Cargando…
Circulating metabolic biomarkers of renal function in diabetic and non-diabetic populations
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 an...
Autores principales: | , , , , , , , , , , , , , , , , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2018
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6189123/ https://www.ncbi.nlm.nih.gov/pubmed/30323304 http://dx.doi.org/10.1038/s41598-018-33507-7 |
_version_ | 1783363303209172992 |
---|---|
author | Barrios, Clara Zierer, Jonas Würtz, Peter Haller, Toomas Metspalu, Andres Gieger, Christian Thorand, Barbara Meisinger, Christa Waldenberger, Melanie Raitakari, Olli Lehtimäki, Terho Otero, Sol Rodríguez, Eva Pedro-Botet, Juan Kähönen, Mika Ala-Korpela, Mika Kastenmüller, Gabi Spector, Tim D. Pascual, Julio Menni, Cristina |
author_facet | Barrios, Clara Zierer, Jonas Würtz, Peter Haller, Toomas Metspalu, Andres Gieger, Christian Thorand, Barbara Meisinger, Christa Waldenberger, Melanie Raitakari, Olli Lehtimäki, Terho Otero, Sol Rodríguez, Eva Pedro-Botet, Juan Kähönen, Mika Ala-Korpela, Mika Kastenmüller, Gabi Spector, Tim D. Pascual, Julio Menni, Cristina |
author_sort | Barrios, Clara |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-6189123 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-61891232018-10-22 Circulating metabolic biomarkers of renal function in diabetic and non-diabetic populations Barrios, Clara Zierer, Jonas Würtz, Peter Haller, Toomas Metspalu, Andres Gieger, Christian Thorand, Barbara Meisinger, Christa Waldenberger, Melanie Raitakari, Olli Lehtimäki, Terho Otero, Sol Rodríguez, Eva Pedro-Botet, Juan Kähönen, Mika Ala-Korpela, Mika Kastenmüller, Gabi Spector, Tim D. Pascual, Julio Menni, Cristina Sci Rep Article 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. Nature Publishing Group UK 2018-10-15 /pmc/articles/PMC6189123/ /pubmed/30323304 http://dx.doi.org/10.1038/s41598-018-33507-7 Text en © The Author(s) 2018 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/. |
spellingShingle | Article Barrios, Clara Zierer, Jonas Würtz, Peter Haller, Toomas Metspalu, Andres Gieger, Christian Thorand, Barbara Meisinger, Christa Waldenberger, Melanie Raitakari, Olli Lehtimäki, Terho Otero, Sol Rodríguez, Eva Pedro-Botet, Juan Kähönen, Mika Ala-Korpela, Mika Kastenmüller, Gabi Spector, Tim D. Pascual, Julio Menni, Cristina Circulating metabolic biomarkers of renal function in diabetic and non-diabetic populations |
title | Circulating metabolic biomarkers of renal function in diabetic and non-diabetic populations |
title_full | Circulating metabolic biomarkers of renal function in diabetic and non-diabetic populations |
title_fullStr | Circulating metabolic biomarkers of renal function in diabetic and non-diabetic populations |
title_full_unstemmed | Circulating metabolic biomarkers of renal function in diabetic and non-diabetic populations |
title_short | Circulating metabolic biomarkers of renal function in diabetic and non-diabetic populations |
title_sort | circulating metabolic biomarkers of renal function in diabetic and non-diabetic populations |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6189123/ https://www.ncbi.nlm.nih.gov/pubmed/30323304 http://dx.doi.org/10.1038/s41598-018-33507-7 |
work_keys_str_mv | AT barriosclara circulatingmetabolicbiomarkersofrenalfunctionindiabeticandnondiabeticpopulations AT ziererjonas circulatingmetabolicbiomarkersofrenalfunctionindiabeticandnondiabeticpopulations AT wurtzpeter circulatingmetabolicbiomarkersofrenalfunctionindiabeticandnondiabeticpopulations AT hallertoomas circulatingmetabolicbiomarkersofrenalfunctionindiabeticandnondiabeticpopulations AT metspaluandres circulatingmetabolicbiomarkersofrenalfunctionindiabeticandnondiabeticpopulations AT giegerchristian circulatingmetabolicbiomarkersofrenalfunctionindiabeticandnondiabeticpopulations AT thorandbarbara circulatingmetabolicbiomarkersofrenalfunctionindiabeticandnondiabeticpopulations AT meisingerchrista circulatingmetabolicbiomarkersofrenalfunctionindiabeticandnondiabeticpopulations AT waldenbergermelanie circulatingmetabolicbiomarkersofrenalfunctionindiabeticandnondiabeticpopulations AT raitakariolli circulatingmetabolicbiomarkersofrenalfunctionindiabeticandnondiabeticpopulations AT lehtimakiterho circulatingmetabolicbiomarkersofrenalfunctionindiabeticandnondiabeticpopulations AT oterosol circulatingmetabolicbiomarkersofrenalfunctionindiabeticandnondiabeticpopulations AT rodriguezeva circulatingmetabolicbiomarkersofrenalfunctionindiabeticandnondiabeticpopulations AT pedrobotetjuan circulatingmetabolicbiomarkersofrenalfunctionindiabeticandnondiabeticpopulations AT kahonenmika circulatingmetabolicbiomarkersofrenalfunctionindiabeticandnondiabeticpopulations AT alakorpelamika circulatingmetabolicbiomarkersofrenalfunctionindiabeticandnondiabeticpopulations AT kastenmullergabi circulatingmetabolicbiomarkersofrenalfunctionindiabeticandnondiabeticpopulations AT spectortimd circulatingmetabolicbiomarkersofrenalfunctionindiabeticandnondiabeticpopulations AT pascualjulio circulatingmetabolicbiomarkersofrenalfunctionindiabeticandnondiabeticpopulations AT mennicristina circulatingmetabolicbiomarkersofrenalfunctionindiabeticandnondiabeticpopulations |