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. 2025 May 9;44(1):148.
doi: 10.1186/s41043-025-00878-3.

Association between insulin resistance and multiple chronic diseases: a cross-sectional study from CHARLS

Wen-Ze Jiang  1 , Zhen-Liang Fan  2 , Meng-Li Xu  1 , En-Hui Qian  1 , Ke-Da Lu  3
Affiliations

Association between insulin resistance and multiple chronic diseases: a cross-sectional study from CHARLS

Wen-Ze Jiang et al. J Health Popul Nutr. .

Abstract

Background: Chronic disease is a global public health problem. This study aimed to explore the association between insulin resistance (IR)-related indices and various chronic diseases, and to evaluate the predictive capacity of IR-related indices for these diseases.

Methods: The data used in this study came from CHARLS. Binary logistic regression analysis and RCS were used to analyze the relationship between IR-related indices, including TyG, TyG-BMI, TyG-WHtR, METS-IR and eGDR, with nine chronic diseases. Subgroup analysis was performed to test the stability of the results. Finally, the predictive power of IR-related indices for chronic diseases was tested by ROC curve.

Results: A total of 8,177 participants were included in this study. The study found that elevated prevalence of multiple chronic diseases is positively associated with increases in TyG, TyG-BMI, TyG-WHtR, and METS-IR, and negatively associated with eGDR. ROC analysis revealed that IR-related indices had the best accuracy in predicting dyslipidemia compared to other diseases, with TyG being the best predictor.

Conclusions: IR-related indices were positively associated with the prevalence of multiple chronic diseases. The burden of chronic diseases can be reduced by improving IR in middle-aged and older people.

Keywords: CHARLS; Chronic disease; Insulin resistance.

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Conflict of interest statement

Declarations. Ethics approval and consent to participate: In accordance with the Declaration of Helsinki, Peking University examined and authorized the studies involving human subjects in the China Health and Retirement Longitudinal Study (CHARLS) (IRB00001052–11015). The patients/participants provided their written informed consent to participate in CHARLS. Our study used publicly available deidentified data from the CHARLS website ( http://charls.pku.edu.cn/index.htm ) and waived informed consent. Consent for publication: Not applicable. Competing interests: The authors declare no competing interests.

Figures

Fig. 1
Fig. 1
Flowchart of the sample selection from CHARLS 2015. CHARLS China Health and Retirement Longitudinal Study, IR insulin resistance
Fig. 2
Fig. 2
Results of RCS analysis. Associations between IR-related indices with the risk of hypertension (A), heart disease (B, G), digestive disease (C, E, H), hyperuricemia (D, I) and dyslipidemia (F) were evaluated by restricted cubic spline after adjustment for the covariables in model 3. The solid blue lines correspond to the central estimates, and the light blue regions indicate the 95% confidence intervals. The dashed lines parallel to the X-axis indicate that odd ratio = 1
Fig. 3
Fig. 3
Forest plot of TyG. Forest plot of triglyceride glucose index (TyG) association with the risk of A hypertension, B asthma, C dyslipidemia and D hyperuricemia
Fig. 4
Fig. 4
Forest plot of TyG-BMI. Forest plot of triglyceride glucose-body mass index (TyG-BMI) association with the risk of A hypertension, B heart disease, C digestive disease, D dyslipidemia and E hyperuricemia
Fig. 5
Fig. 5
Forest plot of TyG-WHtR. Forest plot of triglyceride glucose-waist-to-height ratio (TyG-WHtR) association with the risk of A hypertension, B heart disease, C digestive disease, D asthma, E dyslipidemia and F hyperuricemia
Fig. 6
Fig. 6
Forest plot of METS-IR. Forest plot of metabolic score of IR (METS-IR) association with the risk of A hypertension, B heart disease, C digestive disease, D dyslipidemia and E hyperuricemia
Fig. 7
Fig. 7
Forest plot of eGDR. Forest plot of estimated glucose disposal rate (eGDR) association with the risk of A hypertension, B heart disease, C digestive disease, D dyslipidemia and E hyperuricemia
Fig. 8
Fig. 8
Diagnostic efficacy of metabolic score of IR-related indices for chronic diseases
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