Development and Preliminary Validation of a Risk Association Model for Dyslipidemia in Adults

Hanyi LI; Huayu SUN; Xin LI; Yishuang YUAN; Xingpei WANG; Yitao ZHANG; Yafen ZHANG; Liju LI

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Article Navigation > Journal of Kunming Medical University > 2026 > Accepted Manuscript

Hanyi LI, Huayu SUN, Xin LI, Yishuang YUAN, Xingpei WANG, Yitao ZHANG, Yafen ZHANG, Liju LI. Development and Preliminary Validation of a Risk Association Model for Dyslipidemia in Adults[J]. Journal of Kunming Medical University.

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Development and Preliminary Validation of a Risk Association Model for Dyslipidemia in Adults

1.
Department of Public Health，Shilin Yi Autonomous County Center for Disease Control and Prevention，Kunming Yunnan 652200
2.
School of Public Health，Kunming Medical University，Kunming Yunnan 650500
3.
Department of Chronic Non-communicable Disease Prevention and Control，Shilin Yi Autonomous County Center for Disease Control and Prevention，Kunming Yunnan 652200
4.
Guishan Town Health Center，Shilin Yi Autonomous County，Kunming Yunnan 652204
5.
Dake Township Health Center，Shilin Yi Autonomous County，Kunming Yunnan 652202
6.
Shilin Health Center，Shilin Yi Autonomous County，Kunming Yunnan 652208，China

Received Date: 2024-02-04

Abstract

Abstract

Objective To develop a risk association model for dyslipidemia providing a scientific tool to support early screening and precision interventions. Methods Based on a cross-sectional survey of the Yunnan Provincial Chronic Disease and Risk Factor Surveillance Project conducted in Shilin Yi Autonomous County from November 2022 to April 2023, a total of 1577 eligible participants were enrolled. Data were collected through questionnaires, physical examinations, and laboratory tests. After preprocessing, 46 variables were included, and the dataset was randomly split into training and test sets at a ratio of 7∶3. Univariate analysis was first performed on the training set to identify statistically significant variables. Subsequently, four machine learning algorithms—Lasso, Boruta, SWSFS, and XGBoost—were employed for feature selection. Variables commonly identified by all four algorithms were retained as key variables to construct a multivariable logistic regression model, which was visualized as a nomogram. Model performance was systematically assessed using the test set via the confusion matrix, area under the ROC curve （AUC）, decision curve analysis （DCA）, and calibration curves. Results The prevalence of dyslipidemia in this study was 43.37%. Six important variables were ultimately identified in the model: BMI, waist circumference, glycated hemoglobin, heart rate, serum uric acid, and walking or cycling. Among them, glycated hemoglobin exhibited a significant nonlinear association （P for nonlinearity=0.003）, whereas BMI （P = 0.002） and serum uric acid （P < 0.001） showed linear positive correlations; establishing them as key risk factors for dyslipidemia. Walking or cycling was a significant protective factor （P < 0.001）. The associations of waist circumference （P = 0.384） and heart rate （P = 0.078） with dyslipidemia did not reach statistical significance. The model achieved an AUC of 0.663 in the testing set; with a sensitivity of 67.1% and specificity of 63.2%. DCA showed a significant clinical net clinical benefit within the threshold probability range of 0.25–0.65, and the calibration curve indicated good model fit. Conclusion The proposed dyslipidemia risk association model demonstrates moderate discriminatory performance and clinical utility, offering a reference for the early identification of high-risk individuals with dyslipidemia and targeted interventions.
- Dyslipidemia,
- Machine learning,
- Risk factors,
- Risk association model,
- Nomogram

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References(26)

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