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Published on: June 17, 2020
Virtual diagnosis of diabetic nephropathy using metabolomics in place of kidney biopsy: The DIAMOND study
Da Woon Kim1, Hyo Jin Kim2, Eun Young Seong2
1Department of Internal Medicine and Biomedical Research Institute, Pusan National University Hospital, 179 Gudeok-ro, Seo-gu, Busan 49241, Republic of Korea.
Aims:
To explore the clinical factors and urinary metabolites that predict biopsy-confirmed diabetic nephropathy (DN) in patients with type 2 diabetes mellitus (T2DM).
Methods:
Data from the medical records of 126 patients with T2DM who underwent kidney biopsy between January 2010 and October 2020 at a single-center were retrospectively reviewed to investigate the clinical factors that predict DN. Urine samples were collected to perform urine metabolomics in patients with T2DM divided by biopsy-confirmed DN, immunoglobulin A, and membranous nephropathy, and a control group of healthy participants. Each group comprised 11 age- and sex-matched participants. A prediction model was developed using a combination of clinical factors and urinary metabolites, and a multivariate receiver operating characteristic (ROC) analysis was conducted.
Results:
Age, presence of proliferative diabetic retinopathy, T2DM duration, and hemoglobin A1c levels were clinical factors predictive of DN. Four urinary metabolites (alanine, choline, N-phenylacetylglycine, and trigonelline) had variable importance in projection scores > 1 and were predictive of DN. When conducting multivariate ROC analysis with a combination of clinical factors and urinary metabolites, the area under the curve was 1.000.
Conclusions:
The combination of clinical factors and urinary metabolites is highly valuable for predicting biopsy-confirmed DN in patients with T2DM.
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