Differential metabolomic signatures of declining renal function in Types 1 and 2 diabetes

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Manca , M L , Solini , A , Haukka , J K , Sandholm , N , Forsblom , C , Groop , P H & Ferrannini , E 2021 , ' Differential metabolomic signatures of declining renal function in Types 1 and 2 diabetes ' , Nephrology Dialysis Transplantation , vol. 36 , no. 10 , pp. 1859-1866 . https://doi.org/10.1093/ndt/gfaa175

Title: Differential metabolomic signatures of declining renal function in Types 1 and 2 diabetes
Author: Manca, Maria Laura; Solini, Anna; Haukka, Jani K.; Sandholm, Niina; Forsblom, Carol; Groop, Per Henrik; Ferrannini, Ele
Contributor organization: HUS Abdominal Center
Faculty of Medicine
University of Helsinki
Nefrologian yksikkö
CAMM - Research Program for Clinical and Molecular Metabolism
Research Programs Unit
Clinicum
Medicum
Institute for Molecular Medicine Finland
Diabetes and Obesity Research Program
Department of Medicine
Per Henrik Groop / Principal Investigator
Date: 2021-10-01
Language: eng
Number of pages: 8
Belongs to series: Nephrology Dialysis Transplantation
ISSN: 0931-0509
DOI: https://doi.org/10.1093/ndt/gfaa175
URI: http://hdl.handle.net/10138/340442
Abstract: Background: Chronic kidney disease (CKD) shows different clinical features in Types1 (T1D) and 2 diabetes (T2D). Metabolomics have recently provided useful contribution to the identification of biomarkers of CKD progression in either form of the disease. However, no studies have so far compared plasma metabolomics between T1D and T2D in order to identify differential signatures of progression of estimated glomerular filtration rate (eGFR) decline. Methods: We used two large cohorts of T1D (from Finland) and T2D (from Italy) patients followed up to 7 and 3 years, respectively. In both groups, progression was defined as the top quartile of yearly decline in eGFR. Pooled data from the two groups were analysed by univariate and bivariate random forest (RF), and confirmed by bivariate partial least squares (PLS) analysis, the response variables being type of diabetes and eGFR progression. Results: In progressors, yearly eGFR loss was significantly larger in T2D [-5.3 (3.0), median (interquartile range)mL/min/1.73 m2/year] than T1D [-3.7 (3.1) mL/min/1.73 m2/year; P = 0.018]. Out of several hundreds, bivariate RF extracted 22 metabolites associated with diabetes type (all higher in T1D than T2D except for 5-methylthioadenosine, pyruvate and β-hydroxypyruvate) and 13 molecules associated with eGFR progression (all higher in progressors than non-progressors except for sphyngomyelin). Three of the selected metabolites (histidylphenylalanine, leucylphenylalanine, tryptophylasparagine) showed a significant interaction between disease type and progression. Only eight metabolites were common to both bivariate RF and PLS. Conclusions: Identification of metabolomic signatures of CKD progression is partially dependent on the statistical model. Dual analysis identified molecules specifically associated with progressive renal impairment in both T1D and T2D.
Description: Publisher Copyright: © 2020 The Author(s) 2020. Published by Oxford University Press on behalf of ERA-EDTA. All rights reserved.
Subject: 3121 General medicine, internal medicine and other clinical medicine
GFR
metabolomics
nephropathy
Type 1 diabetes
Type 2 diabetes
METABOLITES
MAST-CELLS
RISK
NEPHROPATHY
SERUM URIC-ACID
INDIVIDUALS
PLASMA
DYSFUNCTION
CHRONIC KIDNEY-DISEASE
ASSOCIATION
Peer reviewed: Yes
Rights: unspecified
Usage restriction: openAccess
Self-archived version: acceptedVersion


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