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Abstract: TH-PO236

Novel Urinary Metabolite Biomarkers in Diagnosis of Diabetic Kidney Disease

Session Information

Category: Diabetic Kidney Disease

  • 602 Diabetic Kidney Disease: Clinical

Authors

  • Shi, Caifeng, Second Affiliated Hospital of Nanjing Medical University, Nanjing, China
  • He, Aiqin, Second Affiliated Hospital of Nanjing Medical University, Nanjing, China
  • Wu, Xiaomei, Second Affiliated Hospital of Nanjing Medical University, Nanjing, China
  • Yang, Junwei, Second Affiliated Hospital of Nanjing Medical University, Nanjing, China
  • Zhou, Yang, Second Affiliated Hospital of Nanjing Medical University, Nanjing, China
Background

To exploration and application of novel urinary metabolite biomarkers in diagnosis of diabetic kidney disease (DKD).

Methods

Healthy individuals and type 2 diabetes (T2DM) patients admitted to the Second Affiliated Hospital of Nanjing Medical University from January to December 2020 were enrolled. Two separated cohorts-discovery cohort (n = 116) and validation cohort (n = 119) were included, which were divided into three groups: healthy control group, simple T2DM group, and T2DM + DKD group. Demographic and laboratory data were collected, and the urinary metabolites were detected by ultraperformance liquid chromatography coupled to tandem mass spectrometry. Partial least squares discriminant analysis and orthogonal partial least squares discriminant analysis were used for multi-dimensional modeling. Machine learning based on Boruta algorithm was used to screen potential markers. Wilcox test was used for comparison of metabolite data between two groups, and Kruskal-Wallis test was used for comparison between multiple groups. Binary logistic regression analysis was used for statistical modeling, and receiver operating characteristic (ROC) curve was used to evaluate diagnostic efficacy.

Results

A total of 160 metabolites were detected in morning urine samples by targeted quantitative metabolomics. Sixty-two differential metabolites were screened out from the discovery cohort, mainly enriched in amino acid metabolic pathway, and 17 of them were candidate markers for diagnosis of DKD. Fifteen differential metabolites and seven candidate markers (isovaleric acid; isobutyric acid; leucine; s-adenosylhomocysteine; propionic acid; oxoadipic acid; propionylcarnitine) were verified in the validation cohort. The diagnostic model of the combined marker was constructed by integrating the 7-candidate urinary metabolic markers. The diagnostic efficacy of the combined marker in both the discovery cohort and the validation cohort (the area under ROC curve of the discovery cohort was 0.888, 95%CI 0.814—0.963; the area under ROC curve of the validation cohort was 0.811, 95%CI 0.734—0.887) were significantly higher than that of a single metabolic marker.

Conclusion

There were significant differences in urine metabolites between simple T2DM patients and T2DM with DKD patients. The combination of various urine metabolic markers may be a novel strategy to diagnose DKD.

Funding

  • Government Support – Non-U.S.