Urinary Metabolite Signatures for Predicting Elderly Stroke Survivors with Depression

Abstract

Background: Post-stroke depression (PSD) is a major complication in stroke survivors, especially in elderly stroke survivors. But there are still no objective methods to diagnose depression in elderly stroke survivors. Thus, this study was conducted to identify potential biomarkers for diagnosing elderly PSD subjects.

Methods: Elderly (60 years or older) stroke survivors with depression were assigned into the PSD group, and elderly stroke survivors without depression and elderly healthy controls (HCs) were assigned into the non-depressed group. Urinary metabolite signatures obtained from gas chromatography-mass spectrometry (GC-MS)-based metabolomic platform were collected. Both univariate and multivariate statistical analysis were used to find the differential urinary metabolites between the two groups.

Results: The 78 elderly HCs, 122 elderly stroke survivors without depression and 124 elderly PSD subjects were included. A set of 13 differential urinary metabolites responsible for distinguishing PSD subjects from non-depressed subjects were found. The Phenylalanine, tyrosine and tryptophan biosynthesis, Phenylalanine metabolism and Galactose metabolism were found to be significantly changed in elderly PSD subjects. The phenylalanine was significantly negatively correlated with age and depressive symptoms. Meanwhile, a biomarker panel consisting of 3-hydroxyphenylacetic acid, tyrosine, phenylalanine, sucrose, palmitic acid, glyceric acid, azelaic acid and α-aminobutyric acid was identified.

Conclusion: These results provided candidate molecules for developing objective methods to diagnose depression in elderly stroke survivors, suggested that taking supplements of phenylalanine might be an effective method to prevent depression in elderly stroke survivors, and would be helpful for future revealing the pathophysiological mechanism of PSD.

Keywords: biomarker; metabolomics; post-stroke depression.

Figure 1

Metabolomic analysis of urine samples from different groups: (A) OPLS-DA model built with training set showed that the elderly PSD subjects (blue dot) could be intelligibly separated from the elderly non-depressed subjects (green dot); (B) T-predicted scatter plot showed that the model could effectively predict the elderly PSD subjects (blue dot) and elderly non-depressed subjects (green dot) from the testing set.

Figure 2

Relative concentrations of these eight urinary metabolite biomarkers for elderly PSD (mean±standard deviation).

Figure 3

Diagnostic performance of potential biomarker panel for elderly PSD: (A) AUC value in the training set; (B) AUC value in the testing set.

Figure 4

Pathway analysis using the differential urinary metabolites: (A) three metabolic pathways were found to be significantly affected in elderly PSD subjects; (B) the main metabolites involved in these pathways. Red and green ellipses represent the significantly increased and decreased metabolites, respectively, identified in this study.