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Background <p>Previous studies have shown that obesity and metabolic and psychological factors are related to cardiometabolic comorbidities, and this study focuses on the BRI, TyG index and depression and explores the independent and combined effects of these three factors on cardiometabolic comorbidities from multiple perspectives.</p> Methods <p>We analyzed data from 5,199 participants (baseline 2011) in the China Health and Retirement Longitudinal Study (CHARLS). The participants were stratified into four groups on the basis of the median TyG index (8.62) and BRI (4.03). Logistic regression models were used to assess associations between TyG-BRI strata, depression (CES-D score ≥ 10), and incident CMM (cardiometabolic multimorbidity), incorporating multiplicative interaction terms (BRI×TyG×Depression) and additive interaction indices (RERI/AP). A composite score was developed using standardized biomarker z scores and depression status, with predicti·ve performance evaluated via ROC-AUC comparisons (DeLong’s test) and restricted cubic splines.</p> Results <p>During the 7-year follow-up period, 331 incident CMM cases were documented. Machine learning models (random forest and XGBoost) identified BRI and TyG as the top predictors of CMM. Logistic regression revealed the highest risk in the high-BRI &amp; high-TyG group (OR = 3.89, 95% CI = 3.02–5.01 vs. low-BRI and low-TyG). Tripartite exposure (high BRI and high TyG and depression) was associated with a striking 14-fold increased risk (OR = 14.2, 95% CI = 7.95–27.6), with a significant multiplicative interaction (<i>p</i> = 0.016). The composite cardiometabolic‒psychosocial score showed superior predictive capacity (AUC = 0.70 vs. BRI = 0.66, TyG = 0.65, depression = 0.56; DeLong’s <i>p</i> &lt; 0.001) and dose‒responsive risk escalation (P-overall &lt; 0.001, P for nonlinearity = 0.70).</p> Conclusion <p>This study identifies BRI, TyG, and depression as independent and synergistic predictors of CMM. Our findings advocate integrated clinical strategies for effective CMM prevention.</p>

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Obesity-depression-metabolism triad: amplification of cardiometabolic multimorbidity risk in Chinese adults (CHARLS 2011–2018)

  • 期刊论文

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  • 发表日期：

    2025年9月29日

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  • Qinglin He,
  • Wen Yu,
  • Chubing Long,
  • Dong Lin,
  • Feng Lu,
  • Maoling Zhong,
  • Wei Lin,
  • Shupeng Huang,
  • Shuiying Hua,
  • Fuzhou Hua,
  • Xifeng Wang

摘要

Background

Previous studies have shown that obesity and metabolic and psychological factors are related to cardiometabolic comorbidities, and this study focuses on the BRI, TyG index and depression and explores the independent and combined effects of these three factors on cardiometabolic comorbidities from multiple perspectives.

Methods

We analyzed data from 5,199 participants (baseline 2011) in the China Health and Retirement Longitudinal Study (CHARLS). The participants were stratified into four groups on the basis of the median TyG index (8.62) and BRI (4.03). Logistic regression models were used to assess associations between TyG-BRI strata, depression (CES-D score ≥ 10), and incident CMM (cardiometabolic multimorbidity), incorporating multiplicative interaction terms (BRI×TyG×Depression) and additive interaction indices (RERI/AP). A composite score was developed using standardized biomarker z scores and depression status, with predicti·ve performance evaluated via ROC-AUC comparisons (DeLong’s test) and restricted cubic splines.

Results

During the 7-year follow-up period, 331 incident CMM cases were documented. Machine learning models (random forest and XGBoost) identified BRI and TyG as the top predictors of CMM. Logistic regression revealed the highest risk in the high-BRI & high-TyG group (OR = 3.89, 95% CI = 3.02–5.01 vs. low-BRI and low-TyG). Tripartite exposure (high BRI and high TyG and depression) was associated with a striking 14-fold increased risk (OR = 14.2, 95% CI = 7.95–27.6), with a significant multiplicative interaction (p = 0.016). The composite cardiometabolic‒psychosocial score showed superior predictive capacity (AUC = 0.70 vs. BRI = 0.66, TyG = 0.65, depression = 0.56; DeLong’s p < 0.001) and dose‒responsive risk escalation (P-overall < 0.001, P for nonlinearity = 0.70).

Conclusion

This study identifies BRI, TyG, and depression as independent and synergistic predictors of CMM. Our findings advocate integrated clinical strategies for effective CMM prevention.