Plasma Metabolomics Identifies Markers of Impaired Renal Function: A Meta-analysis of 3089 Persons with Type 2 Diabetes
Author(s) -
Nete Tofte,
Nicole Vogelzangs,
Dennis O. MookKanamori,
Adela Brahimaj,
Jano,
Fariba Ahmadizar,
Ko Willems van Dijk,
Marie FrimodtMøller,
Ilja C.W. Arts,
Joline W. J. Beulens,
Femke Rutters,
Amber A. van der Heijden,
Maryam Kavousi,
Coen D.A. Stehouwer,
Giel Nijpels,
Marleen M. J. van Greevenbroek,
Carla Kallen,
Peter Rossing,
Tarunveer S. Ahluwalia,
Leen M. ‘t Hart
Publication year - 2020
Publication title -
the journal of clinical endocrinology and metabolism
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.206
H-Index - 353
eISSN - 1945-7197
pISSN - 0021-972X
DOI - 10.1210/clinem/dgaa173
Subject(s) - meta analysis , type 2 diabetes , diabetes mellitus , medicine , metabolomics , renal function , function (biology) , bioinformatics , endocrinology , biology , genetics
Context There is a need for novel biomarkers and better understanding of the pathophysiology of diabetic kidney disease. Objective To investigate associations between plasma metabolites and kidney function in people with type 2 diabetes (T2D). Design 3089 samples from individuals with T2D, collected between 1999 and 2015, from 5 independent Dutch cohort studies were included. Up to 7 years follow-up was available in 1100 individuals from 2 of the cohorts. Main outcome measures Plasma metabolites (n = 149) were measured by nuclear magnetic resonance spectroscopy. Associations between metabolites and estimated glomerular filtration rate (eGFR), urinary albumin-to-creatinine ratio (UACR), and eGFR slopes were investigated in each study followed by random effect meta-analysis. Adjustments included traditional cardiovascular risk factors and correction for multiple testing. Results In total, 125 metabolites were significantly associated (PFDR = 1.5×10–32 − 0.046; β = −11.98-2.17) with eGFR. Inverse associations with eGFR were demonstrated for branched-chain and aromatic amino acids (AAAs), glycoprotein acetyls, triglycerides (TGs), lipids in very low-density lipoproteins (VLDL) subclasses, and fatty acids (PFDR < 0.03). We observed positive associations with cholesterol and phospholipids in high-density lipoproteins (HDL) and apolipoprotein A1 (PFDR < 0.05). Albeit some metabolites were associated with UACR levels (P < 0.05), significance was lost after correction for multiple testing. Tyrosine and HDL-related metabolites were positively associated with eGFR slopes before adjustment for multiple testing (PTyr = 0.003; PHDLrelated < 0.05), but not after. Conclusions This study identified metabolites associated with impaired kidney function in T2D, implying involvement of lipid and amino acid metabolism in the pathogenesis. Whether these processes precede or are consequences of renal impairment needs further investigation.
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