Farsad Afshinnia1, Thekkelnaycke M Rajendiran2,3, Chenchen He4, Jaeman Byun4, Daniel Montemayor5,6, Manjula Darshi5,6, Jana Tumova5,6, Jiwan Kim5,6, Christine P Limonte7,8, Rachel G Miller9, Tina Costacou9, Trevor J Orchard9, Tarunveer S Ahluwalia10,11, Peter Rossing12,13, Janet K Snell-Bergeon13, Ian H de Boer7,8,14, Loki Natarajan15, George Michailidis16, Kumar Sharma17,6, Subramaniam Pennathur1,2,18. 1. Department of Internal Medicine-Nephrology, University of Michigan, Ann Arbor, MI fafshin@med.umich.edu sharmak3@uthscsa.edu spennath@umich.edu. 2. Michigan Regional Comprehensive Metabolomics Resource Core, University of Michigan, Ann Arbor, MI. 3. Department of Pathology, University of Michigan, Ann Arbor, MI. 4. Department of Internal Medicine-Nephrology, University of Michigan, Ann Arbor, MI. 5. Division of Nephrology, University of Texas Health Science Center San Antonio, San Antonio, TX. 6. Center for Renal Precision Medicine, Division of Nephrology, Department of Medicine, University of Texas Health Science Center San Antonio, San Antonio, TX. 7. Division of Nephrology, Department of Medicine, University of Washington, Seattle, WA. 8. Kidney Research Institute, University of Washington, Seattle, WA. 9. Department of Epidemiology, Graduate School of Public Health, University of Pittsburgh, Pittsburgh, PA. 10. Steno Diabetes Center Copenhagen, Copenhagen, Denmark. 11. Department of Biology, The Bioinformatics Center, University of Copenhagen, Copenhagen, Denmark. 12. Department of Clinical Medicine, University of Copenhagen, Copenhagen, Denmark. 13. Barbara Davis Center for Diabetes, University of Colorado Anschutz Medical Campus, Aurora, CO. 14. Puget Sound Veterans Affairs Healthcare System, Seattle, WA. 15. Division of Biostatistics and Bioinformatics, Herbert Wertheim School of Public Health and Human Longevity Science and Moores Cancer Center, University of California San Diego, La Jolla, CA. 16. Department of Statistics and the Informatics Institute, University of Florida, Gainesville, FL. 17. Division of Nephrology, University of Texas Health Science Center San Antonio, San Antonio, TX fafshin@med.umich.edu sharmak3@uthscsa.edu spennath@umich.edu. 18. Department of Molecular and Integrative Physiology, University of Michigan, Ann Arbor, MI.
Abstract
OBJECTIVES: Patients with type 1 diabetes (T1D) exhibit modest lipid abnormalities as measured by traditional metrics. This study aimed to identify lipidomic predictors of rapid decline of kidney function in T1D. RESEARCH DESIGN AND METHODS: In a case-control study, 817 patients with T1D from three large cohorts were randomly split into training and validation subsets. Case was defined as >3 mL/min/1.73 m2 per year decline in estimated glomerular filtration rate (eGFR), while control was defined as <1 mL/min/1.73 m2 per year decline over a minimum 4-year follow-up. Lipids were quantified in baseline serum samples using a targeted mass spectrometry lipidomic platform. RESULTS: At individual lipids, free fatty acid (FFA)20:2 was directly and phosphatidylcholine (PC)16:0/22:6 was inversely and independently associated with rapid eGFR decline. When examined by lipid class, rapid eGFR decline was characterized by higher abundance of unsaturated FFAs, phosphatidylethanolamine (PE)-Ps, and PCs with an unsaturated acyl chain at the sn1 carbon, and by lower abundance of saturated FFAs, longer triacylglycerols, and PCs, PEs, PE-Ps, and PE-Os with an unsaturated acyl chain at the sn1 carbon at eGFR ≥90 mL/min/1.73 m2. A multilipid panel consisting of unsaturated FFAs and saturated PE-Ps predicted rapid eGFR decline better than individual lipids (C-statistic, 0.71) and improved the C-statistic of the clinical model from 0.816 to 0.841 (P = 0.039). Observations were confirmed in the validation subset. CONCLUSIONS: Distinct from previously reported predictors of GFR decline in type 2 diabetes, these findings suggest differential incorporation of FFAs at the sn1 carbon of the phospholipids' glycerol backbone as an independent predictor of rapid GFR decline in T1D.
OBJECTIVES: Patients with type 1 diabetes (T1D) exhibit modest lipid abnormalities as measured by traditional metrics. This study aimed to identify lipidomic predictors of rapid decline of kidney function in T1D. RESEARCH DESIGN AND METHODS: In a case-control study, 817 patients with T1D from three large cohorts were randomly split into training and validation subsets. Case was defined as >3 mL/min/1.73 m2 per year decline in estimated glomerular filtration rate (eGFR), while control was defined as <1 mL/min/1.73 m2 per year decline over a minimum 4-year follow-up. Lipids were quantified in baseline serum samples using a targeted mass spectrometry lipidomic platform. RESULTS: At individual lipids, free fatty acid (FFA)20:2 was directly and phosphatidylcholine (PC)16:0/22:6 was inversely and independently associated with rapid eGFR decline. When examined by lipid class, rapid eGFR decline was characterized by higher abundance of unsaturated FFAs, phosphatidylethanolamine (PE)-Ps, and PCs with an unsaturated acyl chain at the sn1 carbon, and by lower abundance of saturated FFAs, longer triacylglycerols, and PCs, PEs, PE-Ps, and PE-Os with an unsaturated acyl chain at the sn1 carbon at eGFR ≥90 mL/min/1.73 m2. A multilipid panel consisting of unsaturated FFAs and saturated PE-Ps predicted rapid eGFR decline better than individual lipids (C-statistic, 0.71) and improved the C-statistic of the clinical model from 0.816 to 0.841 (P = 0.039). Observations were confirmed in the validation subset. CONCLUSIONS: Distinct from previously reported predictors of GFR decline in type 2 diabetes, these findings suggest differential incorporation of FFAs at the sn1 carbon of the phospholipids' glycerol backbone as an independent predictor of rapid GFR decline in T1D.
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