In this study, we performed an integrated analysis of the metabolic profiles of the peripheral system of patients with and without PSD. We found 47 metabolites that were differentially expressed between patients with and without PSD. Of these identified metabolites, 14 (L-glutamic acid, D-glucose, uric acid, L-lactic acid, 2-Hydroxybutyric acid, L-phenylalanine, formic acid, L-arabitol, azelaic acid, glyceric acid, pseudouridine, 5-Hydroxyhexanoic acid, L-tyrosine, and homocysteine) have also been reported in previous studies as potential biomarkers for PSD (Tang et al., 2016; Levada and Troyan, 2018). Residual metabolites were first reported as differentially expressed metabolites between patients with and without PSD. We found that the expression patterns of metabolites were inconsistent or even opposite among different studies. This may be related to the small sample size, differences in the subjects included, different sources of tissue types of metabolites and different metabolomic techniques used of previous studies. We have integrated these 47 metabolites into biological signaling pathways to investigate the biological functions associated with these metabolic alterations, and the results showed that they are mainly involved in the disruption of amino acid metabolism, particularly "phenylalanine metabolism" and "phenylalanine, tyrosine and tryptophan biosynthesis". L-glutamic acid, L-phenylalanine, and L-tyrosine were enriched in at least three of the six metabolic pathways identified. In addition, metabolites were classified according to tissue types and metabolomic techniques, and pathway analysis was used to investigate the metabolites. The subgroup and the overall results were similar, indicating good reliability of the results.
Glutamate is a major excitatory neurotransmitter in the central nervous system (Murrough et al., 2017), the most abundant free amino acid in the brain, involved in a variety of metabolic pathways in the body (Zhou and Danbolt, 2014), and a key transmitter in the degeneration of signaling neurons after stroke(Lai et al., 2014). Glutamate is associated with various aspects of the pathophysiological processes associated with depression (Murrough et al., 2017) and is increasingly recognized for its role in stress-related illnesses, including anxiety and depression (Ashe et al., 2019). Additionally, stroke was independently associated with glutamate levels (Lai et al., 2014; Frank et al., 2019). PSD is accompanied by changes in frontal glutamate levels, which may reflect abnormal glutamate transmission immediately after stroke (Glodzik-Sobanska et al., 2006; Wang et al., 2012). In a study of rats with middle cerebral artery occlusion, Frank et al. (Frank et al., 2019) found changes in glutamate levels in rats with PSD. Li et al. (Li et al., 2019) also found significant changes in glutamate content in rats with PSD. Furthermore, glutamate was one of 25 metabolites that differed between PSD rats and control and stroke rats (Jiang et al., 2021). Glutamate levels have also been shown to be independently associated with PSD in clinical studies (Cheng et al., 2014). Moreover, early PSD after acute ischemic stroke was independently associated with glutamate levels (Geng et al., 2017). This study also found that glutamate was differentially expressed in the peripheral system of patients with and without PSD.
In this study, the pathway was divided into three modules. The first module was mainly composed of amino acid metabolism pathways. This study showed that “phenylalanine metabolism” (metabolites: L-phenylalanine, hippurate, and L-tyrosine) was the most significantly altered pathway. “Phenylalanine metabolism” was also identified as one of the three pathways most associated with PSD in a previous study (Chen et al., 2021) and has been identified as an important pathway in the acute phase of ischemic stroke (Sidorov et al., 2020). As one of eight essential amino acids, phenylalanine may have the potential to relieve pain and depression (Chen et al., 2021). Phenylalanine metabolism was also found to be associated with depression in a depressive rat model(Xu et al., 2019; Yang et al., 2020a). Imbalance of central and peripheral phenylalanine metabolism was found in rats subjected to chronic unpredictable mild stress (Han et al., 2019). Plasma phenylalanine levels showed an association with the Diagnostic and Statistical Manual of Mental Disorders, Third Edition depression subgroup (dysthymic disorders, major recurrent depression, and bipolar depression) (Chiaroni et al., 1990). Urine metabolic phenotypes of patients with MDD were analyzed, and phenylalanine metabolism was significantly affected in middle-aged patients with MDD (Chen et al., 2019). The determination of phenylalanine metabolism can provide a reasonable biochemical pathway for the pathogenesis of neuropsychiatric disorders, support individualized treatment, and predict the outcome (Strasser et al., 2017). In vivo, phenylalanine is mainly catalyzed by phenylalanine hydroxylase, which produces tyrosine (Xu et al., 2019). A recent meta-analysis of 15,428 participants showed a significant association between tyrosine and depression (Bot et al., 2020). Ke et al. (Ke et al., 2019) identified tyrosine as a consistent biomarker associated with PSD. An animal study also showed that tyrosine is associated with the development of depression (Han et al., 2019). Changes in central and peripheral tyrosine and phenylalanine concentrations are thought to be related to the pathogenesis of depression (Ogawa et al., 2018). In addition, Ormstad et al. (Ormstad et al., 2016) found that tyrosine and phenylalanine were biomarkers for the diagnosis of acute ischemic stroke. Phenylalanine and tyrosine are essential for synthesis of the neurotransmitters dopamine, norepinephrine, and epinephrine (Teraishi et al., 2018; Wang et al., 2020) and are directly linked to the development of depression. Chen et al. (Chen et al., 2021) also found that "phenylalanine metabolism" and "phenylalanine, tyrosine and tryptophan biosynthesis", two pathways related to phenylalanine metabolism, were significantly affected in patients with PSD, which was consistent with the results of our study. In addition, phenylalanine and tyrosine are metabolic by-products of intestinal microbiota. In recent years, the relationship between intestinal microbiota and depression has become a focus of research (Zheng et al., 2016), and the results have suggested that the occurrence and development of PSD may also be related to the disturbance of intestinal microflora. The second module consisted mainly of nucleotide metabolism pathways. Among them, the "aminoacyl-tRNA biosynthesis" pathway contained the most associated molecules. Simultaneously, “aminoacyl-tRNA biosynthesis” is an important metabolic pathway in human psychiatric disorders such as MDD and attention-deficit/hyperactivity disorder (Yang et al., 2020b). A combined analysis of feces, serum, liver and hippocampal metabolites from mice that underwent fecal microbiota transplantation from MDD patients showed significant changes in "aminoacyl-tRNA biosynthesis" (Li et al., 2018). The third module was mainly composed of glucose metabolism pathways. Depression is well known to be linked to disorders of glucose metabolism. A study of mice with MDD found that the "glycolysis/gluconeogenesis" pathway in the hippocampus of mice was affected after drug use, as shown by changes in metabolite levels and connected metabolite level ratios (Weckmann et al., 2014).
This study has several obvious limitations. First, only a small number of studies were available in the database, and the data available for analysis were limited. Therefore, the results identifying significant metabolites should be interpreted with caution. Second, a small amount of metabolite information may be lost in the standardized naming process of candidate metabolites because of the non-standard naming methods used in some studies. Third, both ischemic stroke and hemorrhagic stroke patients were included in this study, but these two types of patients were not analyzed separately. The metabolites associated with ischemic stroke and hemorrhagic stroke may differ because of their different pathophysiological mechanisms. Therefore, future studies should analyze these two groups of patients separately. Fourth, we did not verify the relevant pathways, which should be further verified in future studies. Finally, we only analyzed metabolites in peripheral tissues (plasma, serum, and urine), and future studies should further analyze the metabolites in the brain.
In conclusion, by analyzing the metabolite levels of patients with and without PSD, this study found that 47 metabolites were differentially expressed in the peripheral tissues. We also predicted six signaling pathways that may be involved in the development of PSD, among which the most significantly altered pathway is phenylalanine metabolism. These pathways were roughly divided into three modules, which are involved in amino acid metabolism, nucleotide metabolism, and glucose metabolism. Subgroup analyses indicated that the results have good reliability. Our findings may contribute to elucidate the molecular mechanism of PSD and may provide clues for the early objective diagnosis of PSD. However, because of limited evidence and multiple confounding factors in the sample, more large sample studies are needed to determine the strength of these associations.