Research Paper Volume 16, Issue 4 pp 3420—3530
Insights into serum metabolic biomarkers for early detection of incident diabetic kidney disease in Chinese patients with type 2 diabetes by random forest
- 1 Department of Nephrology, Shenzhen Traditional Chinese Medicine Hospital, The Fourth Clinical Medical College of Guangzhou University of Chinese Medicine, Shenzhen, China
- 2 The Research Centre for Chinese Medicine Innovation and Department of Applied Biology and Chemical Technology, The Hong Kong Polytechnic University, Hong Kong, China
- 3 State Key Laboratory of Chinese Medicine and Molecular Pharmacology (Incubation), Shenzhen, China
Received: April 7, 2023 Accepted: December 6, 2023 Published: February 12, 2024
https://doi.org/10.18632/aging.205542How to Cite
Copyright: © 2024 Jiang et al. This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Abstract
Diabetic kidney disease (DKD) is a leading cause of end-stage renal disease (ESRD) worldwide. Early detection is critical for the risk stratification and early intervention of progressive DKD. Serum creatinine (sCr) and urine output are used to assess kidney function, but these markers are limited by their delayed changes following kidney pathology, and lacking of both sensitivity and accuracy. Hence, it is essential to illustrate potential diagnostic indicators to enhance the precise prediction of early DKD. A total of 194 Chinese individuals include 30 healthy participants (Stage 0) and 164 incidents with type 2 diabetes (T2D) spanning from DKD’s Stage 1a to 4 were recruited and their serums were subjected for untargeted metabolomic analysis. Random forest (RF), a machine learning approach, together with univariate linear regression (ULR) and multivariate linear regression (MvLR) analysis were applied to characterize the features of untargeted metabolites of DKD patients and to identify candidate DKD biomarkers. Our results indicate that 2-(α-D-mannopyranosyl)-L-tryptophan (ADT), succinyladenosine (SAdo), pseudouridine and N,N,N-trimethyl-L-alanyl-L-proline betaine (L-L-TMAP) were associated with the development of DKD, in particular, the latter three that were significantly elevated in Stage 2-4 T2D incidents. Each of the four metabolites in combination with sCr achieves better performance than sCr alone with area under the receiver operating characteristic curve (AUC) of 0.81-0.91 in predicting DKD stages. An average of 3.9 years follow-up study of another cohort including 106 Stage 2-3 patients suggested that “urinary albumin-to-creatinine ratio (UACR) + ADT + SAdo” can be utilized for better prognosis evaluation of early DKD (average AUC = 0.9502) than UACR without sexual difference.
Abbreviations
β2-MG: β2-microglobulin; BSA: body surface-area; CKD: chronic kidney disease; DKD: diabetic kidney disease; ESRD: end-stage renal disease; FBG: fasting blood glucose; QC: quality control; RRI: renal resistive index; SBP: systolic blood pressure; L,L-TMAP: N,N,N-trimethyl-L-alanyl-L-proline betaine; RMSEs: root mean square errors; UPLC-Orbitrap-MS: Ultra-Performance Liquid Chromatography-Orbitrap-Mass Spectrometry; UACR: urinary albumin-to-creatinine ratio; SAM: significantly altered metabolites; ADT: 2-(α-D-mannopyranosyl)-L-tryptophan; SAdo: succinyladenosine; sCr: serum creatinine; MS-sCr: Mass Spectrometry detected serum creatinine; T2D: type 2 diabetes; T1D: type 1 diabetes; CDBs: candidate DKD biomarkers; PCA: partial correlation analysis; BeGFR: CDB-predicted eGFR; SD: standard deviation.