Diabetology & metabolic syndrome · 2026
The discordance of remnant cholesterol and low-density lipoprotein cholesterol as a predictor of diabetes, diabetic microvascular diseases, and cardiovascular disease.
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Wang Yao, Li Na, Shao Xian, Xu Guangya, Wang Jingyu, Wu Meng, Ning Xiangming, He Xingyu, Li Hongling, Ma Jun, Wei Wenjun, Lin Jiayi, Zhang Jinghao, Lv Niluo, Wang Kejie, Li Shiyun, Yang Ting, Wu Yuqing, Shi Zheng
Аннотация
BACKGROUND: The impact of discordance between remnant cholesterol (RC) and low-density lipoprotein cholesterol (LDL-c) on diabetes, diabetic kidney disease (DKD), diabetic retinopathy (DR) and cardiovascular disease (CVD) remains unclear. This study aims to explore the association between the discordance and these outcomes, using data from the National Health and Nutrition Examination Survey (NHANES) 1999.1-2020.3 and Clinical Medical College & Affiliated Hospital of Chengdu University.
METHODS: We prespecified a ± 15 percentile-point cutoff for discordance between RC and LDL-c, defined as RC percentile minus LDL-c percentile. Using this rule, 11,826 NHANES participants (cohort 1) and 306 participants from the Clinical Medical College & Affiliated Hospital of Chengdu University (cohort 2) were categorized as low discordance ( ≤ - 15), concordant (- 15 to + 15), or high discordance ( ≥ + 15). Key variables were screened by the Boruta algorithm. Logistic regression analysis models, restricted cubic spline (RCS), and subgroup analyses were used to assess the associations of discordance with outcomes. Receiver operating characteristic (ROC) and three machine learning models were used to assess the predictive value of the discordance for outcomes.
RESULTS: High discordance was significantly associated with increased risks of diabetes (OR -cohort 1: 2.371, 95% CI: 1.848-3.055; OR -cohort 2: 4.064, 95% CI: 1.750-10.020), DKD (OR: 2.593, 95% CI: 1.930-3.521), DR (OR: 2.205, 95% CI: 1.404-3.556), and CVD (OR -cohort 1: 2.299, 95% CI: 1.900-2.791; OR -cohort 2: 3.220, 95% CI: 1.266-8.175). In cohort 1, the RCS analysis showed linear relationships for these outcomes. Hypertension, HOMA-IR ≥ 3.1 and HOMA-β < 100 were identified as significant modifiers in subgroup analyses. In cohort 2, the RCS analysis showed linear relationships for diabetes and non-linear for CVD. All machine learning models demonstrated great predictive value of the discordance for diabetes and CVD.
CONCLUSION: The discordance is a significant predictor of diabetes, diabetic microvascular diseases, and cardiovascular disease.
KEY MESSAGE: (1) The discordance between RC and LDL-c is significantly associated with diabetes, diabetic microvascular diseases, and cardiovascular disease. (2) In NHANES, the RCS analysis showed linear relationships for diabetes, DKD, DR and CVD. In clinical cohort, the RCS analysis showed linear relationships for diabetes and non-linear for CVD. (3) The association of discordance with diabetes, DKD and CVD was more prevalent in individuals with hypertension and HOMA-IR ≥3.1, while individuals with hypertension were more sensitive to DR and individuals with HOMA-β <100 were more sensitive to diabetes. (4) The discordance showed the certain predictive ability for all outcomes.
METHODS: We prespecified a ± 15 percentile-point cutoff for discordance between RC and LDL-c, defined as RC percentile minus LDL-c percentile. Using this rule, 11,826 NHANES participants (cohort 1) and 306 participants from the Clinical Medical College & Affiliated Hospital of Chengdu University (cohort 2) were categorized as low discordance ( ≤ - 15), concordant (- 15 to + 15), or high discordance ( ≥ + 15). Key variables were screened by the Boruta algorithm. Logistic regression analysis models, restricted cubic spline (RCS), and subgroup analyses were used to assess the associations of discordance with outcomes. Receiver operating characteristic (ROC) and three machine learning models were used to assess the predictive value of the discordance for outcomes.
RESULTS: High discordance was significantly associated with increased risks of diabetes (OR -cohort 1: 2.371, 95% CI: 1.848-3.055; OR -cohort 2: 4.064, 95% CI: 1.750-10.020), DKD (OR: 2.593, 95% CI: 1.930-3.521), DR (OR: 2.205, 95% CI: 1.404-3.556), and CVD (OR -cohort 1: 2.299, 95% CI: 1.900-2.791; OR -cohort 2: 3.220, 95% CI: 1.266-8.175). In cohort 1, the RCS analysis showed linear relationships for these outcomes. Hypertension, HOMA-IR ≥ 3.1 and HOMA-β < 100 were identified as significant modifiers in subgroup analyses. In cohort 2, the RCS analysis showed linear relationships for diabetes and non-linear for CVD. All machine learning models demonstrated great predictive value of the discordance for diabetes and CVD.
CONCLUSION: The discordance is a significant predictor of diabetes, diabetic microvascular diseases, and cardiovascular disease.
KEY MESSAGE: (1) The discordance between RC and LDL-c is significantly associated with diabetes, diabetic microvascular diseases, and cardiovascular disease. (2) In NHANES, the RCS analysis showed linear relationships for diabetes, DKD, DR and CVD. In clinical cohort, the RCS analysis showed linear relationships for diabetes and non-linear for CVD. (3) The association of discordance with diabetes, DKD and CVD was more prevalent in individuals with hypertension and HOMA-IR ≥3.1, while individuals with hypertension were more sensitive to DR and individuals with HOMA-β <100 were more sensitive to diabetes. (4) The discordance showed the certain predictive ability for all outcomes.