Lipidomic Signature of Progression of Chronic Kidney Disease in the Chronic Renal Insufficiency Cohort

Human studies report conflicting results on the predictive power of serum lipids on the progression of chronic kidney disease. We aimed to systematically identify the lipids that predict progression to end-stage kidney disease. Methods: From the Chronic Renal Insufficiency Cohort, 79 patients with chronic kidney disease stages 2 to 3 who progressed to end-stage kidney disease over 6 years of follow-up were selected and frequency matched by age, sex, race, and diabetes with 121 nonprogressors with less than 25% decline in estimated glomerular filtration rate during the follow-up. The patients were randomly divided into training and test sets. We applied liquid chromatography-mass spectrometry-based lipidomics on visit year 1 samples. Results: We identified 510 lipids, of which the top 10 coincided with false discovery threshold of 0.058 in the training set. From the top 10 lipids, the abundance of diacylglycerols and cholesteryl esters was lower, but that of phosphatidic acid 44:4 and monoacylglycerol 16:0 was significantly higher in progressors. Using logistic regression models, a multimarker panel consisting of diacylglycerols and monoacylglycerol independently predicted progression. The c-statistic of the multimarker panel added to the base model consisting of estimated glomerular filtration rate and urine protein-to-creatinine ratio as compared with that of the base model was 0.92 (95% confidence interval: 0.88–0.97) and 0.83 (95% confidence interval: 0.76–0.90, P < 0.01), respectively, an observation that was validated in the test subset. Discussion: We conclude that a distinct panel of lipids may improve prediction of progression of chronic kidney disease beyond estimated glomerular filtration rate and urine protein-to-creatinine ratio when added to the base model.