Differences in the gut microbiota and plasma metabolome of major depressive disorder patients with and without ischemic stroke

Differences in the gut microbiota and plasma metabolome of major depressive disorder patients with and without ischemic stroke | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Differences in the gut microbiota and plasma metabolome of major depressive disorder patients with and without ischemic stroke Huiru Zhang, Dongsheng Lyu, Xingguang Zhang, Ning Cao This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3948912/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Major depressive disorder (MDD) and ischemic stroke (IS) are prominent contributors to disease burden worldwide, and MDD has been recognized as a significant risk factor for IS in epidemiology studies; however, the specific mechanisms that explain the coexistence of MDD and IS have not been identified. Multiple studies have shown a strong association between the gut microbiota and both IS and MDD. We propose that the gut microbiota may play a role in the development of IS in individuals with MDD. This study aimed to investigate the mechanisms linking the gut microbiota and increased risk of IS development in patients with MDD. Methods We included 30 hospitalized individuals diagnosed with MDD with IS and 30 individuals diagnosed with MDD without IS using the matching method and used 16S rRNA gene sequencing and the nontarget metabolome to analyze the gut microbiota composition and plasma metabolic profiles of the included patients. Results MDD patients with IS and MDD patients without IS have different gut microbiota structures and plasma metabolic profiles. MDD patients with IS had more bacteria with lipopolysaccharide (LPS) structures and lacked bacteria that produce butyrate. Alloprevotella and Bacteroides massiliensis , along with their associated metabolites, facilitated precise differentiation between patients with and without IS. The area under the curve (AUC) for these bacteria was 0.998 (95% confidence interval: 0.992-1.000) and 0.992 (95% confidence interval: 0.978-1.000). Conclusions Compared with MDD patients without IS, patients with MDD who also had IS exhibited distinct changes in their gut microbiome and metabolite profiles. Changes in the gut microbiome are evident by an elevated abundance of bacteria with LPS structures and a reduced abundance of bacteria that produce butyrate. Additionally, the abundances of Alloprevotella and Bacteroides massiliensis , along with their related metabolites, strongly predict IS in patients with MDD. Major depressive disorder Ischemic stroke Gut microbiota Plasma metabolome Figures Figure 1 Figure 2 Figure 3 Figure 4 Background Major depressive disorder (MDD) is a prevalent and severe mental illness characterized by a persistent feeling of sadness and a decrease in cognitive function and is frequently accompanied by a lack of motivation and slower thought processes[ 1 , 2 ]. MDD accounts for 49.4 million disability-adjusted life years (DALYs) globally, positioning it as a prominent contributor to the disease burden in 2020[ 3 ]. Furthermore, NAFLD is a significant public health concern that leads to reduced patient functioning and increased mortality rates[ 4 ]. The global 12-month incidence of MDD is approximately 6%, and more than 20% of individuals experience at least one episode of depression during their lifetime[ 5 ]. In addition, individuals with MDD have a death rate almost twice as high as that of the general population[ 6 ]. MDD has been recognized as a significant risk factor for ischemic stroke (IS) in numerous extensive cohort studies and meta-analyses of cohort studies[ 7 – 10 ]. IS is a serious condition that imposes a substantial burden on individuals and society and is one of the primary causes of disability and mortality worldwide[ 11 ]. In 2019, the worldwide incidence of IS, the number of deaths caused by IS, and the number of DALYs lost were 77.63 million, 3.23 million, and 63.48 million, respectively[ 12 ]. Reducing the risk of developing IS in patients with MDD has important implications for improving the survival of MDD patients and decreasing the overall burden of the disease. Although there is an epidemiologic association between MDD and IS, the specific mechanisms that explain the coexistence of MDD and IS have still not been identified. The advancement of next-generation sequencing technology is anticipated to be a pivotal factor in uncovering the mechanism behind the occurrence of IS in patients with MDD, particularly in relation to the gut microbiota. The gut microbiota is commonly referred to as the "second genome" of humans. The gut microbiota can impact the physiological processes of various systems in the body through different pathways, hence playing a role in the development of diseases. A disordered body, in contrast, might result in an imbalance of the gut microbiota through various mechanisms. Multiple studies undertaken in recent years have shown a strong association between the gut microbiota and both IS and MDD. Several clinical studies and meta-analyses have demonstrated significant disparities in the gut microbiota and its metabolites between persons with MDD and healthy control subjects[ 13 – 17 ]. In the same way, the gut microbiota can play a role in the development of IS through specific microbiota-derived metabolites, such as LPS, short-chain fatty acids (SCFAs), trimethylamine-N-oxide (TMAO), and phenylacetylglutamine (PAGln)[ 18 – 22 ]. Therefore, we propose that the gut microbiota may play a role in the development of IS in individuals with MDD. However, the specific mechanism involved has not been fully comprehended. This study aimed to initially investigate the mechanisms linking the gut microbiota and increased risk of IS development in patients with MDD. To achieve this goal, we sequenced the gut microbiota and plasma metabolome of MDD patients with and without IS. The findings of this study aim to establish a basis for further research on the role of the gut microbiota in the development of IS in MDD patients. Materials and Methods Study subjects and Sample Collection Procedures We selected individuals with MDD at the Inner Mongolia Autonomous Region Mental Health Center in China. The study period spanned from December 2021 to May 2023. We included hospitalized individuals among the MDD patients in the IS group who fulfilled the following specified criteria: (i) 18 years of age or older; (ii) met the Diagnostic and Statistical Manual of Mental Disorders, 4th Edition (DSM-IV) criteria for MDD according to the Chinese version of the Mini-International Neuropsychiatric Interview (MINI); and (iii) were diagnosed with IS within the last 3 years after being diagnosed with MDD. The diagnostic criteria for IS were derived from the "Chinese guidelines for the diagnosis and treatment of acute ischemic stroke 2018". The exclusion criteria included the presence of other mental disorders diagnosed according to the 4th Edition (DSM-IV) criteria; treatment-resistant depression; depression with severe suicidal and self-injurious tendencies; schizophrenia; bipolar disorder; and neurodegenerative diseases. Additionally, individuals with other diagnostic diseases, such as chronic inflammatory disorders, thyroid disease, or cancer, were excluded. Pregnant or breastfeeding females, those who had used antibiotics within 1 month prior to sampling, and individuals who reported changes in diet habits or had special dietary requirements (e.g., food allergy, food intolerance, vegetarianism) were also excluded. Using the matching method, each MDD patient with a history of IS was paired with an MDD patient without a history of IS who was admitted to the hospital simultaneously. The patients were matched based on sex, antidepressant medication, and age difference of no more than two years. Patients who did not have IS were subsequently allocated to the non-IS group. The ultimate sample included 60 participants—30 individuals diagnosed with MDD with IS and 30 individuals diagnosed with MDD without IS. The degree of depression was assessed using the 24-item Hamilton Depression Rating Scale (HAMD-24). The individuals were also queried about their smoking habits, and their blood pressure (BP), blood glucose (GLU), blood lipid, and past medication history were documented. Fecal and blood samples were collected from all the research subjects within 48 hours of hospital admission. Participants deposited their feces into prepared, clean, and dry receptacles specifically designed for fecal collection. Fecal samples were obtained from the middle section using a sterile fecal sampler with a volume of 15 ml. For each scenario, a tube with a minimum of 2 grams of sample was retained, and the stool samples were then stored at a temperature of -80°C until they were utilized. Licensed nurses collected whole blood, which was then left to coagulate for 15 minutes at room temperature. After that, the mixture was centrifuged at 5000 rpm for 10 minutes. The resulting plasma was kept and promptly stored at a temperature of -80°C until needed. 16S rRNA Gene Sequencing of the Gut Microbiome After the fecal samples were thawed, DNA was extracted using either cetyltrimethylammonium bromide (CTAB) or sodium dodecyl sulfonate (SDS) techniques. The concentration and purity of the DNA were assessed using agarose gels, and the DNA was subsequently diluted to a concentration of 1 ng/µl using sterile water. The V4 region of the 16S rRNA gene was amplified using a PCR technique that employed particular primers, including barcodes (515F: GTGCCAGCMGCCGCGGTAA and 806R: GGACTACHVGGGTWTCTAAT). Amplification was carried out using Phusion® High-Fidelity PCR Master Mix with GC Buffer from New England Biolabs (USA). The PCR products were purified using a Qiagen Gel Extraction Kit manufactured by Qiagen in Germany. A TruSeq® DNAPCR-Free Sample Preparation Kit (Illumina, USA) was used to construct sequencing libraries following the manufacturer's recommendations. The library quality was evaluated using a Qubit® 2.0 Fluorometer (Thermo Fisher, USA) and QPCR. Finally, the library was sequenced on a NovaSeq 6000 system (Illumina, USA). After sequencing, the raw data were preprocessed to obtain effective tags. The UPARSE method (UPARSE v7.0.1001, http://www.drive5.com/uparse/ ) was used to perform optical transform unit (OTU) clustering analysis on sequences with 97% similarity. The resulting representative OTU sequences were subsequently matched with a microbial taxonomy database to obtain information about representative species. The community makeup of the two groups of samples was determined at each taxonomic level (phylum, class, order, family, genus, and species) using the results of the OTU analysis. The Shannon diversity index, Simpson's index of diversity, Chao1 estimator, and abundance-based coverage estimator (ACE) were calculated using Mothur software (v1.48) to examine the abundance and diversity of species within each sample. The Bray‒Curtis method was used to conduct a principal coordinate analysis (PCoA) to assess the extent of the variation in species diversity, community composition, and structure across the samples. Linear discriminant analysis effect size (LEfSe) analysis was used to discern bacterial markers with high-dimensional characteristics among the different groupings. Ultimately, the anticipated functional composition profiles of the 16S rRNA sequences were condensed into Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways using Tax4Fun2. Plasma Metabolomic analysis The plasma metabolites were examined using liquid chromatography‒mass spectrometry (LC‒MS). The ProteoWizard software converted the initial data file obtained from the LC‒MS analysis into the mzML format. The process of identifying, filtering, and aligning peaks was carried out using the R package XCMS. The "SVR" technique was employed to rectify the peak area. The metabolite annotations of the LC‒MS data were validated after quality control using the laboratory's proprietary database, publicly available database, artificial intelligence prediction database, and metDNA technology. The R program was used for statistical analysis. The statistical analysis comprises two techniques: principal component analysis (PCA) and orthogonal partial least-squares discriminant analysis (OPLS-DA). The differentially abundant metabolites were selected based on the following criteria: variable importance in projection (VIP) score ≥ 1; P value ˂ 0.05; and fold change ≥ 2 or ≤ 0.5. Local discriminant analysis (LDA) effect size plots and volcano plots were generated to illustrate the differences in metabolites across different groups. The KEGG pathway was used to refer to the metabolic pathways that were enriched with the differentially abundant metabolites. Statistical analysis All the statistical analyses were performed using R software version 4.2.0. Continuous variables are presented as the mean ± standard deviation (SD), and statistical comparisons among the various groups were performed using one-way analysis of variance (ANOVA) or a t test. Categorical data were analyzed by the chi-square test. Spearman correlation tests were applied to determine the correlation between the gut microbiome and metabolome. P < 0.05 was considered to indicate statistical significance. Results Clinical characteristics of the participants In this study, we conducted a comprehensive examination of the gut microbiota and plasma metabolome in 60 samples from two groups: MDD patients with IS and MDD patients without IS. The two groups did not show any significant differences in demographic indicators, such as sex, age, HAMD-24 score, blood pressure, blood glucose, or blood lipids (Table 1 ). The well-matched samples were utilized to detect particular characteristics of the gut microbiota and metabolites in MDD patients with IS. Table 1 Clinical characteristics of the participants MDD with IS MDD t / χ ༒ P Age (mean ± SD ) 68.33 ± 6.799 68.17 ± 6.859 0.095 0.925 Gender (Men), n (%) 12(40%) 12(40%) 0 1.0 score of HAMD-24 (mean ± SD ) 29.87 ± 8.127 26.17 ± 7.231 1.863 0.068 Smoking status (Yes), n (%) 6(20%) 4(13.8%) 0.424 0.525 systolic blood pressure (SBP) (mmHg) (mean ± SD ) 126.60 ± 14.053 130.51 ± 15.120 1.035 0.305 diastolic blood pressure (DBP) (mmHg) (mean ± SD ) 78.60 ± 8.677 78.63 ± 9.238 0.014 0.989 blood glucose (GLU) (mmol/L) (mean ± SD ) 5.75 ± 1.658 5.95 ± 1.119 0.559 0.579 total cholesterol (TC) (mmol/L) (mean ± SD ) 4.37 ± 1.293 4.51 ± 1.041 0.465 0.643 triglyceride (TG) (mmol/L) (mean ± SD ) 1.75 ± 1.101 1.86 ± 1.312 0.357 0.722 low-density lipoprotein cholesterol (LDL-C) (mmol/L) (mean ± SD ) 2.77 ± 0.994 1.21 ± 0.283 0.768 0.873 high density lipoprotein cholesterol (HDL-C) (mmol/L) (mean ± SD ) 1.22 ± 0.279 1.21 ± 0.283 0.161 0.446 There was no difference in the diversity of the IS patients compared to the non-IS patients The gut microbiome of MDD patients with IS and MDD patients without IS was assessed using 16S rRNA gene sequencing. Initially, we employed alpha (α)-diversity as a measure of both the number of species present and their relative abundance. The findings indicated that MDD patients with IS exhibited a tendency toward decreased gut microbiota diversity, as evidenced by lower Shannon diversity indices (Fig. 1 A), Simpson's indices (Fig. 1 B), Chao1 (Fig. 1 C), and ACE (Fig. 1 D) indices than did MDD patients without IS. However, it is important to note that none of these differences reached statistical significance. PCoA was also conducted to evaluate the beta (β)-diversity of the microbiota. However, we did not observe distinct segregation between the two groups. The Adonis test results, based on Bray‒Curtis dissimilarity, did not reveal any significant differences in the bacteria between the two groups (Fig. 1 E). Taxonomic and functional characterization of the gut microbiota in IS and non-IS patients. The samples were subsequently assessed at six taxonomic levels, namely, phylum, class, order, family, genus, and species, to determine whether there was any variation in the taxonomic composition between the IS patients and non-IS patients. We employed the LEfSe technique to ascertain the taxa that served as biomarkers for each group. Bar plots depicting the relative abundances at the phylum level clearly revealed distinct variations in the gut microbiota between patients with IS and those without IS. The phyla Firmicutes and Bacteroidetes were the most abundant in both groups, with relative abundances greater than 10%. While Proteobacteria was not the most common phylum in either group, its relative abundance was significantly greater in the IS group than in the non-IS group (Fig. 2 A). At the order level, the IS group exhibited a greater abundance of Enterobacterales than the non-IS group, with an LDA score greater than 2. Similar results were observed at the family level, where the abundance of Enterobacteriaceae was greater in the non-IS group than in the IS group. Enterobacteriaceae are gram-negative bacteria that have LPS structures. In addition, the abundance of Veillonella parvula, which are gram-positive bacteria that possess an outer membrane with LPS similar to that of gram-negative bacteria, was greater in the IS group than in the IS group. The results revealed that the gut microbiota of the IS group included an increase in the presence of bacteria with LPS structures. The distributions of SCFA-producing bacteria in the gut microbiota of patients with IS and those without IS were ultimately distinct. The abundance of propionic acid-producing bacteria, including Veillonella parvula and Alloprevotella, was greater in the non-IS group than in the IS group, as was the abundance of two subspecies of Prevotella , namely, Prevotella sp. Marseille P2931 and Prevotella stercorea. Conversely, there was a lower abundance of butyric acid-producing bacteria, such as Acidaminococcaceae , Roseburia , and Fusicatenibacter , in the IS group than in the non-IS group (with an LDA score greater than 2). Finally, at the species level, the IS group displayed a lower prevalence of Bacteroides massiliensis and Bacteroides finegoldii than did the non-IS group (Fig. 2 B, C). We employed receiver operating characteristic (ROC) curve analysis to examine the aforementioned differential taxa to discover bacteria that may be valuable in the early detection of MDD patients with IS. Ultimately, we discovered that Alloprevotella and Bacteroides massiliensis were successfully differentiated between MDD patients with IS and those without IS. The area under the curve (AUC) values were 0.749 (95% CI = 0.621, 0.876) and 0.783 (95% CI = 0.664, 0.902) for Fig. 2 D and Fig. 2 E, respectively. We employed Tax4Fun2 to predict the functional capacity of microbial communities in both IS and non-IS patients. This was accomplished by analyzing the 16S rRNA gene content, which is specifically designed to assess the functional capabilities of microbial communities identified using 16S rRNA sequencing. The pathways linked to the IS subjects involved primarily the metabolism of terpenoids and polyketides, such as the biosynthesis of siderophore group nonribosomal peptides. Additionally, these findings were associated with energy metabolism, specifically sulfur and nitrogen metabolism. Furthermore, these genes were related to the metabolism of cofactors and vitamins, including ubiquinone and other terpenoid-quinone biosynthesis products. Finally, carbohydrate metabolism, specifically inositol phosphate metabolism, was also a significant aspect of these subjects. Conversely, the pathways linked to the non-IS participants were primarily involved in translation (ribosome, aminoacyl-tRNA biosynthesis), nucleotide metabolism (pyrimidine metabolism), amino acid metabolism (lysine biosynthesis), replication and repair (homologous recombination, DNA replication). It is important to emphasize that this is a speculative forecast derived from the analysis of 16S rRNA composition, and it does not conclusively assess functional capabilities or transcriptional activity (Fig. 2 F). Metabolome differences in IS and non-IS patients LC–MS–based metabolomic analysis was used to evaluate the differences in the plasma metabolic signatures between patients with IS and patients without IS. A model based on OPLS-DA was used to examine the disparities in metabolites between the two groups. Based on the OPLS-DA results, the IS and non-IS groups were clearly separated into distinct regions (Fig. 3 A). The goodness-of-fit and predictive ability values (R 2 X = 0.153, R 2 Y = 0.988, Q 2 = 0.753, P value < 0.05) suggested that the OPLS-DA model had a good fit and effective predictive capacity (Fig. 3 B). Using the OPLS-DA data, we employed the VIP as a criterion to further identify metabolites that exhibited significant differences between the two groups. Metabolites with a VIP ≥ 1.0, a P value < 0.05, or a fold change ≥ 2 or ≤ 0.5 were considered differentially abundant metabolites. A total of 38 metabolites exhibited significant differences between the IS and non-IS groups. Similarly, compared with those in the non-IS group, the levels of 17 metabolites in the IS group were significantly greater, whereas the levels of 21 metabolites were significantly lower (Fig. 3 C). The differentially abundant metabolites mostly consisted of amino acids and their metabolites, benzene and its substituted derivatives, glycerophospholipids, and heterocyclic chemicals. To determine the metabolic pathways associated with these genes, the differentially abundant metabolites were compared to those in the KEGG database. The identified differentially abundant metabolites were found to be involved in various metabolic pathways, such as amino acid metabolism (specifically tryptophan), lipid metabolism (including steroid hormone and sphingolipid), carbohydrate metabolism (ascorbate and aldarate, glycolysis/gluconeogenesis), signal transduction (sphingolipid signaling pathway), cell growth and death (necroptosis), the endocrine and nervous system (adipocytokine signaling pathway, neurotrophin signaling pathway), and endocrine and metabolic diseases. This finding suggested that disruption of multiple pathways may play a role in the progression of IS (Fig. 3 D). Correlation analysis between the gut microbiota and metabolites Spearman's correlation coefficient was calculated to ascertain the functional correlations between alterations in the gut microbiome and plasma metabolism based on the disparities in the gut microbes and metabolites observed between individuals with IS and those without IS. The correlations were deemed statistically significant when∣ r ∣≥ 0.5 and the P value was < 0.05. The study revealed negative correlations between the levels of glucobrassicin, daidzein, 5-hydroxypseudobaptigenin, k-strophanthoside, arbutin 6-phosphate, 1-pentadecanoyl-2-lignoceroyl-sn-glycerol, and 1-stearoyl-2-myristoyl-sn-glycero-3-phosphocholine and the abundance of Alloprevotella . The r values were − 0.575, -0.554, -0.553, -0.539, -0.626, -0.508, -0.573, and − 0.501. The P values for all correlations were less than 0.001. The level of 7(14)-farnesene-9,12-diol was positively correlated with the abundance of Alloprevotella ( r = 0.515, P < 0.001; Fig. 4 A). Glucobrassicin, daidzein, 5-hydroxypseudobaptigenin, and dinoseb were shown to be positively correlated with Bacteroides massiliensis . The r values were 0.635, 0.602, 0.511, and 0.539, respectively. The P values for all correlations were less than 0.001 (Fig. 4 B). These findings indicate that the modified gut microbiome may interact with metabolites to influence the development of ischemic stroke. Concurrently, we employed receiver operating characteristic (ROC) curve analysis to evaluate Alloprevotella and its associated metabolites, as well as Bacteroides massiliensis and its associated metabolites, to determine the best model that may be valuable for the early detection of MDD patients with IS. The findings indicated that the AUC for the combination of Alloprevotella and its associated metabolites was 0.998 (with a confidence interval of 0.992 to 1.000). Similarly, the AUC for the combination of Bacteroides massiliensis and its related metabolites was 0.992 (with a confidence interval of 0.978 to 1.000) (Fig. 4 C and D). These results suggest that the presence of Alloprevotella and its associated metabolites, as well as Bacteroides massiliensis and its associated metabolites, are good indicators of increased risk for ischemic stroke in individuals with MDD. Discussion Previous studies have established a connection between MDD and IS at the population level, but the specific mechanisms that explain the co-occurrence of MDD and IS have not been identified. Due to the close connection between the gut microbiota and these two illnesses, we hypothesized that the gut microbiota may also play a critical role in the development of IS in individuals with MDD. To elucidate the mechanisms associated with the gut microbiota, we analyzed the gut microbiota and metabolome in MDD patients with IS and MDD patients without IS. The present study showed through gut microbiota and metabolomic analyses that there are significant changes in the makeup of the gut microbiota community and metabolites in MDD patients with IS and MDD patients without IS. Our research provides a fresh point of view on the association between MDD and IS. In the examination of the taxonomic composition disparity between the two groups, we observed that at the phylum level, the proportion of Proteobacteria was greater in IS patients than in non-IS patients. A Proteobacteria increase is considered an indicator of microbial dysbiosis[ 23 ]. Prior studies have demonstrated that individuals with MDD exhibit disrupted gut microbiota features, although there is much heterogeneity in the methods and reporting of these studies[ 16 , 17 , 24 , 25 ]. Moreover, our study showed that MDD patients with IS exhibit more severe disruptions in their gut microbiota. The elevated abundance of Proteobacteria can be ascribed to the enrichment of Enterobacteriaceae , and these findings align with a previous investigation conducted on individuals with IS during both the acute and recovery phases[ 20 ]. Enterobacteriaceae are bacteria characterized by their LPS structures. LPS is a crucial constituent of the outer membrane's external layer in gram-negative bacteria. It can trigger an immune response and induce inflammation in the host[ 26 – 28 ]. The presence of a high level of Enterobacteriaceae in the intestines, in conjunction with an impaired gut barrier, promptly triggers widespread inflammation throughout the body by producing a substantial quantity of LPS. This process may contribute to the development of IS, and higher levels of LPS are associated with worse outcomes in patients with IS[ 20 , 29 – 31 ]. In addition, the abundance of Veillonella parvula, which is a gram-positive bacterium that possesses an outer membrane with LPS similar to that of gram-negative bacteria, was similarly greater in IS patients than in non-IS patients[ 32 ]. The distributions of SCFA-producing bacteria in the gut microbiota of patients with IS and those without IS were ultimately distinct. Patients with IS exhibited elevated levels of propionic acid-producing bacteria, including Veillonella parvula , Alloprevotella , and two subspecies of Prevotella ( Prevotella_sp_Marseille_ P2931 and Prevotella _stercorea). Conversely, there was a lower abundance of butyric acid-producing bacteria, such as Acidaminococcaceae , Roseburia , and Fusicatenibacter . These findings suggest that various SCFAs have distinct functions in the progression of IS in individuals with MDD. Prior studies have demonstrated that individuals with acute IS with diabetes have decreased levels of bacteria that produce butyric acid[ 33 ]; furthermore, even those without a previous history of stroke but with a high risk of experiencing one also showed a reduction in the level of butyric acid-producing bacteria[ 34 ], implying that bacteria that produce butyrate may play a protective role in the development and prognosis of IS. The aforementioned results indicate that the gut microbiota of patients with MDD during the recovery phase after IS is similar to that of patients with acute-phase IS. The primary characteristics of this gut microbiota include an increase in the presence of bacteria with LPS structures and a decrease in the quantity of bacteria that produce butyrate. Bacteroides massiliensis is a bacterium that is found at relatively low abundances in people with IS. Prior research examining the gut microbiota of patients with cancer, including prostate cancer[ 35 ], colorectal cancer[ 36 ], and melanoma[ 37 ], revealed that the abundance of Bacteroides massiliensis was greater in patients with tumors. However, it has also been shown that individuals with diabetes and those with diabetes-related cardiovascular issues have significantly lower levels of Bacteroides massiliensis than healthy individuals[ 38 ]. This finding demonstrates the complexity of the relationship between Bacteroides massiliensis and disease. Additional investigations are needed to determine whether Bacteroides massiliensis contributes to the development of IS in patients with MDD. Metabolomes represent the combined effects of internal physiological processes and external factors. The human gut microbiota engages in significant interactions with the host through the cometabolism of substrates and the exchange of metabolites[ 39 ]. Consequently, we conducted an analysis of the patients' plasma metabolomics. We observed the considerable impact of the presence or absence of IS on the metabolites of these strains. We discovered two distinct compounds, daidzein and glucobrassicin, that are strongly linked to the presence of two specific types of bacteria, Alloprevotella and Bacteroides massiliensis . Daidzein is a type of flavonoid compound that is found in soybeans and several soy-based products and exists in the form of glucosides[ 40 ]. The gut microbiota, which includes Bacteroidetes , Firmicutes , Entererococcus , Lactobacillus , and Bifidobacterium , contains genes that encode various glycosidase enzymes, such as β-glucosidase. These enzymes can breakdown and transform soy sapogenins, which are glycosides, into free sapogenins and glucose[ 41 ]. Once separated, the free sapogenins undergo a range of reactions, including dihydroxylation, reduction, pyrone ring cleavage, or demethylation. These events result in the formation of a new molecule that exhibits potent estrogenic activity, such as equol, or a product that is inactive, such as O-desmethylangolensin[ 40 , 42 ]. Equol has demonstrated significant promise in the prevention of cardiovascular disease (CVD). Equol may decrease the likelihood of developing coronary heart disease by enhancing its anti-atherogenic properties, improving arterial stiffness, and reducing LDL-C levels in overweight people[ 43 ]. Glucobrassicin, an important secondary metabolite found in cruciferous vegetables, can be broken down to form indole-3-methanol (I3C). Both I3C and its primary in vivo derivative, 3,3’-diindolylmethane (DIM), have demonstrated efficacy as cancer chemopreventive agents in preclinical models and have shown potential in clinical trials. I3C also exhibited significant neuroprotective effects in a rat model of clonidine-induced depression[ 44 ]. Similarly, I3C has been shown to demonstrate significant protective or therapeutic effects against diabetes[ 45 , 46 ], hypertension[ 47 , 48 ], and a rat model of cerebral ischemia/reperfusion[ 49 ]. These effects may be attributed to its antioxidant activity and capacity to inhibit the inflammatory response. While previous research has relied on animal tests, the current study revealed that individuals with MDD and IS had reduced levels of glucobrassicin. These findings suggested that glucobrassicin may have a protective effect on patients with MDD complicated with IS. Moreover, there was an inverse relationship between daidzein and glucobrassicin with Alloprevotella , while a positive relationship was found with Bacteroides massiliensis. These findings suggested that daidzein and glucobrassicin may have an impact on patients with MDD by interacting with these two bacteria. However, further investigation is required to determine the exact mechanism of action involved. Ultimately, the random forest analysis identified Alloprevotella and Bacteroides massiliensis as reliable discriminators between IS patients and non-IS patients. Nevertheless, we found that the addition of related metabolites can significantly enhance the accuracy of the prediction. Hence, the gut microbiota and metabolites may also prove beneficial in the prompt detection of patients with MDD who have IS. We also acknowledge various limitations of this study. Initially, the hospital's lockdown as a result of the COVID-19 pandemic made sample collection more challenging. The IS patients we collected were in the recovery phase rather than the acute phase. However, previous studies have shown a significant increase in the abundance of Enterobacteriaceae in patients with acute and recovering IS[ 20 ]. Consequently, our research can offer a reference regarding the composition of the gut microbiota in patients with MDD during the acute stage of IS. Furthermore, 16S rRNA sequencing lacks the ability to provide genetically modified annotations that are precise enough to identify the specific species. Therefore, shotgun metagenomic sequencing should be performed for further microbiome analysis. Conclusions In conclusion, this study revealed that, compared with MDD patients without IS, patients with MDD who also had IS had distinct changes in their gut microbiome and metabolite profiles. Changes in the gut microbiome manifest as an increase in the abundance of bacteria with LPS structures and a decrease in the abundance of bacteria that produce butyrate. Significantly altered microbes and metabolites may contribute to the development of IS in patients with MDD. Additionally, this study emphasized that the presence of the bacteria Alloprevotella and Bacteroides Massiliensis , in addition to their related metabolites, are strong prognostic indicators of IS in patients with MDD. The findings provide new insights into the mechanisms that contribute to the coexistence of MDD and IS. Abbreviations MDD major depressive disorder IS ischemic stroke LPS lipopolysaccharide DALY disability-adjusted life years SCFAs short-chain fatty acids HAMD: Hamilton Depression Rating Scale BP blood pressure GLU blood glucose OTU optical transform unit ACE abundance-based coverage estimator PCoA Principal Coordinate Analysis LEfSe linear discriminant analysis effect size KEGG Kyoto Encyclopedia of Genes and Genomes LC‒MS: liquid chromatography‒mass spectrometry PCA principal component analysis OPLS-DA orthogonal partial least-squares discriminant analysis VIP variable importance in projection LDA Local Discriminant Analysis ROC receiver operating characteristic AUC: area under the curve I3C indole-3-methanol Declarations Ethics approval and consent to participate Ethical approval for the study was approved by the Ethics Committee of Mental Health Center of Inner Mongolia Autonomous Region (2022009), Hohhot, China. The study was conducted in accordance with a protocol that received approval from the Medical Ethics Research Committee of our hospital. In the presence of clinicians, patients’ families were informed of the study and provided informed consent. All the research in this study was carried out in compliance with the Declaration of Helsinki, ensuring the protection of patients' privacy rights. Consent for publication Not applicable. Availability of data and materials Not applicable. Competing interests The authors declare that they have no competing interests in relation to this manuscript. Funding This study was supported by the Scientific Research Project of Universities in Inner Mongolia Autonomous Region (NJZY22617); the Inner Mongolia Medical University General Program (YKD2024MS003); the National Natural Science Foundation of China (82160639); the Inner Mongolia Autonomous Region Health Science and Technology Program (202202104); the Science and Technology Project of High-level Clinical Specialties Construction in Public Hospitals of Inner Mongolia Autonomous Region Health Commission (2023SGGZ049); and the Science and Technology Program of the Joint Fund for Scientific Research in Public Hospitals (2023GLLH0160). Authors' contributions All the authors participated in the study design. HR Zhang and DS Lyu collected the fecal and blood samples and drafted the manuscript. Ning Cao performed the statistical analysis and interpretation of the data. XG Zhang designed and supervised the study. Final approval was obtained from all the authors of the version submitted for publication. Acknowledgments We thank all the doctors, nurses and clinical scientists who worked in the hospital during the period of patient recruitment as well as the patients who were involved in this study. We also thank Wuhan MetWare Biotechnology Co., Ltd., for sample detection. References Otte C, Gold SM, Penninx BW, Pariante CM, Etkin A, Fava M, et al. Major depressive disorder. Nat Rev Dis Primer. 2016;2:1–20. Su Y, Ye C, Xin Q, Si T. Major depressive disorder with suicidal ideation or behavior in Chinese population: A scoping review of current evidence on disease assessment, burden, treatment and risk factors. J Affect Disord. 2023;340:732–42. COVID-19 Mental Disorders Collaborators. Global prevalence and burden of depressive and anxiety disorders in 204 countries and territories in 2020 due to the COVID-19 pandemic. Lancet Lond Engl. 2021;398:1700–12. 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Association Between Depressive Symptoms and Incident Cardiovascular Diseases. JAMA. 2020;324:2396–405. Alexopoulos GS. Depression and Cerebrovascular Disease: What is to be Done? Am J Geriatr Psychiatry. 2017;25:129–30. Zahodne LB, Gilsanz P, Glymour MM, Gibbons LE, Brewster P, Hamilton J, et al. Comparing Variability, Severity, and Persistence of Depressive Symptoms as Predictors of Future Stroke Risk. Am J Geriatr Psychiatry Off J Am Assoc Geriatr Psychiatry. 2017;25:120–8. Saini V, Guada L, Yavagal DR. Global Epidemiology of Stroke and Access to Acute Ischemic Stroke Interventions. Neurology. 2021;97:S6–16. GBD 2019 Stroke Collaborators. Global, regional, and national burden of stroke and its risk factors, 1990–2019: a systematic analysis for the Global Burden of Disease Study 2019. Lancet Neurol. 2021;20:795–820. Liu L, Wang H, Chen X, Zhang Y, Zhang H, Xie P. Gut microbiota and its metabolites in depression: from pathogenesis to treatment. eBioMedicine. 2023;90:104527. 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A Cardiovascular Disease-Linked Gut Microbial Metabolite Acts via Adrenergic Receptors. Cell. 2020;180:862–877.e22. Xu K, Gao X, Xia G, Chen M, Zeng N, Wang S, et al. Rapid gut dysbiosis induced by stroke exacerbates brain infarction in turn. Gut. 2021;70:1486–94. Chen R, Xu Y, Wu P, Zhou H, Lasanajak Y, Fang Y, et al. Transplantation of fecal microbiota rich in short chain fatty acids and butyric acid treat cerebral ischemic stroke by regulating gut microbiota. Pharmacol Res. 2019;148:104403. Peh A, O’Donnell JA, Broughton BRS, Marques FZ. Gut Microbiota and Their Metabolites in Stroke: A Double-Edged Sword. Stroke. 2022;53:1788–801. Shin N-R, Whon TW, Bae J-W. Proteobacteria: microbial signature of dysbiosis in gut microbiota. Trends Biotechnol. 2015;33:496–503. Lai W-T, Deng W-F, Xu S-X, Zhao J, Xu D, Liu Y-H, et al. Shotgun metagenomics reveals both taxonomic and tryptophan pathway differences of gut microbiota in major depressive disorder patients. Psychol Med. 2021;51:90–101. McGuinness AJ, Davis JA, Dawson SL, Loughman A, Collier F, O’Hely M, et al. A systematic review of gut microbiota composition in observational studies of major depressive disorder, bipolar disorder and schizophrenia. Mol Psychiatry. 2022;27:1920–35. Gorman A, Golovanov AP. Lipopolysaccharide Structure and the Phenomenon of Low Endotoxin Recovery. Eur J Pharm Biopharm Off J Arbeitsgemeinschaft Pharm Verfahrenstechnik EV. 2022;180:289–307. Garcia-Vello P, Di Lorenzo F, Zucchetta D, Zamyatina A, De Castro C, Molinaro A. Lipopolysaccharide lipid A: A promising molecule for new immunity-based therapies and antibiotics. Pharmacol Ther. 2022;230:107970. Di Lorenzo F, De Castro C, Silipo A, Molinaro A. Lipopolysaccharide structures of Gram-negative populations in the gut microbiota and effects on host interactions. FEMS Microbiol Rev. 2019;43:257–72. Klimiec E, Pasinska P, Kowalska K, Pera J, Slowik A, Dziedzic T. The association between plasma endotoxin, endotoxin pathway proteins and outcome after ischemic stroke. Atherosclerosis. 2018;269:138–43. Wei Y-H, Bi R-T, Qiu Y-M, Zhang C-L, Li J-Z, Li Y-N, et al. The gastrointestinal-brain-microbiota axis: a promising therapeutic target for ischemic stroke. Front Immunol. 2023;14:1141387. Xu H, Xu Z, Long S, Li Z, Jiang J, Zhou Q, et al. The role of the gut microbiome and its metabolites in cerebrovascular diseases. Front Microbiol. 2023;14:1097148. Poppleton DI, Duchateau M, Hourdel V, Matondo M, Flechsler J, Klingl A, et al. Outer Membrane Proteome of Veillonella parvula: A Diderm Firmicute of the Human Microbiome. Front Microbiol. 2017;8:1215. Fecal Transplantation from db/db Mice Treated with Sodium Butyrate Attenuates Ischemic Stroke Injury [Internet]. [cited 2023 Nov 30]. Available from: https://journals.asm.org/doi/epdf/ 10.1128/spectrum.00042-21 Zeng X, Gao X, Peng Y, Wu Q, Zhu J, Tan C, et al. Higher Risk of Stroke Is Correlated With Increased Opportunistic Pathogen Load and Reduced Levels of Butyrate-Producing Bacteria in the Gut. Front Cell Infect Microbiol. 2019;9:4. Garbas K, Zapała P, Zapała Ł, Radziszewski P. The Role of Microbial Factors in Prostate Cancer Development—An Up-to-Date Review. J Clin Med. 2021;10:4772. Hasan R, Bose S, Roy R, Paul D, Rawat S, Nilwe P, et al. Tumor tissue-specific bacterial biomarker panel for colorectal cancer: Bacteroides massiliensis, Alistipes species, Alistipes onderdonkii, Bifidobacterium pseudocatenulatum, Corynebacterium appendicis. Arch Microbiol. 2022;204:348. Peters BA, Wilson M, Moran U, Pavlick A, Izsak A, Wechter T, et al. Relating the gut metagenome and metatranscriptome to immunotherapy responses in melanoma patients. Genome Med. 2019;11:61. 陈茜, Xi C, 薛勇, Yong X, 宋晓峰, Xiaofeng S, et al. 糖尿病及糖尿病心血管并发症患者肠道菌群的特征 [Internet]. 微生物学报; 2019 [cited 2024 Jan 23]. Available from: http://journals.im.ac.cn/html/actamicrocn/2019/9/20190904.htm#outline_anchor_7 Gong X, Liu Y, Liu X, Li A, Guo K, Zhou D, et al. Disturbance of Gut Bacteria and Metabolites Are Associated with Disease Severity and Predict Outcome of NMDAR Encephalitis: A Prospective Case–Control Study. Front Immunol. 2022;12:791780. Laddha AP, Kulkarni YA. Pharmacokinetics, pharmacodynamics, toxicity, and formulations of daidzein: An important isoflavone. Phytother Res. 2023;37:2578–604. Xu J, Chen H-B, Li S-L. Understanding the Molecular Mechanisms of the Interplay Between Herbal Medicines and Gut Microbiota. Med Res Rev. 2017;37:1140–85. Mayo B, Vázquez L, Flórez AB. Equol: A Bacterial Metabolite from The Daidzein Isoflavone and Its Presumed Beneficial Health Effects. Nutrients. 2019;11:2231. Usui T, Tochiya M, Sasaki Y, Muranaka K, Yamakage H, Himeno A, et al. Effects of natural S-equol supplements on overweight or obesity and metabolic syndrome in the Japanese, based on sex and equol status. Clin Endocrinol (Oxf). 2013;78:365–72. El-Naga RN, Ahmed HI, Abd Al Haleem EN. Effects of indole-3-carbinol on clonidine-induced neurotoxicity in rats: Impact on oxidative stress, inflammation, apoptosis and monoamine levels. NeuroToxicology. 2014;44:48–57. Poornima J, Mirunalini S. Regulation of carbohydrate metabolism by indole-3-carbinol and its metabolite 3,3’-diindolylmethane in high-fat diet-induced C57BL/6J mice. Mol Cell Biochem. 2014;385:7–15. Jayakumar P, Pugalendi KV, Sankaran M. Attenuation of hyperglycemia-mediated oxidative stress by indole-3-carbinol and its metabolite 3, 3’- diindolylmethane in C57BL/6J mice. J Physiol Biochem. 2014;70:525–34. Leader CJ, Clark BJ, Hannah AR, Sammut IA, Wilkins GT, Walker RJ. Breeding Characteristics and Dose-dependent Blood Pressure Responses of Transgenic Cyp1a1-Ren2 Rats. Comp Med. 2018;68:360–6. Pedras MSC, Nycholat CM, Montaut S, Xu Y, Khan AQ. Chemical defenses of crucifers: elicitation and metabolism of phytoalexins and indole-3-acetonitrile in brown mustard and turnip. Phytochemistry. 2002;59:611–25. Chichai AS, Popova TN, Kryl’skii ED, Oleinik SA, Razuvaev GA. Indole-3-carbinol mitigates oxidative stress and inhibits inflammation in rat cerebral ischemia/reperfusion model. Biochimie. 2023;213:1–11. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-3948912","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":272575715,"identity":"669925a4-ada3-4366-8909-2c9961bfba55","order_by":0,"name":"Huiru Zhang","email":"","orcid":"","institution":"Inner Mongolia Medical University","correspondingAuthor":false,"prefix":"","firstName":"Huiru","middleName":"","lastName":"Zhang","suffix":""},{"id":272575716,"identity":"7c7a17a5-d87c-4037-a292-881e02d282c0","order_by":1,"name":"Dongsheng Lyu","email":"","orcid":"","institution":"Mental Health Center of Inner Mongolia Autonomous Region","correspondingAuthor":false,"prefix":"","firstName":"Dongsheng","middleName":"","lastName":"Lyu","suffix":""},{"id":272575717,"identity":"1e372a6c-4bf5-4f69-95af-2d8298adeb14","order_by":2,"name":"Xingguang Zhang","email":"","orcid":"","institution":"Inner Mongolia Medical University","correspondingAuthor":false,"prefix":"","firstName":"Xingguang","middleName":"","lastName":"Zhang","suffix":""},{"id":272575718,"identity":"8fdd6a41-72c7-4e5a-a8b2-85abe74c140c","order_by":3,"name":"Ning Cao","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA0klEQVRIiWNgGAWjYLCCBAYGHjZm9oMPEipqiNciw8/Ok2zw4Mwx4i2ykexnMJN82MJMWCl/++FjDx7uqOUxOMyQVpHYwAYU6U7Aq0XiTFq6QeKZ40AtjMduJO6QAYqc3YBXiwFDjplEYtsxsC03Es+wMRhI5BLQwv8GrsWsILGNmQgtEmBbangkmxnMGIjSInHjWRpQywEefmaeZImEM8d4CPqFvz/5mOTPtjp7Nv7jBz/+qKiR42/vxa8FCg7DWTzEKAeBOmIVjoJRMApGwUgEAJ5dRf4/eR+LAAAAAElFTkSuQmCC","orcid":"","institution":"Inner Mongolia Medical University","correspondingAuthor":true,"prefix":"","firstName":"Ning","middleName":"","lastName":"Cao","suffix":""}],"badges":[],"createdAt":"2024-02-11 16:36:43","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3948912/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3948912/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":51141709,"identity":"30d9a71d-0b6c-47d6-b7c2-9d8b19655de5","added_by":"auto","created_at":"2024-02-14 20:23:11","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":676438,"visible":true,"origin":"","legend":"\u003cp\u003eGut microbiome characteristics in patients with IS versus non-IS patients. The Shannon diversity index (A), Simpson's index of diversity (B), Chao1 index (C), and ACE index (D) are presented using boxplots for the IS group (in red) and the non-IS group (in blue). The boxplots represent the median, 25th and 75th quartiles, as well as the minimum and maximum values. The study consisted of two groups: the IS group, which included 30 participants; and the non-IS group, which also included 30 participants. The statistical significance of the alpha diversity was assessed using the Wilcoxon rank-sum test. (E) The bacterial signatures did not show any notable differences between the two groups (Bray‒Curtis distance, Adonis test, \u003cem\u003eP\u003c/em\u003e= 0.161).\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-3948912/v1/b26085d45f866a8afb7e4157.png"},{"id":51141710,"identity":"e90d9d7f-267d-4551-a5ac-cdf003a98a72","added_by":"auto","created_at":"2024-02-14 20:23:11","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":641213,"visible":true,"origin":"","legend":"\u003cp\u003eDifferences in taxonomic composition between IS patients and non-IS patients. A stacked bar plot indicating the average relative abundance of phyla within the IS patients and non-IS patients (A). LDA coupled with effect size measurements in the IS and non-IS patients. Enriched taxa in the IS (red) and non-IS (green) groups are displayed with LDA scores. Only taxa with LDA scores above the threshold of 2.0 are shown (B). Cladogram representation of the gut microbiota in the IS patients versus the non-IS patients. by 16S rRNA sequencing. Enriched taxa in the IS (red) and non-IS (green) groups are indicated. The brightness of each dot is correlated with its LDA effect size (C). A receiver operating characteristic (ROC) curve was used to identify the microbiota that could predict the occurrence of IS in patients with MDD. The abundances of \u003cem\u003eAlloprevotella\u003c/em\u003e (D)\u003cem\u003e \u003c/em\u003eand \u003cem\u003eMassiliensis Bacteroides\u003c/em\u003e (E) could be used to discriminate the two groups according to the area under the curve (AUC). The functional potential of the microbial communities of IS and non-IS patients via Tax4Fun2. Statistical analysis of the OTU-based relative abundance of functional annotations in the IS and non-IS groups by Student’s \u003cem\u003et\u003c/em\u003e test (F).\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-3948912/v1/5f7c9356904439311120dd13.png"},{"id":51141711,"identity":"6323bf11-aa0f-4c66-85c8-62c2a455fb0f","added_by":"auto","created_at":"2024-02-14 20:23:11","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":2710104,"visible":true,"origin":"","legend":"\u003cp\u003eNontarget metabolome of IS and non-IS patients. OPLS-DA was performed to determine the metabolite profile (IS: n = 30, non-IS: n = 30). Plasma metabolomics OPLS-DA score plot of the IS (red) vs. non-IS (green) groups. Each dot denotes an individual subject (A). Permutation test for the OPLS-DA model: 999 permutations led to intercepts of R\u003csup\u003e2\u003c/sup\u003eX = 0.153, R\u003csup\u003e2\u003c/sup\u003eY = 0.988, Q\u003csup\u003e2\u003c/sup\u003e = 0.753, \u003cem\u003eP\u003c/em\u003e value\u0026lt; 0.05, implying an acceptable model minus overfitting (B). Volcano plot showing differentially abundant metabolites between the IS patients and non-IS patients. Each dot denotes a metabolite (C). Each significantly selected subpathway is denoted by a circle and described by three parameters. The circle size shows how many of the metabolites were selected in the subpathway (see the legend in gray to the right of the plot for relative sizes). Circles from purple to red denote selected subpathway significance levels based on −log10 (p value) (see the legend to the right of the plot for the relative color gradient). Pathways were markedly enriched if \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05, which was comparable to a −log10(\u003cem\u003eP \u003c/em\u003evalue) \u0026gt; 1.3. The circle position along the rich factor axis shows the abundance of the selected metabolites from the subpathway against all subpathway metabolites (D).\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-3948912/v1/9dbef8e1e74dbc8d2d1ae969.png"},{"id":51142303,"identity":"d53bb42b-8fd6-4391-8314-41c1c4ec6b80","added_by":"auto","created_at":"2024-02-14 20:31:11","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":679364,"visible":true,"origin":"","legend":"\u003cp\u003eCorrelation analysis between the gut microbiota and metabolites Chord plots of correlation analysis between \u003cem\u003eAlloprevotella \u003c/em\u003eand related metabolites (A) and between \u003cem\u003eBacteroides massiliensis \u003c/em\u003eand related metabolites (B). The breadth of the link represents the relative coefficient between the differential species and the metabolite. A greater correlation is indicated by a wider link. Pink denotes a positive correlation, whereas blue denotes a negative correlation. ROC curves were used to identify gut microbiota and metabolite characteristics that could predict the occurrence of IS in patients with MDD. The abundances of \u003cem\u003eAlloprevotella \u003c/em\u003eand its related metabolites (C)\u003cem\u003e \u003c/em\u003eand\u003cem\u003e Bacteroides\u003c/em\u003e \u003cem\u003emassiliensis \u003c/em\u003eand its related metabolites (D) could be used to discriminate the two groups with the AUC.\u003c/p\u003e","description":"","filename":"Figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-3948912/v1/f3c13eec4b79ef6547f79f63.png"},{"id":51896606,"identity":"375a12b9-f62d-4228-8272-7051f3473246","added_by":"auto","created_at":"2024-03-02 11:22:35","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1199886,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3948912/v1/513b2fd4-5e7f-4204-83e5-653bef1f7652.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Differences in the gut microbiota and plasma metabolome of major depressive disorder patients with and without ischemic stroke","fulltext":[{"header":"Background","content":"\u003cp\u003eMajor depressive disorder (MDD) is a prevalent and severe mental illness characterized by a persistent feeling of sadness and a decrease in cognitive function and is frequently accompanied by a lack of motivation and slower thought processes[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. MDD accounts for 49.4\u0026nbsp;million disability-adjusted life years (DALYs) globally, positioning it as a prominent contributor to the disease burden in 2020[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Furthermore, NAFLD is a significant public health concern that leads to reduced patient functioning and increased mortality rates[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. The global 12-month incidence of MDD is approximately 6%, and more than 20% of individuals experience at least one episode of depression during their lifetime[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. In addition, individuals with MDD have a death rate almost twice as high as that of the general population[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eMDD has been recognized as a significant risk factor for ischemic stroke (IS) in numerous extensive cohort studies and meta-analyses of cohort studies[\u003cspan additionalcitationids=\"CR8 CR9\" citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. IS is a serious condition that imposes a substantial burden on individuals and society and is one of the primary causes of disability and mortality worldwide[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. In 2019, the worldwide incidence of IS, the number of deaths caused by IS, and the number of DALYs lost were 77.63\u0026nbsp;million, 3.23\u0026nbsp;million, and 63.48\u0026nbsp;million, respectively[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Reducing the risk of developing IS in patients with MDD has important implications for improving the survival of MDD patients and decreasing the overall burden of the disease.\u003c/p\u003e \u003cp\u003eAlthough there is an epidemiologic association between MDD and IS, the specific mechanisms that explain the coexistence of MDD and IS have still not been identified. The advancement of next-generation sequencing technology is anticipated to be a pivotal factor in uncovering the mechanism behind the occurrence of IS in patients with MDD, particularly in relation to the gut microbiota. The gut microbiota is commonly referred to as the \"second genome\" of humans. The gut microbiota can impact the physiological processes of various systems in the body through different pathways, hence playing a role in the development of diseases. A disordered body, in contrast, might result in an imbalance of the gut microbiota through various mechanisms. Multiple studies undertaken in recent years have shown a strong association between the gut microbiota and both IS and MDD. Several clinical studies and meta-analyses have demonstrated significant disparities in the gut microbiota and its metabolites between persons with MDD and healthy control subjects[\u003cspan additionalcitationids=\"CR14 CR15 CR16\" citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. In the same way, the gut microbiota can play a role in the development of IS through specific microbiota-derived metabolites, such as LPS, short-chain fatty acids (SCFAs), trimethylamine-N-oxide (TMAO), and phenylacetylglutamine (PAGln)[\u003cspan additionalcitationids=\"CR19 CR20 CR21\" citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Therefore, we propose that the gut microbiota may play a role in the development of IS in individuals with MDD. However, the specific mechanism involved has not been fully comprehended.\u003c/p\u003e \u003cp\u003eThis study aimed to initially investigate the mechanisms linking the gut microbiota and increased risk of IS development in patients with MDD. To achieve this goal, we sequenced the gut microbiota and plasma metabolome of MDD patients with and without IS. The findings of this study aim to establish a basis for further research on the role of the gut microbiota in the development of IS in MDD patients.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy subjects and Sample Collection Procedures\u003c/h2\u003e \u003cp\u003eWe selected individuals with MDD at the Inner Mongolia Autonomous Region Mental Health Center in China. The study period spanned from December 2021 to May 2023. We included hospitalized individuals among the MDD patients in the IS group who fulfilled the following specified criteria: (i) 18 years of age or older; (ii) met the Diagnostic and Statistical Manual of Mental Disorders, 4th Edition (DSM-IV) criteria for MDD according to the Chinese version of the Mini-International Neuropsychiatric Interview (MINI); and (iii) were diagnosed with IS within the last 3 years after being diagnosed with MDD. The diagnostic criteria for IS were derived from the \"Chinese guidelines for the diagnosis and treatment of acute ischemic stroke 2018\". The exclusion criteria included the presence of other mental disorders diagnosed according to the 4th Edition (DSM-IV) criteria; treatment-resistant depression; depression with severe suicidal and self-injurious tendencies; schizophrenia; bipolar disorder; and neurodegenerative diseases. Additionally, individuals with other diagnostic diseases, such as chronic inflammatory disorders, thyroid disease, or cancer, were excluded. Pregnant or breastfeeding females, those who had used antibiotics within 1 month prior to sampling, and individuals who reported changes in diet habits or had special dietary requirements (e.g., food allergy, food intolerance, vegetarianism) were also excluded.\u003c/p\u003e \u003cp\u003eUsing the matching method, each MDD patient with a history of IS was paired with an MDD patient without a history of IS who was admitted to the hospital simultaneously. The patients were matched based on sex, antidepressant medication, and age difference of no more than two years. Patients who did not have IS were subsequently allocated to the non-IS group. The ultimate sample included 60 participants\u0026mdash;30 individuals diagnosed with MDD with IS and 30 individuals diagnosed with MDD without IS.\u003c/p\u003e \u003cp\u003eThe degree of depression was assessed using the 24-item Hamilton Depression Rating Scale (HAMD-24). The individuals were also queried about their smoking habits, and their blood pressure (BP), blood glucose (GLU), blood lipid, and past medication history were documented. Fecal and blood samples were collected from all the research subjects within 48 hours of hospital admission. Participants deposited their feces into prepared, clean, and dry receptacles specifically designed for fecal collection. Fecal samples were obtained from the middle section using a sterile fecal sampler with a volume of 15 ml. For each scenario, a tube with a minimum of 2 grams of sample was retained, and the stool samples were then stored at a temperature of -80\u0026deg;C until they were utilized. Licensed nurses collected whole blood, which was then left to coagulate for 15 minutes at room temperature. After that, the mixture was centrifuged at 5000 rpm for 10 minutes. The resulting plasma was kept and promptly stored at a temperature of -80\u0026deg;C until needed.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e16S rRNA Gene Sequencing of the Gut Microbiome\u003c/h2\u003e \u003cp\u003eAfter the fecal samples were thawed, DNA was extracted using either cetyltrimethylammonium bromide (CTAB) or sodium dodecyl sulfonate (SDS) techniques. The concentration and purity of the DNA were assessed using agarose gels, and the DNA was subsequently diluted to a concentration of 1 ng/\u0026micro;l using sterile water. The V4 region of the 16S rRNA gene was amplified using a PCR technique that employed particular primers, including barcodes (515F: GTGCCAGCMGCCGCGGTAA and 806R: GGACTACHVGGGTWTCTAAT). Amplification was carried out using Phusion\u0026reg; High-Fidelity PCR Master Mix with GC Buffer from New England Biolabs (USA). The PCR products were purified using a Qiagen Gel Extraction Kit manufactured by Qiagen in Germany. A TruSeq\u0026reg; DNAPCR-Free Sample Preparation Kit (Illumina, USA) was used to construct sequencing libraries following the manufacturer's recommendations. The library quality was evaluated using a Qubit\u0026reg; 2.0 Fluorometer (Thermo Fisher, USA) and QPCR. Finally, the library was sequenced on a NovaSeq 6000 system (Illumina, USA).\u003c/p\u003e \u003cp\u003eAfter sequencing, the raw data were preprocessed to obtain effective tags. The UPARSE method (UPARSE v7.0.1001, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.drive5.com/uparse/\u003c/span\u003e\u003cspan address=\"http://www.drive5.com/uparse/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) was used to perform optical transform unit (OTU) clustering analysis on sequences with 97% similarity. The resulting representative OTU sequences were subsequently matched with a microbial taxonomy database to obtain information about representative species. The community makeup of the two groups of samples was determined at each taxonomic level (phylum, class, order, family, genus, and species) using the results of the OTU analysis. The Shannon diversity index, Simpson's index of diversity, Chao1 estimator, and abundance-based coverage estimator (ACE) were calculated using Mothur software (v1.48) to examine the abundance and diversity of species within each sample. The Bray‒Curtis method was used to conduct a principal coordinate analysis (PCoA) to assess the extent of the variation in species diversity, community composition, and structure across the samples. Linear discriminant analysis effect size (LEfSe) analysis was used to discern bacterial markers with high-dimensional characteristics among the different groupings. Ultimately, the anticipated functional composition profiles of the 16S rRNA sequences were condensed into Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways using Tax4Fun2.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003ePlasma Metabolomic analysis\u003c/h2\u003e \u003cp\u003eThe plasma metabolites were examined using liquid chromatography‒mass spectrometry (LC‒MS). The ProteoWizard software converted the initial data file obtained from the LC‒MS analysis into the mzML format. The process of identifying, filtering, and aligning peaks was carried out using the R package XCMS. The \"SVR\" technique was employed to rectify the peak area. The metabolite annotations of the LC‒MS data were validated after quality control using the laboratory's proprietary database, publicly available database, artificial intelligence prediction database, and metDNA technology. The R program was used for statistical analysis. The statistical analysis comprises two techniques: principal component analysis (PCA) and orthogonal partial least-squares discriminant analysis (OPLS-DA). The differentially abundant metabolites were selected based on the following criteria: variable importance in projection (VIP) score\u0026thinsp;\u0026ge;\u0026thinsp;1; \u003cem\u003eP\u003c/em\u003e value ˂ 0.05; and fold change\u0026thinsp;\u0026ge;\u0026thinsp;2 or \u0026le;\u0026thinsp;0.5. Local discriminant analysis (LDA) effect size plots and volcano plots were generated to illustrate the differences in metabolites across different groups. The KEGG pathway was used to refer to the metabolic pathways that were enriched with the differentially abundant metabolites.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eAll the statistical analyses were performed using R software version 4.2.0. Continuous variables are presented as the mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation (SD), and statistical comparisons among the various groups were performed using one-way analysis of variance (ANOVA) or a \u003cem\u003et\u003c/em\u003e test. Categorical data were analyzed by the chi-square test. Spearman correlation tests were applied to determine the correlation between the gut microbiome and metabolome. \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered to indicate statistical significance.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eClinical characteristics of the participants\u003c/h2\u003e \u003cp\u003eIn this study, we conducted a comprehensive examination of the gut microbiota and plasma metabolome in 60 samples from two groups: MDD patients with IS and MDD patients without IS. The two groups did not show any significant differences in demographic indicators, such as sex, age, HAMD-24 score, blood pressure, blood glucose, or blood lipids (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The well-matched samples were utilized to detect particular characteristics of the gut microbiota and metabolites in MDD patients with IS.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eClinical characteristics of the participants\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMDD with IS\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMDD\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003et\u003c/em\u003e/\u003cem\u003eχ\u003c/em\u003e\u003csup\u003e༒\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (mean\u0026thinsp;\u0026plusmn;\u0026thinsp;\u003cem\u003eSD\u003c/em\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e68.33\u0026thinsp;\u0026plusmn;\u0026thinsp;6.799\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e68.17\u0026thinsp;\u0026plusmn;\u0026thinsp;6.859\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.095\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.925\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender (Men), \u003cem\u003en\u003c/em\u003e (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12(40%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12(40%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003escore of HAMD-24 (mean\u0026thinsp;\u0026plusmn;\u0026thinsp;\u003cem\u003eSD\u003c/em\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e29.87\u0026thinsp;\u0026plusmn;\u0026thinsp;8.127\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e26.17\u0026thinsp;\u0026plusmn;\u0026thinsp;7.231\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.863\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.068\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSmoking status (Yes), \u003cem\u003en\u003c/em\u003e (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6(20%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4(13.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.424\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.525\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003esystolic blood pressure (SBP) (mmHg) (mean\u0026thinsp;\u0026plusmn;\u0026thinsp;\u003cem\u003eSD\u003c/em\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e126.60\u0026thinsp;\u0026plusmn;\u0026thinsp;14.053\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e130.51\u0026thinsp;\u0026plusmn;\u0026thinsp;15.120\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.035\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.305\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ediastolic blood pressure (DBP) (mmHg) (mean\u0026thinsp;\u0026plusmn;\u0026thinsp;\u003cem\u003eSD\u003c/em\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e78.60\u0026thinsp;\u0026plusmn;\u0026thinsp;8.677\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e78.63\u0026thinsp;\u0026plusmn;\u0026thinsp;9.238\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.014\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.989\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eblood glucose (GLU) (mmol/L) (mean\u0026thinsp;\u0026plusmn;\u0026thinsp;\u003cem\u003eSD\u003c/em\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.75\u0026thinsp;\u0026plusmn;\u0026thinsp;1.658\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.95\u0026thinsp;\u0026plusmn;\u0026thinsp;1.119\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.559\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.579\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003etotal cholesterol (TC) (mmol/L) (mean\u0026thinsp;\u0026plusmn;\u0026thinsp;\u003cem\u003eSD\u003c/em\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.37\u0026thinsp;\u0026plusmn;\u0026thinsp;1.293\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.51\u0026thinsp;\u0026plusmn;\u0026thinsp;1.041\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.465\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.643\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003etriglyceride (TG) (mmol/L) (mean\u0026thinsp;\u0026plusmn;\u0026thinsp;\u003cem\u003eSD\u003c/em\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.75\u0026thinsp;\u0026plusmn;\u0026thinsp;1.101\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.86\u0026thinsp;\u0026plusmn;\u0026thinsp;1.312\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.357\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.722\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003elow-density lipoprotein cholesterol (LDL-C) (mmol/L) (mean\u0026thinsp;\u0026plusmn;\u0026thinsp;\u003cem\u003eSD\u003c/em\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.77\u0026thinsp;\u0026plusmn;\u0026thinsp;0.994\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.21\u0026thinsp;\u0026plusmn;\u0026thinsp;0.283\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.768\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.873\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ehigh density lipoprotein cholesterol (HDL-C) (mmol/L) (mean\u0026thinsp;\u0026plusmn;\u0026thinsp;\u003cem\u003eSD\u003c/em\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.22\u0026thinsp;\u0026plusmn;\u0026thinsp;0.279\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.21\u0026thinsp;\u0026plusmn;\u0026thinsp;0.283\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.161\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.446\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eThere was no difference in the diversity of the IS patients compared to the non-IS patients\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThe gut microbiome of MDD patients with IS and MDD patients without IS was assessed using 16S rRNA gene sequencing. Initially, we employed alpha (α)-diversity as a measure of both the number of species present and their relative abundance. The findings indicated that MDD patients with IS exhibited a tendency toward decreased gut microbiota diversity, as evidenced by lower Shannon diversity indices (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA), Simpson's indices (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB), Chao1 (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC), and ACE (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eD) indices than did MDD patients without IS. However, it is important to note that none of these differences reached statistical significance. PCoA was also conducted to evaluate the beta (β)-diversity of the microbiota. However, we did not observe distinct segregation between the two groups. The Adonis test results, based on Bray‒Curtis dissimilarity, did not reveal any significant differences in the bacteria between the two groups (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eE).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eTaxonomic and functional characterization of the gut microbiota in IS and non-IS patients.\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThe samples were subsequently assessed at six taxonomic levels, namely, phylum, class, order, family, genus, and species, to determine whether there was any variation in the taxonomic composition between the IS patients and non-IS patients. We employed the LEfSe technique to ascertain the taxa that served as biomarkers for each group. Bar plots depicting the relative abundances at the phylum level clearly revealed distinct variations in the gut microbiota between patients with IS and those without IS. The phyla \u003cem\u003eFirmicutes\u003c/em\u003e and \u003cem\u003eBacteroidetes\u003c/em\u003e were the most abundant in both groups, with relative abundances greater than 10%. While \u003cem\u003eProteobacteria\u003c/em\u003e was not the most common phylum in either group, its relative abundance was significantly greater in the IS group than in the non-IS group (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA). At the order level, the IS group exhibited a greater abundance of \u003cem\u003eEnterobacterales\u003c/em\u003e than the non-IS group, with an LDA score greater than 2. Similar results were observed at the family level, where the abundance of \u003cem\u003eEnterobacteriaceae\u003c/em\u003e was greater in the non-IS group than in the IS group. \u003cem\u003eEnterobacteriaceae\u003c/em\u003e are gram-negative bacteria that have LPS structures. In addition, the abundance of \u003cem\u003eVeillonella\u003c/em\u003e parvula, which are gram-positive bacteria that possess an outer membrane with LPS similar to that of gram-negative bacteria, was greater in the IS group than in the IS group. The results revealed that the gut microbiota of the IS group included an increase in the presence of bacteria with LPS structures. The distributions of SCFA-producing bacteria in the gut microbiota of patients with IS and those without IS were ultimately distinct. The abundance of propionic acid-producing bacteria, including Veillonella parvula and Alloprevotella, was greater in the non-IS group than in the IS group, as was the abundance of two subspecies of \u003cem\u003ePrevotella\u003c/em\u003e, namely, \u003cem\u003ePrevotella\u003c/em\u003e sp. \u003cem\u003eMarseille\u003c/em\u003e P2931 and \u003cem\u003ePrevotella\u003c/em\u003e stercorea. Conversely, there was a lower abundance of butyric acid-producing bacteria, such as \u003cem\u003eAcidaminococcaceae\u003c/em\u003e, \u003cem\u003eRoseburia\u003c/em\u003e, and \u003cem\u003eFusicatenibacter\u003c/em\u003e, in the IS group than in the non-IS group (with an LDA score greater than 2). Finally, at the species level, the IS group displayed a lower prevalence of \u003cem\u003eBacteroides massiliensis\u003c/em\u003e and \u003cem\u003eBacteroides finegoldii\u003c/em\u003e than did the non-IS group (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB, C).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eWe employed receiver operating characteristic (ROC) curve analysis to examine the aforementioned differential taxa to discover bacteria that may be valuable in the early detection of MDD patients with IS. Ultimately, we discovered that \u003cem\u003eAlloprevotella\u003c/em\u003e and \u003cem\u003eBacteroides massiliensis\u003c/em\u003e were successfully differentiated between MDD patients with IS and those without IS. The area under the curve (AUC) values were 0.749 (95% CI\u0026thinsp;=\u0026thinsp;0.621, 0.876) and 0.783 (95% CI\u0026thinsp;=\u0026thinsp;0.664, 0.902) for Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eD and Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eE, respectively.\u003c/p\u003e \u003cp\u003eWe employed Tax4Fun2 to predict the functional capacity of microbial communities in both IS and non-IS patients. This was accomplished by analyzing the 16S rRNA gene content, which is specifically designed to assess the functional capabilities of microbial communities identified using 16S rRNA sequencing. The pathways linked to the IS subjects involved primarily the metabolism of terpenoids and polyketides, such as the biosynthesis of siderophore group nonribosomal peptides. Additionally, these findings were associated with energy metabolism, specifically sulfur and nitrogen metabolism. Furthermore, these genes were related to the metabolism of cofactors and vitamins, including ubiquinone and other terpenoid-quinone biosynthesis products. Finally, carbohydrate metabolism, specifically inositol phosphate metabolism, was also a significant aspect of these subjects. Conversely, the pathways linked to the non-IS participants were primarily involved in translation (ribosome, aminoacyl-tRNA biosynthesis), nucleotide metabolism (pyrimidine metabolism), amino acid metabolism (lysine biosynthesis), replication and repair (homologous recombination, DNA replication). It is important to emphasize that this is a speculative forecast derived from the analysis of 16S rRNA composition, and it does not conclusively assess functional capabilities or transcriptional activity (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eF).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eMetabolome differences in IS and non-IS patients\u003c/h2\u003e \u003cp\u003eLC\u0026ndash;MS\u0026ndash;based metabolomic analysis was used to evaluate the differences in the plasma metabolic signatures between patients with IS and patients without IS. A model based on OPLS-DA was used to examine the disparities in metabolites between the two groups. Based on the OPLS-DA results, the IS and non-IS groups were clearly separated into distinct regions (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA). The goodness-of-fit and predictive ability values (R\u003csup\u003e2\u003c/sup\u003eX\u0026thinsp;=\u0026thinsp;0.153, R\u003csup\u003e2\u003c/sup\u003eY\u0026thinsp;=\u0026thinsp;0.988, Q\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.753, \u003cem\u003eP\u003c/em\u003e value\u0026thinsp;\u0026lt;\u0026thinsp;0.05) suggested that the OPLS-DA model had a good fit and effective predictive capacity (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eUsing the OPLS-DA data, we employed the VIP as a criterion to further identify metabolites that exhibited significant differences between the two groups. Metabolites with a VIP\u0026thinsp;\u0026ge;\u0026thinsp;1.0, a \u003cem\u003eP\u003c/em\u003e value\u0026thinsp;\u0026lt;\u0026thinsp;0.05, or a fold change\u0026thinsp;\u0026ge;\u0026thinsp;2 or \u0026le;\u0026thinsp;0.5 were considered differentially abundant metabolites. A total of 38 metabolites exhibited significant differences between the IS and non-IS groups. Similarly, compared with those in the non-IS group, the levels of 17 metabolites in the IS group were significantly greater, whereas the levels of 21 metabolites were significantly lower (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC). The differentially abundant metabolites mostly consisted of amino acids and their metabolites, benzene and its substituted derivatives, glycerophospholipids, and heterocyclic chemicals.\u003c/p\u003e \u003cp\u003eTo determine the metabolic pathways associated with these genes, the differentially abundant metabolites were compared to those in the KEGG database. The identified differentially abundant metabolites were found to be involved in various metabolic pathways, such as amino acid metabolism (specifically tryptophan), lipid metabolism (including steroid hormone and sphingolipid), carbohydrate metabolism (ascorbate and aldarate, glycolysis/gluconeogenesis), signal transduction (sphingolipid signaling pathway), cell growth and death (necroptosis), the endocrine and nervous system (adipocytokine signaling pathway, neurotrophin signaling pathway), and endocrine and metabolic diseases. This finding suggested that disruption of multiple pathways may play a role in the progression of IS (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eD).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eCorrelation analysis between the gut microbiota and metabolites\u003c/h2\u003e \u003cp\u003eSpearman's correlation coefficient was calculated to ascertain the functional correlations between alterations in the gut microbiome and plasma metabolism based on the disparities in the gut microbes and metabolites observed between individuals with IS and those without IS. The correlations were deemed statistically significant when∣\u003cem\u003er\u003c/em\u003e∣\u0026ge; 0.5 and the P value was \u0026lt;\u0026thinsp;0.05. The study revealed negative correlations between the levels of glucobrassicin, daidzein, 5-hydroxypseudobaptigenin, k-strophanthoside, arbutin 6-phosphate, 1-pentadecanoyl-2-lignoceroyl-sn-glycerol, and 1-stearoyl-2-myristoyl-sn-glycero-3-phosphocholine and the abundance of \u003cem\u003eAlloprevotella\u003c/em\u003e. The \u003cem\u003er\u003c/em\u003e values were \u0026minus;\u0026thinsp;0.575, -0.554, -0.553, -0.539, -0.626, -0.508, -0.573, and \u0026minus;\u0026thinsp;0.501. The \u003cem\u003eP\u003c/em\u003e values for all correlations were less than 0.001. The level of 7(14)-farnesene-9,12-diol was positively correlated with the abundance of \u003cem\u003eAlloprevotella\u003c/em\u003e (\u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.515, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001; Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA). Glucobrassicin, daidzein, 5-hydroxypseudobaptigenin, and dinoseb were shown to be positively correlated with \u003cem\u003eBacteroides massiliensis\u003c/em\u003e. The \u003cem\u003er\u003c/em\u003e values were 0.635, 0.602, 0.511, and 0.539, respectively. The \u003cem\u003eP\u003c/em\u003e values for all correlations were less than 0.001 (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB). These findings indicate that the modified gut microbiome may interact with metabolites to influence the development of ischemic stroke.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eConcurrently, we employed receiver operating characteristic (ROC) curve analysis to evaluate \u003cem\u003eAlloprevotella\u003c/em\u003e and its associated metabolites, as well as \u003cem\u003eBacteroides massiliensis\u003c/em\u003e and its associated metabolites, to determine the best model that may be valuable for the early detection of MDD patients with IS. The findings indicated that the AUC for the combination of \u003cem\u003eAlloprevotella\u003c/em\u003e and its associated metabolites was 0.998 (with a confidence interval of 0.992 to 1.000). Similarly, the AUC for the combination of \u003cem\u003eBacteroides massiliensis\u003c/em\u003e and its related metabolites was 0.992 (with a confidence interval of 0.978 to 1.000) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eC and D). These results suggest that the presence of \u003cem\u003eAlloprevotella\u003c/em\u003e and its associated metabolites, as well as \u003cem\u003eBacteroides massiliensis\u003c/em\u003e and its associated metabolites, are good indicators of increased risk for ischemic stroke in individuals with MDD.\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003ePrevious studies have established a connection between MDD and IS at the population level, but the specific mechanisms that explain the co-occurrence of MDD and IS have not been identified. Due to the close connection between the gut microbiota and these two illnesses, we hypothesized that the gut microbiota may also play a critical role in the development of IS in individuals with MDD. To elucidate the mechanisms associated with the gut microbiota, we analyzed the gut microbiota and metabolome in MDD patients with IS and MDD patients without IS. The present study showed through gut microbiota and metabolomic analyses that there are significant changes in the makeup of the gut microbiota community and metabolites in MDD patients with IS and MDD patients without IS. Our research provides a fresh point of view on the association between MDD and IS.\u003c/p\u003e \u003cp\u003eIn the examination of the taxonomic composition disparity between the two groups, we observed that at the phylum level, the proportion of \u003cem\u003eProteobacteria\u003c/em\u003e was greater in IS patients than in non-IS patients. A \u003cem\u003eProteobacteria\u003c/em\u003e increase is considered an indicator of microbial dysbiosis[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Prior studies have demonstrated that individuals with MDD exhibit disrupted gut microbiota features, although there is much heterogeneity in the methods and reporting of these studies[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Moreover, our study showed that MDD patients with IS exhibit more severe disruptions in their gut microbiota. The elevated abundance of \u003cem\u003eProteobacteria\u003c/em\u003e can be ascribed to the enrichment of \u003cem\u003eEnterobacteriaceae\u003c/em\u003e, and these findings align with a previous investigation conducted on individuals with IS during both the acute and recovery phases[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. \u003cem\u003eEnterobacteriaceae\u003c/em\u003e are bacteria characterized by their LPS structures. LPS is a crucial constituent of the outer membrane's external layer in gram-negative bacteria. It can trigger an immune response and induce inflammation in the host[\u003cspan additionalcitationids=\"CR27\" citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. The presence of a high level of \u003cem\u003eEnterobacteriaceae\u003c/em\u003e in the intestines, in conjunction with an impaired gut barrier, promptly triggers widespread inflammation throughout the body by producing a substantial quantity of LPS. This process may contribute to the development of IS, and higher levels of LPS are associated with worse outcomes in patients with IS[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan additionalcitationids=\"CR30\" citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. In addition, the abundance of \u003cem\u003eVeillonella\u003c/em\u003e parvula, which is a gram-positive bacterium that possesses an outer membrane with LPS similar to that of gram-negative bacteria, was similarly greater in IS patients than in non-IS patients[\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. The distributions of SCFA-producing bacteria in the gut microbiota of patients with IS and those without IS were ultimately distinct. Patients with IS exhibited elevated levels of propionic acid-producing bacteria, including \u003cem\u003eVeillonella parvula\u003c/em\u003e, \u003cem\u003eAlloprevotella\u003c/em\u003e, and two subspecies of \u003cem\u003ePrevotella\u003c/em\u003e (\u003cem\u003ePrevotella_sp_Marseille_\u003c/em\u003eP2931 and \u003cem\u003ePrevotella\u003c/em\u003e_stercorea). Conversely, there was a lower abundance of butyric acid-producing bacteria, such as \u003cem\u003eAcidaminococcaceae\u003c/em\u003e, \u003cem\u003eRoseburia\u003c/em\u003e, and \u003cem\u003eFusicatenibacter\u003c/em\u003e. These findings suggest that various SCFAs have distinct functions in the progression of IS in individuals with MDD. Prior studies have demonstrated that individuals with acute IS with diabetes have decreased levels of bacteria that produce butyric acid[\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]; furthermore, even those without a previous history of stroke but with a high risk of experiencing one also showed a reduction in the level of butyric acid-producing bacteria[\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e], implying that bacteria that produce butyrate may play a protective role in the development and prognosis of IS. The aforementioned results indicate that the gut microbiota of patients with MDD during the recovery phase after IS is similar to that of patients with acute-phase IS. The primary characteristics of this gut microbiota include an increase in the presence of bacteria with LPS structures and a decrease in the quantity of bacteria that produce butyrate.\u003c/p\u003e \u003cp\u003e \u003cem\u003eBacteroides massiliensis\u003c/em\u003e is a bacterium that is found at relatively low abundances in people with IS. Prior research examining the gut microbiota of patients with cancer, including prostate cancer[\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e], colorectal cancer[\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e], and melanoma[\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e], revealed that the abundance of \u003cem\u003eBacteroides massiliensis\u003c/em\u003e was greater in patients with tumors. However, it has also been shown that individuals with diabetes and those with diabetes-related cardiovascular issues have significantly lower levels of \u003cem\u003eBacteroides massiliensis\u003c/em\u003e than healthy individuals[\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. This finding demonstrates the complexity of the relationship between \u003cem\u003eBacteroides massiliensis\u003c/em\u003e and disease. Additional investigations are needed to determine whether \u003cem\u003eBacteroides massiliensis\u003c/em\u003e contributes to the development of IS in patients with MDD.\u003c/p\u003e \u003cp\u003eMetabolomes represent the combined effects of internal physiological processes and external factors. The human gut microbiota engages in significant interactions with the host through the cometabolism of substrates and the exchange of metabolites[\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. Consequently, we conducted an analysis of the patients' plasma metabolomics. We observed the considerable impact of the presence or absence of IS on the metabolites of these strains. We discovered two distinct compounds, daidzein and glucobrassicin, that are strongly linked to the presence of two specific types of bacteria, \u003cem\u003eAlloprevotella\u003c/em\u003e and \u003cem\u003eBacteroides massiliensis\u003c/em\u003e. Daidzein is a type of flavonoid compound that is found in soybeans and several soy-based products and exists in the form of glucosides[\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. The gut microbiota, which includes \u003cem\u003eBacteroidetes\u003c/em\u003e, \u003cem\u003eFirmicutes\u003c/em\u003e, \u003cem\u003eEntererococcus\u003c/em\u003e, \u003cem\u003eLactobacillus\u003c/em\u003e, and \u003cem\u003eBifidobacterium\u003c/em\u003e, contains genes that encode various glycosidase enzymes, such as β-glucosidase. These enzymes can breakdown and transform soy sapogenins, which are glycosides, into free sapogenins and glucose[\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. Once separated, the free sapogenins undergo a range of reactions, including dihydroxylation, reduction, pyrone ring cleavage, or demethylation. These events result in the formation of a new molecule that exhibits potent estrogenic activity, such as equol, or a product that is inactive, such as O-desmethylangolensin[\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. Equol has demonstrated significant promise in the prevention of cardiovascular disease (CVD). Equol may decrease the likelihood of developing coronary heart disease by enhancing its anti-atherogenic properties, improving arterial stiffness, and reducing LDL-C levels in overweight people[\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. Glucobrassicin, an important secondary metabolite found in cruciferous vegetables, can be broken down to form indole-3-methanol (I3C). Both I3C and its primary in vivo derivative, 3,3\u0026rsquo;-diindolylmethane (DIM), have demonstrated efficacy as cancer chemopreventive agents in preclinical models and have shown potential in clinical trials. I3C also exhibited significant neuroprotective effects in a rat model of clonidine-induced depression[\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]. Similarly, I3C has been shown to demonstrate significant protective or therapeutic effects against diabetes[\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e], hypertension[\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e, \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e], and a rat model of cerebral ischemia/reperfusion[\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e]. These effects may be attributed to its antioxidant activity and capacity to inhibit the inflammatory response. While previous research has relied on animal tests, the current study revealed that individuals with MDD and IS had reduced levels of glucobrassicin. These findings suggested that glucobrassicin may have a protective effect on patients with MDD complicated with IS. Moreover, there was an inverse relationship between daidzein and glucobrassicin with \u003cem\u003eAlloprevotella\u003c/em\u003e, while a positive relationship was found with \u003cem\u003eBacteroides\u003c/em\u003e massiliensis. These findings suggested that daidzein and glucobrassicin may have an impact on patients with MDD by interacting with these two bacteria. However, further investigation is required to determine the exact mechanism of action involved. Ultimately, the random forest analysis identified \u003cem\u003eAlloprevotella\u003c/em\u003e and \u003cem\u003eBacteroides massiliensis\u003c/em\u003e as reliable discriminators between IS patients and non-IS patients. Nevertheless, we found that the addition of related metabolites can significantly enhance the accuracy of the prediction. Hence, the gut microbiota and metabolites may also prove beneficial in the prompt detection of patients with MDD who have IS.\u003c/p\u003e \u003cp\u003eWe also acknowledge various limitations of this study. Initially, the hospital's lockdown as a result of the COVID-19 pandemic made sample collection more challenging. The IS patients we collected were in the recovery phase rather than the acute phase. However, previous studies have shown a significant increase in the abundance of \u003cem\u003eEnterobacteriaceae\u003c/em\u003e in patients with acute and recovering IS[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Consequently, our research can offer a reference regarding the composition of the gut microbiota in patients with MDD during the acute stage of IS. Furthermore, 16S rRNA sequencing lacks the ability to provide genetically modified annotations that are precise enough to identify the specific species. Therefore, shotgun metagenomic sequencing should be performed for further microbiome analysis.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eIn conclusion, this study revealed that, compared with MDD patients without IS, patients with MDD who also had IS had distinct changes in their gut microbiome and metabolite profiles. Changes in the gut microbiome manifest as an increase in the abundance of bacteria with LPS structures and a decrease in the abundance of bacteria that produce butyrate. Significantly altered microbes and metabolites may contribute to the development of IS in patients with MDD. Additionally, this study emphasized that the presence of the bacteria \u003cem\u003eAlloprevotella\u003c/em\u003e and \u003cem\u003eBacteroides Massiliensis\u003c/em\u003e, in addition to their related metabolites, are strong prognostic indicators of IS in patients with MDD. The findings provide new insights into the mechanisms that contribute to the coexistence of MDD and IS.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eMDD major depressive disorder\u003c/p\u003e\n\u003cp\u003eIS ischemic stroke\u003c/p\u003e\n\u003cp\u003eLPS lipopolysaccharide\u003c/p\u003e\n\u003cp\u003eDALY disability-adjusted life years\u003c/p\u003e\n\u003cp\u003eSCFAs short-chain fatty acids\u003c/p\u003e\n\u003cp\u003eHAMD: Hamilton Depression Rating Scale\u003c/p\u003e\n\u003cp\u003eBP blood pressure\u003c/p\u003e\n\u003cp\u003eGLU blood glucose\u003c/p\u003e\n\u003cp\u003eOTU optical transform unit\u003c/p\u003e\n\u003cp\u003eACE abundance-based coverage estimator\u003c/p\u003e\n\u003cp\u003ePCoA Principal Coordinate Analysis\u003c/p\u003e\n\u003cp\u003eLEfSe linear discriminant analysis effect size\u003c/p\u003e\n\u003cp\u003eKEGG Kyoto Encyclopedia of Genes and Genomes\u003c/p\u003e\n\u003cp\u003eLC‒MS: liquid chromatography‒mass spectrometry\u003c/p\u003e\n\u003cp\u003ePCA principal component analysis\u003c/p\u003e\n\u003cp\u003eOPLS-DA orthogonal partial least-squares discriminant analysis\u003c/p\u003e\n\u003cp\u003eVIP variable importance in projection\u003c/p\u003e\n\u003cp\u003eLDA Local Discriminant Analysis\u003c/p\u003e\n\u003cp\u003eROC receiver operating characteristic\u003c/p\u003e\n\u003cp\u003eAUC: area under the curve\u003c/p\u003e\n\u003cp\u003eI3C indole-3-methanol\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eEthical approval for the study was approved by the Ethics Committee of Mental Health Center of Inner Mongolia Autonomous Region (2022009), Hohhot, China. The study was conducted in accordance with a protocol that received approval from the Medical Ethics Research Committee of our hospital. In the presence of clinicians, patients\u0026rsquo; families were informed of the study and provided informed consent. All the research in this study was carried out in compliance with the Declaration of Helsinki, ensuring the protection of patients\u0026apos; privacy rights.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests in relation to this manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was supported by the Scientific Research Project of Universities in Inner Mongolia Autonomous Region (NJZY22617); the Inner Mongolia Medical University General Program (YKD2024MS003); the National Natural Science Foundation of China (82160639); the Inner Mongolia Autonomous Region Health Science and Technology Program (202202104);\u0026nbsp;the\u0026nbsp;Science and Technology Project of High-level Clinical Specialties Construction in Public Hospitals of Inner Mongolia Autonomous Region Health Commission (2023SGGZ049); and the Science and Technology Program of the Joint Fund for Scientific Research in Public Hospitals (2023GLLH0160).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll the authors participated in the study design. HR Zhang\u0026nbsp;and DS Lyu collected the fecal and blood samples and drafted the manuscript.\u0026nbsp;Ning Cao performed the statistical analysis and interpretation of the data. XG Zhang designed and supervised the study. Final approval was obtained from all the authors of the version submitted for publication.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe thank all the doctors, nurses and clinical scientists who worked in the hospital during the period of patient recruitment as well as the patients who were involved in this study. We also\u0026nbsp;thank Wuhan MetWare Biotechnology Co., Ltd., for sample detection.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eOtte C, Gold SM, Penninx BW, Pariante CM, Etkin A, Fava M, et al. Major depressive disorder. 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Biochimie. 2023;213:1\u0026ndash;11.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Major depressive disorder, Ischemic stroke, Gut microbiota, Plasma metabolome","lastPublishedDoi":"10.21203/rs.3.rs-3948912/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3948912/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eMajor depressive disorder (MDD) and ischemic stroke (IS) are prominent contributors to disease burden worldwide, and MDD has been recognized as a significant risk factor for IS in epidemiology studies; however, the specific mechanisms that explain the coexistence of MDD and IS have not been identified. Multiple studies have shown a strong association between the gut microbiota and both IS and MDD. We propose that the gut microbiota may play a role in the development of IS in individuals with MDD. This study aimed to investigate the mechanisms linking the gut microbiota and increased risk of IS development in patients with MDD.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eWe included 30 hospitalized individuals diagnosed with MDD with IS and 30 individuals diagnosed with MDD without IS using the matching method and used 16S rRNA gene sequencing and the nontarget metabolome to analyze the gut microbiota composition and plasma metabolic profiles of the included patients.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eMDD patients with IS and MDD patients without IS have different gut microbiota structures and plasma metabolic profiles. MDD patients with IS had more bacteria with lipopolysaccharide (LPS) structures and lacked bacteria that produce butyrate. \u003cem\u003eAlloprevotella\u003c/em\u003e and \u003cem\u003eBacteroides massiliensis\u003c/em\u003e, along with their associated metabolites, facilitated precise differentiation between patients with and without IS. The area under the curve (AUC) for these bacteria was 0.998 (95% confidence interval: 0.992-1.000) and 0.992 (95% confidence interval: 0.978-1.000).\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eCompared with MDD patients without IS, patients with MDD who also had IS exhibited distinct changes in their gut microbiome and metabolite profiles. Changes in the gut microbiome are evident by an elevated abundance of bacteria with LPS structures and a reduced abundance of bacteria that produce butyrate. Additionally, the abundances of \u003cem\u003eAlloprevotella\u003c/em\u003e and \u003cem\u003eBacteroides massiliensis\u003c/em\u003e, along with their related metabolites, strongly predict IS in patients with MDD.\u003c/p\u003e","manuscriptTitle":"Differences in the gut microbiota and plasma metabolome of major depressive disorder patients with and without ischemic stroke","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-02-14 20:23:06","doi":"10.21203/rs.3.rs-3948912/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"88d43aa5-a6f7-4aa2-9a94-a6e8cbb86d9f","owner":[],"postedDate":"February 14th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-03-07T06:50:02+00:00","versionOfRecord":[],"versionCreatedAt":"2024-02-14 20:23:06","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-3948912","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3948912","identity":"rs-3948912","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}