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.

Asymmetrical flow field-flow fractionation for improved characterization of human plasma lipoproteins

“…Specific fragmentation in MS2 allows accurate identification of unique lipids in MS1 as described previously. [32, 41] Figure 4 (ESM Table S1) shows that 277 lipids species in the LDL fraction and 278 species in the HDL fraction are identified in UC-prepared samples in positive MS mode. Similarly, 141 lipids in both LDL and HDL fractions are found in UC-prepared samples in negative mode.…”

“…[32] In brief, the modified Bligh-Dyer method[33] is used to prepare samples. The lipoprotein fractions collected by SP-AF4 and lipoproteins isolated by UC are brought to room temperature.…”

A Pre-transplant Blood-based Lipid Signature for Prediction of Antibody-mediated Rejection in Kidney Transplant Patients

“…In a human study comparing healthy controls and CKD patients, Reis et al found that the content of total PC and Ceramides were decreased along with the ratio of LPC/LPE(18). In a study comparing patients with CKD progression compared to control patients, Afshinnia et al reported that CKD progression was associated with lower Cholesteryl ester (CE), diacylglycerols (DG), PC, plasmenylcholine (PC-P), PE-P, and phosphatidic acid (PA), and elevated PE and monoacylglycerols (MAG)(19). This finding suggests that patients with CKD progression with a decrease of longer acyl chains and polyunsaturated lipids might benefit from the effects of polyunsaturated fatty acid supplementation, as some previous studies have suggested(20,21).…”

An integrative systems biology approach for precision medicine in diabetic kidney disease

“…High‐throughput multi‐omics approaches (transcriptomics, proteomics, metabolomics and lipidomics) have been used to profile patients' biospecimen (kidney biopsy, plasma and urine samples). These multi‐omics data sets contributed to the discovery of biomarker candidates that are associated with disease progression . Table includes molecular biomarkers that were discovered using systems biology approaches with demonstrated association with kidney outcomes in patient‐related studies.…”

“…These multiomics data sets contributed to the discovery of biomarker candidates that are associated with disease progression. 17,[22][23][24][25][26][27][28][29] Table 2 includes molecular biomarkers that were discovered using systems biology approaches with demonstrated association with kidney outcomes in patient-related studies.…”