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. 2019 Aug 13:12:1379-1386.
doi: 10.2147/DMSO.S215187. eCollection 2019.

Urinary biomarkers for diagnosing poststroke depression in patients with type 2 diabetes mellitus

Zi-Hong Liang  1 , Yan-Bo Jia  2 , Zi-Ru Li  1 , Min Li  1 , Mei-Ling Wang  1 , Yong-Li Yun  1 , Li-Jun Yu  1 , Lei Shi  1 , Run-Xiu Zhu  1
Affiliations

Urinary biomarkers for diagnosing poststroke depression in patients with type 2 diabetes mellitus

Zi-Hong Liang et al. Diabetes Metab Syndr Obes. .

Abstract

Background: Depression can seriously affect the quality of life of type 2 diabetes mellitus (T2DM) patients after stroke. However, there were still no objective methods to diagnose T2DM patients with poststroke depression (PSD). Therefore, we conducted this study to deal with this problem.

Methods: Gas chromatography-mass spectroscopy (GC-MS)-based metabolomics profiling method was used to profile the urinary metabolites from 83 nondepressed T2DM patients after stroke and 101 T2DM patients with PSD. The orthogonal partial least-squares discriminant analysis was conducted to explore the metabolic differences in T2DM patients with PSD. The logistic regression analysis was performed to identify the optimal and simplified biomarker panel for diagnosing T2DM patients with PSD. The receiver operating characteristic curve analysis was used to assess the diagnostic performance of this biomarker panel.

Results: In total, 23 differential metabolites (7 decreased and 16 increased in T2DM patients with PSD) were found. A panel consisting of pseudouridine, malic acid, hypoxanthine, 3,4-dihydroxybutyric acid, fructose and inositol was identified. This panel could effectively separate T2DM patients with PSD from nondepressed T2DM patients after stroke. The area under the curve was 0.965 in the training set and 0.909 in the validation set. Meanwhile, we found that the galactose metabolism was significantly affected in T2DM patients with PSD.

Conclusion: Our results could be helpful for future development of an objective method to diagnose T2DM patients with PSD and provide novel ideas to study the pathogenesis of depression.

Keywords: metabolite; metabolomics; post-stroke depression; type 2 diabetes mellitus.

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Conflict of interest statement

The authors declare no financial or other conflicts of interest in this work.

Figures

Figure 1
Figure 1
Metabolomic analysis of urine samples from the two groups: (A) OPLS-DA model built with training set (green dot, nondepressed T2DM patients after stroke; blue dot, T2DM patients with PSD); (B) T-predicted scatter plot built with validation set (green dot, nondepressed T2DM patients after stroke; blue dot, T2DM patients with PSD); (C) 399-item permutation test. Abbreviations: T2DM, type 2 diabetes mellitus; PSD, post-stroke depression; OPLS-DA, orthogonal partial least-squares discriminant analysis.
Figure 2
Figure 2
Correlation coefficients of the differential metabolites.
Figure 3
Figure 3
Heatmap constructed using molecular features of the differential metabolites. Abbreviations: T2DM, type 2 diabetes mellitus; PSD, post-stroke depression.
Figure 4
Figure 4
Pathway analysis of the differential metabolites: (A) galactose metabolism was significantly affected (p-value<0.05, impact>0 and false discovery rate (FDR)<0.1); (B) metabolite–metabolite interaction analysis of the differential metabolites.
Figure 5
Figure 5
Results of logistic regression analysis and receiver operating characteristic curve analysis. Abbreviations: AIC, Akaike’s information criterion; AUC, area under the curve.
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