Precision Epidemiological Approaches to Cardiometabolic Disease Prevention and Management.

Cardiometabolic diseases, including hypertension, diabetes mellitus, obesity, dyslipidaemia, and cardiovascular diseases, are among the leading causes of morbidity and mortality globally. The burden is increasing rapidly in low- and middle-income countries (LMICs), particularly in Africa, where urbanization, lifestyle changes, environmental exposures, and limited healthcare access contribute to poor health outcomes. Conventional prevention and treatment approaches often rely on generalized strategies that do not adequately account for individual and population differences in genetic susceptibility, environmental risk factors, lifestyle behaviours, and therapeutic response.To address these challenges, precision epidemiology has emerged as an essential discipline. While personalised and precision medicine focus on clinical diagnosis and individual-level therapeutic interventions, and precision public health emphasizes the targeted deployment of population-level policies, precision epidemiology is defined as the systematic integration of high-resolution genomic, clinical, environmental, behavioral, and population-level data to quantify distribution patterns, identify multi-factorial etiological pathways, and refine risk stratification in populations. It is distinguished from purely clinical or molecular sciences by its core commitment to population-level design, generalizability, and causal inference. Advances in genomics, pharmacogenomics, biomarker discovery, artificial intelligence, digital health, and data analytics provide new opportunities to identify high-risk populations and design targeted interventions.This Research Topic aims to provide a multidisciplinary platform for advancing precision epidemiological approaches to the prevention and management of cardiometabolic diseases. We seek innovative research that integrates genomic, clinical, environmental, behavioral, and population-level data to improve understanding of disease risk, progression, therapeutic response, and health outcomes. Particular emphasis is placed on identifying population-specific determinants of cardiometabolic diseases and addressing health disparities in underrepresented populations, especially in African and other LMIC settings.We welcome original research articles, reviews, systematic reviews, meta-analyses, perspectives, and methodological studies focused on precision epidemiological approaches to cardiometabolic disease prevention and management. All submitted work must situate its findings within an epidemiological framework, demonstrating a clear population-level orientation. Studies that are purely laboratory-based, translational, or focused on individual-level clinical outcomes without a population or epidemiological foundation are outside the scope of this collection.Submissions should align with one of the following five key areas:1. Genomics and Pharmacogenomics in Cardiometabolic Diseases: Studies integrating genomic and pharmacogenomic data with population-level variables to evaluate genetic susceptibility and treatment response variability.2. Biomarkers and Molecular Epidemiology: Research utilizing molecular, cellular, and biochemical markers, provided they are explicitly grounded in population-level epidemiological inquiry rather than isolated laboratory or clinical models.3. Risk Prediction and Precision Prevention Strategies: Development and validation of predictive models to identify high-risk cohorts and optimize preventive interventions.4. Population-Specific and African Precision Health: Studies addressing the unique methodological challenges of conducting precision epidemiology in resource-limited settings. We strongly encourage submissions addressing data infrastructure constraints, biobank availability, the representativeness of genomic reference populations, and implementation fidelity.5. Digital and Data-Driven Epidemiological Tools: Research with direct epidemiological utility, such as disease surveillance systems, electronic health record-based phenotyping, real-world data analyses, and population-level risk modelling. Submissions focusing on clinical decision-support systems or individual-level health technologies are outside the scope and fall more appropriately under clinical informatics. Topic editors: Charity Masilela, Sithandiwe Mazibuko Mbeje, Sihle Mabhida, SINENHLANHLA MTHEMBU Source: https://www.frontiersin.org/research-topics/83409/precision-epidemiological-approaches-to-cardiometabolic-disease-prevention-and-management