Abstract
Using targeted NMR spectroscopy of 227 fasting serum metabolic traits, we searched for novel metabolic signatures of renal function in 926 type 2 diabetics (T2D) and 4838 non-diabetic individuals from four independent cohorts. We furthermore investigated longitudinal changes of metabolic measures and renal function and associations with other T2D microvascular complications. 142 traits correlated with glomerular filtration rate (eGFR) after adjusting for confounders and multiple testing: 59 in diabetics, 109 in non-diabetics with 26 overlapping. The amino acids glycine and phenylalanine and the energy metabolites citrate and glycerol were negatively associated with eGFR in all the cohorts, while alanine, valine and pyruvate depicted opposite association in diabetics (positive) and non-diabetics (negative). Moreover, in all cohorts, the triglyceride content of different lipoprotein subclasses showed a negative association with eGFR, while cholesterol, cholesterol esters (CE), and phospholipids in HDL were associated with better renal function. In contrast, phospholipids and CEs in LDL showed positive associations with eGFR only in T2D, while phospholipid content in HDL was positively associated with eGFR both cross-sectionally and longitudinally only in non-diabetics. In conclusion, we provide a wide list of kidney function–associated metabolic traits and identified novel metabolic differences between diabetic and non-diabetic kidney disease.
Highlights
IntroductionII, Helmholtz Zentrum München - German Research Center for Environmental Health, Neuherberg, Germany. 8Research Unit Molecular Epidemiology, Helmholtz Zentrum München - German Research Center for Environmental Health, Neuherberg, Germany. 9Chair of Epidemiology, Ludwig-Maximilians-Universität München, UNIKA-T, Augsburg, Germany. 10Research Centre of Applied and Preventive Cardiovascular Medicine, University of Turku, Turku, Finland. 11Department of Clinical Physiology and Nuclear Medicine, Turku University Hospital, Turku, Finland. 12Department of Clinical Chemistry, Fimlab Laboratories and Finnish Cardiovascular Research Center-Tampere, Faculty of Medicine and Life Sciences, University of Tampere, Tampere, Finland. 13Department of Nephrology, Consorci Sanitari del Garraf, Barcelona, Spain. 14Department of Endocrinology and Nutrition, Hospital del Mar, Institut Mar d’Investigacions Mediques, Barcelona, Spain. 15Department of Clinical Physiology, University of Tampere and Tampere University Hospital, Tampere, Finland. 16Systems Epidemiology, Baker Heart and Diabetes Institute, Melbourne, Victoria, Australia. 17Population Health Science, Bristol Medical School, University of Bristol, Bristol, UK. 18Medical Research Council Integrative Epidemiology Unit at the University of Bristol, Bristol, UK. 19Computational Medicine, Faculty of Medicine, University of Oulu and Biocenter Oulu, Oulu, Finland. 20NMR Metabolomics Laboratory, School of Pharmacy, University of Eastern Finland, Kuopio, Finland. 21Department of Epidemiology and Preventive Medicine, School of Public Health and Preventive Medicine, Faculty of Medicine, Nursing and Health Sciences, The Alfred Hospital, Monash University, Melbourne, Victoria, Australia. 22Present address: Weill Cornell www.nature.com/scientificreports/
To assess the confounding effect of drug usage, we ran the same models in 1054 individuals from TwinsUK adjusting for statin and hormone replacement therapy (HRT), and in 655 individuals from GenodiabMar adjusting for statin usage
The strongest cross-sectional associations with eGFR were observed for glycine and phenylalanine (P < 0.001) with association magnitudes of −8.37 [−9.73: −7.02] and −7.92 [−9.27: −6.57], respectively, for the diabetic group and −1.29 [−1.66: −0.92] and −1.69 [−2.07: −1.32] for the non-diabetic group (Fig. 2)
Summary
II, Helmholtz Zentrum München - German Research Center for Environmental Health, Neuherberg, Germany. 8Research Unit Molecular Epidemiology, Helmholtz Zentrum München - German Research Center for Environmental Health, Neuherberg, Germany. 9Chair of Epidemiology, Ludwig-Maximilians-Universität München, UNIKA-T, Augsburg, Germany. 10Research Centre of Applied and Preventive Cardiovascular Medicine, University of Turku, Turku, Finland. 11Department of Clinical Physiology and Nuclear Medicine, Turku University Hospital, Turku, Finland. 12Department of Clinical Chemistry, Fimlab Laboratories and Finnish Cardiovascular Research Center-Tampere, Faculty of Medicine and Life Sciences, University of Tampere, Tampere, Finland. 13Department of Nephrology, Consorci Sanitari del Garraf, Barcelona, Spain. 14Department of Endocrinology and Nutrition, Hospital del Mar, Institut Mar d’Investigacions Mediques, Barcelona, Spain. 15Department of Clinical Physiology, University of Tampere and Tampere University Hospital, Tampere, Finland. 16Systems Epidemiology, Baker Heart and Diabetes Institute, Melbourne, Victoria, Australia. 17Population Health Science, Bristol Medical School, University of Bristol, Bristol, UK. 18Medical Research Council Integrative Epidemiology Unit at the University of Bristol, Bristol, UK. 19Computational Medicine, Faculty of Medicine, University of Oulu and Biocenter Oulu, Oulu, Finland. 20NMR Metabolomics Laboratory, School of Pharmacy, University of Eastern Finland, Kuopio, Finland. 21Department of Epidemiology and Preventive Medicine, School of Public Health and Preventive Medicine, Faculty of Medicine, Nursing and Health Sciences, The Alfred Hospital, Monash University, Melbourne, Victoria, Australia. 22Present address: Weill Cornell www.nature.com/scientificreports/. Several studies investigated metabolic profiles associated with renal function[4,5] in the general population[6] and in type 1 diabetic (T1D) patients[7,8,9] to identify biomarkers for disease progression[8] and mortality[10]. To gain insights in potential mechanisms of the cross-sectional associations, we investigated longitudinal changes of metabolite levels and renal function and associations with other microvascular complications of T2D
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