The association between residual cholesterol to high-density lipoprotein cholesterol ratio and type 2 diabetes risk
Jinting Zhang1, Zhaoxiang Wang1, Qianqian Wang1
1Department of Endocrinology, Kunshan Hospital Affiliated to Jiangsu University, Kunshan, Jiangsu, China.
Insights
Elevated remnant cholesterol to high-density lipoprotein cholesterol ratio (RC/HDL-C) is linked to increased type 2 diabetes (T2D) risk in older adults. This finding suggests RC/HDL-C can aid in predicting T2D in elderly populations.
Area of Science:
- Cardiovascular Research
- Metabolic Disease Epidemiology
- Biomarker Discovery
Background:
- The remnant cholesterol to high-density lipoprotein cholesterol ratio (RC/HDL-C) is a recognized biomarker for metabolic disturbances.
- Prior research links elevated RC/HDL-C to cardiovascular, cerebrovascular, and metabolic diseases.
- Limited longitudinal data exist on RC/HDL-C and type 2 diabetes (T2D) risk, especially in elderly populations.
Purpose of the Study:
- To investigate the association between RC/HDL-C and incident T2D in Chinese adults.
- To provide evidence for early T2D prevention and detection strategies.
- To clarify the role of RC/HDL-C in T2D risk among high-risk elderly individuals.
Main Methods:
- A prospective follow-up study in Kunshan, China (2018-2021) included 7655 participants aged 60+.
- Cox proportional hazards models assessed the independent effect of RC/HDL-C on T2D risk.
- Kaplan-Meier curves and restricted cubic splines (RCS) visualized the relationship between RC/HDL-C and T2D risk.
Main Results:
- During 3.87 years of follow-up, 783 participants (10.23%) developed T2D.
- A positive, independent association was found between RC/HDL-C and T2D risk (HR=1.12, P<0.001).
- RCS analysis indicated a non-linear relationship, and ROC analysis showed modest predictive ability (AUC=0.601).
Conclusions:
- Elevated RC/HDL-C levels are associated with a higher risk of incident T2D in older adults.
- RC/HDL-C serves as an independent predictor of T2D in the elderly.
- RC/HDL-C can potentially enhance risk stratification in existing T2D prediction models.
Purpose:
The remnant cholesterol to high-density lipoprotein cholesterol ratio (RC/HDL-C) is a novel biomarker of metabolic disturbances. Prior studies have indicated a close association between elevated RC/HDL-C and the occurrence and progression of cardiovascular, cerebrovascular, and metabolic diseases. However, longitudinal data on the relationship between RC/HDL-C and type 2 diabetes (T2D) risk remain limited, particularly among high-risk elderly populations. This study aims to clarify the association between RC/HDL-C and incident T2D in Chinese adults, offering evidence for early prevention and detection of T2D.
Patients And Methods:
A follow-up study was conducted in Kunshan, China, from January 2018 to July 2021, involving 7655 participants aged 60 years or older. Cox proportional hazards models were used to evaluate the independent effect of the RC/HDL-C level on the risk of T2D. The relationship between RC/HDL-C and T2D risk was visually demonstrated using the Kaplan-Meier method and restricted cubic splines (RCS).
Results:
During a median follow-up of 3.87 years, 783 participants (10.23%) developed T2D. A fully adjusted Cox proportional hazards model showed a positive and independent association between RC/HDL-C and T2D risk (HR = 1.12, 95% CI: 1.06-1.17, P < 0.001). The highest RC/HDL-C quartile (Q4) had the highest cumulative incidence of T2D (Log-rank test, P < 0.001). Furthermore, RCS analysis revealed a non-linear relationship between RC/HDL-C and T2D risk. Receiver operating characteristic (ROC) curve analysis revealed that the RC/HDL-C ratio exhibited the largest area under the curve (AUC = 0.601, 95% CI: 0.580-0.623), indicating modest but statistically significant predictive ability. Subgroup analysis further validated the robustness of these results.
Conclusion:
We found that older adults with elevated RC/HDL-C levels have a higher risk of incident T2D. RC/HDL-C is an independent predictor of incident T2D in the elderly and may serve as a valuable adjunct to enhance risk stratification within existing prediction models.
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