Heart failure (HF) causes a heavy disease burden globally. Identifying etiological risk factors is essential for informing disease prevention and developing new treatment strategies. This review synthesizes insights from published Mendelian randomization (MR) studies on risk factors associated with HF. MR analyses support potential causal associations between various risk factors and HF, including lifestyle habits (e.g., physical activity, smoking, sleep patterns), cardiometabolic factors (e.g., obesity, hypertension, type 2 diabetes, dyslipidemia), nutritional exposures (e.g., 25-hydroxyvitamin D, sodium intake, diet), emerging biomarkers (e.g., fibroblast growth factor 23), gut microbiota, and potential druggable targets. These studies uncover molecular processes that may contribute to HF pathogenesis and provide leads for therapeutic intervention. However, findings are not always consistent across studies, and discrepancies may arise from differences in study design, genetic instruments, population characteristics, and analytical methods. Key assumptions, including the absence of horizontal pleiotropy and the validity of instrumental variables, may not always be fully satisfied, since the interpretation of MR results requires caution. Future MR studies integrating multi-omics data, advanced methodologies and diverse populations will be essential to refine causal inference and enhance translational relevance. Overall, MR provides valuable insights into the potential causal determinants of HF and may contribute to improved prevention strategies and identification of therapeutic targets, although further validation and integration with clinical evidence are required.
Background The relationship between cumulative triglyceride-glucose-body mass index (TyG-BMI) exposure and the risk of cardiovascular disease (CVD) has been unclear. This study investigated the association between cumulative TyG-BMI exposure and the risk of CVD in the Chinese population using data from the large-scale, prospective community-based Kailuan Study. Methods The Kailuan Study included 47,577 individuals without a history of CVD or cancer who underwent health examinations in 2006, 2008, and 2010. Cumulative TyG-BMI exposure was calculated as the weighted sum of the mean TyG-BMI for each time interval (value × time). Participants were stratified into four groups based on the cumulative TyG-BMI exposure quartile. Cox proportional hazards regression models were established to calculate hazard ratios and 95% confidence intervals for evaluation of the relationship between cumulative TyG-BMI exposure and risk of CVD. The area under the receiver operating characteristic (ROC) curve was calculated to compare the predictive power of cumulative TyG-BMI, TyG, and BMI for CVD. Results A total of 3,514 incident cardiovascular events occurred during a median follow-up of 10 years. The risk of CVD increased with increasing cumulative TyG-BMI exposure quartile. After adjusting for potential confounders, Cox regression analysis yielded respective hazard ratios (95% confidence intervals) of 1.32 (1.18–1.49), 1.33 (1.18–1.49), and 1.44 (1.29–1.62) for the Q2, Q3, and Q4 groups in comparison with the Q1 group. The subgroup analysis showed a significant interaction between cumulative TyG-BMI index and age or hypertension, but there was no interaction between sex, Diabetes mellitus and cumulative TyG-BMI index. The restricted cubic spline analysis revealed a significant non-linear relationship between cumulative TyG-BMI index and the risk of CVD. The area under the ROC curve (AUC) of cumulative TyG-BMI was 0.6047, demonstrating modestly higher discriminative performance than TyG (AUC: 0.5602) and BMI (AUC: 0.5612). Conclusions High cumulative TyG-BMI exposure is associated with an increased risk of CVD. The TyG-BMI value may help to identify individuals at high risk of developing CVD.
Peng Fu, Yuxian Wang, Kuangyi Wu et al.· Frontiers in Cardiovascular...· 0 citations
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