Risk Factors for Cardiovascular Disease: Epidemiology, Screening, Prevention, and Therapeutic Interventions

Figures

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  FIGURE 1. Global burden of cardiovascular disease and an integrated framework linking risk factors to clinical events. (Draft diagram created in WPS office PPT with GPT 5.5‐assisted layout suggestions. Final image refined and annotated by the authors. All pathway details were cross‐checked against experimental data.) (A) Global distribution of CVD burden—Regional burden of cardiovascular disease measured by disability‐adjusted life years (DALYs). (B) Mortality trends by income level—Trends in CVD mortality across countries with different income levels from 1990 to 2025. (C) Impact of obesity—Contribution of obesity to the global burden of cardiovascular disease. (D) Impact of diabetes—Contribution of diabetes to cardiovascular disease risk and burden. (E) Impact of population aging—Age‐related changes in cardiovascular disease burden and risk. (F) Impact of PM2.5 exposure—Cardiovascular burden attributable to ambient particulate air pollution.

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  FIGURE 2. Systems‐level landscape of cardiovascular risk factors: integration of traditional and emerging determinants through molecular nodes and a mechanistic axis driving CVD progression. (Draft diagram created in WPS office PPT with GPT 5.5‐assisted layout suggestions. Final image refined and annotated by the authors. All pathway details were cross‐checked against experimental data.) (A) Hypertension, diabetes, dyslipidemia, smoking, obesity, age, sex, and physical inactivity. (B) Sleep disorders, stress, socioeconomic factors, PM2.5, microplastics, gut dysbiosis, chronic inflammation, and genetic susceptibility. (C) Endothelial dysfunction, inflammation, oxidative stress, mitochondrial dysfunction, plaque instability, and thrombosis. (D) From risk exposure to vascular injury, atherosclerosis, myocardial infarction, stroke, and heart failure.

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  FIGURE 3. Clinical pipeline for CVD screening and risk assessment: from traditional risk scores to AI‐driven multimodal prediction and personalized intervention. (Draft diagram created in WPS office PPT with GPT 5.5‐assisted layout suggestions. Final image refined and annotated by the authors. All pathway details were cross‐checked against experimental data.) (A) Framingham, SCORE2, and ASCVD used to estimate baseline cardiovascular risk. (B) hsCRP, Lp(a), troponin, BNP, and multiomics panels for enhanced risk detection. (C) Coronary artery calcium score (CACS) and coronary CTA to assess structural disease. (D) Carotid intima‐media thickness (IMT) and cardiac MRI for functional and anatomical evaluation. (E) Combining multimodal data to optimize risk stratification and guide personalized prevention. (F) Wearable devices, heart rate variability (HRV), and continuous monitoring to enable dynamic risk assessment and early intervention.

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  FIGURE 4. Multilayer prevention and therapeutic intervention landscape for cardiovascular disease (CVD): linking interventions to mechanisms and reduction of major adverse cardiovascular events. (Draft diagram created in WPS office PPT with GPT 5.5‐assisted layout suggestions. Final image refined and annotated by the authors. All pathway details were cross‐checked against experimental data.) (A) Lifestyle interventions (diet, exercise, smoking cessation) targeting LDL, blood pressure, and inflammation to reduce CVD risk in the general population. (B) Medication‐based management for high‐risk individuals, including statins, antihypertensives, and anti‐inflammatory strategies, to prevent disease progression. (C) Intensive management for patients with established CVD, incorporating novel approaches such as precise anti‐inflammatory treatment (NLRP3/IL‐1β/IL‐6), gene/RNA lipid‐lowering therapy (PCSK9, ANGPTL3, Lp(a)), and gut microbiome‐targeted interventions (TMAO pathway) to reduce major adverse cardiovascular events.

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  FIGURE 5. Closed‐loop systems medicine framework for future CVD management that integrates multiomics, digital biomarkers, AI, and policy actions. (Draft diagram created in WPS office PPT with GPT 5.5‐assisted layout suggestions. Final image refined and annotated by the authors. All pathway details were cross‐checked against experimental data.) (A) Multimodal data acquisition—Collection of multiomics, microbiome, wearable/digital biomarkers, and social–environmental determinants (e.g., PM2.5). (B) AI‐driven data integration—Fusion engine integrates multimodal data for refined risk stratification and individualized intervention planning. (C) Intervention strategies—Personalized care (genotype/metabotype‐guided therapy, lifestyle prescriptions), population‐level public health measures, and environmental/policy actions. (D) Longitudinal monitoring and feedback—Continuous assessment and iterative optimization of risk prediction and preventive interventions to reduce cardiovascular events.