Front. Public Health

Sec. Aging and Public Health

Volume 12 - 2024 | doi: 10.3389/fpubh.2024.1462483

Provisionally accepted

Jiang Hailong 1 Xiaoting Geng 2 Jie Shi 1 Chi Zhang 3 Chang Li 1 Ying Gai 1 Jia Mei 1 Shuying Li 2* 1 Chengde Medical University, Chengde, China 2 Affiliated Hospital of Chengde Medical University, Chengde, Hebei, China 3 Chengde Nursing Vocational College, Chengde, Hebei, China

The final, formatted version of the article will be published soon.

You have multiple emails registered with Frontiers:

Please enter your email address:

If you already have an account, please login

You don't have a Frontiers account ? You can register here

The incidence of dyslipidemia as a risk factor for many serious diseases is increasing year by year. This study aimed to construct and visualize a risk prediction model for dyslipidemia in middleaged and elderly people.The subjects of our study are derived from CHARLS. Participants were allocated to training and validation groups in a 7:3 ratio at random. To identify potential predictors of dyslipidemia, we employed univariate analysis, lasso regression, and multivariate binary logistic regression analyses. A nomogram was constructed based on logistic regression results, and a ROC curve was used to evaluate its predictive performance. The accuracy and discriminatory capability were assessed using calibration curve analysis, while the net clinical benefit rate was evaluated through decision curve analysis (DCA).Our study included a total of 12589 participants, of which 1,514 were detected with dyslipidemia syndrome. Model construction: Based on the results of the logistic regression analysis of the training set, six variables were selected to construct the model, which were ranked in order of importance as comorbid hypertension, comorbid diabetes, waistline, comorbid gastrointestinal system, residence address, and comorbid liver disease. The ROC curve results indicated that the prediction model exhibited moderate discriminatory ability (AUC > 0.7). Additionally, the calibration curve confirmed the model's strong predictive accuracy. The decision curve analysis (DCA) illustrated a positive net benefit associated with the prediction model.The prediction model of dyslipidemia risk in middle-aged and elderly people constructed in this study has good efficacy and helps to screen high-risk groups.

Keywords: Dyslipidemia, middleaged and older adults, Prediction model, nomogram, CHARLS

Received: 10 Jul 2024; Accepted: 04 Nov 2024.

Copyright: © 2024 Hailong, Geng, Shi, Zhang, Li, Gai, Mei and Li. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

* Correspondence: Shuying Li, Affiliated Hospital of Chengde Medical University, Chengde, Hebei, China

Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.