Circulating metabolic biomarkers of renal function in diabetic and non-diabetic populations View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2018-12

AUTHORS

Clara Barrios, Jonas Zierer, Peter Würtz, Toomas Haller, Andres Metspalu, Christian Gieger, Barbara Thorand, Christa Meisinger, Melanie Waldenberger, Olli Raitakari, Terho Lehtimäki, Sol Otero, Eva Rodríguez, Juan Pedro-Botet, Mika Kähönen, Mika Ala-Korpela, Gabi Kastenmüller, Tim D. Spector, Julio Pascual, Cristina Menni

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. More... »

PAGES

15249

Identifiers

URI

http://scigraph.springernature.com/pub.10.1038/s41598-018-33507-7

DOI

http://dx.doi.org/10.1038/s41598-018-33507-7

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1107514524

PUBMED

https://www.ncbi.nlm.nih.gov/pubmed/30323304


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426 schema:name Chair of Epidemiology, Ludwig-Maximilians-Universität München, UNIKA-T, Augsburg, Germany
427 Institute of Epidemiology II, Helmholtz Zentrum München - German Research Center for Environmental Health, Neuherberg, Germany
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429 https://www.grid.ac/institutes/grid.5386.8 schema:alternateName Cornell University
430 schema:name Department for Twin Research, King’s College London, London, UK
431 Institute of Bioinformatics and Systems Biology, Helmholtz Zentrum München - German Research Center for Environmental Health, Neuherberg, Germany
432 Weill Cornell Medical College, New York City, USA
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434 https://www.grid.ac/institutes/grid.5509.9 schema:alternateName University of Tampere
435 schema:name Department of Clinical Chemistry, Fimlab Laboratories and Finnish Cardiovascular Research Center-Tampere, Faculty of Medicine and Life Sciences, University of Tampere, Tampere, Finland
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437 https://www.grid.ac/institutes/grid.7737.4 schema:alternateName University of Helsinki
438 schema:name Nightingale Health Ltd, Helsinki, Finland
439 Research Programs Unit, Diabetes and Obesity, University of Helsinki, Helsinki, Finland
440 rdf:type schema:Organization
 




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