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

INTRODUCTION

Human studies report conflicting results on the predictive power of serum lipids on progression of chronic kidney disease (CKD). 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 CKD stage 2 to 3 who progressed to ESKD over 6 years of follow up were selected and frequency-matched by age, sex, race, and diabetes with 121 non-progressors with less than 25% decline in estimated glomerular filtration rate (eGFR) 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 (DAGs) and cholesteryl esters was lower, but that of phosphatidic acid 44:4 and monoacylglycerol (MAG) 16:0 was significantly higher in progressors. Using logistic regression models a multi-marker panel consisting of DAGs, and MAG independently predicted progression. The c-statistic of the multimarker panel added to the base model consisting of eGFR and urine protein-creatinine ratio (UPCR) as compared to that of the base model was 0.92 (95% Confidence Interval [CI]: 0.88-0.97), and 0.83 (95% CI: 0.76-0.90, P<0.01), respectively; an observation which was validated in the Test subset.

CONCLUSION

We conclude that a distinct panel of lipids may improve prediction of progression of CKD beyond eGFR and UPCR when added to the base model.