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Huang, J. ; Huth, C. ; Covic, M. ; Troll, M. ; Adam, J. ; Zukunft, S. ; Prehn, C. ; Wang, L. ; Nano, J. ; Scheerer, M.F. ; Neschen, S. ; Kastenmüller, G. ; Suhre, K.* ; Laxy, M. ; Schliess, F.* ; Gieger, C. ; Adamski, J. ; Hrabě de Angelis, M. ; Peters, A. ; Wang-Sattler, R.

Machine learning approaches reveal metabolic signatures of incident chronic kidney disease in individuals with prediabetes and type 2 diabetes.

Diabetes 69, 2756-2765 (2020)
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Open Access Green
Early and precise identification of individuals with pre-diabetes and type 2 diabetes (T2D) at risk for progressing to chronic kidney disease (CKD) is essential to prevent complications of diabetes. Here, we identify and evaluate prospective metabolite biomarkers and the best set of predictors of CKD in the longitudinal, population-based Cooperative Health Research in the Region of Augsburg (KORA) cohort by targeted metabolomics and machine learning approaches. Out of 125 targeted metabolites, sphingomyelin C18:1 and phosphatidylcholine diacyl C38:0 were identified as candidate metabolite biomarkers of incident CKD specifically in hyperglycemic individuals followed during 6.5 years. Sets of predictors for incident CKD developed from 125 metabolites and 14 clinical variables showed highly stable performances in all three machine learning approaches and outperformed the currently established clinical algorithm for CKD. The two metabolites in combination with five clinical variables were identified as the best set of predictors, and their predictive performance yielded a mean area value under the receiver operating characteristic curve of 0.857. The inclusion of metabolite variables in the clinical prediction of future CKD may thus improve the risk prediction in people with prediabetes and T2D. The metabolite link with hyperglycemia-related early kidney dysfunction warrants further investigation.
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Publication type Article: Journal article
Document type Scientific Article
Keywords Risk; Sphingomyelin; Prediction; Albuminuria; Progression; Population; Children
Language english
Publication Year 2020
HGF-reported in Year 2020
ISSN (print) / ISBN 0012-1797
e-ISSN 1939-327X
Journal Diabetes
Quellenangaben Volume: 69, Issue: 12, Pages: 2756-2765 Article Number: , Supplement: ,
Publisher American Diabetes Association
Publishing Place Alexandria, VA.
Reviewing status Peer reviewed
Institute(s) Institute of Epidemiology (EPI)
Institute of Experimental Genetics (IEG)
Molekulare Endokrinologie und Metabolismus (MEM)
Institute of Bioinformatics and Systems Biology (IBIS)
Institute of Health Economics and Health Care Management (IGM)
POF-Topic(s) 30202 - Environmental Health
90000 - German Center for Diabetes Research
30201 - Metabolic Health
30505 - New Technologies for Biomedical Discoveries
Research field(s) Genetics and Epidemiology
Enabling and Novel Technologies
PSP Element(s) G-504091-003
G-504090-001
G-504000-006
G-504091-002
G-504091-004
G-501900-061
G-505600-003
G-504000-002
G-501900-062
G-503700-001
G-505300-002
G-500600-001
G-504000-010
G-501900-401
Grants Biomedical Research Program funds at Weill Cornell Medicine -Qatar, a program - Qatar Foundation
EIT, a body of the European Union
European Institute of Innovation and Technology (EIT) Health
European Union Seventh Framework Programme (EU FP7)
Munich Center of Health Sciences (MC-Health), Ludwig-Maximilians-Universitat, as part of LMUinnovativ
State of Bavaria
Helmholtz Zentrum Munchen -German Research Center for Environmental Health - German Federal Ministry of Education and Research (BMBF)
DOI 10.2337/db20-0586
WOS ID WOS:000594819600021
Scopus ID 85096524249
PubMed ID 33024004
Erfassungsdatum 2020-10-15
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