Cardiometabolic index as a predictor of gallstone incidence in U.S. adults: insights from NHANES 2017–2020 - Ji Li; Shou-jun Bai (BMC Gastroenterology)
Abstract Background Gallstone disease (GSD) is associated with obesity. The Cardiometabolic Index (CMI), a metric that accurately assesses central adiposity and visceral fat, has not been extensively studied in relation to GSD risk. This study investigates the link between CMI and GSD incidence in U.S. adults. Methods This study utilized data from the National Health and Nutrition Examination Survey(NHANES)(2017–2020) to assess the association between CMI and GSD, adjusting for confounders such as age, sex, race, chronic diseases, and lifestyle factors. Multivariable logistic regression models and subgroup analyses were employed. Generalized Additive Models (GAM) and advanced curve fitting techniques were used to explore potential non-linear relationships, with threshold effects determined via piecewise linear regression if such relationships were identified. Receiver Operating Characteristic (ROC) curves evaluated and compared the predictive performance of CMI, Body Mass Index (BMI), and Waist Circumference (WC), establishing optimal cutoff values along with their sensitivity and specificity. Results This study included 3,706 participants, of whom 10.6% (392) had GSD. Participants with GSD showed significantly higher CMI values (0.57 vs. 0.44, P = 0.0002). The GSD group included more females and older adults, with increased risks for hypertension, diabetes, higher serum cholesterol and creatinine levels, and a higher risk of cancer. Logistic regression analysis revealed that higher CMI was significantly associated with greater GSD incidence (OR = 1.19, 95% CI = 1.02–1.38, P < 0.0001). The ROC curve demonstrated superior predictive performance (AUC = 0.778), outperforming conventional metrics like BMI and WC. GAM analysis indicated a non-linear positive correlation between CMI and GSD, with an optimal threshold of 0.996. Subgroup analysis found the strongest association among females, individuals aged 20–39, non-Hispanic Whites, those without a history of coronary heart disease, and alcohol consumers. Conclusion Our study reveals a nonlinear positive correlation between the CMI and the incidence of GSD among U.S. adults, with a threshold value of 0.996. Despite limitations in sample size that constrained the analysis of a fully adjusted model, after adjusting for confounding factors, the AUC for predicting GSD using CMI reached 0.778, surpassing traditional metrics. These findings underscore the importance of CMI as a critical risk factor and emphasize the necessity of targeted interventions for high-risk populations.