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. 2022 May 9:9:819311.
doi: 10.3389/fmed.2022.819311. eCollection 2022.

Serum Metabolomics Benefits Discrimination Kidney Disease Development in Type 2 Diabetes Patients

Xiaofeng Peng  1   2   3   4 , Xiaoyi Wang  1   2   3   4   5 , Xue Shao  1   2   3   4 , Yucheng Wang  1   2   3   4 , Shi Feng  1   2   3   4 , Cuili Wang  1   2   3   4 , Cunqi Ye  1   2   3   4   6 , Jianghua Chen  1   2   3   4 , Hong Jiang  1   2   3   4
Affiliations

Serum Metabolomics Benefits Discrimination Kidney Disease Development in Type 2 Diabetes Patients

Xiaofeng Peng et al. Front Med (Lausanne). .

Abstract

Background: Diabetic kidney disease (DKD) is the primary cause of end-stage renal disease, raising a considerable burden worldwide. Recognizing novel biomarkers by metabolomics can shed light on new biochemical insight to benefit DKD diagnostics and therapeutics. We hypothesized that serum metabolites can serve as biomarkers in the progression of DKD.

Methods: A cross-sectional study of 1,043 plasma metabolites by untargeted LC/MS among 89 participants identified associations between proteinuria severity and metabolites difference. Pathway analysis from differently expressed metabolites was used to determine perturbed metabolism pathways. The results were replicated in an independent, cross-sectional cohort of 83 individuals. Correlation and prediction values were used to examine the association between plasma metabolites level and proteinuria amount.

Results: Diabetes, and diabetic kidney disease with different ranges of proteinuria have shown different metabolites patterns. Cysteine and methionine metabolism pathway, and Taurine and hypotaurine metabolism pathway were distinguishable in the existence of DKD in DC (diabetes controls without kidney disease), and DKD with different ranges of proteinuria. Two interesting tetrapeptides (Asn-Met-Cys-Ser and Asn-Cys-Pro-Pro) circulating levels were elevated with the DKD proteinuria progression.

Conclusions: These findings underscore that serum metabolomics provide us biochemical perspectives to identify some clinically relevant physiopathologic biomarkers of DKD progression.

Keywords: biomarker discovery; diabetic kidney disease; metabolomics; progression; proteinuria.

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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

Figure 1
Figure 1
Experiment outline of this research.
Figure 2
Figure 2
Overall similarity and differences between samples by PCA and OPLS-DA analysis. (A) PCA score plots of healthy controls (HC), diabetic controls (DC), diabetic kidney disease patients [DKD, i.e., Diabetic nephropathy (DN)]. (B,C) OPLS-DA score and model overview plots of HC, DC, and DKD. (D) 1,000-times permutation test of the model showed its high strong reliability.
Figure 3
Figure 3
Visualization of serum metabolites difference between healthy controls and diabetic patients. (A) Volcano plot comparing serum metabolites in diabetic controls (DC) (n = 30) and healthy controls (HC) (n = 30). The vertical dashed lines indicate the threshold for the 1.5-fold abundance difference. The horizontal dashed line indicates the P = 0.05 threshold. X-axis, log2[average_FoldChange]. Y-axis, –log10[adjusted-P value]. P-value computed using a two-sided unpaired t-test without adjustment for multiple comparisons. (B) Volcano plot comparing serum metabolites in diabetic kidney disease patients (DKD) (n = 29) and healthy controls (HC) (n = 30). Refer to (A) for the description of the figure. (C) Volcano plot comparing serum metabolites in diabetic kidney disease patients (DKD) (n = 29) and diabetic controls (DC) (n = 30). Refer to (A) for the description of the figure. (D) Volcano plot comparing serum metabolites in diabetic kidney disease with heavy proteinuria (DKD-heavy) (n = 17) and diabetic kidney disease with moderate proteinuria (DKD-moderate) (n = 12). Refer to (A) for the description of the figure.
Figure 4
Figure 4
Disturbed cysteine and methionine metabolism and hypotaurine metabolism pathway with significance identified from Metabanalyst in the comparison of DKD vs. DC, DKD-H vs. DKD-M. (A) Disturbed metabolic pathways were identified from the changed metabolites from the comparison of DKD vs. DC and DKD-H vs. DKD-M using serum samples. All matched pathways according to the p values from the pathway enrichment analysis and pathway impact values from the pathway topology analysis. The color of each node (varying from yellow to red) means the metabolites are in the data with different levels of significance, the size of each node represents the pathway impact values. (B) Altered serum metabolites in the cysteine and methionine metabolism pathway. The left square refers to the comparison of DKD vs. DC, the right square refers to the comparison of DKD-H vs. DKD-M. Red means upregulated more than 1.1 fold, Green means downregulated less than 0.91 fold, and gray means unchanged whose range between 0.91 and 1.1 in each comparison. (C) Altered serum metabolites in the hypotaurine metabolism pathway. Refer to (B) for the description of the figure.
Figure 5
Figure 5
Asn-Met-Cys-Ser and Asn-Cys-Pro-Pro peak count changed with the progression of diabetic kidney disease. (A) Venn diagrams showing the number of upregulated (fold change ≥ 1.5) metabolites in DKD vs. DC and DKD-heavy vs. DKD-moderate (p < 0.05). (B) Venn diagrams showing the number of downregulated (fold change ≤ 0.67) metabolites in DKD vs. DC and DKD-heavy vs. DKD-moderate (p < 0.05). (C) Heatmap of 4 metabolites, Asn-Met-Cys-Ser, Asn-Cys-Pro-Pro, Thr-Cys-Cys and Isorhamnetin 3-(3″, 6″-di-p-coumarylglucoside) changed significantly (fold change ≥ 2 or ≤ 2) in the same direction in the comparison of DKD vs. DC and DKD-heavy vs. DKD-moderate. (D) Chemical structural formula, exact mass, and molecular weight of Asn-Met-Cys-Ser and Asn-Cys-Pro-Pro. (E,F) Confirmation of peak counts of Asn-Met-Cys-Ser and Asn-Cys-Pro-Pro in the validation group. Healthy control (HC) (n = 23), diabetic kidney disease with moderate proteinuria (DKD-M) (n = 25), and diabetic kidney disease with heavy proteinuria (DKD-H) (n = 35). The data are shown as the mean ± SEM. *P < 0.05 vs. the corresponding control group.
Figure 6
Figure 6
Correlation with clinical parameters and prediction value. (A) Correlation of urine protein (g/L) and UACR (g/mol·Cr) with Asn-Met-Cys-Ser. (B) Correlation of urine protein (g/L) and UACR (g/mol·Cr) with Asn-Cys-Pro-Pro. (C) Individual value plots of Asn-Met-Cys-Ser in the validation group. (D) Area under the curve (AUC) of prediction models based on Asn-Met-Cys-Ser. (E) Individual value plots of Asn-Cys-Pro-Pro in the validation group. (F) Area under the curve (AUC) of prediction models based on Asn-Cys-Pro-Pro. The data are shown as the mean ± SEM. *P < 0.05 vs. the corresponding control group.
Figure 7
Figure 7
Schematic illustration of the present study.
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