DB20-0586_rv2_SOM_up2.pdf (1.03 MB)
Download fileMachine learning approaches revealed metabolic signatures of incident chronic kidney disease in persons with pre-and type 2 diabetes
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posted on 2020-11-04, 21:24 authored by Ada AdminAda Admin, Jialing Huang, Cornelia Huth, Marcela Covic, Martina Troll, Jonathan Adam, Sven Zukunft, Cornelia Prehn, Li Wang, Jana Nano, Markus F. Scheerer, Susanne Neschen, Gabi Kastenmüller, Karsten Suhre, Michael Laxy, Freimut Schliess, Christian Gieger, Jerzy Adamski, Martin Hrabe de Angelis, Annette Peters, Rui Wang-SattlerEarly and precise identification of individuals with pre-diabetes and
type 2 diabetes (T2D) at risk of 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 (SM) C18:1 and
phosphatidylcholine diacyl (PC aa) 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 persons with pre- and T2D.
The metabolite link with hyperglycemia-related early kidney dysfunction
warrants further investigation.