Objective  To explore the relationship between blood lipid indicators and different clinical classifications of dyslipidemia and diabetic kidney disease (DKD) and to compare the value of different clinical classifications of dyslipidemia for predicting DKD.

  Methods  Continuously enrollment of subjects was conducted at the First Affiliated Hospital of Chongqing Medical University and the Yongchuan Hospital of Chongqing Medical University between October 2020 and October 2021. A total of 356 type 2 diabetes mellitus (T2DM) patients admitted to the two hospitals were enrolled. They were divided into DKD group (n=126) and simple T2DM group (n=230) according to whether their T2DM was combined with DKD. In addition, 250 healthy individuals undergoing physical examination during the same period were enrolled for the control group. The blood pressure, blood lipid, blood glucose, and the kidney function indicators of the three groups were measured. The effects of different classifications of dyslipidemia on DKD were analyzed with unconditional logistic regression models, the receiver operating characteristic (ROC) curve was constructed, the area under the curve (AUC) of ROC was calculated, and the value of different classifications of dyslipidemia for predicting DKD was analyzed.

  Results  The diastolic blood pressure (DBP), systolic blood pressure (SBP), total cholesterol (TC), triacylglycerol (TG), low-density lipoprotein cholesterol (LDL-C), serum creatinine (Scr), uric acid (UA), and glycosylated hemoglobin A1c (HbA1c) of the DKD group and the simple T2DM group were significantly higher than those of the control group, while the high-density lipoprotein cholesterol (HDL-C) levels of the DKD group and the simple T2DM group were lower than that of the control group (all P<0.05). The disease course of T2DM, DBP, SBP, TC, TG, Scr, UA and HbA1c of the DKD group were significantly higher than those of the T2DM group (all P<0.05). After adjusting for the effects of T2DM disease course, DBP, SBP, Scr, UA and HbA1c, the results showed that TC (OR=1.426, 95%CI: 1.088-1.868) and TG (OR=1.404, 95%CI: 1.075-1.833) were independent risk factors for DKD, and that hypercholesterolemia (OR=1.817, 95%CI: 1.040-3.177) and mixed hyperlipidemia (OR=2.148, 95%CI: 1.110-4.159) were independent risk factors for DKD (all P<0.05). The AUC (95%CI) of hypercholesterolemia was 0.789 (0.729-0.871). The AUC (95%CI) of mixed hyperlipidemia was 0.671 (0.579-0.760). Hypercholesterolemia showed better predictive value for the diagnosis and prediction of DKD.

  Conclusion  Among the blood lipid indicators, TC and TG are independent risk factors of DKD. In the clinical classifications of dyslipidemia, hypercholesterolemia and mixed hyperlipidemia are independent risk factors of DKD. Hypercholesterolemia can be used as a predictor to screen for DKD among T2DM patients and is well suited for extensive application in outpatient screening.