Lipid phenotypes and diabetic kidney disease: a cluster-based study integrating traditional and nontraditional lipid parameters

This study aimed to identify lipid phenotypes integrating traditional and nontraditional lipid parameters and examine their associations with diabetic kidney disease (DKD).

The study analyzed 4,936 Chinese adults with type 2 diabetes mellitus recruited from 24 primary medical institutions. K-means clustering was performed using triglycerides (TG), high-density lipoprotein cholesterol (HDL), low-density lipoprotein cholesterol (LDL), atherogenic index of plasma, and remnant cholesterol (RC). Multivariable logistic regression was used to evaluate associations between lipid phenotypes and DKD and interactions with C-reactive protein were assessed on multiplicative and additive scales.

Two lipid phenotypes were identified: a relatively higher LDL/HDL phenotype (n = 4,095, 82.96%) and a higher TG/RC phenotype (n = 841, 17.04%). The higher TG/RC phenotype exhibited more severe metabolic-inflammatory status and a higher prevalence of DKD than the other phenotype (52.44% vs 44.00%). After full adjustment, the higher TG/RC phenotype was associated with higher odds of DKD (OR 1.370, 95% CI 1.172-1.602), early DKD (OR 1.238, 95% CI 1.059-1.446), and reduced eGFR (OR 2.062, 95% CI 1.420-2.994). Participants with both the higher TG/RC phenotype and elevated C-reactive protein levels showed the highest odds of DKD (OR 1.881, 95% CI 1.540-2.297), with significant multiplicative and additive interactions. The clustered phenotype also provided incremental discriminative information beyond individual lipid markers, and the findings remained consistent across sensitivity analyses.

Integrating traditional and nontraditional lipid measures may help identify clinically relevant lipid phenotypes associated with DKD and support more targeted renal assessment in T2DM patients.