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

Abstract

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.

Keywords: Chronic Kidney Disease; Lipids; proteinuria.

Figure 1

Flow of identification and validation of the independent predictors of progression by different classification methods in the study subsets. FDR, false discovery rate; IQR, interquartile range; LR, logistic regression; PLS-DA, partial least square-discriminant analysis; RF, Random Forest.

Figure 2

Volcano plot in the training set illustrating the statistical significance of the fold change of the mean values of detected features in progressors to end-stage kidney disease versus nonprogressors derived from the compound-by-compound t-test. 1 = DAG 36:0; 2 = CE 20:5; 3 = CE 22:5; 4 = DAG 34:5; 5 = CE 20:3; 6 = CE 18:2; 7 = DAG 34:0; 8 = DAG 32:0; 9 = MAG 16:0; 10 = PA 44:4; lipids with a nominal P value ≤ 0.0027 are shown in color. CE, cholesterol esters; DAG, diacylglycerol; MAG, monoacylglycerol; PA, phosphatidic acid.

Figure 3

Odds ratio and 95% confidence interval of progression to end-stage kidney disease according to the FDR proposed (top panel), RF proposed (middle panel), and PLS-DA proposed lipids (bottom panel) by change of each 1 SD in abundance of candidate lipids using (a) the logistic regression model in unadjusted model, (b) adjusted models by eGFR and other factors (age, sex, race, diabetes, hypertension, and congestive heart failure), (c) adjusted by the urine protein-to-creatinine ratio and other factors, and (d) adjusted by eGFR, urine protein-to-creatinine ratio, and other factors in the training set. DAG, diacylglycerol; eGFR, estimated glomerular filtration rate; FDR, false discovery rate; MAG, monoacylglycerol; PC, phosphatidylcholine; PLS-DA, partial least square-discriminant analysis; RF, Random Forest.

Figure 4

Intraclass comparison of the distribution of the mean of log2 peak intensities of significant metabolites that passed the FDR threshold (q < 0.05) by case and control groups; CE: n = 10; DAG: n = 7; PC: n = 2; pPC: n = 2; PA: n = 4; pPE: n = 2; PE: n = 6; MAG: n = 2. The box represents median and interquartile ranges, and error bars present 1.5-fold × the interquartile range below the 25th and above the 75th percentile. Means were compared using the t-test, *P < 0.05; ***P < 0.001. CE, cholesterol esters; DAG, diacylglycerol; FDR, false discovery rate; MAG, monoacylglycerol; PA, phosphatidic acid; pPC, plasmenyl-phosphatidylcholine; pPE, plasmenyl-phosphatidylethanolamine.

Figure 5

Comparing the distribution of the false discovery rate proposed lipids in cases and controls in patients with and without diabetes. The box represents median and interquartile ranges, and error bars present 1.5-fold × the interquartile range below the 25th and above the 75th percentile. Means were compared using the t-test, *P < 0.05; **P < 0.01, ***P < 0.001. DAG, diacylglycerol; MAG, monoacylglycerol.

Figure 6

ESKD:non-ESKD mean ratio of log2 peak intensities by the number of bonds and carbon number in different classes of lipids. Only statistically significant P values are shown. CE, cholesterol ester; DAG, diacylglycerol; ESKD, end-stage kidney disease; MAG, monoacylglycerol; pPC, plasmenyl-phosphatidylcholine; pPE, plasmenyl-phosphatidylethanolamine; TAG, triacylglycerol.

Figure 7

The correlation network displaying metabolic differences between progressors and nonprogressors according to the top 102 differentially regulated lipids. Node color reflects fold changes. Lipids that have passed the FDR statistical significance with significantly lower and higher abundance in progressors are shown in green and red, respectively. Edge thickness represents the significance of adjusted partial correlation coefficients between the nodes (Supplementary Table S6). In most cases, the correlations within the same class of lipids are stronger than interclass correlations that are evident from the network structure. CE, cholesterol ester; DAG, diacylglycerol; FDR, false discovery rate; MAG, monoacylglycerol; PA, phosphatidic acid; pPE, plasmenyl-phosphatidylethanolamine; TAG, triacylglycerol.