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Patients with occlusion myocardial infarction (OMI) and no ST-elevation on presenting electrocardiogram (ECG) are increasing in numbers. These patients have a poor prognosis and would benefit from immediate reperfusion therapy, but, currently, there are no accurate tools to identify them during initial triage. Here we report, to our knowledge, the first observational cohort study to develop machine learning models for the ECG diagnosis of OMI. Using 7,313 consecutive patients from multiple clinical sites, we derived and externally validated an intelligent model that outperformed practicing clinicians and other widely used commercial interpretation systems, substantially boosting both precision and sensitivity. Our derived OMI risk score provided enhanced rule-in and rule-out accuracy relevant to routine care, and, when combined with the clinical judgment of trained emergency personnel, it helped correctly reclassify one in three patients with chest pain. ECG features driving our models were validated by clinical experts, providing plausible mechanistic links to myocardial injury. A machine learning algorithm, developed to detect occlusion myocardial infarction with no-ST elevation from electrocardiogram, outperforms clinicians in diagnostic assessments.

The discovery of genetic loci associated with complex diseases has outpaced the elucidation of mechanisms of disease pathogenesis. Here we conducted a genome-wide association study (GWAS) for coronary artery disease (CAD) comprising 181,522 cases among 1,165,690 participants of predominantly European ancestry. We detected 241 associations, including 30 new loci. Cross-ancestry meta-analysis with a Japanese GWAS yielded 38 additional new loci. We prioritized likely causal variants using functionally informed fine-mapping, yielding 42 associations with less than five variants in the 95% credible set. Similarity-based clustering suggested roles for early developmental processes, cell cycle signaling and vascular cell migration and proliferation in the pathogenesis of CAD. We prioritized 220 candidate causal genes, combining eight complementary approaches, including 123 supported by three or more approaches. Using CRISPR–Cas9, we experimentally validated the effect of an enhancer in MYO9B , which appears to mediate CAD risk by regulating vascular cell motility. Our analysis identifies and systematically characterizes >250 risk loci for CAD to inform experimental interrogation of putative causal mechanisms for CAD. Genome-wide association analyses, functionally informed fine-mapping and complementary gene prioritization approaches identify new risk loci and candidate effector genes for CAD.

Myocardial infarction is a leading cause of death worldwide 1 . Although advances have been made in acute treatment, an incomplete understanding of remodelling processes has limited the effectiveness of therapies to reduce late-stage mortality 2 . Here we generate an integrative high-resolution map of human cardiac remodelling after myocardial infarction using single-cell gene expression, chromatin accessibility and spatial transcriptomic profiling of multiple physiological zones at distinct time points in myocardium from patients with myocardial infarction and controls. Multi-modal data integration enabled us to evaluate cardiac cell-type compositions at increased resolution, yielding insights into changes of the cardiac transcriptome and epigenome through the identification of distinct tissue structures of injury, repair and remodelling. We identified and validated disease-specific cardiac cell states of major cell types and analysed them in their spatial context, evaluating their dependency on other cell types. Our data elucidate the molecular principles of human myocardial tissue organization, recapitulating a gradual cardiomyocyte and myeloid continuum following ischaemic injury. In sum, our study provides an integrative molecular map of human myocardial infarction, represents an essential reference for the field and paves the way for advanced mechanistic and therapeutic studies of cardiac disease. A time-resolved high-resolution map of human cardiac remodelling after myocardial infarction, integrating single-cell transcriptomic, chromatin accessibility and spatial transcriptomic data, provides a valuable resource for the field.

We report a genome-wide association study (GWAS) of coronary artery disease (CAD) incorporating nearly a quarter of a million cases, in which existing studies are integrated with data from cohorts of white, Black and Hispanic individuals from the Million Veteran Program. We document near equivalent heritability of CAD across multiple ancestral groups, identify 95 novel loci, including nine on the X chromosome, detect eight loci of genome-wide significance in Black and Hispanic individuals, and demonstrate that two common haplotypes at the 9p21 locus are responsible for risk stratification in all populations except those of African origin, in which these haplotypes are virtually absent. Moreover, in the largest GWAS for angiographically derived coronary atherosclerosis performed to date, we find 15 loci of genome-wide significance that robustly overlap with established loci for clinical CAD. Phenome-wide association analyses of novel loci and polygenic risk scores (PRSs) augment signals related to insulin resistance, extend pleiotropic associations of these loci to include smoking and family history, and precisely document the markedly reduced transferability of existing PRSs to Black individuals. Downstream integrative analyses reinforce the critical roles of vascular endothelial, fibroblast, and smooth muscle cells in CAD susceptibility, but also point to a shared biology between atherosclerosis and oncogenesis. This study highlights the value of diverse populations in further characterizing the genetic architecture of CAD. To overcome limitations of previous genome-wide association studies of coronary artery disease, this study incorporates a cohort of individuals containing large fractions of Black and Hispanic individuals to provide a wider view of the genetic landscape of this disease.

Identification of individuals at highest risk of coronary artery disease (CAD)—ideally before onset—remains an important public health need. Prior studies have developed genome-wide polygenic scores to enable risk stratification, reflecting the substantial inherited component to CAD risk. Here we develop a new and significantly improved polygenic score for CAD, termed GPS Mult , that incorporates genome-wide association data across five ancestries for CAD (>269,000 cases and >1,178,000 controls) and ten CAD risk factors. GPS Mult strongly associated with prevalent CAD (odds ratio per standard deviation 2.14, 95% confidence interval 2.10–2.19, P  < 0.001) in UK Biobank participants of European ancestry, identifying 20.0% of the population with 3-fold increased risk and conversely 13.9% with 3-fold decreased risk as compared with those in the middle quintile. GPS Mult was also associated with incident CAD events (hazard ratio per standard deviation 1.73, 95% confidence interval 1.70–1.76, P  < 0.001), identifying 3% of healthy individuals with risk of future CAD events equivalent to those with existing disease and significantly improving risk discrimination and reclassification. Across multiethnic, external validation datasets inclusive of 33,096, 124,467, 16,433 and 16,874 participants of African, European, Hispanic and South Asian ancestry, respectively, GPS Mult demonstrated increased strength of associations across all ancestries and outperformed all available previously published CAD polygenic scores. These data contribute a new GPS Mult for CAD to the field and provide a generalizable framework for how large-scale integration of genetic association data for CAD and related traits from diverse populations can meaningfully improve polygenic risk prediction. A polygenic risk score for coronary artery disease developed using data from individuals of five different ancestries has increased accuracy across diverse populations.

This Review provides an epidemiological overview of global mortality from acute coronary syndromes (ACS). Across the regions of the world where data are available, mortality from ACS — including premature (age <70 years) mortality from ACS — was higher in men than in women. In both sexes, age-standardized mortality rates (ASMRs) for ACS in 2020 were highest in lower-income global regions. However, 20 years earlier, ASMRs for ACS were highest in higher-income global regions, including Europe, Northern America and Oceania. These higher-income regions have seen progressive reductions in mortality from ACS during the past 20 years, which is in contrast to the more stable levels of mortality from ACS in Asia and in Latin America and the Caribbean. In the seven African countries with data available, a small upwards trend in ASMRs for ACS was observed, reflecting an epidemiological transition that is already well advanced in these regions. Consistent with these changes during the past 20 years were >50% reductions in ASMRs for ACS in the high-income countries of the world compared with <15% reductions in lower-middle-income countries. Policymakers need more complete epidemiological data across and within global regions to identify those countries in which the burden of death from ACS is greatest and the need to implement preventive strategies is most pressing.Acute coronary syndromes (ACS) are a major contributor to death from cardiovascular disease. In this Review, Timmis and colleagues present data on mortality from ACS for 122 countries and examine the regional and temporal changes in the epidemiology of ACS over the past 20 years.

Despite prompt epicardial recanalization in patients presenting with ST-segment elevation myocardial infarction (STEMI), coronary microvascular obstruction and dysfunction (CMVO) is still fairly common and is associated with poor prognosis. Various pharmacological and mechanical strategies to treat CMVO have been proposed, but the positive results reported in preclinical and small proof-of-concept studies have not translated into benefits in large clinical trials conducted in the modern treatment setting of patients with STEMI. Therefore, the optimal management of these patients remains a topic of debate. In this Review, we appraise the pathophysiological mechanisms of CMVO, explore the evidence and provide future perspectives on strategies to be implemented to reduce the incidence of CMVO and improve prognosis in patients with STEMI.Coronary microvascular obstruction and dysfunction (CMVO) is common in patients with ST-segment elevation myocardial infarction (STEMI) despite prompt reperfusion. In this Review, the authors discuss the mechanisms of CMVO as well as strategies to reduce its incidence and improve prognosis.

Despite advances in our understanding of the pathophysiology of many cardiovascular diseases (CVDs) and expansion of available therapies, the global burden of CVD-associated morbidity and mortality remains unacceptably high. Important gaps remain in our understanding of the mechanisms of CVD and determinants of disease progression. In the past decade, much research has been conducted on the human microbiome and its potential role in modulating CVD. With the advent of high-throughput technologies and multiomics analyses, the complex and dynamic relationship between the microbiota, their ‘theatre of activity’ and the host is gradually being elucidated. The relationship between the gut microbiome and CVD is well established. Much less is known about the role of disruption (dysbiosis) of the oral microbiome; however, interest in the field is growing, as is the body of literature from basic science and animal and human investigations. In this Review, we examine the link between the oral microbiome and CVD, specifically coronary artery disease, stroke, peripheral artery disease, heart failure, infective endocarditis and rheumatic heart disease. We discuss the various mechanisms by which oral dysbiosis contributes to CVD pathogenesis and potential strategies for prevention and treatment.In this Review, Ntusi and colleagues examine the mechanistic links between dysbiosis of the oral microbiome and the pathogenesis of cardiovascular diseases, and explore potential strategies for prevention and treatment.

Complex diseases, such as coronary artery disease (CAD), are often multifactorial, caused by multiple underlying pathological mechanisms. Here, to study the multifactorial nature of CAD, we performed comprehensive clinical and multi-omic profiling, including serum metabolomics and gut microbiome data, for 199 patients with acute coronary syndrome (ACS) recruited from two major Israeli hospitals, and validated these results in a geographically distinct cohort. ACS patients had distinct serum metabolome and gut microbial signatures as compared with control individuals, and were depleted in a previously unknown bacterial species of the Clostridiaceae family. This bacterial species was associated with levels of multiple circulating metabolites in control individuals, several of which have previously been linked to an increased risk of CAD. Metabolic deviations in ACS patients were found to be person specific with respect to their potential genetic or environmental origin, and to correlate with clinical parameters and cardiovascular outcomes. Moreover, metabolic aberrations in ACS patients linked to microbiome and diet were also observed to a lesser extent in control individuals with metabolic impairment, suggesting the involvement of these aberrations in earlier dysmetabolic phases preceding clinically overt CAD. Finally, a metabolomics-based model of body mass index (BMI) trained on the non-ACS cohort predicted higher-than-actual BMI when applied to ACS patients, and the excess BMI predictions independently correlated with both diabetes mellitus (DM) and CAD severity, as defined by the number of vessels involved. These results highlight the utility of the serum metabolome in understanding the basis of risk-factor heterogeneity in CAD. Multi-omic profiling of patients with coronary artery disease reveals links between alterations in the serum metabolome with genetics, diet and the microbiome and also implicates these metabolomic alterations with metabolic impairment preceding clinically overt coronary artery disease.

Although guidelines recommend fixed cardiac troponin thresholds for the diagnosis of myocardial infarction, troponin concentrations are influenced by age, sex, comorbidities and time from symptom onset. To improve diagnosis, we developed machine learning models that integrate cardiac troponin concentrations at presentation or on serial testing with clinical features and compute the Collaboration for the Diagnosis and Evaluation of Acute Coronary Syndrome (CoDE-ACS) score (0–100) that corresponds to an individual’s probability of myocardial infarction. The models were trained on data from 10,038 patients (48% women), and their performance was externally validated using data from 10,286 patients (35% women) from seven cohorts. CoDE-ACS had excellent discrimination for myocardial infarction (area under curve, 0.953; 95% confidence interval, 0.947–0.958), performed well across subgroups and identified more patients at presentation as low probability of having myocardial infarction than fixed cardiac troponin thresholds (61 versus 27%) with a similar negative predictive value and fewer as high probability of having myocardial infarction (10 versus 16%) with a greater positive predictive value. Patients identified as having a low probability of myocardial infarction had a lower rate of cardiac death than those with intermediate or high probability 30 days (0.1 versus 0.5 and 1.8%) and 1 year (0.3 versus 2.8 and 4.2%; P  < 0.001 for both) from patient presentation. CoDE-ACS used as a clinical decision support system has the potential to reduce hospital admissions and have major benefits for patients and health care providers. A clinical decision support system for diagnosis of myocardial infarction, based on machine learning models that use a single measurement of high-sensitivity troponin, outperforms clinical guidelines that use fixed cardiac troponin thresholds for diagnosis.