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. 2023 Nov;55(11):1563-1572.
doi: 10.1007/s00726-023-03330-0. Epub 2023 Sep 22.

Metabolomic profiling of amino acids study reveals a distinct diagnostic model for diabetic kidney disease

Jiao Wang  1   2   3 , Chunyu Zhou  2   3   4 , Qing Zhang  5   6 , Zhangsuo Liu  7   8   9   10
Affiliations

Metabolomic profiling of amino acids study reveals a distinct diagnostic model for diabetic kidney disease

Jiao Wang et al. Amino Acids. 2023 Nov.

Abstract

Diabetic kidney disease (DKD), a highly prevalent complication of diabetes mellitus, is a major cause of mortality in patients. However, identifying circulatory markers to diagnose DKD requires a thorough understanding of the metabolic mechanisms of DKD. In this study, we performed ultra-performance liquid chromatography tandem mass spectrometry (UPLC-MS/MS) to reveal altered metabolic profiles of amino acids (AAs) in patients with DKD. We found decreased plasma levels of histidine and valine, increased urine levels of proline, decreased urine levels of histidine and valine, and increased saliva levels of arginine in patients with DKD compared with the levels in patients with type 2 diabetes mellitus (T2DM) and in healthy controls. Our analyses of the key metabolites and metabolic enzymes involved in histidine and valine metabolism indicated that the AAs level alterations may be due to enhanced carnosine hydrolysis, decreased degradation of homocarnosine and anserine, enhanced histidine methylation, and systemic enhancement of valine metabolism in patients with DKD. Notably, we generated a distinct diagnostic model with an AUC of 0.957 and an accuracy up to 92.2% on the basis of the AA profiles in plasma, urine and saliva differing in patients with DKD using logistic regression and receiver operating characteristic analyses. In conclusion, our results suggest that altered AA metabolic profiles are associated with the progression of DKD. Our DKD diagnostic model on the basis of AA levels in plasma, urine, and saliva may provide a theoretical basis for innovative strategies to diagnose DKD that may replace cumbersome kidney biopsies.

Keywords: Amino acids; Diabetic kidney disease; Diagnostic model; LC–MS/MS; Metabolomics.

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Conflict of interest statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Figures

Fig. 1
Fig. 1
The metabolic pathways of histidine and valine are significantly different among DKD, T2DM and CON groups. Diagram of histidine metabolic pathway (A). Diagram of valine metabolic pathway (B). Circulating levels of metabolite of histidine and valine metabolism (C). *P < 0.05, **P < 0.01, ***P < 0.001
Fig. 2
Fig. 2
Circulating levels of major enzymes of histidine and valine metabolism. CNDP1 was detected by an enzyme-linked immunosorbent assay in plasma, while CNDP2, SETD3, Histidase, BCAT1, BCAT2, BCKDHA, and BCKDHB mRNA expression levels were detected using quantitative real-time PCR of whole blood cells. CNDP1, carnosinase 1; CNDP2, carnosinase 2; BCAT1, branched-chain aminotransferase 1; BCAT2, branched-chain aminotransferase 2; BCKDHA, branched-chain ketoacid dehydrogenase A; BCKDHB, branched-chain ketoacid dehydrogenase B; SETD3, SET domain-containing 3. *P < 0.05, **P < 0.01, ***P < 0.001
Fig. 3
Fig. 3
Association between the amino acids concentrations in the plasma and urine. Correlation is significant at the 0.0001 level (2-tailed). Pearson’s Correlations Coefficient: 0.538
Fig. 4
Fig. 4
ROC curve and AUC for different model. Plasma histidine (6C89C0 line), plasma valine (A5CEE9 line), urine histidine (FD8008 line), urine proline (FFC000 line), urine valine (FFE38B line), saliva arginine (92D050 line), Logit(P) (FF0000 line). Accuracy = (A × sensitivity + B × specificity)/(A + B), where A is the number of participants in the corresponding disease group and B is the number of participants in the corresponding control group
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