Development and validation of a cognitive dysfunction risk prediction model for the abdominal obesity population

作者：Chun Lei, Gangjie Wu, Yan Cui, Hui Xia, Jianbing Chen, Xiaoyao Zhan, Yanlan Lv, Meng Li, Ronghua Zhang, Xiaofeng Zhu · 发表于：Frontiers in Endocrinology · 年份：2024 · DOI：10.3389/fendo.2024.1290286 · 被引用次数：8 · 研究领域：Dementia and Cognitive Impairment Research、Bariatric Surgery and Outcomes、Nutrition and Health in Aging

Objectives: This study was aimed to develop a nomogram that can accurately predict the likelihood of cognitive dysfunction in individuals with abdominal obesity by utilizing various predictor factors. Methods: A total of 1490 cases of abdominal obesity were randomly selected from the National Health and Nutrition Examination Survey (NHANES) database for the years 2011-2014. The diagnostic criteria for abdominal obesity were as follows: waist size ≥ 102 cm for men and waist size ≥ 88 cm for women, and cognitive function was assessed by Consortium to Establish a Registry for Alzheimer's Disease (CERAD), Word Learning subtest, Delayed Word Recall Test, Animal Fluency Test (AFT), and Digit Symbol Substitution Test (DSST). The cases were divided into two sets: a training set consisting of 1043 cases (70%) and a validation set consisting of 447 cases (30%). To create the model nomogram, multifactor logistic regression models were constructed based on the selected predictors identified through LASSO regression analysis. The model's performance was assessed using several metrics, including the consistency index (C-index), the area under the receiver operating characteristic (ROC) curve (AUC), calibration curves, and decision curve analysis (DCA) to assess the clinical benefit of the model. Results: < 0.05). These predictors were incorporated into the nomogram. The C-indices for the training and validation sets were 0.814 (95% CI: 0.875-0.842) and 0.805 (95% CI: 0.758-0.851), respecti...