research paper on lifestyle disease

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• v.149(Suppl 1); 2019 Jan

LIFESTYLE DISEASES: Keeping fit for a better tomorrow

Prashant mathur.

ICMR- National Centre for Disease Informatics and Research, Bengaluru, India

Leena Mascarenhas

“Manaevam Manushayanam Kdranam Bandha Mokshayoh” (Man's captivity or freedom is dependent on the state of his mind. From this it follows that whether a man is healthy or unhealthy depends on himself. Illness is the result not only of our actions but also of our thoughts.)

The country has been undergoing a rapid transition in health over the past several decades – a shift from infectious diseases to non-communicable diseases (NCDs). This burden of NCDs had to be matched with appropriate response in research, and ICMR decided to set up National Centre for Disease Informatics and Research (NCDIR), Bengaluru in 2011 with the purpose of collection, analysis and reporting of etiological, clinical, epidemiological, and public health of NCDs – cancer, diabetes, cardiovascular diseases, stroke and other determinants. The ICMR National Cancer Registry Programme (NCRP), since 1982, collects and reports data in cancer incidence, burden and trends. Additionally, registries on stroke and heart failure have also been initiated in 2017 along with a national survey on NCD risk factors. NCDIR functions in a disease informatics hub for non-communicable diseases to inform policy, programme and decision-making.

Mahatma Gandhi inaugurating Kamla Nehru Hospital, Allahabad in Swaraj Bhawan campus on February 28, 1941. Presently, ICMR-NCDIR Cancer Registry operates from the Regional Cancer Centre.

CHANGING HEALTH SCENARIO IN INDIA

ICMR along with Public Health Foundation of India (PHFI) and Institute for Health Metrics & Evaluation (IHME) published state-level disease burden reports mapping the pattern of disease burden during 1990 to 2016 which showed the rising burden due to non-communicable diseases. Over the past century, India has transitioned from an era dominated by disease burden attributed to infectious diseases, childhood and maternal deaths to an era of lifestyle disease-related chronic diseases – Non-communicable Diseases (NCDs). In 2017, India witnessed 61.8 per cent deaths due to non-communicable diseases. These are a group of chronic diseases which begin in early phase of life and continue to progress if not appropriately intervened over the span of life leading to sickness and untimely death. NCDs majorly refers to cancers, diabetes, hypertension, cardiovascular diseases, mental health and others. They are together governed by a cluster of risk factors and their determinants, like tobacco and alcohol use, unhealthy diet, lack of physical activity, overweight & obesity, pollution (air, water and soil) and stress. As can be seen, most of them are human behaviour-driven and can be prevented at individual, family and societal levels. Over the decades, we are witnessing a narrowing of the gap between urban and rural health profiles due to NCDs. Thus, with the unfinished agenda of health, India faces a triple burden of health to deal with (infectious, NCDs and injuries). Elements adding to the escalation of burden due to NCDs include increasing life expectancy, affluence, industrialization and globalization.

GANDHIJI’S VIEWPOINTS FOR LIFESTYLE DISEASES

In Young India , August 8, 1929, Gandhiji wrote:

“Instead of using the body as a temple of God we use it as a vehicle for indulgences, and are not ashamed to run to medical men for help in our effort to increase them and abuse the earthly tabernacle.”

The above statement very well summarizes the occurrence of lifestyle related NCDs – neglect of prevention, indulgence in excesses and then seeking medical assistance to cure diseases!

Mahatma Gandhi led an extremely simple and altruistic way of life, setting an ideal example for every one of us to remain healthy. Not just his reasoning of truth and peacefulness has motivated us all over the world, but his lessons keep on inspiring us to lead a healthy way of life.

His words remind us every day,

“It is Health that is real Wealth and not pieces of gold and silver.”

While jailed in the Aga Khan Palace at Poona between 1942–44 he wrote the book Key to Health , which has covered different parts of well-being including the human body, air, water, nourishment, brahmacharya, tea, coffee and cocoa, intoxicants, opium and tobacco – all very relevant NCD risk factors. He hoped that any individual would have the genuine key to open the entryways, driving him to great well-being. He won’t have to visit specialists or vaidyas .

In this chapter it would be befitting to align our thoughts with Gandhiji's philosophy as relevant to NCDs and their risk factors.

Appropriate diet

“The body was never meant to be treated as a refuse bin, holding all the foods that the palate demands.”

The rise in obesity amongst adults and children in the country is propelled by over-consumption of food high in fats, carbohydrates and salt, and thus seeks our immediate attention to limit its damages due to diabetes, heart diseases, cancers, etc. It's estimated that there are 19 per cent overweight men and 21 per cent women and, 2 per cent overweight and obese children in the country. In his book Diet and Diet Reforms , Gandhiji's enthusiasm for nutrition emerged mostly from his anxiety for people around him. A productive method to help nature do this and keep the body in well-being was through an appropriate eating regimen. He thus became involved in research about diet.

He has spelt out his views on taking a balanced diet which has the right amount of fats, carbohydrates, proteins, salt and sugar. Through different phases of his life, he tried different things with different weight control plans and rehearsed self-limit. He picked food he discovered fortifying and surrendered those nourishments that made him powerless. He promoted fasting to provide the body an opportunity to detox, cleanse the stomach, use up sources of nutrition like fat, and help in coping with any infection that might be present.

Physical activity

“Today I know that physical training should have as much place in the curriculum as mental training.” The Story of my Experiments with Truth (1929)

Fast-paced industrialization and globalization have reduced physical activity levels and is making us lead an increasingly sedentary lifestyle. The physically inactive adult population was found to be 54.4 per cent, with 65 per cent in urban areas, and women more than men. Gandhiji himself was a very active person undertaking frequent long marches for the freedom struggle and always found time in his busy schedule for his regular morning walks. The Dandi March set him out, solo, on a 390 km walk protesting the tax on salt, and was joined by 10,000 followers. He regained his physical strength by resting for 4–5 hours per night.

Mahatma Gandhi and others on an evening walk at Panchgani, July 1944.

Gandhi on his daily walk, Wardha, 1934.

From Gujarat Vidyapeeth to Sabarmati Ashram, Mahatma Gandhi bicycled for the first time since his South African days to reach evening prayers in time, 1928.

Mahatma Gandhi swimming at Cape Comorin, January 22, 1934.

Tobacco and alcohol use

“Tobacco has created havoc for mankind.” “Those who take to drinking, ruin themselves and their people.” – Key to Health (1948)

Tobacco use contributed to nearly 5.9 per cent of NCDs and about 30 per cent of all cancers in India. Alcohol use is 29 per cent among men and 1 per cent among women. The youth of the country is attracted to these addictions which have grave health as well as societal consequences. Gandhiji preached self-restraint from tobacco and alcohol as addictive behaviours as they are intoxicants.

Air and environmental pollution

“Air, water and grains are the three chief kinds of food. Air is free to all, but, if it is polluted, it harms our health. Doctors say that bad air is more harmful than bad water. Inhalation of bad air is harmful by itself and this is the reason we [sometimes] need change of air.” – Ahmedabad meeting on January 1, 1918

Air pollution contributed a 9.8 per cent burden to NCDs in India in 2016 and poses a new and major public health challenge. He recognized its importance and in his speech at Ahmedabad in January 1918, explained the importance of purity of air, water and food. One hundred years ago, even though during his time environmental problems were not familiar, Gandhiji with his stunning premonition and knowledge anticipated that environmental deterioration was leading mankind to the wrong course.

National Programmes on Non-Communicable Diseases.

PRESENT EFFORTS TO TACKLE LIFESTYLE DISEASES

The interventions for NCDs and their risk factors today focus on health promotion, prevention, treatment and rehabilitation. Policy interventions can help in reducing exposure to behavioural risk factors. Additionally, it is indispensable to provide an enabling environment so that individuals can support their modified lifestyle. The National Programme for Prevention and Control of Diabetes, Cardiovascular Diseases, Cancer and Stroke (NPCDCS) emphasizes the above approach. India has committed to achieve the WHO global NCD targets by 2025 and the Sustainable Development Goals by 2030.

The changing health scenarios in India are driven by the risk factors and exposure to vectors. Even though the life expectancy has increased to 76 years, life today is burdened by lifestyle-related chronic NCDs. ICMR-NCDIR, through its research programmes, measures and monitors major NCDs and NCD risk factors towards developing appropriate interventions and measurement of their impact.

Gandhiji promoted a healthy lifestyle, and also proposed few cures for basic sicknesses in his book Guide to Health . Many of his hypotheses on health were revolutionary then and are valid today also. Gandhian ideas give an intriguing, and ideally successful end towards the NCD epidemic.

Perhaps the facts contained in this chapter can fill in as motivation for an extensive battle against NCDs. As citizens of this country we must strive to follow the Gandhian principles of healthy living!

ICMR-National Centre for Disease Informatics and Research, Bengaluru.

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• Published: 10 July 2023

Healthy lifestyle is linked to gains in disease-free life expectancy in China

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A healthy lifestyle is associated with longer total life expectancy and a larger proportion of remaining years lived without a major noncommunicable disease in the Chinese population. Public health initiatives that promote healthy lifestyles may have a role in realizing the Healthy China 2030 strategic plan.

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EDITORIAL article

Editorial: understanding the link between lifestyle and neurodegenerative diseases.

• Med-X Research Institute, School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China

Editorial on the Research Topic Understanding the link between lifestyle and neurodegenerative diseases

In the intricate tapestry of neurodegenerative diseases, Alzheimer's stands as a formidable challenge, affecting millions globally. The quest to understand and mitigate its impact has led researchers to explore the dynamic interplay between lifestyle choices and the onset of Alzheimer's disease (AD). Among the multifaceted factors under scrutiny, the role of cholesterol profile and insulin resistance emerges as a crucial nexus.

In this series, we highlighted five papers to showcase the progress in the field. The work “ Physical exercise may increase plasma concentration of high-density lipoprotein-cholesterol in patients with Alzheimer's disease ” by Jensen et al. , sheds light on a promising avenue in the realm of non-pharmacological interventions. This randomized controlled trial delves into the intricate relationship between physical exercise and lipid profiles in individuals grappling with AD. The foundation of this investigation lies in the recognition that lifestyle factors significantly influence the risk of developing Alzheimer's later in life. A cholesterol profile teetering toward the unfavorable and insulin resistance are identified as potent precursors to the onset of AD. Here, the spotlight turns to exercise, a modality that has shown cognitive benefits in healthy individuals. The paramount question addressed in this study: Can physical exercise wield a positive influence on the lipid profile, insulin, and glucose levels in patients with Alzheimer's?

The research, spanning 16 weeks, engaged 172 patients in a rigorous exploration of the effects of moderate-to-high intensity exercise. The cohort was divided into those embracing the exercise regimen and a control group undergoing treatment as usual. Intriguingly, the study considered the influence of apolipoproteinE genotype on key lipid components: total cholesterol, low-density lipoprotein-cholesterol (LDL-C), high-density lipoprotein-cholesterol (HDL-C), and triglycerides (TG) in plasma.

The findings resonate with a note of optimism. Significant attention was given to the “high exercise sub-group,” revealing a marked increase in plasma HDL-C levels. A 4.3% surge in HDL-C was observed in the high-exercise group compared to a marginal 0.7% decrease in the control group, after adjusting for statin use. These results, coupled with a stringent analysis of exercise adherence, propel the conclusion that short-term physical activity may indeed hold a key to enhancing the cholesterol profile in patients with AD.

The implications of this study extend beyond the immediate context, opening a door to interventions that embrace the vitality of lifestyle in our quest for healthier minds.

In the second installment of our series, we delve into a comprehensive review that underscores the interconnectedness of metabolic disorders and neurodegenerative diseases. Titled “ Animal models of metabolic disorders in the study of neurodegenerative diseases: an overview ” by de Bem et al. , the authors navigate the complex landscape where obesity, diabetes, and hypercholesterolemia converge with Alzheimer's and Parkinson's disease.

Metabolic disorders are on the rise globally, mirroring the trajectory of neurodegenerative diseases. The review posits obesity, diabetes, and hypercholesterolemia as early harbingers of sporadic Alzheimer's and Parkinson's disease. Beyond the surface, these conditions share molecular and cellular signatures, including protein aggregation, oxidative stress, neuroinflammation, and blood-brain barrier dysfunction—hallmarks contributing to cognitive impairment and neuronal death.

The paper leverages rodent models of metabolic disorders as valuable tools for unraveling the phenotypic features and pathogenic mechanisms of neurodegenerative disorders. As we traverse the intricate terrain of animal models, we gain insights into the shared pathological aspects of Alzheimer's and Parkinson's disease.

The third paper, “ LPC-DHA/EPA-enriched diets increase brain DHA and modulate behavior in mice that express human APOE4 ” by Scheinman et al. , spotlights the intricate dance between APOE genotype and docosahexaenoic acid (DHA). APOE4, associated with heightened cognitive decline and increased risk of neurodegenerative disorders, prompts the quest for supplements targeting genotype-modulated processes. The study introduces a promising avenue—lysophosphatidylcholine (LPC)-DHA enriched diets, showcasing enhanced bioavailability and a potential shield against age-related neurodegeneration.

As we pivot to the fourth paper, “ Healthy lifestyles and wellbeing reduce neuroinflammation and prevent neurodegenerative and psychiatric disorders ” by Kip et al. , the focus sharpens on the societal paradigm shift. From prioritizing productivity to emphasizing health and wellbeing, this paper asserts that embracing a balanced lifestyle is a win-win for individuals and societies. The integrated “healthy” lifestyle approach emerges as a cornerstone for preventing, rather than merely managing, neurodegenerative diseases.

The fifth and final paper, “ Liver's influence on the brain through the action of bile acids ” by Yeo et al. , unveils the liver's pivotal role as a sensor and effector in peripheral metabolic changes. Aberrations in liver function, especially pertaining to bile acid composition, cast a far-reaching impact on the brain. The review explores the intricate interplay between liver dysfunction, bile acids, and neurological disorders, shedding light on an often-overlooked aspect of neurodegeneration.

Through this Research Topic, the threads connecting lifestyle choices to the intricate mechanisms of neurodegenerative diseases become more pronounced. From exercise-induced shifts in lipid profiles to the nuanced interaction between diet, genetics, and cognitive function, we navigate a landscape where lifestyle choices not only influence but might hold the key to mitigating the impact of neurodegenerative disorders.

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Keywords: neurodegenerative diseases, lifestyle, metabolism, physical exercise, APOE

Citation: Wang Y (2024) Editorial: Understanding the link between lifestyle and neurodegenerative diseases. Front. Neurosci. 18:1365734. doi: 10.3389/fnins.2024.1365734

Received: 04 January 2024; Accepted: 09 January 2024; Published: 19 January 2024.

Edited and reviewed by: Einar M. Sigurdsson , New York University, United States

Copyright © 2024 Wang. 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.

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Research Status of Lifestyle-Related Diseases in China Based on Big Data Analysis

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To investigate the research status and development trend of Lifestyle-Related Diseases in China based on big data analysis, and to provide reference for their further research. METHODS: The databases of CNKI, Wanfang and Superstar Discovery were retrieved, and bibliometrics was used to analyze the big data of Chinese medical network literature on lifestyle-related diseases. RESULTS: A total of 7,636 kinds of literature were retrieved, and after screening there are 473 types of literature, including 397 journal papers, 50 conference papers, 15 master’s degree papers and 11 monographs. The first paper was published in 1987; the provinces/municipalities with more than 20 publications were Beijing, Shandong, Jiangsu, Guangdong, Hubei and Liaoning. There were 1,002 authors involved, and the degree of cooperation was 2.12; 29 papers were supported by the fund (6.28%); 164 papers were not cited (35.50%). CONCLUSION: In recent years, the study on Lifestyle-Related Diseases has attracted much attention, and the amount of relative published papers is on the rise, however the profundity and the depth of the study is not far enough. There are not many experts deeply working on this field. Even for the researchers in this field, the published amount of the articles is quite small and limited. The lack of continuity on the research of Lifestyle-Related Diseases is another major problem. Therefore, China needs to scale up the study and improve the research quality to obtain more research results, and provide more reference for future researchers.

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Chen, Y., Peng, X. (2021). Research Status of Lifestyle-Related Diseases in China Based on Big Data Analysis. In: Xu, Z., Parizi, R.M., Loyola-González, O., Zhang, X. (eds) Cyber Security Intelligence and Analytics. CSIA 2021. Advances in Intelligent Systems and Computing, vol 1342. Springer, Cham. https://doi.org/10.1007/978-3-030-70042-3_66

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Moving Biosurveillance Beyond Coded Data Using AI for Symptom Detection From Physician Notes: Retrospective Cohort Study

Authors of this article:

Original Paper

• Andrew J McMurry 1, 2 , PhD   ; 
• Amy R Zipursky 1, 3 , MD, MBI   ; 
• Alon Geva 1, 4, 5 , MD, MPH   ; 
• Karen L Olson 1, 2 , PhD   ; 
• James R Jones 1 , MPhil   ; 
• Vladimir Ignatov 1 , MFA   ; 
• Timothy A Miller 1, 2 , PhD   ; 
• Kenneth D Mandl 1, 2, 6 , MD, MPH  

1 Computational Health Informatics Program, Boston Children's Hospital, Boston, MA, United States

2 Department of Pediatrics, Harvard Medical School, Boston, MA, United States

3 Division of Pediatric Emergency Medicine, Department of Pediatrics, The Hospital for Sick Children, Toronto, ON, Canada

4 Division of Critical Care Medicine, Department of Anesthesiology, Critical Care, and Pain Medicine, Boston Children's Hospital, Boston, MA, United States

5 Department of Anaesthesia, Harvard Medical School, Boston, MA, United States

6 Department of Biomedical Informatics, Harvard Medical School, Boston, MA, United States

Corresponding Author:

Kenneth D Mandl, MD, MPH

Computational Health Informatics Program

Boston Children's Hospital

Landmark 5506 Mail Stop BCH3187, 401 Park Drive

Boston, MA, 02215

United States

Phone: 1 6173554145

Email: [email protected]

Background: Real-time surveillance of emerging infectious diseases necessitates a dynamically evolving, computable case definition, which frequently incorporates symptom-related criteria. For symptom detection, both population health monitoring platforms and research initiatives primarily depend on structured data extracted from electronic health records.

Objective: This study sought to validate and test an artificial intelligence (AI)–based natural language processing (NLP) pipeline for detecting COVID-19 symptoms from physician notes in pediatric patients. We specifically study patients presenting to the emergency department (ED) who can be sentinel cases in an outbreak.

Methods: Subjects in this retrospective cohort study are patients who are 21 years of age and younger, who presented to a pediatric ED at a large academic children’s hospital between March 1, 2020, and May 31, 2022. The ED notes for all patients were processed with an NLP pipeline tuned to detect the mention of 11 COVID-19 symptoms based on Centers for Disease Control and Prevention (CDC) criteria. For a gold standard, 3 subject matter experts labeled 226 ED notes and had strong agreement ( F 1 -score=0.986; positive predictive value [PPV]=0.972; and sensitivity=1.0). F 1 -score, PPV, and sensitivity were used to compare the performance of both NLP and the International Classification of Diseases, 10th Revision (ICD-10) coding to the gold standard chart review. As a formative use case, variations in symptom patterns were measured across SARS-CoV-2 variant eras.

Results: There were 85,678 ED encounters during the study period, including 4% (n=3420) with patients with COVID-19. NLP was more accurate at identifying encounters with patients that had any of the COVID-19 symptoms ( F 1 -score=0.796) than ICD-10 codes ( F 1 -score =0.451). NLP accuracy was higher for positive symptoms (sensitivity=0.930) than ICD-10 (sensitivity=0.300). However, ICD-10 accuracy was higher for negative symptoms (specificity=0.994) than NLP (specificity=0.917). Congestion or runny nose showed the highest accuracy difference (NLP: F 1 -score=0.828 and ICD-10: F 1 -score=0.042). For encounters with patients with COVID-19, prevalence estimates of each NLP symptom differed across variant eras. Patients with COVID-19 were more likely to have each NLP symptom detected than patients without this disease. Effect sizes (odds ratios) varied across pandemic eras.

Conclusions: This study establishes the value of AI-based NLP as a highly effective tool for real-time COVID-19 symptom detection in pediatric patients, outperforming traditional ICD-10 methods. It also reveals the evolving nature of symptom prevalence across different virus variants, underscoring the need for dynamic, technology-driven approaches in infectious disease surveillance.

Introduction

Real-time emerging infection surveillance requires a case definition that often involves symptomatology. To detect symptoms, population health monitoring systems and research studies tend to largely rely on structured data from electronic health records, including the International Classification of Diseases, 10th Revision (ICD-10) codes [ 1 ]. However, symptoms are not diagnoses and, therefore, may not be consistently coded, leading to incorrect estimates of the prevalence of COVID-19 symptoms [ 2 ]. Natural language processing (NLP) of unstructured data from electronic health records has proven useful in recognizing COVID-19 symptoms and identifying additional signs and symptoms compared to structured data alone [ 3 , 4 ]. However, surveillance of COVID-19 symptoms is nuanced as symptoms have been shown to differ by variant eras [ 5 , 6 ] and by age, with pediatric patients generally experiencing milder symptoms [ 7 ]. For example, while loss of taste or smell was reported with early COVID-19 variants, it was less commonly reported during the Omicron wave and in younger patients who more frequently experience fever and cough [ 8 - 11 ]. Understanding symptom patterns in children during different COVID-19 variant eras is important. Early in the pandemic, the availability of molecular testing was extremely limited. The less severe course of infection and varying presentations may lead to under testing due to mild symptoms [ 12 ], potentially underestimating pediatric COVID-19 cases. Additionally, relatively asymptomatic children can still transmit the virus. Tailoring interventions based on age-specific manifestations contribute to effective control of virus transmission within communities.

We sought to validate and test an open-source artificial intelligence (AI)–based NLP pipeline that includes a large language model (LLM) to detect COVID-19 symptoms from physician notes. As a formative use case, we sought to illustrate how this pipeline could detect COVID-19 symptoms and differentiate symptom patterns across SARS-CoV-2 variant eras in pediatric patients. We specifically study patients presenting to the emergency department (ED) who can be sentinel cases in an outbreak.

Study Design and Setting

This was a retrospective cohort study of all patients up to 21 years of age presenting to the ED of a large, free-standing, university-affiliated, pediatric hospital between March 1, 2020, and May 31, 2022.

Ethical Considerations

The Boston Children’s Hospital Committee on Clinical Investigation performed ethical, privacy, and confidentiality reviews of the study and found it to be exempt from human subjects oversight. A waiver of consent was obtained to cover the targeted extraction and secure review of clinical notes by approved study personnel in protected environments within the hospital firewall.

Study Variables

The main dependent variables were a set of 11 COVID-19 symptoms based on Centers for Disease Control and Prevention (CDC) criteria [ 13 ]—fever or chills, cough, shortness of breath or difficulty breathing, fatigue, muscle or body aches, headache, new loss of taste or smell, sore throat, congestion or runny nose, nausea or vomiting, and diarrhea. We identified these symptoms by both NLP and ICD-10 codes. For the formative use case, the study period was divided into 3 variant eras defined using Massachusetts COVID-19 data from Covariant [ 14 ]. The pre-Delta era was from March 1, 2020, to June 20, 2021; the Delta era was from June 21, 2021, to December 19, 2021; and the Omicron era was from December 20, 2021, onward. A diagnosis of COVID-19 was defined as a positive SARS-CoV-2 polymerase chain reaction (PCR) test or the presence of ICD-10 code U07.1 for COVID-19 during the same ED encounter in which symptoms were evaluated.

AI/NLP Pipeline Development

A total of 3 reviewers reached a consensus on a symptom concept dictionary [ 15 ] to capture each of the 11 COVID-19 symptoms. They relied on the Unified Medical Language System [ 16 ], which has a nearly comprehensive list of symptom descriptors [ 17 ], including SNOMED (SNOMED International) coded clinical terms [ 18 ], ICD-10 codes for administrative billing, abbreviations, and common language for patients [ 19 ]. The open-source and free Apache cTAKES (Apache Software Foundation) NLP pipeline was tuned to recognize and extract coded concepts for positive symptom mentions (based on the dictionary) from physician notes [ 20 ]. Apache cTAKES uses a NegEx algorithm which can help address negation [ 20 - 23 ]. To further address negation, we incorporated an LLM, Bidirectional Encoder Representations from Transformers, that was fine-tuned for negation classification on clinical text [ 24 , 25 ].

Gold Standard

A total of 2 reviewers established a gold standard by manually reviewing physician ED notes. After all notes were labeled by the cTAKES pipeline, a test set of 226 ED notes was loaded into Label Studio [ 26 ], an open-source application for ground truth labeling. These notes were from patients both with and without COVID-19 and were selected to ensure that each of the 11 symptoms was mentioned in at least 30 ED notes. Some notes mentioned more than 1 symptom. Using an annotation guide ( Multimedia Appendix 1 ), 2 reviewers, who were masked from the terms identified by the NLP pipeline for note selection, each labeled 113 notes for mention of the 11 COVID-19 symptoms. As per the guide, only symptoms relevant to the present illness were considered positive mentions. Symptoms were not considered positive mentions if stated as past medical history, family history, social history, or an indication for a medication unrelated to the encounter.

Interrater Reliability

The F 1 -score was used to assess consistency in manual chart review. The F 1 -score is the balance of sensitivity and positive predictive value (PPV) [ 27 ]. It was computed by comparing the annotations of each of the 2 initial reviewers to those of a third reviewer, who independently labeled a subset (56/226, 25%) of notes annotated by the other reviewers. The choice of F 1 -score as the metric for agreement was informed by the observed high frequency of true negative annotations when they were assigned by chance [ 20 , 27 , 28 ]. Reliability analyses used Python (version 3.10; Python Software Foundation).

AI/NLP and ICD-10 Accuracy

Accuracy measures of the true symptom percentages in the test set for each symptom included F 1 -score, PPV, sensitivity, and specificity [ 29 , 30 ].

Formative Use Case

The impact of pandemic variant era on COVID-19 symptomatology was examined. Descriptive statistics were used to characterize patients presenting to the ED during each pandemic era. The percentage of patients in the ED with symptoms of COVID-19 was assessed in separate analyses for each symptom using chi-square analyses of 3×2 tables (pandemic era × symptom presence or absence) with α set at .05. Post hoc chi-square tests were used to compare each pandemic era with all others using a Bonferroni adjusted α of .017. To assess the effect of pandemic era, COVID-19 status, and the interaction of these variables on whether or not a patient had each symptom, logistic regression was used in separate analyses for each symptom. Bonferroni adjusted confidence limits were used for post hoc analyses. If the interaction term was not significant, the main effects of COVID-19 and variant era were reported. Data were analyzed using SAS (version 9.4; SAS Institute Inc).

Study Population

There were 59,173 unique patients with 85,678 ED encounters during the study period. For each ED encounter, there was 1 final physician ED note that aggregated all ED physician documentation. Characteristics of the entire study cohort and variant-specific cohorts are summarized in Table 1 . A patient could appear in the cohort more than once if they had multiple ED encounters.

a PCR: polymerase chain reaction.

b ICD-10: International Classification of Diseases, 10th Revision.

High consistency was demonstrated between reviewer 3, who labeled a subset of notes, and both reviewers 1 and 2, who each labeled half of the notes chosen to establish the gold standard. The F 1 -scores for the 2 reviewers were 0.988 and 0.984, respectively. The PPV was 0.976 and 0.968 and sensitivity was 1.0 for both.

AI or NLP ICD-10 Accuracy

As shown in Table 2 , the F 1 -score for NLP was higher and thus more accurate at identifying encounters in the test set with patients that had any of the COVID-19 symptoms than ICD-10. NLP also had higher F 1 -score for each individual symptom. In addition, NLP sensitivity of true positive symptoms was higher than ICD-10. However, NLP accuracy of true negative symptoms (specificity) was somewhat lower compared to ICD-10.

a NLP: natural language processing.

c F 1 -score: accuracy measure balancing PPV and sensitivity.

d PPV: positive predictive value.

The 2 most prevalent symptoms, cough and fever, had sensitivity scores for NLP that were among the highest of the symptoms, and much higher than those for ICD-10 codes. The greatest discrepancy between NLP and ICD-10 F 1 -scores was for congestion or runny nose. The smallest difference was for diarrhea.

Prevalence of Symptoms Over Time

The percentage of ED encounters with patients with COVID-19 who had symptoms was estimated using the NLP pipeline and ICD-10 codes. As shown in Figure 1 , during each month of the study, the percentage of encounters with no symptoms detected was much lower using NLP compared to ICD-10. Using NLP, the range was from 0% to 19% of encounters (mean 6%, SD 4%), while with ICD-10, the range was 22% to 52% (mean 38%, SD 7%).

The percentage of encounters with patients with COVID-19 who presented with each symptom by month was higher using NLP than ICD-10 ( Multimedia Appendix 2 ). The 2 most common symptoms, cough and fever, are shown in Figures 2 and 3 . On average, cough was identified during 52% (SD 13%) of the encounters each month using NLP, but only 15% (SD 5%) using ICD-10. On average, fever characterized 70% (SD 11%) of encounters using NLP, but 41% (SD 9%) using ICD-10.

Using ICD-10, there were many months where individual symptoms were not detected. Of the 27 study months, loss of taste or smell was not detected using ICD-10 during 24 months, nor were muscle or body aches during 13 months. A total of 3 more symptoms had at least 3 consecutive months where each was not detected using ICD-10. These were congestion or runny nose (9 total months, not all consecutive), sore throat (8 months), and fatigue (7 months). Sporadic months without detection using ICD-10 were observed for headache (5 months), diarrhea (2 months), cough (1 month), and nausea or vomiting (1 month). Using NLP, sporadic months without detection were observed for just 2 symptoms, loss of taste or smell (6 months) and sore throat (2 months).

Prevalence of Symptoms Across Variant Eras

The prevalence estimates of symptoms across variant eras for encounters with patients with COVID-19 differed for each symptom identified by NLP, except for nausea or vomiting and sore throat ( Table 3 ). Post hoc analyses revealed several patterns. New loss of taste or smell was the only symptom that varied across all 3 eras. It was most common in the pre-Delta era, followed by the Delta era, and then the Omicron era. Congestion or runny nose, cough, and fever or chills were more common during the Delta and Omicron era than during the pre-Delta era, but the Delta era did not differ from the Omicron era. Muscle or body aches were more common during the pre-Delta era than both the Delta and Omicron eras, but the Delta era did not differ from the Omicron era. Diarrhea, fatigue, headache, and shortness of breath were more common during the pre-Delta era than the Omicron era but were not different than the Delta era, and the Delta era did not differ from the Omicron era. Nausea or vomiting and sore throat did not differ by variant era. The chi-square results are in Multimedia Appendix 3 .

a,b,c Variant eras with the same superscript across a row did not differ in post hoc analyses.

Symptoms by COVID-19 Status and Variant Era

The interaction of COVID-19 status and variant era on the presence of each symptom is shown in Table 4 . However, because the interaction was not significant for 2 symptoms, fever and chills, and sore throat, the main effects for COVID-19 status are shown for both ( P <.001). The odds ratios (ORs) indicate that patients with COVID-19 were more likely to have each of these 2 symptoms than patients without this disease. These symptoms were also more likely to occur during the Delta and Omicron era than during the pre-Delta era. For the remaining symptoms, the interaction term was significant and the ORs in each variant era are shown in the table. The ORs comparing patients with COVID-19 to those without the disease differed among the variant eras. Several patterns were observed. Patients with COVID-19 were more likely to exhibit each of the symptoms of congestion or runny nose, cough, fatigue, headache, muscle or body aches, new loss of taste or smell, or shortness of breath or difficulty breathing. However, effect sizes (ORs) differed among pandemic eras. For diarrhea, this symptom was more likely for patients with COVID-19 in the pre-Delta and Delta eras, but not during the Omicron era. And nausea was more likely only in the pre-Delta era. Significant ORs ranged in size from 1.3 to 26.7 (mean 4.6, SD 5.3). The logistic regression results are in Multimedia Appendix 4 .

a Odds ratios compare patients with COVID-19 at an ED encounter to patients without the disease.

b CL: Bonferroni adjusted confidence limits in post hoc analyses.

c If the interaction term was significant, the effect of COVID-19 during each variant era is shown. Otherwise, the effect for COVID-19 is shown.

d Type 3 test of the interaction term (variant era × COVID-19) in a logistic regression analysis.

Principal Findings

We find evidence that AI-based NLP of physician notes is a superior method for capturing patient symptoms for real-time biosurveillance than reliance on traditional approaches using ICD-10. NLP was more sensitive than ICD-10 codes in identifying symptoms and some symptoms could only be detected using NLP. As a form of internal validation, the symptoms identified by the CDC as associated with COVID-19 were more common in patients with than without this disease.

Comparison With Prior Work

The study was also able to capture a nuanced picture of symptom prevalence and odds across different SARS-CoV-2 variant eras. Consistent with previous literature, symptom patterns changed over time as new variants emerged. Variants may present with differences in symptomatology as a result of a number of factors including differences in mutations in spike proteins, receptor binding, and ability to escape host antibodies [ 31 ]. As has been previously reported [ 11 , 32 - 35 ], we found that fever or chills were the most common COVID-19 symptom across the variants. In our cohort, shortness of breath was less common during the Omicron era than during the pre-Delta era. The Omicron variant has less of an ability to replicate in the lungs compared to the bronchi, which may explain why this symptom became less common [ 36 ]. Studies have reported sore throat as a common symptom in the Omicron era, but we did not observe a significant difference across eras [ 8 , 9 ]. It is possible that we did not see a higher percentage of sore throats in the Omicron era because it may be more challenging for pediatric patients to describe this symptom. One study found that sore throat was observed more often in those of 5-20 years of age compared to those of 0-4 years of age [ 8 ]. Similarly, a study reported that sore throat was more common in those greater than or equal to 13 years of age in the Omicron era compared to the Delta era [ 37 ]. In our study cohort, approximately half of the patients were younger than 5 years of age. As children this age may not be able to describe their symptoms well, symptoms that are also signs, such as fever or cough, might be more commonly documented in physician notes than symptoms such as sore throat. New loss of taste or smell was most common in the pre-Delta era, followed by the Delta era and then the Omicron era in this study. This symptom has been reported less commonly in the Omicron era [ 8 , 9 ]. Studies have postulated that patients with the Omicron variant are less likely to present with loss of taste or smell as this variant has less penetration of the mucus layer and therefore, may be less likely to infect the olfactory epithelium [ 38 ].

Limitations

There were important limitations in our use of NLP. The NLP pipeline was tested with a set of notes where some symptoms were more frequent in the test set (eg, loss of taste or smell) than in the formative use case. This was done to have sufficient data to evaluate the symptom pipeline. The NLP pipeline does not account for vital signs and so fever may not have been detected with the pipeline if it was documented in a patient’s vital signs rather than the clinical text. The cTAKES tool in the pipeline lacks the temporal context to ascertain if the mention of a symptom in a note is a new symptom or a prior symptom. We modified our technique because of this but nevertheless may have overestimated the prevalence of symptoms in our study. Future work will involve filtering by note section so that certain components of a note like past medical history are not included. We used 2 techniques to recognize negation, but some negated symptoms (eg, “patient had no cough”) were still captured as positive symptom mentions leading to a possible overestimation of symptom prevalence. Finally, this NLP pipeline did involve substantial preprocessing. We plan to evaluate the implementation of Generative Pre-trained Transformer (GPT) for this task. GPT-4 was able to extract COVID-19 symptoms in a recent study [ 39 ] and it may limit the need for preprocessing.

Our formative study had some limitations. First, we examined COVID-19 symptoms in patients presenting to a single urban pediatric ED. Patients presenting to outpatient settings, who likely had milder symptoms, were not included and our results may reflect patients with more severe symptoms. And because the setting was a single site, results may not generalize to other EDs. Second, we defined COVID-19 status as positive if a patient had a PCR positive test for COVID-19 or an appropriate ICD-10 code at the ED encounter. Patients who were COVID-19 positive on a test at home or at an outside center may not have been captured by this definition even if they presented to the ED with COVID-19 [ 40 ]. Additionally, symptoms may have differed across variant eras as a result of COVID-19 vaccinations or previous infections rather than variant differences. Literature in adults shows that vaccination is associated with a decrease in systemic symptoms [ 41 ]. The United States Food and Drug Administration authorized the use of the COVID-19 vaccine in October 2021, during the Delta era and prior to the Omicron era, for children 5-11 years of age [ 42 ]. Vaccination rates for pediatric patients vary by age group in Massachusetts, as of April 3, 2023, of those 0-19 years of age, 3% to 57% have received a primary series but have not been boosted, and 3% to 18% have been boosted since September 1, 2022 [ 43 ]. As such, some patients in the Delta and Omicron eras may have been vaccinated or had previous COVID-19 infections [ 44 ].

Conclusions

In an era where rapid and accurate infectious disease surveillance is crucial, this study underscores the transformative potential of AI-based NLP for real-time symptom detection, significantly outperforming traditional methods such as ICD-10 coding. The dynamic adaptability of NLP technology allows for the nuanced capture of evolving symptomatology across different virus variants, offering a more responsive and precise tool kit for biosurveillance efforts. Its integration into existing health care infrastructure could be a game changer, elevating our capabilities to monitor, understand, and ultimately control the spread of emerging infectious diseases.

Acknowledgments

This study was supported by the Centers for Disease Control and Prevention (CDC) of the US Department of Health and Human Services (HHS) as part of a financial assistance award. The contents are those of the authors and do not necessarily represent the official views of, nor an endorsement by CDC, HHS, or the US Government. Support was also obtained from the National Center for Advancing Translational Sciences, National Institutes of Health Cooperative Agreement (U01TR002623). ARZ was supported by a training grant from the National Institute of Child Health and Human Development (T32HD040128). Generative artificial intelligence (AI) was not used to design or conduct this study.

Data Availability

All data analyzed during this study for the formative use case are in Multimedia Appendix 5 of this published article.

Authors' Contributions

KDM, AJM, and TAM contributed to the conceptualization. KDM contributed to the funding. AJM, ARZ, AG, and KLO performed the formal analysis. AJM, JRJ, and VI contributed to the software. AJM, ARZ, and KDM contributed to writing original drafts. KLO and AG contributed to writing review and edits.

Conflicts of Interest

TAM is a member of the advisory council for Lavita AI. Others declare no conflicts of interest.

COVID-19 symptoms annotation guide.

Detection of COVID-19 symptoms using NLP and ICD-10 by month for emergency department encounters with patients with COVID-19. ICD-10: International Classification of Diseases, 10th Revision; NLP: natural language processing.

The chi-square analysis of COVID-19 symptom prevalence by pandemic variant era for emergency department encounters with patients with COVID-19, symptoms were detected using NLP. NLP: natural language processing.

Logistic regression analysis of the effect of COVID-19 status, pandemic variant era, and their interaction on symptom status for ED encounters, symptoms were detected using NLP. ED: emergency department; NLP: natural language processing.

Data files for the time series figures, the chi-square analysis of symptom prevalence, and the logistic regression analysis of the effects of COVID-19 status and pandemic variant era on symptom status.

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• Bhattacharyya RP, Hanage WP. Challenges in inferring intrinsic severity of the SARS-CoV-2 Omicron variant. N Engl J Med. 2022;386(7):e14. [ FREE Full text ] [ CrossRef ] [ Medline ]

Abbreviations

Edited by T de Azevedo Cardoso; submitted 06.10.23; peer-reviewed by D Liebovitz; comments to author 09.11.23; revised version received 30.11.23; accepted 27.02.24; published 04.04.24.

©Andrew J McMurry, Amy R Zipursky, Alon Geva, Karen L Olson, James R Jones, Vladimir Ignatov, Timothy A Miller, Kenneth D Mandl. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 04.04.2024.

This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in the Journal of Medical Internet Research, is properly cited. The complete bibliographic information, a link to the original publication on https://www.jmir.org/, as well as this copyright and license information must be included.

Membrane Trafficking in Disease: Mark A. McNiven

Hepatitis b virus.

Image of hepatocytes infected with HBV secreting large numbers of exosomes (highlighted in purple), which also can contain hepatitis B virions.

Hepatitis B virus (HBV) infection is a global health problem because individuals infected with the virus are at risk of chronic hepatitis, liver cirrhosis and hepatocellular carcinoma. It is estimated that 350 to 400 million people have chronic HBV infection despite the availability of effective vaccines. Thus, a better understanding of the HBV life cycle and the mechanisms by which this virus usurps the established vesicle trafficking, autophagic, secretory and endocytic pathways of the hepatocyte host is essential to enable the identification of new antiviral targets.

Selected Publications

Ninomiya M, Inoue J, Krueger EW, Chen J, Cao H, Masamune A, McNiven MA. The exosome-associated tetraspanin CD63 contributes to the efficient assembly and infectivity of the hepatitis B virus . Hepatology Communications. 2021; doi:10.1002/hep4.1709.

Inoue J, Krueger EW, Chen J, Cao H, Ninomiya M, McNiven MA. HBV secretion is regulated through the activation of endocytic and autophagic compartments mediated by Rab7 stimulation . Journal of Cell Science. 2015; doi:10.1242/jcs.158097.

Abdulkarim AS, Cao H, Huang B, McNiven MA. The large GTPase dynamin is required for hepatitis B virus protein secretion from hepatocytes . Journal of Hepatology. 2003; doi:10.1016/s0168-8278(02)00326-4.

Accola MA, Huang B, Al Masri A, McNiven MA. The antiviral dynamin family member, MxA, tubulates lipids and localizes to the smooth endoplasmic reticulum . Journal of Biological Chemistry. 2002; doi:10.1074/jbc.M201641200.

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Study: Life expectancy increased as world addressed major killers, though poor pandemic management slowed progress

by Institute for Health Metrics and Evaluation

Global life expectancy increased by 6.2 years since 1990 according to a new study published in The Lancet . Over the past three decades, reductions in death from leading killers have fueled this progress, including diarrhea and lower respiratory infections, as well as stroke and ischemic heart disease.

When the COVID-19 pandemic arrived in 2020, however, it derailed progress in many locations. This is the first study to compare deaths from COVID-19 to deaths from other causes globally.

Despite the challenges presented by the COVID-19 pandemic, the researchers found that the super-region of Southeast Asia, East Asia, and Oceania had the largest net gain in life expectancy between 1990 and 2021 (8.3 years), largely due to reductions in mortality from chronic respiratory diseases , stroke, lower respiratory infections, and cancer.

The super-region's strong management of the COVID-19 pandemic helped preserve these gains. South Asia had the second-largest net increase in life expectancy among super-regions between 1990 and 2021 (7.8 years), primarily thanks to steep declines in deaths from diarrheal diseases .

"Our study presents a nuanced picture of the world's health," said Dr. Liane Ong, co-first author of the study and a Lead Research Scientist at the Institute for Health Metrics and Evaluation (IHME). "On one hand, we see countries' monumental achievements in preventing deaths from diarrhea and stroke," she said. "At the same time, we see how much the COVID-19 pandemic has set us back."

The study also highlights how COVID-19 radically altered the top five causes of death for the first time in 30 years. COVID-19 displaced a long-dominant killer—stroke—to become the second-leading cause of death globally. The research presents updated estimates from the Global Burden of Disease Study (GBD) 2021.

The authors found that the super-regions where the COVID-19 pandemic hit hardest were Latin America and the Caribbean and sub-Saharan Africa, which lost the most years of life expectancy due to COVID-19 in 2021. While documenting the enormous loss of life caused by the COVID-19 pandemic, the researchers also pinpointed the reasons behind the improvements in life expectancy in every super-region.

Looking across different causes of death, the study reveals sharp drops in deaths from enteric diseases—a class of diseases that includes diarrhea and typhoid. These improvements increased life expectancy worldwide by 1.1 years between 1990 and 2021.

Reductions in deaths from lower respiratory infections added 0.9 years to global life expectancy during this period. Progress in preventing deaths from other causes also drove up life expectancy around the world, including stroke, neonatal disorders, ischemic heart disease , and cancer. For each disease, reductions in deaths were most pronounced between 1990 and 2019.

At the regional level, Eastern sub-Saharan Africa experienced the largest increase in life expectancy, which jumped by 10.7 years between 1990 and 2021. Control of diarrheal diseases was the leading force behind improvements in this region. East Asia had the second-largest gain in life expectancy; the region's success in slashing deaths from chronic obstructive pulmonary disease played a key role.

The GBD 2021 study measures mortality by cause of death and years of life lost at global, regional, national, and subnational levels. The analysis links specific causes of death to changes in life expectancy.

The study illuminates not only the diseases that have driven increases and decreases in life expectancy , but also looks at how patterns of disease have shifted across locations over time, presenting, as the authors write, an "opportunity to deepen our understanding of mortality-reduction strategies…[which] might reveal areas where successful public health interventions have been implemented."

GBD 2021 highlights places that have made huge strides in preventing deaths from major diseases and injuries. It also emphasizes how some of the most burdensome diseases are now concentrated in certain locations, underscoring opportunities for intervention. For example, in 2021, deaths from enteric diseases were largely concentrated in sub-Saharan Africa and South Asia.

For another disease, malaria, the researchers found that 90% of deaths occurred in an area inhabited by just 12% of the world's population in a stretch of land ranging from Western sub-Saharan Africa through Central Africa to Mozambique.

"We already know how to save children from dying from enteric infections including diarrheal diseases, and progress in fighting this disease has been tremendous," said Professor Mohsen Naghavi, the study's co-first author and the Director of Subnational Burden of Disease Estimation at IHME.

"Now, we need to focus on preventing and treating these diseases, strengthening and expanding immunization programs, and developing brand-new vaccines against E. coli, norovirus, and Shigella," he added.

In addition to providing new insights on COVID-19, the study reveals growing threats from non-communicable diseases , such as diabetes and kidney diseases, which are increasing in every country. The researchers also point to uneven progress against conditions such as ischemic heart disease, stroke, and cancer. High-income countries have driven down deaths from many types of non-communicable diseases, but many low-income countries have not.

"The global community must ensure that the lifesaving tools that have cut deaths from ischemic heart disease, stroke, and other non-communicable diseases in most high-income countries are available to people in all countries, even where resources are limited," said Eve Wool, senior author of the study and a Senior Research Manager at IHME.

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Computer Science > Machine Learning

Title: towards system modelling to support diseases data extraction from the electronic health records for physicians research activities.

Abstract: The use of Electronic Health Records (EHRs) has increased dramatically in the past 15 years, as, it is considered an important source of managing data od patients. The EHRs are primary sources of disease diagnosis and demographic data of patients worldwide. Therefore, the data can be utilized for secondary tasks such as research. This paper aims to make such data usable for research activities such as monitoring disease statistics for a specific population. As a result, the researchers can detect the disease causes for the behavior and lifestyle of the target group. One of the limitations of EHRs systems is that the data is not available in the standard format but in various forms. Therefore, it is required to first convert the names of the diseases and demographics data into one standardized form to make it usable for research activities. There is a large amount of EHRs available, and solving the standardizing issues requires some optimized techniques. We used a first-hand EHR dataset extracted from EHR systems. Our application uploads the dataset from the EHRs and converts it to the ICD-10 coding system to solve the standardization problem. So, we first apply the steps of pre-processing, annotation, and transforming the data to convert it into the standard form. The data pre-processing is applied to normalize demographic formats. In the annotation step, a machine learning model is used to recognize the diseases from the text. Furthermore, the transforming step converts the disease name to the ICD-10 coding format. The model was evaluated manually by comparing its performance in terms of disease recognition with an available dictionary-based system (MetaMap). The accuracy of the proposed machine learning model is 81%, that outperformed MetaMap accuracy of 67%. This paper contributed to system modelling for EHR data extraction to support research activities.

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Diabetes drug shows promise against Parkinson's

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WASHINGTON – A drug used to treat diabetes slowed the progression of motor issues associated with Parkinson’s disease, a study published in the New England Journal of Medicine said on April 3.

Parkinson’s is a devastating nervous system disorder affecting 10 million people worldwide, with no current cure. Symptoms include rhythmic shaking known as tremors, slowed movement, impaired speech and problems balancing, which get worse over time.

Researchers have been interested in exploring a class of drugs called GLP-1 receptor agonists – which mimic a gut hormone and are commonly used to treat diabetes and obesity – for their potential to protect neurons.

So far however, evidence of clinical benefits in patients has been limited and early studies have proved inconclusive.

In the new paper, 156 patients with early-stage Parkinson’s were recruited across France and then randomly chosen to receive either lixisenatide, which is sold under the brand names Adlyxin and Lyxumia and made by Sanofi, or a placebo.

After one year of follow up, the group on the treatment, which is given as an injection, saw no worsening of their movement symptoms, while those on the placebo did.

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The effect was “modest” according to the paper, and was noticeable only when assessed by professionals “who made them do tasks; walking, standing up, moving their hands, etc” senior author Olivier Rascol, a neurologist at Toulouse University, told AFP.

But, he added, this may just be because Parkinson’s disease worsens slowly, and with another year of follow up, the differences might become much starker.

“This is the first time that we have clear results, which demonstrate that we had an impact on the progression of the symptoms of the disease and that we explain it by a neuroprotective effect,” said Rascol.

Gastrointestinal side effects were common on the drug and included nausea, vomiting and reflux, while a handful of patients experienced weight loss.

Both Rasol and co-author Wassilios Meissner, a neurologist at Bordeaux University Hospital, both stressed more study would be required to confirm safety and efficacy before the treatment should be given to patients.

Michael Okun, medical director of the Parkinson’s Foundation, told AFP that from a practical standpoint, the differences in patient outcomes were not clinically significant, but “statistically and compared to other studies, this type of difference should draw our interest and attention”.

“Experts will likely argue whether this study meets a minimum threshold for neuroprotection, and it likely does not,” continued Okun, adding the weight loss side effect was concerning for Parkinson’s patients.

Rodolfo Savica, a professor of neurology at the Mayo Clinic in Minnesota added: “The data so far are suggestive of a possible effect – but we need to replicate the study for sure.”

He added that, while this study lumped together patients aged 40-75, separating them by age group might have revealed ages at which the treatment is more effective.

The authors of the new study said they were looking forward to the results from other forthcoming trials that may help confirm their findings. – AFP

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Doctors Say Diagnosis of Catherine’s Cancer Is a Familiar Scenario

The Princess of Wales did not reveal the type of cancer she has, but oncologists say the disease is often identified during other procedures.

By Gina Kolata

Gina Kolata previously reported on King Charles III’s cancer diagnosis .

• Published March 22, 2024 Updated March 25, 2024

Although it is not known what type of cancer Princess Catherine has, oncologists say that what she described in her public statement that was released on Friday — discovering a cancer during another procedure, in this case a “major abdominal surgery” — is all too common.

“Unfortunately, so much of the cancer we diagnose is unexpected,” said Dr. Elena Ratner, a gynecologic oncologist at Yale Cancer Center who has diagnosed many patients with ovarian cancer, uterine cancer and cancers of the lining of the uterus.

Without speculating on Catherine’s procedure, Dr. Ratner described situations in which women will go in for surgery for endometriosis, a condition in which tissue similar to the lining of the uterus is found elsewhere in the abdomen. Often, Dr. Ratner says, the assumption is that the endometriosis has appeared on an ovary and caused a benign ovarian cyst. But one to two weeks later, when the supposedly benign tissue has been studied, pathologists report that they found cancer.

In the statement, Princess Catherine said she is getting “a course of preventive chemotherapy.”

That, too, is common. In medical settings, it is usually called adjuvant chemotherapy.

Dr. Eric Winer, director of the Yale Cancer Center, said that with adjuvant chemotherapy, “the hope is that this will prevent further problems” and avoid a recurrence of the cancer.

It also means that “you removed everything” that was visible with surgery, said Dr. Michael Birrer, director of the Winthrop P. Rockefeller Cancer Institute at the University of Arkansas for Medical Sciences. “You can’t see the cancer,” he added, because microscopic cancer cells may be left behind. The chemotherapy is a way to attack microscopic disease, he explained.

Other parts of Catherine’s statement also hit home for Dr. Ratner, particularly her concern for her family.

“William and I have been doing everything we can to process and manage this privately for the sake of our young family,” Catherine said, and “It has taken us time to explain everything to George, Charlotte, and Louis in a way that is appropriate for them, and to reassure them that I am going to be OK.”

Those are sentiments that Dr. Ratner hears on a regular basis and reveal, she says, “how hard it is for women to be diagnosed with cancer.”

“I see this day in and day out,” she said. “Women always say, ‘Will I be there for my kids? What will happen with my kids?’

“They don’t say, ‘What will happen to me?’”

Gina Kolata reports on diseases and treatments, how treatments are discovered and tested, and how they affect people. More about Gina Kolata

The Fight Against Cancer

Colon and rectal cancers are increasing among people younger than 50. Experts have a few ideas about why .

Risk calculators can offer a more personalized picture of an individual patient’s breast cancer risk. But experts warn that the results need to be interpreted with the help of a doctor .

Early detection is a powerful weapon in preventing deaths from colon cancer, but many patients are reluctant to undergo colonoscopies or conduct at-home fecal tests. Doctors see potential in another screening method .

The human papillomavirus vaccine provides powerful protection against the leading cause of cervical cancer and against a strong risk factor for anal cancer. Here’s what to know about the shot.

A recent study adds to growing evidence that exercise is an important part of preventing prostate cancer , the second most common and second most fatal cancer in the United States for men.

No single food can prevent cancer on its own, but experts say that there are some that may help you build the best defense .