Nomogram-based risk prediction model employing serum biomarkers to assess intestinal injury risk in patients with metabolic syndrome - Chen, Yongqing; Wen, Guangxu; Wang, Hongmei; Yang, Qiyu; Luo, Zilang; Wang, Xin; Ouyang, Jing; Yang, Jiadan (Frontiers in Endocrinology, 2025)
ObjectivePatients with metabolic syndrome (MetS) are more likely to have intestinal injury that may accelerate the disease process. We developed a risk prediction model for the non-invasive, rapid, and accurate assessment of intestinal injury in patients with MetS based on serum biomarkers.MethodsPatients with MetS who underwent colonoscopy were enrolled in this study. Based on the results of the colonoscopy, the participants were divided into the intestinal injury and non-intestinal injury groups. Blood samples were collected to detect laboratory indicators and quantify serum biomarkers. Univariate and multivariate logistic regression analyses were employed to identify predictors of intestinal injury in patients with MetS and to construct a nomogram-based risk prediction model. We employed bootstrapping and 5-fold cross-validation to validate the model internally, with the area under the curve (AUC) used to assess the predictive efficacy, the calibration curve utilized to evaluate the calibration degree, and decision curve analysis (DCA) used to evaluate the clinical practicability of the model.ResultsThe study included 263 participants. Our multivariate logistic regression analysis indicated that clinical features such as age, body mass index, neutrophil percentage, as well as serum biomarkers including diamine oxidase and lipopolysaccharide, were predictive factors for intestinal injury in patients with MetS. The model had strong repeatability (bootstrap method: precision: 0.873, 5-fold cross-validation: AUC: 0.948 ± 0.012), differentiation (AUC: 0.957), and accuracy (Hosmer-Lemeshow χ2 = 3.985, P = 0.858), while DCA results confirmed the clinical utility of the nomogram.ConclusionsSerum biomarkers are effective variables to assess intestinal injury in patients with MetS via our nomogram-based risk prediction model.Clinical trial registrationhttps://www.chictr.org.cn/, identifier ChiCTR2400088476.