Why the study?
To evaluate sdLDL-C dynamics across the spectrum from prediabetes to T2DM with complications, validate existing estimation formulas in Chinese populations, and develop an accurate population-specific formula.
Does a novel population-specific formula improve the accuracy of sdLDL-C estimation compared to existing formulas in Chinese patients with prediabetes and T2DM?
Does a novel population-specific formula improve the accuracy of sdLDL-C estimation compared to existing formulas in Chinese patients with prediabetes and T2DM?
A novel, population-specific formula improves the estimation of highly atherogenic sdLDL-C in Chinese patients with T2DM, offering a cost-effective tool for ASCVD risk stratification.
Answers are grounded in this paper and cite their sources.
Should not yet change practice; leaves open prospective validation before using this formula for ASCVD risk stratification.
OBJECTIVE: This study aimed to (1) evaluate small dense low-density lipoprotein cholesterol (sdLDL-C) dynamics from prediabetes to type 2 diabetes mellitus (T2DM) with complications, (2) validate existing sdLDL-C estimation formulas (Sampson’s, Srisawasdi’s, Han’s) in Chinese populations, and (3) develop a population-specific formula for enhanced accuracy. METHODS: A multicenter study recruited 1,944 participants (216 controls, 70 with prediabetes, 212 with newly diagnosed T2DM, 164 with treated T2DM, and 1,286 in validation cohorts). Lipid profiles, including sdLDL-C (measured via enzymatic assays), were analyzed. Formula performance was assessed using spearman correlation, intraclass correlation coefficients (ICC), and multivariable linear regression. A novel formula was derived via multivariable regression. RESULTS: Atherogenic lipid triad manifestations emerged early: sdLDL-C was significantly elevated in participants with prediabetes (1.07 [0.73, 1.40] vs. 0.57 [0.44, 0.72] mmol/L in controls, P < 0.05) and further increased in those with T2DM, correlating strongly with triglycerides (TG; r = 0.59), non-high-density lipoprotein cholesterol (nonHDL-C; r = 0.69), and apolipoprotein B (ApoB; r = 0.62). Existing formulas overestimated sdLDL-C in controls and treated T2DM (P < 0.05), though both Han’s and Sampson’s formula performed better in newly diagnosed T2DM (P > 0.05). A novel sdLDL-C estimation formula, incorporating low-density lipoprotein cholesterol (LDL-C), TG, nonHDL-C, age, and sex, achieved superior accuracy (R²=0.743; ICC = 0.681) and minimized residuals (Δ = 0.16 vs. 0.28–0.29, P < 0.05). CONCLUSION: sdLDL-C elevation begins in prediabetes, highlighting its value for early atherosclerotic cardiovascular disease (ASCVD) risk assessment. Current formulas show population-specific limitations, whereas the new model provides improved accuracy for Chinese T2DM patients, enabling cost-effective sdLDL-C estimation and personalized lipid management.
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Feng et al. (2025) studied this question.
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