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The effects of pharmacological blood pressure lowering at normal or high-normal blood pressure ranges in people with or without pre-existing cardiovascular disease remains uncertain. We analysed individual participant data from randomised trials to investigate the effects of blood pressure lowering treatment on the risk of major cardiovascular events by baseline levels of systolic blood pressure. We did a meta-analysis of individual participant-level data from 48 randomised trials of pharmacological blood pressure lowering medications versus placebo or other classes of blood pressure-lowering medications, or between more versus less intensive treatment regimens, which had at least 1000 persons-years of follow-up in each group. Trials exclusively done with participants with heart failure or short-term interventions in participants with acute myocardial infarction or other acute settings were excluded. Data from 51 studies published between 1972 and 2013 were obtained by the Blood Pressure Lowering Treatment Trialists' Collaboration (Oxford University, Oxford, UK). We pooled the data to investigate the stratified effects of blood pressure-lowering treatment in participants with and without prevalent cardiovascular disease (ie, any reports of stroke, myocardial infarction, or ischaemic heart disease before randomisation), overall and across seven systolic blood pressure categories (ranging from <120 to ≥170 mm Hg). The primary outcome was a major cardiovascular event (defined as a composite of fatal and non-fatal stroke, fatal or non-fatal myocardial infarction or ischaemic heart disease, or heart failure causing death or requiring admission to hospital), analysed as per intention to treat. Data for 344 716 participants from 48 randomised clinical trials were available for this analysis. Pre-randomisation mean systolic/diastolic blood pressures were 146/84 mm Hg in participants with previous cardiovascular disease (n=157 728) and 157/89 mm Hg in participants without previous cardiovascular disease (n=186 988). There was substantial spread in participants' blood pressure at baseline, with 31 239 (19·8%) of participants with previous cardiovascular disease and 14 928 (8·0%) of individuals without previous cardiovascular disease having a systolic blood pressure of less than 130 mm Hg. The relative effects of blood pressure-lowering treatment were proportional to the intensity of systolic blood pressure reduction. After a median 4·15 years' follow-up (Q1–Q3 2·97–4·96), 42 324 participants (12·3%) had at least one major cardiovascular event. In participants without previous cardiovascular disease at baseline, the incidence rate for developing a major cardiovascular event per 1000 person-years was 31·9 (95% CI 31·3–32·5) in the comparator group and 25·9 (25·4–26·4) in the intervention group. In participants with previous cardiovascular disease at baseline, the corresponding rates were 39·7 (95% CI 39·0–40·5) and 36·0 (95% CI 35·3–36·7), in the comparator and intervention groups, respectively. Hazard ratios (HR) associated with a reduction of systolic blood pressure by 5 mm Hg for a major cardiovascular event were 0·91, 95% CI 0·89–0·94 for partipants without previous cardiovascular disease and 0·89, 0·86–0·92, for those with previous cardiovascular disease. In stratified analyses, there was no reliable evidence of heterogeneity of treatment effects on major cardiovascular events by baseline cardiovascular disease status or systolic blood pressure categories. In this large-scale analysis of randomised trials, a 5 mm Hg reduction of systolic blood pressure reduced the risk of major cardiovascular events by about 10%, irrespective of previous diagnoses of cardiovascular disease, and even at normal or high–normal blood pressure values. These findings suggest that a fixed degree of pharmacological blood pressure lowering is similarly effective for primary and secondary prevention of major cardiovascular disease, even at blood pressure levels currently not considered for treatment. Physicians communicating the indication for blood pressure lowering treatment to their patients should emphasise its importance on reducing cardiovascular risk rather than focusing on blood pressure reduction itself. British Heart Foundation, UK National Institute for Health Research, and Oxford Martin School.

Heart attack is a leading cause of mortality, necessitating timely and precise diagnosis to improve patient outcomes. However, timely diagnosis remains a challenge due to the complex and nonlinear relationships between clinical indicators. Machine learning (ML) and deep learning (DL) models have the potential to predict cardiac conditions by identifying complex patterns within data, but their “black-box” nature restricts interpretability, making it challenging for healthcare professionals to comprehend the reasoning behind predictions. This lack of interpretability limits their clinical trust and adoption. The proposed approach addresses this limitation by integrating predictive modeling with Explainable AI (XAI) to ensure both accuracy and transparency in clinical decision-making. The proposed study enhances heart attack prediction using the University of California, Irvine (UCI) dataset, which includes various heart analysis parameters collected through electrocardiogram (ECG) sensors, blood pressure monitors, and biochemical analyzers. Due to class imbalance, the Synthetic Minority Over-sampling Technique (SMOTE) was applied to enhance the representation of the minority class. After preprocessing, various ML algorithms were employed, among which Artificial Neural Networks (ANN) achieved the highest performance with 96.1% accuracy, 95.7% recall, and 95.7% F1-score. To enhance the interpretability of ANN, two XAI techniques, specifically SHapley Additive Explanations (SHAP) and Local Interpretable Model-Agnostic Explanations (LIME), were utilized. This study incrementally benchmarks SMOTE, ANN, and XAI techniques such as SHAP and LIME on standardized cardiac datasets, emphasizing clinical interpretability and providing a reproducible framework for practical healthcare implementation. These techniques enable healthcare practitioners to understand the model’s decisions, identify key predictive features, and enhance clinical judgment. By bridging the gap between AI-driven performance and practical medical implementation, this work contributes to making heart attack prediction both highly accurate and interpretable, facilitating its adoption in real-world clinical settings.

Given the significant risk that heart disease, particularly heart attacks, poses to individuals’ lives, it is crucial to develop effective techniques for early detection. Advanced machine learning and deep learning algorithms have the ability to predict heart attacks by analyzing a patient’s medical history and overall health. These algorithms can process large datasets, extracting valuable insights that help mitigate the risk of fatal outcomes. This study integrates a deep learning approach to predict and detect heart attacks early by classifying patient data as normal or abnormal. The proposed model combines a Convolutional Neural Network (CNN) with self-attention, leveraging the self-attention mechanism to focus on the most critical aspects of the sequence. Since heart attack risk is closely tied to the changes in vital signs over time, this approach enables the model to learn and assign appropriate weights to each input component. Improvements and modifications to the hybrid model resulted in a 98.71% accuracy rate during testing. The model’s strong performance on evaluation metrics shows its potential effectiveness in detecting heart attacks.

Background Heart disease and stroke are among the leading causes of death in Native Americans. Knowledge of heart attack and stroke symptomology are essential for prompt identification of symptoms and for appropriate action in seeking care. Knowledge of heart attack and stroke symptoms among US Native American adults was this study’s focus. Methods Multivariate techniques were used to analyze national surveillance data. Native American adults comprised the study population. Low heart attack and stroke knowledge score was the dependent variable. Results Logistic regression analysis yielded that Native American adults with low heart attack and stroke composite knowledge scores were more likely to be: older, less educated, poorer, uninsured, a rural resident, male, without a primary health care provider, and lacking a recent medical checkup. Conclusions The identified characteristics of Native American adults with heart attack and stroke knowledge deficits or disparities should guide educational initiatives by health care providers focusing on improving such knowledge.

Exosomes, as functional paracrine units of therapeutic cells, can partially reproduce the reparative properties of their parental cells. The constitution of exosomes, as well as their biological activity, largely depends on the cells that secrete them. We isolated exosomes from explant-derived cardiac stromal cells from patients with heart failure (FEXO) or from normal donor hearts (NEXO) and compared their regenerative activities in vitro and in vivo. Patients in the FEXO group exhibited an impaired ability to promote endothelial tube formation and cardiomyocyte proliferation in vitro. Intramyocardial injection of NEXO resulted in structural and functional improvements in a murine model of acute myocardial infarction. In contrast, FEXO therapy exacerbated cardiac function and left ventricular remodeling. microRNA array and PCR analysis revealed dysregulation of miR-21-5p in FEXO. Restoring miR-21-5p expression rescued FEXO's reparative function, whereas blunting miR-21-5p expression in NEXO diminished its therapeutic benefits. Further mechanistic studies revealed that miR-21-5p augmented Akt kinase activity through the inhibition of phosphatase and tensin homolog. Taken together, the heart failure pathological condition altered the miR cargos of cardiac-derived exosomes and impaired their regenerative activities. miR-21-5p contributes to exosome-mediated heart repair by enhancing angiogenesis and cardiomyocyte survival through the phosphatase and tensin homolog/Akt pathway.

Prompt coronary catheterization and revascularization have markedly improved the outcomes of myocardial infarction, but have also resulted in a growing number of surviving patients with permanent structural damage of the heart, which frequently leads to heart failure. There is an unmet clinical need for treatments for this condition 1 , particularly given the inability of cardiomyocytes to replicate and thereby regenerate the lost contractile tissue 2 . Here we show that expression of human microRNA-199a in infarcted pig hearts can stimulate cardiac repair. One month after myocardial infarction and delivery of this microRNA through an adeno-associated viral vector, treated animals showed marked improvements in both global and regional contractility, increased muscle mass and reduced scar size. These functional and morphological findings correlated with cardiomyocyte de-differentiation and proliferation. However, subsequent persistent and uncontrolled expression of the microRNA resulted in sudden arrhythmic death of most of the treated pigs. Such events were concurrent with myocardial infiltration of proliferating cells displaying a poorly differentiated myoblastic phenotype. These results show that achieving cardiac repair through the stimulation of endogenous cardiomyocyte proliferation is attainable in large mammals, however dosage of this therapy needs to be tightly controlled. MicroRNAs delivered by adeno-associated viral vectors improve global and regional contractility, increase muscle mass and reduce scar size in a porcine model of myocardial infarction.

Background In the American population, the relationship between the triglyceride-glucose (TyG) index and TYG combined with indicators of obesity and cardiovascular disease (CVD) and its mortality has been less well studied. Methods This cross-sectional study included 11,937 adults from the National Health and Nutrition Examination Survey (NHANES) 2003–2018. Cox proportional hazards model, binary logistic regression analyses, restricted cubic spline (RCS), and receiver operating characteristic (ROC) were used to analyze the relationship between TyG and its combined obesity-related indicators and CVD and its mortality. Mediation analysis explored the mediating role of glycated hemoglobin and insulin in the above relationships. Results In this study, except for no significant association between TyG and CVD mortality, TyG, TyG-WC, TyG-WHtR, and TyG-BMI were significantly and positively associated with CVD and CVD mortality. TyG-WHtR is the strongest predictor of CVD mortality (HR 1.66, 95% CI 1.21–2.29). The TyG index correlated better with the risk of coronary heart disease (OR 2.52, 95% CI 1.66–3.83). TyG-WC correlated best with total CVD (OR 2.37, 95% CI 1.77–3.17), congestive heart failure (OR 2.14, 95% CI 1.31–3.51), and angina pectoris (OR 2.38, 95% CI 1.43–3.97). TyG-WHtR correlated best with myocardial infarction (OR 2.24, 95% CI 1.45–3.44). RCS analyses showed that most of the above relationships were linear (P-overall < 0.0001, P-nonlinear > 0.05). Otherwise, ROC curves showed that TyG-WHtR and TyG-WC had more robust diagnostic efficacy than TyG. In mediation analyses, glycated hemoglobin mediated in all the above relationships and insulin-mediated in partial relationships. Conclusions TyG-WC and TyG-WtHR enhance CVD mortality prediction, diagnostic efficacy of CVD and its mortality, and correlation with some CVD over and above the current hottest TyG. TyG-WC and TyG-WtHR are expected to become more effective metrics for identifying populations at early risk of cardiovascular disease and improve risk stratification.

Innate immune cells play important roles in tissue injury and repair following acute myocardial infarction (MI). Although reprogramming of macrophage metabolism has been observed during inflammation and resolution phases, the mechanistic link to macrophage phenotype is not fully understood. In this study, we found that myeloid-specific deletion (mKO) of mitochondrial complex I protein, encoded by Ndufs4, reproduced the proinflammatory metabolic profile in macrophages and exaggerated the response to LPS. Moreover, mKO mice showed increased mortality, poor scar formation, and worsened cardiac function 30 days after MI. We observed a greater inflammatory response in mKO mice on day 1 followed by increased cell death of infiltrating macrophages and blunted transition to the reparative phase during post-MI days 3-7. Efferocytosis was impaired in mKO macrophages, leading to lower expression of antiinflammatory cytokines and tissue repair factors, which suppressed the proliferation and activation of myofibroblasts in the infarcted area. Mitochondria-targeted ROS scavenging rescued these impairments, improved myofibroblast function in vivo, and reduced post-MI mortality in mKO mice. Together these results reveal a critical role of mitochondria in inflammation resolution and tissue repair via modulation of efferocytosis and crosstalk with fibroblasts. These findings have potential significance for post-MI recovery as well as for other inflammatory conditions.

The cardiac extracellular matrix (ECM) not only provides mechanical support, but also transduces essential molecular signals in health and disease. Following myocardial infarction, dynamic ECM changes drive inflammation and repair. Early generation of bioactive matrix fragments activates proinflammatory signaling. The formation of a highly plastic provisional matrix facilitates leukocyte infiltration and activates infarct myofibroblasts. Deposition of matricellular proteins modulates growth factor signaling and contributes to the spatial and temporal regulation of the reparative response. Mechanical stress due to pressure and volume overload and metabolic dysfunction also induce profound changes in ECM composition that contribute to the pathogenesis of heart failure. This manuscript reviews the role of the ECM in cardiac repair and remodeling and discusses matrix-based therapies that may attenuate remodeling while promoting repair and regeneration.

Macrophages promote both injury and repair after myocardial infarction, but discriminating functions within mixed populations remains challenging. Here we used fate mapping, parabiosis and single-cell transcriptomics to demonstrate that at steady state, TIMD4 + LYVE1 + MHC-II lo CCR2 − resident cardiac macrophages self-renew with negligible blood monocyte input. Monocytes partially replaced resident TIMD4 – LYVE1 – MHC-II hi CCR2 − macrophages and fully replaced TIMD4 − LYVE1 − MHC-II hi CCR2 + macrophages, revealing a hierarchy of monocyte contribution to functionally distinct macrophage subsets. Ischemic injury reduced TIMD4 + and TIMD4 – resident macrophage abundance, whereas CCR2 + monocyte-derived macrophages adopted multiple cell fates within infarcted tissue, including those nearly indistinguishable from resident macrophages. Recruited macrophages did not express TIMD4, highlighting the ability of TIMD4 to track a subset of resident macrophages in the absence of fate mapping. Despite this similarity, inducible depletion of resident macrophages using a Cx3cr1 -based system led to impaired cardiac function and promoted adverse remodeling primarily within the peri-infarct zone, revealing a nonredundant, cardioprotective role of resident cardiac macrophages. Epelman and colleagues use fate mapping and single-cell transcriptomics to describe the dynamics of resident and recruited cardiac macrophages during ischemic injury.