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Afshinnia F, Rajendiran TM, Karnovsky A, et al. Lipidomic Signature of Progression of Chronic Kidney Disease in the Chronic Renal Insufficiency Cohort. Kidney Int Rep. 2016;1(4):256-268doi: 10.1016/j.ekir.2016.08.007.
Afshinnia, F., Rajendiran, T. M., Karnovsky, A., Soni, T., Wang, X., Xie, D., Yang, W., Shafi, T., Weir, M. R., He, J., Brecklin, C. S., Rhee, E. P., Schelling, J. R., Ojo, A., Feldman, H., Michailidis, G., & Pennathur, S. (2016). Lipidomic Signature of Progression of Chronic Kidney Disease in the Chronic Renal Insufficiency Cohort. Kidney international reports, 1(4), 256-268. https://doi.org/10.1016/j.ekir.2016.08.007
Afshinnia, Farsad, et al. "Lipidomic Signature of Progression of Chronic Kidney Disease in the Chronic Renal Insufficiency Cohort." Kidney international reports vol. 1,4 (2016): 256-268. doi: https://doi.org/10.1016/j.ekir.2016.08.007
Afshinnia F, Rajendiran TM, Karnovsky A, Soni T, Wang X, Xie D, Yang W, Shafi T, Weir MR, He J, Brecklin CS, Rhee EP, Schelling JR, Ojo A, Feldman H, Michailidis G, Pennathur S. Lipidomic Signature of Progression of Chronic Kidney Disease in the Chronic Renal Insufficiency Cohort. Kidney Int Rep. 2016 Nov;1(4):256-268. doi: 10.1016/j.ekir.2016.08.007. Epub 2016 Aug 18. PMID: 28451650; PMCID: PMC5402253.
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Kidney Int Rep. 2016 Nov;1(4):256-268. doi: 10.1016/j.ekir.2016.08.007. Epub 2016 Aug 18.

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

Kidney international reports

Farsad Afshinnia, Thekkelnaycke M Rajendiran, Alla Karnovsky, Tanu Soni, Xue Wang, Dawei Xie, Wei Yang, Tariq Shafi, Matthew R Weir, Jiang He, Carolyn S Brecklin, Eugene P Rhee, Jeffrey R Schelling, Akinlolu Ojo, Harold Feldman, George Michailidis, Subramaniam Pennathur

Affiliations

  1. Division of Nephrology Department of Internal Medicine, University of Michigan.
  2. Department of Pathology, University of Michigan.
  3. Michigan Regional Comprehensive Metabolomics Resource Core.
  4. Department of Computational Medicine & Bioinformatics, University of Michigan.
  5. Department of Biostatistics and Epidemiology, University of Pennsylvania.
  6. Johns Hopkins University School of Medicine.
  7. Division of Nephrology, Department of Medicine, University of Maryland School of Medicine.
  8. Tulane University School of Medicine.
  9. University of Illinois at Chicago.
  10. Massachusetts General Hospital.
  11. Case Western Reserve University, MetroHealth Medical Center.
  12. Department of Statistics, University of Michigan.

PMID: 28451650 PMCID: PMC5402253 DOI: 10.1016/j.ekir.2016.08.007

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

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