research article of myocardial infarction

• Open access
• Published: 22 April 2023

The global prevalence of myocardial infarction: a systematic review and meta-analysis

• Nader Salari 1 , 2 ,
• Fatemeh Morddarvanjoghi 3 ,
• Amir Abdolmaleki 4 ,
• Shabnam Rasoulpoor 5 ,
• Ali Asghar Khaleghi 6 ,
• Leila Afshar Hezarkhani 7 ,
• Shamarina Shohaimi 8 &
• Masoud Mohammadi 6  

BMC Cardiovascular Disorders volume  23 , Article number:  206 ( 2023 ) Cite this article

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Myocardial infarction (MI) is one of the life-threatening coronary-associated pathologies characterized by sudden cardiac death. The provision of complete insight into MI complications along with designing a preventive program against MI seems necessary.

Various databases (PubMed, Web of Science, ScienceDirect, Scopus, Embase, and Google scholar search engine) were hired for comprehensive searching. The keywords of “Prevalence”, “Outbreak”, “Burden”, “Myocardial Infarction”, “Myocardial Infarct”, and “Heart Attack” were hired with no time/language restrictions. Collected data were imported into the information management software (EndNote v.8x). Also, citations of all relevant articles were screened manually. The search was updated on 2022.9.13 prior to the publication.

Twenty-two eligible studies with a sample size of 2,982,6717 individuals (< 60 years) were included for data analysis. The global prevalence of MI in individuals < 60 years was found 3.8%. Also, following the assessment of 20 eligible investigations with a sample size of 5,071,185 individuals (> 60 years), this value was detected at 9.5%.

Due to the accelerated rate of MI prevalence in older ages, precise attention by patients regarding the complications of MI seems critical. Thus, determination of preventive planning along with the application of safe treatment methods is critical.

Peer Review reports

Myocardial Infarction (MI) is one of the life-threatening coronary events with SCD [ 1 ] and the most severe clinical presentation of coronary artery disease (CAD) [ 2 ]. This pathology is divided into two categories of ST-elevation MI (STE-MI) and non-ST-elevation MI (NSTE-MI). Since unstable angina is the imminent background for MI, it is also considered an acute coronary syndrome (ACS) status [ 3 ].

More than 3 million individuals develop STE-MI each year, and more than 4 million people represent STE-MI pathology. Although MI is mainly detected in developed countries, it is also detected commonly in developing countries [ 4 , 5 , 6 , 7 ]. In a published study with 19,781 CAD patients, the MI prevalence was found 23.3% [ 8 ]. In recent years, a considerable decreasing trend in STE-MI incidence was detected in European countries and the United States [ 9 , 10 ].

MI is the main cause of human death, globally [ 11 ]. Although the global rate of MI-associated mortality was totally decreased, the incidence of heart failure (HF) is at a high level [ 12 ]. The mortality and morbidity rates are high in MI-related HF [ 13 , 14 ]. HF induces detrimental impacts on the healthcare systems of the United States, affecting 6 million individuals, 300,000 deaths per year, and approximately $40 billion in costs [ 15 ]. Also, the economic impact of MI is at a high rate. In 2010, more than 1.1 million hospitalizations following MI attacks were reported in the United States, with an estimated direct cost of $450 billion [ 16 ]. Body weakness is a common complication in cardiovascular diseases and is also a common syndrome among the elderly causing weight loss, fatigue, physical manipulation, decreased walking speed, and low body activity [ 17 ]. Obesity, sedentary lifestyle, hypertriglyceridemia, or inflammation markers (such as high-sensitivity C-reactive protein [hs-CRP]), are mostly independent cardiovascular (CV) risk factors associated with insulin [ 18 ]. Various published articles represented a general increase in the prevalence of cardiovascular risk factors (especially diabetes, cholesterol and obesity, and even smoking) [ 19 , 20 , 21 , 22 ]. In MI patients < 55 years, smoking was found a unique cardiovascular risk factor in 80% of cases [ 23 ].

The present systematic review and meta-analysis study seems beneficial for health system policymakers requiring the prevalence of MI patients during the allocation of health care resources. We believe that elimination of the complications and reduction in mortality rate need comprehensive assessment approaches.

In this study, the primary search was conducted on June 6, 2022. Databases of PubMed, Web of Science, ScienceDirect, Scopus, Embase, and Google scholar search engine were hired for definition of searching strategy. Also, the main keywords of “Prevalence”, “Outbreak”, “Burden”, “Myocardial Infarction”, “Myocardial Infarct”, and “Heart Attack” were used for comprehensive searching with no time and language-associated restrictions. Following paper selection, the related citations were imported to the information management software (EndNote v.8x). Finally, in order to secondary screening, all citations of the collected articles were reviewed manually. The searching was also updated on September 13, 2022.

Inclusion and exclusion criteria

All gathered studies reporting the MI prevalence, available full texts, and studies with sufficient data (number of samples, percentage of MI prevalence) were totally included in this study. Also, case–control studies, cohort investigations, case series, case reports, reviews, repetitive papers, studies with insufficient data, papers with unavailable full texts, and conference studies were excluded.

Study selection

The Endnote software (v. X8) was hired to organize the selected studies. Duplicate studies were detected and merged together. In primary screening, irrelevant studies were removed following assessment of the titles and abstracts. Then, the full texts of the remaining articles were screened according to the inclusion and exclusion criteria. All screening protocols were conducted by two independent authors in order to accelerate the credibility index and inhibit the potential searching bias. Corresponding author was also responsible for the management of possible disagreements among the researchers. Finally, 33 studies were included for quality control assessment.

Quality control assessment

For validation and the quality control assessment, an observational study-associated checklist (The Strengthening the Reporting of Observational Studies in Epidemiology checklist (STROBE)) was used. This STROBE checklist consisted of six assessment scales of Title, Abstract, Introduction, Methodology, Results, and Discussion with 32 evaluation items including Title, Problem Statement, Study Objectives, Type of Study, Statistical Population, Sampling Method, Appropriate Sample Size Determination, Variables Definition, and the Procedures, Data Collection Tools, Statistical Analysis Methods and Findings. The article with STROBE scoring ≥ 16 was considered good and moderate (included in the study), and articles < 16 were poor quality (excluded from the study).

Data extraction

The eligible data were extracted by two researchers based on the previously prepared checklist (containing the Author's name, Year of publication, Research region, Sample size, Disease prevalence, and Age).

Data analysis

The heterogeneity of the studies was assessed using I 2 test. Also, the Egger test was used for publication bias assessment. All statistical analysis was applied in Comprehensive Meta-Analysis software (Version 2).

Whole eligible data (6462 studies systematically and 134 investigations manually) regarding the prevalence of MI were collected based on the PRISMA guideline and categorized into two groups of individuals < 60 and ≥ 60 years. All the papers were imported into the information management software (EndNote v.X8). Among the total number of 6596 studies, 4566 duplicate investigations were detected and merged together. During the primary screening, the Title and Abstract of the remaining studies were assessed. Subsequently, 1879 investigations were excluded due to the irrelevant contents. Following the secondary screening, the full texts of the papers were assessed (118 studies were also excluded in this stage). Eligible collected papers were assessed based on the STROBE checklist, and the studies with poor-quality methodology were removed from the investigation. Finally, 32 high-quality papers were included for systematic review and meta-analysis study (Table 1 ) (Fig.  1 ).

Reviewing, screening and extracting articles based on PRISMA process

Data analysis of 20 eligible studies with a sample size of 5.071.185 individuals > 60 years was conducted, and I 2 index represented a high heterogeneity rate ( I 2  = 99.7%). Meta-analysis assessment revealed that the global prevalence of MI in individuals > 60 years was 9.5% (95%CI: 7.7–11.6) (Fig.  2 ). Also, no publication bias ( p  = 0.113) was found in this age group (Fig.  3 ). Following data analysis of 22 eligible studies with a sample size of 29.826.717 individuals < 60 years, the I 2 index showed a high heterogeneity rate ( I 2  = 99.9). The global MI prevalence in this age group was found 3.8% (95%CI:2.7–5.3) (Fig.  4 ). Also, no publication bias ( p  = 0.064) was detected (Fig.  5 ).

Forest plot representing the global prevalence of myocardial infarction in age group > 60 years based on the random effects model

Funnel plot representing the distribution bias of eligible collected studies

Forest plot representing the global prevalence of myocardial infarction in the age group < 60 years (random effect model)

Funnel plot representing publication bias in eligible collected studies

This systematic review and meta-analysis study in the first investigation examine the global prevalence of MI in two groups of individuals < 60 and > 60 years. The global prevalence of MI < 60 years was detected 3.8% according to 22 studies with a sample size of 29.826.717 individuals. This value was also found 9.5% in the remaining 20 studies with a sample size of 5.071.185 patients > 60 years.

Following gender categorization, the prevalence of MI in males was found almost 5 folds greater than the females [ 44 ]. In a large number of other published studies, a high prevalence of MI was reported in males (> 60%) compared to females [ 56 , 57 , 58 , 59 , 60 , 61 , 62 , 63 , 64 , 65 , 66 , 67 , 68 , 69 , 70 , 71 , 72 , 73 , 74 , 75 , 76 , 77 , 78 , 79 ]. On the contrary, other literature reported higher MI prevalence in females, probably due to the sedentary lifestyle, metabolic syndrome, and similar risk factors [ 80 ].

Based on the geographical distribution, there were different results representing the MI prevalence including 10.4%, 0.1%, 0.2%, and 2.5% in Sudan, Senegal, Nigeria, and Kenya, respectively. These geographical differences in MI prevalence were probably associated with lifestyle, disease prevention plans, and the level of availability of medical diagnosis resources [ 81 , 82 , 83 , 84 ].

Extracted data from a large, diverse, community-based population represented a considerable decrease in MI prevalence (after 2000) and incidence of ST-segment elevation (in recent decades) [ 2 ]. Although the statistical analysis of CAD prevalence and the related mortality rate showed a decremental trend, the statistics of published literature (before 2002) had no report [ 85 , 86 , 87 , 88 , 89 , 90 , 91 , 92 ].

Various studies conducted in the United States (after 2000) revealed a considerable decremental trend in the incidence of AMI and the rate of hospitalization [ 2 , 93 ]. The rate of AMI incidence also decreased in Sweden between 2001 to 2008 which was higher in males [ 94 ]. A similar trend conducted in the Netherlands from 1998 to 2007 also reported the same results [ 95 ]. Respectively, 33% and 31% reduction of AMI rates in males and females were reported in England (2002 to 2010) [ 96 ]. Another study showed a steady decline in AMI and mortality rates in most regions of Europe [ 10 ]. This study was consistent with the findings of the present investigation reporting that a reduction in MI prevalence was probably associated with innovation of preventive medical protocols and a parallel improvement in risk factors management [ 95 , 97 , 98 , 99 ].

The prevalence of angina and MI decreased considerably over the 12-year period. The reduction in the prevalence of cardiovascular diseases (CVD), including angina and MI, may result from application of preventive medical procedures and management of risk factors [ 45 ]. On the contrary, a high prevalence of undiagnosed MI (26.9%) was also reported. Consequently, more participants (17%) had un-diagnosed MI, and others (9.6%) represented diagnosed MI [ 47 ]. In another study, the incidence of definitive MI diagnosis in hospitalized patients was 272/100,000 individuals (aged 30–74) [ 87 ].

The high rate and increased severity of CAD in patients with a family background were directly related to the risk of MI in younger ages and both genders [ 40 ]. The scientists also found that cocaine addicts are 7 times more at risk of heart attack [ 100 ]. Notably, an increased rate of MI incidence was detected in people < 55 years during 1997–2005 [ 101 ]. In parallel, various studies reported an annual increase (4%) in the incidence of AMI among women aged 35 to 54 years in Western Australia (from 1996 to 2007) and an increase among women aged 20 to 49 years (from 1994 to 2004). In these studies, the accelerated prevalence of smoking (especially among young females), obesity, and the lack of physical activity have been reported in adolescents and young adults [ 102 , 103 , 104 , 105 , 106 , 107 , 108 ].

In this study, a higher prevalence was reported in people over the age of 60. In the results reported in the global epidemiology study of ischemic heart disease, which was based on the results of the global burden of disease study, it was reported that ischemic heart disease has a high upward trend with It shows increasing age and the growing trend continues until the age of 89 [ 109 ].

Limitations

Since the age range explained in published studies had no similarity to the age groups in the present study, some eligible papers were excluded. Although, almost half of the studies were conducted in specific subpopulations (such as other heart disease and diabetic patients admitted to the emergency department); difficult conclusions regarding the MI prevalence in general population were possible.

According to the findings of the present study, the prevalence of MI in people < 60 and > 60 years old were 3.8% and 9.5%, respectively. Therefore, based on the results of the studies that have been reviewed and included in the meta-analysis, the high prevalence of MI was reported to be higher in individuals > 60 years which is considered a warning for health policymakers regarding the importance of this age for diagnosis and screening procedures of MI.

Availability of data and materials

Datasets are available through the corresponding author upon reasonable request.

Abbreviations

Web of Science

Preferred Reporting Items for Systematic Reviews and Meta-Analysis

Strengthening the reporting of observational studies in epidemiology for cross-sectional study

• Myocardial infarction

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Salari, N., Morddarvanjoghi, F., Abdolmaleki, A. et al. The global prevalence of myocardial infarction: a systematic review and meta-analysis. BMC Cardiovasc Disord 23 , 206 (2023). https://doi.org/10.1186/s12872-023-03231-w

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Research Article

Health outcomes after myocardial infarction: A population study of 56 million people in England

Roles Conceptualization, Data curation, Formal analysis, Funding acquisition, Investigation, Methodology, Project administration, Validation, Visualization, Writing – original draft, Writing – review & editing

* E-mail: [email protected]

Affiliations Leeds Institute of Cardiovascular and Metabolic Medicine, University of Leeds, Leeds, United Kingdom, Leeds Institute for Data Analytics, University of Leeds, Leeds, United Kingdom

Roles Formal analysis, Methodology, Writing – original draft, Writing – review & editing

Affiliation Leeds Institute for Health Sciences, University of Leeds, Leeds, United Kingdom

Roles Formal analysis, Methodology, Supervision, Writing – review & editing

Affiliations Leeds Institute for Data Analytics, University of Leeds, Leeds, United Kingdom, Wolfson Institute of Population Health, Queen Mary University of London, London, United Kingdom

Roles Formal analysis, Software, Visualization, Writing – review & editing

Roles Formal analysis, Methodology, Visualization, Writing – review & editing

Roles Conceptualization, Methodology, Supervision, Writing – review & editing

Affiliations Biostatistics Research Group, Department of Population Health Sciences, University of Leicester, Leicester, United Kingdom, Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden

Affiliations Institute of Health Informatics, University College London, London, United Kingdom, Health Data Research UK, University College London, London, United Kingdom, NIHR Biomedical Research Centre, University College London Hospitals NHS Foundation Trust, University College London, London, United Kingdom, Charité Universitätsmedizin, Berlin, Germany

Roles Conceptualization, Supervision, Writing – review & editing

Affiliations Leeds Institute of Cardiovascular and Metabolic Medicine, University of Leeds, Leeds, United Kingdom, Leeds Institute for Data Analytics, University of Leeds, Leeds, United Kingdom, Department of Cardiology, Leeds Teaching Hospitals NHS Trust, Leeds, United Kingdom

• Marlous Hall, 
• Lesley Smith, 
• Jianhua Wu, 
• Chris Hayward, 
• Jonathan A. Batty, 
• Paul C. Lambert, 
• Harry Hemingway, 
• Chris P. Gale

• Published: February 15, 2024
• https://doi.org/10.1371/journal.pmed.1004343
• Peer Review
• Reader Comments

The occurrence of a range of health outcomes following myocardial infarction (MI) is unknown. Therefore, this study aimed to determine the long-term risk of major health outcomes following MI and generate sociodemographic stratified risk charts in order to inform care recommendations in the post-MI period and underpin shared decision making.

Methods and findings

This nationwide cohort study includes all individuals aged ≥18 years admitted to one of 229 National Health Service (NHS) Trusts in England between 1 January 2008 and 31 January 2017 (final follow-up 27 March 2017). We analysed 11 non-fatal health outcomes (subsequent MI and first hospitalisation for heart failure, atrial fibrillation, cerebrovascular disease, peripheral arterial disease, severe bleeding, renal failure, diabetes mellitus, dementia, depression, and cancer) and all-cause mortality. Of the 55,619,430 population of England, 34,116,257 individuals contributing to 145,912,852 hospitalisations were included (mean age 41.7 years (standard deviation [SD 26.1]); n = 14,747,198 (44.2%) male). There were 433,361 individuals with MI (mean age 67.4 years [SD 14.4)]; n = 283,742 (65.5%) male). Following MI, all-cause mortality was the most frequent event (adjusted cumulative incidence at 9 years 37.8% (95% confidence interval [CI] [37.6,37.9]), followed by heart failure (29.6%; 95% CI [29.4,29.7]), renal failure (27.2%; 95% CI [27.0,27.4]), atrial fibrillation (22.3%; 95% CI [22.2,22.5]), severe bleeding (19.0%; 95% CI [18.8,19.1]), diabetes (17.0%; 95% CI [16.9,17.1]), cancer (13.5%; 95% CI [13.3,13.6]), cerebrovascular disease (12.5%; 95% CI [12.4,12.7]), depression (8.9%; 95% CI [8.7,9.0]), dementia (7.8%; 95% CI [7.7,7.9]), subsequent MI (7.1%; 95% CI [7.0,7.2]), and peripheral arterial disease (6.5%; 95% CI [6.4,6.6]). Compared with a risk-set matched population of 2,001,310 individuals, first hospitalisation of all non-fatal health outcomes were increased after MI, except for dementia (adjusted hazard ratio [aHR] 1.01; 95% CI [0.99,1.02]; p = 0.468) and cancer (aHR 0.56; 95% CI [0.56,0.57]; p < 0.001).

The study includes data from secondary care only—as such diagnoses made outside of secondary care may have been missed leading to the potential underestimation of the total burden of disease following MI.

Conclusions

In this study, up to a third of patients with MI developed heart failure or renal failure, 7% had another MI, and 38% died within 9 years (compared with 35% deaths among matched individuals). The incidence of all health outcomes, except dementia and cancer, was higher than expected during the normal life course without MI following adjustment for age, sex, year, and socioeconomic deprivation. Efforts targeted to prevent or limit the accrual of chronic, multisystem disease states following MI are needed and should be guided by the demographic-specific risk charts derived in this study.

Author summary

Why was this study done.

• Myocardial infarction (MI; heart attack) can have major long-term impact on individuals and result in a wide range of further health conditions.
• Existing studies have focussed on determining the short-term risk of a second heart attack, stroke, or major bleeding, but research describing the long-term risk of major health outcomes for specific age, sex, and deprivation groups was lacking.
• Nationally representative and robust information of a wide range of long-term health outcomes following a heart attack is critical for the development of treatment recommendations, which take account of an individuals’ specific risk.

What did the researchers do and find?

• From the population of 56 million adults in England, we analysed hospital records for 34 million adults admitted to hospital (constituting 145 million admission records) to investigate the long-term health outcomes following a heart attack compared with individuals without a heart attack.
• Of 433,361 individuals with a heart attack, up to a third developed heart failure or renal failure, 7% had further heart attacks, and 38% died within the 9-year study period.
• Heart failure, atrial fibrillation, stroke, peripheral arterial disease, severe bleeding, renal failure, diabetes, and depression occurred more frequently for people who had a heart attack compared with those who did not, but the risk of cancer was lower overall and the risk of dementia did not differ overall.

What do these findings mean?

• Efforts should be made to prevent or limit the development of long-term health outcomes that follow a heart attack—the likelihood of which differ depending on the age, sex, and deprivation of an individual.
• These findings are based on the full population of adults admitted to hospital in England, address limitations of previous studies, and can be used to inform preventative strategies tailored to specific individuals surviving a heart attack.
• The study was limited to hospitalisation data only—therefore, some diagnoses made outside of hospital may have been missed.

Citation: Hall M, Smith L, Wu J, Hayward C, Batty JA, Lambert PC, et al. (2024) Health outcomes after myocardial infarction: A population study of 56 million people in England. PLoS Med 21(2): e1004343. https://doi.org/10.1371/journal.pmed.1004343

Academic Editor: Andre P. Kengne, South African Medical Research Council, SOUTH AFRICA

Received: June 26, 2023; Accepted: January 5, 2024; Published: February 15, 2024

Copyright: © 2024 Hall 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: The data underlying the results presented in the study are available from NHS England's Data Access Request Service https://dataaccessrequest.hscic.gov.uk/ . HES data are managed and released by NHS Digital. The specific extract provided to the research team can only be used for the stated purpose of the study and for the length of time necessary to conduct the study. The extract cannot be shared outside of the research team or for any other purpose according to the legally binding terms under which they were released. Please see our privacy notice for further information on the purpose and legal basis of our use of these data: https://digital.nhs.uk/data-and-information/data-tools-and-services/data-services/hospital-episode-statistics . Access to HES data is available by direct application to NHS Digital and is available to anyone who has a legal basis for accessing these data, meets the requirements for safe and secure use of these data and intends to use these data for demonstrable benefit to health and social care in the UK. A full HES data dictionary, information of how to apply and the costs associated with data applications are available publicly via the NHS digital website: https://digital.nhs.uk . All diagnostic and procedure codes used to define specific study outcomes are provided in the supplementary online material released at time of publication. Aggregated data of the age, sex and deprivation specific post MI absolute risk of new onset disease (as presented in heat maps ( Fig 5 )) are available to explore freely via: https://multimorbidity-research-leeds.github.io/research-resources Anyone wishing to use these aggregate data to generate their own graphical summaries may do so providing full reference is given to this publication.

Funding: MH received funding from the Wellcome Trust https://wellcome.org/ (Sir Henry Wellcome Postdoctoral Fellowship ref: 206470/Z/17/Z), British Heart Foundation https://www.bhf.org.uk/ (ref: PG/19/54/34511) and British Heart Foundation-Alan Turing Cardiovascular Data Science Award https://www.bhf.org.uk/for-professionals/information-for-researchers/what-we-fund/bhf-turing-cardiovascular-data-science-awards (ref: BHF-Turing-19/02/1022). JAB was funded by Wellcome Trust 4ward North Clinical Research Training Fellowship (ref: 227498/Z/23/Z). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. The researchers have acted independently from funders and all authors had access to the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis.

Competing interests: I have read the journal’s policy and the authors of this manuscript have the following competing interests: MH declares research grant income from the Wellcome Trust, British Heart Foundation and Alan Turing Institute. JAB declares research grant income from the Wellcome Trust. CPG has received funding, not in relation to this study, from Abbott Diabetes, Bristol Myers Squibb and the European Society of Cardiology, and consulting fees from AI Nexus, AstraZeneca, Amgen, Bayer, Bristol Myers Squibb, Boehrinher-Ingleheim, CardioMatics, Chiesi, Daiichi Sankyo, GPRI Research B.V., Menarini, Novartis, iRhyth, Organon as well as payment for honoraria or lectures from AstraZeneca, Boston Scientific, Menarini, Novartis, Raisio Group, Wondr Medical, Zydus. CPG declares participation on Data Safety Monitoring or Advisory boards for the DANBLCOK and TARGET CTCA trials and editorial and committee membership of the NICE Indicator Advisory Committee, EHJ Quality of Care and Clinical Outcomes and ESC Quality Indicator Committee. CH, LS, JW, HH, and PCL have no competing interests to declare.

Abbreviations: aHR, adjusted hazard ratio; CABG, coronary artery bypass graft; CI, confidence interval; CIF, cumulative incidence function; DAPT, dual antiplatelet therapy; GDPR, General Data Protection Regulation; GP, general practitioner; HDR UK, Health Data Research United Kingdom; HES, Hospital Episode Statistics; IMD, Index of Multiple Deprivation; LASER, Leeds Analytic Secure Environment for Research; MI, myocardial infarction; MINAP, Myocardial Ischaemia National Audit Project; NHS, National Health Service; PCI, percutaneous coronary intervention; PH, proportional hazard; PPIE, patient and public involvement and engagement; SD, standard deviation

Introduction

Information about the health outcomes of people with myocardial infarction (MI) is required to determine individual health needs, enable earlier detection and treatment of new onset disease, and inform health service planning. MI is a major contributor to further cardiovascular, renometabolic, and neuropsychiatric conditions [ 1 – 5 ]. Although estimating 10-year cardiovascular disease risk is an established part of primary prevention [ 6 , 7 ], comprehensive evidence for the long-term impact of MI on subsequent major health outcomes is lacking. Such information is critical not only for the development of guideline recommendations but also to underpin shared decision-making in the post-MI period [ 8 ]. Effective communication of the likely course of disease and risk of adverse long-term outcomes between individuals and healthcare professionals can promote positive lifestyle changes, facilitate treatment compliance, and improve patient understanding and quality of life [ 9 , 10 ].

Electronic health records are a powerful resource for understanding a diverse range of health outcomes over many years of follow-up [ 11 ]. While the largest study to date of post-MI health outcomes provides temporal trends over 2 decades (4.3 million patients in the United States (US)), outcomes were limited to 1-year mortality, readmission, and recurrent MI [ 4 ]. Indeed, the majority of studies of new onset disease following MI focussed only on short-term recurrent MI, bleeding, or stroke [ 12 – 26 ] (literature review S1 Text and S1 Table ). Short- and long-term heart failure incidence following MI has been studied extensively [ 27 – 32 ]—but estimates vary widely (14% to 36%) [ 33 ]. While studies reporting the determinants of heart failure account for confounding and competing risk of death—absolute risk was commonly reported without adjustment, which is prone to bias and lacks generalisability [ 3 , 34 – 36 ]. Long-term post-MI incidence of atrial fibrillation [ 37 – 39 ] and depression [ 40 – 42 ] has been reported without adjustment for sociodemographic factors, pre-existing disease, and differential exposure times, and studies of depression were small (<300 patients). While data on the post-MI incidence of cancer (9% within 17 years; n = 2.1 million) [ 43 ] and dementia (9% within 35 years, n = 314,911) [ 2 ] were more robust—patient demographic-specific absolute risks remain unknown. To our knowledge, there were no contemporary, nationally representative studies of new onset peripheral arterial disease, chronic renal failure, or diabetes for survivors of MI (except one study of newly diagnosed diabetes at time of MI in young adults) [ 44 ].

To our knowledge, none of the studies reporting non-fatal health outcomes post-MI to date account for confounding as well as censoring and competing risks of death to quantify the absolute risk of outcomes over continuous follow-up time.

Understanding the clinical and public health importance of health outcomes after MI requires consideration of the absolute and relative risks beyond age- and sex-matched general populations. To the best of our knowledge, there are no studies of the long-term relative, absolute, and detailed patient demographic-specific risk of major cardiovascular and non-cardiovascular health outcomes following MI.

Therefore, we used hospital admission data in England to determine the risk of all-cause mortality and 11 non-fatal health outcomes following MI, including (1) health outcomes targeted through existing secondary prevention guidelines following MI (subsequent MI and heart failure); (2) health outcomes with shared risk factor profiles with MI, but which were not part of secondary prevention (peripheral arterial disease, cerebrovascular disease, and chronic renal failure); (3) health outcomes for which early detection is crucial for improved outcomes (severe bleeding, diabetes, atrial fibrillation, and cancer); and, finally, (4) health outcomes, which are difficult to prevent but have significant impact on individuals quality of life or life expectancy (depression and dementia). We hypothesised that post-MI disease incidence differed to that expected during a life course without MI. Therefore, we determined the excess incidence, adjusted absolute risk, and age, sex, deprivation, and time-specific risk for each of these health outcomes following MI compared with matched controls.

We conducted a cohort study of all individuals aged ≥18 years admitted to one of 229 National Health Service (NHS) Trusts in England between 1 January 2008 and 31 January 2017. We analysed 11 non-fatal health outcomes identified a priori (subsequent MI and first hospitalisation for heart failure, atrial fibrillation, cerebrovascular disease, peripheral arterial disease, severe bleeding, renal failure, diabetes mellitus, dementia, depression, and cancer) and all-cause mortality for individuals hospitalised with MI compared with a risk-set matched control cohort.

Our study hypothesis was that risk of major health outcomes following MI differed from that expected during a life course without MI. The hypothesis and methodology were defined a priori—exceptions to this, including data-driven decisions and analyses conducted in response to peer review, are labelled as such throughout.

Data access

Hospital Episode Statistics (HES) data were extracted from the Admitted Patient Care dataset by NHS Digital and linked with all-cause mortality data from the Office for National Statistics (censoring date 27 March 2017). HES data contain prospectively collected clinical, demographic, and organisational data for every hospitalisation to NHS hospitals in England, as previously described [ 45 ]. In brief, an individual’s admission to hospital is recorded via a number of single episodes each containing a primary diagnosis, up to 19 secondary diagnoses and 24 operations, procedures, or interventions (coded according to the International Classification of Disease [ICD-10] and Office of Population Censuses and Surveys Classification of Interventions and Procedures [OPCS4.5], respectively) [ 45 ].

Phenotype definitions

Individuals with MI and each of the 11 non-fatal health outcomes were identified using ICD-10 and OPCS4.5 codes derived from the Health Data Research United Kingdom (HDR UK) Phenotype Library ( healthdatagateway.org ) ( S2 Table ) [ 46 ]. In addition, we identified ICD-10 codes for key subgroups including stroke, aortic disease, gastrointestinal bleeding, vascular dementia, acute and chronic renal failure, and colorectal, lung, breast, and prostate cancer ( S2 Table ).

Index MI, as well as the first occurrence of each outcome, was extracted from all primary and secondary diagnostic codes and all procedure codes across all hospitalisation episodes per individual. To identify index events within our study period, all patients with MI or any of the a priori health outcomes in any HES record prior to the study start date were excluded. We ascertained index MI from all diagnoses fields, as planned a priori, given that the presence of another dominant disease may impact on ascertainment from the first diagnostic position only [ 47 ]. Subsequent MI was defined as any MI more than 2 months from initial MI. We conducted sensitivity analyses for all outcomes in which follow-up was restricted to 2 or more months following MI to assess the impact of potential bias from the high number of events observed shortly after study entry. This was a data-driven decision ( S2 Text ). Data cleaning steps are outlined in S3 Text .

Analytical cohort and matching process

Individuals were categorised into (1) a primary analytical cohort of those with an MI hospitalisation record and (2) a cohort of all hospitalised individuals who did not have MI ( Fig 1 ). Our a priori analyses plans focussed on the comparison of outcomes between these 2 cohorts. However, in order to minimise bias arising from different demographic profiles between groups as well as to avoid immortal time bias, we instead employed an exact risk-set matching procedure [ 48 ]. This was determined prior to peer review, in favour of propensity score matching, to avoid the need for pruning of data resulting in efficiency loss and potential risk of reintroducing bias [ 49 ].

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a Population estimates extracted from the Office for National Statistics Population Estimates for England, Wales, Scotland, and Northern Ireland . b Heart failure, atrial fibrillation, cerebrovascular disease, peripheral vascular disease, severe bleeding, renal failure, diabetes mellitus, dementia, depression, and cancer. c Duplicate HES episodes, which contain the same data across admission start and end dates, episode start and end dates, primary and secondary diagnoses codes, and procedure codes, are administrative duplications where incorrect or new entries have been created and are removed from analyses ( S3 Text ). d Records that are missing core and essential information are deemed of too poor a quality to be included in analyses. These include records with missing, conflicting, or out-of-range finished consultant episode start and end dates, unknown spell begin and end indicators, or unknown episode order. e Individuals were matched according to single year of age, sex, month and year of hospital admission, and NHS Trust using a 5:1 risk-set matching approach. HES, Hospital Episode Statistics; MI, myocardial infarction; NHS, National Health Service.

https://doi.org/10.1371/journal.pmed.1004343.g001

Risk-set matching involved matching any new case of MI occurring at time t to any 5 individuals who had not yet developed MI by time t . Matching was based on single year of age, sex, month and year of hospital admission, and NHS Trust. Due to the longitudinal nature of our data, and as per risk-set matching guidance, individuals who later went on to develop MI were censored at the date of MI for the control cohort but contribute to both MI and matched control cohorts in analyses [ 48 ]. Study entry was either the date of the first episode with MI or first matched episode.

Statistical analyses

Patient demographics including age (continuous), sex (male/female), year (single year of study entry), socioeconomic deprivation (Index of Multiple Deprivation [IMD] [ 50 ]—the official score of relative deprivation for small areas of England categorised into groups from least deprived [quintile 1] to most deprived [quintile 5]), crude mortality (Kaplan–Meier failure rate at 30 days, 1 year, and 5 years), total diagnoses by ICD-10 chapter heading, total person years of follow-up, and percentage of missing data for each demographic variable were summarised for the MI, non-MI, and matched control cohorts, respectively. Following peer review, baseline data relating to cardiovascular risk (including hypertension, dyslipidaemia, obesity, tobacco smoking, and alcohol excess) and use of an invasive coronary strategy for index MI (invasive coronary angiography, percutaneous coronary intervention [PCI], or coronary artery bypass graft [CABG]) (ICD-10 and OPCS4.5 code lists provided; S2 Table ) were included. Baseline cardiovascular risk was based on diagnoses observed prior to, or on study entry, within the study period only as historical data were not available in-house due to information governance minimisation requirements (see Data governance section).

Excess rate of health outcomes and all-cause mortality.

Unadjusted rates of disease per 1,000 person-years of follow-up, attained age, and adjusted excess rate of post-MI disease for each outcome compared with matched controls were calculated. Excess rate of post-MI disease was based on adjusted hazard ratios (aHRs) and 95% confidence intervals (CIs) from flexible parametric survival models per outcome [ 51 ]. Models adjusted for age, sex, calendar year, and deprivation score. Nonlinearity of age was modelled using restricted cubic splines (3 degrees of freedom). To provide an overall estimate of excess risk of post-MI disease, comparable with existing literature, aHRs were modelled as standard proportional hazards [PHs] (i.e., fixed constant over time). Subsequently, the PH assumption was relaxed to provide higher resolution insight into absolute risk of outcomes over continuous follow-up time (described below).

Absolute risk of health outcomes and all-cause mortality.

The adjusted absolute risk of each outcome was calculated through standardised cumulative incidence functions (CIFs) stratified by a cohort over 9 years of follow-up, based on the same set of adjusted survival models specified above, treating death without outcome as a competing risk and additionally including a time-dependent effect for MI versus matched controls to relax the PH assumption and reflect variation in the difference in cumulative incidence between cohorts over continuous follow-up time (using “standsurv” in Stata MP v17) [ 52 ].

Age, sex, and deprivation-specific risk charts.

Standardised cumulative incidence for each outcome were calculated for all combinations of age group (<40, 40 to <50, 50 to <60, 60 to <70, 70 to <80, 80 to <90, and ≥90 years of age), sex, and socioeconomic deprivation quintile. Following peer review, age, sex, and deprivation-specific risk for the MI cohort were additionally adjusted for receipt of invasive coronary angiography, PCI, or CABG at time of MI. Risk charts were generated for each cohort at 60 days, 1 year, and 5 years of follow-up and presented in heat maps and an interactive web-based application.

Multiple imputation for missing data was not performed owing to (i) the minimal amount of missing data in core data fields and (ii) the significant increase in computational power required versus the minimal gain in analytical accuracy for data of this scale.

Data governance, IT infrastructure, ethics, and reporting standards

This research adheres to General Data Protection Regulation (GDPR) ( privacy notice ). Data minimisation standards were met through pseudonymisation, month/year aggregation of date, exclusion of patients aged <18 years old, and individuals with pre-existing conditions from a priori outcomes at source. Data were stored and processed within the Leeds Analytic Secure Environment for Research ( LASER ), University of Leeds. Ethical approval was not required for this study, which solely relies on the secondary use of routinely collected, non-confidential healthcare data. This study is reported as per the Reporting of studies Conducted using Observational Routinely-collected Data ( RECORD ) guideline and CODE-EHR minimum standards ( S1 and S2 Checklists).

Patient and public involvement and engagement (PPIE)

In the period prior to obtaining research funding, the research team consulted with individuals who have had, or cared for someone who had, an MI. Shared experiences of those individuals shaped early study design and reporting of this research. Individuals raised concerns about the lack of information provided regarding future health risks following their heart attack. They noted that there was a particular focus on changes in diet and exercise in the immediate period after a heart attack, with little or no information available of “red flags to look out for” in the long term. Individuals identified the need for tools to enable doctors to “risk assess us” and tell us more about future health prospects. These discussions directly informed our research design and ensured we retained a long-term focus, instead of curtailing outcomes at 1-year post-MI to align with existing evidence. Furthermore, we developed an interactive, open-access tool that can be used by healthcare professionals, individual patients, and their carers to better understand and communicate the absolute risks of developing a range of health outcomes motivated by our patient and public involvement and engagement (PPIE) discussions. Individuals felt that this knowledge may provide greater incentivisation for positive lifestyle changes following a heart attack as well as allow individuals and healthcare professionals to “act early rather than react late.”

Finally, the research team hosted a workshop attended by a further 8 individuals with cardiovascular and other multiple long-term health conditions providing direct feedback on this study and guiding the dissemination strategy and direction of follow-on studies. The PPIE group identified the need for joined up thinking between different healthcare providers, given the risk of both cardiovascular and non-cardiovascular conditions, which we raise in our manuscript discussion. They noted the adoption of a long-term perspective is particularly important given that many expect good life expectancy after MI. The PPIE group advocated for the importance of dissemination to general practitioners (GPs) as well as cardiologists and identified the need for clear lay summaries of the work to ensure it is accessible by all. The research team will coproduce lay summaries with our PPIE members and disseminate findings to relevant patient groups, including through the British Heart Foundation’s Heart Voices network.

Of the 55,619,430 populace of England, 34,116,257 individuals aged 18 years and above were admitted to hospital amounting to 145,912,852 hospital episodes in NHS hospitals in England over the study period. The analytical cohorts comprised 433,361 individuals with MI (2,972,215 episodes), 33,429,669 individuals without MI (129,307,574 episodes), and a subset of 2,001,310 matched controls (17,304,985 episodes) ( Fig 1 ). There were 18,322 matched controls who went on to develop MI (0.92%) and therefore contribute data to both cohorts. Individuals with MI were admitted to hospital at a mean age of 67.4 years (standard deviation [SD] 14.4), were predominantly male (65.5% [ n = 283,742]), and had a 30-day mortality rate of 9.9% [ n = 42,882] ( Table 1 ). For matched controls, the age and sex profile was similar to those with MI by design (mean age 66.8 [SD 14.2] and [65.7% male, n = 1,314,388]) with 3.1% ( n = 59,991) 30-day mortality. There were minimal differences in deprivation between cohorts (20.7% [ n = 77,008] versus 19.2% [ n = 375,734] were in the most deprived category for MI and matched controls, respectively). The proportion of individuals with hypertension, dyslipidaemia, obesity, and tobacco smoking were higher among those with MI compared with matched controls but lower for alcohol excess ( Table 1 ). A total of 319,439 (73.7%) individuals with MI received an invasive coronary management strategy for index MI. Missing data in core data fields were limited, including 50,438 (0.1%) for age, 33,871 (0.1%) for sex, 0 missing for month and year of admission, but higher for deprivation ( n = 4,025,757, 11.8%).

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There were 18,343,361 diagnoses codes covering all conditions for individuals with MI throughout the study period, of which 24.7% [ n = 4,526,829] were unique nonfatal diagnoses codes per individual ( Table 1 ). The majority of diagnosis codes related to the circulatory system (44.3% [ n = 2,003,429]), which, along with “endocrine, nutritional, and metabolic diseases” (9.6% [ n = 433,545]), appeared more frequently than in matched controls (20.2% [ n = 3,631,165] and 9.0% [ n = 1,616,515] for circulatory and “endocrine, nutritional, and metabolic diseases,” respectively). For matched controls, diagnoses relating to neoplasms (20.2%, [ n = 3,631,165]) and diseases of the digestive system (14.7% [ n = 2,646,415]) occurred most frequently ( Table 1 ).

Excess rate of health outcomes and all-cause mortality

The most frequent health outcomes following MI, prior to standardisation, were heart failure (crude rate 86.4; 95% CI [85.9,86.9] per 1,000 person-years [1,000 pyrs]), atrial fibrillation (64.3; 95% CI [63.9,64.7] per 1,000 pyrs), renal failure (56.5; 95% CI [56.1,56.9] per 1,000 pyrs), and diabetes mellitus (53.7; 95% CI [53.3,54.1] per 1,000 pyrs) ( S3 and S4 Tables). There was an excess rate of heart failure (aHR 4.93; 95% CI [4.89,7.97]; p < 0.001), atrial fibrillation (aHR 1.98; 95% CI [1.97,2.00]; p < 0.001), cerebrovascular disease (aHR 1.25; 95% CI [1.23,1.26]; p < 0.001), peripheral arterial disease (aHR 1.86; 95% CI [1.83,1.89]; p < 0.001), severe bleeding (aHR 1.22; 95% CI [1.20,1.23]; p < 0.001), renal failure (aHR 1.77; 95% CI [1.75,1.78]; p < 0.001), diabetes mellitus (aHR 1.62; 95% CI [1.61,1.64]; p < 0.001), vascular dementia (aHR 1.13; 95% CI [1.10,1.16]; p < 0.001), and depression (aHR 1.06; 95% CI [1.04,1.07]; p < 0.001) following MI compared with matched controls ( Fig 2 ). There was no difference in the rate of dementia overall (aHR 1.01; 95% CI [0.99,1.02]; p = 0.468) and a reduced rate of cancer (aHR 0.56; 95% CI [0.56,0.57]; p < 0.001) ( Fig 2 ).

a Excess rate of post-MI hospitalisations and all-cause mortality presented as aHRs and 95% CIs based on a series of flexible parametric survival models for each outcome adjusted for age at admission, sex, calendar year of admission, and deprivation score. Age was modelled using restricted cubic spline functions with 3 degrees of freedom to allow for its potential nonlinear association with outcomes, and death without event was treated as a competing risk. Complimentary sensitivity analyses, in which follow up was restricted to begin a minimum of 2 months after study entry, are provided in S8 Table . aHR, adjusted hazard ratio; CI, confidence interval; MI, myocardial infarction; NA, not applicable; SD, standard deviation.

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Absolute risk health outcomes and all-cause mortality

Overall, the adjusted cumulative incidence at 9 years post-MI was highest for all-cause mortality (37.8%; 95% CI [37.6,37.9]) followed by heart failure (29.6%; 95% CI [29.4,29.7]), renal failure (27.2%; 95% CI [27.0,27.4]), atrial fibrillation (22.3%; 95% CI [22.2,22.5]), severe bleeding (19.0%; 95% CI [18.8,19.1]), diabetes mellitus (17.0%; 95% CI [16.9,17.1]), cancer (13.5%, 95% CI [13.3,13.6]), cerebrovascular disease (12.5%; 95% CI [12.4,12.7]), depression (8.9%; 95% CI [8.7,9.0]), dementia (7.8%; 95% CI [7.7,7.9]), subsequent MI (7.1%; 95% CI [7.0─7.2]), and peripheral arterial disease (6.5%; 95% CI [6.3,6.5]) (Figs 3 and 4 and S5 Table ). Cumulative incidence was greater among the MI cohort compared with matched controls for all outcomes except gastrointestinal bleeding—where it was higher in the short term (3.8%; 95% CI [3.8,3.9] versus 3.2% 95% CI [3.1,3.2] at 1 year) and similar in the long term (9.0%; 95% CI [8.9,9.1] versus 8.8%; 95% CI [8.8,8.9]); dementia—where incidence was higher in the short term (2.1%; 95% CI [2.1,2.2] versus 1.79; 95% CI [1.77,1.81] at 60 days) and lower in the long term (7.8%; 95% CI [7.7,7.9] vesus 8.34%; 95% CI [8.28,8.41] at 9 years); and cancer—where incidence was lower throughout the follow-up period (13.5%; 95% CI [13.3,13.6] versus 21.5%; 95% CI [21.4,21.6] at 9 years) (sensitivity analyses S1 and S2 Figs and S6 Table ; numbers at risk S7 Table ).

a Calculated according to the standardised CIF, treating death without outcome as a competing risk, adjusted for nonlinear age using restricted cubic splines, sex, calendar year and deprivation score and a time-dependent effect for MI versus matched controls. Full CIFs and CIs by time point provided in S5 Table , and sensitivity analyses, in which follow-up was restricted to begin a minimum of 2 months after study entry, presented in S1 Fig and S6 Table . Numbers at risk at 1, 5, and 9 years of follow-up are provided in S7 Table . b Individuals were matched according to single year of age, sex, month and year of hospital admission, and NHS Trust using a 5:1 risk-set matching approach. c y-Axis range for all-cause mortality differs to plots for nonfatal health outcomes. CI, confidence interval; CIF, cumulative incidence function; ICD, International Classification of Disease; MI, myocardial infarction; NHS, National Health Service.

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a Calculated according to the standardised CIF, treating death without outcome as a competing risk, adjusted for nonlinear age using restricted cubic splines, sex, calendar year and deprivation score and a time-dependent effect for MI versus matched controls. Full CIFs and CIs by time point provided in S5 Table , and sensitivity analyses, in which follow-up was restricted to begin a minimum of 2 months after study entry, presented in S2 Fig and S6 Table . Numbers at risk at 1, 5, and 9 years of follow-up are provided in S7 Table . b Individuals were matched according to single year of age, sex, month and year of hospital admission, and NHS Trust using a 5:1 risk-set matching approach. c Includes all cancer types (ICD10 codes C00–C97), i.e., this category is not restricted to the sum of breast, prostate, lung, and colorectal cancer). CI, confidence interval; CIF, cumulative incidence function; ICD, International Classification of Disease; MI, myocardial infarction; NHS, National Health Service.

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Age, sex, and deprivation-specific risk charts for 11 nonfatal health outcomes and all-cause mortality following MI

There was an increasing risk with age post-MI for men and women across all deprivation quintiles for heart failure (cumulative incidence at 5 years ranging from 13.5%; 95% CI [13.0,14.0] to 48.9%; 95% CI [48.0,49.7] for men in deprivation quintile 3 aged <40 years and ≥90 years, respectively), atrial fibrillation (2.5%; 95% CI [2.3,2.7] to 36.6%; 95% CI [35.8,37.4] for men in deprivation quintile 3 aged <40 years and ≥90 years, respectively), and renal failure (4.0%; 95% CI [3.7,4.3] to 46.8%; 95% CI [45.9,47.7]) for men in deprivation quintile 3 aged <40 years and ≥90 years, respectively) ( Fig 5 ). The association of age with subsequent MI, peripheral arterial disease, cerebrovascular disease, diabetes, and cancer was less pronounced. In contrast, post-MI depression risk was highest among younger age groups at each time point, particularly for those in the most deprived quintile (5-year CIFs for men in deprivation quintile 5 were 11.5%; 95% CI [10.9,12.0]; 10.0%; 95% CI [9.7,10.3]; 8.5%; 95% CI [8.2,8.7]; 6.8%; 95% CI [6.6,6.9]; 5.3%; 95% CI [5.1,5.5]; 4.4%; 95% CI [4.3,3.6]; and 3.8%; 95% CI [3.5,4.0] for those aged <40 years, 40 to <50 years, 50 to <60 years, 60 <70 years, 70 to <80 years, 80 to <90 years, and ≥90 years respectively). The risk of depression post-MI was also higher among women compared with men (5-year CIFs for women 21.5%; 95% CI [20.5,22.5], 18.9%; 95% CI [18.3,19.5]; 16.1%; 95% CI [15.6,16.6]; 13.0%; 95% CI [12.6,13.4]; 10.3%; 95% CI [10.0,10.6]; 8.7%; 95% CI [8.4,9.0]; 7.5%; 95% CI [7.0,7.9] for those aged <40 years, 40 to <50 years, 50 to <60 years, 60 to <70 years, 70 to <80 years, 80 to<90 years, and ≥90 years, respectively ( Fig 5 ). Complimentary risk charts for matched controls and sensitivity analyses provided in S3 , S4 and S5 Figs and interactive versions accessed via https://multimorbidity-research-leeds.github.io/research-resources .

a Calculated according to the standardised CIF, treating death without outcome as a competing risk and adjusted for nonlinear age using restricted cubic spline functions, sex, calendar year, deprivation score, and receipt of invasive coronary angiography, percutaneous coronary intervention, or coronary artery bypass graft. b Deprivation is measured using the IMD where 1 indicates those in the least deprived fifth, and 5 indicates those in the most deprived fifth. S3 , S4 and S5 Figs show CIFs for the matched control cohort and the main and matched control sensitivity analyses. Interactive version of these data are provided ( https://multimorbidity-research-leeds.github.io/research-resources ). CIF, cumulative incidence function; IMD, Index of Multiple Deprivation; MI, myocardial infarction.

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In this study of over 145 million hospitalisations in England, we provide nationwide evidence from a single health system of the specific burden of a wide range of health outcomes following MI. Up to a third of patients with MI developed heart failure or renal failure, 13% cerebrovascular disease, 9% depression, 7% had further MI or peripheral arterial disease, and 38% died within 9 years (compared with 35% deaths for individuals without MI). Rates of all health outcomes, except dementia and cancer, were significantly higher than expected during the normal life course without MI.

Increased incidence of heart failure post-MI is well recognised [ 3 ], but estimates have been inconsistent, often lack confounder adjustment, and were unavailable by detailed demographic groups. Our study provides adjusted estimates of 21.2% heart failure at 1 year, rising to 29.6% at 9 years following MI compared with 2.9% and 9.8% at 1 and 9 years for matched controls, respectively. Further, our study shows earlier onset of heart failure following MI for the most socioeconomically deprived individuals. While we did not assess secondary preventative medication directly, our findings may reflect previously reported underuse of secondary preventative medication among socioeconomically deprived groups after MI [ 53 ]. We found that almost one-fifth of patients were admitted to hospital with severe bleeding following MI. While we were unable to study dual antiplatelet therapy (DAPT), recent data indicated that a reduction of major bleeding complications may be achieved through shortened DAPT regimes [ 54 ]. The incidence of post-MI diabetes mellitus, peripheral vascular disease, and renal failure had not been previously reported. Here, we quantify a small excess incidence of diabetes mellitus (17% versus 14%) and peripheral arterial disease (6% versus 4%) at 9 years, and a marked difference in the incidence of renal failure (27% versus 20%) following MI compared with controls.

New hospitalisations for depression occurred in 1 in 11 individuals after MI and was more frequent at younger ages of MI, for those in the most deprived quintile, and among women. Given the increasing trend in cardiovascular risk factors among young adults [ 55 ], and the increasing proportion of MI among young adults and women [ 56 ], the incidence of depression following MI will likely rise.

Our study showed a lower incidence of cancer overall following MI, as well as for breast, colorectal, lung, and prostate cancer compared with controls. Two existing large-scale studies also describe significantly reduced risks of breast and prostate cancer among individuals with MI [ 43 ] and cardiovascular disease more broadly [ 57 ]. Our study builds on this, providing demographic-specific absolute risk overall and for key cancer subgroups. In contrast with previously reported data, we additionally show a reduced risk of lung and colorectal cancer and all cancers combined (compared with previously reported increased incidence of lung cancer and nonsignificant difference in colorectal cancer following MI [ 43 ] and increased risk of lung, colorectal, and all cancers for individuals with cardiovascular disease [ 57 ]). The reason for conflicting evidence may be explained by (1) different population demographics of previously published work (smaller and younger population of MI [ 43 ] and broader cardiovascular population [ 57 ]) and (2) lack of accounting for competing risk of death [ 57 ], which was likely to have a marked impact on findings given the high early mortality rate observed for individuals with cardiovascular disease. Mechanisms underpinning reduced risk of cancer following MI remain unclear and warrant further investigation. While trial data have assessed the role of aspirin in the onset, progression, and mortality of specific cancer subtypes [ 58 – 60 ], broader evidence for its beneficial effect remains unclear [ 61 ]. We are unable to state whether aspirin contributed to the reduced risk of cancer observed, given medication data were unavailable. While some evidence points to reduced screening for cancer among individuals with cardiovascular disease [ 62 ], a surveillance bias may also act in the opposite direction. Finally, given that we focussed specifically on development of new cancer following MI, we included only individuals living long enough cancer-free to develop MI before cancer, and this may further explain our findings.

The incidence of dementia overall was 7.8% and 2.3% for vascular dementia within 9 years of MI. There was no difference in the risk of dementia overall, but there was a 13% increased risk of vascular dementia following MI compared with controls—consistent with previous work [ 2 ]. We improve on previous data by showing an increased risk of dementia overall in the short term (2.1% versus 1.8% at 60 days) but reduced risk in the long term (7.8% versus 8.3% at 9 years) and a consistently increased risk of vascular dementia at all time points following MI compared with controls.

While we use large-scale nationwide data and robust methodology to produce generalisable results, we do acknowledge the study limitations. Our focus was on hospitalised events and we did not have access to diagnoses made in primary care–with may have (1) underestimated the totality of post-MI disease and (2) led to some individuals with pre-existing comorbidities being missed from our exclusion criteria—which may vary by outcome. While the steep increase in events after study entry could in part reflect underestimation of pre-existing disease, the observed pattern of events was expected given that individuals enter the study at a key clinical event, signifying more severe or complex disease, rather than in a healthy state. To further mitigate this risk, we present sensitivity analyses delaying follow up to 60 days after study entry, while acknowledging that our primary findings are substantiated by other studies reporting high rates of rehospitalisation within 30 days following MI [ 63 ], reflecting conditions likely diagnosed shortly after MI due to screening for risk factors (e.g., diabetes) and sequelae (e.g., heart failure) or due to complications (e.g., bleeding). We further ensured high ascertainment of preexisting disease by making use of a look-back period in excess of 10 years and capturing conditions via comprehensive code lists. We acknowledge that, for some individuals, the look-back period may have been limited (e.g., due to immigration), which we could not quantify.

Case ascertainment of MI within HES is known to be high; indeed, individuals with long-term conditions as well as MI are more likely to be captured by HES than by the UK’s Myocardial Ischaemia National Audit Project (MINAP) [ 47 ]. We acknowledge that case ascertainment and changes in coding practices may vary by health outcome and that reliance on ICD coding alone may have led to under reporting of some conditions such as dementia and depression. While we were unable to account for this directly, confounding of temporal changes in ascertainment and coding practices is partially captured by inclusion of calendar year. Furthermore, high sensitivity and specificity of comorbidity recording have been reported for HES, in particular for diabetes (97.7% sensitivity, 96.1% specificity [ 64 ]). While dementia diagnoses are delayed by approximately 1.6 years in HES versus primary care, case ascertainment is high (85%) [ 65 ]. We did not account for severity of hospitalisation for individuals without MI and were unable to distinguish between subtypes of MI; however, we include the full range of admissions without restriction to less severe disease, and specific MI subtype coding criteria were implemented towards the end of our study period, making future stratification possible [ 66 ]. We acknowledge the likely under reporting of lifestyle-related risk factors within hospitalisation records, and baseline cardiovascular risk data, which were restricted to within the study period only. These data were therefore provided in summary format only and represent only the extremes of the population distribution. HES does not capture information with regard to secondary preventative medication, and there is currently no national individual-level hospital prescribing database for England for linkage without consent for research [ 45 ]. While we could not quantify the impact of medication on post-MI incidence of health outcomes, we do adjust for invasive management of MI. Finally, we acknowledge that the largest proportion of data relate to outcomes within 1 year of MI due to a drop-off in numbers at risk over follow up, but note that there were sufficient data to provide statistically robust estimates of long-term outcomes (>110,000 individuals and 2,400 individuals per outcome at 5 and 9 years, respectively).

The use of nationally representative health record data provided a depth of analyses allowing risk stratification into clinically relevant groups, for many outcomes. While high-quality system wide healthcare databases are increasingly accessible, major barriers remain in (1) timely data access; (2) access to scalable computational facilities to handle size, complexity, and security standards; and (3) stringent data minimisation, which limited the scope to conditions identified a priori, rather than the full breadth of possible outcomes.

Although clearly a public health focus must be the prevention of MI, evidence from our study has implications for clinical care in Cardiology and beyond. We evidence the excess incidence of conditions that are targeted through current secondary preventative guidelines (heart failure), conditions not currently directly included in secondary preventative guidelines (chronic renal failure and cerebrovascular disease), conditions that would benefit from early detection for improved outcomes (severe bleeding and atrial fibrillation), and conditions that have a significant impact on quality of life (depression). While we did not assess impact of secondary preventative medication on outcomes directly, implications of our research in context of previous studies indicate that (1) improved secondary preventative medication for younger individuals in the most socioeconomically deprived group may tackle the high incidence of post-MI heart failure observed among this demographic [ 53 ]; and (2) a reduction in the long-term high incidence of major bleeding complications may be achieved through shortened DAPT regimes [ 54 ] and longer-term surveillance following MI. High incidences of chronic renal failure, cerebrovascular disease, and peripheral arterial disease following MI suggest opportunity for intensified secondary prevention of shared modifiable risk factors and enhanced post-MI health surveillance to mitigate against increased healthcare usage and premature death. Moreover, screening interventions for the most at risk of post-MI depression (including individuals who are younger, female, or socioeconomically deprived) should be considered. While the incidence of vascular dementia contributes only a small proportion of the post-MI disease burden, causal links and opportunities for secondary prevention between MI and vascular dementia warrant further study, given the excess incidence observed.

When extrapolated to the 1.4 million survivors of MI in the UK in 2022, our study implies an estimated 414,400 new diagnoses of heart failure, 312,200 atrial fibrillation, 175,000 cerebrovascular disease, peripheral vascular disease, 266,000 severe bleeding, 380,800 renal failure, 238,000 diabetes mellitus, 109,200 dementia, 124,600 depression, and 189,000 cancer in the next decade, in addition to 99,400 individuals with subsequent MI and up to 529,200 dying within 9 years of first MI.

Our sociodemographic stratified risk charts provide a crucial step in translating future health outcomes to support informed and shared healthcare decision-making. Effective communication of the likely course of disease and risk of adverse long-term outcomes between individuals and healthcare professionals promote positive lifestyle changes, facilitate treatment compliance, and improve patient understanding and quality of life [ 9 , 10 ]. Informed by PPIE, our graphics have been designed in an easy-to-use format via a publicly accessible website, providing healthcare professionals and patients with a tool to discuss relevant demographic-specific risk to direct appropriate care. Moreover, these data have the potential to underpin public health policies aimed at reducing the health inequalities observed and reducing the significant ongoing burden of disease for the increasing number of survivors of MI—some of whom have many potential years of life left. Future work should focus on stratifying risk by specific MI phenotype and identifying modifiable risk factors associated with the increased burden of health outcomes evidenced.

In conclusion, individuals frequently accrue major comorbidities across a range of body systems in the decade following MI—with 3 in 10 developing heart failure or renal failure and 4 in 10 dying. Health inequalities relating to age, sex, and socioeconomic deprivation are clearly evidenced—socioeconomically deprived individuals are more likely to have MI earlier in their life course and experience an increased burden of post-MI health outcomes at an earlier age. Improved post-MI preventative strategies, encompassing enhanced surveillance and detection, are required to tackle the high incidences of heart failure, atrial fibrillation, cerebrovascular disease, and renal failure observed in this population. Finally, sociodemographic stratified risk charts should be used to inform decision-making about health and well-being for specific patient groups in the post-MI period and underpin public health policies aimed at reducing health inequalities.

Supporting information

S1 checklist. reporting of studies conducted using observational routinely collected data (record) standard..

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S2 Checklist. CODE-EHR framework: Best practice checklist to report on the use of structured electronic healthcare records in clinical research date of completion: 20 January 2023.

Study name: Health outcomes after myocardial infarction : A population study of 56 million people in England .

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S1 Text. Review of the prior evidence.

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S2 Text. Sensitivity analyses methods.

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S3 Text. Hospital Episode Statistics (HES) data cleaning.

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S1 Table. Summary of the available evidence of post-MI new onset disease incidence, 1946–October 2023.

ACEi/ARB, angiotensin-converting enzyme inhibitors and angiotensin II receptor blockers; ACS, acute coronary syndromes; AF, atrial fibrillation; CAD, coronary artery disease; CIF, cumulative incidence function; COPD, coronary obstructive pulmonary disease; CPRD, Clinical Practice Research Database; EHR, electronic healthcare record; HES, Hospital Episode Statistics; HF, heart failure; HFrEF, heart failure with reduced ejection fraction; HR, hazard ratio; IRR, incidence rate ratio; IQR, interquartile range; KM, Kaplan–Meier; MI, myocardial infarction; MINAP, Myocardial Ischaemia National Audit Project; NSETMI, non ST-elevation myocardial infarction; PCI, percutaneous coronary intervention; PH, proportional hazards; PPCI, primary percutaneous coronary intervention; PTSD, posttraumatic stress disorder; SCAD, spontaneous coronary artery dissection; SD, standard deviation; STEMI, ST-elevation myocardial infarction; USA, United States of America; vs., versus.

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S2 Table. Code definitions for health outcomes, vascular risk factors, and invasive coronary strategy for MI according to the International Classification of Diseases (ICD10) and operating Procedure Code Supplement Classification of Interventions and Procedures (OPCS4.5).

ICD10 and OPCS Coding lists adapted from published: https://www.caliberresearch.org/portal . NEC, not elsewhere classifiable; NOC, not otherwise specified.

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S3 Table. Crude rate of post-MI disease per 1,000 person years (pyrs) for those with MI compared with a matched control group a in England, 2008–2017.

a Individuals were matched according to single year of age, sex, month and year of hospital admission, and NHS Trust using a 5:1 risk-set matching approach. b Cases within the matched control cohort who went on to develop MI were censored at time of first MI; therefore, estimates of subsequent MI for this cohort were not included. CI, confidence interval; MI, myocardial infarction; NA, not applicable; NHS, National Health Service.

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S4 Table. Crude rate of post-MI disease occurring more than 2 months following study entry (sensitivity analyses) per 1,000 person years (pyrs) for those with MI compared with a matched-control group a in England, 2008–2017.

a Individuals were matched according to single year of age, sex, month and year of hospital admission, and NHS Trust using a 5:1 risk-set matching approach. b Cases within the matched control cohort who went on to develop MI were censored at time of first MI; therefore, estimates of subsequent MI for this cohort were not included. CI, confidence interval; MI, myocardial infarction; NA, not applicable; NHS, National Health Service; SD, standard deviation.

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S5 Table. Cumulative incidence a of all-cause mortality and first hospitalisation for all outcomes following MI treating death without event as a competing risk compared with matched controls b at 60 days, 1 year, 5 years, and 9 years of follow-up in England, 2008–2017.

a Cumulative incidences are presented as percentage of cases expected to develop each outcome by each respective time point and adjusted for nonlinear age using restricted cubic spline functions, sex, calendar year, and deprivation score—treating death without outcome as a competing risk. b Individuals were matched according to single year of age, sex, month and year of hospital admission, and NHS Trust using a 5:1 risk-set matching approach. c Cases within the matched control cohort who went on to develop MI were censored at time of first MI; therefore, estimates of subsequent MI for this cohort were not included. CI, confidence interval; MI, myocardial infarction; NA, not applicable; NHS, National Health Service; SD, standard deviation.

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S6 Table. Cumulative incidence a of all-cause mortality and first hospitalisation for all outcomes at least 2 months following MI (sensitivity analyses) treating death without outcome as a competing risk, compared with matched controls b at 1 year, 5 years, and 9 years of follow-up in England, 2008–2017.

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S7 Table. Numbers at risk for the cumulative incidence analysis over time for post-MI outcomes in England, 2008–2017.

a Numbers at risk at 1, 5, and 9 years follow-up are equal for those in the main analyses and the sensitivity analyses by design. MI, myocardial infarction.

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S8 Table. Excess rate a of all-cause mortality and first hospitalisation for all outcomes at least 2 months following MI (sensitivity analyses) over and above matched controls b in England, 2008–2017.

a Excess rate is presented as the aHR for each outcome comparing matched controls with individuals with MI and adjusted for nonlinear age using restricted cubic spline functions, sex, calendar year, and deprivation score—treating death without outcome as a competing risk. b Individuals were matched according to single year of age, sex, month and year of hospital admission, and NHS Trust using a 5:1 risk-set matching approach. c Cases within the matched control cohort who went on to develop MI were censored at time of first MI; therefore, estimates comparing the HR of subsequent MI between the MI cohort and matched controls are not applicable. aHR, adjusted hazard ratio; CI, confidence interval; MI, myocardial infarction; NA, not applicable.

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S1 Fig. Adjusted absolute risk a over continuous time of subsequent MI, heart failure, atrial fibrillation, cerebrovascular disease, peripheral arterial disease, and severe bleeding occurring at least 2 months following index MI (sensitivity analyses) compared with matched controls b in England.

a Calculated according to the standardised CIF, treating death without outcome as a competing risk and adjusted for nonlinear age using restricted cubic spline functions, sex, calendar year, and deprivation score. b Individuals were matched according to single year of age, sex, month and year of hospital admission, and NHS Trust using a 5:1 risk-set matching approach. CIF, cumulative incidence function; MI, myocardial infarction; NHS, National Health Service.

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S2 Fig. Adjusted absolute risk a over continuous time of renal failure, diabetes mellitus, dementia, depression, and cancer occurring at least 2 months following index MI (sensitivity analyses) compared with matched controls b in England.

a Calculated according to the standardised CIF, treating death without outcome as a competing risk and adjusted for nonlinear age using restricted cubic spline functions, sex, calendar year, and deprivation score. b Individuals were matched according to single year of age, sex, month and year of hospital admission, and NHS Trust using a 5:1 risk-set matching approach. c Includes all cancer types (ICD10 codes C00–C97), i.e., this category is not restricted to the sum of breast, prostate, lung, and colorectal cancer). CIF, cumulative incidence function; MI, myocardial infarction; NHS, National Health Service.

https://doi.org/10.1371/journal.pmed.1004343.s015

S3 Fig. Absolute risk a of 11 non-fatal health outcomes occurring at least 2 months following MI at 1 year and 5 years of follow-up (sensitivity analyses) as well as by age group, sex, and deprivation b in England ( N = 433,361).

a Calculated according to the standardised CIF, treating death without outcome as a competing risk and adjusted for nonlinear age using restricted cubic spline functions, sex, calendar year, deprivation score, and receipt of invasive coronary angiography, percutaneous coronary intervention, or coronary artery bypass graft. b Deprivation is measured using the IMD where 1 indicates those in the least deprived quintile, and 5 indicates those in the most deprived quintile. Interactive version of these data are provided ( https://multimorbidity-research-leeds.github.io/research-resources ). CIF, cumulative incidence function; IMD, Index of Multiple Deprivation; MI, myocardial infarction.

https://doi.org/10.1371/journal.pmed.1004343.s016

S4 Fig. Absolute risk a of 11 non-fatal health outcomes and all-cause mortality at 60 days, 1 year, and 5 years of follow-up by age group, sex, and deprivation b among matched individuals without MI in England ( N = 2,001,310).

a Calculated according to the standardised CIF, treating death without outcome as a competing risk and adjusted for nonlinear age using restricted cubic spline functions, sex, calendar year, and deprivation score. b Deprivation is measured using the IMD where 1 indicates those in the least deprived fifth, and 5 indicates those in the most deprived fifth. Interactive version of these data are provided ( https://multimorbidity-research-leeds.github.io/research-resources ). CIF, cumulative incidence function; IMD, Index of Multiple Deprivation; MI, myocardial infarction.

https://doi.org/10.1371/journal.pmed.1004343.s017

S5 Fig. Absolute risk a of 11 non-fatal health outcomes occurring at least 2 months following study entry (sensitivity analyses) at 1 year and 5 years of follow-up and by age group, sex, and deprivation b among matched individuals without MI in England ( N = 2,001,310).

https://doi.org/10.1371/journal.pmed.1004343.s018

Acknowledgments

We acknowledge the work by NHS Digital in providing access to these data for the purposes of our study and the staff in the Leeds Institute for Data Analytics DAT team, University of Leeds involved in the data management and the secure and safe storage of data for this project. We would like to acknowledge the work by Dr Charlotte Sturley in enhancing our PPIE activities and establishing an ongoing PPIE group for individuals with cardiovascular disease and multiple long-term conditions to support future research, and that of Heart Voices for their help in disseminating our PPIE opportunities. Finally, we would like to acknowledge all the patients and carers who have taken the time to share their experiences of post-heart attack care for their valuable contributions to setting the direction and design of the research, their feedback, and their ongoing involvement in dissemination of the work.

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Pathophysiology of Myocardial Infarction

Affiliation.

• 1 The Wilf Family Cardiovascular Research Institute, Division of Cardiology, Department of Medicine, Albert Einstein College of Medicine, Bronx, New York, USA.
• PMID: 26426469
• DOI: 10.1002/cphy.c150006

Myocardial infarction is defined as sudden ischemic death of myocardial tissue. In the clinical context, myocardial infarction is usually due to thrombotic occlusion of a coronary vessel caused by rupture of a vulnerable plaque. Ischemia induces profound metabolic and ionic perturbations in the affected myocardium and causes rapid depression of systolic function. Prolonged myocardial ischemia activates a "wavefront" of cardiomyocyte death that extends from the subendocardium to the subepicardium. Mitochondrial alterations are prominently involved in apoptosis and necrosis of cardiomyocytes in the infarcted heart. The adult mammalian heart has negligible regenerative capacity, thus the infarcted myocardium heals through formation of a scar. Infarct healing is dependent on an inflammatory cascade, triggered by alarmins released by dying cells. Clearance of dead cells and matrix debris by infiltrating phagocytes activates anti-inflammatory pathways leading to suppression of cytokine and chemokine signaling. Activation of the renin-angiotensin-aldosterone system and release of transforming growth factor-β induce conversion of fibroblasts into myofibroblasts, promoting deposition of extracellular matrix proteins. Infarct healing is intertwined with geometric remodeling of the chamber, characterized by dilation, hypertrophy of viable segments, and progressive dysfunction. This review manuscript describes the molecular signals and cellular effectors implicated in injury, repair, and remodeling of the infarcted heart, the mechanistic basis of the most common complications associated with myocardial infarction, and the pathophysiologic effects of established treatment strategies. Moreover, we discuss the implications of pathophysiological insights in design and implementation of new promising therapeutic approaches for patients with myocardial infarction.

Copyright © 2015 John Wiley & Sons, Inc.

Publication types

• Research Support, N.I.H., Extramural
• Cell Respiration
• Myocardial Infarction / metabolism
• Myocardial Infarction / pathology
• Myocardial Infarction / physiopathology*
• Myocytes, Cardiac / metabolism*
• Myocytes, Cardiac / pathology

Grants and funding

• R01 HL76246/HL/NHLBI NIH HHS/United States
• R01 HL85440/HL/NHLBI NIH HHS/United States

• Open supplemental data
• Reference Manager
• Simple TEXT file

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Original research article, characterization of myocardial injury phenotype by thermal liquid biopsy.

• 1 Division of Cardiovascular Medicine, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, United States
• 2 Department of Mathematics and Statistics, Northern Arizona University, Flagstaff, AZ, United States
• 3 Department of Chemistry and Biochemistry, New Mexico State University, Las Cruces, NM, United States
• 4 Molecular Biology and Interdisciplinary Life Sciences Program, New Mexico State University, Las Cruces, NM, United States
• 5 UofL Health–Brown Cancer Center and Division of Medical Oncology and Hematology, Department of Medicine, University of Louisville, Louisville, KY, United States

Background and aims: With the advent and implementation of high-sensitivity cardiac troponin assays, differentiation of patients with distinct types of myocardial injuries, including acute thrombotic myocardial infarction (TMI), acute non-thrombotic myocardial injury (nTMi), and chronic coronary atherosclerotic disease (cCAD), is of pressing clinical importance. Thermal liquid biopsy (TLB) emerges as a valuable diagnostic tool, relying on identifying thermally induced conformational changes of biomolecules in blood plasma. While TLB has proven useful in detecting and monitoring several cancers and autoimmune diseases, its application in cardiovascular diseases remains unexplored. In this proof-of-concept study, we sought to determine and characterize TLB profiles in patients with TMI, nTMi, and cCAD at multiple acute-phase time points (T 0 h, T 2 h, T 4 h, T 24 h, T 48 h) as well as a follow-up time point (Tfu) when the patient was in a stable state.

Methods: TLB profiles were collected for 115 patients (60 with TMI, 35 with nTMi, and 20 with cCAD) who underwent coronary angiography at the event presentation and had subsequent follow-up. Medical history, physical, electrocardiographic, histological, biochemical, and angiographic data were gathered through medical records, standardized patient interviews, and core laboratory measurements.

Results: Distinctive signatures were noted in the median TLB profiles across the three patient types. TLB profiles for TMI and nTMi patients exhibited gradual changes from T0 to Tfu, with significant differences during the acute and quiescent phases. During the quiescent phase, all three patient types demonstrated similar TLB signatures. An unsupervised clustering analysis revealed a unique TLB signature for the patients with TMI. TLB metrics generated from specific features of TLB profiles were tested for differences between patient groups. The first moment temperature ( T FM ) metric distinguished all three groups at time of presentation (T0). In addition, 13 other TLB-derived metrics were shown to have distinct distributions between patients with TMI and those with cCAD.

Conclusion: Our findings demonstrated the use of TLB as a sensitive and data-rich technique to be explored in cardiovascular diseases, thus providing valuable insight into acute myocardial injury events.

1 Introduction

Each year, over 12 million patients present with suspected acute myocardial infarction (MI) to the emergency departments in North America and Europe ( 1 ). A systematic review by the Agency for Healthcare Research and Quality of the US Department of Health and Human Services (AHRQ Report) ( 2 ) showed that ∼5.7% of emergency department patients receive an incorrect diagnosis, with MI ranking second among conditions associated with the most serious harm due to misdiagnosis.

The etiology of acute MI is complex. Although coronary thrombus overlying a disrupted atherosclerotic plaque is the hallmark and therapeutic target of acute MI, multiple non-thrombotic etiologies, such as coronary vasospasm and demand ischemia, are now known to exist and necessitate different treatments ( 3 , 4 ). Multiple studies have reported that non-thrombotic MI is at least as common as thrombotic MI ( 5 ). While current guidelines distinguish between thrombotic (Type 1) MI and non-thrombotic causes of myocardial injury ( 4 ), clinically actionable criteria to distinguish between these two types of myocardial injuries do not exist. Because both types of MI are associated with myocyte injury, they both lead to an increase in circulating levels of troponin, the current gold standard for MI diagnosis. The limitations of current diagnostic strategies are highlighted by the fact that 70% of the ∼6 million US patients presenting to hospital with chest pain are given a benign diagnosis at a cost of approximately $10 billion/year ( 6 – 8 ). Despite the expense of this diagnostic work-up, 2%–5% of patients discharged home with a benign diagnosis are subsequently found to have an acute MI with a worse prognosis than those correctly diagnosed on the initial encounter ( 6 – 8 ). In patients with thrombotic MI, lack of accurate and rapid diagnosis could delay necessary, time-sensitive, anti-thrombotic, anti-coagulant, fibrinolytic, and procedural revascularization therapies, whereas in patients with non-thrombotic myocardial injury, these therapies could lead to unnecessary bleeding/procedural risks without the possibility of clinical benefit ( 9 – 11 ).

Thermal liquid biopsy (TLB) utilizing differential scanning calorimetry (DSC) is a powerful tool that may be applied to characterize and differentiate myocardial injury events, without the need for costly or more invasive procedures. DSC is a thermoanalytical method employed to analyze the heat profiles associated with the denaturation of biomolecules and their interactions with different metabolites. TLB is based on the analysis of non-solid biological tissues (e.g., blood plasma) that captures complex mixtures of heat release and heat absorption that reflect the overall biomolecular makeup of blood plasma at the time of collection ( Figure 1 ). This detects alterations in protein concentration, post-translational modifications, or interaction with other analytes that affect the thermal stability of the plasma proteome ( 12 – 14 ). Previous studies have successfully employed TLB to better understand complex factors contributing to diseases status including cancer ( 14 – 21 ), autoimmune ( 22 – 25 ), and other diseases ( 15 , 26 – 29 ). Although TLB offers a comprehensive measure of disease status, with potential for novel characterization and monitoring of diseases, its application in cardiovascular diseases remains unexplored.

Figure 1 . Flowchart illustrating the use of TLB in myocardial injury assessment. The process begins with blood collection and plasma separation in patients with suspected MI. TLB involves DSC analysis to capture the comprehensive protein denaturation behavior of the patient plasma sample. The resulting TLB profiles are used to differentiate between the different forms of myocardial injury (cCAD, TMI, and nTMi), which complements other clinical data in the clinical decision-making process.

Given that atherothrombosis results from an imbalance between thrombotic and fibrinolytic proteins, individual biomolecule measurements may not reflect the complex interplay of multiple biological factors contributing to a pathological state. We hypothesized that TLB may capture the collective biomolecular constitution of blood plasma at the time of sampling, providing a signature TLB profile of acute changes associated with patients with distinct types of myocardial injuries. We sought to characterize TLB at the time of the acute clinical event and quiescent follow-up time points in three patient types: acute thrombotic myocardial infarction (TMI), acute non-thrombotic myocardial injury (nTMi), and chronic coronary atherosclerotic disease (cCAD) (the stable underlying disease necessary for acute TMI). This approach represents a novel use of TLB in the assessment of acute myocardial injury events.

2 Materials and methods

2.1 study design and patient recruitment.

This investigation is a prospective cohort study to evaluate the utility of TLB for differentiating myocardial injury subtypes. Patients with suspected acute myocardial injury (TMI and nTMi) and suspected cCAD were recruited from two hospitals in Louisville, KY, USA, between September 2014 and January 2020. The study was approved by the University of Louisville Internal Review Board (IRB #14.0437) and both participating hospitals. All patients provided written informed consent.

Patient interviews and medical records were used in the collection of pertinent medical history, physical, electrocardiographic, histological, biochemical, and angiographic data. Coronary angiograms were assessed in a blinded fashion with standardized criteria by the Johns Hopkins Quantitative Angiographic Core Laboratory ( 30 ). Laboratory data (troponin I, creatinine, blood cell, and platelet counts) were obtained from the treating hospital clinical laboratory and research blood samples were collected and processed at standardized study time points: baseline/time of invasive angiogram (T0) and 2 (T2), 4 (T4), 24 (T24), and 48 (T48) h post angiogram (unless the patient was discharged from the hospital prior to this time point). In addition, troponin I levels were measured using Beckman Access assay from T0 to T48 to assess peak troponin relative to the upper reference limit (URL of 0.04 ng/ml). Follow-up history, physical exam results, laboratory data and research blood samples were collected at a single follow-up (Tfu) visit 3–12 (median, 3.98) months after the procedure or hospitalization for acute myocardial injury, when the patient was in a stable condition.

2.2 Analytical cohort

The analytical cohort was designed to identify two etiological types of acute myocardial injury (TMI and nTMi) and a non-acute but diseased control (cCAD) ( Table 1 ). The criteria were chosen to maximize analytical group specificity with the expectation of differences in both clinical features and pathobiology ( Table 1 , Figure 2 ). The patients themselves served as their own controls, from the time of the acute event (time of invasive coronary angiography for acute myocardial injury or chronic coronary atherosclerosis) to the quiescent state (stable for ≥3 months). This study design allows for the identification of characteristics specific to the acute clinical event (within patients) and differences between event types by comparison between myocardial injury patient types. As compared with TMI, individuals with acute nTMi serve as control for ischemia/necrosis; and individuals with cCAD serve as control for the underlying disease state, atherosclerosis, and diagnostic evaluation (cardiac catheterization).

Table 1 . Description of study analytical phenotypes (cCAD, TMI, nTMi).

Figure 2 . Flowchart for inclusion of patients into cCAD, TMI, and nTMi analytical groups.

2.2.1 Acute thrombotic MI and acute non-thrombotic myocardial injury

Enrollment criteria for acute myocardial injury, which includes TMI and nTMi groups, required that each patient be >18 years of age and scheduled for non-elective coronary angiography within 48 h after admission. Those enrolled in either of the acute phenotypes must have had at least one of the following four criteria: (1) new or presumably new ST-segment depression >0.1 mV; (2) elevated cardiac troponin I >99th percentile for a healthy reference population specific to the assay used and >30% elevation from lowest acute-phase troponin to peak troponin within 24 h of enrollment; (3) ≥1 mm ST-segment elevation in ≥2 contiguous electrocardiogram (ECG) leads; or (4) ≥1 mm ST-segment depression in V1 and V2 (posterior wall infarct) ( Table 1 ) ( 24 ). Patients who received fibrinolysis were not eligible. All troponin measurements were performed in a Clinical Laboratory Improvement Amendments certified laboratory.

The criteria for differentiating between TMI and nTMi were based upon those previously proposed by our group ( 31 ), as described in Table 1 . The definition of TMI included the criteria for acute myocardial injury as well as presence of a histologically confirmed coronary thrombus (by blinded pathological assessment, CVPath Institute, Inc., Gaithersburg, MD, USA) ( Table 1 ). nTMi was defined as meeting the same four criteria for acute myocardial injury as TMI, but with no recovery of a histologically confirmed thrombus, and satisfaction of all of the following six criteria in all coronary vessels via core laboratory blinded angiogram assessment: (1) no stenosis greater than 50%, (2) no filling defects, (3) no abrupt vessel cutoff with persistence of contrast, (4) no intraluminal staining, (5) thrombolysis in myocardial infarction (TIMI) flow = 3, and (6) TIMI myocardial perfusion grade (TIMI MPG) = 3 ( 24 ) ( Table 1 , Figure 2 ).

2.2.2 Chronic coronary atherosclerotic disease

Patients enrolled in the suspected cCAD group were required to have presented for coronary angiography as an elective procedure, with evidence of significant coronary atherosclerosis with stenosis greater than 50% in at least one coronary vessel; or had a past medical history of atherosclerosis as evidenced by coronary artery bypass graft (CABG), percutaneous coronary intervention (PCI), stroke/ transient ischemic attack (TIA), carotid endarterectomy, peripheral artery bypass procedure, or abdominal aortic aneurysm repair. Additional criteria included normal TIMI flow and TIMI MPG in all vessels via core laboratory blinded angiogram assessment as well as pre-procedure cardiac troponin I <99th percentile for a healthy reference population specific to the assay used. Patients in the suspected cCAD group were excluded on the basis of any one of the following criteria: (1) hospitalization for acute coronary syndrome or clinical instability within 4 weeks prior to planned enrollment; CABG within 1 year prior to planned enrollment; or PCI, stroke, or TIA within 12 weeks prior to planned enrollment; (2) presence of unstable angina or symptoms refractory to maximal medical therapy; (3) presence of significant comorbidities likely to cause death within 2 years; (4) significant active history of substance abuse within 5 years of enrollment; or (5) unable to return to the medical campus for a 3-month stable follow-up ( Table 1 , Figure 2 ). Acute samples of patients with cCAD were censored, after T0, if troponin level increased >99th percentile for a healthy reference population specific to the assay used. This step excluded patient samples after a type 4 myocardial infarction, related to percutaneous coronary intervention.

2.3 Sample collection and preparation for DSC analysis

Samples from a total of 115 patients (cCAD, TMI, and nTMi) were collected at multiple acute-phase time points (T0, T2, T4, T24, T48) as well as a follow-up (Tfu) when the patient was in a stable state. Enrollment sample collection via an arterial sheath took place at the time of the coronary angiography after a 5–10 ml waste draw. All available follow-up samples (T2, T4, T24, T48, and ≥3 months) were collected from a peripheral vein, utilizing a blood pressure cuff as a gentle tourniquet (maximum pressure of <40 mmHg), after >10 ml of clinical blood collection or waste draw, and into a tube containing ethylenediamine tetraacetic acid (EDTA). Plasma was processed with a standardized protocol 45 min after collection.

Longitudinal plasma samples encompassing multiple time points during the acute time course (T0, T2, T4, T24, and T48) and a stable cardiac state at the 3–12-month follow-up (Tfu) were randomly batched into sets of 14 samples to ensure all sample handling and data collection could be completed within 7 days after sample thawing. We previously validated all aspects of our experimental approach for the analysis of plasma samples (specimen processing and storage, sample preparation and batching for DSC analysis, instrument settings and analysis replicates, and data processing) across thousands of analyses ( 32 ). Each batch of samples was prepared for DSC analysis by dialyzing against a standard phosphate buffer (1.7 mM KH 2 PO 4 , 8.3 mM K 2 HPO 4 , 150 mM NaCl, 14.7 mM sodium citrate, pH 7.5) to achieve normalization of buffer conditions for all samples. Specifically, each plasma sample (150–200 µl) was split between two Slide-A-Lyzer MINI dialysis units (MWCO 3500, 0.1 ml; Pierce, Rockford, IL, USA) and dialyzed at 4 °C against 1 L of phosphate buffer for a total dialysis time of 24 h, with buffer changes after 3 h of dialysis, then after two periods of 4 h, followed by a final overnight dialysis period of 14 h. After dialysis, the samples were recovered from dialysis units and filtered to remove particulates using centrifuge tube filters (0.45 μm cellulose acetate; Pall Corporation, New York, NY, USA). The final dialysis buffer was also filtered (0.2 μm polyethersulfone; Pall Corporation) and used for all sample dilutions and as a reference solution for DSC studies. Dialyzed samples were diluted 25-fold with a final dialysis buffer to obtain a suitable protein concentration for DSC analysis (∼ 2 mg/ml). The exact protein concentration of each plasma sample analyzed by DSC was determined using the bicinchoninic acid protein assay kit microplate protocol (Pierce), using absorbance measurements taken with a Tecan Spark plate reader (Tecan US, Research Triangle Park, NC, USA).

2.4 TLB profile determination

TLB profiles were generated from DSC data collected with a Nano DSC Autosampler System (TA instruments, New Castle, DE, USA), which was serviced according to the manufacturer's procedures. Interim instrument performance was assessed using the biological standard lysozyme and was within the manufacturer's specifications. The plasma samples and matched final dialysis buffer to load the instrument sample and reference chambers, respectively, were transferred to 96-well plates and loaded into the instrument autosampler maintained at 4 °C until analysis. Sample volumes of 950 μl were required to provide sufficient volume to ensure proper rinsing and filling of the 300 μl thermal sensing area. DSC scans were recorded from 20 to 110 °C at a scan rate of 1 °C/min following a pre-scan equilibration period of 900 s at 20 °C. The instrument was cycled overnight by running multiple water scans followed the next morning by at least three buffer scans to condition the instrument chambers before running the batch of samples. Buffer scans collected at the beginning and end of a sample set and after single or consecutive sample scans were examined to determine acceptable reproducibility and effective rinsing of the instrument chambers. Duplicate DSC scans were obtained for each of the TLB profiles shown in the results to ensure the profile was reproducible. Raw DSC scans were corrected for instrument baseline by subtraction of a suitable buffer reference scan, normalized for sample protein concentrations, and corrected for non-zero sample baselines by application of a linear baseline function using Origin 7 software (OriginLab Corporation, Northampton, MA, USA). TLB profiles were plotted as excess specific heat capacity, C p ex (cal/°C.g), vs. temperature (°C) with final analysis performed on a temperature range of 45–90 °C with an interval size of 0.1 °C.

2.5 Statistical analysis and data visualization

The baseline characteristics of the patient cohort grouped into three myocardial injury groups were summarized with mean and standard deviation, or median, first quartile (25th percentile) and third quartile (75th percentile), if the distribution showed substantial visual evidence of non-normality or skew. Categorical characteristics were summarized with frequency and proportion within each study group. Since the analytical cohorts were different by design, statistical testing of differences was not performed.

A panel of 19 TLB metrics ( Figure 3 ) was utilized to characterize all TLB profiles at baseline (T0) and quiescent phase (Tfu) time points. The changes in all 19 TLB metrics within patients, between the quiescent state and the acute phase presentation (ΔTfu − T0), were also evaluated. The 19 TLB metrics were as follows: peak amplitudes corresponding to the temperature region 60–66 °C ( Peak 1 ), 67–73 °C ( Peak 2 ), and 73–81 °C ( Peak 3 ); the temperature of Peak 1 ( T Peak 1 ), Peak 2 ( T Peak 2 ), and Peak 3 ( T Peak 3 ); the ratio of Peak 1 and Peak 2 amplitudes ( Peak 1/Peak 2 ); the ratio of Peak 1 and Peak 3 amplitudes ( Peak 1/Peak 3 ); the ratio of Peak 2 and Peak 3 amplitudes ( Peak 2/Peak 3 ); the minimum (valley) between Peak 1 and Peak 2 ( V1.2 ); temperature of V1.2 ( T V1.2 ); the ratio of V1.2 and Peak 1 ( V1.2/Peak 1 ); the ratio of V1.2 and Peak 2 ( V1.2/Peak 2 ); the ratio of V1.2 and Peak 3 ( V1.2/Peak 3 ); the maximum TLB profile amplitude ( Max ); the temperature of Max ( T Max ); the first moment temperature ( T FM ); TLB profile width at half height ( Width ); and the total area of the TLB profile ( Area ). Peak identification was based on a predetermined temperature range of three major transition ranges typically observed in TLB profiles—a major transition ( Peak 1 ) in the range 60–66 °C, a smaller transition ( Peak 2 ) in the range 67–73 °C, and a shoulder transition ( Peak 3 ) in the range 73–81 °C, within which the maximum amplitude was recognized as the peak position ( 12 , 28 , 33 , 34 ). The valleys were determined by locating the lowest amplitude between any two given peaks. All TLB metrics were derived using the tlbparam R package available at http://www.github.com/BuscagliaR/tlbparam .

Figure 3 . Representation of TLB metrics evaluated in this study. TLB profile width at half height ( Width ); total area of the TLB profile ( Area ), maximum profile amplitude ( Max ); temperature of the maximum profile amplitude ( T Max ); first moment temperature ( T FM ); peak amplitudes corresponding to the temperature regions 60–66 °C ( Peak 1 ), 67–73 °C ( Peak 2 ), and 73–81 °C ( Peak 3 ); temperature of Peak 1 ( T Peak 1 ), Peak 2 ( T Peak 2 ), and Peak 3 ( T Peak 3 ); the minimum (valley) between Peak 1 and Peak 2 ( V1.2 ); and temperature of V1.2 ( T V1.2 ).

Non-parametric testing was used to alleviate failed normality assumptions of linear models. The Kruskal–Wallis test with correction for multiple comparisons was utilized to determine if there was evidence of differences in TLB metric distribution across the patient groups. Statistical significance indicates that the values of a TLB metric are consistently larger/smaller in at least one group, suggesting a systematic difference in the metric's distribution by patient group. To explore pairwise differences between groups, the Wilcoxon signed-rank test was utilized, with group comparisons visualized by box and whisker plots.

Using the full TLB profile, the ability to differentiate myocardial injury type at baseline (T0) was investigated using unsupervised methodologies that required no a priori assumption regarding patient status via clustering of patient TLB profiles related to an acute myocardial injury event, followed by an assessment of cluster purity and characteristics. Importantly, the clustering process exclusively utilized only TLB profiles, remaining unaffected by additional clinical factors or patient information, such as myocardial injury phenotype. The numbers of clusters were assessed based on within-sum-of-squares and silhouette analysis, providing independent measures of the optimal number of clusters ( Supplementary Figure S1 ). Final unsupervised clusters were chosen based on cluster statistics and cluster purity.

All statistical conclusions were based on a 5% significance level. The analyses reported in the current work were conducted using the statistical programming language R and the following packages: dplyr , tidyr , ggplot2 , stat , and factoextra ( 35 – 37 ).

The baseline cohort characteristics for the three patient groups analyzed, acute TMI ( n  = 60), acute nTMi ( n  = 20), and cCAD ( n  = 35), are displayed in Table 2 . Patients with TMI were younger, predominantly male, with more being smokers as compared with patients with cCAD or nTMi. Patients with cCAD were more likely to be White, former smokers, dyslipidemic, diabetic, hypertensive, and had a prior history of atherosclerosis, heart failure, and lower platelet counts as compared with patients with TMI or nTMi. At baseline, ST elevation was observed in 78% of the patients with TMI, and 30% of the patients with nTMi ( Table 2 ). Differences in history of atherosclerosis, coronary stenosis ≥75%, median troponin at enrollment, and peak troponin varied as expected based on the criteria used to define the study cohorts. From baseline to T48, 88% of the patients with TMI had a peak troponin >100 times the URL. Most of the patients with nTMi fell within the range of 10–100 times the URL, whereas patients with cCAD had peak troponin below the threshold of 1 URL.

Table 2 . Characteristics of the patients in the study cohort.

3.1 Baseline TLB profiles

By visual inspection, median TLB profiles demonstrate regions of differentiation among all three patient groups at baseline (T0) ( Figure 4 ). At the time of an acute event (T0), both TMI and nTMi had a lower Peak 1 as compared with cCAD, and TMI had a higher Peak 3 as compared with both cCAD and nTMi.

Figure 4 . Time-course evaluation of myocardial injury phenotypes presented as median TLB profile (black) and 95% quantile interval (gold shading). Note: TLB profiles of blood plasma samples are commonly characterized by three main transitions: a major transition ( Peak 1 , 60–66 °C), with a smaller transition ( Peak 2 , 67–73 °C), and a shoulder transition ( Peak 3 , 73–81 °C), as represented by the gray, orange, and blue bands, respectively.

Among the 19 TLB metrics, 13 were different between at least two of the patient groups at T0 ( Table 3 ). Differences in 10 out of the 13 metrics, Peak 1 , Peak 3 , T Peak 2 , Peak 1 / Peak 2 , Peak 1/Peak 3 , Peak 2/Peak 3 , V1.2/Peak 2 , V1.2/Peak 3 , T FM , and T Max , were observed between patients with TMI vs. those with cCAD or nTMi vs. patients with cCAD at T0 ( Figure 5 , Supplementary Figure S2 ). Differences in three out of 13 metrics were observed between patients with TMI and those with cCAD at T0. Importantly, one metric, T FM , showed significant differences between all three groups and was able to distinguish TMI from cCAD, nTMi from cCAD, as well as TMI from nTMi at T0 ( Figure 5 , Supplementary Tables S1, S2 ). The TLB profiles grouped by clinical phenotype at T0 are provided in Supplementary Figure S3 .

Table 3 . Summary of the analysis assessing differences in distributions of the values of 19 TLB metrics across myocardial injury patient groups.

Figure 5 . Box plots illustrating selected TLB profile metrics at baseline (T0) and quiescent phase (Tfu) comparing cCAD, TMI, and nTMi. Among the 19 evaluated TLB metrics, T FM , Peak 3, and Peak 1/Peak 3 emerge as the most clinically significant, with the potential to differentiate between TMI and nTMi. Pairwise Wilcoxon signed-rank tests demonstrate distinct TLB differentiation at T0 among all three groups in ( A ) while ( B , C ) differentiate acute myocardial injury (TMI and nTMi) from cCAD at T0. ( D–F ) show a similar distribution of TLB metric values among all groups at Tfu. Note: T FM : first moment temperature; Peak 3 : peak amplitude corresponding to the temperature region 73–81 °C; Peak 1/Peak 3 : ratio of Peak 1 and Peak 3 amplitudes.

3.2 Quiescent phase TLB profiles

By visual inspection, median TLB profiles are similar for all three patient types (cCAD, TMI, nTMi) at the quiescent phase (Tfu) ( Figure 4 ). TLB profiles at Tfu, following the resolution of the acute myocardial injury event, have a large Peak 1 amplitude, a lower Peak 2 amplitude, and a small Peak 3 shoulder. Patients with cCAD maintain the least profile variability across the time course, in contrast to both those with TMI or nTMi. In addition, all 19 TLB metrics were found to have no statistical differences in distribution across the myocardial injury groups at Tfu ( Figure 4 , Table 3 ).

3.3 Time-course TLB profiles: contrasting baseline and quiescent phase

Changes between baseline and quiescent phase, within the patient groups, are least pronounced in the patients with cCAD as compared with those with TMI or nTMi. An acute disease state results in a diminished Peak 1 and more prominent Peaks 2 and 3 , compared with a dominant Peak 1 TLB signature for the quiescent state ( Figure 4 ). The time course represents an enriched view for tracking changes in myocardial injury, with all patient groups demonstrating recovery of the dominant Peak 1 TLB signature at the quiescent time point. The TLB of patients with TMI demonstrated a highly diminished Peak 1 and elevated Peaks 2 and 3 at T0 as compared with the group of patients with non-acute diseased cCAD that received the same invasive diagnostic angiogram at T0 but were not having an acute myocardial event. For patients with nTMi, the TLB profile at T0 was also distinct from those with cCAD, with a diminished Peak 1 and elevated Peak 2 , and was further distinct from those with TMI with distinctive time-dependent changes of the TLB profile. The time course for both TMI and nTMi demonstrated a gradual change in the median TLB profile to the quiescent state TLB profile, but with differences in the dynamics of the recovery of the dominant Peak 1 TLB signature across the time course.

Figure 6 presents the mean TLB difference profile observed for the differences between Tfu and T0 TLB profiles. cCAD showed a smaller mean change between T0 and Tfu with a lower amplitude of Peak 1 at T0, with minimal change in the Peak 2 and Peak 3 regions. TMI showed the most extreme mean differences, with a large positive change in Peak 1 and a large negative change in Peak 3 between T0 and Tfu time points. This reflects a change from a depressed Peak 1 and large 80 °C peak at T0, to a TLB signature with a prominent Peak 1 and no significant signal at 80 °C at Tfu. nTMi shows diversity from these groups in its difference in the region between 68 and 75 °C, while having a slightly smaller change in Peak 1 compared with TMI. The TLB difference profiles for all patients having paired TLB profiles (Tfu − T0) are presented in Supplementary Figure S4 .

Figure 6 . Mean difference TLB profiles between stable cardiac state at the 3–12-month follow-up (Tfu) and baseline (T0) colored by myocardial injury phenotype.

Among the 19 TLB metrics assessed across the three study groups, five metrics showed distinct changes from T0 to Tfu, including T FM , T Max , Peak 1 , Peak 3 , and T V1.2 ( Supplementary Tables S1, S2 ).

Unsupervised clustering was employed to identify unique groupings of TLB profiles. Through the use of k-means clustering, it was determined that an optimal cluster size included k  = 3 cluster centers, with the assessment across cluster sizes provided in Supplementary Figure S1. The finalized clusters are presented in Figure 7 colored by clinical phenotype, with phenotype purity presented in Table 4 . Cluster 1 predominantly comprises patients with cCAD (47%) but includes those with TMI (34.8%) and nTMi (18.2%) as well. Cluster 2 contains a mix of all patient groups but is predominantly TMI (>70%). It is characterized by a loss of Peak 1 definition, and a tendency to shift toward higher peak temperatures. Cluster 3 shows a distinct pattern, unlike the other clusters, having small Peak 1 and Peak 2 amplitudes, with a dominant and clearly defined 80 °C peak rarely observed at such a large amplitude. Cluster 3 only contains patients with TMI, with a TLB profile distinct from that observed within the Cluster 2 patients with TMI who show substantial shifting of the TLB profile without the development of the 80 °C peak.

Figure 7 . Faceted TLB profile clusters (arbitrarily labeled clusters 1, 2, and 3) for three k-mean centers at baseline (T0). The columns correspond to the unsupervised cluster label. All samples within a given cluster are shown colored by clinical phenotype. The phenotypes and corresponding colors are provided in the legend.

Table 4 . Cluster purities for three k-mean centers at baseline (T0).

4 Discussion

This study demonstrates the potential utility of TLB as a serological assay for distinguishing and characterizing myocardial injury events. The key findings include the following: (1) distinctive patterns in TLB profile among the three clinically relevant myocardial injury groups (cCAD, TMI, nTMi) at the time of acute event/evaluation; (2) the TLB profile was substantially altered for TMI and nTMi at the time of the acute event compared with the quiescent phase; (3) relatively less pronounced change in TLB profile from the time of the acute evaluation to the quiescent phase for the cCAD group as compared with the TMI and nTMi; (4) at quiescent phase, TLB profiles for all three patient groups were similar; (5) TLB characteristics can differentiate acute events (TMI/nTMi) from cCAD; (6) an identifiable TLB signature unique to the TMI group is observed in unsupervised clustering and with one TLB metric. Understanding the pathobiology of acute myocardial injury phenotypes has the potential to foster the development of innovative diagnostic, prognostic, preventative, and therapeutic modalities specific to etiologically unique and clinically important disease phenotypes.

The distinct TLB profiles observed during event presentation among myocardial injury groups underscores the diverse pathobiology within the three patient groups (cCAD, TMI, nTMi). However, the areas of similarities in TLB profiles between TMI and nTMi may indicate underlying TLB-captured mechanisms of shared resultant myocardial injuries. The current diagnostic criteria for acute MI lack the ability to delineate the cause of MI in a clinically actionable manner, resulting in non-specific treatments and missed opportunities to intervene prior to irreversible myocardial necrosis, even with the inclusion of high-sensitivity cardiac troponin (hs-cTn) ( 38 ).

The current study identified one TLB metric ( T FM ) that distinguished between TMI, nTMi, and cCAD. This TLB metric may be reflective of the specific pathobiological state, including plaque disruption and atherothrombosis, that is distinct from the shared biology of myocardial injury and chronic atherosclerosis. These differences may be further investigated by combining proteomic, lipidomic, or metabolomic data with the TLB profile signatures ( 18 ). In a prior investigation conducted by our group, we characterized 1,032 plasma metabolites by mass spectrometry in a subset of the patients with TMI, nTMi, and cCAD. We identified a 17-metabolite model that was able to uniquely identify TMI, nTMi, and cCAD at the time of acute myocardial injury event or stable disease evaluation ( 30 ). The robust application of TLB in conjunction with biochemical data has identified biochemical mechanisms to better understand thermal stability shifts in major plasma proteins in multiple myeloma phenotypes ( 15 ). Similarly, the TLB approach led to a TLB-based prognostic classification for early renal function decline in type 1 diabetes ( 27 ) and differentiation of premalignant from benign pancreatic cysts ( 39 ). In addition, several proof-of-principle studies demonstrated distinctive TLB signatures for patients with glioblastoma ( 21 ), melanoma ( 32 ), and psoriasis ( 25 ), indicating the potential utility of TLB as a minimally invasive monitoring tool for such diseases. Interestingly, Velazquez-Campoy et al. ( 32 ) observed a similar TLB profile for melanoma patients with no evidence of disease and healthy controls, demonstrating TLB as a useful tool for monitoring disease remission, and response to treatment. Although healthy controls were not evaluated in this study, the time-course TLB profiles for our cCAD control group and quiescent stage follow-up (Tfu) for all three patient groups were similar to the dominant Peak 1 TLB signature for quiescent/control groups reported from other previous TLB studies ( 12 , 14 , 15 , 32 ).

Our study was limited by sample size but mitigated by our unique study design that used patients as their own controls to identify change from the time of an acute event to a quiescent state, and compared this with a control group of patients with cCAD with the same underlying disease state (atherosclerosis) who were undergoing the same diagnostic procedure (invasive angiography). A larger sample size would allow for more in-depth analysis of TLB profiles related to clinical factors at the time of acute myocardial injury. Another limitation is that the differentiation of patients with myocardial injuries might be related to the magnitude of myocardial damage. Further studies are warranted to better understand the association between TLB signatures and patients with myocardial injuries, irrespective of the extent of myocardial injuries indicated by peak troponin levels. An additional limitation was that the clustering results of TLB profiles showed impurities in the differentiation of cCAD and nTMi from the pervasive TMI phenotype; however, larger sample numbers could allow for additional machine learning and statistical approaches that improve diagnostic performance. Findings from this study warrant further investigation in larger cohorts given the potential for TLB, and the combination of TLB with additional omics datasets, to provide complementary diagnostic approaches and new insights into the biological underpinnings of distinct, clinically relevant myocardial injury events.

5 Conclusion

This study represents the first report of the application of TLB as a sensitive and data-rich technique to be explored in the identification and differentiation of acute myocardial injury etiological subtypes.

Data availability statement

The datasets presented in this article are not readily available because they are being utilized to develop clinically available diagnostics tests/diagnostic aids for the identification and classification of acute myocardial injury. Commercial partnership is fostering this research. All data requests will be individually reviewed and honored in a fashion that is specific to the question being asked without divulging trade secrets. Requests to access the datasets should be directed to the corresponding authors at [email protected] or [email protected].

Ethics statement

The studies involving humans were approved by University of Louisville Internal Review Board. The studies were conducted in accordance with the local legislation and institutional requirements. The participants provided their written informed consent to participate in this study.

Author contributions

KL: Conceptualization, Data curation, Visualization, Writing – review & editing. RB: Conceptualization, Data curation, Visualization, Writing – review & editing, Formal Analysis, Investigation, Methodology, Software, Supervision, Validation, Writing – original draft. PT: Conceptualization, Data curation, Formal Analysis, Investigation, Methodology, Supervision, Writing – review & editing, Validation. ST: Conceptualization, Data curation, Visualization, Writing – review & editing. AK: Data curation, Investigation, Validation, Writing – review & editing. ADF: Conceptualization, Data curation, Funding acquisition, Investigation, Methodology, Project administration, Resources, Supervision, Visualization, Writing – review & editing, Validation. NG: Conceptualization, Data curation, Funding acquisition, Investigation, Methodology, Project administration, Resources, Supervision, Validation, Writing – review & editing.

The authors declare that financial support was received for the research, authorship, and/or publication of this article.

This work was supported by a grant to NCG from the National Institute of Allergy and Infectious Diseases under award number R01AI129959. This work was supported in part by grants from the American Heart Association (11CRP7300003) and the National Institute of General Medical Sciences (P20GM103492 and SC1GM139730). The funders were not involved in the study design, collection, analysis and interpretation of data, the writing of this article, or the decision to submit it for publication.

Acknowledgments

The authors thank all of the study patients.

Conflict of interest

ADF and NG are co-inventors of a patent assigned to and owned by the University of Louisville describing the use of DSC to differentially diagnose myocardial infarction types (US Patent No. 11,835,529). During study completion and analysis, ADF and NG were founders and had an equity interest in a start-up company, DSC Technologies LLC, which was involved in the development of DSC technologies; NG was a consultant for the calorimetry instrument supplier TA Instruments, Inc. involved in education for microcalorimetry applications and the characterization of microcalorimetry instrument performance.

The remaining 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.

Supplementary material

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fcvm.2024.1342255/full#supplementary-material .

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Keywords: thermal liquid biopsy, myocardial injury, myocardial infarction, unsupervised clustering analysis, longitudinal study

Citation: Lidani KCF, Buscaglia R, Trainor PJ, Tomar S, Kaliappan A, DeFilippis AP and Garbett NC (2024) Characterization of myocardial injury phenotype by thermal liquid biopsy. Front. Cardiovasc. Med. 11:1342255. doi: 10.3389/fcvm.2024.1342255

Received: 28 November 2023; Accepted: 18 March 2024; Published: 4 April 2024.

Reviewed by:

© 2024 Lidani, Buscaglia, Trainor, Tomar, Kaliappan, DeFilippis and Garbett. 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: Andrew P. DeFilippis [email protected] Nichola C. Garbett [email protected]

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• Published: 27 March 2024

S100A8/A9 as a prognostic biomarker with causal effects for post-acute myocardial infarction heart failure

• Jie Ma 1 , 2   na1 ,
• Yang Li 1 , 2   na1 ,
• Ping Li 1   na1 ,
• Xinying Yang 1 , 2 ,
• Shuolin Zhu 1 , 2 ,
• Ke Ma 1 , 2 ,
• Fei Gao 1 ,
• Hai Gao 1 ,
• Hui Zhang 3 ,
• Xin-liang Ma   ORCID: orcid.org/0000-0002-9041-0876 4 ,
• Jie Du 1 , 2 &
• Yulin Li   ORCID: orcid.org/0000-0001-7909-0763 1 , 2  

Nature Communications volume  15 , Article number:  2701 ( 2024 ) Cite this article

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• Myocardial infarction
• Predictive markers

Heart failure is the prevalent complication of acute myocardial infarction. We aim to identify a biomarker for heart failure post-acute myocardial infarction. This observational study includes 1062 and 1043 patients with acute myocardial infarction in the discovery and validation cohorts, respectively. The outcomes are in-hospital and long-term heart failure events. S100A8/A9 is screened out through proteomic analysis, and elevated circulating S100A8/A9 is independently associated with heart failure in discovery and validation cohorts. Furthermore, the predictive value of S100A8/A9 is superior to the traditional biomarkers, and the addition of S100A8/A9 improves the risk estimation using traditional risk factors. We finally report causal effect of S100A8/A9 on heart failure in three independent cohorts using Mendelian randomization approach. Here, we show that S100A8/A9 is a predictor and potentially causal medicator for heart failure post-acute myocardial infarction.

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Introduction

Despite therapeutic advancements, patients with acute myocardial infarction (AMI) exhibit a high risk of adverse cardiovascular outcomes 1 . Heart failure (HF), a common complication following the first AMI 2 , is strongly associated with reduced in-hospital and long-term survival 3 . Current guidelines recommend that patients undergo early post-AMI risk assessment for appropriate therapy provision. Early and accurate risk stratification to identify patients with impending HF is crucial to guide treatment and improve prognosis 4 , 5 . Clinical risk scores, including the Thrombolysis in Myocardial Infarction and Global Registry of Acute Coronary Events scores, can only predict recurrent myocardial infarction (MI) and death 6 , 7 . Existing clinical biomarkers, including myocardial necrosis (cardiac troponin I, cTnI), stress (B-type natriuretic peptide, BNP), and inflammation (high-sensitivity C-reactive protein, hs-CRP), are insufficient for precise HF prediction in patients with AMI 5 . Most existing biomarkers only demonstrate correlation, rather than a causal relationship, with HF, and may emerge because of confounders or reverse causation, allowing them to act as passive bystanders rather than drivers of HF. If a biomarker is causally associated with HF, it helps predict HF, and serves as the target of intervention.

Unlike traditional hypothesis-driven approaches, unbiased and high-throughput strategies enable the discovery of novel biomarkers with improved predictions and novel mechanisms that provide insights into disease development. Proteomic profiles represent sources of new candidate biomarkers with diagnostic and prognostic value 8 . Mendelian randomization (MR) study is used to infer causality from observational data because germline genetic variations are defined at conception and are generally not associated with conventional confounders in observational studies 9 . If genetically predicted portion of biomarker is associated with the outcome and meets several strict assumptions, the measured marker might has a causal effect on outcomes.

In this study, we aim to identify biomarkers associated with HF development in patients with AMI. We select S100A8/A9 as a potential biomarker using proteomic analyses. S100A8 and S100A9, as endogenous alarmins, are constitutively expressed in myeloid cells and stored as granules that are ready to be released in response to infectious or bacteria-free inflammation. They exist in several forms but preferentially form a heterodimeric complex of S100A8/A9, which is necessary for their biological effects 10 . The secretion of S100A8/A9 is partly dependent on the reactive oxygen species (ROS) and potassium Efflux. S100A8/A9 is also released during NETosis. In the MI setting, both excessive ROS and NETosis are conductive to S100A8/A9 release into the heart and circulation 11 . Consequently, we prospectively validate the predictive values of S100A8/A9 in two independent cohorts and evaluate the causal relationship between S100A8/A9 and post-AMI HF.

Patient characteristics

The study design is shown in Fig.  1 and includes three steps. The clinicopathological characteristics of patients with AMI ( n  = 20) and healthy controls (HCs) ( n  = 10) in step 1 are presented in Supplementary Table  1 . Baseline information of the discovery ( n  = 1062) and validation ( n  = 1043) cohorts in steps 2 and 3 was displayed by HF status (Table  1 , Supplementary Fig.  1 ). The proportions of ST-segment myocardial infraction (STEMI) vs. non-ST-segment myocardial infraction (NSTEMI) were 75% vs. 25% in discovery cohort and 61.6% vs. 38.4% in validation cohort. During follow-up, 118 in-hospital (11%) and 178 long-term (17%) HF events were recorded in the discovery cohort, and 110 in-hospital (11%) and 82 long-term (8%) HF events were recorded in the validation cohort (Supplementary Tables  2 and 3 ). In the discovery cohort, patients with HF were older; had a higher proportion of Killip classification III; higher serum creatinine, fasting glucose, neutrophil counts, and biomarkers (cTnI, BNP, hs-CRP) levels; lower blood pressure (systolic and diastolic blood pressure) and left ventricular ejection fraction (LVEF); larger infarct size; and more left main lesions. Medications and multivessel disease were similar between patients with and without HF. In the validation cohort, patients with AMI and HF had lower blood pressure, larger infarct size, and higher creatinine, neutrophil counts, and biomarker levels. The incidence of HF events was higher in the discovery cohort than in the validation cohort (28% vs. 18%) owing to the longer follow-up period.

This study comprised the following three steps: (1) HF-related biological processes and proteins were identified using serum proteomics in a cross-sectional set of patients with AMI who developed HF during hospitalization ( n  = 10), patients with AMI without HF ( n  = 10), and HCs ( n  = 10). (2) The association between candidate proteins and HF was prospectively evaluated in the discovery (HF: n  = 296, no-HF: n  = 766) and validation (HF: n  = 192, no-HF: n  = 851) cohorts. (3) The causal relationship between HF-associated proteins and HF was confirmed using MR analysis. For individual-level one-sample MR analysis, the causal association of genetic instruments with post-AMI HF events was assessed in the validation cohort (HF: n  = 192, no-HF: n  = 851). For the two-sample MR analysis, estimates of the association between the genetic instruments and S100A8/A9 levels from the validation cohort ( n  = 1043) and the association between the genetic instruments and post-AMI HF from the UKB cohort ( n  = 1144) were used to examine the causal effect of S100A8/A9 levels on post-AMI HF. Moreover, a statistical summary of the association between genetic instruments and S100A9 levels from the validation cohort and a statistical summary of the GWAS from the finn-b-I9_HEARTFAIL study ( n  = 208178) were used to evaluate the causal association between genetic instruments and general HF. AMI acute myocardial infarction, HC healthy control, HF heart failure, MR Mendelian randomization, UKB UK Biobank.

Some differences in HF risk characteristics can be observed among patients in the validation cohort compared to those in the discovery cohort (Supplementary Table  4 ). For example, the validation cohort had more younger and male patients, higher systolic blood pressure at admission, and fewer patients with a history of comorbidities than the discovery cohort. Several laboratory indicators, including glucose, lipid, creatinine, BNP, cTnI, and CRP levels, were also significantly lower in the validation cohort than in the discovery cohort.

The baseline information of the general population ( n  = 588) in step 3 is presented in Supplementary Table  5 .

Screening of prognostic biomarkers

For non-biased screening of HF-related proteins, we quantified 1000 proteins in serum samples from 20 patients with AMI (HF, n  = 10; no-HF, n  = 10) and ten HCs at admission (Supplementary Fig.  2 , Supplementary Data  1 ). Differential expression analysis identified 362 differentially expressed proteins (DEPs) in patients with AMI vs. HCs, 142 DEPs in patients without HF vs. those with HF development, and 134 overlapping HF-related DEPs between the two comparisons. Among them, 108 proteins had higher levels, while the remaining 26 proteins had lower levels in patients with HF than in HCs or patients with AMI without HF (Supplementary Fig.  3a ). The 134 HF-related proteins included those with established prognostic values for HF (BNP, CRP, cTnT, and several emerging candidates) (Supplementary Table  6 ). S100A12/A8/A9 were the top-ranked DEPs by either fold change or statistical significance in the HF vs. no-HF groups (Supplementary Table  6 ). Subsequently, in the least absolute shrinkage and selection operator regression analyses based on 134 HF-related proteins, the best prognostic candidates were S100A8/A9 and S100A12 (Supplementary Fig.  4 ). We investigated the potential biological functions of the HF-related proteins. In the Reactome enrichment analysis, HF-related proteins were primarily enriched in inflammatory pathways (false discovery rate < 0.05), including toll-like receptor (TLR) and interleukin signaling (Supplementary Fig.  3b ), suggesting an inflammatory activation signature in patients progressing to HF. Collectively, the serum proteomics suggests that S100A8/A9 and S100A12 are the best prognostic candidates for HF in patients with AMI.

Predictive values of S100A8/A9 and S100A12 for HF in discovery cohort

To examine the association between the candidate proteins and HF, we measured circulating S100A8/A9 and S100A12 levels at admission in the discovery cohort. S100A8/A9 and S100A12 were increased in patients with HF compared to patients without HF. The levels of S100A8/A9 and S100A12 were increased by 1.5-fold and 1.1-fold in patients with HF compared to those without HF, respectively (Fig.  2a and Supplementary Fig.  5a ). In univariable analysis, the hazard ratio (HR) for post-AMI HF per standard deviation (SD) of S100A8/A9 was 1.92 (1.69–2.19), P  < 0.001, and that of S100A12 was 1.09 (0.97–1.24), P  = 0.157 (Fig.  2b , Supplementary Table  7 ). In multivariate analysis, the association between S100A8/A9 and HF remained significant after adjustment for sex and significant clinical variables chosen from the univariable analysis ( P  < 0.05) (HR per SD: model 1: 2.05 [95% confidence interval [CI]: 1.80–2.35], P  < 0.001; model 2: 2.15 [95% CI: 1.88–2.46], P  < 0.001; model 3: 2.03 [95% CI: 1.77–2.33], P  < 0.001) (Fig.  2b ). The associated between S100A12 and HF was not significant after adjustment (HR per SD for model 3: 1.08 [95% CI: 0.95–1.22], P  = 0.261) (Supplementary Fig.  5b ). Spline regression adjusted for the variables in model 3 showed a positive linear dose-response association between S100A8/A9 and HF (Supplementary Fig.  6 ). Moreover, a significant competing risk of events was observed in post-AMI populations. For instance, death not caused by HF was a competing risk factor for post-AMI HF. After controlling for competitive risk events, the Fine-Gray (FG) model demonstrated that S100A8/A9 was associated with a higher incidence of post-AMI HF in the discovery cohort (HR: 2.03; 95% CI: 1.76–2.34; P  < 0.001). In the subgroup analysis, S100A8/A9 levels were independently associated with HF across all pre-specified subgroups, including age, sex, various complications, and infarction severity (Supplementary Fig.  7 ), implying that S100A8/A9 had no interaction with these factors.

S100A8/A9 levels distribution at admission in the HF events ( n  = 296) and non-HF events ( n  = 766) groups of discovery cohort ( a ), as well as in the HF events ( n  = 192) and non-HF events ( n  = 851) groups of validation cohort ( d ). Red, patients who experienced HF events; blue, patients who did not experience HF events. The scatter plots in this figure show the median (center line), 25th, and 75th percentile (lower and upper boundary). Differences between the two groups were analyzed using a two-sided Wilcoxon-test. P  < 0.05 was considered as significant. Unadjusted and adjusted HRs for HF events from Cox proportional hazards regression analysis were shown for discovery ( n  = 1062) and validation ( n  = 1043) cohort ( b and e ), which are indicated with the points and the bars showing the 95% confidence interval. S100A8/A9 was measured on admission. Model 1: adjusted for age and sex; model 2: adjusted for model 1+systolic blood pressure, Killip classification at admission, fasting glucose, creatinine, left main artery disease; model 3: adjusted for model 2+neutrophil count, cTnI, BNP, hs-CRP, left ventricular ejection fraction at admission and estimated infarct size (CK-MB AUC0-72). P -values reported are two-tailed from COX proportional-hazard regression analyses. P   < 0.05 was considered as significant. ROC curves for biomarkers levels at admission in the discovery ( c ) and validation ( f ) cohort for HF events. Blue, hs-CRP; green, cTnI; orange, BNP; yellow, S100A12; red, S100A8/A9. HF heart failure, HRs hazard ratios, ROC receiver operating characteristics. Source data are provided as a Source Data file.

The predictive value of S100A8/A9 was compared to that of existing clinical biomarkers/variables. In the receiver operating characteristic analysis, S100A8/A9 demonstrated better predictive capabilities for HF than cTnI, BNP, and hs-CRP (Fig.  2c ). Among the biomarkers, S100A8/A9 had the highest C-statistic (Supplementary Table  8 ). We further investigated whether adding S100A8/A9 to the known clinical variables would improve risk estimation. S100A8/A9 increased C-statistic significantly when added to the reference model (ΔC-statistic: 0.04 [95% CI: 0.02–0.05], P  < 0.001) in the discovery cohort (Table  2 ). The addition of S100A8/A9 improved reclassification of the reference model (net reclassification index [NRI]: 0.23 [95% CI: 0.12–0.32], P  < 0.001; HF NRI: 0.09 [95% CI: 0.01–0.12]; no-HF NRI: 0.14 [95% CI: 0.09–0.23]) (Table  2 and Supplementary Table  9 ). However, adding S100A12 to the reference model did not improve risk stratification. Collectively, S100A8/A9 provided incremental information on known clinical biomarkers/variables for HF prediction. We conducted decision curve analysis to gain a more precise understanding of the improvement in the model by adding S100A8/A9. Compared to the reference model, the model including S100A8/A9 would on average identify ∼ 34 additional cases, without identifying any additional false positives, in a population of 1000 patients with an incidence of HF events of 28% (Supplementary Fig. 8a ).

Validation of the predictive value of S100A8/A9 for HF in validation cohort

Circulating S100A8/A9 levels were an independent HF predictor after adjusting for the same variables in the validation cohort (HR: 2.15 [95% CI: 1.79–2.58], P  < 0.001), confirming S100A8/A9 as a robust HF predictor (Fig.  2d, e ). After controlling for competitive risk events, the FG model showed that S100A8/A9 was associated with a higher incidence of post-AMI HF in the validation cohort (HR: 2.14; 95% CI:1.76–2.60; P  < 0.001). Additionally, S100A8/A9 better predicted HF than cTnI, BNP, and hs-CRP (Fig.  2f and Supplementary Table  10 ). Furthermore, consistent with the results from the discovery cohort, adding S100A8/A9 to the reference model also improved risk stratification (Table  2 and Supplementary Table  11 ). Compared to the reference model, the model, including S100A8/A9, would on average identify ∼ 19 additional cases, without identifying any additional false positives, in a population of 1000 patients with an 18% incidence of HF events (Supplementary Fig. 8b ).

The Killip class is a specialized indicator of cardiac function in patients with AMI. We observed that the S100A8/A9 concentrations were higher in Killip class II and III patients than in Killip class I patients in the combined cohort (Supplementary Fig.  9 ). Due to the limited sample size in patients with Killip class III (20 and 14 in discovery and validation cohort), a rising trend was only observed between Killip class III and class II.

The follow-up time for both the discovery and validation cohorts extends beyond 2020, presenting the possibility of confounding from the COVID-19 pandemic on HF risk 12 . We performed a sensitivity analysis of the association between S100A8/A9 and HF before and after COVID-19 pandemic (January 1, 2020). S100A8/A9 levels remained independently associated with HF risk (Supplementary Table  12 ).

Kaplan–Meier curves illustrated that patients in the higher risk categories stratified by the quartile of S100A8/A9 exhibited a higher risk of post-AMI HF events in both cohorts. The cutoff values of S100A8/A9 in the high-risk group were 5059 ng/mL and 4877 ng/mL in the discovery and validation cohorts, respectively, suggesting that a S100A8/A9 level exceeding 5000 ng/mL may be a reference for a higher risk of HF (Fig.  3a, b ). Supplementary Table  13 illustrates S100A8/A9 plasma concentrations in relation to all the clinical characteristics.

Kaplan–Meier curves illustrate the timing of HF events in the four strata of S100A8/A9 levels and S100A8/A9 genetic score. The quartile of S100A8/A9 (ng/ml) was used to classify patients into four risk categories, including low, low-intermediate, intermediate-high, and high-risk in the discovery cohort ( a ) and validation cohort ( b ). Similarly, the quartile of the S100A8/A9 genetic score was used to classify patients in the validation cohort into four risk categories ( c ). The vertical black dashed line means the median follow-up time (4.2 and 2.9 years for discovery and validation cohorts respectively). The maximum follow-up time is 5.8 years and 5.3 years for discovery and validation cohorts respectively. P -values reported are two-tailed from log-rank tests. P  < 0.05 was considered as significant. Source data are provided in the Source Data File.

Causal relationship between S100A8/A9 and HF

We next explored whether elevated S100A8/A9 levels drive HF development. The causal effects of HF-associated proteins were firstly investigated using individual-level one-sample MR analysis. To establish genetic instruments associated with the S100A8/A9 level, we search for genome-wide association studies ( GWAS) summary statistics of plasma proteins for all 4907 aptamers at https://www.Decode.com/summarydata/ . However, we could not find GWAS summary statistics for S100A8/A9 or S100A8. Because S100A9 regulates S100A8/A9 complex functions through various mechanisms, including protecting S100A8 from degradation, and given that S100A9 levels were strongly correlated with S100A8/A9 levels in the plasma ( r  = 0.92, P  < 0.01, Supplementary Fig.  10 ), we selected 50 cis -protein quantitative single nucleotide polymorphisms (SNPs) of S100A9 from a GWAS of 35,559 Icelanders. The 24 SNPs with beta > 0 were associated with elevated S100A8/A9 concentrations, and 26 SNPs with beta < 0 were associated with decreased S100A8/A9 concentrations (Supplementary Table  14 ). Among the 50 protein quantitative trait loci (pQTLs), 20 with minor allele frequency >0.01 in East Asians were used for linkage disequilibrium analysis, and six tag SNPs ( r 2  < 0.8) were included in the S100A8/A9 genetic score (Supplementary Fig.  11 ). The genotype-tissue expression (GTEx) portal strongly supported that 17/20 pQTLs were associated with differential S100A8/A9 mRNA expression (Supplementary Tables  15 and 16 ). Because estimated effect of the SNPs on S100A8/A9 levels were obtained in Icelanders, and the genetic backgrounds of Icelanders and Chinese were heterogeneous, we confirmed the association between the genetic score and S100A8/A9 levels in a general Chinese population. Among these 588 HCs, the increase in the S100A8/A9 genetic score was significantly associated with the high-risk S100A8/A9 levels (refer to S100A8/A9 levels are greater than the mean plus one standard deviation of this cohort) (odds ratio [OR] per SD: 1.40 [95% CI: 1.11–1.76], P  = 0.004). Additionally, the association was not affected by age and sex adjustment (OR per SD: 1.39 [95% CI: 1.11–1.75]; P  = 0.005).

We then conducted genotyping for the six tag SNPs and calculated the S100A8/A9 genetic score for each individual in the validation cohort. Rs12033317 (β = 0.33) and rs12119788 (β = 0.89) were associated with increased S100A8/A9 levels, and rs1560832 (β = –0.52), rs3014874 (β = –0.47), rs3014875(β = –0.42), and rs59961408 (β = –0.16) were associated with decreased S100A8/A9 levels, consistent with the directions in the GWAS summary statistics. A scatter plot illustrated the positive association between the genetic score and S100A8/A9 plasma concentrations for validation cohort (Supplementary Fig.  12 ). For a 1-SD increase in S100A8/A9 genetic scorers, the incidence of S100A8/A9 levels exceeding 4877 ng/mL (cutoff value for high risk of HF) increased by 43%, both in raw data and after adjusting for sex and age. We also provided a Kaplan–Meier curve comparing the S100A8/A9 genetic score to HF (Fig.  3c ). At the median follow-up time, the patients in the higher risk categories stratified by the quartile of S100A8/A9 genetic score exhibited a higher risk of post-AMI HF events. The stratification was less significant due to the limited patients at the late follow-up time. We subsequently performed one-sample MR and observed that genetically predicted S100A8/A9 values were associated with HF (OR per SD: 1.20 [95% CI: 1.003–1.44], P  = 0.047) after adjusting for age, sex, systolic blood pressure, Killip classification at admission, fasting glucose, creatinine, left main artery disease, neutrophil count, cTnI, BNP, CRP, LVEF at admission, and estimated infarct size (CK-MB AUC 0– 72 ).

To confirm this result, we conducted a two-sample MR analysis and verified the causal effect of S100A8/A9 on post-AMI HF (Supplementary Fig.  13 ). In 1114 patients with AMI from the UK Biobank (UKB) database (Supplementary Table  17 ), owing to the absence of rs12119788, S100A8/A9 genetic scores comprised the other five SNPs. For a 1-SD increase in S100A8/A9 genetic scorers, the risk of HF increased by 30% (raw) and 29% (adjusted for sex and age). Two-sample MR analysis showed a genetically instrumented per-SD S100A8/A9 was associated with higher odds of post-AMI HF in Wald ratio analysis (OR: 2.06 [95% CI: 1.25–3.39]; P  = 0.004) and inverse-variance-weighted (IVW) analysis (OR: 1.55 [95% CI: 1.15–2.09]; P  = 0.004) (Table  3 ). The Egger method provided no evidence of pleiotropy (Egger’s intercept, P  = 0.744).

We further validated this finding in general HF using data from the finn-b-I9_HEARTFAIL study (Supplementary Fig.  13 ). Using cis-pQTLs effect estimates on S100A8/A9 levels from the validation cohort, increased genetically predicted S100A8/A9 levels were associated with increased risk of HF (IVW estimate of OR per SD: 1.04 [95% CI: 1.01–1.07]; P  = 0.016) (Table  3 ). We provided dose-response curves showing the causal effect of S100A8/A9 levels on post-AMI/general HF, with a line of best drawn (Supplementary Fig.  14 ). The slope is the causal estimate of S100A8/A9 levels on post-MI/general HF.

Colocalization analyses can strengthen the evidence for a causal effect. However, guidelines for colocalization analysis recommend only testing for colocalization where P  < 10 − 6 for both traits in question. Due to the small sample size of the post-AMI cohort, there is insufficient power to test colocalization in our cohort. Although the sample size for general HF is much larger, the minimum P -value of the SNPs within the target region is 0.0068, which does not meet the criteria for conducting colocalization analysis. However, we provided a visual comparison of the pQTL and GWAS signals at the locus for both UKB cohort and finn-b-19_HEARTFAIL cohort by showing LocusZoom plots of the pQTL and GWAS signals side-by-side (Supplementary Fig.  15 ). The plots showed that the pQTL and GWAS for post-AMI HF signals were all around the S100A9 gene.

In our proteomic analyses, high inflammation status, particularly increased S100A8/A9 level, was observed in patients with post-AMI HF. In two independent prospective cohorts, S100A8/A9 robustly predicted HF. We demonstrated that a genetically determined portion of S100A8/A9 was associated with post-AMI HF risk, suggesting that S100A8/A9 acts as an intermediate causal phenotype in post-AMI HF.

Although data regarding the association between high inflammatory status and post-AMI HF exist 13 , the existing known biomarkers are insufficient for precise HF prediction in patients with AMI. High S100A8/A9 levels during the acute event were reportedly associated with increased hospitalization for HF during follow-up in patients with MI 14 . However, this study had certain limitations, including a small sample size (HF: n  = 41, no-HF: n  = 483) of patients with ACS, lack of validation, and no significant result after adjusting for multiple factors. Therefore, the prognostic value of S100A8/A9 in AMI needs to be further established. Our study revealed the prognostic value of S100A8/A9 in post-AMI HF in two independent prospective cohorts. Adjustments for age, sex, cardiovascular risk factors, and well-established risk indicators did not significantly affect the association between S100A8/A9 and post-AMI HF. Moreover, S100A8/A9 was superior to established biomarkers, including cTnI, BNP, and CRP, and added discrimination/reclassification value to a reference model of traditional and established risk indicators.

MR analyses of S100A8/A9 enhanced the significance of our compared to other observational studies on prognostic markers. MR can reveal novel the pathological mechanisms and predict novel drug targets for several cardiovascular diseases 15 , 16 . More recently, Lumbers et al. reported a large-scale MR analysis of incident HF that combined observational associations of 90 cardiovascular proteins with a systematic appraisal of causal effects. However, S100A8/A9 was not included in the Olink-Proseek Multiplex proximity extension assay for cardiovascular protein assessment, preventing the discovery of a correlation or causal relationship between S100A8/A9 and HF 17 . Therefore, our study is the first to demonstrate the causal effect of S100A8/A9 on post-AMI HF. Because of the close correlation between S100A8 and S100A9 levels and the dependence of S100A8 protein stability on S100A9 expression 18 , we chose cis- variants, as opposed to trans- variants, of S100A9 as genetic instruments. In this regard, the scope for violating the exclusion restriction assumption was limited because the pQTL variant effects on the outcome were possibly mediated through the expression of the protein under consideration (no horizontal pleiotropy) 19 . The association between genetic variants and exposure was strongly supported by the GTEx portal, suggesting the effectiveness of S100A9 genetic instruments. Among the 20 SNPs shown in Supplementary Table  14 , rs3014874 was both a pQTL and an expression quantitative trait locus with the most significant P -value. Rs3014874 may be a functional variant because of its location in the distance enhancer E1385341. Additionally, rs12119788 and rs2070864 were located in a promoter-like element, E1385328, and another distance enhancer, E1385333, implying that the target region has multiple functional SNPs.

In older patients, the prognosis of AMI is influenced by many other diseases, and the role of genetics in S100A8/A9 levels is also influenced by more confounding factors. Therefore, we selected patients with early-onset AMI in the validation cohort to reduce confounding influences and highlight the role of genetic variants. The validation cohort comprised unusually young patients with AMI. To exclude the possibility that the observed causal effect of S100A8/A9 on HF risk only applies to patients with early-onset MI, we used the UKB cohort for external validation of the causal effect, in which the median age at AMI onset was 56.0 [52.6–59.2] in patients with HF and 55.7 [51.6–58.4] in patients without HF (Supplementary Table  17 ). Additionally, there is a causal effect of S100A8/A9 on post-AMI HF in the UKB cohort, indicating that the observed causal effect of S100A8/A9 on HF risk is not limited to individuals with early-onset MI.

The pathological role of S100A8/A9 has been established in experimental ischemia/ischemia-reperfusion injury 14 , 20 , 21 . Mitochondrial dysfunction and oxidative stress can cause cardiomyocyte death. S100A8/A9 directly induces mitochondrial dysfunction by suppressing mitochondrial complex I activity 20 . S100A8/A9 interacts with NADPH oxidase complex by binding to p67phox and Rac in neutrophils, thereby promoting oxidative stress 22 . Persistent and excessive inflammation might contribute to myocardial injury aggravation and adverse cardiac remodeling 23 . During ischemia/ischemia-reperfusion injury, S100A8/A9 promoted cardiac inflammatory response by stimulating leukocyte activation/infiltration, amplifying NF-κB signaling activation, and proinflammatory cytokine secretion 14 , 20 , 21 , 24 . Additionally, microvascular obstruction caused by thrombosis contributed to infarct extension after percutaneous coronary intervention 25 . S100A8/A9 modulated platelet function and promoted thrombus formation 26 . Collectively, S100A8/A9 is an important driver of various pathological processes during ischemia/ischemia-reperfusion injury. These functions demonstrate that S100A8/A9 can be used as a strong predictor of post-AMI HF and support its causal effect in the MR analysis.

Although neutrophils express S100A8/A9/12, there are several possible explanations for the improved predictive ability of S100A8/A9 compared to S100A12. First, the S100A8/A9 levels in both the serum and PBC were much higher than the S100A12 levels in patients with AMI. Second, once secreted, extracellular S100A8/A9 and S100A12 function by activating the pattern recognition receptors, including TLR-4 or receptor for advanced glycation end-products (RAGE) 27 . We previously showed that S100A8/A9-induced cardiomyocyte death depended on TLR-4, rather than RAGE 20 . Moreover, S100A8/A9 promoted granulopoiesis by interacting with TLR-4, subsequently priming the inflammasome 21 . RAGE is the target protein of S100A12 28 , and in vivo affinity of S100A12 to RAGE is higher than that of the S100 protein family 29 . Moreover, there are insufficient studies on the direct effect of S100A12 on ischemia/ischemia-reperfusion injury, though S100A12 promotes atherosclerosis and vascular calcification 30 .

Measuring S100A8/A9 levels at admission may identify patients at high risk of post-AMI and is suitable for the early use of medications and careful post-discharge follow-up. Elevated S100A8/A9 levels drive the post-AMI progression of the inflammatory response and mitochondrial dysfunction. Several anti-inflammatory agents or mitochondria-targeting peptides are promising drugs for cardiovascular disease 31 , 32 , and these treatments may be more effective for patients with high S100A8/A9 levels. S100A8/A9 levels are elevated before overt HF, and S100A9 blockade reportedly improves cardiac function in experimental MI and ischemia-reperfusion 14 , 20 , 21 . In present study, we prove the causal effect of S100A8/A9 on post-AMI HF in human. Both human and experimental study indicate that S100A8/A9 is a promising therapeutic target for post-AMI HF. Administration with ABR238901(an orally active and potent S100A8/A9 blocker) or a S100a9 neutralizing antibody, could prevent cardiac injury and HF post-AMI in animal model 14 , 20 . Collectively, future randomized controlled trials are required to elucidate whether targeting S100A8/A9 have clinical benefits in post-AMI HF prevention.

This study had some limitations. First, it was completed at a single center. therefore, further research is required using multicenter prospective cohorts. However, the consistency in the prognostic value of S100A8/A9 in the two independent cohorts suggested that S100A8/A9 is a reliable predictive biomarker. Second, S100A8/A9 was measured at admission, and a repeated-measures analysis may have captured a larger proportion of the S100A8/A9 variance. However, this early time point is the most useful for identifying a possible biomarker-based risk stratification. Third, due to the high cost of protein profiling experiments, we performed the initial screening step in a small number of subjects. As a result, other causal proteins may have been missed due to the low detection power in the initial screening step. Lastly, the urine albumin-creatinine ratio (UACR) is a prognostic marker of adverse HF outcomes in patients with ACS with type 2 diabetes 33 , 34 . We have not conducted this analysis because of the missing of UACR in present study. However, in our discovery and validation cohorts, 67% and 80% patients have no history of type 2 diabetes, respectively. Collectively, our major findings remain solid and robust despite these limitations.

In conclusion, high S100A8/A9 levels are associated with an increased post-AMI HF risk, providing an efficient approach for identifying patients at high HF risk. MR analysis revealed a causal effect of S100A8/A9 on post-AMI HF, supporting the possible role of anti-S100A8/A9 interventions in HF prevention. S100A8/A9 can improve post-AMI HF risk stratification, and its causal effects can help elucidate the pathological mechanisms of post-AMI HF.

The written informed consent was obtained from all the participants, and the Ethics Committee of Beijing Anzhen Hospital Capital Medical University approved the study protocol (approval number: 2018010). All patients participated in the study process without compensation. This study followed the Strengthening the Reporting of Observational Studies in Epidemiology reporting guideline 35 and the Declaration of Helsinki and was registered at ClinicalTrials.gov (ID: NCT03752515).

Study design

This study comprised the following three steps (Fig.  1 ): (1) HF-related candidate proteins were identified using serum proteomics in a cross-sectional set of patients with AMI who developed HF during hospitalization, patients with AMI without HF, and HCs. (2) The association between candidate proteins and HF was prospectively evaluated in the discovery (HF: n  = 296, no-HF: n  = 766) and validation (HF: n  = 192, no-HF: n  = 851) cohorts. (3) The causal relationship between HF-associated proteins and HF was confirmed using MR analysis. Genetic instruments associated with the S100A8/A9 level were identified from published GWAS 36 . For individual-level one-sample MR analysis, the causal association of genetic instruments with post-AMI HF events was assessed in the validation cohort (HF: n  = 192, no-HF: n  = 851). For the two-sample MR analysis, estimates of the association between the genetic instruments and S100A8/A9 levels from the validation cohort and the association between the genetic instruments and post-AMI HF from the UKB cohort were used to examine the causal effect of S100A8/A9 levels on post-AMI HF. Moreover, a statistical summary of the association between genetic instruments and S100A9 levels from the validation cohort and a statistical summary of the GWAS from the finn-b-I9_HEARTFAIL study were used to evaluate the causal association between genetic instruments and general HF.

Study participants and sampling

For the discovery cohort, we recruited consecutive 1324 patients with AMI at the Beijing Anzhen Hospital of Capital Medical University between August 1, 2015, and November 30, 2017. Patients with cardiogenic shock (Killip class IV), active infection, systemic inflammatory disease, known malignant disease, or surgery within the previous 3 months were excluded, while 1,062 patients were enrolled according to stringent criteria (Supplementary Fig.  1 ).

For the validation cohort, we recruited 1183 patients with AMI (age: 18–45 years) from Beijing Anzhen Hospital of Capital Medical University between February 1, 2016, and January 30, 2020, with similar exclusion criteria, and finally included 1043 patients with AMI (Supplementary Fig.  1 ).

HCs were recruited among individuals receiving regular physical examinations at Beijing Anzhen Hospital, and HC status was confirmed using electrocardiogram, transthoracic echocardiography, and laboratory examination to exclude MI, HF, or abnormal cardiac structure/function.

For patients with AMI from the UKB cohort, we used data collected at the UKB assessment centers at baseline, combined with information on incident disease events from the hospital and death registry. Notably, 1144 individuals diagnosed with MI and <60 years of age were included in our study. The UKB study was approved by the North West Multi-Center Research Ethics Committee, and all participants provided written informed consent to participate in the UKB study. Post-AMI HF includes HF and mortality due to cardiovascular disease (CVD). HF was defined using the ICD-10 code I50. Death due to CVD was defined using the ICD-10 codes for different endpoints from the Death Registry.

Study definitions

AMI was defined as continuous chest pain for >30 min, new ischemic electrocardiogram changes, and elevated cTnI levels with at least one value above the 99th percentile upper reference limit 37 . Two independent cardiologists diagnosed each patient. In-hospital HF included new HF onset (HF symptoms/signs after initial presentation and imaging evidence of pulmonary congestion), worsening HF (Killip class II progressing to III or IV, and Killip class III progressing to IV), cardiogenic shock diagnosis, and in-hospital death due to HF or cardiogenic shock. Long-term HF included HF progression resulting in rehospitalization and/or death due to HF after the initial discharge. Supplementary Table  3 presents the specific definitions of each event type. All HF events were adjudicated by the consensus of two experienced cardiologists who were blinded to the study results by review of outpatient clinics or hospitalization records and telephone interview. Any disagreements were resolved through discussion and by seeking a third opinion from another blinded, experienced cardiologist as required. Current smokers were defined as those who have smoked 100 cigarettes in their lifetime and had smoked cigarettes in the past 30 days.

Study outcomes and follow-up

The study outcomes were the in-hospital and long-term post-discharge HF incidence. Patients were followed up at 6–12-month intervals for HF events by telephone interviews and review of outpatient clinics or hospitalization records. During follow-up, 118 in-hospital (11%) and 178 long-term (17%) HF events were recorded in the discovery cohort, and 110 in-hospital (11%) and 82 long-term (8%) HF events were recorded in the validation cohort. The follow-up ended at death or termination (May 31, 2021; the maximum follow-up for both the discovery and validation cohorts). The median follow-up times were 4.2 years (interquartile range [IQR]: 1.7–5.1) and 2.9 years (IQR: 1.6–4.2) in the discovery and validation cohorts, and 52 and 44 patients were lost to follow-up, respectively.

Blood sample collection

Venous blood samples were collected on admission. The samples were placed into gel-containing vacutainer tubes and centrifuged within 1 h at 1800 g for 25 min; the serum, plasma, and blood cells were stored at –80 °C until use.

Serum proteomics and analysis

Human antibody array.

A human antibody array (a combination of Human L-507 and Human L-493, RayBiotech Inc.) was performed on the serum of HCs and patients with AMI with or without HF events at admission ( n  = 10 per group), according to the manufacturer’s instructions. Each serum sample was hybridized to the arrays overnight at 4 °C. All slides were scanned using a GenePix 4000 B Microarray Scanner and analyzed using GenePix Pro 6.0 software. The protein levels were normalized to the internal controls.

Selection of prognostic candidates

DEPs were identified based on a P- value < 0.05 in AMI vs. HC or no-HF events vs. HF events. DEPs at the intersection of the two comparisons were identified as HF-related DEPs. Based on the HF-related DEPs, Reactome enrichment analysis was used to determine the pathways that were significantly associated with HF. The least absolute shrinkage and selection operator (LASSO) is a regression analysis method that minimizes the sum of least squares in a linear regression model and shrinks the selected beta coefficients using penalties 38 , by incorporating shrinkage, this method provides a rigid variable selection and coefficient estimation. LASSO analysis excludes the least informative variables and selects the features of greatest importance for the outcome of interest in the imputed dataset. Among the selected HF-related proteins, LASSO analysis was conducted with the glmnet package in R, with a 10-fold cross-validation step to define the λ parameter that resulted in the minimum value of the mean square error of the regression model 39 .

Biochemistry measurement

Serum S100A8/A9 levels were measured using a standard enzyme-linked immunosorbent assay (ELISA) kit (S100A8/9; R&D Systems Europe, Abingdon, Oxford, UK). The assay employed a quantitative sandwich enzyme immunoassay technique. Briefly, a monoclonal antibody specific to the human S100A8/S100A9 heterodimer was pre-coated onto a microplate. Subsequently, the samples were pipetted into the wells. After washing away the unbound substances, an enzyme-linked monoclonal antibody specific for the human S100A8/S100A9 heterodimer was added to each well. All steps strictly followed the operation procedure of ELISA protocol. S100A12 levels were measured using a standard ELISA kit (S100A12: RayBiotech, Norcross, GA, USA). S100A9 levels were measured using a standard ELISA kit (S100A9: RayBiotech, Norcross, GA, USA).

All samples were assessed in duplicate in a blinded manner. The inter- and intra-assay coefficients of variation in patients with AMI were 3.62% and 3.37% respectively for S100A8/A9, 4.73% and 4.51% for S100A12, and 3.63% and 3.91% for S100A9. For randomly selected samples, the levels of biomarkers in fresh samples at admission correlated well with those in the same samples stored for 6 months (S100A8/A9: r  = 0.974, P  < 0.001; S100A12: r  = 0.961, P  < 0.001, S100A9: r  = 0.975, P  < 0.001), suggesting that storage at –80 °C for up to 6 months did not significantly affect the stability of serum S100A8/A9, S100A12 and S100A9.

cTnI, BNP, and hs-CRP levels were measured simultaneously with S100A8/A9 levels in both cohorts. cTnI, BNP, and hs-CRP levels were analyzed using the same assays in the two cohorts. Serum cTnI levels were determined using a chemiluminescence assay (Beckman Coulter, Access AccuTnI+3), and the 99th percentile of cTnI was 0.04 ng/mL. Plasma BNP levels were assessed using an Alere Triage immunoassay and read on an automated DxI800 platform (Beckman Coulter Diagnostics); normal levels were considered to be <100 pg/mL. Plasma hs-CRP levels were measured using a turbid metric inhibition immunoassay (Beckman Coulter AU5800 automatic biochemical analyser); normal levels were considered <3 mg/L. Serum cholesterol, glucose, and creatinine levels were measured using routine laboratory methods.

Infarct size estimation

The infarct size was evaluated using the area under the curve (AUC) for the CK-MB enzyme over the first 72 h after admission (CK-MB AUC 0-72 ) 40 . CK-MB levels were recorded at baseline (time of enrolment) and at 6, 24, 48, and 72 h after enrolment. The CK-MB measurements were performed in a central laboratory. The AUC was calculated using the linear trapezoidal method 41 based on the five CK-MB values (Supplementary Fig.  16 ).

Genotyping and quality control in the validation cohort

Human genomic DNA was isolated from EDTA-anticoagulated blood using the proteinase K methods 42 . SNPs were genotyped in all populations using TaqMan technology according to the manufacturer’s protocol. Negative controls without DNA were used for each plate to ensure no contamination between the samples and genotyping reagents. Genotypes that could not be automatically measured using Sequence Detection System 2.1 software were excluded. Direct Sanger sequencing was used to confirm the accuracy of genotyping.

Genetic instruments

We downloaded published GWAS from >30,000 individuals 36 to identify the pQTLs of S100A8/A9. Because no record of S100A8 or S100A8/A9 was found in these public data, and given that S100A8 protein stability depends on S100A9 expression, we selected cis-pQTLs within 20 kb on either side of the S100A9 gene (153357854-153361023 [grch38.p14]). Subsequently, SNPs with minor allele frequency >0.01 in East Asians (1000 Genome Project) were used for linkage disequilibrium analysis using Haploview software. The S100A8/A9 genetic score was constructed by combining variants in low-linkage disequilibrium ( r 2  < 0.8) with all other variants. Supplementary Table  14 shows that rs12119788 and rs12033317 are associated with elevated S100A8/A9 plasma concentrations, while rs59961408, rs1560832, rs3014874, and rs3014875 are associated with decreased S100A8/A9 plasma concentrations.

Sample size

To estimate the sample size required to assess the predictive value of candidate proteins, we assumed an event rate of 10% and covariate prediction of approximately 30% of the biomarker variance; a sample size of 913 patients provided 90% power (at P  < 0.05) to detect a biomarker HR of 1.5. The number of patients included in this study met the inclusion criteria.

For MR, the sample size calculation was based on the results of an online tool using several parameters 43 , including type-I error rate, power, OR of exposure and outcome, event rate, and variance explained by the selected genetic instruments.

Statistical analyses

Data for categorical and continuous variables are presented as absolute numbers with percentages and medians with interquartile ranges (IQR: 25th–75th percentiles). χ² or Fisher’s exact test was used for categorical variables. Continuous variables were compared between the two groups using either Student’s t -test or the Mann–Whitney U -test, as appropriate. Participants with missing data were excluded as they represented a small group. P -values < 0.05 were considered statistically significant. Analyses were performed using the IBM SPSS software (version 24.0; SPSS Inc., Chicago, IL, USA) or R (version 4.1.1; R Foundation for Statistical Computing, Vienna, Austria).

Associations of candidate-protein levels with HF

HF-related proteins were identified in the discovery cohort by univariate and multivariate analyses using a Cox regression model. Univariate models were used to identify potential confounders (Supplementary Table  7 ). Known risk factors (including sex) and potential confounders ( P  < 0.05 in univariate analysis) were included in the multivariate analysis. We constructed three multivariate models by adding variables for multivariate adjustment: model 1 (sex and age), model 2 (model 1 + systolic blood pressure [SBP], Killip classification at admission, fasting glucose, creatinine, and left main artery disease), and model 3 (model 2 + neutrophil count, cTnI, BNP, CRP, LVEF, and estimated infarct size [CK-MB AUC 0-72 ]).

The proportional-hazard assumption was assessed for time-to-event outcomes using the Schoenfeld residuals test, and no proportional-hazard assumption was violated for the biomarker variables. For continuous variables, HRs and the corresponding 95% confidence intervals (CIs) per 1-SD higher measure were calculated. Spline regression models were used to explore the shapes of the associations between the prognostic biomarkers and outcomes by fitting a restricted cubic spline function 44 . The analyses were multivariate-adjusted and used three knots (5th, 50th, and 95th percentiles). We examined the association between candidate proteins and time-to-event ratios in the different subgroups. This approach allowed us to estimate subgroup-specific HR and compare the HRs in the two categories of differing subgroup variables. Kaplan–Meier cumulative-event curves were used to display outcomes independent of prognostic biomarker-guided risk status; group-wise comparisons were based on the log-rank test.

As the rate of an event and the corresponding event risk do not have a direct relationship under the presence of competing events, the Fine-Gray competing risk model based on the R package “cmprsk” was used to examine the associations while accounting for death (insufficient evidence to determine death from HF, non-cardiac death) as a competing risk. The Fine-Gray model computes the HR adjusted for sex, age, SBP, Killip classification at admission, fasting glucose, creatinine, left main artery disease, neutrophil count, cTnI, BNP, CRP, LVEF at admission, and estimated infarct size (CK-MB AUC 0-72 ) 45 .

Incremental predictive value of candidate proteins

Since no established/reliable post-MI HF prediction models exist, we constructed reference models using risk factors with P  < 0.05 in the univariate analysis (age, sex, SBP, Killip classification at admission, fasting glucose, creatinine, left main artery disease, neutrophil count, cTnI, BNP, CRP, LVEF at admission, and estimated infarct size (CK-MB AUC 0-72 )). The added predictive ability of the candidate biomarkers beyond that of the reference model was assessed using Harrell’s concordance C-statistic calculated from the Cox regression model and logistic model-based categorical NRI. Harrell’s concordance C-statistic and time-dependent receiver operating characteristic (ROC) curve analysis were used to compare the predictive accuracy of the candidate proteins and clinical biomarkers. The clinical usefulness of S100A8/A9 was evaluated using decision curve analysis (DCA) 46 , by estimating the net benefit of adding S100A8/A9 to risk stratify patients according to different decision thresholds of HF-event risk compared with the reference model.

Causality between S100A8/A9 and HF

Individual-level one-sample and two-sample MR analyses were used to investigate the causality between S100A8/A9 and HF incidence. Three general assumptions of MR were applied: (i) robust S100A8/A9 association, (ii) no association with HF confounders, and (iii) association only with HF based on their effect on S100A8/A9.

One-sample MR analysis

Individual-level one-sample MR analysis was used to investigate causality between S100A8/A9 and HF. For one-sample MR analysis, the S100A8/A9 genetic score was calculated for each participant in the validation cohort by summing the number of effect alleles that a participant inherited at each variant in the score, weighted by the effect of each variant on S100A9 levels (β value in the GWAS summary). We used a two-stage least squares method to regress S100A8/A9 levels on the S100A8/A9 genetic score and used estimation prediction to generate a genetically predicted plasma S100A8/A9 value for each validation cohort participant. These values were tested for HF risk associations using multivariable regression. The analyses were performed using the R package “ivreg”.

Two-sample MR analysis to assess the causal effect of S100A8/A9 on post-AMI HF

Two-sample MR analysis is less prone to false-positive bias, which is possible in one-sample MR analysis 47 . We obtained the effect estimates of the S100A8/A9 genetic score on S100A8/A9 levels in the validation cohort and the effect estimates of the S100A8/A9 genetic score on post-AMI HF in the UKB (1144 patients with AMI). We estimated the causal effect of S100A8/A9 levels on post-AMI HF using the Wald ratio. Furthermore, we calculated causal effects by combining variant-specific causal estimates in an IVW fixed-effects meta-analysis. Specifically, we obtained the effect estimates of the five SNPs on S100A8/A9 levels in the validation cohort and the effect estimates of the five SNPs on post-AMI HF from the UKB (1144 patients with AMI). Subsequently, we estimated the causal effect of S100A8/A9 levels on post-AMI HF using the Wald ratio and used the IVW method to generate an estimate of all SNPs. Pleiotropy was assessed using the MR-Egger regression.

Two-sample MR analysis to assess the causal effect of S100A8/A9 on general HF

To determine the effect size of SNPs on S100A8/A9, we used association statistics between the six SNPs and S100A9 levels in a validation cohort of 1043 patients with AMI. We used an association statistical summary between the six SNPs and HF from the finn-b-I9_HEARTFAIL study (13,087 patients with HF and 19,5091 controls) to obtain the effect size estimates of the association between target SNPs and HF. Causal-effect estimates of S100A8/A9 levels on HF events were obtained separately for each SNP by Wald ratio 48 , and to generate an estimate using all the SNPs, we used the inverse-variance-weighted method for primary analysis and MR-Egger for sensitivity analysis.

Reporting summary

Further information on research design is available in the  Nature Portfolio Reporting Summary linked to this article.

Data availability

The raw data supporting the findings of the study have been provided in source data. Due to ethical and legal restrictions, the clinical data are not publicly available. Any individual affiliated with an academic institution may request access to the clinical data from Yulin Li, PhD ([email protected]) for research purposes. This includes submitting a proposal to the management team, where upon approval, data will be provided with a signed data access agreement. The timeframe for responding to an access to information is a 20-working day from the date of receipt. Source data are provided with this paper.Three public data were used in the MR analysis. The GWAS summary statistics for S100A9 “5339_49_S100A9_calgranulin_B.txt.gz” be downloaded at https://www.decode.com/summarydata/ . The data of AMI patients in UK Biobank cohort were download from at https://www.ukbiobank.ac.uk/ and received under the data request application no.68808. The HF GWAS of summary statistics from finn-b-I9_HEARTFAIL study are publicly available at https://gwas.mrcieu.ac.uk/datasets/finn-b-I9_HEARTFAIL/ .  Source data are provided with this paper.

Code availability

We used publicly available software for the analyses, and all software used is listed and described in the Methods section of our manuscript. Statistical analyses were conducted in R statistical software. Proteome analysis was used the R glmnet package ( https://cran.r-project.org/web/packages/glmnet/index.html ) and pathway enrichment analyses were conducted using the clusterProfiler package in R ( https://pubmed.ncbi.nlm.nih.gov/22455463/ ) and the ReactomePA R package ( https://bioconductor.org/packages/release/bioc/html/ReactomePA.html ). MR analyses were conducted using the TwoSampleMR package in R. ( https://mrcieu.github.io/TwoSampleMR/ ), genetic colocalization analyses were conducted using the coloc package in R .

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Acknowledgements

We appreciate all the patients who participated and thank Dr. Jian Cui (Shanghai BioGenius Biotechnology Co., Ltd.) for providing bioinformatics assistance. We thank Dr. Jie Zhao (School of Public Health, University of Hong Kong) and Dr. Lan Liu (School of Statistics, University of Minnesota, Twin Cities) for their assistance on statistics. This study was funded by the National Science Foundation of China (82230013, 81770245, 81970215 to YL.L), Key Laboratory of Remodeling-Related Cardiovascular Diseases, Ministry of Education, China, Beijing Municipal Public Welfare Development and Reform Pilot Project for Medical Research Institutes (JYY2023-9), Beijing Municipal Health Commission (11000023T000002039525), and Beijing Hospitals Authority’s Ascent Plan.

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These authors contributed equally: Jie Ma, Yang Li, Ping Li.

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Beijing Anzhen Hospital of Capital Medical University, Beijing, China

Jie Ma, Yang Li, Ping Li, Xinying Yang, Shuolin Zhu, Ke Ma, Fei Gao, Hai Gao, Jie Du & Yulin Li

Beijing Institute of Heart Lung and Blood Vessel Diseases, Beijing, China

Jie Ma, Yang Li, Xinying Yang, Shuolin Zhu, Ke Ma, Jie Du & Yulin Li

Department of Preventive Medicine, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA

Department of Emergency Medicine, Thomas Jefferson University, Philadelphia, PA, USA

Xin-liang Ma

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L.Y.L. designed the study. J.M., P.L., Y.X.Y., F.G. and H.G. collected samples and completed the follow-up of AMI patients. J.M., Y.L., L.S.Z., K.M. and H.Z. performed the experiments and data analysis. L.Y.L. and Y.L. wrote the manuscript. L.X.M., J.D. and L.Y.L. organized and supervised the study.

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Ma, J., Li, Y., Li, P. et al. S100A8/A9 as a prognostic biomarker with causal effects for post-acute myocardial infarction heart failure. Nat Commun 15 , 2701 (2024). https://doi.org/10.1038/s41467-024-46973-7

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DOI : https://doi.org/10.1038/s41467-024-46973-7

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April 5, 2024

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Clinical trial: First cardiac bioimplants for treatment of myocardial infarction using umbilical cord stem cells

by Germans Trias i Pujol Research Institute

The results of a pioneering study support the safety of the bioimplants called PeriCord, made from stem cells of the umbilical cord and pericardium from a tissue donor, which aid in the regeneration and revascularization of the affected area. The study has monitored seven interventions of this pioneering tissue engineering surgery over three years, noting excellent biocompatibility and no rejection in patients.

The therapy has been developed by the research group ICREC (Heart Failure and Cardiac Regeneration) at Germans Trias i Pujol Research Institute (IGTP) and Banc de Sang i Teixits (BST). PeriCord has anti-inflammatory properties and opens the door to creating other drugs for conditions beyond the heart.

The paper is published in the journal eBioMedicine .

The promising results obtained in a clinical trial with a pioneering advanced therapy drug named PeriCord, which aims to repair the heart of patients who have suffered a heart attack , confirm the feasibility of new therapies based on the application of stem cells and tissue engineering to promote the regeneration of damaged tissues.

This new medicine, derived from umbilical cord and pericardium stem cells from tissue donors, is a world-first tissue engineering product (a type of advanced therapy combining cells and tissues optimized in the laboratory). The drug is applied in patients undergoing coronary bypass , utilizing the procedure to repair the scar in the heart area affected by the infarction, which has lost the ability to beat when blood flow stopped.

The first intervention of this new therapy was almost 4 years ago, resulting from a collaboration between the ICREC at IGTP and BST. Following its success, a study was initiated to demonstrate its clinical safety. The study included 12 coronary bypass candidates, 7 treated with bioimplants and 5 without, to compare the outcomes.

Dr. Antoni Bayés, ICREC researcher and first author of the article, says, "This pioneering human clinical trial comes after many years of research in tissue engineering, representing a very innovative and hopeful treatment for patients with a heart scar resulting from a heart attack," referring to PeriCord.

While the current study aimed to demonstrate the safety of this new drug in the context of myocardial infarction, its positive outcomes have shown that PeriCord possesses other exceptional properties. It has proven to be a medicine with excellent biocompatibility, drastically minimizing the risk of rejection and ensuring perfect tolerance by the body.

Additionally, it has anti-inflammatory properties, paving the way for broader applications in pathologies involving inflammation. "Its potential could be much wider; we believe it can be a valuable tool for modulating inflammatory processes," explains Dr. Sergi Querol, head of the Cellular and Advanced Therapies Service at BST.

Severe but stable patients

The patients included in the therapy are individuals who have suffered a heart attack and have reduced quality and life expectancy. The bypass ensures blood circulation in the area, and the bioimplant goes a step further to stimulate the scar, initiating cellular mechanisms involved in tissue repair.

"Voluntarily provided substances of human origin are used, both in terms of multi-tissue donor pericardial tissue and mesenchymal stem cells from umbilical cord donors at the birth of a baby," explains Querol. It is very gratifying to think that "thanks to this and the donors, we provide a new therapeutic tool that can improve a patient's quality of life."

PeriCord consists of a membrane that comes from the pericardium of a tissue donor, which BST has decellularized and lyophilized. It has then been recellularized with these umbilical cord stem cells.

Once in the operating theater, surgeons attach the laboratory-generated bioimplant to the affected area of the patient's heart. After a year, the implanted tissue adheres and adapts perfectly to the structure of the heart, covering the scar left by the heart attack.

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Myocardial Infarction in Young Individuals: A Review Article

Anupam sood.

1 Department of Emergency Medicine, Jawarhalal Nehru Medical College, Datta Meghe Insititute of Higher Education and Research, Wardha, IND

Akhilesh Singh

Charuta gadkari.

Although myocardial infarction (MI) primarily affects patients over the age of 45, it can also affect young women and men. Still, when it occurs at an early age, it has severe morbidity and psychological and financial burdens for the patient and his or her relatives. Four classes can be used to categorize the causes of MI in individuals below the age of 45. These are drug abuse-related MI, hyper-coagulable conditions, atheromatous coronary artery disease (CAD), and non-atheromatous CAD. There is a significant overlap between each category. Elevated blood pressure, smoking, diabetes, obesity, high cholesterol, inactivity, an unbalanced diet, binge drinking alcohol, and related substances are all risk factors. The primary mechanism of an MI is typically the total obstruction of a vessel caused by breaking an atheromatous plaque. This article covers the research and focuses on the practical concerns related to young adults with MI.

Introduction and background

The most significant cause of death for individuals in the West is coronary heart disease (CHD) [ 1 - 3 ]. The fatal symptom of CHD is myocardial infarction (MI), which might appear as a sudden demise. Although MI primarily affects individuals over the age of 45, it may also be seen in young men or women. Luckily, it rarely occurs in a population under the age of 45 [ 4 ]. When it occurs at a younger age, this illness has severe morbidity, psychological impacts, and financial burdens for the patient and his or her relatives. The protection provided to youth has gradually been destroyed by the rising prevalence of CHD risk factors (RF) in young adults, such as cigarette smoking, increased weight, and inactivity. The term MI describes the loss of cardiac muscle tissue (infarction) brought on by ischemia injury or the deprivation of oxygen to the myocardium. It is one type of acute coronary syndrome (ACS), which is described as an abrupt or brief shift in symptoms associated with blood flow in the heart. In contrast to unstable angina, the other type of ACS, MI, happens when cell death occurs, which can be confirmed by a blood test for biomarkers like cardiac troponin [ 5 ].

Methodology

We searched the Central and MEDLINE databases using the Cochrane Library and PubMed. The PubMed search technique was thus customized for each database: ("risk factors for coronary artery disease "[Title/Abstract] AND "young onset myocardial infarction"[Title/Abstract]) OR "Smoking and myocardial infarction"[Title/Abstract] OR " [Title/Abstract] OR "myocardial infarction"[Title/Abstract]. We also looked through the reference lists of potentially relevant papers to find additional studies. These electronic searches yielded studies, which were then analyzed alongside relevant sources in their bibliographies. Original studies written in English that evaluated RF, diagnosis, and treatment were included. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) research methodology is depicted in Figure ​ Figure1 1 .

Epidemiology

CHD is becoming less seen across all age groups in the United Kingdom. In reality, between 1992 and 2012, the condition was discovered to be present in 0.5% of males and 0.18% of women between the ages of 35 and 44, as well as 20.5% of men and 17.1% of women over the age of 60 [ 6 ]. In fact, due to atypical presentations and reluctance to submit themselves for further examinations, the number of younger patients may be lower than what they are [ 7 ]. However, it was discovered that just 3% of all CHD patients were in the younger group, defined as those under the age of 40 [ 8 ]. In the near future, this will lead to a rise in disease burden. Cigarette smoking is the leading RF for cardiac problems and has been proven to be more and more widespread in young individuals, where it can reach up to 8-10%. In the United Kingdom, it was discovered that girls who smoked more frequently and for a more extended period had a heavier smoking burden [ 9 ]. This would affect the protection of the heart provided by hormones like estrogen in young women. Increased weight is a significant concern in young people and children, and it has been elevated by three times in the United Kingdom in the past 20 years [ 10 - 12 ]. The use of cocaine is one of the frequent causes of manifestation of pain in the chest among younger individuals and can develop in MI [ 13 , 14 ]. It is almost transparent that the incidence of CHD, as predicted, has increased in patients aged less than 45.

Risk factors

Cigarette smoking, cholesterol levels, diabetes, hypertension, increased weight, food habits, physical inactivity, and alcohol consumption were all considered RF. Psychological stress and the male gender are the major RF in younger individuals [ 15 , 16 ]. Post-COVID-19 infection and post-COVID-19 vaccination may also trigger MI in young people. More critical factors included smoking, lipid problems, hypertension, and diabetes. Cigarette smoking is the central and most common RF among younger individuals, while in the case of the elder population, hypertension, diabetes, and dyslipidemia are the most common RF [ 17 , 18 ]. Smoking is the only element that may be changed entirely. Smoking tobacco speeds up the onset of atherosclerosis by decreasing tissue oxygenation, harming the vascular endothelium, and increasing sympathetic nervous system activity. Smoking also increases platelet aggregatory activity, which aids in developing intravascular clots [ 19 ]. The most common RF in the 17 to 45 age category were smoking (approximately 57%), dyslipidemia (approximately 52%), and hypertension (50%), and approximately 91% of patients had at least one RF. One out of five patients had diabetes and obesity, and one out of every 10 people who had their first acute myocardial infarction (AMI) also used drugs. The most common conditions in people aged 45 to 59 were hypertension (60%), dyslipidemia (57.5%), and smoking (52%), and 92% of patients had at least one RF. At the time of the first AMI, diabetes mellitus was there in one in four instances, obesity in one out of six, and drug usage in one out of 20 cases. In both age groups, Hispanic patients have a higher rate of diabetes mellitus than other groups; Asian/Pacific/Islander patients had increased rates of dyslipidemia; Black patients had a higher frequency of elevated blood pressure (BP), increased weight, and substance misuse; and White patients had increased rates of smoking than other racial groups. The most common risk element during an initial AMI is dyslipidemia in individuals who were White (55%), Hispanic (50%), and Asian (approximately 56%) racial groups, while elevated BP was one of the most common RF in Blacks (about 64%). It is alarming that some RF are highly prevalent in the younger population, particularly elevated BP (about 50%) and dyslipidemia (about 51.8%). These figures are significantly higher than the current estimates of hypertension (12.8% in men and 9.4% in women) and dyslipidemia (approximately 13% in men and 8% in women) in the general adult population of the United States aged 20 to 44 [ 20 ]. The 18 to 44 years patient group had a rate of 22.6%, with a tendency to increase, which is significantly more than the 4% prevalence rate of diabetes mellitus in the United States. The risk of recurrent AMI or fatal CHD in the first five years after a first AMI in people over 45 is as high as 17-20%. The elevated risk of such occurrences is linked to the presence of specific changeable RF through lifestyle modification [ 21 ]. Figure ​ Figure2 2 summarizes RF for the development of ischemic heart disease.

CMD: coronary microvascular dysfunction

Source: Original

Pathogenesis

There are four categories in which the pathogenesis of MI in individuals below 45 can be described.

Atherosclerotic CHD

The atheromatous process begins in infancy. In a necropsy examination of 760 young adults who passed away for various reasons, 20% of men and 5% of women had advanced CHD. The pathogenic causes of young people's atherosclerosis were connected to the typical RF that is the same as in adults. Smoking was linked to atheromatous processes for young patients, according to reports shown to be prevalent up to 92%. Smoking was found to be more in patients under 40 years of age than in those who were beyond 60. Patients who had a MI while they were younger than 45 years old were shown to have higher rates of lipid abnormalities, particularly hypertriglyceridemia and low high-density lipoprotein. In addition to overt diabetes, a 65% prevalence of decreased glucose tolerance was identified in survivors of MI under 45 years old. In younger people, traditional RF is more important. In addition, the development of hyperhomocysteinemia and variable level of lipoprotein-A may have the same clinical effects [ 22 , 23 ].

Non-atheromatous Coronary Artery Abnormalities

Congenital coronary artery abnormalities are a possibility when MI strikes young adults. These are highly uncommon. Myocardial bridging (coronaries embedded within a tunnel in the myocardium) includes systolic contraction, which can result in substantial ischemia during contraction and can lead to MI. Surgical procedures and percutaneous intervention were both found to be medical management options more effective in this group of patients [ 22 , 23 ].

Recreational Drug Use

The use of cocaine is linked to many cardiac issues, including MI. Cocaine usage was associated with the clinical presentation in 48% of young patients who were referred to the emergency room with non-traumatic chest discomfort. A thorough background is essential since cocaine effects might manifest up to 72 hours after consumption. The majority of individuals who misuse cocaine are also chronic smokers, which increases their risk of MI. In addition to MI, cardio-myopathy, tachyarrhythmias, and endocarditis have all been linked to cocaine usage. MI can arise from using marijuana and amphetamines. Additionally, excessive alcohol consumption has been reported to be connected to a young person acquiring MI even though the mechanism is not evident [ 23 , 24 ].

Hyper-Coagulable State

Recurrent arterial and venous thrombosis is linked to the anti-phospholipid syndrome. Young adults in their 30s are frequently affected by it. It could be primary or secondary and connected with autoimmune conditions, such as systemic lupus erythematous. These patients often exhibit enhanced platelet adhesiveness and early atherosclerosis [ 23 , 24 ].

Clinical presentation

Ninety percent of males and females reported experiencing chest pain, such as pressure, tightness, or discomfort. Women had an increased rate of pain symptoms (61.9%) which was not related to the chest compared to males (54.8%), which included gastrointestinal distress (nausea and stomachache), elevated heart rate, and breathlessness [ 25 ]. Various signs and symptoms include chest pain that feels like pressure, aching, tightness, and squeezing pain, which radiates to the left arm, shoulder, or back, sweating, exhaustion, heartburning sensation or indigestion, dizziness, and shortness of breath. The adult male presents more frequently with chest discomfort and sweating [ 22 ], but there is a significant overlap between the symptoms of men and women. On the other hand, women tend only to experience non-chest pain discomfort, presenting symptoms such as pain in the back or neck or "nausea or vomiting," which was discovered to be poorly understood [ 26 ]. Similar to how it applies to younger people, older people also exhibit more unusual symptoms [ 27 - 29 ]. One reason women and the elderly experience higher fatality rates may be due to this lack of awareness of unusual symptoms. One study found that only 24% of young adults with documented coronary artery disease (CAD) had stable angina [ 30 ]. Sixty-nine percent of people under the age of 45 who had a MI denied having any chest pain prior to the MI. Most patients' symptoms were observed to last less than a week. A thorough history would provide crucial information for the differential diagnosis of chest discomfort. The initial focus of clinical inspection should be on hemodynamic stability. Signs of sympathetic hyperactivity, in the form of elevated heart rate, perspiration, and any indications of prior abuse of injectable drugs, are essential [ 31 ].

Diagnosis of MI

Within 10 minutes of admission, a doctor should order and interpret an ECG, which is a vital diagnostic tool [ 32 ]. In patients with significant ST elevation on electrocardiography or suspected new left bundle branch block (BBB), immediate coronary angiography should be performed. Risk stratification and an algorithm for risk-based diagnosis and treatment should be applied to all patients. ECG criteria were modified in such a way that it also takes into account the age of the patient and gender-specific differences about leads V2-V3. ST-segment elevations are significant when the J-point is elevated by less than 0.15 mV in women, less than 0.25 mV in men under 40, and less than 0.2 mV in males above 40. An increase of less than 0.1 mV is diagnostic in all other leads. Patients with left or right BBB, early repolarisation, persistent ST elevations from a remnant aneurysm, purely posterior MI, or poorly positioned leads may have trouble interpreting their ECG. Concordant ST elevations may be the strongest predictor of continued AMI in individuals with established left BBB. At the same time, more complicated algorithms do not seem to offer enough diagnostic assurance [ 33 ]. Cocaine consumption is linked to abnormal ECG, commonly seen as dynamic ST-segment elevation. Suppose the patient presents to the hospital emergency room as soon as possible after the commencement of chest pain. In that case, ST elevation on ECG may be detected. If precious time is lost, the pain in the chest and ECG abnormalities quickly disappear and may be missed. Vasodilators are given to individuals with coronary cocaine use-related arterial spasms [ 34 ]. After 12 hours of chest discomfort, patients have a high chance of abnormal Q waves. An arbitrary T-wave modification, ST wave inversion, and depression are observed in patients with coronary arteries that are partially blocked. With pleuritic pain and concave upward ST-segment elevation in lateral leads, myopericarditis may be present [ 35 ].

Cardiac Troponin: A Gold Standard Biomarker

Cardiac enzyme levels are consistently elevated. Troponin T elevation (cardiac specific) is the most precise indicator of cardiac injury. There are three subtypes of cardiac troponin, which regulate the myocardial contractile machinery (T, I, and C). Elevated levels signify myocardial damage because cardiac troponin T (cTnT) or troponin I (cTnI) expression occurs solely in cardiomyocytes [ 36 ]. A biphasic release kinetics causes an early peak to be seen within 24 hours, and the contraction apparatus proteolytic degradation causes a plateau after 48-72 hours [ 37 , 38 ]. While constant readings throughout serial measures indicate chronic myocardial injury, a distinct spike and decline in troponin levels or a considerably raised level of troponin during admission indicate AMI. A more likely occurrence of AMI is connected to a highly noticeable shift. If initial troponin readings are elevated, an earlier American National Academy for Clinical Biochemistry Guidance regarded a delta change of 20% or more as noteworthy. A rise or decline of 50% or more was suggested by a committee of the European Society of Cardiology (ESC). The 0-h/1-h rule-in and rule-out algorithms are defined by assay-specific absolute cut-off levels in the 2015 ESC guideline on managing non-ST-elevation ACS. High-sensitivity assays should only be described as having detectable troponin values in more than 50% of healthy people [ 39 ]. Patients may have a false-positive creatinine kinase rise with the abuse of cocaine.

Echocardiography

The diagnosis of non-ischemic causes of chest discomfort, such as myocarditis, valvular illness, cardiomyopathies, pulmonary embolism, or aortic dissection, is also aided by echocardiography. Echocardiography is also the preferred technique for identifying problems such as ventricular wall rupture or subsequent mitral valve regurgitation following papillary muscle rupture or ischemia [ 40 ].

Although less common and accessible than echocardiography, cardiac MRI is particularly useful in diagnosing myocardial illness.

Coronary Angiography

It gives essential information about the presence of any abnormalities in the coronary artery, and if present, it tells about the extent and identification of the offending lesion. New regional wall motion abnormality in echocardiography, myocardial scarring in MRI or nuclear testing, or an intra-coronary thrombus during coronary angiography, along with a significant spike or decline in cardiac troponin, are currently accepted diagnostic criteria for MI.

Younger patients' first care for MI deviates slightly from typical adult management. All patients should be given first doses of oxygen, nitrates, diamorphine, and aspirin. Statins are also used, which have anti-inflammatory properties. For those patients that have a history of cocaine abuse, beta-blockers should be avoided in them because the chest ache paradoxically becomes worse. Benzodiazepines are recommended for the initial treatment of MI in cocaine abuse. These patients should continue receiving nitrates to prevent coronary spasms. Expert opinion on instability should be obtained among those with unstable hemodynamics, and coronary angiography and intervention should be considered. Thrombolytic treatment should be made available to patients with persistent ST elevation due to cocaine that has not improved with the help of nitrates. Younger individuals appear to tolerate thrombolytic drugs better. Risk stratification should be used after the initial management of patients with non-ST-segment elevation MI. Based on persistent or dynamic ECG abnormalities, a greater degree of cardiac increase enzymes, and extra RF such as diabetes mellitus, high-risk patients should be directed to experts so they can determine whether early coronary angiography and intervention are necessary or not. Many younger individuals have normal coronary arteries. Therefore, coronary angiography is not always administered to them. Exercise stress testing is a valuable tool for risk categorization in patients with existing MI. The majority of the young patients who completed stage three of the Bruce regimen (nine minutes or longer) were discovered to have no abnormalities in their coronary arteries. The majority of MI patients often undergo coronary angiography. As was previously noted, due to the greater likelihood of discovering a normal coronary artery, this may not be given as a standard option to every affected patient. The possibility of finding an aberrant coronary artery increases in people at high risk, such as diabetes mellitus, dyslipidemia, and a family history of early CHD. People with severe left ventricular dysfunction should be provided with coronary angiography since early revascularization in the form of percutaneous transluminal coronary angioplasty and coronary artery bypass graft surgery improves their prognosis [ 38 - 40 ].

Conclusions

Fortunately, MI is rare in young individuals under the age of 45. However, it can still be a severe issue for the patient and the managing physician. It has a terrible impact on young people with sedentary lifestyles. A patient's age, various RF, clinical manifestations, and prognosis must all be considered. The increased frequency of CHD and various RF indicates the beginning of an alarming trend. In suspected MI patients who are less than 45 years old, abuse of substances, abnormalities in a coronary artery, premature CAD, and hypercoagulable status must be considered. After early stabilization, risk stratification should come next. Stratification and early revascularization should be provided since they will produce better clinical results. There is a lot of significance of secondary preventable measures for all newly hospitalized MI patients; if not prevented, the long-term mortality rate can be as high as one-third of the cases.

The authors have declared that no competing interests exist.