Ambient air pollution drives cardiovascular disease (CVD), yet single-pollutant models overlook how risk emerges within broader environmental and structural contexts. This review applies the Public Health Exposome (PHE) framework, which integrates natural, built, social, and policy environments, to show how multilevel systems shape CVD vulnerability. Extreme heat, humidity, and climate variability modify pollutant toxicity, while structural inequities, discriminatory policies, and inadequate planning intensify exposures in marginalized communities. Cumulative psychosocial stress further amplifies inflammatory responses, activating the endo-exposome. Multi-omics studies reveal that pollution alters epigenomic, transcriptomic, and metabolomic pathways linked to inflammation, oxidative stress, and endothelial dysfunction. Emerging tools like machine learning, high-resolution exposure modeling, graph-based analytics, and wearable sensing, enable integration of environmental data and support systems-level approaches. The PHE framework illustrates how layered environmental and structural stressors accumulate to elevate CVD risk. Reducing this burden requires coupling PHE science with equitable public policy.

Purpose of ReviewCardiovascular disease (CVD) is the major complication and leading cause of death among individuals with type 2 diabetes (T2D). Women and younger adults with T2D face a disproportionately higher risk of CVD, compared with their male or older counterparts. This review summarizes clinical and epidemiological evidence on the mechanisms underlying these disparities and highlights directions for future research and clinical practice.Recent FindingsWomen with T2D lose the cardioprotection typically seen in the general population and face disproportionately higher cardiovascular risk. This excess risk is influenced by adverse metabolic profiles preceding T2D onset; female-specific factors such as polycystic ovary syndrome and gestational complications; and non-biological contributors, including delayed diagnosis and less optimal healthcare utilization and delivery in women compared with men. Young adults with early-onset T2D also experience a heightened cardiovascular burden, driven by a more aggressive disease course, prolonged exposure to metabolic abnormalities, and distinctive psychosocial stressors that compound their risk. Despite these disparities, both female and young adult patients with T2D remain understudied, hindering the development of precision prevention and management strategies.SummaryFuture mechanistic and interventional research that integrates sex and age as key biological factors will be critical for advancing precision approaches and reducing disparities in diabetes care and outcomes.

PurposeElectronic health record (EHR) data have become essential and commonplace in epidemiological and clinical research. In this narrative review on the use of EHR data in epidemiology, I discuss appropriate research questions, common biases, and potential sensitivity analyses focusing on recent work that has been done to improve the internal and external validity of EHR-based studies.Recent FindingsAn appropriate research question addresses issues of EHR-data availability and accessibility, while patient selection forces into healthcare may result in a sample that lacks representativeness. Natural language processing tools are becoming widespread and tailored to EHR use for operationalizing unstructured data. Common biases identified in the literature include misclassification and measurement error, informed presence bias, selection bias and sampling error, and residual confounding. SummaryEHR data are unlike other observational data sources and carry assumptions about patient selection and clinical documentation that can impact the validity of the analyses. Potential sensitivity analyses including quantitative bias analysis can help to understand the impact of one or more of these biases on the study findings.

Real-world data (RWD) from electronic health records (EHRs) and administrative claims databases are used increasingly to generate real-world evidence (RWE). RWE is used to support clinical evidence packages for medicines that inform decision-makers. In this review of current issues in the use of RWD-derived external comparator groups to support regulatory filings, we assess a series of topics that generally apply across many disease indications. However, most of the examples and illustrations focus on the oncology clinical research setting. The topics include an overview of current uses of RWD in drug development, a discussion of regulatory filings using RWD-derived external comparators, a brief overview of guidance documents and white papers pertaining to external comparators, a summary of some limitations and methodological issues in the use of external comparator groups and finally, a look at the future of this area and recommendations.

The purpose of this review was to summarize the current state of the literature on the use of "mHealth" (the use of mobile devices for health promotion) for injury prevention and control. mHealth is being used to measure, predict, and prevent the full spectrum of injuries. However, most literature remains preliminary or in a pilot stage. Use of best-of-class design principles (e.g., user-centered design, theory-based development) is uncommon, and wide-scale dissemination of effective monitoring or intervention tools is rare. mHealth for injury prevention holds promise, but further work is needed across the full spectrum of development and translation.