Association of Lipid Parameters with the Risk of Chronic Kidney Disease: A Longitudinal Study Based on Populations in Southern China

Abstract

Objective

To investigate which plasma lipid parameters are useful for detecting chronic kidney disease (CKD) in a Chinese population without known CKD or renal impairment.

Methods

This was a prospective study. In southern Chinese cities from 2012 to 2013, a total of 1037 subjects aged ≥ 18 years old received a survey. Logistic regression and multiple linear regression analyses were performed. The lipid parameters studied included total cholesterol (TC), triglycerides (TG), high-density lipoprotein cholesterol (HDL-C), non-high-density lipoprotein cholesterol (nHDL-C), TG/HDL-C ratio, TC/HDL-C ratio and nHDL-C/HDL-C ratio.

Results

After adjusting for confounding factors, the fourth percentile of logTG/HDL-C was observed to be an independent risk factor for CKD (OR = 2.453, P < 0.001), and the highest quantile of the logTG/HDL-C ratio was associated with a higher prevalence of CKD (P < 0.05). This risk was reduced when the model was adjusted with Insulin resistance (IR) (OR = 2.034, P < 0.05). In the group of women, glucose metabolism disorders, high uric acid, and obesity, this risk was increased. Multiple regression models showed that log TG and nonHDL-C/HDL-C were negatively correlated with eGFR (P < 0.05), while log TG and TC were positively correlated with logACR (P < 0.05). The area under the curve (ROC) of lgTG/HDL was 0.623 (p < 0.001).

Conclusion

The serum logTG/HDL-C ratio is the only suitable predictor of CKD, and IR may be the mechanism. This risk needs to be controlled in a specific population. Log TG and nonHDL-C/HDL-C were negatively correlated with eGFR, while log TG and TC were positively correlated with logACR.

Keywords:

• lipid
• chronic kidney disease
• CKD
• urinary albumin to creatinine ratio
• ACR
• estimated glomerular filtration rate
• eGFR

Funding

This study was supported by the “Risk factors and prediction model of chronic kidney disease caused by metabolic syndrome: A multicentric prospective cohort study” clinical trial training project of Southern Medical University (LC2016PY047, 2016), Science and Technique Program of Guangzhou (201604020015, 2015), South Wisdom Valley Innovative Research Team Program (CXTD-004, 2014), and The National Natural Science Foundation of China (81873620).

Disclosure

The authors report no conflicts of interest in this work.