuscript. RHA read and approved the final manuscript. All authors read and approved the final manuscript.

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Supplementary information

Additional file 1..

PRISMA checklist.

Additional file 2.

Search strategies for the six databases, from January 1, 2000 to July 12, 2018.

Additional file 3

Funnel plots examining small-study effects on the pooled T2DM prevalence among women of childbearing age. Egger’s test p <0.0001.

Additional file 4

Funnel plots examining small-study effects on the pooled pre-DM prevalence among women of childbearing age. Egger’s test p <0.0001.

Additional file 5.

Weighted prevalence of T2DM and pre-DM in childbearing age women in MENA countries according to age group.

Additional file 6.

Sub-regional weighted prevalence of T2DM in women of childbearing age according to the tested population, data collection period, T2DM ascertainment, sample size, and overall, in 14 MENA countries.

Additional file 7.

Sub-regional weighted prevalence of T2DM (Figure 1 ) and pre-DM (Figure 2 ) in women of childbearing age from 2000 to 2009 and from 2010 to 2018. Square represents the estimated prevalence and lines around the square represent the upper and lower limit of the 95% confidence interval of the prevalence.

Additional file 8.

Timeline view of the weighted prevalence of T2DM (Figure 1 ) and pre-DM (Figure 2 ) in women of childbearing age, by publication year.

Additional file 9.

Univariate and multivariable meta-regression analyses to identify sources of heterogeneity in studies reporting on T2DM prevalence in women of childbearing age by the different measured characteristics.

Additional file 10.

Sub-regional weighted prevalence of pre-DM in childbearing age women according to the tested population, data collection period, Pre-DM ascertainment, sample size, and overall, in the four sub regions of the 10 MENA countries.

Additional file 11.

Univariate and multivariable meta-regression analyses to identify sources of heterogeneity in studies reporting on pre-DM prevalence in women of childbearing age by the different measured characteristics.

Additional file 12.

Quality assessment of the 48 research reports included in the analysis.

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Al-Rifai, R.H., Majeed, M., Qambar, M. et al. Type 2 diabetes and pre-diabetes mellitus: a systematic review and meta-analysis of prevalence studies in women of childbearing age in the Middle East and North Africa, 2000–2018. Syst Rev 8 , 268 (2019). https://doi.org/10.1186/s13643-019-1187-1

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Systematic Reviews

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Literature review of type 2 diabetes mellitus among minority Muslim populations in Israel

Affiliation.

• 1 Yulia Treister-Goltzman, Roni Peleg, the Department of Family Medicine and Siaal Research Center for Family Practice and Primary Care, Faculty of Health Sciences, Ben-Gurion University of the Negev, Beer-Sheva and Clalit Health Services, Southern District, Beer-Sheva 84105, Israel.
• PMID: 25685290
• PMCID: PMC4317312
• DOI: 10.4239/wjd.v6.i1.192

This review surveys the literature published on the characteristics and implications of pre-diabetes and type 2 diabetes mellitus (T2DM) for the Arab and Bedouin populations of Israel. T2DM is a global health problem. The rapid rise in its prevalence in the Arab and Bedouin populations in Israel is responsible for their lower life expectancy compared to Israeli Jews. The increased prevalence of T2DM corresponds to increased rates of obesity in these populations. A major risk group is adult Arab women aged 55-64 years. In this group obesity reaches 70%. There are several genetic and nutritional explanations for this increase. We found high hospitalization rates for micro and macrovascular complications among diabetic patients of Arab and Bedouin origin. Despite the high prevalence of diabetes and its negative health implications, there is evidence that care and counseling relating to nutrition, physical activity and self-examination of the feet are unsatisfactory. Economic difficulties are frequently cited as the reason for inadequate medical care. Other proposed reasons include faith in traditional therapy and misconceptions about drugs and their side effects. In Israel, the quality indicators program is based on one of the world's leading information systems and deals with the management of chronic diseases such as diabetes. The program's baseline data pointed to health inequality between minority populations and the general population in several areas, including monitoring and control of diabetes. Based on these data, a pilot intervention program was planned, aimed at minority populations. This program led to a decrease in inequality and served as the basis for a broader, more comprehensive intervention that has entered the implementation stage. Interventions that were shown to be effective in other Arabic countries may serve as models for diabetes management in the Arab and Bedouin populations in Israel.

Keywords: Arabs; Bedouins; Ethnic differences; Muslims; Pre-diabetes; Risk factors for diabetes; Type 2 diabetes mellitus.

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Serum brain-derived neurotrophic factor levels in type 2 diabetes mellitus patients and its association with cognitive impairment: A meta-analysis

Contributed equally to this work with: Wan-li He, Fei-xia Chang

Roles Conceptualization, Data curation, Formal analysis, Methodology, Writing – original draft

Affiliation Department of Medical Imaging Center, Gansu Provincial Maternal and Child Care Hospital (Gansu Provincial Central Hospital), Lanzhou, Gansu, China

Roles Conceptualization, Data curation, Investigation, Methodology, Writing – original draft

Roles Data curation, Formal analysis, Investigation, Writing – original draft, Writing – review & editing

Roles Investigation, Methodology, Visualization, Writing – original draft, Writing – review & editing

Roles Methodology, Visualization, Writing – original draft, Writing – review & editing

Affiliation Department of Medical Imaging Center, Gansu Provincial Maternal and Child Care Hospital, Lanzhou, Gansu, China

Roles Data curation, Project administration, Visualization, Writing – original draft, Writing – review & editing

* E-mail: [email protected]

Affiliation Department of Radiology, Gansu Provincial Hospital, Lanzhou, Gansu, China

• Wan-li He, 
• Fei-xia Chang, 
• Tao Wang, 
• Bi-xia Sun, 
• Rui-rong Chen, 
• Lian-ping Zhao

• Published: April 22, 2024
• https://doi.org/10.1371/journal.pone.0297785
• Peer Review
• Reader Comments

To compare the serum levels of brain-derived neurotrophic factor (BDNF) in type 2 diabetes mellitus (T2DM) patients with healthy controls (HC) and evaluate the BDNF levels in T2DM patients with/without cognitive impairment.

PubMed, EMBASE, and the Cochrane Library databases were searched for the published English literature on BDNF in T2DM patients from inception to December 2022. The BDNF data in the T2DM and HC groups were extracted, and the study quality was evaluated using the Agency for Healthcare Research and Quality. A meta-analysis of the pooled data was conducted using Review Manager 5.3 and Stata 12.0 software.

A total of 18 English articles fulfilled with inclusion criteria. The standard mean difference of the serum BDNF level was significantly lower in T2DM than that in the HC group (SMD: -2.04, z = 11.19, P <0.001). Besides, T2DM cognitive impairment group had a slightly lower serum BDNF level compared to the non-cognitive impairment group (SMD: -2.59, z = 1.87, P = 0.06).

BDNF might be involved in the neuropathophysiology of cerebral damage in T2DM, especially cognitive impairment in T2DM.

Citation: He W-l, Chang F-x, Wang T, Sun B-x, Chen R-r, Zhao L-p (2024) Serum brain-derived neurotrophic factor levels in type 2 diabetes mellitus patients and its association with cognitive impairment: A meta-analysis. PLoS ONE 19(4): e0297785. https://doi.org/10.1371/journal.pone.0297785

Editor: Purvi Purohit, All India Institute of Medical Sciences, INDIA

Received: July 25, 2023; Accepted: January 12, 2024; Published: April 22, 2024

Copyright: © 2024 He et al. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: All relevant data are within the manuscript and its Supporting Information files.

Funding: The author(s) received no specific funding for this work.

Competing interests: The authors have declared that no competing interests exist.

1. Introduction

Type 2 diabetes mellitus (T2DM) is a chronic systemic metabolic disorder seriously affecting human health, which is triggered by genetic predisposition and environmental factors [ 1 ]. International Diabetes Federation estimates that T2DM occurs in over 400 million people and it is one of the largest epidemics worldwide [ 2 ]. T2DM manifesting through fasting and post-prandial hyperglycemia can induce various life-threatening co-morbidities and complications such as diabetic neuropathy and diabetic nephropathy [ 3 , 4 ]. Cognitive dysfunction is an important complication observed in type T2DM patients [ 5 ]. In addition, T2DM is an important risk factor implicated in cognitive deficits except aging and neurodegenerative disorder [ 6 ]. T2DM patients have a greater decline in cognitive function than those without T2DM [ 7 ]. Besides, it is reported that T2DM accelerates brain aging and cognitive decline [ 8 ]. T2DM is significantly associated with an increased risk of dementia and a large portion of T2DM patients with cognitive impairment eventually progress to dementia [ 9 , 10 ], which may represent a consequence of brain-specific insulin resistance and impaired glucose regulation [ 11 ]. However, the pathophysiological mechanisms of cerebral impairment in T2DM remain elucidated.

Brain-derived neurotrophic factor (BDNF), a member of the neurotrophic family of proteins, is most widely distributed in the central nervous system (CNS) [ 12 ]. It plays an important role in protecting neurons and synaptic activity [ 13 ]. BDNF was released from the brain to peripheral circulation [ 14 ], and there is a correlation between BDNF in serum and CNS, providing an alternative measure of BDNF changes [ 15 ]. Alternation of BDNF is observed in the pathophysiological basis of many neurodegenerative and psychiatric disorders [ 16 ], including Alzheimer’s disease and depression [ 17 , 18 ]. Furthermore, the serum BDNF is a useful biomarker for executive cognitive impairment in schizophrenia patients [ 19 , 20 ]. In addition, the BDNF Val66Met polymorphism may be a major factor in the susceptibility to cognitive impairment which affects the secretion of mature BDNF [ 21 ]. A meta-analysis suggests that BDNF Val66Met is associated with cognitive impairment in Parkinson’s disease [ 22 ], confirming that BDNF is a risk factor for this disorder [ 23 ]. Furthermore, BDNF is related to the regulation of glucose levels [ 24 ]. Exogenous BDNF reduces blood glucose concentrations and glycated hemoglobin in obese diabetic mice [ 25 ], which is consistent with the finding that there was a positive correlation between BDNF and insulin sensitivity [ 26 ]. Previous studies have revealed the relationship between serum BDNF and diabetic conditions in T2DM patients with controversial results [ 27 – 34 ]. However, the precise role of BDNF in the development of T2DM patients as well as in cognitive function remains unclear.

Therefore, our study aims to explore the alteration tendency of the serum BDNF levels in T2DM patients with or without cognitive impairment using meta-analysis with a comprehensive evaluation of relevant literature. The current study will provide a basic foundation for further investigating the neuropathophysiological mechanisms of cerebral damage in T2DM.

2.1. Literature search and selection

A systemic search strategy was used to identify the relevant studies published in PubMed, EMBASE, and the Cochrane Library from inception to December 2022. We applied a search strategy based on the combination of relevant terms. Two independent investigators acquired articles and sequentially screened their titles and abstracts for eligibility. Then, full texts of articles deemed potentially eligible were acquired. Any disagreement would be solved via discussion with the help of a third senior investigator. A screening guide was used to ensure that the selection criteria were constantly applied.

Inclusion criteria: (1) clinical cross-sectional studies concerning the quantitative values of serum BDNF level in T2DM patients; (2) sufficient data were available for mean and standard deviation analysis of BDNF level; (3) original research. Exclusion criteria: (1) review, abstracts only, letters, comments, guidelines, and case reports; (2) studies in vitro or in animal models; (3) duplicate publications; (4) incomplete data.

2.2. Quality evaluation and data extraction

Agency for Healthcare Research and Quality (AHRQ) was used to evaluate the quality of the included cross-sectional studies. The AHRQ included 8 items with a total score of 8 points. Two independent researchers assessed the quality of the literature and reached a consensus after consultation when necessary.

Calculate standard deviation from confidence interval:

(2) Calculate the standard deviation from an interquartile range:

(3) Calculate standard deviation by p -value:

2.3. Statistical analysis

All the meta-analyses were performed on Review Manager 5.3 and STATA12.0 with a significance level of P <0.05. To calculate the effect size for each study, the summary standard mean difference (SMD) and 95% confidence interval were applied to evaluate the serum BDNF values between T2DM and healthy control (HC), T2DM with or without cognitive impairment. Pooled SMD and corresponding 95% confidence interval were calculated using the inverse variances method. Heterogeneity was estimated using the Cochran Q ( P ) and the inconsistency index where a P value less than 0.05 and I 2 value greater than 50% indicated the presence of significant heterogeneity across the enrolled studies. If notable heterogeneity was observed, a random-effect model was applied and subgroup analyses were used to determine factors that contributed to the heterogeneity and to explore how those factors influenced the results. Subgroup analysis was stratified by the BDNF measuring instruments brand (China or USA; same brand in China or USA), ethnicity (Asian or European), and population [adults or the aged (years≥60)]. In addition, sensitivity analysis was performed to evaluate the reliability of included studies using STATA 12.0. The Egger’s test and the Begg’s test were applied to evaluate potential publication bias using STATA 12.0.

3.1. Search and selection results

The main search strategy is illustrated in Table 1 . Studies selection was managed using EndNote X7. A total of 678 records were initially identified, but only 501 records remained after the elimination of duplicates. Only 51 records were remaining after screening titles, and subsequently, 29 records remained after reading the abstract. After reading full texts, 11 articles with incomplete data were excluded and finally, 18 articles were enrolled. The flow diagram is shown in Fig 1 .

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3.2. Characteristics and quality evaluation

Eighteen articles were included in the meta-analysis. The basic characteristics and quality evaluation of the studies are shown in Table 2 . Among them, 17 articles had T2DM and HC groups, and 3 articles divided the T2DM group into two subgroups according to the presence of cognitive impairment. Of the 18 articles included, 13 were done in China, 2 in Japan, and 1 in each of the following countries (USA, Italy, and Turkey). The sample’s mean age was >18 years in 15 articles and >60 years in 3 articles. The diagnostic criteria of T2DM as recommended by the World Health Organization were adopted in 11 articles; whereas the American Diabetes Association was employed in 1 article, but the remaining articles were not mentioned. Measurement of BDNF using ELISA in 17 articles, but 1 article was not mentioned. All of the 18 included studies were cross-sectional studies. Based on the quality evaluation of AHRQ, 11 studies scored 8, 4 studies scored 7, 1 study scored 5, and 1 study scored 4.

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3.3. Meta-analysis

We compared the BDNF level between T2DM and HC groups ( P < 0.001, I 2 = 99%), and between the T2DM with or without cognitive impairment groups ( P < 0.001, I 2 = 90%) using a random-effect model since the heterogeneity test showed the I 2 value >50%.

Seventeen articles contained 2966 T2DM cases and 3580 HCs. The serum BDNF level in the T2DM group was significantly lower than that in the HC group [SMD: -2.04, z = 11.19, P < 0.001] ( Fig 2A ) . The number of T2DM patients with or without cognitive impairment was 672 and 1913, respectively. The serum BDNF levels in T2DM with cognitive impairment group had a marginal difference from those without cognitive impairment [SMD: -2.59, z = 1.87, P = 0.06] ( Fig 2B ) .

(A) The different BDNF levels between T2DM and HC. (B) The different BDNF levels in T2DM patients with or without cognitive impairment. Abbreviations: BDNF, brain-derived neurotrophic factor; T2DM, type 2 diabetes mellitus; HC, healthy controls.

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3.4. Sensitivity analysis

Sensitivity analyses were conducted to evaluate the robustness of the findings by excluding 1 study at a time to assess if the results were driven by any one study. The significance of the meta-analysis outcome for T2DM and HC group changed when ruling out any one of 6/17 studies and the results also changed in T2DM with or without cognitive impairment group after ruling out 1/3 study, suggesting the results were unstable.

3.5. Subgroup analysis

Subgroup analysis based on the BDNF measuring instruments (either China or USA) exhibited that there were significant differences in BDNF values between T2DM and HC (China: P = 0.05; USA: P < 0.001; Total: P < 0.001), with large heterogeneity (China: P < 0.001 and I 2 = 100%; USA: P < 0.001 and I 2 = 99%; Total: P < 0.001 and I 2 = 99%) ( Fig 3 ) . Then, subgroup analysis was performed on the same instrument brand in China or the USA, respectively and similar results were observed (China: P <0.001; USA: P = 0.002; Total: P <0.001). The heterogeneity was only observed in the same brand from the USA, but not in the same brand from China [China: (P = 0.84 and I 2 = 0%; USA: P < 0.001 and I 2 = 98%; Total: P < 0.001 and I 2 = 96%)] ( Fig 4 ) . Subgroup analysis based on ethnicity and population distribution presented that the BDNF values were significantly different, except for the European (Asian: P < 0.001; European: P = 0.59; Total: P < 0.001). In addition, there was significant difference in the adults in T2DM and HC, except for the aged (adults: P < 0.001; the aged: P = 0.25; Total: P < 0.001), and there were no marked decrease in heterogeneity (Asian: P < 0.001 and I 2 = 99%; European: P < 0.001 and I 2 = 99%; Total: P < 0.001 and I 2 = 99%) (Adults: P < 0.001 and I 2 = 99%; the aged: P = 0.03 and I 2 = 80%; Total: P < 0.001 and I 2 = 99%) (Figs 5 and 6 ) .

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3.6. Publication bias analysis

After Egger’s and Begg’s test, the studies of T2DM and HC group, T2DM with or without cognitive impairment group showed no significant publication bias ( P = 0.606, P = 0.672; P = 0.202, P = 1.000).

4. Discussion

Our study is the first meta-analysis to evaluate the levels of serum BDNF in T2DM patients and HCs and compare the levels between T2DM patients with or without cognitive impairment. We found that the serum BDNF levels were lower in T2DM compared with HC. Furthermore, the serum BDNF levels had a decreasing tendency in T2DM patients with cognitive impairment compared with those without cognitive impairment.

The BDNF plays a key role in the pathophysiology of T2DM due to improving glucose metabolism and insulin sensitivity [ 44 – 46 ]. Previous studies have reported that T2DM patients exhibited significantly lower levels of serum BDNF compared with normal controls [ 27 – 32 , 43 ], which is consistent with our research. Additionally, the cerebral output of BDNF is inhibited under hyperglycemia, logically decreased serum BDNF may be detected in the uncontrolled T2DM patients [ 14 ]. This is in line with the findings that there is an inverse correlation between serum BDNF levels and long-standing diabetes, in males and aged T2DM patients [ 43 ]. Interestingly, upregulated serum BDNF levels in T2DM patients were also reported [ 33 , 34 ]. Such discrepancy is possibly related to physical exercise, obesity, and a balanced diet in T2DM patients [ 47 – 50 ]. In addition, the serum BDNF levels are increased in T2DM patients who received metformin treatment [ 3 ]. This may also link to a compensatory mechanism of serum BDNF release in T2DM [ 33 ], which is supported by the findings that the upregulated serum BDNF levels control blood glucose in newly diagnosed T2DM patients, but this control ability might be lost in a long term T2DM patients [ 35 ]. Our explanation is further supported by a resting state fMRI report showing enhanced functional connectivity of the left hippocampus (a major source of BDNF) with the left inferior frontal gyrus in the early stage of T2DM, which might contribute to adaptive compensation of hippocampal function [ 51 ]. Taken together, the serum BDNF could be a useful biological marker to monitor the development of T2DM and the cerebral impairment in T2DM.

T2DM has reduced the number of new neurons in the hippocampus, and hippocampal neurogenesis plays an important role in learning and memory function throughout life [ 52 ]. The Hippocampal perhaps regulates BDNF to provide neuroprotection and control of synaptic interactions [ 53 – 56 ]. In the present meta-analysis, the serum BDNF levels presented a decreasing tendency in T2DM patients with cognitive impairment compared with those patients without cognitive impairment. Such downregulation was also observed in Alzheimer’s disease, showing that the serum BDNF levels may be involved in the progression of cognitive impairment [ 57 ]. Such findings showed that serum BDNF levels may be involved in the progression of cognitive impairment in patients with T2DM. Thus, the reduction of BDNF might contribute to the neuropathophysiology of brain damage in T2DM, especially relating to cognitive impairment in T2DM.

However, substantial heterogeneity existed in the present meta-analysis. The heterogeneity could be generated from related factors, including the different brands of instruments for measuring BDNF, times and methods of blood collection, population distributions, and ethnicities. Such heterogeneity has been eliminated in the subgroup analysis by comparing the data from the same brands of instruments.

There are some limitations in the study. Firstly, the meta-analysis mostly included Chinese Han populations, which may not reflect the entire population/race. Secondly, different diagnostic criteria for diabetes were applied which might also compromise the data analysis. Although internationally recognized scales were utilized, the lack of a standard protocol for cognitive impairment could lead to inconsistent results.

In conclusion, the present meta-analysis suggests that the decrease in serum BDNF levels in T2DM patients has resolved the inconsistencies in previous studies. The serum BDNF levels in T2DM patients with cognitive impairment had a downward trend compared with those patients without cognitive impairment. Moreover, the reduction of serum BDNF may be a vital neuropathophysiological mechanism of cognitive impairment in T2DM patients.

Supporting information

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https://doi.org/10.1371/journal.pone.0297785.s001

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https://doi.org/10.1371/journal.pone.0297785.s002

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SYSTEMATIC REVIEW article

Recurrent urinary tract infections and type 2 diabetes mellitus: a systematic review predominantly in women.

• 1 Medical School, University of Texas Southwestern Medical Center, Dallas, TX, United States
• 2 Department of Urology, University of Texas Southwestern Medical Center, Dallas, TX, United States

Background: Type 2 diabetes mellitus is considered a risk factor for developing recurrent urinary tract infections. This review examined current knowledge on the incidence rates, bacterial strains, risk factors, treatments, and outcomes of recurrent urinary tract infections in type 2 diabetes, predominantly in women.

Methods: A systematic review was conducted for all English language articles from inception to June 2022 utilizing the Cochrane and Preferred Reporting Items for Systematic Reviews and Meta-Analyses standards in the databases PubMed, OVID Embase, and Cochrane Library. References were cross-examined for further articles. Data collected described the prevalence, characteristics, and management of recurrent urinary tract infections. Risk of bias assessments were performed for all studies.

Results: From 3342 identified articles, 597 met initial study criteria. Fifteen studies from 10 countries were included after full-text reviews. Four studies found higher recurrent urinary tract infection rates in diabetics versus non-diabetics meanwhile others reported recurrence rates from 23.4% to 37%. Four of five studies found diabetes to be a risk factor for recurrent urinary tract infection. E. coli was the most frequent causative pathogen. Antibiotic prescription results varied; however, multiple studies determined that longer treatment (≥ 5 days) did not correlate with lower recurrence rates. Risk of bias assessments found the most frequent study weakness to be identification of confounding variables.

Conclusion: This review covered multiple subtopics, with few comprehensive or generalizable results, suggesting a need for more research on how recurrent urinary tract infections can be better evaluated and managed in women with type 2 diabetes.

Introduction

Urinary tract infection (UTI) is the most common adult bacterial infection in the world, affecting over 60% of women at least once in their lifetime and becoming a recurrent urinary tract infection (rUTI) in more than a quarter of women ( 1 – 5 ). With the growing issue of antibiotic resistance, there is an urgent need to expand our understanding of UTIs, especially recurrent infections ( 6 ). Unfortunately, there are few published studies on rUTIs to guide clinical diagnosis and treatment ( 2 , 5 , 6 ).

Patients with type II diabetes mellitus (T2DM) are of special interest to researchers as many studies have shown that individuals with T2DM suffer from UTIs at a higher rate than those without T2DM ( 7 – 9 ). The incidence of diabetes in the US is rapidly increasing, therefore, UTIs are likely to become even more prevalent ( 10 ). A 2021 systematic review summarized the current literature on UTIs and diabetes ( 11 ). However, there is limited literature that focuses on rUTIs in women with T2DM.

Until recently, there were multiple limitations on rUTI research which contributed to the lack of rUTI studies in various populations. Criteria for rUTI diagnosis were not well defined and studies in humans were lacking ( 12 ). In a 2018 study, various diagnostic criteria for rUTIs were compared across studies, highlighting the need for one clearly defined, uniform diagnostic criteria ( 13 ). With the increase in both antibiotic resistant infections and T2DM across the world, understanding the relationship between recurrent urinary tract infections and diabetes is crucial ( 1 , 14 ). Given this context and several gaps in knowledge, our goal was to analyze current literature to understand where rUTI research in T2DM populations stands. We aimed to identify all English language articles on the topic of recurrent urinary tract infections in adult, type II diabetic women and compare research outcomes across studies on rUTI diagnostic criteria, rUTI incidence rates, characterization of rUTIs, risk factors for rUTIs, workup and diagnostic methods, UTI treatment durations, antibiotic prescription rates, antibiotic resistances, and the correlation of SGLT2 inhibitor use with rUTIs. We hypothesized that rUTI incidence rates would be higher in T2DM women than non-T2DM women.

Study design

We aimed to collect all studies with relevant information on the workup, characteristics, and treatment of rUTIs in women with T2DM including data on rUTI incidence rates, common bacterial strains, symptoms of infection, risk factors for rUTI, rUTI treatments, and post-treatment outcomes.

Systematic review

A systematic review was performed in accordance with Cochrane and Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) standards ( 15 ). We reviewed articles published from inception to June 2022 in PubMed/MEDLINE, OVID Embase, and Cochrane Library. The references of relevant articles were hand searched by the reviewers to identify any additional articles. The study criteria outlined below were used in this review:

Inclusion criteria

- Full text, English-language, prospective cohort, retrospective cohort, and randomized control trial studies of adult female patients.

- Studies focused on rUTI and T2DM; studies with an initial focus on UTI with relevant, clearly defined rUTI data, were included.

Exclusion criteria

- Abstract-only texts, individual case reports, review articles, non-human studies. Review articles were not excluded until full-text examination and reference screening to ensure comprehensive article identification.

- Studies with strictly male or pediatric populations; due to the limited dataset, studies including men or pediatric patients were not excluded if the study included a significant proportion of women. Such studies were reported separately.

- Asymptomatic bacteriuria, pyelonephritis, and unspecified genitourinary infections.

- Type I diabetic populations only; due to the limited dataset, groups with both Type I and Type II diabetes were included and independently analyzed.

The search was conducted using the keywords [recur*] AND [urinary tract infection*] AND [diabetes] including MeSH terms. Alternative spellings, names, and abbreviations, such as “rUTI”, “chronic”, cystitis”, “T2DM” and “adult-onset diabetes” were thoroughly searched in all combinations. Additional keywords for diabetes medications such as “metformin,” “Ozempic,” and “sodium-glucose co-transporter 2 inhibitor” were used as alternative terms to find all possible additional diabetic populations. Keywords appeared at least once in the title, abstract, keywords, or full text. Findings were compared between reviewers and differences were reconciled after careful examination and discussion.

Through a multi-database and cross-reference search, 3342 records were identified. The titles of the articles were reviewed by two independent reviewers and were excluded if they did not meet study criteria or were duplicates. This step yielded 597 abstracts which were then further reviewed. 153 full-text studies were assessed for initial eligibility and were excluded based on format and topic exclusions. A total of 15 articles met all eligibility criteria.

Fifteen studies were included in the final review ( Figure 1 ). This included 4 prospective studies, 10 retrospective studies, and 1 study that was both prospective and retrospective. The countries of origin were the United States (n = 3), Netherlands (n = 2), Taiwan (n = 2), India (n = 2), Germany (n = 1), Greece (n = 1), Spain (n = 1), Israel (n = 1), Japan (n = 1), and Italy (n = 1).

Figure 1 PRISMA 2020 flow diagram for new systematic reviews which included searches of databases and registers only. Adapted from: Page MJ, McKenzie JE, Bossuyt PM, Boutron I, Hoffmann TC, Mulrow CD, et al. The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. BMJ 2021;372:n71. doi: 10.1136/bmj.n71. UTI, urinary tract infection; rUTI, recurrent urinary tract infection; T2DM, type II diabetes mellitus.

RUTI diagnosis criteria and incidence rates

The definitions and diagnostic criteria of recurrent UTI are included for each study ( Table 1 ). Ten studies defined rUTI with a combination of symptomatology, urine culture, and prescription patterns and five studies used only one criterion. Nine studies set time frames in which multiple UTI diagnoses had to be made to consider the infection “recurrent”, most commonly 1 year.

Table 1 Overview of current RUTI and diabetes studies including women.

Twelve studies reported rUTI incidence rates. Grandy et al. reported both UTI and rUTI rates for their T2DM group, meanwhile Fu et al. studied general rUTI diagnosis rates in newly diagnosed T2DM patients. Other studies looked at recurrence rates in diabetic vs non-diabetic populations after a UTI diagnosis. Schneeberger et al. and Gorter et al. both found that women with DM had higher recurrence rates than their non-DM counterparts, however, neither of these studies distinguished between Type I and Type II diabetic patients. In contrast, two studies did not find significant differences in diabetic vs non-diabetic rUTI rates; once again, these studies did not differentiate Type 1 Diabetes Mellitus (T1DM) from T2DM.

Characterization of RUTI

Of the five studies that reported specific strains, all 5 found Escherichia coli (E. coli) to be the most frequent causative agent for UTI (56.1% - 96.2%) both in diabetics and non-diabetics. None of the studies reported a significant difference between the two groups. Two studies reported pathogen rates in their studies specific to rUTI, both of which found E. coli to be the most frequently rUTI pathogen. Aswani et al. reported a higher prevalence of extended-spectrum beta-lactamase (ESBL) E. coli in diabetics vs. non-diabetics; in contrast, Yoon et al. found no significant differences in pathogen distribution between groups.

Risk factors for RUTI

Diabetes has been shown to be a risk factor for UTI across many former studies ( 11 ). Of the 15 studies included in this review, five studies sought out to determine if diabetes was a risk factor for developing recurrent UTI, specifically. Yoon et al. found that having diabetes was significantly associated with the progression of acute to recurrent cystitis while Moustakas et al. found that DM was a risk factor for rUTI. Similarly, Grigoryan et al. found that the presence of DM was a determinant of late recurrence of UTI. For studies looking at risk factors for rUTI in diabetic populations, results varied greatly. Gorter et al. reported several risk factors for rUTI in women, including insulin treatment. Contrary to this finding, Wilke et al. reported that insulin treatment was not associated with rUTI risk.

Workup, diagnosis, and treatment durations

Eight studies discussed the diagnosis, treatment, or outcomes of rUTIs in diabetes. Grigoryan et al. reported that women with diabetes and acute cystitis were less likely to receive workup for new cystitis events but were more likely to receive longer durations of antibiotics. They also found that treating UTI episodes for longer did not correlate with lower rates of recurrence. Similarly, Schneeberger et al. reported that women with DM received longer and more potent antimicrobial treatment for UTIs but had higher recurrence rates than non-DM women.

Antibiotic prescription rates

Gorter et al. found that both women with and without DM received significantly different antibiotic prescriptions between their first episode of UTI and their recurrent episodes, but that diabetes did not influence antibiotic prescription patterns. Moustakas and Grigoryan et al. both reported that fluoroquinolones were the most prescribed antibiotic class; the latter also found that there was no clinically meaningful difference in prescription patterns between DM status groups. Similarly, Schneeberger et al. found no statistically significant difference in fluoroquinolone prescription between groups but reported that postmenopausal patients with DM were more likely to receive norfloxacin with longer treatment duration.

Antibiotic resistance

Antibiotic resistance patterns were discussed in three studies. Aswani et al. reported similar patterns in both DM and non-DM populations; similarly, Bonadio et al. reported slight differences that did not reach statistical significance. Neither of these studies specified the impact of antibiotic resistance on rUTIs. Moustakas et al. looked at how resistance to antimicrobials was associated with multiple UTIs. They found that resistance to colistin and imipenem was associated with a history of >2 UTI episodes but observed only in a few patients.

SGLT2 inhibitors

Two studies focused on the effects of sodium-glucose co-transporter 2 inhibitors (SGLT2i) on UTIs and their recurrences. The study by Lin YH et al. investigated the risk factors related to genitourinary tract infections with SGLT2i use. The authors found a 28.2% recurrence rate. The other study, Lorenzo et al. found that 10 out of 691 patients interrupted their SGLT2i use due to rUTIs.

Risk of bias assessment

The Joanna Briggs Institute (JBI) critical appraisal checklist was used to analyze the risk of bias for the thirteen cohort studies and two cross-sectional analyses in this review. The results are summarized in ( Table 2 ). The studies included in this review were not focused on acute treatment of a single UTI infection, and as a result, certain criteria for the risk of bias assessment categories were not applicable. The articles otherwise scored highly for similarities in the groups, exposure measurement, and statistical analysis. Some of the studies did not identify and/or address confounding variables, however, these studies were still high enough quality for inclusion.

Table 2 Risk of bias assessment for cohort and analytical cross-sectional studies.

This systematic review of existing literature on rUTIs in women with diabetes was done according to PRISMA guidelines. In addition to a relative dearth of publications, we observed that only two studies reported exclusively on rUTIs in T2DM women. The others included UTI data as well as information on diabetic men. RUTI definitions were heterogeneous and most did not comply with the more recently adopted criteria of two symptomatic UTIs in six months of three in a year ( 13 ). In the end, fifteen articles were identified as relevant to this topic, covering various subtopics including rUTI incidence rates, characteristics, symptoms, risk factors, treatments, and outcomes.

RUTI incidence rates in diabetic populations were the most reported findings across the studies. Overall, four studies reported non-comparative recurrence rates ranging from 23.4% to 37% in different diabetic populations. Six studies compared rUTI rates between populations, most commonly diabetics vs. non-diabetics. Four of these yielded statistically significant differences between groups, although not all studies fully addressed possible confounding variables. Two studies found differences in rUTI rates between diabetics and non-diabetics that were not statistically significant. These differences between studies are possibly the result of variable diabetes groups (T2DM vs all diabetics), small study populations, short study durations, and a lack of rUTI focus.

Another aim of this review was to assess the risk factors for rUTI. Four of five studies found that diabetes was a determining risk factor for rUTI, but no specific conclusion can be drawn regarding T2DM as a risk factor, specifically. Additional risk factors for rUTI in diabetic patients mentioned across the studies included retinopathy, overactive bladder, incontinence, kidney problems, narrow/blocked arteries, insulin treatment, age, and duration of diabetes. However, many of these findings were inconsistent across studies. This is again likely the result of the differences in study populations, methodology, and rUTI definitions.

Antibiotic usage and resistance were the focus of treatment data across studies. Due to the varying geographical locations of the studies included in this review, guidelines for antibiotic prescriptions varied greatly, limiting our ability to compare antibiotic prescription findings. Despite these differences, it was reported that longer durations of antibiotic treatment in diabetic patients does not correlate with less UTI recurrences. Otherwise, no significant findings were reported for antibiotic resistance patterns between diabetic and non-diabetic groups.

E. coli was found to be the most frequent causative agent of both UTI and rUTI across studies, both for diabetic and non-diabetic groups. Only one study found a significant difference between the causative agent of UTI/rUTI in diabetic and non-diabetic groups; ESBL was determined to be higher in diabetics. For symptoms of rUTI specifically, only one study reported relevant findings. Moustakas et al. found that urinary urgency, abdominal pain, and the absence of genital symptoms were correlated with having ≥3 UTIs in a year.

Areas of gaps of knowledge

As underscored by this review, several gaps in knowledge in the field of rUTI research in diabetics were identified. To our knowledge, this review is the first formal systematic review of the limited literature available on the topic of recurrent urinary tract infections in type II diabetics, with a focus on female populations. This project was initiated to better understand the gaps in knowledge in this growing field and aging population. Although there has been a recent suggestion for a standardized definition of rUTI, many of the studies in this review used different diagnostic criteria. In addition, there were so few rUTI studies in T2DM women specifically, that studies with unspecified types of diabetes (T1DM and T2DM not separated) and studies that included some men had to be included. Due to these large differences in study populations, methodologies, and aims, performing a valuable statistical comparison between studies, such as a meta-analysis, was not possible. UTI recurrence and incidence rates were difficult to compare. Additionally, treatment options varied greatly between countries because of guidelines as well as high rates of antibiotic-resistant organisms and antibiotic allergies ( 6 , 13 , 18 , 20 – 22 , 28 ). This resulted in an inability to compare treatment results across studies. Lastly, patients with diabetes often had several comorbidities that were difficult to control for, and multiple studies did not identify confounding variables.

Conclusions

This systematic review summarizes the literature on recurrent urinary tract infections in diabetic women. Fifteen studies from 10 countries met study criteria, providing a heterogenous population. The included articles covered subtopics from recurrent UTI rates, risk factors, symptoms, characteristics, treatments, and disease outcomes. Several studies focused on UTIs and diabetes as their primary goal, and recurrent UTIs only as their secondary target. Hence, rUTI specific results included in this review were limited and not generalizable. However, multiple studies found diabetes to be a risk factor for rUTI, supporting our initial hypothesis that rUTI rates are higher in diabetics than non-diabetics. The findings of this review indicate an urgent need for more research, specifically well-structured prospective studies to determine how best to evaluate and manage recurrent urinary tract infections in diabetic patients.

Data availability statement

The original contributions presented in the study are included in the article/supplementary material. Further inquiries can be directed to the corresponding author.

Author contributions

SP: Data curation, Formal Analysis, Investigation, Methodology, Writing – original draft, Writing – review & editing. PZ: Conceptualization, Data curation, Formal Analysis, Investigation, Methodology, Project administration, Resources, Writing – original draft, Writing – review & editing.

The author(s) declare financial support was received for the research, authorship, and/or publication of this article.

Conflict of interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Publisher’s note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

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Keywords: diabetes, recurrent, UTI, urinary infection, type II diabetes

Citation: Papp SB and Zimmern PE (2023) Recurrent Urinary tract infections and type 2 diabetes mellitus: a systematic review predominantly in women. Front. Urol. 3:1275334. doi: 10.3389/fruro.2023.1275334

Received: 09 August 2023; Accepted: 22 November 2023; Published: 12 December 2023.

Reviewed by:

Copyright © 2023 Papp and Zimmern. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Philippe E. Zimmern, [email protected]

Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.

• Open access
• Published: 24 April 2024

Effects of sodium-glucose cotransporter 2 inhibitors on bone metabolism in patients with type 2 diabetes mellitus: a systematic review and meta-analysis

• Jing Wang 1 ,
• Yang Li 3 &
• Chen Lei 4  

BMC Endocrine Disorders volume  24 , Article number:  52 ( 2024 ) Cite this article

Metrics details

Sodium glucose cotransporter 2 (SGLT2) inhibitors are widely used in type 2 diabetes mellitus (T2DM) therapy. The impact of SGLT2 inhibitors on bone metabolism has been widely taken into consideration. But there are controversial results in the study on the effect of SGLT2 inhibitors on bone metabolism in patients with T2DM. Therefore, we aimed to examine whether and to what extent SGLT2 inhibitors affect bone metabolism in patients with T2DM.

A literature search of randomized controlled trials (RCTs) was conducted through PubMed, Web of Science, Embase, Cochrane databases, and Scopus from inception until 15 April 2023. Eligible RCTs compared the effects of SGLT2 inhibitors versus placebo on bone mineral density and bone metabolism in patients with T2DM. To evaluate the differences between groups, a meta-analysis was conducted using the random effects inverse-variance model by utilizing standardized mean differences (SMD).

Through screening, 25 articles were finally included, covering 22,828 patients. The results showed that, compared with placebo, SGLT2 inhibitors significantly increased parathyroid hormone (PTH, SMD = 0.13; 95%CI: 0.06, 0.20), and cross-linked C-terminal telopeptides of type I collagen (CTX, SMD = 0.11; 95%CI: 0.01, 0.21) in patients with T2DM, decreased serum alkaline phosphatase levels (ALP, SMD = -0.06; 95%CI: -0.10, -0.03), and had no significant effect on bone mineral density (BMD), procollagen type 1 N-terminal propeptide (P1NP), 25-hydroxy vitamin D, tartrate resistant acid phosphatase-5b (TRACP-5b) and osteocalcin.

Conclusions

SGLT2 inhibitors may negatively affect bone metabolism by increasing serum PTH, CTX, and decreasing serum ALP. This conclusion needs to be verified by more studies due to the limited number and quality of included studies.

Systematic review registration

PROSPERO, identifier CRD42023410701

Peer Review reports

Research in context

SGLT2 inhibitors have been widely used in clinical practice for their good cardiorenal protective and hypoglycemic effects. However, their effects on bones are still controversial. The drug has been shown to have a potential adverse effect on bone in multiple animal experiments. However, in the latest meta-analysis, it was not found that the risk of fracture increased in patients with type 2 diabetes mellitus (T2DM) treated with SGLT2 inhibitors.

Can SGLT2 inhibitors affect bone mineral density and bone metabolism in patients with T2DM?

We found that SGLT2 inhibitors may have a negative effect on bone in patients with T2DM.

When T2DM is treated in clinical work, doctors will pay more attention to the monitoring of bone safety. And we provided a reference for the use of SGLT2 inhibitors.

Introduction

It is well known that type 2 diabetes mellitus (T2DM) is characterized by persistently elevated blood glucose or elevated postprandial blood glucose containing carbohydrates [ 1 ]. As a chronic non-communicable disease, its prevalence is increasing worldwide, especially related to the gradual entry of people into an aging society, high calorie intake, and a sedentary lifestyle [ 2 ]. Recent studies have shown that in addition to the cardiovascular, ocular, renal and neurological complications of the disease in patients, bone strength is also impaired and leads to an increased risk of fractures [ 3 ]. The presence of T2DM is associated with a prevalent metabolic disorder that has detrimental effects on bone metabolism, leading to an increased susceptibility to fractures [ 4 , 5 ]. Among the various types of osteoporotic fractures, individuals with T2DM face a heightened risk for hip fractures, which are considered the most severe, as well as limb fractures such as those occurring in the leg or ankle [ 6 ].

The anti-diabetic drugs currently applied clinically have certain effects on the bone metabolism of patients [ 7 ]. Sodium–glucose cotransporter 2 (SGLT2) inhibitor is one of the new hypoglycemic drugs. It can reduce glucose re-absorption by inhibiting SGLT2 in proximal tubules of the kidney, thus promoting urine glucose excretion and reducing blood glucose [ 8 ]. In recent years, studies on the effects of SGLT2 inhibitors on bone metabolism have been continuously released, and the existing relationship between the two is still controversial. Theoretically, SGLT2 inhibitors increase renal tubular reabsorption of phosphate and serum parathyroid hormone concentration [ 9 ].

Considering the significant economic and social burden caused by bone health issues and associated fracture risks, it is imperative to conduct a comprehensive evaluation of the impact of SGLT2 inhibitors on fractures and bone metabolism. In view of the fact that there are still controversial results in the study on the effect of SGLT2 inhibitors on bone metabolism in patients with T2DM, we conducted a systematic and comprehensive analysis of the existing research results in order to provide reference for the selection of SGLT2 inhibitors in the treatment of T2DM in clinical work.

Protocol and registration

The protocol of this systematic review and meta-analysis has been registered in PROSPERO (registration no. CRD42023410701).

Eligibility criteria

We included randomized controlled trials (RCTs) comparing the efficacy of SGLT2 inhibitors versus placebo, in English only. Eligible participants were adults with T2DM, regardless of background hypoglycemic therapy. Interventions should last for at least 12 weeks and the outcomes should include at least one of bone mineral density or bone metabolism.

Search strategy

We searched PubMed, Web of Science, Embase, Cochrane databases, and Scopus on 15 April 2023 for English-language studies. Detailed information about our search strategy was presented in the electronic supplementary material (Table S1 ). To avoid omitting any eligible studies, any terms related to “SGLT2 inhibitor” were searched.

Selection process

All search results were downloaded into EndNote (version X9, Thomson Reuters, Philadelphia, PA, USA) to eliminate duplication. Two reviewers independently performed a preliminary screening of the title and abstract. Remaining articles were read through the full text to determine inclusion, and the reasons for excluded articles were recorded. Any disagreements were resolved by a third reviewer. Articles that could not get the required data were also excluded. Articles for which the required data were not available after contacting the corresponding author were also excluded.

Data collection and risk of bias assessment

Data extraction was done by two independent reviewers and arbitrated by a third reviewer. The relevant information extracted from the included articles mainly included: (1) Basic information: first author, publication year, sample size, and the number of experimental and control groups. (2) Characteristics of research subjects: gender, age, glycated hemoglobin, BMI, SGLT2 inhibitor type and dose, and duration of treatment; (3) Outcomes: Mean ± standard deviation (SD) of post-treatment relative baseline changes in bone mineral density (BMD) and bone metabolism-related indicators including parathyroid hormone (PTH), cross-linked C-terminal telopeptides of type I collagen (CTX), alkaline phosphatase (ALP), 25-hydroxy vitamin D, procollagen type 1 N-terminal propeptide (P1NP), osteocalcin, and Tartrate resistant acid phosphatase-5b (TRACP-5b); (4) Relevant information described in the literature that can be used to assess the risk of bias.

The risk of bias will be assessed by two authors independently using the RoB2 tool for the included RCTs [ 10 ]. Using the RoB2 tool, we will assess domains such as randomization process, assignment and adhering to intervention, missing data and measurement of outcome, and finally categorize the studies as having a low, some concern, or high risk of bias.

Statistical analysis

We will pool the results using a random-effects meta-analysis, using standard mean difference (SMD) for continuous outcomes, and calculate 95% confidence interval (CI). A p -value < 0.05 was considered statistically significant. The Chi-square test combined with I-value analysis was used to judge the heterogeneity among the articles. When the heterogeneity of the studies in each group was relatively large ( P  < 0.05, I 2  ≥ 50%), the source of heterogeneity needed to be clarified. Subsequent subgroup analysis or sensitivity analysis was conducted to explain the reasons for heterogeneity. Egger’s tests were performed to assess publication bias. R (version 4.2.3) and the statistical package ‘meta’ were used for analysis.

Search results

According to the established retrieval strategy, we screened a total of 8554 studies from 5 databases. After a series of screenings, 25 studies ultimately met the eligibility criteria, totaling 22,828 unique participants. Twenty-three studies included in the analysis were RCTs [ 11 , 12 , 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 , 30 , 31 , 32 , 33 ], and two studies were for RCTs Pooled analysis [ 34 , 35 ] (Fig.  1 ).

Flow diagram of the identification of eligible trials

Study characteristics

The study characteristics were summarized in Table  1 . A total of 22,828 participants from 25 RCTs were randomly assigned to one of five SGLT2 inhibitors (canagliflozin, dapagliflozin, empagliflozin, ipragliflozin, and ertugliflozin) or placebo. Sample sizes for individual trials ranged from 40 to 12,620 participants, and the average trial duration was 55 weeks (range 12–104 weeks).

The risk of bias in the 25 RCTs is summarized in Fig.  2 . Most of the trials included in the meta-analysis were judged to have a low risk of bias.

Risk of bias assessments of included studies

Meta-analysis results

• Bone mineral density

A total of 3 studies [ 11 , 24 , 26 ] reported the effects of SGLT2 inhibitors on BMD in patients with T2DM. The results of the overall and subgroup meta-analysis are presented in Fig.  3 . There was no significant difference in BMD after treatment between the SGLT2 inhibitor group and the placebo group (SMD = -0.02; 95%CI: -0.09, 0.05). In subgroup analyses of bone sites, there was also no significant change in BMD in the two groups (lumbar spine, SMD = − 0.02, 95%CI: −0.13, 0.10; femoral neck, SMD = 0.05, 95%CI: −0.11, 0.22; total hip, SMD = -0.08, 95%CI: −0.27, 0.12; and distal forearm, SMD = − 0.06, 95%CI: −0.18, 0.06). No evidence of publication bias was observed (Table S2 ).

Meta-analysis of the effect of Sodium–glucose cotransporter 2 (SGLT2) inhibitors on BMD compared with placebo. BMD, bone mineral density

• Bone metabolism

13 studies [ 11 , 12 , 13 , 14 , 15 , 16 , 19 , 23 , 24 , 28 , 31 , 32 , 35 ] reported PTH levels after SGLT2 inhibitor treatment (Fig.  4 ). 7 papers compared CTX [ 11 , 19 , 23 , 24 , 26 , 28 , 32 ] and 25-hydroxy vitamin D [ 11 , 14 , 15 , 23 , 31 , 32 , 35 ] levels after treatment (Fig.  5 A-B). 15 papers [ 11 , 15 , 16 , 18 , 20 , 21 , 22 , 25 , 27 , 29 , 30 , 34 , 35 ] reported ALP levels after treatment (Fig.  6 ). 3 papers compared P1NP [ 11 , 14 , 24 ] and osteocalcin [ 14 , 26 , 32 ] levels after treatment (Fig.  7 A-B). 2 papers [ 23 , 28 ] reported TRACP-5b levels after treatment (Fig.  7 C). Except for osteocalcin ( P  = 0.02, I 2  = 75%), no significant heterogeneity was observed. Meta results showed that, compared with placebo, SGLT2 inhibitors significantly increased PTH levels (SMD = 0.13; 95%CI: 0.06, 0.20) and CTX levels (SMD = 0.11; 95%CI: 0.01, 0.21), while significantly decreased ALP levels (SMD = -0.06; 95%CI: -0.10, -0.03). However, there was no significant difference in 25-hydroxy vitamin D (SMD = 0.09; 95%CI: 0.00, 0.18), P1NP (SMD = 0.13; 95%CI: -0.02, 0.28), osteocalcin (SMD = 0.19; 95%CI: -0.16, 0.54), and TRACP-5b (SMD = 0.05; 95%CI: -0.17, 0.28) after treatment between the SGLT2 inhibitor group and the placebo group.

Meta-analysis of the effect of Sodium–glucose cotransporter 2 (SGLT2) inhibitors on PTH compared with placebo. PTH, parathyroid hormone

Meta-analysis of the effect of Sodium–glucose cotransporter 2 (SGLT2) inhibitors on CTX ( A ) and 25-hydroxy vitamin D ( B ) compared with placebo. CTX, Cross-linked C-terminal telopeptides of type I collagen

Meta-analysis of the effect of Sodium–glucose cotransporter 2 (SGLT2) inhibitors on ALP compared with placebo. ALP, Alkaline phosphatase

Meta-analysis of the effect of Sodium–glucose cotransporter 2 (SGLT2) inhibitors on P1NP ( A ), osteocalcin ( B ) and TRACP-5b ( C ) compared with placebo. P1NP, Procollagen type 1 N-terminal propeptide; TRACP-5b, Tartrate resistant acid phosphatase-5b

In addition, no evidence of publication bias was observed for any of the above outcomes (Table S2 ).

The combined detection of BMD and bone turnover markers can be used to evaluate bone metabolism in patients. However, the changes of bone turnover markers are more sensitive [ 36 ]. In this study, after a comprehensive literature search and analysis, 25 studies were finally included for meta-analysis. Our results suggested that SGLT2 inhibitors had no significant effect on BMD in patients with T2DM compared to placebo. However, due to the short follow-up period and limited number of the RCTs included in the studies, more long-term studies are needed to accurately determine the impact of SGLT2 inhibitors on BMD.

In terms of bone metabolism, we observed that SGLT2 inhibitors significantly increased serum PTH and CTX levels and decreased serum ALP levels in patients with T2DM. This presents a seemingly paradoxical situation, as it is traditionally understood that elevated levels of PTH normally stimulate bone formation, which in turn increases levels of ALP, the active marker of bone formation [ 37 ]. This reflects the discrepancy between increased PTH levels and decreased ALP levels in patients using SGLT2 inhibitors underscores the complexity of the drugs’ impact on bone metabolism. It suggests a multifactorial influence involving immediate metabolic changes, differential effects on bone remodeling phases, the intricate role of RAAS activation, and the body’s broader compensatory responses [ 38 ]. In addition, no statistically significant effect of SGLT2 inhibitors on P1NP, TRACP-5b, 25-hydroxy vitamin D, and osteocalcin was observed in this study. However, although CTX and ALP levels change significantly in the meta-analysis, no single report shows a significant increase in CTX and only one study found a significant reduction of ALP. The reason for these phenomena can be attributed to the short duration of the study. The studies included this time are up to just over 3 months (104 days). Current research suggests that short-term studies (3 months) may not sufficiently capture significant changes in bone metabolism markers due to the physiological lag between alterations in glucose metabolism and their impact on bone remodeling processes []. In contrast, studies extending beyond 6 to 12 months are considered more likely to demonstrate meaningful changes in these markers [ 37 , 39 ]. Further research, particularly studies with longer follow-up periods and detailed analyses of bone quality and turnover markers, is needed to fully elucidate these relationships.

The exact mechanism of the negative effects of SGLT2 inhibitors on bone health remains unknown. A study has shown that SGLT2 is not expressed in either the osteoblast lineage or the osteoclast lineage [ 40 ]. SGLT1 was detected in MC3T3-E1 differentiated osteoblasts, but its expression level was low. Therefore, the effects of these drugs on bone may be indirect [ 41 ]. SGLT2 inhibitors destroy serum calcium, phosphate, and vitamin D homeostasis [ 42 ]. As reabsorption of sodium in the proximal renal tubules decreases, the activity of sodium-phosphate co-transporters at the apical membrane increases. Serum phosphate levels further increase, inducing parathyroid cells and osteoblasts to secrete PTH and fibroblast growth factor 23 (FGF23). PTH causes bone resorption. While FGF23 promotes urinary phosphate excretion, inhibition of 1-αhydroxylase causes a decrease in 1,25-dihydroxvitamin D levels [ 43 ]. The decrease in blood sodium concentration can also directly affect osteoclasts, leading to an increase in bone fragility [ 44 ]. In the opposite way, calcium is reabsorbed by sodium-calcium cotransporters. The inhibition of SGLT2 leads to increased excretion of urine glucose and urine calcium, and the decrease of serum calcium causes secondary hyperparathyroidism [ 9 ]. It has been verified that the main results in our study suggested SGLT2 inhibitors could significantly increase serum PTH. Unfortunately, there are no more clinical studies reporting the effects of SGLT2 inhibitors on FGF23 in patients with T2DM.

SGLT2 inhibitors provide modest weight loss. A reduction in mechanical pressure on the bone tissue may decrease bone density and enhance bone turnover [ 45 ]. This may partly explain the reduction in total hip bone density in T2DM patients with canagliflozin. Weight loss also decreases aromatase activity, resulting in decreased estradiol levels that severely affect bone density and bone turnover [ 46 , 47 ]. In addition to the indirect effects of SGLT2 inhibitors on bone metabolism, adverse events associated with these agents due to osmotic diuresis and volume consumption (orthostatic hypotension, postural dizziness, etc.) may increase the risk of falls and fractures [ 48 ].

There are some limitations to consider in this study. Most studies containing SGLT2 inhibitors focused on the cardiorenal effects. The main outcomes did not include bone health or relevant data were not shown. Therefore, some types of SGLT2 inhibitors received few articles and participants. Important confounding factors such as diet, exercise level, and solar radiation were not reported in some original studies and cannot be corrected. Since T2DM requires a combination of drugs in most cases, the background treatment for each patient cannot be unified, and there may be other drugs that also affect bones, leading to error in the results.

Although further studies are needed, the results of our study have demonstrated the possible negative effects of SGLT2 inhibitors on bone health in patients with T2DM. However, there is still a lack of human studies regarding the effects of SGLT2 inhibitors on bone microarchitectural changes in patients with T2DM. Further preclinical or clinical data are needed to elucidate the effects on bone matrix mineralization and collagen fiber distribution. SGLT2 inhibitors have a good hypoglycemic effect and cardiorenal protection, but they may have a secondary effect on bone turnover. The long-term safety of this effect on bones deserves continued monitoring as the use of this drug becomes more routine in patients with T2DM.

Data availability

Datasets used in this article are available from the corresponding author on reasonable request.

Abbreviations

Alkaline phosphatase

Cross-linked C-terminal telopeptides of type I collagen

Fibroblast growth factor 23

Procollagen type 1 N-terminal propeptide

Parathyroid hormone

Randomized controlled trials

Sodium–glucose cotransporter 2

Standard mean difference

Tartrate resistant acid phosphatase-5b

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This study was supported by Key Science and technology project in Ningxia (2020BFG02011); Key Science and technology project in Ningxia (2023BEG02022); Ningxia natural science foundation (2023AAC03614) and Ningxia natural science foundation (2023AAC03597).

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XL, CL and JH designed the study. XL and YL identified and acquired reports of trials and extracted data. HT, QD, WS, SZ, YS and JH performed all data analyses, checked for statistical inconsistency, and interpreted data. HT, QD, WS, SZ, YS and JH contributed to data interpretation. HT drafted the report and all other authors critically reviewed the report. All authors approved the final version of manuscript. JH is the guarantor of this work.

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Wang, J., Li, X., Li, Y. et al. Effects of sodium-glucose cotransporter 2 inhibitors on bone metabolism in patients with type 2 diabetes mellitus: a systematic review and meta-analysis. BMC Endocr Disord 24 , 52 (2024). https://doi.org/10.1186/s12902-024-01575-8

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Related Editorials:   Should Metformin Continue as First-Line Pharmacotherapy for Patients With Type 2 Diabetes?

Yes: Metformin Is Still the Best Choice

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Type 2 diabetes mellitus is a chronic disease that is increasing in global prevalence. An individualized approach to pharmacotherapy should consider costs, benefits beyond glucose control, and adverse events. Metformin is the first-line therapy due to its low cost and effectiveness. Sulfonylureas and thiazolidinediones are additional low-cost oral hypoglycemic classes available in the United States; however, evidence shows variability in weight gain and hypoglycemia. Thiazolidinediones increase fluid retention and are not recommended in patients with New York Heart Association class III or IV heart failure. Newer medications, including glucagon-like peptide-1 receptor agonists and sodium-glucose cotransporter-2 inhibitors, have demonstrated weight loss, reduced cardiovascular events, decreased renal disease, and improved all-cause morbidity and mortality. Sodium-glucose cotransporter-2 inhibitors are recommended for people with known cardiovascular disease, heart failure, and chronic kidney disease but carry an increased risk of urinary tract and mycotic infections. Glucagon-like peptide-1 receptor agonists are contraindicated in patients with active multiple endocrine neoplasia type 2 or a personal or family history of medullary thyroid carcinoma; adverse effects include gastrointestinal upset and pancreatitis. Dipeptidyl-peptidase-4 inhibitors have a low risk of hypoglycemia but may increase the risk of pancreatitis and require a renal dose adjustment. Public and private programs to increase access to newer hypoglycemic medications are increasing; however, there are limitations to access, particularly for uninsured and underinsured people.

More than 38 million people in the United States have diabetes mellitus; 90% to 95% have type 2. 1 In 2022, medical and economic costs of $412.9 billion in the United States were attributed to diabetes. 2 In the next 10 years, estimates predict a near doubling of the number of affected people in the United States, with a greater prevalence among low-income populations. 3 , 4 People with annual incomes below the poverty level have the highest prevalence of a diabetes diagnosis and complication rates. 1 Food insecurity, food deserts, and lack of access to safe exercise spaces in lower socioeconomic areas complicate efforts to prevent and control type 2 diabetes. 5 , 6 Racial and ethnic disparities also exist. A total of 14.5% of American Indians and Alaska Natives, 12.1% of non-Hispanic Blacks, 11.7% of Hispanics, 9.1% of non-Hispanic Asians, and 6.9% of non-Hispanic Whites are diagnosed with diabetes; however, millions more people are undiagnosed. 7 – 9

Social determinants of health (i.e., the conditions in which individuals are born, work, and live) are strongly associated with diabetes outcomes. 7 , 10 Education, income, occupation, and environment affect the ability to prevent and control diabetes. 7 To improve health equity, federal programs, including the Affordable Care Act and Medicaid, have expanded access to care for more people with diabetes. Patient assistance programs have also increased access to newer, more expensive medications. 7 , 11

Prevention and Nonpharmacologic Management

The goal of diabetes care is to optimize quality of life by preventing macrovascular and microvascular complications, including cardiovascular, renal, neurologic, ophthalmic, and gastrointestinal disorders. 12

Risk factors for developing diabetes include elevated visceral adiposity, polycystic ovary syndrome, fasting plasma glucose level of 100 mg per dL (5.55 mmol per L) or greater, A1C of 5.7% or greater, a family history of type 2 diabetes, poor dietary habits, and a history of gestational diabetes. 13 Dietary modifications alone do not sustain A1C improvement. A meta-analysis of randomized controlled trials demonstrated that a low-carbohydrate diet was associated with significant A1C reduction at 3 months but not at 6 or 12 months and a body mass index reduction at 6 months but not at 12 months. 14 Multifaceted programs such as the Diabetes Prevention Program have provided strong evidence for lifestyle modifications to prevent diabetes and control glucose levels for people with type 2 diabetes. Modifications include 150 minutes or more of moderate-intensity exercise or 75 minutes or more of vigorous-intensity exercise per week, weight loss of 7% or greater, and a reduction in dietary fat and caloric intake. 15

Patient-Centered Glycemic Control

An A1C is the average blood glucose level over 3 months measured by the percentage of glycosylated red blood cells. Anemia, end-stage renal disease, alcoholism, and certain hemoglobinopathies can affect the accuracy of an A1C. 16 The American Diabetes Association recommends a target A1C of less than 7% for healthy adults younger than 65 years and less than 7.5% for healthy adults 65 years and older with intact cognition and functional status. 17 Less stringent A1C goals below 8% may be more appropriate for some patients. Several clinical trials found that severe hypoglycemia is a marker for a high absolute risk of cardiovascular events and mortality. 17 – 20 Individualized therapy is based on factors including the risk of hypoglycemia, drug-drug interactions, adverse effects, disease duration, life expectancy, comorbidities, established vascular complications, patient preference and resources, and the patient's support system. 17 – 19 , 21 , 22 In some patients, continuous glucose monitoring may improve glycemic control and reduce hypoglycemia by providing real-time data on blood glucose levels. 20

Pharmacologic Management

Eight classes of hypoglycemic medications ( Table 1 23 , 24 and Table 2 21 , 25 – 29 ) are discussed in this review. Figure 1 provides an approach to prescribing type 2 diabetes medications based on patient risk factors. 12 , 23 , 26 , 28 , 30 Unless contraindicated, metformin is the first-line therapy. 23 Accessibility is the next consideration. For example, if a patient has an indication for a newer medication (e.g., sodium-glucose cotransporter-2 [SGLT-2] inhibitors: microalbuminuria, chronic kidney disease, coronary artery disease; glucagon-like peptide-1 [GLP-1] receptor agonists: coronary artery disease), it is critical to ensure that the patient can obtain the medication through insurance or be assisted in applying for a program to cover the cost. 17 , 31 , 32 Otherwise, lower-cost medications should be considered. Treatment regimens should support weight management goals. 23 If noninsulin therapy is maximized or exhausted and glycemic control is not achieved, insulin should be considered. 3

ALPHA-GLUCOSIDASE INHIBITORS

The primary mechanism of action of alpha-glucosidase inhibitors is to inhibit the alpha-glucosidase enzyme, which is found in the small intestine cells brush border that catalyzes complex carbohydrates. This action reduces postprandial hyperglycemia, although patients must have concurrent food intake to receive its effects. Therapy should be initiated at the lowest effective dose and titrated slowly every two to four weeks. 29 Alpha-glucosidase inhibitors are excreted renally and contraindicated in people with a serum creatinine of 2.0 mg per dL (176.8 μmol per L) or greater. 29 The alpha-glucosidase inhibitor, acarbose, increases serum transaminase levels and should not be used in patients with liver cirrhosis. 24

Metformin is the preferred first-line oral blood glucose–lowering medication to manage type 2 diabetes. 33 Metformin decreases hepatic glucose production and intestinal absorption of glucose to improve insulin sensitivity and is effective, safe, and inexpensive; data suggest that it decreases the risk of cardiovascular events and death. 33 – 35 The principal adverse effect of metformin is gastrointestinal intolerance, including bloating, nausea, abdominal discomfort, and diarrhea, which can be reduced by slow titration and concurrent food intake. Patients with risk factors for lactic acidosis, including concurrent use of carbonic anhydrase inhibitors and those 65 years or older with recent use of iodinated contrast, undergoing surgery, or in a hypoxic state, should be monitored during treatment. 33 , 36 Metformin is cleared by renal filtration; caution must be taken in the setting of chronic kidney disease. In 2016, the U.S. Food and Drug Administration (FDA) revised the metformin label to reflect its safety in people with an estimated glomerular filtration rate (eGFR) of 30 mL per minute per 1.73 m 2 or greater. When the eGFR is between 30 and 45 mL per minute per 1.73 m 2 , a maximum daily dosage of 1,000 mg and close monitoring of renal function are recommended. 33 – 35 Metformin is contraindicated in patients with an eGFR of less than 30 mL per minute per 1.73 m 2 and acute or chronic metabolic acidosis. Metformin is also contraindicated for patients with hepatic impairment and unstable heart failure. 33 – 35

DIPEPTIDYL-PEPTIDASE-4 INHIBITORS

Dipeptidyl-peptidase-4 (DPP-4) inhibitors block DPP-4, an enzyme that degrades incretin peptides GLP-1 and -2. DPP-4 inhibitors activate glucose-dependent insulinotropic polypeptides to stimulate beta cells to secrete insulin. 37 They are weight-neutral and have a low risk of hypoglycemia. DPP-4 inhibitors are often combined with other hypoglycemics (e.g., metformin, thiazolidinediones), but use with GLP-1 receptor agonists does not provide additive glucose-lowering effects. 24 , 38 , 39 An association between DPP-4 inhibitor use and bullous pemphigoid and other dermatoses has been observed; they have also been associated with pancreatitis in clinical trials, although causality has not been established. 30 , 40 Unlike SGLT-2 inhibitors and GLP-1 receptor agonists, DPP-4 inhibitors have not demonstrated improved cardiovascu lar outcomes. Sitagliptin (Januvia), saxagliptin (Onglyza), and alogliptin (Nesina) did not show significant differences in cardiovascular events between treatment and placebo groups. 27 , 41 , 42 In April 2016, the FDA issued a boxed warning that saxagliptin and alogliptin may increase the risk of heart failure, particularly in people with preexisting heart failure or renal impairment. 28

GLP-1 RECEPTOR AGONISTS

GLP-1 receptor agonists are medications approved to treat diabetes and obesity. Glucagon-like peptides and glucose-dependent insulinotropic poly peptides stimulate insulin secre -tion after glucose ingestion via the incretin effect, a natural process that may be decreased or absent in patients with type 2 diabetes. 25 , 43 GLP-1 receptor agonists are subcutaneous injectable formulations, except for semaglutide, which is also available in an oral form (Rybelsus), and orforglipron and danuglipron, which are currently being reviewed for oral formulations. 25 , 44 – 47 Increasing data support the effectiveness of GLP-1 receptor agonists and SGLT-2 inhibitors. A 2018 systematic review (816 trials; n = 471,038) found that GLP-1 receptor agonists and SGLT-2 inhibitors reduced cardiovascular-related deaths, nonfatal myocardial infarction, hospital admissions, end-stage renal disease, and all-cause mortality. 26 Another study showed that GLP-1 receptor agonists decreased nonfatal stroke rates, whereas SGLT-2 inhibitors demonstrated greater effectiveness in decreasing end-stage renal disease. 26 , 48 GLP-1 receptor agonists also promote weight loss (mean loss = 6.4 lb [2.9 kg]). 26 , 46 Adverse effects and potential adverse events include gastrointestinal upset, pancreatitis, and cholelithiasis/cholecystitis. 46 , 49 Liraglutide (Saxenda), semaglutide, and dulaglutide (Trulicity) are also associated with an increased risk of diabetic retinopathy. 50

MEGLITINIDES

Meglitinides directly stimulate the pancreatic beta cells via sulfonylurea receptor-1, sulfonylurea receptor-1A, and sulfonylurea receptor-1B, causing insulin release. 51 Meglitinides have a short half-life and a rapid onset of action. This may be beneficial for patients who eat once per day because it can be taken once with the largest meal to reduce postprandial hyperglycemia. Repaglinide and nateglinide are the medications in this class in the United States. The most common adverse effects are weight gain and hypoglycemia. 24

SGLT-2 INHIBITORS

SGLT-2 inhibitors decrease blood glucose by increasing urinary excretion of glucose. They are expressed in the nephron's proximal tubule, mediating 90% of the filtered glucose reabsorption. By blocking glucose reabsorption, SGLT-2 inhibitors increase urinary excretion and decrease plasma glucose levels. 24 , 52 Dosing adjustments are required in renal insufficiency, and the glucose-lowering effect of SGLT-2 inhibitors decreases as the eGFR declines. 35 , 53 A large randomized clinical trial (n = 7,020) showed that empagliflozin (Jardiance) reduced a composite outcome of myocardial infarction, stroke, and cardiovascular death in people with established arteriosclerotic cardiovascular disease compared with placebo. 43 , 54 Because of these benefits, SGLT-2 inhibitors are indicated in chronic kidney disease and heart failure management and in patients with diabetes who have microalbuminuria, chronic kidney disease, or chronic heart failure. 45 SGLT-2 inhibitors also promote weight loss (mean = 1.5 to 7.7 lb [0.68 to 3.49 kg]), reduce potassium in hyperkalemia, and increase magnesium in hypomagnesemia. 55 The FDA issued a boxed warning that SGLT-2 inhibitors may lead to euglycemic ketoacidosis and are not recommended for people with a history of diabetic ketoacidosis; patients should be educated on the ketoacidosis risk. 23 , 53 , 54 , 56

SULFONYLUREAS

Sulfonylureas are insulin-secretagogues that directly stimulate insulin release from pancreatic beta cells, requiring these cells to function. 30 Sulfonylureas also minimally improve insulin resistance in tissues. 30 Sulfonylureas do not decrease all-cause mortality, but they are low cost and may offer one of the few options for patients to achieve glycemic control. Glimepiride, glyburide, and glipizide are the most prescribed sulfonylureas in the United States. Hypoglycemia is the most common adverse effect and may be worse in older adults, patients with renal dysfunction, and those concurrently using insulin. 30 There are significant variations with different sulfonylureas for weight gain and hypoglycemia. Some studies suggest little to no weight gain with glimepiride and that glyburide causes a greater risk of hypoglycemia due to a longer half-life. 24 , 55 Most adverse effects, including cardiovascular events, occur at doses beyond clinical effectiveness. 55 If there is a need for glycemic control at one-half of the maximum dosage, an alternative agent should be considered. 55

THIAZOLIDINEDIONES

Thiazolidinediones improve glycemic control by enhancing insulin sensitivity in muscle and adipose tissue and inhibiting hepatic glucose production. 24 Thiazolidinediones substantially decrease insulin resistance, particularly when combined with other agents. 24 The most common adverse effects are weight gain and fluid retention. Physicians should avoid or minimally dose thiazolidinediones for people with New York Heart Association class III or IV heart failure. Thiazolidinediones have an FDA boxed warning for increased risk of bone fractures, primarily affecting distal upper and lower limbs, and should be avoided in patients with osteopenia or osteoporosis. 24 There are conflicting data about the association of bladder cancer with thiazolidinedione use. 57 , 58 Pioglitazone is an effective treatment for patients with coexisting nonalcoholic steatohepatitis. 59

Patient Assistance Programs

Most patients with type 2 diabetes are resource-limited. 1 , 60 Patient assistance programs sponsored by pharmaceutical manufacturers, public funding, and nonprofit organizations provide financial assistance to increase medication access. 61 Successful enrollment is associated with improved glycemic control. 62 Educating clinicians on medication cost barriers (including barriers for patients with insurance) is critical to increasing access to newer medications. 63 Increasingly robust programs, such as the Michigan Collaborative for Type 2 Diabetes, continue to reduce medication barriers by incorporating quality initiatives to promote patient and clinician awareness. 11 , 63 , 64

This article updates previous articles on this topic by George, et al. 24 ; Luna and Feinglos 65 ; and Riddle . 66

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Prevalence of metabolic syndrome and associated factors among patient with type 2 diabetes mellitus in Ethiopia, 2023: asystematic review and meta analysis

• Betelhem Mesfin Demissie 1 ,
• Fentaw Girmaw 2 ,
• Nimona Amena 3 &
• Getachew Ashagrie 2  

BMC Public Health volume  24 , Article number:  1128 ( 2024 ) Cite this article

Metrics details

Metabolic syndrome is a complex pathophysiologic state which characterized by abdominal obesity, insulin resistance, hypertension, and hyperlipidaemia. The Adult Treatment Panel III report (ATP III) of the National Cholesterol Education Programme identified the metabolic syndrome as a serious public health issue in the modern era. In Western and Asian nations, the frequency of metabolic syndrome is rising, especially in developing regions experiencing rapid socio-environmental changes, in Sub-Saharan Africa; metabolic syndrome may be present in more than 70% of people with type 2 diabetes mellitus. Therefore the objective of our study was to estimate the pooled prevalence of metabolic syndrome and associated factors among type II diabetes mellitus patient.

This systematic review and meta-analysis included original articles of cross sectional studies published in the English language. Searches were carried out in PubMed, Web of Science, Google Scholar, and grey literature Journals from 2013 to June 2023. A random-effects model was used to estimate the pooled prevalence of metabolic syndrome among type II Diabetes mellitus patient in Ethiopia. Heterogeneity was assessed using the I 2 statistic. Subgroup analysis was also conducted based on study area. Egger’s test was used to assess publication bias. Sensitivity analysis was also conducted.

Out of 300 potential articles, 8 cross sectional studies were included in this systematic review and meta-analysis study. The pooled prevalence of metabolic syndrome among patient with type II diabetes mellitus in Ethiopia was found to be 64.49% (95% CI: 62.39, 66.59) and 52.38% (95% CI: 50.05, 54.73) by using NCEP/ATP III and IDF criteria, respectively. The weighted pooled prevalence of metabolic syndrome among type II diabetes mellitus patients by sub group analysis based on the study region was 63.79% (95% CI: 56.48, 71.11) and 52.23% (95%CI: 47.37, 57.22) by using NCEP/ATP III and IDF criteria, respectively. Being female and increased body mass index were factors associated with metabolic syndrome among type II diabetes mellitus patients.

The prevalence of metabolic syndrome among type II patient is high. Therefore, policymakers, clinicians, and concerned stakeholders shall urge effective strategies in the control, prevention, and management of metabolic syndrome among type II diabetes mellitus.

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Introduction

The complicated pathophysiologic condition known as the metabolic syndrome is characterised by insulin resistance, hypertension, hyperlipidaemia, and abdominal obesity and which originate primarily from an imbalance between energy expenditure and calorie intake [ 1 ]. Even though the NCEP-ATPIII, IDF, and WHO criteria are the most often utilised clinical criteria for the diagnosis of metabolic syndrome, there are numerous similarities between them, there are also notable differences in the perspectives on the underlying causes of the metabolic syndrome [ 2 ]. The prevalence of metabolic syndrome is increasing in Western and Asian countries, particularly in emerging areas that are undergoing fast socio-environmental change. Numerous studies have demonstrated that metabolic syndrome is a major risk factor for type 2 diabetes mellitus, cardiovascular disease (CVD), and overall mortality [ 3 ].

Global estimates suggest that about one-third of the world’s population, primarily in developing countries, may have metabolic syndrome [ 4 ]. The National Cholesterol Education Programme’s Adult Treatment Panel III report (ATP III) recognised metabolic syndrome as a significant contemporary public health concern. It is a multiplex risk factor for cardiovascular disease (CVD) that requires more therapeutic care, and it also raises the risk of cancer, mental problems, renal illness, and early mortality [ 2 , 5 ].

Compared to those in Western countries, the urban population in several developing nations has a greater prevalence of metabolic syndrome. The two main causes of this disease’s growth are the increase in fast food consumption—high-calorie, low-fiber foods—and the decline in physical activity brought on by sedentary leisure activities and the use of automated transportation [ 1 ].

In Sub-Saharan Africa, metabolic syndrome may be present in more than 70% of people with type 2 diabetes mellitus. According to research which is done in two rural clinics of Ghana, the prevalence of metabolic syndrome among type II diabetes mellitus patients was 68.6% (95% CI: 64.0-72.8), and having diabetes for more than five years, being female, and being overweight are significantly associated with metabolic syndrome [ 4 ]. A study done by Lira Neto JCG et al., Stated that among 201 study participants, 50.7% were diagnosed with metabolic syndrome [ 5 ].

A cross sectional study conducted in Ethiopia reported that the prevalence of metabolic syndrome was 20.3% among 325 study participants [ 6 ]. A Systematic Review and Meta-analysis study conducted in Ethiopia stated that the pooled prevalence of metabolic syndrome in Ethiopia was found to be 34.89% (95% CI: 26.77, 43.01) and 27.92% (95% CI: 21.32, 34.51) by using NCEP/ATP III and IDF criteria, respectively. Subgroup analysis based on the study subjects using NCEP/ATP III showed that the weighted pooled prevalence was 63.78%(95% CI: 56.17, 71.40) among type 2 Diabetes Mellitus patients [ 7 ].

Even though this topic has been studied before, our work is unique since we look at aspects outside prevalence. Thus, our study’s goal was to analyse the pooled prevalence of metabolic syndrome and associated factors among patients with type II diabetes mellitus. How much is the prevalence of metabolic syndrome among Ethiopian patients with type II diabetes mellitus? And what variables are associated with metabolic syndrome patients with type II diabetes in Ethiopian?

Protocol and search strategy

The systematic review and meta-analysis was reported according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement guideline (Fig.  1 ). The study protocol was registered in the PROSPERO International Prospective Register of Systematic Reviews (CRD42023442704). An inclusive literature search was conducted to identify studies about the prevalence of metabolic syndrome among patients with type II diabetes mellitus reported among the Ethiopian population of various study subjects. Both electronic and gray literature searches were carried out systematically. PubMed, Web of Science, Google Scholar, and grey literature, were searched for material between 2013 and 2023. The search terms were used separately and in combination using Boolean operators like “OR” or “AND.” An example of keywords used in PubMed to select relevant studies was as follows: (((“Metabolic Syndrome“[Mesh] OR Metabolic syndrome*[tiab]) AND (“Diabetes Mellitus, Type 2“[Mesh] OR “Diabetes Mellitus, Type 2*“[tiab])) AND (“Prevalence“[Mesh] OR “Magnitude*“[tiab])) AND (“Ethiopia“[Mesh] OR “Ethiopia*“[tiab]). Moreover, each database’s specific search parameters were customized accordingly.

Study selection (inclusion and exclusion criteria)

Inclusion criteria.

Studies were selected according to the following criteria: study design, participants, exposures and condition or outcome(s) of interest. Eligible studies were only quantitative full- text, and observational studies (cross-sectional) reporting prevalence and associated factors in terms of the odds ratio. Only articles written in English were retrieved for review. We included studies involving only type II diabetes mellitus patients aged greater than 30.

The primary outcome was the prevalence of metabolic syndrome. We used author reported definitions (according ATP III &IDF) (Table  1 ). Secondary outcome was factors associated with metabolic syndrome among type 2 diabetes mellitus patients. We were used unpublished articles to identify any potential studies that might have been missed from our search.

Exclusion criteria

We excluded reviews, case reports, case series, qualitative studies, and opinion articles. We were exclude abstract-only papers.

Data extraction and quality assessment

Data extraction was done independently by the two reviewers in a pre-piloted data extraction form created in MS Excel. Any discrepancies in the extracted data were resolved by consensus or discussion with a third reviewer. The following details will be extracted from each study:- details of the study (first author’s last name, year of publication), study region, study design, sample size, Prevalence of metabolic syndrome, associated factors.

The Joanna Brigg Institute’s quality evaluation criteria’s (JBI) were used to evaluate the studies’ quality. We assessed each of the chosen publications using the JBI assessment checklist. Research with a quality score of at least 50% was deemed to be of high quality.

Data analysis

Version 17 of STATA was used to analyse the retrieved data after they were imported into Microsoft Excel. To get a general summary estimate of the prevalence across trials, a random-effects model was employed. We employed point estimate with a 95% confidence interval. Sensitivity analysis was used to evaluate each study’s contribution to the outcome by eliminating each one individually. Using Egger’s test, the existence of publication bias was evaluated. The Cochran’s Q statistic and I2 statistics were used to assess the heterogeneity of the studies. Moreover, meta-regression has been conducted that represents linear predictions for the metabolic syndrome among type 2 diabetes mellitus patients prevalence as a function of published year. Subgroup analysis was performed based on study region and study subjects since there was unexplained significant heterogeneity.

Publication bias

Funnel plot and Egger’s test was used to assess publication bias and a P-value of less than 0.05 was used to declare the publication bias. The included studies were assessed for potential publication bias and separate analyses were done based on IDF and NCEP/ATPIII criteria (p values were 0.58 and 0.88, respectively) which indicated the absence of publication bias (Figs.  2 and 3 ).

PRISMA flow diagram of study selection for systematic review and meta-analysis of prevalence of metabolic syndrome among type II Diabetes mellitus patients in Ethiopia [ 11 ]

Forest plot showing the pooled prevalence of metabolic syndrome among type II Diabetes mellitus patients in Ethiopia (according to NCEP ATP III Criteria)

Forest plot showing the pooled prevalence of metabolic syndrome among type II Diabetes mellitus patients in Ethiopia (according to IDF Criteria)

Characteristics of included studies

The title and abstract screening of 300 potential articles yielded 102 that were included relevant to the topic of interest; the full-text screening of 50 of these articles indicated their eligibility for full-text assessment; 8 of these articles, involving 2375 study participants, were found to be eligible for systematic review and meta-analysis. Based on the NCEP/ATPIII and IDF criteria, the prevalence of metabolic syndrome in patients with type 2 diabetes mellitus was assessed among the Ethiopian population of different study participants. Five studies reported the prevalence of metabolic syndrome among type 2 diabetes mellitus based on both IDF and NCEP/ATPIII criteria, whereas seven studies based on NCEP/ATPIII criteria only and six studies by IDF criteria only (Table  2 ).

Prevalence of metabolic syndrome among type 2 diabetes mellitus patients using IDF and NCEP ATP III Criteria

The random-effects model was applied since the heterogeneity index of the studies were significant. The pooled prevalence of metabolic syndrome was found to be 64.49% (95% CI: 62.39, 66.59) by using NCEP/ATP III (Figs.  4 ) and 52.38% (95% CI: 50.05, 54.73) by using IDF criteria (Fig.  5 ). Subgroup analysis based on the study region using NCEP/ ATP III showed that the weighted pooled prevalence was 63.79% (95% CI: 56.48, 71.11) among type 2 diabetes patients (Fig.  6 ). Using IDF criteria, subgroup analysis based on the study region showed that the weighted pooled prevalence was 52.23% (95%CI: 47.37, 57.22).

Sub group analysis based on study region using NCEP ATP III Criteria

the pooled odds ratio of the association between sex and prevalence of metabolic syndrome among type II diabetes mellitus patients

The pooled odds ratio of the association between BMI and prevalence of metabolic

Factors associated with metabolic syndrome among type II DM

Association between sex and prevalence of metabolic syndrome.

The association between being female and prevalence of metabolic syndrome was examined based on the finding from five studies ( 1 , 2 , 4 , 5 and 7 ). The pooled odds ratio (AOR: 0.5, 95% CI: -0.32-1.31) showed that prevalence of metabolic syndrome associated with being female. The studies showed very high heterogeneity (I²=86.3% and P  = 0.00) (Fig.  7 ). Hence, a random effects model was employed to do the final analysis.

Funnel plot for prevalence of metabolic syndrome according to NCE ATPIII

Association between BMI and prevalence of metabolic syndrome

The association between BMI and prevalence of metabolic syndrome was examined based on the finding from four studies ( 1 , 2 , 4 and 8 ). The pooled odds ratio (AOR: 3.86, 95% CI: 2.57–5.15) showed that prevalence of metabolic syndrome associated with BMI. The studies showed high heterogeneity (I²= 68.2% and P  = 0.034) (Fig.  8 ). Hence, a random effects model was employed to do the final analysis.

Funnel plot for prevalence of metabolic syndrome according to IDF

Sensitivity analysis

Sensitivity analysis was carried out by gradually removing each research from the analytic process according to the two provided diagnostic criteria (NCEP/ATP III and IDF) in order to evaluate the impact of each study on the pooled estimated prevalence of metabolic syndrome among type II diabetes mellitus patients. The result showed that excluded studies led to significant changes in the shared estimation of the prevalence of metabolic syndrome (Figs.  9 and 10 ).

Sensitivity analysis based on NCEP/ATP III diagnostic criteria

Sensitivity analysis based on IDF diagnostic criteria

This systematic review and meta- analysis study provides evidence of an estimated pooled prevalence of metabolic syndrome among type II diabetes mellitus patients of the Ethiopian population. According to this review, the combined pooled prevalence of Metabolic syndrome among type II diabetes mellitus patients were 64.49% (95% CI: 62.39, 66.59) and 52.38% (95% CI: 50.05, 54.73) by using NCEP/ATP III and IDF criteria, respectively.

The finding of this study similar with the study conducted in African populations, which reported that the prevalence of metabolic syndrome among type 2 diabetes mellitus was 66.9%(95%CI: 60.3–73.1) [ 9 ]. In addition, the study which was done in sub-Saharan African countries in line with this finding which reports that prevalence of metabolic syndrome among type II diabetes melituse patients were 64.8% (95% CI: 54.74, 74.86) and 57.15% by using NCEP/ATP III and IDF criteria, respectively.

Furture more, the study which was done in Sub-Saharan Africa reported that, among others sub-Saharan Africa countries the prevalence of metabolic syndrome was highest in Ethiopia, (61.14%, 95% CI: 51.74, 70.53), which is almost similar with our study findings [ 8 ].

Similarly the study conducted in Ethiopian population showed that the weighted pooled prevalence of metabolic syndrome among type II diabetes mellitus patient was 63.78% (95% CI: 56.17, 71.40) [ 7 ].

This study result supported by the fact that metabolic syndrome has been associated with type 2 diabetes due to its high prevalence worldwide since it is both related to the increase in obesity and a sedentary lifestyle. Several studies suggest that individuals with Metabolic syndrome are 5 times more likely to develop type 2 diabetes [ 10 ].

This meta-analysis assessed factors determining metabolic syndrome among type II diabetes mellitus patients. The current meta-analysis demonstrated that the prevalence rate of metabolic syndrome was higher in females compared to that in males. This has been shown in all the Middle Eastern countries, and the prevalence was much higher among women than men [ 6 ]. The prevalence rate of metabolic syndrome associated with the individual’s body mass index.

This study has implications for clinical practice. Determining the prevalence of metabolic syndrome among type 2 diabetic patients is critical to guide healthcare professionals to minimize the risk of metabolic syndrome by providing guidance to the patient who has undergone diabetic care follow up. Moreover, it gives information about the burden and public health impact of metabolic syndrome for possible consideration during routine diabetic patient care.

This meta-analysis study has its own limitations that should be considered in the future research. Few studies are included due to limited research in Ethiopia which makes it difficult to generalize the findings to all type 2 diabetic patients in the countery and which makes the discussion part more shallow.

In conclusion, according to this systematic review the prevalence of metabolic syndrome among type II patient is high in Ethiopia and recommends an urgent attention from both the clinical and public health viewpoint Therefore, policymakers, clinicians, and concerned stakeholders shall urge effective strategies in the control, prevention, and management of metabolic syndrome among type II diabetes mellitus. In addition country context-specific preventive strategies should be developed to reduce the burden of metabolic syndrome.

Data availability

The data used to support the findings of this study are available from the corresponding author upon request.

Abbreviations

Cardiovascular Disease

National Cholesterol Education Program–Adult Treatment Panel III

International diabetes federation

Preferred Reporting Items for Systematic Reviews and Meta-Analyses

World Health Organization

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“B.M involved in the entire work of the manuscript which is a design of the study, review of literature, and data analysis, “F.G.” was involved in, design of the study, review of the literature, data extraction, and statistical analysis. “N.A”. and “G.A” were involved in data analysis and interpretation and drafting of the manuscript. All authors critically revised the paper, and they agreed to be accountable for all aspects of the work.

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Demissie, B.M., Girmaw, F., Amena, N. et al. Prevalence of metabolic syndrome and associated factors among patient with type 2 diabetes mellitus in Ethiopia, 2023: asystematic review and meta analysis. BMC Public Health 24 , 1128 (2024). https://doi.org/10.1186/s12889-024-18580-0

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Service quality: perspective of people with type 2 diabetes mellitus and hypertension in rural and urban public primary healthcare centers in Iran

• Shabnam Iezadi 1 ,
• Kamal Gholipour 1 ,
• Jabraeil Sherbafi 2 ,
• Sama Behpaie 3 ,
• Nazli soltani 2 ,
• Mohsen Pasha 2 &
• Javad Farahishahgoli 2  

BMC Health Services Research volume  24 , Article number:  517 ( 2024 ) Cite this article

Metrics details

This study aimed to assess the service quality (SQ) for Type 2 diabetes mellitus (T2DM) and hypertension in primary healthcare settings from the perspective of service users in Iran.

The Cross-sectional study was conducted from January to March 2020 in urban and rural public health centers in the East Azerbaijan province of Iran. A total of 561 individuals aged 18 or above with either or both conditions of T2DM and hypertension were eligible to participate in the study. The study employed a two-step stratified sampling method in East Azerbaijan province, Iran. A validated questionnaire assessed SQ. Data were analyzed using One-way ANOVA and multiple linear regression statistical models in STATA-17.

Among the 561 individuals who participated in the study 176 (31.3%) were individuals with hypertension, 165 (29.4%) with T2DM, and 220 (39.2%) with both hypertension and T2DM mutually. The participants’ anthropometric indicators and biochemical characteristics showed that the mean Fasting Blood Glucose (FBG) in individuals with T2DM was 174.4 (Standard deviation (SD) = 73.57) in patients with T2DM without hypertension and 159.4 (SD = 65.46) in patients with both T2DM and hypertension. The total SQ scores were 82.37 (SD = 12.19), 82.48 (SD = 12.45), and 81.69 (SD = 11.75) for hypertension, T2DM, and both conditions, respectively. Among people with hypertension and without diabetes, those who had specific service providers had higher SQ scores (b = 7.03; p  = 0.001) compared to their peers who did not have specific service providers. Those who resided in rural areas had lower SQ scores (b = -6.07; p  = 0.020) compared to their counterparts in urban areas. In the group of patients with T2DM and without hypertension, those who were living in non-metropolitan cities reported greater SQ scores compared to patients in metropolitan areas (b = 5.09; p  = 0.038). Additionally, a one-point increase in self-management total score was related with a 0.13-point decrease in SQ score ( P  = 0.018). In the group of people with both hypertension and T2DM, those who had specific service providers had higher SQ scores (b = 8.32; p  < 0.001) compared to the group without specific service providers.

Study reveals gaps in T2DM and hypertension care quality despite routine check-ups. Higher SQ correlates with better self-care. Improving service quality in primary healthcare settings necessitates a comprehensive approach that prioritizes patient empowerment, continuity of care, and equitable access to services, particularly for vulnerable populations in rural areas.

Peer Review reports

Diabetes and hypertension, recognized as major contributors to premature mortality, stand as primary risk factors for heart attacks, strokes, and kidney diseases [ 1 , 2 ]. Diabetes, in particular, may result in blindness and lower limb amputations [ 1 ]. The prevalence of diabetes is on the rise globally, especially in low- and middle-income countries (LMICs), where approximately two-thirds of individuals with hypertension reside [ 3 , 4 ]. Existing literature underscores the high prevalence of Type 2 Diabetes Mellitus (T2DM) and/or hypertension in Iran, akin to other LMICs, posing substantial threats to patients and healthcare systems if not effectively managed [ 4 , 5 , 6 ]. Alarmingly, evidence indicates that the rates of treatment and control for both T2DM and hypertension in Iran are notably lower than in higher-income countries, magnifying the potential for severe consequences [ 4 , 7 ].

The global healthcare community has increasingly emphasized the importance of quality of care since the Institute of Medicine’s landmark publication, “Crossing the Quality Chasm,” urging essential changes to bridge the quality gap by the end of the 21st century [ 8 ]. Despite these efforts, many health systems, particularly those in LMICs, grapple with low-quality care [ 8 ]. Poor quality of care stands out as a significant factor contributing to inadequate control of hypertension and T2DM [ 9 , 10 ]. Studies have consistently shown a positive correlation between receiving high-quality care for diabetic or hypertensive conditions and achieving better health outcomes [ 9 , 10 , 11 ]. Consequently, gaining a deeper understanding of the quality of care provided to patients with T2DM and/or hypertension is crucial for effective community management.

Assessing quality is a foundational step toward enhancing care for individuals with chronic health conditions [ 12 ]. Quality of care can be assessed from various perspectives, including technical and service quality. Technical quality assesses the adherence of services to established guidelines [ 13 ], while service quality examines the overall quality of services provided to patients [ 14 ]. SQ primarily describes how the received care is perceived and influenced by various factors such as physical, social, and cultural contexts, as well as aspects like accessibility, respect, and confidentiality [ 14 ]. Most studies examining the quality of T2DM and/or hypertension care have predominantly focused on technical aspects, with only a handful exploring service quality [ 15 , 16 ]. Notably, despite the higher prevalence of T2DM and hypertension in LMICs, the majority of studies examining service quality for these conditions originate from high-income countries, underscoring the imperative for additional research in LMICs [ 3 , 4 , 17 ]. This study aims to fill this gap by assessing service quality for T2DM and hypertension in primary healthcare settings from the perspective of service users in Iran.

Study design

This cross-sectional study was conducted from January to March 2020 in the East Azerbaijan province of Iran. We adhered to The Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guidelines to prepare our study report [ 18 ].

Study settings and participants

The target population included individuals seeking healthcare from health centers in the East-Azerbaijan province of Iran. Eligible participants were aged 18 or above, diagnosed with T2DM and/or hypertension at least 12 months before data collection.

We employed a two-step stratified sampling method. Initially, all 20 districts in East-Azerbaijan province were categorized into metropolitan, densely populated urban, and predominantly rural areas. Subsequently, we randomly selected districts (Tabriz, Marand, Bostanabad, Varzaqhan, Ajabshir) and health centers within those districts. Participants were then randomly selected from lists of eligible individuals in each selected health center.

Sample size calculation

Using the G-Power program (Heinrich-Heine-Universität Düsseldorf, Düsseldorf, Germany), we calculated a sample size of 637 based on 95% power, 0.05 α and an effect size of 0.07 to consider the stratified sampling, considering a linear regression test based on a fixed model.

Participants’ recruitment

Health workers in selected centers communicated with potential participants during routine care visits. They explained the study’s purpose, introduced the research team, and obtained written consent from willing participants. To safeguard privacy, participants could complete the anonymous questionnaire in a separate room.

Data collection

Data was collected from January to March 2020 using a standard SQ questionnaire (the validity and reliability were already approved in similar contexts) [ 19 , 20 , 21 ]. The questionnaire included four main parts. The first part consisted of the demographic characteristics (age, gender, place of birth, current residency, language, employment status, health insurance status, and education level). The second part encompassed questions related to disease conditions (medical history, type of treatment, complications, and smoking status), and the third part contained questions related to self-management conditions. The final part included 37 questions in 13 dimensions of service quality (SQ), including choice of care provider (2 questions), communication (4 questions), autonomy (4 questions ), availability of support groups (3 questions), continuum of care (2 questions), basic amenities (4 questions), dignity (4 questions), timeliness (4 questions), safety (2 questions), prevention services (2 questions), accessibility of services (2 questions), confidentiality (2 questions) and dietary counseling (2 questions).

Despite previous validation, the face validity of the questionnaire was reviewed and confirmed by health management specialists and cardiologists at Tabriz University of Medical Sciences, and its reliability was confirmed according to the Cronbach’s alpha coefficient (α = 0.81) in a pilot study on 30 participants. We recruited 13 participants from urban and 17 participants from rural center in pilot study. Cronbach’s alpha coefficient ranged from (α = 0.67) for “timeliness”, to (α = 0.83) for “dietary counseling”. Additionally, according to previous studies, an SQ score of less than “nine” indicates a failure in the quality of care and a significant gap for improvement [ 19 , 20 , 21 ]. We excluded the participants of the pilot study from the main sample size to avoid any bias.

Data analysis

For each question, participants were asked to report the importance of the item and their perception of the quality of care they had received about that item (performance) during the last 12 months. Questions related to the importance of the SQ items were scored on a four-point Likert scale, which was then scaled from 1 to 10 (1 = not important, 3 = somehow important, 6 = important, and 10 = very important). Questions related to the perceived performance of services were also scored on a four-point scale ranging from ‘‘never, sometimes, usually, and always’’ or ‘‘poor, fair, good, and excellent’’. For analysis, this scale was dichotomized, say, 0 = usually/always or good/excellent and 1 = never/sometimes or poor/fair [ 22 , 23 ].

An overall measure of SQ was calculated for each SQ dimension by combining the importance and performance scores using the following formula [ 22 , 23 ]:

Service Quality = 10 – (importance × performance).

SQ score ranges from 0 (worse) to 10 (best). The SQ score of each dimension was calculated as mean SQ scores of that dimension’s questions and total SQ was calculated as mean SQ scores of all 37 questions. Finally, the service quality score was reported on a scale of 0-100.We assessed and confirmed the normality of data by one sample Kolmogorov–Smirnov test ( n  = 561, Z = 0.07, P _Value = 0.06). We reported frequencies and percentages for categorical variables and mean and standard deviation for the numerical variables, including, age and SQ score and its dimensions. We used the One-Way ANOVA test to analyze the differences between the anthropometric indices and biochemical characteristics and dimensions of SQ in categorical variables.

We employed a two-step linear regression analysis as the entering method for our data analysis. Variables identified as related with Service Quality (SQ) in the univariate analysis were included in the multiple linear regression model. The significance thresholds for the entry and removal of variables in the stepwise regression model were set at 0.05 and 0.25, respectively. Additionally, age, education, continuous care by specialists, and self-evaluation of disease were included as control variables.

To ensure the validity of our regression analysis, we conducted several checks. Normality of residuals was assessed and confirmed through the normal probability plot, while the homogeneity of residual variances was verified via the residual versus predicted values plot. We further ensured residual independence and addressed multicollinearity by employing Durbin-Watson Statistics and Variance Inflation Factor, respectively. These steps were taken to fulfill all assumptions of multiple linear regression. Also, reference categories in regression analysis were selected based on the research team’s theoretical interest and previous studies.

Statistical significance was determined at a p -value threshold of < 0.05. The data were meticulously analyzed using the STATA version 17 (StatsCorp, College Station, TX, USA).

Among the 637 contacted patients, an impressive 561 individuals participated in the study, reflecting a robust response rate of 91.1%. The majority of participants were female (69%), hailing from metropolitan areas (36%), predominantly speaking Azeri (94%), unemployed (74%), lacking supplementary health insurance (65%), and reporting illiteracy (41%) (Table  1 ).

he anthropometric indices and biochemical characteristics of the participants revealed a predominant occurrence of overweight status. Notably, the mean Fasting Blood Glucose (FBG) levels in individuals with Type 2 Diabetes Mellitus (T2DM) were elevated, measuring 174.4 (73.57) in patients with T2DM without hypertension and 159.4 (65.46) in patients with both T2DM and Hypertension. Additional details regarding the participants’ anthropometric indices and biochemical characteristics can be found in Table  2 .

Statement of principal findings

In this study, the evaluation of service quality (SQ) for Type 2 Diabetes Mellitus (T2DM) and hypertension in primary healthcare settings in Iran revealed that SQ scores for participants with T2DM without Hypertension, those with hypertension without T2DM, and those with both conditions were at an average level. The primary weaknesses identified in SQ were related to the availability of support groups, self-care training, and dietary counseling.

In our study, participants reported higher scores for “dignity” and “confidentiality” items in service provision compared to the other dimensions of the SQ, while the lowest score was reported for the availability of support groups. The significant role of the support groups in controlling patients with T2DM and/or hypertension, especially in LMICs with a rising burden of diabetes, is well documented. For example, studies have reported that support groups can enhance diabetes knowledge and psychosocial functioning [ 24 , 25 ], improve diabetes outcomes [ 26 , 27 ], and enhance self-management behaviors [ 27 , 28 ]. Therefore, it is of fundamental importance to take advantage of support groups when providing services for patients with diabetes or other chronic health conditions. However, this principle component of care seems to be ignored in the care process of patients with T2DM and/or hypertension in Iran.

In addition to access to support groups, the dimensions of “nutrition counseling”, “disease prevention services”, and “the right to choose service providers” had the lowest scores among all dimensions of SQ in all three groups of patients. However, a strong body of evidence has shown that due to the important role of nutrition interventions in improving glucose metabolism, weight, BMI, and waist circumference in T2DM [ 29 ], nutrition counseling is essential for patients with T2DM [ 30 ]. Other studies, on the other hand, have highlighted the role of social interactions in the effective control of T2DM and/or hypertension and in guiding the self-management tasks. For instance, one study showed that risks of uncontrolled hypertension are lower among those with higher social interactions who discuss their health issues with others in a social group [ 31 ]. Due to the importance of these elements in care process of the patients with T2DM and/or hypertension, it is critical for the health system to employ plans to monitor the performance of the healthcare provider with regard to service quality of chronic health conditions.

To achieve desirable outcomes in treating patients with T2DM and/or hypertension, healthcare providers need to be very concrete about providing self-management and dietary counseling. Moreover, considering the progressive nature of T2DM and hypertension and the need for constant monitoring of progress and any complications of the disease, it is necessary to provide them with accurate training and self-management advice by the service providers. In addition, the authorities of the health system should take measures to continuously evaluate the status of these services and care in the healthcare center.

Based on the results of this study, the patients’ self-care status was not favorable. Poor performance in implementing self-care programs indicates that healthcare providers may have failed to achieve care goals for patients with chronic conditions. The results of the study also showed that generally the better the self-care status the higher the SQ score. This finding may imply that empowered patients can receive better care from service providers [ 32 ].

The results of our study identified that people who received their services from a specific provider reported significantly higher scores for SQ than those without a specific service provider. This highlights the need for stability in service providers, especially when dealing with chronic situations, which require long-term coordination between the patient and the service provider. Receiving services from a specific healthcare provider for chronic health conditions is one of the main elements of the continuum of care [ 33 ]. Studies have shown that continuum of care is connected to greater glycemic control [ 34 , 35 ], improvement of health-related quality of life [ 36 ], and lower odds for mortality in patients with T2DM [ 35 ].

Additionally, based on the results of the current study, patients in small cities reported a higher quality of services than those in rural areas. Aligning with our results, several studies have shown that patients with diabetes in rural areas are less likely to receive adequate and high-quality care compared to their non-rural counterparts [ 37 , 38 ]. A systematic review has summarized several interventions targeting patients, professionals, and health systems to improve the quality of care for patients with diabetes in rural areas, including patient education, clinician education, and electronic patient registry [ 39 ]. Recent studies from LMICs also have reported the improvement of diabetes and/or hypertension care as a result of interventions such as patient education by health workers/nurses [ 40 ] and professionals’ and patients’ joint advocacy for health system reform to improve the access to medication and disease management/prevention services in rural areas [ 41 ].

Implications for policy, practice, and research

The results of this study are crucial for enhancing health authorities’ understanding of the quality of healthcare services for patients with Type 2 Diabetes Mellitus (T2DM) and/or hypertension, along with identifying determinant factors. This knowledge is foundational for initiating improvements in service quality and addressing the specific needs of patients with chronic health conditions. A deep understanding of the healthcare service landscape for patients with chronic health conditions is deemed monumental. This understanding serves as the initial step towards implementing targeted interventions and strategies to enhance the overall quality of services provided to patients. It lays the groundwork for addressing challenges and optimizing care delivery. The emphasis of the World Health Organization (WHO) on universal health coverage and the management of chronic diseases, particularly in developing countries, aligns with the significance of this study’s results. The holistic views presented on the quality of services for individuals with T2DM and/or hypertension, encompassing both rural and urban areas in a Low- and Middle-Income Country (LMIC), contribute to global health priorities. In summary, the study’s implications extend to informing policy decisions, guiding practice improvements, and shaping the trajectory of future research endeavors. The holistic perspective provided by this research contributes to the ongoing global efforts to enhance healthcare services for individuals with chronic conditions, particularly in LMICs.

Limitations

We acknowledge that there are some limitations to this study. First, the main health outcomes of T2DM and hypertension, such as Hemoglobin HA1c and blood pressure, were missed from patients’ medical records and, therefore, were not included in the data analysis. Second, the samples were patients with T2DM and/or hypertension who received healthcare services from the public sector and those who were visited by physicians in their private offices were not included in the study. As a result, we were not able to compare the SQ in the private and public sectors. Despite these limitations, this study could provide more insight into how SQ of T2DM and hypertension may be varied among patients with different characteristics and different geographical residencies.

The results of the current study revealed that even though the primary health system has initiated delivering routine check-ups for patients with T2DM and/or hypertension in primary health centers a decade ago, there is a gap in the quality of services provided. While SQ scores across participant groups were generally average, significant weaknesses were identified in the availability of support groups, self-care training, and dietary counseling. Notably, higher SQ scores correlated with better self-care status, suggesting the importance of patient empowerment in improving care outcomes. Stability in healthcare providers was also highlighted as essential for continuity of care, particularly in managing chronic conditions like T2DM and hypertension. Notably, higher SQ scores correlated with better self-care status, suggesting the importance of patient empowerment in improving care outcomes. Furthermore, disparities in service quality between small cities and rural areas were evident, with rural populations facing greater challenges in accessing adequate care. Addressing these disparities requires targeted interventions such as patient and clinician education initiatives, as well as health system reforms to improve access to medication and disease management services in rural areas. Overall, enhancing service quality in primary healthcare settings necessitates a comprehensive approach that prioritizes patient empowerment, continuity of care, and equitable access to services, particularly for vulnerable populations in rural areas.

The findings regarding self-reported hypertension self-management status indicated that among individuals with hypertension without Type 2 Diabetes Mellitus (T2DM), the majority adhered to the “regular use of prescription drugs” (approximately 94%). Conversely, “regular blood pressure measurement at home” was the least adhered-to item, with an adherence rate of around 61%. In contrast, among patients with both T2DM and hypertension, a substantial proportion reported adherence to a “recommended diet” (approximately 90%) and being “aware of the side effects of high blood pressure” (roughly 88%). The results of Fisher’s Exact Test and Independent Samples Test demonstrated no statistically significant relationship between hypertension self-management status and the presence of T2DM among individuals with hypertension, neither for sub-items nor for the total score. Comprehensive details on the hypertension self-management status of participants are presented in Table  3 .

The self-reported Type 2 Diabetes Mellitus (T2DM) self-management status revealed that the majority of participants adhered to the “regular use of prescription drugs” (approximately 97%). Conversely, “regular glucose measurement at home” emerged as the least adhered-to items, with adherence rates of approximately 58% among patients with T2DM without hypertension and 47% among patients with both T2DM and hypertension. A comprehensive overview of the T2DM self-management status of patients is presented in Table  4 .

Among all 13 dimensions of Service Quality (SQ), confidentiality and dignity exhibited the highest scores across all groups. The total SQ scores were 82.37 (12.19), 82.48 (12.45), and 81.69 (11.75) for hypertension, Type 2 Diabetes Mellitus (T2DM), and both conditions (Hypertension & T2DM), respectively. Notably, there were no statistically significant differences in total SQ scores between the groups ( P  = 0.780). Detailed results of SQ scores for each group are presented in Table  5 .

The Multiple Regression model results unveiled several relationships with Service Quality (SQ) scores in different patient groups. Among individuals with hypertension and without diabetes, those with specific service providers demonstrated higher SQ scores (b = 7.03; p  < 0.001) compared to those without specific service providers. Moreover, individuals in rural areas with hypertension and without diabetes exhibited lower SQ scores (b = -6.07; p  < 0.05) than their urban counterparts.

In the group of patients with Type 2 Diabetes Mellitus (T2DM) and without hypertension, those residing in non-metropolitan cities reported higher SQ scores compared to patients in metropolitan areas (b = 5.09; p  < 0.05). Additionally, a one-point increase in self-management total score was related with a 0.13-point decrease in SQ score ( P  < 0.05).

For people with both hypertension and T2DM, those with specific service providers demonstrated higher SQ scores (b = 8.32; p  < 0.001) compared to those without specific service providers. Patients with both conditions who had a diabetes history of over 10 years exhibited higher SQ scores than those with less than two years of diabetes history (b = 4.47; p  < 0.05).

Data availability

The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.

Abbreviations

Body Mass Index

Mean Fasting Blood Glucose

Service Quality

Standard Deviation

Type2 Diabetes Mellitus

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Acknowledgements

We are deeply grateful for the contributions of district health centers’ employees and urban health centers’/posts’ on data collection and we appreciate the financial support of Tabriz University of Medical Sciences, the Office of the Vice-Chancellor for Health at Tabriz University of Medical Sciences. Also, special thanks to all participants for their patience and participation in this study.

This study was funded by Tabriz University of medical science, Approval ID IR.TBZMED.REC.1398.428 (grant number: 61696).

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East Azerbaijan Provincial Health Centre, Tabriz University of Medical Sciences, Tabriz, Iran

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Student Research Committee, Department of Health Policy and Management, School of Management and Medical Informatics, Tabriz University of Medical Sciences, Tabriz, Iran

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All authors developed the study design. KG, JS, SB, NS, and MP participated in data collection. SI and KG performed the data synthesis. SI, KG, and SB drafted the manuscript. SI and KG edited the manuscript grammatically. All authors conducted a literature review. All authors read the manuscript and approved it after any comments.

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Iezadi, S., Gholipour, K., Sherbafi, J. et al. Service quality: perspective of people with type 2 diabetes mellitus and hypertension in rural and urban public primary healthcare centers in Iran. BMC Health Serv Res 24 , 517 (2024). https://doi.org/10.1186/s12913-024-10854-y

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Self-care and type 2 diabetes mellitus (T2DM): a literature review in sex-related differences

Irene baroni.

1 Department of Biomedicine and Prevention, University of Rome Tor Vergata, Rome, Italy

2 Health Professions Research and Development Unit, IRCCS Policlinico San Donato, Milano, Italy

Rosario Caruso

3 Department of Biomedical Sciences for Health, University of Milan, Milan, Italy

Federica Dellafiore

4 Department of Public Health, Experimental and Forensic Medicine, Section of Hygiene, University of Pavia, Pavia, Italy

Davide Ausili

5 Department of Medicine and Surgery, University of Milan-Bicocca, Monza, Italy

Serena Barello

6 EngageMinds HUB – Consumer, Food & Health Engagement Research Center, Università Cattolica del Sacro Cuore, Milano and Cremona, Italy

Arianna Magon

Gianluca conte.

7 Nursing Degree Course, section Istituti Clinici di Pavia e Vigevano, Pavia, Italy

Ida Vangone

8 Department of Oncology and Hematology-Oncology, Istituto Europeo di Oncologia, Milan, Italy

Luca Guardamagna

9 Istituto Clinico Beato Matteo, Vigevano, (PV), Italy

Cristina Arrigoni

Background and aim of the work..

Type 2 Diabetes Mellitus (T2DM) is a multifactorial disease, and it is considered a worldwide challenge for its increasing prevalence and its negative impact on patients’ wellbeing. Even if it is known that self-care is a key factor in reaching optimal outcomes, and males and females implement different self-care behaviors, sex-related differences in self-care of patients with T2DM have been poorly investigated. Especially, an overall view of the available evidence has not yet been done. Accordingly, this review aims to summarize, critically review, and interpret the available evidence related to the sex-related differences in self-care behaviors of patients with T2DM.

An extensive literature review was performed with a narrative synthesis following the PRISMA statement and flowchart through four databases: PubMed, CINAHL, Scopus, and Embase.

From the 5776 identified records by the queries, only 29 articles were included, having a high-quality evaluation. Both females and males with T2DM must improve their self-care: more males reported performing better behaviors aimed at maintaining health and clinical stability (i.e., self-care maintenance) than females, but mainly in relation to physical activity. On the other hand, more females reported performing adequate behaviors aimed at monitoring their signs and symptoms (i.e., self-care monitoring) but with worse glycemic control and diabetic complications (i.e., self-care management).

Conclusions.

This review firstly provides an overall view of different self-care behaviors implemented by males and females with T2DM, showing that self-care management should be improved in both sexes. Health education must include the problems related to the diabetic pathology and the patient’s own characteristics, such as sex. ( www.actabiomedica.it )

Introduction

World Health Organization (WHO) estimates that Type II Diabetes Mellitus (T2DM) is the third-highest risk factor for premature mortality worldwide, preceded only by high blood pressure and tobacco ( 1 ). Moreover, extensive epidemiologic studies show that T2DM incidence is increasing worldwide. It affects 463 million people, with the overall figure predicted to rise to 629 million by 2045 and accounts for approximately 90% of all patients with diabetes ( 2 ). T2DM management is mainly focused on monitoring blood glucose levels, taking medication, and educating patients to maintain healthy behaviors (i.e., self-care behaviors) ( 3 ), which is fundamental to achieving good clinical outcomes and quality of life ( 3 , 4 ).

Self-care is a complex and natural decision-making process to maintain health, especially amongst chronic patients ( 4 – 6 ), such as patients with T2DM. According to Riegel’s theory, self-care behaviors could be influence actions aimed at maintaining both physiological and emotional stability (i.e., self-care maintenance), which facilitates the perception of specific signs and symptoms (i.e., self-care monitoring) and is directed at managing these upon onset (i.e., self-care management) ( 4 , 6 ). Overall, self-care maintenance, monitoring, and management are influenced by self-care self-efficacy, which is the level of confidence people have in their ability to perform adequate self-care, and self-care self-efficacy ( 4 , 7 , 8 ).

Since the strategic role of self-care behaviors implemented on the health outcomes of patients with chronic pathology is evident and well recognized ( 9 – 12 ), recently, many authors have been concentrating on the study of self-care determinants that can determine clinical outcomes, such as sex ( 9 , 10 , 13 – 17 ). In fact, understanding sex-related differences in self-care among chronic patients has a key role into designing evidence-based educational interventions and improving health outcomes. Also, in epidemiology, a significant difference between men and women in patients with T2DM, and other sex-related differences are highlighted in the presence of different genes and hormones, partially determined by environmental exposure ( 18 , 19 ). The field of sex-related differences is also studied in metabolic response to treatments ( 20 ), quality of life ( 21 ), and psychosocial variables ( 22 ). However, these differences should be considered together with the most common T2DM health determinants, which strongly influence treatment outcomes in people with T2DM ( 17 , 22 ), such as wellbeing, self-care and self-efficacy ( 23 ).

Therefore, to the best of our knowledge, there is a lack of synthesis regarding the sex-related differences in self-care behaviors in patients with T2DM. Indeed, the recent literature presents few empirical studies, and globally, these differences are confused and fragmented ( 24 ). This gap represents a drawback for the current need to plan strategic education interventions to support self-care in patients with T2DM.

The aim of this study was to summarize, critically review, and interpret the evidence related to sex-related differences in self-care behaviors of patients with T2DM.

Study design

It was performed an extensive literature search with a narrative synthesis ( 25 ). The articles included are published until September 2020, without an initial temporal limit, and they explored and described the sex-related differences regarding self-care behaviors in patients with T2DM.

Search method and study selection

The selection of studies for inclusion was performed according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement and flowchart ( 26 ). The PRISMA statement is an evidence-based minimum set of items to ensure the rigor of systematic searches and decrease selection bias, while the PRISMA flowchart identifies four phases (i.e., identification, screening, eligibility, and inclusion) that help guide the choice of records ( 26 ) ( Figure 1 ). For this review, a three-stage approach was used for data synthesis because it was not possible to perform a meta-analysis due to the methodological and clinical heterogeneities among the included studies. Using keywords, such as ‘Self-care behaviors’; ‘Sex-related difference’; ‘Type 2 diabetes mellitus’ and their synonyms, the following literature databases were used: PubMed, Scopus, Embase and CINAHL. In addition, was performed an open search on Google Scholar . The main inclusion criteria were: (a) focused on sex-related differences in self-care behaviors of patients with T2DM, (b) primary research with quantitative study designs, (c) published in English. Finally, we also carried out backwards and forward citation tracking and we examined the reference lists (citation chasing) of included studies for the identification of additional studies ( 27 ). The only exclusion criterion was the low quality of papers after the appraisal of the eligible articles as requested for phase 3 of the PRISMA flow chart, as described below. The search syntax for bibliographic database searching is shown in Table 1 .

PRISMA flow chart

The search syntax for bibliographic database searching

The selection process ( Figure 1 ) was independently conducted by two authors (IB and FD), and, with a third researcher (CA), was conducted a discussion to reach a consensus to solve potential disagreements at each phase. During phase one (i.e., identification) were to find 5776 records, of which 97 records remain after duplicates removal and after the screening based on the title. Accordingly, in this screening phase (i.e. phase two), each screened record’s abstracts were assessed, and 55 records were excluded because their content did not meet the inclusion criteria (i.e. not written in English, were either based on qualitative research methodology or they were not related to the research question).

The quality appraisal of the 29 papers in the inclusion phase (i.e., phase 3) was assessed by the independent work of two authors (IB, FD), using standardized Checklist from the Joanna Briggs Institute Critical Appraisal tools for use in JBI Systematic Reviews ( 28 ). The JBI Checklist critical appraisal tool consists of 8 criteria related to qualitative research philosophy, research design, and trustworthiness ( Table 2 ). The overall scoring, which indicates the two authors’ assessment (i.e., Yes, No, Unclear or Not/Applicable), was discussed within a consensus meeting. The authors’ agreement had to concern with their degree of credibility. Any disagreements between the authors were solved by consensus discussion or referred to a third reviewer (CA).

JBI Checklist - Critical appraisal

FD: Federica Dellafiore; IB: Irene Baroni

Data Extraction, Analysis and Synthesis

The included articles were analyzed according to a narrative analysis approach ( 25 ). Specifically, the authors read the full texts several times to get an overview of the content of each paper ( Table 3 ) in accordance with the following format: (a) first author and publication’s years, (b) population/geographic area (c) study aim, (d) study methodology and design and (f) main results. After the entire research team provided an account of the main findings and seeing that a meta-analysis was not feasible for high heterogeneity, the authors, to allow better interpretation and usability of the results within the scientific literature, agreed to consider the ‘Middle-Range Theory of Self-Care of Chronic Illness’ as the framework that better represented these results ( 6 ). Therefore, the results were grouped into self-care maintenance, self-care monitoring, self-care management, and determinants of self-care (i.e. some psychosocial factors can be considered associated to self-care activities, such as social support and self-efficacy) ( 29 ) ( Table 4 ).

Study characteristics and main results

Data Synthesis within the Middle-Range Theory of Self-Care of Chronic Illness framework

Of the 29 included articles, 25 reported cross-sectional data collection ( Table 3 ), and most of them (n=12) were conducted in American countries (USA, Canada, and Mexico). Of the other articles, eight were conducted in European countries (Italy, United Kingdom, Spain, Germany, Austria, and Norway), and nine in Middle Eastern countries (Japan, China, Nepal, Thailand, Oman, Iran, and South Korea). The included articles’ data extraction identified four main areas belonging to self-care maintenance, self-care monitoring, self-care management, and determinants of self-care ( Table 4 ).

In general, some articles reported results on the self-care construct in general which highlight the non-homogeneity of the results that emerged from this review. Caruso et al. 2020 ( 17 ) found that females perform equal or better self-care than males, while the same authors in 2017 ( 30 ) reported that there were no significant sex-related differences related to self-care. Previous studies showed the same disagreement: Yin J. 2016 ( 31 ) reported that females were more intensively treated and had better self-care behaviors with high levels of treatments’ adherence; while Choi J.S., 2015 ( 32 ) stated that older (≥ 70 years) men scored slightly higher on diabetes self-care behaviours compared to older women but with no statistically significant difference, and, finally, Mansyur C. L. 2015 ( 33 ) reported that women had significantly lower self-care adherence than men. Additionally, a more specifical narrative analysis was performed, and four main areas describing an overall view of sex-related differences emerged from the results of 29 included articles ( Table 4 ).

Self-care Maintenance: diet and weight control, exercise and physical activity, medications and insulin therapy

Eighteen studies reported results on self-care maintenance activities. Overall, despite substantial evidence showing that males practised more adequate physical activity than females, conflictual results were found on dietary, weight and concomitant factors (i.e. foot care, smoke and alcohol).

Boonsatean W. et al. 2018 ( 34 ) showed that men performed higher scores of dietary control than women, similarly to Vaccaro J. A.2012 ( 35 ) and Shrestha A. D.2013 ( 36 ) that reported that lesser women were following the recommended healthy diet practices than men, and to Misra R.2009 ( 37 ) that found that females were also more likely to report difficulty with dietary adherence. Conversely, Avedzi H. M. et al. 2017 ( 38 ) and Vitale M. et al. 2016 ( 39 ) stated that women consumed more legumes, vegetables, fruits, eggs, and milk and avoided high fats/caloric foods than men, like Naicker K. et al. 2017 ( 40 ) that reported a greater tendency of avoiding saturated fats in women. In this regard, Vitale M. et al. 2016 ( 39 ) highlighted some discordant points in the eating behaviour of males and females: it seems that the proportion of energy from total fat and saturated fat was significantly higher in women than in men, but in women, the intake of fibre was significantly higher than in men with a lower global glycaemic load of the diet. And again, Chiu C. et al. 2011 ( 41 ) stated that women have better dietary habits but significantly higher BMI than men. Finally, Taru C. et al. 2008 ( 42 ) assumed that for females, “reducing the amount of cooking salt” may be more effective for reducing dietary intake than simply emphasizing the need to reduce such intake.

Specifically, on weight control, Tokunaga-Nakawatase Y. et al. 2019 ( 43 ) reported that non-obese-but-overeating female patients showed higher levels of HbA1c than not-overeating females, unlike the males. Aghili R. 2017 ( 44 ) affirm that women tend to have higher BMI compared to men. Similarly, Mansyur C. L. 2015 ( 33 ) found that women tended to be significantly more obese than men, although both T2DM men and women tended to be obese on average; and Rossi M. C. et al., 2017 ( 45 ) report that higher levels of total and LDL-cholesterol, and BMI were found in women. Badawi G. et al., 2012 ( 46 ) add that obesity was associated with poorer health status in women but not in men. Also, Vitale M. et al. 2016 ( 39 ) agree that BMI was significantly higher in women; instead, Kacerovsky-Bielesz G. et al., 2009 ( 22 ) confirm that BMI tended to be higher in women but did not significantly differ compared with men.

Considering the physical activity, Boonsatean W. et al., 2018 ( 34 ) showed that men reported practicing more physical activity than women. Alghafri T.S. et al., 2018 ( 47 ) also agree that compared to males, females were less physically active and tended to report longer sitting time. Also, Rossi M. C. 2017 ( 45 ) and Chiu C. 2011 ( 41 ) reported that women had statistically significant poorer scores for physical functioning and self-care activities dedicated to physical activities. On the same line, Shrestha A. D.et al. 2013 ( 36 ) found that women were straggling behind men in the recommended exercise for at least five days a week. But, Badawi G. et al., 2012 ( 46 ) concluded that physical activity was associated with better health status in men only, and Kacerovsky-Bielesz G. et al., 2009 ( 22 ) reported that more women than men performed regular physical exercise.

Based on medications, insulin therapy, and risk factors control, Brown S.A. et al., 2000 ( 48 ) reported that among individuals treated with diet only, males exhibited higher HbA1c levels than females; conversely, for individuals on insulin only, females showed a higher mean of HbA1c compared with males. Aghili R. et al., 2017 ( 44 ) add that the basal insulin dose was higher in women compared to men, and Chiu C. et al. 2011 ( 41 ) concluded that there were no significant sex-related differences in the medication adherence. Investigating blood pressure as a concomitant factor for diabetes, Aghili R. et al. 2017 ( 44 ) affirm that diastolic blood pressure was significantly higher in men, as well as Vitale M. et al. 2016 ( 39 ) reported that diastolic blood pressure was slightly lower in women than in men. Sahin et a., 2020 ( 49 ) reported that the male sex is a determinant of good foot care behaviour; on the contrary, Rossi M. C. et al., 2017 ( 45 ) reported that women showed higher adherence than men to self-care activities dedicated to foot care. Lastly, Rossi M. C. et al., 2017 ( 45 ) found that women were less frequently smokers than men and Kacerovsky-Bielesz G. 2009 ( 22 ) found that more women than men reported no or very rare alcohol consumption.

Self-care Monitoring: glycaemic control and blood glucose testing

Eleven studies reported results on self-care monitoring activities. Overall, despite substantial evidence showing that females practiced greater adequate blood glucose monitoring than males, these latter seem to have a higher level of glycemic control in terms of lower HbA1c.

Caruso R. et al. 2020 ( 17 ) found that self-care monitoring was lower among males when compared with females, while Lipscombe C. et al. 2016 ( 50 ) stated that more females than males never checked their blood glucose levels, but Cuevas H. E. et al. 2015 ( 51 ) specifies that more females than males checked blood glucose levels more than one time per week. Instead, Rossi M. C. et al. 2017 ( 45 ) and Chiu C. 2011 ( 41 ) reported that women showed higher adherence than men to self-care activities dedicated to self-monitoring of blood glucose even if they have significantly higher levels of HbA1c than men. Alghafri T. S. 2018 ( 47 ) too, reported that compared to males, there were significantly more females with uncontrolled diabetes, in accordance with Boonsatean W. et al. 2018 ( 34 ), Aghili R. 2017 ( 44 ), Shrestha A. D. et al. 2013 ( 36 ), and Hawthorne K. et al. 1999 ( 52 ) that reported that women had poorer glycemic control than men. On the contrary, Vitale M. et al. 2016 ( 39 ) reported that glucose control, evaluated as HbA1c, was marginally better in women, while Kacerovsky-Bielesz G. et al. 2009 ( 22 ) found that in women, HbA1c tended to be higher, but did not significantly differ compared with men.

Self-care Management

Only five studies reported results on self-care management. Studies on this topic are described in detail below.

Boonsatean W. et al. 2018 ( 34 ) and Chiu C. et al. 2011 ( 41 ) found a higher incidence of diabetic complications among women, despite a demonstrated higher mean score for glucose management and healthcare use than men. On the contrary, Rossi M. C. et al. 2017 ( 45 ) found that women had a lower prevalence of known diabetes complications than men. Meanwhile, Hawthorne K. et al. 1999 ( 52 ) reported that knowledge of diabetic complications was generally low, lower in women than in men. Finally, Caruso et al. 2020 ( 24 ) reported that males, having been diagnosed with diabetic neuropathy, were associated with inadequate self-care management; on the contrary, females, having been diagnosed with T2DM from <10 years and having diabetes retinopathy showed a lower risk of inadequate self-care management.

Self-care’s determinants: psychosocial factors associated with self-care activities

Nineteen studies investigated self-care behaviors’ determinants. Overall, a substantial amount of evidence reported more diabetes-related psychological difficulties and a lower perception of social support in females than in males. Furthermore, these studies highlighted the fundamental role of self-efficacy as a self-care determinant and mediator, but its level was found to be significantly lower in females than in males.

Aghili R. et al. 2017 ( 44 ) report that women under the age of 55 reported a higher level of distress, depression, anxiety and worse quality of life compared to men in the same age group, in accordance with Caruso et al. 2017 ( 30 ) that report that men perception of wellbeing is higher than in women, while the level of anxiety and depression are higher in women. Badawi G. et al. 2012 ( 46 ) reported that men were more likely to rate their health as excellent, while women were more likely to rate their health as good, fair or poor. Also, Naicker K. et al. 2017 ( 40 ) reported that more women than men had either depressive or anxious symptoms but was found strong associations between depression/anxiety and glycemic control in men only, while Yin J. et al. 2016 ( 31 ) found an association between poor glycemic control and depressive symptoms both in men and women but was more common in women than in men. Instead, Nau D. P. et al. 2007 ( 53 ) found a significant association between depression severity and worse medication adherence in men, while women were relatively adherent regardless of depressive symptom severity. However, Lipscombe C. et al. 2014 ( 54 ) reported that women were more likely than men to report having mild and moderate-severe anxiety symptoms; moreover, mild anxiety symptoms were associated with an increased odds of inactivity in women only, whereas moderate to severe anxiety symptoms were associated with an increase in the odds of inactivity for men only. The same authors found a similar association in 2016 with diabetes distress ( 50 ). On the same aspect, Rossi M. C. et al. 2017 ( 45 ) reported statistically significant poorer scores for psychological wellbeing, empowerment, diabetes-related distress, satisfaction with treatment, barriers to medication taking, satisfaction with access to chronic care and healthcare communication, and perceived social support in women than men. Shrestha A. D. et al. 2013 ( 36 ) found that women tend to present more depressive somatic symptoms associated with diabetes while men were more likely to present fatigue, muscle aches and sexual dysfunction. According to Chiu C. et al. 2011 ( 41 ), women had lower scores than men on diabetes coping status, perceived control, self-efficacy, and perceived family support but higher scores on depressive symptoms than men. Also, Kacerovsky-Bielesz G et al. .2009 ( 22 ) found that women tended to express a lower degree of satisfaction with social support. On the contrary, Misra R. et al. 2009 ( 37 ) reported that females reported higher social support compared to their male peers.

Caruso R et al. 2017 ( 30 ) found that men have a higher perception of self-efficacy than women. Moreover, Cherrington A. et al. 2010 ( 55 ) reported that there is strong evidence that diabetes self-efficacy mediates the effect of depressive symptoms on glycemic control for males. In the same area, Boonsatean W. et al. 2018 ( 34 ) found that men felt more confident in the treatment given by the health professionals and showed higher confidence in the treatment effectiveness than women. Deepening, Mansyur C. L. et al. 2016 ( 56 ) reported that men who were more discouraged from following a healthful diet but who ate the same foods tended to have lower Self-efficacy, while women who received more support and who ate the same foods as their families tended to have higher Self-efficacy. The same author ( 33 ) found that women had significantly lower self-efficacy (SE) and tended to perceive that they received lower levels of support. Shrestha A. D. et al. 2013 ( 36 ) reported that men who had higher self-efficacy had better dietary practice, while women with lower self-efficacy could not follow a good diet.

Caruso R. et al. 2020 ( 24 ) reported that in females, a higher level of persistence in self-care self-efficacy as a determinant of adequate self-care maintenance, while in both males and females, persistence higher level of self-care self-efficacy was associated with a decrease in the risk of inadequate self-care management. Moreover, among males, being an active worker was associated with inadequate self-care maintenance, while low income was associated with inadequate self-care maintenance only in females. On the same line, Choi J. S. et al. 2015 ( 32 ) stated that the number of diabetes-related complications, diabetes self-efficacy, and depression were significant predictors of self-care behaviors in older men; while in older women, the predictors were diabetes self-efficacy, depression, duration of diabetes, and barriers to diabetes self-care.

Our literature review provided an overall perspective on the evidence related to sex-related differences in self-care behaviors of patients with T2DM. We discovered 29 primary studies emerging by databases search, showing a growing interest in this topic by researchers; accordingly, the literature is extensive, but results are often fragmented and contradictory, undermining the effective possibility for nurses and clinicians to establish interventions to support the lacking areas of diversity between males and females with T2DM in the implementation of their self-care behaviors. So, we wanted to summarize and interpret evidence about sex-related differences in self-care among this population. For this aim, our study can be considered innovative and very useful in clinical practice, as it will allow combining each individual research effort into a global vision.

As already highlighted, self-care has a crucial role in determining a positive clinical trajectory among patients with chronic diseases ( 57 ). Sex-related differences are an important feature of the real world, as men and females perform many aspects of biology, physiopathology, and clinical issues in a specific way. It is possible to recognise males’ and females’ peculiarities in self-care behaviors. Indeed, it was found that males and females with T2DM performed self-care differently; more males reported performing better behaviors aimed at maintaining health and clinical stability (i.e., self-care maintenance) than females, but mainly concerning physical activity. On the other hand, more females reported performing adequate behaviors aimed at monitoring their signs and symptoms (i.e., self-care monitoring) but with worse glycemic control and diabetic complications (i.e., self-care management). These results are relevant because they clarify what is known about this aspect, which is fundamental for designing future research and implementing evidence-based personalized educational interventions for both sexes.

Self-care maintenance refers to those behaviors performed to improve wellbeing, preserve health, or maintain physical and emotional stability ( 5 , 58 ), and it represents the first step to staying healthy. Specifically, most of the included studies explored self-care maintenance behaviors, focusing on physical activity, diet, weight control behaviors, and treatment adherence of patients with T2DM. Many available studies argued that males practiced greater physical activity than females, thus highlighting the need to develop women tailored educational interventions to support physical activity. Even if regular physical activity provides an important benefit to the health of women, in general, women are often more sedentary than men ( 59 ), and participation in physical activity decreases as women age ( 60 ), increasing the risk for cardiovascular disease, diabetes, hypertension, colon cancer, and depression ( 61 ). For women with T2DM, physical activity is more important because empirical evidence shows that increasing physical activity, along with dietary changes, can dramatically decrease the risk of developing type 2 diabetes ( 62 ). However, the current evidence of sex-related differences in diet, weight, foot care, smoke and alcohol need to be improved to settle doubts coming from contradictory results.

Meanwhile, females reported more adequate self-monitoring behaviors (i.e., blood glycemic control) than males, but our results show also that females with T2DM generally report more inadequate glycemic control (i.e. higher level of HbA1c) than males. This result is comparable to what is reported by adults with T1DM ( 9 ). These findings may suggest that other inadequate behaviors, such as self-care maintenance behaviors, often reported by females with diabetes, could negatively influence glycemic control so much that the positive influence of reported adequate levels of self-care monitoring seems exceeded. However, this hypothesis needs to be empirically tested. Future empirical studies would be useful to understand the previous research that showed worse glycemic control among females who performed better blood glucose monitoring than males and vice-versa. These results are contrary to those of studies showing that adequate self-monitoring of blood glucose leads to better glycemic control (lower HbA1c) in patients with diabetes ( 63 , 64 ). This other contradiction can also be explained by the findings of other studies showing the leading role of self-efficacy in determining adequate behaviors e better outcomes ( 65 , 66 ) and that man reports a higher perception of self-efficacy than women ( 30 , 33 ). Therefore, considering these results, the influence of sex in modulating the association between self-monitoring and glycaemic control should be further investigated with empirical research designed to control selection bias for sex and psychosocial variables. Furthermore, these findings suggest that more specific educational interventions should be included in the clinical practice for both sexes.

Considering self-care management (i.e., the ability to respond to signs and symptoms when they occur), only five studies reported results on this topic. Two main components could be referred to as self-care management: autonomous and consultative behaviours, that patients need to perform as the result of their decision-making on specific observed signs and/or symptoms ( 4 ). In line with previous research in T2DM and T1DM populations ( 29 ), this review shows that patients with T2DM generally have difficulties in performing adequate self-care management, especially females that seem to report worse glycemic control e diabetic complications than males. However, this aspect is clearly under-investigated and showed conflictual results, with few studies aimed at investigating sex-related differences in self-care management.

Self-efficacy is one of the most important factors (i.e. self-care determinants) that affect the successfulness of self-care behaviors in patients with T2DM ( 67 ), explaining the 11.4% of variance regarding diabetes self-care and 31.3% variance of diabetes self-care behavioral intention ( 68 ). Additionally, the self-efficacy of patients with T2DM is strongly associated with the perceived quality of life ( 69 ), eating habits ( 70 ) and predicted the patients’ glycemic control ( 71 ). Therefore, it is emerging the strategic role of self-efficacy in determining self-care behaviors of patients with T2DM, and for this reason, clarifying the peculiarities of males and females is essential. In the general population, males have significantly higher self-efficacy strength than their female peers ( 72 ), and our results confirmed this result for women with T2DM. In this regard, it is possible hypnotizing different factors causing these differences. Firstly, the role division and social resources disadvantage for women, gendered family division, and social division of labor. This disadvantage will accumulate the old age, with the increase in chronic illness. Secondly, it has been described that often males overestimate their abilities and performance, with a relatively high level of confidence in facing challenges. Thirdly, the influence of social desirability, with the traditional thinking of “men are more able” is still deeply rooted ( 72 ). Accordingly, all these factors need to be deeply studied in patients with T2DM.

So, the under-investigated areas require empirical investigation to better understand sex-specific patterns of self-care behaviors and self-care determinants. Specifically, researchers need to deeply understand the relationships between each component of self-care and males’ and females’ specific outcomes. Also, this kind of knowledge could be important to improve clinical practice because the awareness of sex-specific patterns in self-care behaviors might create tailored interventions for providing self-care to both sexes.

Conclusions

Understanding sex-related differences in adults with T2DM is important for addressing future research and clinical practice because males and females with T2DM have reported performing self-care differently. Our results showed conflictual results: more males reported performing better behaviors aimed at maintaining health and clinical stability (i.e., self-care maintenance) than females, but mainly in relation to physical activity; On the other hand, more females reported performing adequate behaviors aimed at monitoring their signs and symptoms (i.e., self-care monitoring), but with worse glycemic control and diabetic complications (i.e., self-care management). However, more empirical research is recommended to obtain optimal knowledge for addressing clinical practice.

Considering that the available evidence on this topic is still fragmented, this literature review had some limitations. First, it was not possible to perform a meta-analysis because of the degree of methodological and clinical heterogeneity among the included studies. Second, although a large number of included articles to summarize the evidence related to sex-related differences in patients’ self-care behaviors with T2DM, the search terms and databases we used might not have found all relevant studies because of the numerous behaviors that are referred to as self-care. Third, we have to acknowledge that the majority of the included studies do not have performed statistical analysis stratified by sex, which could have led to biased results.

We suggest that healthcare providers involved in the care management of adults with T2DM give particular attention to supporting females with T2DM to enhance their behaviors aimed at maintaining adequate health and glycemic control, such as regular physical activity, and improved management of the diabetes-related psychological issue and weight-control behaviors. Considering the care management of males with T2DM, we recommend that clinicians pay particular attention to sustaining patients to monitor changes in their signs and symptoms, such as better blood glucose monitoring and dietary intake. We recommend educational interventions for both sexes to enhance patients’ ability in self-care management, such as responding to signs and symptoms when they occur.

Conflict of Interest:

Each author declares that he or she has no commercial associations (e.g., consultancies, stock ownership, equity interest, patent/licensing arrangement) that might pose a conflict of interest in connection with the submitted article

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The health-related quality of life of patients with type 1 and type 2 diabetes mellitus: a meta-analysis

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• Published: 17 April 2024

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• Cuma Fidan   ORCID: orcid.org/0000-0002-8581-5940 1 &
• İsmail Ağırbaş   ORCID: orcid.org/0000-0002-1664-5159 2  

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We need to know about the health-related quality of life (HRQoL) of patients with diabetes mellitus (DM) to assess their health outcomes. Quantitative studies have discussed whether patients with Type 1 DM (T1DM) have better HRQoL than those with Type 2 DM (T2DM) or vice versa. However, no meta-analyses have addressed the HRQoL of patients with T1DM and T2DM together. Therefore, the primary objective of this meta-analysis was to investigate the HRQoL of patients with T1DM and T2DM. The secondary objective of this meta-analysis was to use various scales to assess the HRQoL of patients with T1DM and T2DM.

The inclusion criteria were (1) study participants were diagnosed with T1DM and T2DM and were aged 18 years or older, (2) outcome measure was HRQoL as assessed by appropriate instruments, (3) study written in the English language, (4) research articles using quantitative research methods, (5) study with full-text access, and (6) study reporting the necessary statistics to calculate the effect size. Cohen’s d was used to calculate effect sizes, while the random effect model was used to calculate the joint effect size.

The sample consisted of seven articles, which recruited a total of 4.896 patients with DM. Patients with T1DM and T2DM had similar HRQoL. According to the EQ-5D-5L, patients with T1DM had a higher HRQoL than those with T2DM. According to the EQ-5D-VAS and SF-36, patients with T1DM and T2DM had similar HRQoL. The Egger’s regression analysis indicated no publication bias.

Our results are sample-specific and cannot be generalized to all patients with DM. Therefore, more research is warranted on this topic.

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Datasets related to this article can be found at Fig. 3 (Forest plot), Table 1 (Characteristics of included studies), and can be checked from references.

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Fidan, C., Ağırbaş, İ. The health-related quality of life of patients with type 1 and type 2 diabetes mellitus: a meta-analysis. Endocrine (2024). https://doi.org/10.1007/s12020-024-03824-1

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Received : 06 February 2024

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Published : 17 April 2024

DOI : https://doi.org/10.1007/s12020-024-03824-1

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• Diabetes mellitus
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• Meta-analysis
• Type 1 diabetes mellitus
• Type 2 diabetes mellitus

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