Aug 2026· Journal of Clinical Medicine· Vol 15, pp. 6181· 0 citations· 63 references
Medicine
TL;DR
A deeper understanding of genetic architecture, environmental exposures, and sociocultural factors appear to contribute to earlier onset and more aggressive disease, suggesting an urgent need for South Asian specific HF risk prediction models.
Abstract
Background: Heart failure (HF) represents the final common pathway of diverse cardiovascular disorders, including coronary artery disease, primary myocardial pathology, and abnormalities of cardiac conduction. These conditions arise from an interplay of genetic, environmental, and psychosocial influences, making it essential to understand these determinants to improve risk prediction and prevention. Multiple biological pathways of inflammation, fibrosis, coagulation, oxidative stress, lipid dysregulation, endothelial dysfunction, and metabolic disturbances are shaped by inherited susceptibility and modifiable exposures. Together, these mechanisms drive HF development and progression, though their relative contributions vary across populations. Methods: PubMed and Google Scholar were searched for clinical, biomedical, and interdisciplinary studies published between 1 January 2000, and 31 December 2025. Keywords included “Asian,” “adult,” “India,” “heart failure,” “risk assessment,” “prognosis,” and “predictive value.” Studies were included if they focused on South Asian Indian adults, with priority given to original research, systematic reviews, and meta-analyses. Pediatric studies and those centered on other ethnic groups were excluded. Results: Among South Asian Indians, cardiovascular disease burden remains disproportionately high compared with Western populations. Unique genetic architecture, environmental exposures, and sociocultural factors appear to contribute to earlier onset and more aggressive disease. Identifying population-specific genetic variants, clarifying psychosocial influences, and addressing environmental risks may help reduce these disparities. Conclusions: This qualitative review highlights key gaps in current knowledge. A deeper understanding of these determinants could refine HF risk stratification, guide targeted prevention strategies, and reduce the growing cardiovascular burden in South Asian Indians. There is an urgent need for South Asian specific HF risk prediction models.
Heart failure (HF) causes a heavy disease burden globally. Identifying etiological risk factors is essential for informing disease prevention and developing new treatment strategies. This review synthesizes insights from published Mendelian randomization (MR) studies on risk factors associated with HF. MR analyses support potential causal associations between various risk factors and HF, including lifestyle habits (e.g., physical activity, smoking, sleep patterns), cardiometabolic factors (e.g., obesity, hypertension, type 2 diabetes, dyslipidemia), nutritional exposures (e.g., 25-hydroxyvitamin D, sodium intake, diet), emerging biomarkers (e.g., fibroblast growth factor 23), gut microbiota, and potential druggable targets. These studies uncover molecular processes that may contribute to HF pathogenesis and provide leads for therapeutic intervention. However, findings are not always consistent across studies, and discrepancies may arise from differences in study design, genetic instruments, population characteristics, and analytical methods. Key assumptions, including the absence of horizontal pleiotropy and the validity of instrumental variables, may not always be fully satisfied, since the interpretation of MR results requires caution. Future MR studies integrating multi-omics data, advanced methodologies and diverse populations will be essential to refine causal inference and enhance translational relevance. Overall, MR provides valuable insights into the potential causal determinants of HF and may contribute to improved prevention strategies and identification of therapeutic targets, although further validation and integration with clinical evidence are required.
Background: Heart failure (HF) is a global health burden with high mortality. Sudden cardiac death (SCD) remains a major complication, highlighting the need for accurate risk prediction. Methods: We conducted a systematic review and meta-analysis, guided by the Population, Intervention, Comparator, Outcome, Timing, and Setting framework, to identify risk factors for SCD in HF and assess prediction models. Searches across 8 databases yielded eligible studies. Data extraction, risk of bias assessment using the Prediction Model Risk of Bias Assessment Tool, and statistical analyses were performed. Results: Twelve studies met inclusion criteria, with 8 included in the meta-analysis. New York Heart Association classification and left ventricular ejection fraction emerged as the most robust predictors of SCD. Additional significant factors included age, sex, ischemic etiology, diabetes, heart rate, sodium, potassium, creatinine, estimated glomerular filtration rate, and hemoglobin. Considerable heterogeneity was observed among studies. Conclusion: New York Heart Association class and left ventricular ejection fraction are key predictors of SCD in HF, while demographic, etiological, and laboratory factors further refine risk assessment. Current models show limitations due to heterogeneity and lack of external validation. Future work should integrate refined predictors, treatment responses, and diverse populations to improve the accuracy and clinical utility of SCD risk stratification in HF.
Sudden cardiac death (SCD) accounts for an estimated 4–5 million deaths annually worldwide and remains a major unresolved challenge in cardiovascular medicine. Survival from out-of-hospital cardiac arrest remains below 10% in most regions, and approximately half of SCD events occur as the first clinical manifestation of cardiovascular disease, often in individuals not identified by conventional risk stratification. We conducted a narrative review of the literature in PubMed/MEDLINE, Web of Science, and EMBASE (January 2003 to April 2026), prioritizing prospective cohorts, randomized trials, meta-analyses, and international guidelines. Five major modifiable lifestyle domains are consistently linked to SCD risk: sleep health, physical activity, diet, psychological stress, and tobacco and alcohol use. Sleep duration shows a U-shaped association with cardiovascular mortality, and untreated obstructive sleep apnea may confer substantially elevated risk (subgroup OR 3.87; 95% CI 1.09–13.81), although overall evidence remains heterogeneous. Physical activity also follows a U-shaped pattern, with habitual moderate activity being protective, and brief vigorous incidental activity (∼4 min/day) reducing cardiovascular mortality by 32%–34%, while sedentary behavior and unaccustomed vigorous exertion increase risk. Mediterranean dietary patterns reduce major cardiovascular events by approximately 30%, and psychological stress promotes arrhythmogenesis through neurohormonal activation and adverse remodeling. These exposures converge on four key mechanisms: autonomic dysfunction, systemic inflammation, structural and electrophysiological remodeling, and metabolic-hormonal dysregulation. Integrating lifestyle-based interventions into routine cardiovascular risk assessment offers a low-cost and scalable strategy for primary prevention. Emerging digital health technologies, including AI-enhanced electrocardiography and consumer-grade wearables, provide complementary opportunities for individualized risk prediction and population-level monitoring, particularly in resource-limited settings where conventional device-based prevention remains limited. Given the observational nature of most included evidence and the heterogeneity of SCD definitions across studies, the mechanistic and clinical inferences presented here should be regarded as hypothesis-generating, motivating prospective and where feasible interventional research rather than immediate practice change.
Jin-Hua Zhang, Xiao-Wei Meng, Hyungsoo Shin et al.· Frontiers in Cardiovascular...· 0 citations
A substantial increase in the global burden of CVDs is demonstrated, with prevalence nearly doubling between 1990 and 2019 and mortality continuing to rise worldwide.
Nawfal Hasan Siam, Umme Halima Mithila, S. Tisha et al.· Health Science Reports· 0 citations
Background/Objectives: Type 2 diabetes mellitus (T2DM) is a global epidemic strongly associated with an increased risk of heart failure, independent of coronary artery disease or hypertension. This condition, historically termed diabetic cardiomyopathy (DCM) and recently redefined as “diabetic myocardial disorder,” remains frequently underdiagnosed in its subclinical stages. The objective of this non-systematic review is to synthesize current evidence on the pathophysiological mechanisms, diagnostic advancements, and evolving therapeutic strategies for diabetic myocardial involvement. Methods: A comprehensive review of contemporary literature was conducted, focusing on recent consensus statements from the ESC and AHA, large-scale epidemiological data (IDF/WHO), and pivotal clinical trials (EMPA-REG, DAPA-HF, and LEADER). We analyzed the role of multimodal imaging—specifically speckle-tracking echocardiography (STE) and multiparametric cardiac magnetic resonance (CMR)—and circulating biomarkers in early phenotyping. Results: Pathophysiological drivers include lipotoxicity, oxidative stress, and AGE-mediated fibrosis. Advanced imaging techniques, such as global longitudinal strain (GLS) and CMR T1-mapping/ECV quantification, demonstrate superior sensitivity over LVEF in detecting early subendocardial dysfunction and diffuse fibrosis. Furthermore, NT-proBNP serves as a robust prognostic marker for the HFpEF-like trajectory typical of diabetes. Clinically, the therapeutic landscape has shifted with SGLT2 inhibitors and GLP-1 receptor agonists, which provide significant cardioprotection and reduction in heart failure hospitalizations through mechanisms beyond glycemic control. Conclusions: Diabetic myocardial disorder represents a complex continuum within the cardiometabolic spectrum. Early detection through multimodal imaging and biomarkers is essential for risk stratification. Integrating novel glucose-lowering therapies with proven cardiovascular benefits is now mandatory to alter the natural history of the disease and prevent progression to overt heart failure.
S. D'Elia, Rosa Franzese, Ettore Luisi et al.· Diabetology· 0 citations
Cardiovascular diseases (CVDs) remain the leading cause of morbidity and mortality worldwide, and the burden is particularly severe in low‐income and middle‐income countries. Although traditional risk factors such as hypertension, dyslipidemia, diabetes, and smoking have been well established, new research evidence indicates that inflammation, environmental exposure, psychological and social stress, gut microbiota, and genetic susceptibility play a crucial role in shaping the risk profile of CVDs. This review comprehensively summarizes the epidemiology, pathophysiological mechanisms, screening strategies, prevention, and treatment interventions of CVDs throughout the disease development process. Special emphasis is placed on the integration of multiomics methods, artificial intelligence (AI), and digital health technologies (including wearable devices and AI‐enhanced electrocardiograms), which are transforming risk prediction and personalized prevention approaches. We also summarize current preclinical and clinical evidence, including ongoing trials, and discuss implementation differences in different socioeconomic environments. Despite significant progress, there are still many challenges in translating new biomarkers and technologies into scalable and equitable clinical applications. This review identifies key knowledge gaps and proposes future directions toward precision cardiovascular medicine, aiming to combine mechanistic insights with practical applications, ultimately reducing the burden of CVDs globally.
Mo-Wei Kong, Zaiyong Zheng, Min Huang et al.· MedComm· 0 citations
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