Diet, Big Data and Cardiovascular Risk: Toward Personalised Prevention in Human Populations

Cardiovascular disease (CVD) remains the leading cause of death worldwide, and diet is one of its most important modifiable risk factors. Decades of nutritional epidemiology link dietary patterns such as the Mediterranean, DASH and plant-based diets to lower cardiovascular risk. Yet the same diet does not benefit everyone equally. Age, sex, ethnicity, genetics, metabolic health, medication use and environmental exposures such as air pollution and physical activity can all change how diet relates to cardiovascular outcomes. Large cohorts, electronic health records and newer analytical methods, including causal inference and machine learning, now make it possible to study these differences in human populations at scale. Important gaps remain. Dietary data are often self-reported and noisy, prediction models are rarely validated in independent populations or trials, and evidence from under-represented and low-resource settings is limited. This Research Topic addresses these gaps.This Research Topic aims to advance the evidence on how diet interacts with individual and environmental characteristics to shape cardiovascular risk, and to support more personalised, evidence-based prevention. We welcome studies in human populations that use large cohorts, health records, trials or pooled evidence to identify who benefits most from which dietary approach, to improve the prediction of cardiovascular risk and outcomes, and to test dietary interventions. All submissions must address cardiovascular outcomes, risk factors or prevention. We especially welcome work that is transparent about its methods, validated in independent populations, and attentive to equity and global health., including;Cohort and electronic health record studies of dietary patterns, foods and nutrients in relation to cardiovascular outcomes, including hypertension, coronary heart disease, heart failure, stroke and cardiometabolic risk factors.Differences in the effect of diet by age, sex, ethnicity, socioeconomic status, comorbidity and environmental exposures such as air pollution and physical activity.Machine learning, causal inference and prediction models for cardiovascular risk and for individual response to dietary interventions, with independent validation.Randomised trials, real-world studies, systematic reviews, meta-analyses and umbrella reviews of dietary interventions and supplements for cardiovascular prevention.Human cohort or trial studies using nutritional biomarkers, metabolomics or other omics profiles, and gene–diet interactions, to identify diet-related signatures of cardiovascular risk.Improving dietary assessment, including validated dietary indices, digital and wearable tools, and measurement error.Health disparities, global health, implementation and ethical considerations for personalised nutrition in cardiovascular prevention.Studies that use only cell or animal models, or that address nutrition without cardiovascular outcomes, are outside the scope of this Research Topic. Topic editors: Li-Da Wu, Yacong Bo Source: https://www.frontiersin.org/research-topics/86971/diet-big-data-and-cardiovascular-risk-toward-personalised-prevention-in-human-populations