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. 2025 Aug 20;25:525. doi: 10.1186/s12866-025-04217-8

Fecal metabolites as early-phase biomarkers and prediction panel for ischemic stroke

Ke Xu 1,2, Zhe Ren 1,2, Shuang Zhao 3,4, Yi Ren 1,2, Jiaolin Wang 5, Wentao Wu 5, Zicheng Hu 1, Fei He 5, Dianji Tu 6, Qi Zhong 5, Jianjun Chen 5,✉, Peng Xie 1,2,✉
PMCID: PMC12366175  PMID: 40836221

Abstract

Background

There is growing research on the relationship between gut bacteria and various forms of strokes. This study aimed to investigate the relationship between fecal metabolites and ischemic stroke, providing a new perspective on predicting the latter.

Results

Stool samples were taken from 60 patients with ischemic stroke and 60 healthy individuals, and non-targeted metabolomic analysis was used. The generalized boosted linear model was utilized for co-occurrence analysis to ascertain the noteworthy variation in fecal metabolites. The important differential metabolites were identified by the random forest algorithm, a prediction panel was developed to distinguish ischemic stroke patients from healthy individuals. Specifically, six differential metabolites (Ganoderic acid theta, Fructose-lysine, Pentaethylene glycol, 2-Chlorooctadecanoic acid, PA(2:0/PGF1alpha), and 4-[(E)-5,6-Dihydro-2,3'-bipyridin-3(4H)-ylidenemethyl]-3-methoxyphenol) were identified as potential independent stroke-associated metabolites. A prediction panel consisting of these six metabolites could yield an area under the curve of 0.989 in training set and 0.973 in testing set. There was a substantial correlation between all six independent stroke-associated metabolites and the severity of ischemic stroke, but it was not affected by depression or anxiety.

Conclusions

These six differential metabolites were independent stroke-associated metabolites, and the panel consisting of these metabolites could serve as a potential prediction panel for ischemic stroke. However, future external validation in multi-ethnic cohorts is necessary to confirm broader generalizability.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12866-025-04217-8.

Keywords: Metabolome, Ischemic stroke, Gut, Biomarker

Background

A complex series of neurological, pathological, and physiological processes culminate in brain damage caused by ischemic stroke [1, 2], and its frequency is continuously rising globally [3]. Effective drugs or therapies are currently unavailable for individuals who missed the thrombolysis and surgical embolization therapy windows [4]. Thus, there is an urgent need for efficient alternative therapeutic approaches to encourage brain function restoration and lessen the severity of impairment linked to ischemic stroke [5].

The gut microbiota preserves physical health and is intrinsically connected to the emergence of several illnesses [6–8]. The enteric and central nervous systems can communicate in both directions; this is known as the gut-brain axis of the gut microbiota [9–11]. More than half of ischemic stroke patients experience gastrointestinal problems, and a disturbed gut microbiota may increase the risk of stroke, impact post-stroke mortality, and cause progressive neurological impairments [12, 13]. Recent evidence has also suggested a key role of the gut microbiota and its metabolites in ischemic stroke [14, 15]. Enrichment with Enterobacteriaceae has the potential to increase inflammation and worsen brain infarction, which is a risk factor for poor outcomes for stroke patients on its own [16]. However, it is still mostly unclear how intestinal flora contributes to ischemic stroke.

Following an ischemic stroke, disease progression picks up rapidly [17, 18]. The metabolites generated in the plasma during the beginning of ischemic stroke should be examined in addition to neurological abnormalities and imaging results to guarantee prompt identification and clarify new biological mechanisms linked to functional recovery [19]. The intricacy of the ischemic cascade, the effect of the blood–brain barrier on the diffusion of blood biomarkers, and differing interpretations of laboratory results have all contributed to the restricted use of blood biomarkers of stroke. S-100 protein, brain natriuretic peptides, fibrinogen, and serum-free hemoglobin have all been demonstrated to be potential stroke biomarkers in recent research; however, none of these biomarkers have demonstrated enough sensitivity or specificity for clinical usage [20].

Non-targeted metabolomics is a promising method for investigating biomarkers and their mechanisms in cerebrovascular diseases [21]. Brain illness is increasingly being studied using patient peripheral tissues, such as fecal matter, and some metabolites found in these studies may be useful as potential disease biomarkers [22, 23]. Thus, fecal research is necessary to identify the important metabolites in ischemic stroke and determine whether they have any potential as biomarkers for the objective diagnosis of ischemic stroke. In this study, we assessed the metabolomic analysis of feces in individuals with ischemic stroke patients. Finding possible biomarkers and metabolic changes in ischemic stroke patients was the main aim of our present investigation. Additionally, fecal metabolites that might be useful therapeutic targets and have implications for stroke therapy are shown by our investigation.

Methods

Human subjects

This study was a hospital-based case–control study. The research has been carried out in accordance with the World Medical Association Declaration of Helsinki. Ethical approval was obtained from the Institutional Ethics Committee of the First Affiliated Hospital of Chongqing Medical University (2023–414). Written informed consent was secured from all participants prior to stool sample collection. Between February 2023 and September 2023, inpatients with ischemic stroke were enrolled from the Department of Neurology, the First Affiliated Hospital of Chongqing Medical University, while HCs were recruited from the hospital’s Medical Examination Center. All participants were Han Chinese residents of Chongqing, China. Inclusion criteria for ischemic stroke patients included: (a) aged 18 to 70 years; (b) acute hospitalization within 48 h after onset; (c) confirmed cerebral infarction via intracranial CT or MRI. Exclusion criteria for ischemic stroke patients were as follows: (a) history of transient ischemic attacks, intracerebral hemorrhage, transient cerebral ischemia, or other brain diseases; (b) severe dysfunction of heart, liver, kidney, digestive system, or other organs; (c) psychiatric disorders, cancer, or dementia; (d) pregnancy, breastfeeding, or planned pregnancy; (e) use of anticoagulants, lipid-lowering drugs, probiotics, or antibiotics within the past month; (f) recent infection within the past week. For HCs, exclusion criteria included: (a) history of stroke or neurological disorders; (b) diagnosis of cancer, metabolic, cardiovascular, psychiatric, gastrointestinal, cerebrovascular diseases, or dementia; (c) age < 18 or > 70 years; (d) pregnancy, breastfeeding, or planned pregnancy; (e) intake of anticoagulants, lipid-lowering drugs, probiotics, or antibiotics within the past month; (f) recent infection within the past week.

Clinical assessments and the gathering of samples from human subjects

Clinical baseline data were collected, including age, gender, and BMI. The NIHSS score evaluates stroke severity based on clinical characteristics [24]. Given the well-documented association between ischemic stroke and increased risk of depression and anxiety, which can significantly impair daily functioning and quality of life [25], we also assessed the impact of these psychological symptoms. Severity of depressive and anxious symptoms was measured using the HAMD [26–29] and the HAMA [30, 31]. A HAMA score > 7 indicated the presence of anxiety, while a HAMD score > 17 was used to define depression.

Fecal samples were collected immediately upon patient admission to the hospital, prior to administering any ischemic stroke medications to eliminate potential confounding effects of post-stroke pharmacotherapy on the metabolomic profile. All samples were promptly immersed in absolute ethanol, transported on ice to the laboratory within 2 h, and subsequently stored at −80℃ until analysis.

Untargeted metabolomics analysis

Fecal samples were processed using homogenization, dissociation, and centrifugation, following our previously established protocols [32–34]. Briefly, fecal homogenization was performed as follows: approximately 50 mg of feces were weighed, mixed with 400μL ice-cold extraction solvent (80% methanol), and homogenized using a bead-beater. The supernatant was collected after centrifugation, and the metabolic profile was analyzed by LC–MS/MS. An internal standard (0.02 mg/mL L-2-chlorophenylalanine) was spiked into fecal extracts before LC–MS/MS analysis. Metabolite intensities were normalized to the internal standard to correct for extraction efficiency. The normalization efficiency was also validated by principal component analysis. Moreover, to ensure system stability and data reliability, a pooled quality control (QC) sample was prepared by combining equal volumes (20 μL) of each sample supernatant. QC samples were processed and analyzed identically to experimental samples throughout the workflow.

Using an ACQUITY HSS T3 column (100 mm × 2.1 mm i.d., 1.8 μm; Waters, USA), a Thermo UHPLC-Q Exactive HF-X system was utilized for the untargeted liquid chromatography-mass spectrometry/mass spectrometry (LC–MS/MS) metabolomics investigation at Majorbio Bio-Pharm Technology Co. Ltd. (Shanghai, China). For positive electrospray ionization (ESI +) mode, mobile phases consisted of 0.1% formic acid in water (solvent A) and 0.1% formic acid in acetonitrile (solvent B). In negative ESI-mode, solvents were 6.5 mM ammonium bicarbonate in water (solvent C) and 6.5 mM ammonium bicarbonate in 95% methanol (solvent D). Chromatographic conditions were set as follows: flow rate 0.40 mL/min, column temperature 40 °C, and injection volume 2 μL. The mass spectrometer operated in dual-mode ESI with optimized parameters: source temperature 425 °C, sheath gas flow 50 arb, auxiliary gas flow 13 arb, spray voltage − 3500 V (ESI −)/+ 3500 V (ESI +), and normalized collision energy 20–40-60 V for MS/MS fragmentation.

Using Progenesis QI (Waters Corporation), baseline filtering, peak identification, integration, retention time correction, peak alignment, and normalization were applied to the raw data produced by LC–MS/MS [35]. The Human Metabolome Database (http://www.hmdb.ca), Metlin (https://metlin.scripps.edu), and self-built databases were used to identify metabolites. The highest intensity was determined to be the metabolite expression level [36]. The difference in fecal metabolites between the two groups was determined using OPLS-DA. To assess the developed OPLS-DA model’s goodness-of-fit and predictability, two parameters (R2X and R2Y) were used. In the meantime, a 300-iteration permutation test was run to determine whether the distance between ischemic stroke patients and HCs was not randomly distributed. The resulting OPLS-DA loading plot indicated that the differential fecal metabolites responsible for discriminating between the two groups were those with VIP > 1.0 and p < 0.05. Then, the detected differential fecal metabolites were used to perform pathway enrichment analysis based on the Kyoto Encyclopedia of Genes and Genomes (KEGG) database (http://www.genome.jp/kegg/).

Statistical analysis

The R 4.0.5 software was utilized for all the analyses, and where necessary, the Student’s t-test, Chi-squared test, and Spearman correlation analysis were applied. The method of Benjamini and Hochberg False Discovery Rate was used to conduct multiple testing corrections, and the threshold of adjusted p-value was 0.05. Co-occurrence analysis was performed using the GBLM to determine the significantly different fecal metabolites. In short, within the constructed network, metabolites were categorized as crucial (i.e., stress centrality: the importance of metabolites to maintain the network’s integrity) and influential (i.e., betweenness centrality: the direct and indirect influences of metabolite on other metabolites). After that, a simpler prediction panel for dividing ischemic stroke patients from HCs was created by further analyzing the significant differential fecal metabolites that had been detected using the random forest algorithm. The capacity of this panel to differentiate between ischemic stroke patients and HCs was measured using the ROC curve analysis [37, 38].

Results

Fecal metabolites alterations in ischemic stroke patients

A total of 416 subjects were screened, and 120 subjects (60 ischemic stroke patients and 60 healthy controls (HCs)) were included in the final dataset (Supplementary Fig. S1). The clinical and demographic details of the individuals that were recruited are compiled in Supplementary Table S1. The results of Orthogonal Partial Least-Squares Discriminant Analysis (OPLS-DA) model showed that there was no overlap between the two groups and the values of R2X (0.95) and Q2 (0.71) permutation test (n = 399) quantifying the built model was positive (Fig. 1A), suggesting a robust metabolic difference between ischemic stroke patients and HCs. Moreover, the results of the permutation test showed that the blue regression line of Q2-points intersected the vertical axis (on the left) below zero (Q2 = (0, −0.58)), further demonstrating the validity of the built OPLS-DA model (Fig. 1B).

Fig. 1.

Fig. 1

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Metabolomic analysis of fecal samples from ischemic stroke patients and HCs. A OPLS-DA model built using fecal metabolites showed that ischemic stroke patients were separately from HCs with no overlap, suggesting the divergent fecal metabolic phenotypes between the two groups; B the results of 300-iteration permutation test showed that the built model was robust (Q2 = (0, −0.58)); C 202 differential metabolites (VIP > 1.0, p < 0.05, FDR < 0.05) were identified according to the corresponding OPLS-DA loading plot; D classification of differential metabolites showed that the lipids and lipid-like molecules category ranked at the top, with a proportion of 31.19%. HCs, healthy controls; FDR, false discovery rate; OPLS-DA, orthogonal partial least-squares discriminant analysis; VIP, variable importance in projection

By analyzing the corresponding OPLS-DA loading plot, 96 up-regulated and 106 down-regulated differential fecal metabolites (variable importance in projection (VIP) > 1.0, p < 0.05, false discovery rate (FDR) < 0.05) were identified (Fig. 1C). Among these differential metabolites, 63 (31.19%) metabolites belonged to lipids and lipid-like molecules (Fig. 1D). The detailed information of these differential metabolites was described in Supplementary Table S2.

Enrichment analysis of differential fecal metabolites in ischemic stroke patients

As shown in Fig. 2A, the hierarchical clustering heat-map constructed using these identified differential fecal metabolites showed a consistent clustering pattern within the individual groups, which suggested that the most significant deviations between ischemic stroke patients and HCs could be described by these differential fecal metabolites.

Fig. 2.

Fig. 2

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Pathway enrichment analysis of differential fecal metabolites. A A heat-map analysis of differential fecal metabolites between the two groups was shown; B Pathway enrichment analysis showed that 20 pathways had p < 0.05, and only eight of them had FDR < 0.05: Tyrosine metabolism, Nucleotide metabolism, Synaptic vesicle cycle, ABC transporters, Neuroactive ligand-receptor interaction, Bacterial chemotaxis, Mineral absorption, and Taste transduction. FDR, false discovery rate

Pathway enrichment analysis was conducted here to explore the potential functions of these differential metabolites. The results showed that these differential metabolites were significantly enriched in 20 pathways (p < 0.05), but only eight of them had FDR < 0.05: Tyrosine metabolism (FDR = 0.0001), Nucleotide metabolism (FDR = 0.0027), Synaptic vesicle cycle (FDR = 0.0025), ABC transporters (FDR = 0.0105), Neuroactive ligand-receptor interaction (FDR = 0.0099), Bacterial chemotaxis (FDR = 0.0125), Mineral absorption (FDR = 0.0160) and Taste transduction (FDR = 0.0188) (Fig. 2B). There are seven differential metabolites in Tyrosine metabolism, which was the highest among all the pathways. The detailed information on these eight significantly enriched pathways was described in Supplementary Table S3.

Important differential fecal metabolites in ischemic stroke patients

Spearman correlation analysis was used to explore the potential relationships between differential fecal metabolites and clinical indexes, including age, body mass index (BMI), Hamilton Anxiety Rating Scale (HAMA), 17-item Hamilton Rating Scale of Depression (HAMD), and National Institutes of Health Stroke Scale (NIHSS). The results showed that the differential fecal metabolites were mainly correlated with NIHSS; and they also had some impacts on HAMA and HAMD, but had little relationship with age and BMI (Fig. 3A).

Fig. 3.

Fig. 3

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Important differential fecal metabolites in ischemic stroke patients. A The results of correlation analysis showed that differential metabolites were mainly correlated with NIHSS, and have some and little relationships with HAMA/HAMD and age/BMI, respectively. B GBLM showed that 12 differential metabolites that had higher influence and critical places in the network were viewed as important differential fecal metabolites in stroke patients. Red and blue indicated positive and negative correlations, respectively; and the thicker the line, the bigger the correlation coefficient. BMI, body mass index; GBLM, Generalized boosted linear model; HAMA, Hamilton Anxiety Scale; HAMD, Hamilton Depression Scale; NIHSS, National Institutes of Health Stroke Scale

Among these differential fecal metabolites, 12 metabolites were identified as important differential fecal metabolites by Generalized Boosted Linear Model (GBLM) analysis (Fig. 3B): 4-[(E)−5,6-Dihydro-2,3'-bipyridin-3(4H)-ylidenemethyl]−3-methoxyphenol, Pentaethylene glycol, Fructose-lysine, Calystegine A6, Trans-4-Aminocyclohexanecarboxylic acid, Dihydrocitrinone, Cyclopentanone, Muricatin C, Estradiol benzoate, Ganoderic acid theta (GAT), PA(2:0/PGF1alpha), and 2-Chlorooctadecanoic acid (CA). Moreover, the last five differential fecal metabolites belonged to lipids and lipid-like molecules, and four metabolites (Pentaethylene glycol (PG), Fructose-lysine (FL), 4-[(E)−5,6-Dihydro-2,3'-bipyridin-3(4H)-ylidenemethyl]−3-methoxyphenol (ED), and Trans-4-Aminocyclohexanecarboxylic acid were significantly correlated with lipid metabolism.

Independent stroke-associated metabolites

To find out the independent stroke-associated metabolites, these 12 identified important differential fecal metabolites were further analyzed. Firstly, the included samples were randomly assigned to a training set (40 ischemic stroke patients and 40 HCs) and a testing set (20 ischemic stroke patients and 20 HCs). The training set was used to build a prediction panel, and the testing set was used to independently validate the built prediction panel. Secondly, in training set, after adjusting age, sex, and BMI, the results of the random forest algorithm showed that the following six differential metabolites could be the independent stroke-associated metabolites: GAT, FL, PG, CA, PA(2:0/PGF1alpha) and ED. The mass to charge ratio (m/z), retention time, fragmentation score, formula, and MS/MS spectra of these six metabolites are shown in Supplementary Table S4 and Figure S2. Using R 4.0.5 software, we conducted power calculation (post hoc) using these six independent risk factors, and the results showed that our included sample size could obtain power = 0.87 (alpha = 0.05). Relative levels of these metabolites in two groups are shown in Fig. 4A-F. The metabolite levels, odd rations, and 95% confidence interval of these six stroke-associated metabolites are shown in Supplementary Table S5.

Fig. 4.

Fig. 4

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Independent stroke-associated metabolites. Six independent stroke-associated metabolites were obtained using the random forest algorithm. The relative abundance of GAT (A), FL (B), PG (C), ED (D), CA (E), and PA(2:0/PGF1alpha) (F) in ischemic stroke patients and HCs. The Student’s t-test was used. CA, 2-Chlorooctadecanoic acid; ED, 4-[(E)−5,6-Dihydro-2,3'-bipyridin-3(4H)-ylidenemethyl]−3-methoxyphenol; FL, Fructose-lysine; GAT, Ganoderic acid theta; HCs, Healthy controls; PG, Pentaethylene glycol

In random forest (RF) algorithm analysis, the included samples were randomly assigned to a training dataset (40 ischemic stroke patients and 40 HCs) and a testing dataset (20 ischemic stroke patients and 20 HCs). The training dataset was used to train the RF model and the testing dataset was used to evaluate the RF model. The built RF model could yield an area under the curve (AUC) of 0.998 (95%CI = 0.993–1.000) in training set and 0.943 (95%CI = 0.873–1.000) in testing set. These results showed that the built RF model was validated. The prediction panel consisting of these six metabolites was as follows: P(stroke) = 1/(1 + EXP(−0.065*GAT-4.173*PG-0.463*ED-1.075*FL + 8.998*CA-1.307*PA(2:0/PGF1alpha)−16.584)). Receiver operating characteristic (ROC) curve analysis showed that this prediction panel could yield an AUC of 0.989 (95%CI = 0.970–1.000) in the training set (Fig. 5A). Moreover, in the testing set, this prediction panel could also yield an excellent diagnostic performance for ischemic stroke (AUC = 0.973 (95%CI = 0.938–1.000); Fig. 5B).

Fig. 5.

Fig. 5

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Diagnostic performances of six fecal metabolites in prediction ischemic stroke. Area under the receiver operating characteristic curve (AUC) value of the combination of six metabolites (Ganoderic acid theta, Fructose-lysine, Pentaethylene glycol, 4-[(E)−5,6-Dihydro-2,3'-bipyridin-3(4H)-ylidenemethyl]−3-methoxyphenol, 2-Chlorooctadecanoic acid, and PA(2:0/PGF1alpha)) in diagnosing ischemic stroke patients in training (A) and testing sets (B). The training and testing sets were randomly selected from independent datasets

Assistant analysis of independent stroke-associated metabolites

These identified independent stroke-associated metabolites had no significant correlations with age, sex, BMI, HAMA, and HAMD. However, all six independent stroke-associated metabolites were significantly correlated with NIHSS: NIHSS was positively correlated with GAT (r = 0.41, p = 3.47E-06), FL (r = 0.42, p = 2.52E-07), PG (r = 0.51, p = 1.86E-09), ED (r = 0.40, p = 4.85E-06), CA (r = 0.41, p = 3.47E-06), and PA(2:0/PGF1alpha) (r = 0.44, p = 5.84E-07), and negatively correlated with CA (r = −0.41, p = 3.98E-06) (Fig. 6A).

Fig. 6.

Fig. 6

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Correlations between independent stroke-associated metabolites and NIHSS/HAMA/HAMD. A The independent stroke-associated metabolites were significantly correlated with NIHSS; B The relative abundances of these predictors were similar between ischemic stroke patients with anxiety symptoms and ischemic stroke patients without anxiety symptoms; C The relative abundances of these predictors were similar between ischemic stroke patients with depressive symptoms and ischemic stroke patients without depressive symptoms. ED, 4-[(E)−5,6-Dihydro-2,3'-bipyridin-3(4H)-ylidenemethyl]−3-methoxyphenol; HAMA, Hamilton Anxiety Scale; HAMD, Hamilton Depression Scale; NIHSS, National Institutes of Health Stroke Scale

The relative abundances of these six stroke-associated metabolites were similar between ischemic stroke patients with anxiety symptoms and ischemic stroke patients without anxiety symptoms (Fig. 6B). Meanwhile, the relative abundances of these stroke-associated metabolites were similar between ischemic stroke patients with depressive symptoms and ischemic stroke patients without depressive symptoms (Fig. 6C).

Discussion

Our study first showed that six differential metabolites (GAT, FL, PG, CA, PA(2:0/PGF1alpha), and ED) could be independent stroke-associated metabolites. Meanwhile, the panel consisting of these metabolites could serve as a potential prediction panel for ischemic stroke. In addition, NIHSS was positively correlated with GAT, FL, PG, ED, CA, and PA(2:0/PGF1alpha), and negatively correlated with CA. Our results suggested that these six metabolites in fecal might play an important role in the pathophysiology of ischemic stroke.

A growing body of research indicates that the incidence of ischemic stroke may be influenced by the microbiota and its metabolome [39, 40]. Thus, it is a potential sector to use the gut microbiota and its metabolites as screening techniques for early identification of ischemic stroke. Omics approaches hold great potential for the development of biomarkers because they can produce large-scale, high-throughput data sets with thousands of variables. These data sets may be predictive, making them a profitable approach when applied to biological systems with extremely high levels of complexity, such as the diagnosis of stroke from fecal matter [41, 42]. When it comes to direct interactions between genetic, environmental, and nutritional variables, the fecal metabolome is superior to omics techniques that use biofluids like serum and urine [43]. Therefore, metabolomics studies using fecal samples may find biomarkers more successfully. In addition to offering insights into the relationships between communities from a microbiome sequencing technique, the global nontargeted metabolomics analysis performed here on a fecal sample may make it possible to identify metabolic biomarkers derived from ischemic stroke.

Following an untargeted approach, our study identified 96 up-regulated and 106 down-regulated differential fecal metabolites in the ischemic stroke group, 63 (31.19%) belonged to lipids and lipid-like molecules. Previous research has shown that dyslipidemia is widely thought to be a key factor in the etiology of ischemic stroke [44, 45]. Baseline levels of triglycerides, very low-density lipoprotein (VLDL) triglyceride, VLDL particle number, VLDL size, low-density lipoprotein particle number, high-density lipoprotein (HDL) cholesterol, and HDL size were also linked to future ischemic stroke in a prospective evaluation of lipid and lipoprotein biomarkers associated with incident ischemic stroke [46]. In general, our global nontargeted metabolomics analysis of fecal samples from ischemic stroke patients and HCs revealed distinct and varied metabolic fingerprints. In contrast to blood lipid biomarkers, fecal lipid metabolites reflect the microbial-mediated transformation of dietary lipids, potentially influencing stroke pathogenesis through intestinal barrier dysfunction and neuroinflammatory pathways [47]. This mechanistic link to the gut-brain axis highlights a novel paradigm, as such microbial-metabolic interactions remain underexplored in traditional blood-based biomarker research. Our findings demonstrate that fecal lipid dysregulation is associated with ischemic stroke, offering new insights into gut-brain metabolic crosstalk in cerebrovascular disease.

Multiple biomarkers on a biomarker panel rather than just one might reduce the impact of variance across populations and subgroups, leading to more accurate results. Previous researches [48, 49] indicate that the diagnostic efficacy of combining numerous biomarkers was much higher than that of using each biomarker alone. Our earlier research [27, 50] also showed similar results. Here, we discovered that, when used as independent biomarkers for ischemic stroke detection, the fecal levels of six metabolites (GAT, FL, PG, CA, PA(2:0/PGF1alpha), and ED) demonstrated exceptional diagnostic accuracy. Moreover, we found that the discriminative model consisting of these six metabolites was able to provide a training set AUC of 0.989 which was higher. In the meanwhile, the diagnostic robustness of this discriminative model was demonstrated by its ability to provide an AUC of 0.973 in the testing set. The assessment index used was the AUC; values of 1–0.9, 0.9–0.8, 0.8–0.7, 0.7–0.6, and 0.6–0.5 denoted classification performance that was excellent, good, fair, poor, or fail, respectively [26, 28, 51]. As a result, the discriminative model made up of these six metabolites may be an “excellent” classifier of ischemic stroke patients and HCs.

Following an ischemic stroke, neuropsychiatric problems are common and negatively affect functional results and quality of life [52, 53]. Meanwhile, poststroke depression is thought to be these illnesses’ most common clinical manifestation [54]. Because it influences outcomes, it is crucial to differentiate between ischemic stroke and poststroke depression. Despite this, we discovered that these metabolites were only connected with NIHSS in our investigation and not with HAMD or HAMA. However, the study’s finding is that measurements of these six metabolites have a good discriminant power for identifying individuals who are at risk of ischemic stroke. These fecal indicators, however, are stable and unaffected by emotional swings.

This study has several inherent limitations that warrant consideration. First, the absence of isotope-labeled dietary intervention studies—logistically challenging in human populations—hinders definitive evaluation of fecal metabolite origins. A substantial proportion of gut metabolites arise from dietary intake, gut microbiota activity, medications, and lifestyle factors, making it difficult to distinguish between host-derived and microbially produced metabolites. Second, fecal samples were collected at a single time point, precluding the capture of dynamic metabolic changes across the stroke continuum. Consequently, observed associations reflect acute-phase alterations rather than longitudinal patterns, limiting interpretation to early-stage biomarkers rather than stage-independent risk indicators. Future longitudinal studies with serial sampling across acute, subacute, and chronic phases are needed to characterize temporal metabolite profiles and their association with stroke progression, recovery, or recurrence. Third, the relatively small sample size increases the risk of false-positive findings and limits generalizability. While our results are statistically significant, validation in larger, multicenter cohorts is imperative to confirm the robustness of identified metabolites. Fourth, causal inference remains challenging due to the bidirectional relationship between stroke and gut physiology: large ischemic lesions can disrupt gastrointestinal motility and induce bacterial overgrowth, potentially altering the gut metabolome [35]. Thus, whether the observed metabolite changes are antecedent stroke-associated metabolites or post-stroke adaptations remains unclear. Longitudinal cohort studies with pre-stroke baseline sampling are needed to disentangle causality and differentiate between preexisting biomarkers and disease-induced adaptations. Fifth, the geographic homogeneity of the study population—predominantly Han Chinese residents of Chongqing—may limit generalizability to diverse ethnic groups or populations with distinct dietary habits, future external validation in multi-ethnic cohorts is necessary to confirm broader generalizability. Sixth, although correlations between differential metabolites and NIHSS scores were observed, these associations do not establish direct involvement in ischemic stroke pathogenesis. Mechanistic studies are required to elucidate how these metabolites are involved in ischemic stroke. Collectively, while our findings generate novel hypotheses about gut metabolite-stroke associations, they should be interpreted within these methodological constraints. Future research addressing sample size, longitudinal design, ethnic diversity, and causal pathways will be critical to translating these observations into clinical applications.

In conclusion, we showed how a nontargeted metabolomics technique can effectively distinguish between ischemic stroke and HCs and link various metabolites to phenotypes of illness or well state. Moreover, six fecal metabolites are promising biomarkers for differentiating ischemic stroke from HCs, awaiting additional validation studies conducted over an extended period in bigger cohorts across ethnic landscapes. Additionally, we discovered that these six metabolites together could produce very good diagnostic results in both training and testing sets. While these metabolites show promise as acute-phase indicators, their role in pre-stroke risk prediction requires further investigation. Despite certain limitations, this work provides new opportunities to investigate the gut metabolome in search of biomarkers.

Supplementary Information

Supplementary Material 1. (264.2KB, docx)
Supplementary Material 2. (43.9KB, xlsx)

Acknowledgements

We are grateful to all subjects for their participation in the study.

Abbreviations

AUC

Area under the curve

BMI

Body mass index

CA

2-Chlorooctadecanoic acid

ED

4-[(E)-5,6-Dihydro-2,3'-bipyridin-3(4H)-ylidenemethyl]-3-methoxyphenol

FDR

False discovery rate

FL

Fructose-lysine

GAT

Ganoderic acid theta

GBLM

Generalized boosted linear model

HAMA

Hamilton Anxiety Rating Scale

HAMD

Hamilton Rating Scale of Depression

HCs

Healthy controls

HDL

High density lipoprotein

KEGG

Kyoto encyclopedia of genes and genomes

LC–MS/MS

Liquid chromatography-mass spectrometry/mass spectrometry; NIHSS, national institutes of health stroke scale

OPLS-DA

Orthogonal partial least-squares discriminant analysis

PG

Pentaethylene glycol

RF

Random forest

ROC

Receiver operating characteristic

VIP

Variable importance in projection

VLDL

Very low-density lipoprotein

Authors’ contributions

K.X., J.C., and P.X. conceived and designed the experiments. S.Z., Y.R., J.W., W.W., D.T., and Q.Z. collected the sample. Z.R., Z.H., and F.H. performed the experiments. K.X. and J.C. analyzed the data. K.X. wrote the paper. All authors reviewed and approved the final manuscript.

Funding

This work was supported by National Key R&D Program of China (2017YFA0505700), National Natural Science Foundation of China (81820108015, 82301720), China Postdoctoral Special Foundation (2024T171106), China Postdoctoral Science Foundation (2023MD734128), Chongqing Postdoctoral Special Support Foundation (2023CQBSHTB2010), The First Affiliated Hospital of Chongqing Medical University Postdoctoral Science Foundation (CYYY-BSHPYXM-202310), Qiande Talents Program of the First Affiliated Hospital of Chongqing Medical University (2025QDTS-04), CQMU Program for Youth Innovation in Future Medicine (W0168), and Program for Youth Innovation in Future Medicine, Chongqing Medical University to Peng Zheng.

Data availability

Data is provided within the manuscript and supplementary information files.

Declarations

Ethics approval and consent to participate

The study was approved by the Research Ethics Board at the First Affiliated Hospital of Chongqing Medical University (2023–414). All participants were included after providing written informed consent. The research has been carried out in accordance with the World Medical Association Declaration of Helsinki.

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.

Footnotes

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Contributor Information

Jianjun Chen, Email: chenjianjun@cqmu.edu.cn.

Peng Xie, Email: xiepeng@cqmu.edu.cn.

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