Serum metabolomics detected by LDI-TOF-MS can be used to distinguish between diabetic patients with and without diabetic kidney disease.

Abstract

Diabetic kidney disease (DKD) is an important cause of end-stage renal disease with changes in metabolic characteristics. The objective of this study was to study changes in serum metabolic characteristics in patients with DKD and to examine metabolite panels to distinguish DKD from diabetes with MALDI-TOF-MS. We recruited 40 T2DM patients with or without DKD from a single center for a cross-sectional study. Serum metabolic profiling was performed with MALDI-TOF-MS using a vertical silicon nanowire array. Differential metabolites between DKD and diabetes patients were selected, and their relevance to the clinical parameters of DKD was analyzed. We applied machine learning methods to the differential metabolite panels to distinguish DKD patients from diabetes patients. Twenty-four differential serum metabolites between DKD patients and diabetes patients were identified, which were mainly enriched in butyrate metabolism, TCA cycle, and alanine, aspartate and glutamate metabolism. Among the metabolites, L-kynurenine was positively correlated with urinary microalbumin, urinary microalbumin/creatinine ratio (UACR), creatinine and urea nitrogen content. L-serine, pimelic acid, 5-methylfuran-2-carboxylic acid, 4-methylbenzaldehyde and dihydrouracil were associated with Glomerular filtration rate (eGFR). The panel of differential metabolites could be used to distinguish between DKD and diabetes patients with an AUC value reaching 0.9899-0.9949. Among the differential metabolites, L-kynurenine was related to the progression of DKD. The differential metabolites exhibited excellent performance at distinguishing between DKD and diabetes. This study provides a novel direction for metabolomics-based clinical detection of DKD.