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Ximisinuer Tusongtuoheti,1,2 Guoqing Huang,1,2 Yushan Mao1 1Department of Endocrinology, The First Affiliated Hospital of Ningbo University, Ningbo University, Ningbo, People’s Republic of China; 2Health Science Center, Ningbo University, Ningbo, People’s Republic of ChinaCorrespondence: Guoqing Huang; Yushan Mao, Department of Endocrinology, The First Affiliated Hospital of Ningbo University, 247 Renmin Road, Ningbo, People’s Republic of China, Tel +86 15737939838; +86 13867878937, Email guoqinghuang1992@163.com; maoyushan@nbu.edu.cnPurpose: The aim of this study was to identify independent risk factors for carotid atherosclerosis (CAS) in a population with hyperuricemia (HUA) and develop a CAS risk prediction model.Patients and Methods: This retrospective study included 3579 HUA individuals who underwent health examinations, including carotid ultrasonography, at the Zhenhai Lianhua Hospital in Ningbo, China, in 2020. All participants were randomly assigned to the training and internal validation sets in a 7:3 ratio. Multivariable logistic regression analysis was used to identify independent risk factors associated with CAS. The characteristic variables were screened using the least absolute shrinkage and selection operator combined with 10-fold cross-validation, and the resulting model was visualized by a nomogram. The discriminative ability, calibration, and clinical utility of the risk model were validated using the receiver operating characteristic curve, calibration curve, and decision curve analysis.Results: Sex, age, mean red blood cell volume, and fasting blood glucose were identified as independent risk factors for CAS in the HUA population. Age, gamma-glutamyl transpeptidase, serum creatinine, fasting blood glucose, total triiodothyronine, and direct bilirubin, were screened to construct a CAS risk prediction model. In the training and internal validation sets, the risk prediction model showed an excellent discriminative ability with the area under

Yimeng Shu,1,2 Guoqing Huang,1,2 Yushan Mao1 1Department of Endocrinology, The First Affiliated Hospital of Ningbo University, Ningbo, People’s Republic of China; 2Health Science Center, Ningbo University, Ningbo, People’s Republic of ChinaCorrespondence: Guoqing Huang; Yushan Mao, Department of Endocrinology, The First Affiliated Hospital of Ningbo University, 247 Renmin Road, Ningbo, People’s Republic of China, Tel +86 15737939838 ; +86 13867878937, Email guoqinghuang1992@163.com; maoyushan@nbu.edu.cnPurpose: Hyperuricemia (HUA) is a recognized risk factor for non-alcoholic fatty liver disease (NAFLD). Early diagnosis and the identification of predictive factors are important for the HUA population. This study aimed to investigate the potential relationship between the metabolic score for insulin resistance (METS-IR) index and the triglyceride and glucose (TyG) index with NAFLD in individuals with HUA.Patients and Methods: A total of 5537 HUA participants were included in the study. Hepatic ultrasonography was conducted following the latest diagnostic criteria to diagnose NAFLD. The relationship between the TyG and METS-IR index and NAFLD was evaluated by multivariable logistic regression analysis and restricted cubic spline model (RCS). In addition, the area under the curve (AUC), positive likelihood ratio, and negative likelihood ratio were explored to compare the predictive value of IR surrogates for NAFLD with HUA. The AUCs of the two indicators were compared using the DeLong test.Results: Multivariable logistic regression analysis revealed that elevated TyG (OR = 2.285, 95% CI: 1.525, 3.428) and METS-IR (OR = 1.242, 95% CI: 1.219, 1.266) indices significantly increase the risk of NAFLD. Meanwhile, the RCS analysis revealed a -log-shaped nonlinear relationship between NAFLD risk and the METS-IR index (P non-linear < 0.001), contrasting with the linear association observed with the TyG index (P non-linear = 0.763). Notably, the risk of NAFLD demonstrat