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.