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Background <p>Cardiometabolic index (CMI) is an index integrating visceral obesity and dyslipidemia. This study intends to scrutinize the connection between CMI and gallstone disease (GSD) and to elucidate the association between CMI and insulin resistance (IR) in patients with GSD.</p> Methods <p>To explore the potential nonlinear association and determine the inflection point, a restricted cubic spline (RCS) analysis was performed. Following categorization of CMI based on the identified inflection point, multivariate logistic regression models, subgroup analyses, and interaction tests were utilized to assess the connection between CMI and GSD, as well as between CMI and IR in GSD patients. The homeostasis model assessment for IR (HOMA-IR) and triglyceride-glucose (TyG) index was applied to evaluate IR. Spearman analysis was implemented to investigate the connection between CMI and HOMA-IR. The predictive performance of each indicator was evaluated by the receiver operating characteristic (ROC) curve and the area under the curve (AUC).</p> Results <p>The study included 2311 individuals, with a GSD prevalence of 10.90%. RCS analysis revealed a nonlinear positive correlation between CMI and GSD (nonlinear <i>P</i> &lt; 0.001), as well as between CMI and IR (nonlinear <i>P &lt;</i> 0.001). In the fully adjusted multivariable logistic regression analysis of covariates, compared with the low-category CMI group, the high-category CMI was significantly associated with the risk of GSD (OR = 1.547, 95% CI: 1.143–2.092, <i>P</i> = 0.005), IR (OR = 4.990, 95% CI: 2.517–9.892, <i>P</i> &lt; 0.001). Subgroup analysis demonstrated that the correlation between CMI and GSD was stronger in females. Spearman correlation analysis showed a positive association between CMI and HOMA-IR in GSD patients (<i>r</i> = 0.548, <i>P</i> &lt; 0.001). The ROC curve demonstrated the predictive performance of the CMI model for GSD (AUC = 0.743), which was superior to conventional indicators such as Body Mass Index and Waist Circumference; the predictive performance of CMI (AUC = 0.772) for IR was consistent with that of TyG (AUC = 0.772).</p> Conclusion <p>Our research demonstrates that CMI exhibits a nonlinear positive correlation with the incidence of GSD and IR. This suggests that CMI may serve as a novel and valuable indicator for further investigating the intricate relationships among metabolic syndrome, obesity, and GSD.</p>

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Association of cardiometabolic index with gallstone disease and insulin resistance based on NHANES data

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年5月9日

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  • Liu Yuan,
  • Shuqi Wang,
  • Dong Wang,
  • Enbo Wang

摘要

Background

Cardiometabolic index (CMI) is an index integrating visceral obesity and dyslipidemia. This study intends to scrutinize the connection between CMI and gallstone disease (GSD) and to elucidate the association between CMI and insulin resistance (IR) in patients with GSD.

Methods

To explore the potential nonlinear association and determine the inflection point, a restricted cubic spline (RCS) analysis was performed. Following categorization of CMI based on the identified inflection point, multivariate logistic regression models, subgroup analyses, and interaction tests were utilized to assess the connection between CMI and GSD, as well as between CMI and IR in GSD patients. The homeostasis model assessment for IR (HOMA-IR) and triglyceride-glucose (TyG) index was applied to evaluate IR. Spearman analysis was implemented to investigate the connection between CMI and HOMA-IR. The predictive performance of each indicator was evaluated by the receiver operating characteristic (ROC) curve and the area under the curve (AUC).

Results

The study included 2311 individuals, with a GSD prevalence of 10.90%. RCS analysis revealed a nonlinear positive correlation between CMI and GSD (nonlinear P < 0.001), as well as between CMI and IR (nonlinear P < 0.001). In the fully adjusted multivariable logistic regression analysis of covariates, compared with the low-category CMI group, the high-category CMI was significantly associated with the risk of GSD (OR = 1.547, 95% CI: 1.143–2.092, P = 0.005), IR (OR = 4.990, 95% CI: 2.517–9.892, P < 0.001). Subgroup analysis demonstrated that the correlation between CMI and GSD was stronger in females. Spearman correlation analysis showed a positive association between CMI and HOMA-IR in GSD patients (r = 0.548, P < 0.001). The ROC curve demonstrated the predictive performance of the CMI model for GSD (AUC = 0.743), which was superior to conventional indicators such as Body Mass Index and Waist Circumference; the predictive performance of CMI (AUC = 0.772) for IR was consistent with that of TyG (AUC = 0.772).

Conclusion

Our research demonstrates that CMI exhibits a nonlinear positive correlation with the incidence of GSD and IR. This suggests that CMI may serve as a novel and valuable indicator for further investigating the intricate relationships among metabolic syndrome, obesity, and GSD.