| Title: | The influence of genotype, biomarkers, lifestyle, and psychosocial factors on the occurrence, development, and prognosis of cardiovascular diseases, to establish an early risk prediction model. |
| Lead Institution: | Beijing Anzhen Hospital, Capital Medical University |
| Principal investigator: |
Application 177024
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About
The prevalence and mortality rates of cardiovascular diseases (CVD) are gradually increasing worldwide, leading to a decline in patient quality of life and an escalating economic burden on society. Research on risk factor detection remains urgent for public health, as identifying high-risk factors for CVD is crucial for early prevention and detection. While past studies have elucidated the impact of psychosocial, genetic, and lifestyle factors on certain cardiovascular conditions, the interactions among these factors and their biological mechanisms require further assessment. Building upon previous research, this study integrates large-sample data from databases and employs multi-stage association analyses. It aims to further clarify the influence of psychosocial, genetic, and lifestyle factors on the occurrence, development, and prognosis of CVD. By combining imaging and biomarker data, the research analyzes potential mechanisms through which these factors contribute to CVD in populations with different baseline characteristics, evaluating their weights and laying the foundation for targeted diagnostic and therapeutic approaches. For genetic data, GWAS is conducted for clinical CVD burden analysis, assuming additive genetic effects and adjusting for gender and population-specific principal components. Genetic contribution analysis for major ethnic groups and principal component analysis determine the most suitable population subsets for study. Modifiable factors are screened to generate new lifestyle scores. A multivariate adjustment model is constructed, and Cox proportional hazard models analyze relationships between lifestyle factors, overall lifestyle scores, and risks of all-cause mortality and cause-specific mortality. Analyzing lifestyle and genetic data's impact on CVD occurrence and development, the study categorizes populations by different CVD types. Combining imaging and biomarker data, it explores the mechanisms through which genetic and lifestyle factors contribute to CVD, providing insights through multifactorial analysis. The study has three main objectives: Investigate the impact of genetic variations on CVD through Mendelian randomization and multi-gene risk score analysis, identifying susceptible population subsets for targeted treatments. Explore causal relationships between modifiable environmental, genetic, metabolic, dietary, lifestyle, and psychosocial factors and specific CVD through variable analysis. Evaluate factors influencing CVD prognosis by analyzing biomarker and imaging data, combined with lifestyle and psychosocial data. By providing a comprehensive assessment, this research enables more accurate individual cardiovascular risk assessment, laying the groundwork for personalized prevention and intervention strategies. Understanding the contributions of different factors to cardiovascular health allows governments and health institutions to formulate targeted prevention and intervention strategies, ultimately reducing the overall incidence of cardiovascular diseases.1 Publication
| Pub ID | Title | Author(s) | Year | Journal |
|---|---|---|---|---|
| 18112 | Frailty predicts the onset and dynamic progression of cardio-renal-metabolic multimorbidity: a multistate modeling analysis from the UK Biobank | Lulu Liu (+4) | 2026 | Diabetology & Metabolic Syndrome |
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