Background <p>Hyperlipidemia is a major global public health issue and a significant risk factor for various chronic diseases, including cardiovascular disease and diabetes. Insulin resistance (IR) is closely associated with hyperlipidemia. Estimated glucose disposal rate (eGDR), a non-invasive tool for assessing IR, may have clinical utility in identifying hyperlipidemia and predicting its prognosis.</p> Methods <p>This study is a secondary analysis of retrospective cohort data based on publicly available databases, specifically the U.S. National Health and Nutrition Examination Survey (NHANES) and the China Health and Retirement Longitudinal Study (CHARLS)—incorporating both cross-sectional and longitudinal follow-up data to systematically evaluate the relationship between eGDR and the risk of hyperlipidemia and mortality. Multivariable weighted logistic regression models were employed to analyze the risk of hyperlipidemia, while Cox proportional hazards models were used to assess all-cause and cardiovascular disease (CVD) mortality. Generalized additive models and smooth curve fitting were applied to identify potential nonlinear relationships, and subgroup as well as sensitivity analyses were conducted to verify the robustness of the findings.</p> Results <p>In the NHANES cohort, each standard deviation increase in eGDR was associated with a 11.5% reduction in the risk of hyperlipidemia (OR = 0.885 [0.867, 0.903]), an 8.6% reduction in all-cause mortality (HR = 0.914 [0.892, 0.936]), and a 10.4% reduction in CVD mortality (HR = 0.896 [0.859, 0.936]). In the CHARLS cohort, each SD increase in eGDR was associated with a 7.1% reduction in the risk of hyperlipidemia (OR = 0.929 [0.905, 0.954]) and an 9.2% reduction in all-cause mortality (HR = 0.908 [0.869,0.949]). A nonlinear inverse relationship was observed between eGDR and the risk of hyperlipidemia, with evidence of a significant threshold effect. Kaplan–Meier survival curves demonstrated significantly lower all-cause and CVD mortality among individuals with higher eGDR levels. Stratified analyses indicated that eGDR showed strong consistency and predictive value across different population subgroups, with particularly pronounced effects observed among younger individuals and those with diabetes.</p> Conclusion <p>Our study demonstrates that eGDR, as an indicator of insulin sensitivity, is significantly associated with the risk of hyperlipidemia and mortality, including both all-cause and CVD mortality. Improving eGDR levels may help reduce the health burden associated with hyperlipidemia and supports its potential clinical application in hyperlipidemia management and metabolic disease risk assessment.</p>

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Estimated glucose disposal rate and its dual role in hyperlipidemia risk and mortality: a secondary analysis of retrospective cohort data from U.S. and Chinese

  • Shouxin Wei,
  • Sijia Yu,
  • Chuan Qian,
  • Zhengwen Xu,
  • Yindong Jia,
  • Bo Chen

摘要

Background

Hyperlipidemia is a major global public health issue and a significant risk factor for various chronic diseases, including cardiovascular disease and diabetes. Insulin resistance (IR) is closely associated with hyperlipidemia. Estimated glucose disposal rate (eGDR), a non-invasive tool for assessing IR, may have clinical utility in identifying hyperlipidemia and predicting its prognosis.

Methods

This study is a secondary analysis of retrospective cohort data based on publicly available databases, specifically the U.S. National Health and Nutrition Examination Survey (NHANES) and the China Health and Retirement Longitudinal Study (CHARLS)—incorporating both cross-sectional and longitudinal follow-up data to systematically evaluate the relationship between eGDR and the risk of hyperlipidemia and mortality. Multivariable weighted logistic regression models were employed to analyze the risk of hyperlipidemia, while Cox proportional hazards models were used to assess all-cause and cardiovascular disease (CVD) mortality. Generalized additive models and smooth curve fitting were applied to identify potential nonlinear relationships, and subgroup as well as sensitivity analyses were conducted to verify the robustness of the findings.

Results

In the NHANES cohort, each standard deviation increase in eGDR was associated with a 11.5% reduction in the risk of hyperlipidemia (OR = 0.885 [0.867, 0.903]), an 8.6% reduction in all-cause mortality (HR = 0.914 [0.892, 0.936]), and a 10.4% reduction in CVD mortality (HR = 0.896 [0.859, 0.936]). In the CHARLS cohort, each SD increase in eGDR was associated with a 7.1% reduction in the risk of hyperlipidemia (OR = 0.929 [0.905, 0.954]) and an 9.2% reduction in all-cause mortality (HR = 0.908 [0.869,0.949]). A nonlinear inverse relationship was observed between eGDR and the risk of hyperlipidemia, with evidence of a significant threshold effect. Kaplan–Meier survival curves demonstrated significantly lower all-cause and CVD mortality among individuals with higher eGDR levels. Stratified analyses indicated that eGDR showed strong consistency and predictive value across different population subgroups, with particularly pronounced effects observed among younger individuals and those with diabetes.

Conclusion

Our study demonstrates that eGDR, as an indicator of insulin sensitivity, is significantly associated with the risk of hyperlipidemia and mortality, including both all-cause and CVD mortality. Improving eGDR levels may help reduce the health burden associated with hyperlipidemia and supports its potential clinical application in hyperlipidemia management and metabolic disease risk assessment.