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ORIGINAL RESEARCH article

Front. Public Health
Sec. Aging and Public Health
Volume 13 - 2025 | doi: 10.3389/fpubh.2025.1420596
This article is part of the Research Topic Analyses on Health Status and Care Needs among Older Adults View all 37 articles

Development and Validation of a Hyperlipidemia Risk Prediction Model for Middle-aged and Elderly Chinese Using 2015 CHARLS Data

Provisionally accepted
Li-Xiang Zhang Li-Xiang Zhang Shan-bing Hou Shan-bing Hou Fang-Fang Zhao Fang-Fang Zhao Ting-Ting Wang Ting-Ting Wang Ying Jiang Ying Jiang Xiao-Juan Zhou Xiao-Juan Zhou Jiao-Yu Cao Jiao-Yu Cao *
  • Division of Life Sciences and Medicine, The First Affiliated Hospital of University of Science and Technology of China Anhui Provincial Hospital, Hefei, China

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

    Objective: To develop and validate a predictive model for hyperlipidemia risk among middle-aged and elderly individuals in China, this study aims to offer an effective screening tool for identifying those at risk. Methods: In this study, we included 6,629 middle-aged and elderly individuals, aged 45 and above, who met the inclusion criteria from the 2015 China Health and Retirement Longitudinal Study (CHARLS) as our research subjects. Utilizing the LASSO regression and multivariate Logistic regression method, we analyzed the independent risk factors associated with hyperlipidemia among these subjects. Subsequently, we established a risk prediction model for hyperlipidemia in the middle-aged and elderly population using statistical software Stata 17.0. Results: The prevalence rate of hyperlipidemia among the 6,629 middle-aged and elderly participants was 26.32% (1,745 out of 6,629). The LASSO regression and multivariate Logistic regression analysis all revealed that Body Mass Index (BMI), fasting blood glucose, serum uric acid, C-reactive protein, and white blood cell count were independent risk factors for hyperlipidemia in this demographic. From these findings, a nomogram prediction model was constructed to estimate the risk of hyperlipidemia for middle-aged and elderly individuals. The Area Under the Receiver Operating Characteristic (ROC) Curve (AUC) for the nomograms was 0.717 (95% Confidence Interval (CI): 0.703-0.731), indicating good discrimination. The Decision Curve Analysis (DCA) demonstrated that when the probability of hyperlipidemia in the middle-aged and elderly population falls between 0.11 and 0.61, the application of the nomogram yields the highest net benefit, suggesting that the nomogram model possesses good clinical applicability. the Spiegelhalter's z-statistic test confirmed that the predicted probabilities from the nomogram model are in good agreement with the observed frequencies of hyperlipidemia (P=0.560). The Brier score for the nomogram model was 17.1%, which is below the threshold of 25%, indicating good calibration. Conclusion: The nomogram model, which incorporates the identified risk factors for hyperlipidemia in middle-aged and elderly individuals, has demonstrated good predictive efficiency and clinical applicability. It can serve as a valuable tool to assist healthcare professionals in screening for high-risk groups and implementing targeted preventive interventions.

    Keywords: Chinese, Middle-aged and elderly individuals, Hyperlipidemia, Risk factors, nomogram, Prediction model

    Received: 20 Apr 2024; Accepted: 06 Jan 2025.

    Copyright: © 2025 Zhang, Hou, Zhao, Wang, Jiang, Zhou and Cao. 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: Jiao-Yu Cao, Division of Life Sciences and Medicine, The First Affiliated Hospital of University of Science and Technology of China Anhui Provincial Hospital, Hefei, China

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