Characterization and correlation of gut microbiota in major depressive disorder patients with suicidal ideation

Abstract

Background

This study aimed to analyze the gut microbiota composition in major depressive disorder (MDD) patients with suicidal ideation and explore its correlations with suicidal ideation and plasma levels of C-reactive protein (CRP) and short-chain fatty acids (SCFAs).

Methods

57 MDD patients and 57 healthy controls (HC) were enrolled. The Hamilton Depression Scale-24 (HAMD-24) and Beck Scale for Suicide Ideation-Chinese Version (BSI-CV) assessed the depressive symptoms and suicidal ideation intensity, respectively. According to BSI-CV, MDD patients were divided into those without suicidal ideation (SC0 group) and those with suicidal ideation (SC1 group). All participants provided fecal and blood samples. Gut microbiota composition was analyzed by 16 S ribosomal RNA (16 S rRNA) sequencing. The plasma CRP levels were measured by enzyme-linked immunosorbent assay (ELISA), and SCFAs by targeted gas chromatography-mass spectrometry (GC-MS). Spearman’s correlation analysis was used to evaluate the correlation among gut microbiota, HAMD scores, BSI scores, and plasma levels of CRP and SCFAs. Mediation analysis was performed using Model 4 of the PROCESS (version 3.5). All post-hoc pairwise comparisons in this analysis were adjusted for false discovery rate (FDR), with a FDR-corrected p < 0.05 set as the significance threshold.

Results

1. MDD patients had significantly lower levels of acetic, propionic, and butyric acids than HC (p<0.01), while their CRP levels were significantly higher (p<0.001). 2. The alpha diversity analysis showed the HC group had significantly higher Good’s coverage than SC0 (FDR-corrected p<0.05), higher Pielou’s evenness (FDR-corrected p <0.05) and Simpson indices than SC1 (FDR-corrected p<0.01), while SC0 also had a higher Simpson index than SC1 (FDR-corrected p<0.05). The beta diversity analysis showed differences in the gut microbiota composition among the three groups (pseudo-F = 2.29, FDR-corrected p <0.01). 3. Linear discriminant analysis effect size (LEfSe) identified 24 differentially abundant bacterial taxa across classification levels. 4. The correlation analysis showed that Blautia was positively correlated with BSI scores, HAMD scores, and CRP levels. Bacteroides, Roseburia, and Sutterella were negatively correlated with BSI and HAMD scores. 5. The mediation analysis suggested that CRP might partially mediate the relationship between Blautia and suicidal ideation intensity, but this mediating effect was confounded by depression severity.

Conclusions

This study exploratorily found that MDD patients with suicidal ideation showed preliminary characteristics in gut microbiota diversity, composition, and plasma levels of CRP and SCFAs, and that CRP may play a partial mediating role in the relationship between Blautia and suicidal ideation. These findings were confounded by depression severity, and the underlying mechanisms require further investigation.

Clinical trial number

Not applicable.

Supplementary Information

The online version contains supplementary material available at https://doi.org/10.1186/s12888-026-08290-9.

Introduction

Suicidal ideation refers to thoughts or plans about ending one’s life. According to the World Health Organization, more than 720,000 people die by suicide every year worldwide, with a mortality rate of 9.2 per 100,000 [1]. Previous studies have found an association between suicidal ideation and major depressive disorder (MDD). Suicidal ideation is common in MDD patients and is considered to be a precursor to suicide attempt and suicide completion [2, 3]. The incidence of suicidal ideation in MDD patients is about 53%, and the risk of suicide is significantly increased compared with the general population [4]. Moreover, MDD patients with suicidal ideation have more severe depressive symptoms and a worse response to treatment [5]. Suicidal ideation may develop into suicidal behavior or be alleviated through intervention, but most MDD patients with suicidal ideation do not take the initiative to seek help. Therefore, finding innovative ways to identify and manage suicidal ideation is crucial to counter the rising trend of suicidal ideation in patients with MDD.

The gut-brain axis represents a bidirectional communication system that integrates gut and brain functions. As gut microbiota are now recognized as critical participants in this crosstalk, the concept has been expanded to the microbiota-gut-brain (MGB) axis [6]. Specifically, gut microbiota and their metabolites can influence the central nervous system (CNS) function through various pathways, including neural, endocrine, immune, and metabolic routes, and are implicated in the pathogenesis of MDD. The neural pathway within the MGB axis primarily comprises the enteric nervous system (ENS) and the vagus nerve. Functioning as a critical communication conduit, the ENS conveys signals from the gut lumen to the CNS through the vagus nerve [7]. Not only can the ENS regulate intestinal motility autonomously without CNS input, but it also receives signals from gut microbiota and relays this information to the brain [8]. Impaired ENS function may contribute to the pathogenesis of MDD by disrupting key intestinal functions, including secretion, immune defense, motility, and permeability [9]. The endocrine pathway is primarily mediated by the hypothalamic-pituitary-adrenal (HPA) axis, which serves as the central coordinator of the body’s stress response. Dysregulation of the HPA axis is critically implicated in the pathogenesis of MDD. Perturbations in gut microbiota can elevate levels of microbial antigens and pro-inflammatory cytokines. These factors can compromise the integrity of the blood-brain barrier, leading to HPA axis hyperactivity, thereby contributing to the pathogenesis of MDD [10]. The immune pathway involves peripheral immune activation and subsequent neuroinflammation. Gut microbiota dysbiosis results in a reduction of beneficial metabolites and an increase in harmful ones, thereby enhancing the permeability of both the intestinal and blood-brain barriers. This allows bacteria and their metabolites to enter systemic circulation, triggering a peripheral immune response that promotes neuroinflammation and contributes to the pathophysiology of MDD [11]. The metabolic pathway primarily involves short-chain fatty acids (SCFAs), which are produced by gut microbiota through the fermentation of complex polysaccharides. SCFAs, mainly represented by acetic, propionic, and butyric acids, can enter the brain through the blood circulation and are widely recognized as important regulators of the MGB axis. Oral administration of SCFAs can alleviate depression-like behaviors in mice, and the possible mechanisms include promoting microbial homeostasis, enhancing intestinal mucosal barrier, immunomodulation and neuroprotection [12].

As a special clinical subtype with severe symptoms and poor prognosis, the occurrence and development of MDD with suicidal ideation are also affected by the MGB axis. Maes et al. found that MDD patients with suicidal ideation had significantly different gut microbiota characteristics compared with healthy controls (HC). The beneficial bacteria such as Dorea and Faecalobacterium with anti-inflammatory and SCFAs-producing effects were reduced, the activity of circulating T cells was enhanced, and the levels of pro-inflammatory cytokines were increased. Such dysbiosis and inflammatory activation are specifically associated with suicidal ideation [13]. Furthermore, Maes et al. pointed out that MDD patients with adverse childhood experiences and recurrent episodes have unique enterotypic phenotypes, and one of them is significantly related to suicidal behavior [14]. Chen et al. and Xu et al. concurrently reported significant enrichment of Phascolarctobacterium, Erysipelotrichaceae_UCG-003, and Eubacterium_hallii_group in MDD patients with suicidal ideation, and these expansions were positively correlated with the intensity of suicidal ideation [15, 16]. Ohlsson et al. reported a significant increase in plasma intestinal fatty acid binding protein (I-FABP) coupled with a decrease in zonulin in patients with suicide attempts, implicating increased intestinal barrier permeability. Additionally, I-FABP levels showed a significant association with the severity of depressive symptoms and suicide attempts [17].

Previous studies have found that the diversity and composition of the gut microbiota in MDD patients with or without suicidal ideation show significant differences, but are limited by adverse childhood experiences or perinatal factors [14, 16]. Based on the above studies, we speculate that gut microbiota imbalance and systemic inflammation may play an important role in suicidal ideation in MDD patients. C-reactive protein (CRP) is a widely recognized and stable systemic inflammatory index in clinical practice. Its sensitivity and specificity for systemic inflammatory response have been fully verified, and it has a good correlation with the severity of depression [18]. Therefore, we aimed to identify the differences in gut microbiota characteristics and plasma levels of CRP and SCFAs among MDD patients with suicidal ideation, MDD patients without suicidal ideation, and HC, and further explore their potential role in the occurrence of suicidal ideation in MDD patients.

Methods

Participants

After excluding cases with incomplete data, 57 MDD patients and 57 HC who met the inclusion and exclusion criteria were finally included in the study (Fig. 1). All patients met the diagnostic criteria of MDD in the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM-V). The Hamilton Depression Scale-24 (HAMD-24) was used to measure the severity of depression [19]. The Beck Scale for Suicide Ideation-Chinese Version (BSI-CV) was used to assess the presence and intensity of suicidal ideation [20]. According to BSI-CV, MDD patients were divided into those without suicidal ideation (SC0 group) and those with suicidal ideation (SC1 group). All HC without any disease were recruited voluntarily from surrounding schools and communities through advertisements and online platforms. Subjects were excluded under the following conditions: (1) suffering from other mental disorders; (2) suffering from serious physical diseases or traumatic brain injury; (3) received systematic and standardized antidepressant treatment or physical therapy within the past 1 month; (4) pathological intestinal inflammatory changes, such as inflammatory bowel disease; (5) use of probiotics, antibiotics and other drugs that clearly affect intestinal flora within 3 months; (6) pregnant or lactating women.

Fig. 1.

The flow chart

Beck scale for suicide ideation-Chinese version assessment

The Beck Scale for Suicide Ideation-Chinese Version (BSI-CV) was used to assess suicidal ideation in patients with MDD. There were a total of 19 questions (BSI_19). The higher the score, the stronger the suicidal ideation. Among them, questions 4 and 5 respectively assess the subjects’ active and passive suicidal intentions. If both scores are 0, it means there is no suicidal ideation. Otherwise, it indicates suicidal ideation and further assessment is required for the remaining 14 questions. Suicidal ideation intensity (BSI_SI) and tendency (BSI_ST) were defined by the sum of the first five and the last 14 items, respectively. According to the scores of questions 4 and 5 of BSI-CV, MDD patients were divided into the group with suicidal ideation (SC1 = 33) and the group without suicidal ideation (SC0 = 24). It is worth stating that SC0 represents patients with no current suicidal ideation (as defined by BSI-CV items 4 and 5) but may still support other items related to wish to live/die.

Fecal sample collection and gut microbiota analysis

Fecal samples were collected from all subjects after they completed the general data collection and scale assessment. The entire sampling process is ensured to be free from contamination by urine and the surrounding environment. The samples were transported to a − 80 °C freezer within 30 minutes after collection and stored until further analysis. Fecal DNA was extracted using a Stool Mini Kit (QIAGEN, Hilden, Germany). The purity and integrity of the extracted DNA were assessed by 1% agarose gel electrophoresis. To amplify the V3–V4 hypervariable region of the bacterial 16 S rRNA gene, PCR was performed using the universal primers 338F (5′-ACTCCTACGGGAGGCAGCAG-3′) and 806 R (5′-GGACTACHVGGGTWTCTAAT-3′). Sequencing libraries were constructed and amplified on the Illumina NovaSeq PE250 platform (Personal Biotechnology Co., Ltd., Shanghai, China). Bioinformatic analysis of the microbiota was conducted using QIIME2 (version: 2019.1). After demultiplexing, primer trimming, quality filtering, denoising, merging, and chimera removal, the sequences were clustered into amplicon sequence variants (ASVs) based on 100% sequence similarity. An ASV table was generated to record the abundance and taxonomic classification of ASVs in each sample. After data preprocessing, all samples had a median sequencing depth of 53,650 reads (range: 35,264–93,284 reads). Prior to diversity analysis, all samples were uniformly rarefied to 95% of the minimum sample read count to ensure analyses were based on consistent sequencing depth. The alpha diversity index can reflect the diversity within a habitat. QIIME2 (version: 2019.1) was used to calculate indices such as the Chao1, Faith’s PD, Good’s coverage, Shannon, Simpson, Pielou’s evenness, and Observed species. The beta diversity index reflects the diversity of species composition among different habitat communities. The Principal Coordinates Analysis (PCoA) was conducted based on Bray-Curtis distance matrix. To quantify the statistical significance of the differences between groups, Permutational Multivariate Analysis of Variance (PERMANOVA) was performed on the same distance matrix with 999 permutations. Linear discriminant analysis effect size (LEfSe) was applied to identify differentially abundant microbial taxa among the SC0, SC1, and HC groups (LDA score threshold was set to 3.4). To reduce the influence of rare taxa and low-abundance noise, we filtered out ASVs with a maximum relative abundance below 0.001% across all samples and observed in less than 10% of the samples prior to LEfSe analysis. To control for the potential confounding effect of depression severity on gut microbiota differences between the SC0 and SC1 groups, we performed an exploratory analysis using 1:1 nearest neighbor matching on the HAMD scores with a tolerance of < 3 points. Subsequent analyses on this matched subset were conducted using the Wilcoxon signed rank Test. The post-hoc pairwise comparisons in this analysis were adjusted for false discovery rate (FDR) using the Benjamini-Hochberg method, with a FDR-corrected p < 0.05 set as the significance threshold.

Blood sample collection and metabolite analysis

From all participants, 10 ml of fasting venous blood was drawn and centrifuged at 3,500 rpm for 10 min to isolate plasma. The supernatant was aliquoted and stored at −80 °C. Subsequent analysis quantified SCFAs via targeted gas chromatography-mass spectrometry (GC-MS) and inflammatory factors via enzyme-linked immunosorbent assay (ELISA). ELISA: (1) Thaw the plasma sample naturally at room temperature. (2) Set blank holes, standard holes and sample holes. Add the samples or standards of different concentrations to each hole at a rate of 50 μl respectively, leaving the blank holes empty. (3) Add 100 μl of detection antibody labeled with horseradish peroxidase (HRP) to each standard and sample well, cover with a sealing film, and incubate at 37 °C for 60 minutes. (4) Discard the liquid, dry on absorbent paper, add washing solution (350 μl) to each hole, incubate for 1 minute, discard the washing solution, dry on absorbent paper, and repeat this washing step 5 times. (5) Add 50 μl of substrate chromogen A and B to each hole and incubate at 37 °C in the dark for 15 minutes. (6) Add 50 μl of stop solution to each hole to terminate the reaction. The OD values of each holel were measured at a wavelength of 450 nm within 15 minutes. (7) Plot the standard curve with the concentration of the standard substance as the X-axis and the corresponding OD value as the Y-axis. Linear regression analysis was conducted on the standard curve, and the concentration values of each sample were calculated based on the curve equation. Plasma concentrations of SCFAs were determined by gas GC-MS. A series of standard solutions were prepared for calibration, and 4-methylvaleric acid was used as an internal standard. For sample preparation, plasma was acidified with phosphoric acid, spiked with the internal standard, and extracted with diethyl ether. The extracts were analyzed on a Thermo Trace 1310 GC system coupled to an ISQ LT mass spectrometer (Thermo Fisher Scientific, USA), using an HP-INNOWAX capillary column (30 m × 0.25 mm ID × 0.25 μm). Injection was performed in split mode (10:1) at 250 °C with helium as carrier gas (1.0 ml/min). The oven program started at 90 °C, ramped to 120 °C at 10℃/min, then to 150 °C at 5℃/min, and finally to 250 °C at 25℃/min (held for 2 min). The transfer line and ion source were set at 250 °C and 300 °C, respectively. Mass detection was carried out in selected ion monitoring mode under electron impact ionization (70 eV). Quantification was performed using the internal standard method with 4-methylvaleric acid as the internal standard. A calibration curve was constructed for each analyte using a series of standard solutions, and all concentrations were calculated accordingly and are expressed as μg/ml.

Statistical analyses

Statistical analysis was performed using R Studio (2025.09.02). Demographic and clinical characteristics were compared between the two groups (MDD vs. HC) and across the three groups (SC0, SC1, and HC). Categorical variables including gender and history of suicide attempts were analyzed using the chi-square test. Fisher’s exact test was employed when the assumptions of the chi-square test were not met. Continuous data with normal distribution and homogeneous variance, such as body mass index (BMI), are presented as mean ± standard deviation (SD). The Student’s t test was used for comparison between two groups, and one-way analysis of variance (ANOVA) was used for comparison among three groups. Continuous data with skewed distribution, including age, years of education, HAMD_24 scores, BSI_SI scores, BSI_ST scores, BSI-19 scores, SCFAs levels, and CRP levels are expressed as median (interquartile range) [M (Q1, Q3)]. Inter-group differences for age, years of education, HAMD-24 scores, BSI_SI scores, BSI_ST scores, and BSI-19 scores were compared using the Mann-Whitney U test between two groups and the Kruskal-Wallis test among three groups, with the Bonferroni correction applied for post-hoc pairwise comparisons (P_adj). For plasma CRP and SCFAs levels, group differences were assessed using rank-based analysis of covariance (ANCOVA), with gender, age, years of education, and BMI included as covariates. The effect sizes were estimated using partial η². For significant multi-group comparisons (p < 0.05), post hoc pairwise tests based on adjusted marginal means were performed with the Benjamini-Hochberg correction (FDR-corrected P). The effect size for pairwise comparisons was quantified using an adjusted Cohen’s D. It should be noted that this metric is calculated on the rank-transformed data and it reflects the standardized difference in rank positions between groups, rather than the difference in original measurements. Spearman’s correlation analysis was performed to assess the relationships among gut microbiota, depressive symptoms, suicidal ideation intensity, and plasma levels of CRP and SCFAs. Mediation analysis was conducted using Model 4 of the PROCESS (version 3.5), sampling was repeated 5000 times to estimate confidence intervals for indirect effects using the bias-corrected percentile Bootstrap method. These two analyses are based on Zscore normalized data. Statistical significance was defined as p < 0.05. The threshold for significance was set at an adjusted p < 0.05 for post-hoc pairwise comparisons that used either Bonferroni or Benjamini-Hochberg correction.

Results

Demographic and clinical characteristics among the three groups

The demographic and clinical characteristics of MDD and HC groups are summarized in Table 1. No significant differences were observed between the two groups in terms of gender, age, years of education, or BMI. Compared to the HC group, the MDD group showed significantly higher scores on the HAMD and BSI scales, as well as significantly higher CRP levels (p < 0.001). In contrast, SCFA levels were significantly lower in the MDD group (p < 0.01). The demographic and clinical characteristics of the SC0, SC1, and HC groups and the post hoc comparisons for inter-group differences are presented in Tables 2 and 3. No significant differences were found in gender, age, years of education, or BMI among the groups. HAMD scores were significantly higher in both MDD groups (SC0 and SC1) compared to the HC group (P_adj < 0.001), with no significant difference between the SC0 and SC1 groups. Furthermore, the BSI scores in the SC1 group were significantly higher than those in both the HC and SC0 groups (P_adj < 0.001). Patients in the SC1 group were significantly more likely to have a history of suicide attempts than those in the SC0 group (p < 0.05), reflecting more severe symptomatology and higher risk. CRP levels were significantly higher in both MDD groups (SC0 and SC1) compared to the HC group (FDR-corrected p < 0.001), with no significant difference between the SC0 and SC1 groups. For SCFAs, acetic acid and propionic acid levels in the SC1 and SC0 groups were significantly lower than those in the HC group (FDR-corrected p < 0.01), and butyric acid in the SC1 group was significantly lower than that in the HC group (FDR-corrected p < 0.05), with no significant differences in other group comparisons.

Table 1.

Demographic and clinical characteristics of the MDD and HC groups

Variable	MDD (n = 57)	HC (n = 57)	χ2/Z/t/F	P	Partial η2
Gender (M/F)	16/41	13/44	0.416	0.519a	-
Age (years)	21.00(19.00,29.00)	23.00(21.50,24.00)	1.38	0.169b	-
Years of education	13.00(10.50,16.00)	13.00(11.00,15.00)	0.16	0.877b	-
BMI (kg/m2)	20.97 ± 2.80	21.44 ± 2.65	0.93	0.353c	-
HAMD_24	26.00(22.00,31.00)	1.00(0.00,2.00)	9.25	<0.001b	-
BSI_SI	5.00(2.00,8.50)	0.00(0.00,0.50)	9.19	<0.001b	-
BSI_ST	6.00(0.00,11.00)	0.00(0.00,0.00)	6.66	<0.001b	-
BSI_19	11.00(2.00,19.50)	0.00(0.00,0.50)	9.19	<0.001b	-
Acetic acid (μg/ml)	1.00(0.62,1.45)	1.44(1.07,2.13)	22.42	<0.001d	0.172
Propionic acid (μg/ml)	0.13(0.09,0.20)	0.25(0.22,0.33)	53.12	<0.001d	0.330
Butyric acid (μg/ml)	0.03(0.02,0.05)	0.05(0.03,0.06)	8.49	<0.01d	0.073
CRP (ng/ml)	142.01(117.02,154.04)	107.46(87.68,121.53)	58.92	<0.001d	0.353

HC: healthy controls group; MDD: all MDD patients

a: p-value is χ² test

b: p-value from Mann-Whitney U test between two groups

c: p-value from Student’s t test between two groups

d: p-value from rank-based ANCOVA test

Table 2.

Demographic and clinical characteristics of the three groups

Variable	SC0 (n = 24)	SC1 (n = 33)	HC (n = 57)	F/χ2/H	P	Partial η2
Gender (M/F)	8/16	8/25	13/44	1.02	0.600a	-
Age (years)	22.00(20.00,29.25)	21.00(19.00,29.00)	23.00(21.50,24.00)	2.15	0.341b	-
Years of education	13.00(11.25,15.75)	12.00(9.00,16.00)	13.00(11.00,15.00)	1.23	0.541b	-
BMI (kg/m2)	20.72 ± 2.53	21.15 ± 3.01	21.44 ± 2.65	0.61	0.547c	-
History of suicide attempts (Y/N)	2/22	13/20	0/57	27.47	<0.001d
HAMD_24	22.50(18.00,28.25)	28.00(24.50,31.00)	1.00(0.00,2.00)	87.32	<0.001b	-
BSI_SI	2.00(2.00,3.00)	8.00(6.00,9.00)	0.00(0.00,0.50)	96.34	<0.001b	-
BSI_ST	0.00(0.00,0.00)	11.00(8.50,12.50)	0.00(0.00,0.00)	108.81	<0.001b	-
BSI_19	2.00(2.00,3.00)	19.00(15.00,21.00)	0.00(0.00,0.50)	97.17	<0.001b	-
Acetic acid (μg/ml)	1.00(0.62,1.49)	0.90(0.62,1.45)	1.44(1.07,2.13)	11.26	<0.001e	0.174
Propionic acid (μg/ml)	0.13(0.07,0.20)	0.12(0.10,0.20)	0.25(0.22,0.33)	26.46	<0.001e	0.331
Butyric acid (μg/ml)	0.03(0.02,0.05)	0.03(0.02,0.04)	0.05(0.03,0.06)	4.58	<0.05e	0.079
CRP (ng/ml)	139.64(112.70,156.42)	142.01(119.85,149.94)	107.46(87.68,121.53)	29.52	<0.001e	0.356

HC: healthy controls group; SC0: MDD without suicidal ideation; SC1: MDD with suicidal ideation

a: p-value from χ² test

b: p-value from Kruskai-Wallis H test among multiple groups

c: p-value from ANOVA test among multiple groups

d: p-value from Fisher’s exact test

e: p-value from rank-based ANCOVA test

Table 3.

Results of post hoc test among the three groups

Variable	SC0 vs HC			SC1 vs HC			SC0 vs SC1
	Z/t	r/d_adj	P	χ2/Z/t	r/d_adj	P	χ2/Z/t	r/d_adj	P
History of suicide attempts(Y/N)	-	-	-	26.25	-	<0.001a	6.91	-	<0.05a
HAMD_24	6.29	0.699	<0.001b	8.60	0.907	<0.001b	−1.31	−0.174	0.570b
BSI_SI	3.48	0.387	<0.001b	9.30	0.980	<0.001b	−4.43	−0.587	<0.001b
BSI_ST	0.00	0.000	1.000b	9.85	1.038	<0.001b	−8.03	−1.064	<0.001b
BSI_19	3.43	0.381	<0.001b	9.35	0.986	<0.001b	−4.51	−0.597	<0.001b
Acetic acid(μg/ml)	−3.29	−0.818	<0.01c	−4.30	−0.956	<0.001c	0.50	0.138	0.617c
Propionic acid(μg/ml)	−5.28	−1.311	<0.001c	−6.44	−1.433	<0.001c	0.44	0.122	0.659c
Butyric acid(μg/ml)	−1.69	−0.421	0.140c	−2.92	−0.650	<0.05c	0.83	0.228	0.408c
CRP(ng/ml)	5.44	1.352	<0.001c	6.89	1.533	<0.001c	−0.66	−0.181	0.512c

a: p-value from Fisher’s exact test

b: Bonferroni-corrected p-value from Mann-Whitney U test

c: FDR-corrected p-value from rank-based ANCOVA test

Gut microbiota diversity index among the three groups

Alpha diversity analysis revealed significant intergroup differences (Fig. 2). The Good’s coverage index was significantly higher in the HC group than in the SC0 group (FDR-corrected p < 0.05). The Pielou’s evenness (FDR-corrected p < 0.05) and Simpson indices (FDR-corrected p < 0.01) were both significantly higher in the HC group than in the SC1 group. Furthermore, the Simpson index was also significantly higher in the SC0 group than in the SC1 group (FDR-corrected p < 0.05). Beta diversity, based on the Bray-Curtis distance matrix, was performed to compare the microbial communities across the HC, SC0, and SC1 groups. PERMANOVA revealed a significant overall difference in microbial community structure among the groups (pseudo-F = 2.29, p < 0.01). Subsequent post-hoc pairwise comparisons demonstrated significant differences between SC0 and HC (pseudo-F = 2.53, FDR-corrected p < 0.01) and between SC1 and HC (pseudo-F = 2.94, FDR-corrected p < 0.01), whereas no significant difference was observed between SC0 and SC1 (pseudo-F = 0.94, FDR-corrected p = 0.565) (Fig. 3).

Fig. 2.

Alpha diversity of SC0, SC1 and HC groups (marked by blue, red, and green dots and areas, respectively). *FDR-corrected p < 0.05, **FDR-corrected p < 0.01

Fig. 3.

PCoA of beta diversity based on bray_curtis. Blue represents the SC0 group, red represents the SC1 group, and green represents the HC group. PERMANOVA revealed a significant difference in microbial community structure among the three groups (pseudo-F = 2.29, FDR-corrected p < 0.01)

Analysis of the signatures of gut microbiota

LEfSe analysis identified significant microbial differences among the three groups, with 13, 4, and 7 microbial taxa enriched in the HC, SC0, and SC1 groups, respectively (Fig. 4). At the family level, Bacteroidaceae was most abundant in the HC group, whereas Bifidobacteriaceae was predominant in the SC1 group. At the genus level, the HC group showed high relative abundances of Bacteroides, Roseburia, Dialister, Parabacteroides, and Sutterella; the SC0 group was characterized by Pilimelia and Haemophilus; and the SC1 group was enriched in Bifidobacterium, Blautia, and Dorea. Our exploratory analysis established a subset of 15 matched pairs (30 subjects in total) of SC1 and SC0 patients with highly comparable depression severity. We observed suggestive differences in three microbial taxa: Veillonellaceae (Cohen’s d = 0.33, p < 0.05) and Blautia (Cohen’s d = 0.40, p < 0.05) showed higher abundance in the SC1 group, while Haemophilus was more abundant in the SC0 group (Cohen’s d = −0.38, p < 0.05). However, none of the FDR-corrected p < 0.05.

Fig. 4.

The biomarkers of the SC0, SC1, and HC groups were found by the LEfSe analysis. Blue, red and green represent the enriched populations of the SC0, SC1, and HC groups, respectively. The p, c, o, f, and g before the microbial name represent the phylum, class, order, family, and genus, respectively. Only taxa with FDR-corrected p < 0.05 and a LDA score over 3.4 are shown

Relationship between gut microbiota and clinical parameters

We assessed the correlations between representative microbiota and clinical parameters, including CRP, SCFAs, and psychiatric scale scores (Fig. 5). Blautia demonstrated positive correlations with BSI scores, HAMD scores, and CRP levels. Conversely, Bacteroides, Roseburia, and Sutterella were negatively associated with BSI and HAMD scores, with Dialister and Haemophilus correlating negatively with CRP levels. Acetic, propionic, and butyric acids were inversely correlated with BSI and HAMD scores, in contrast to CRP, which was positively correlated with both.

Fig. 5.

Heatmap of the spearman correlation coefficient matrix between significantly different gut microbiota and HAMD scores, BSI scores, and plasma levels of CRP and SCFAs. Red indicates a positive correlation and blue indicates a negative correlation. *FDR-corrected p < 0.05, **FDR-corrected p < 0.01, ***FDR-corrected p < 0.001

Analysis of mediating effect

To investigate the potential mediating effect of CRP on the relationship between Blautia and suicidal ideation intensity, a mediation model was constructed using the Bootstrap sampling method (Fig. 6). In this model, Blautia was defined as the independent variable, the BSI scores as the dependent variable, and the CRP levels as the mediating variable. To establish a basis for mediation analysis, we first conducted partial correlation analyses. After adjusting for age, sex, BMI, and years of education, the associations among the three factors remained significant (p < 0.01). We also add these demographic variables as covariates in the mediation model to control for confounders. The analysis revealed that Blautia could significantly positively predict the CRP levels (a = 0.2740, p < 0.01), and the CRP levels could significantly positively predict the BSI scores (b = 0.3710, p < 0.001). The direct effect of Blautia on the BSI scores was still significant after controlling the CRP levels (c’ = 0.1885, p < 0.05). The indirect effect value of Blautia on the BSI scores through the CRP levels was 0.1012, Bootstrap SE = 0.0382, and its 95% Bootstrap confidence interval was [0.0412, 0.1929] (Table 4). It is worth noting that although there was no significant difference in HAMD scores between MDD patients with and without suicidal ideation, the mediating effect was still confounded by depression severity. When HAMD scores was included as a covariate, the previously observed mediating effect disappeared (indirect effect = 0.0012, p = 0.159).

Fig. 6.

Mediation models for Blautia, CRP levels, and BSI scores. The numbers in the figure represent the effect size. *p < 0.05, **p < 0.01, ***p < 0.001

Table 4.

Breakdown table of total, direct, indirect and mediating effects

Effect type	Effect	SE/Bootstrap SE	95% CI/Bootstrap CI	Proportion
Total effect	0.2889	0.0904	(0.1096, 0.4681)
Direct effect	0.1877	0.0875	(0.0142, 0.3612)	0.6497
Indirect effect	0.1012	0.0382a	(0.0412, 0.1929)b	0.3503

a: Bootstrap SE

b: Bootstrap CI

Discussion

As mentioned above, patients with MDD exhibit disrupted gut microbiota homeostasis, and this dysbiosis may contribute to the pathogenesis and progression of MDD through neural, immune, endocrine, and metabolic pathways. Our findings demonstrate a significant reduction in alpha diversity of the gut microbiota in MDD patients, as indicated by markedly lower values of Good’s coverage index, Pielou’s evenness, and the Simpson index compared to HC. Furthermore, beta diversity analysis revealed a clear separation in overall microbial community structure between MDD patients and HC, reinforcing the association between gut microbiota dysbiosis and MDD. In contrast, Han et al. reported no significant differences in beta diversity between MDD patients and HC [21]. Additionally, we observed that the plasma CRP levels were significantly elevated in MDD patients, whereas levels of acetic, propionic, and butyric acids were significantly decreased. CRP levels showed a significant positive correlation with depressive symptoms, while acetic, propionic, and butyric acids were negatively correlated with depressive symptoms. Although the plasma CRP levels of MDD patients did not meet the psychiatric definition of “low-grade inflammation” (CRP > 3 mg/L), the significant differences between the groups suggest that the MDD population may be in a higher inflammatory state than the healthy population as a whole. These results are consistent with previous reports, supporting the concept that MDD is not solely a mental disorder but also a systemic disorder linked to gut microbiota dysbiosis, altered metabolite profiles, and a pro-inflammatory state [22–24].

Our results suggest that gut microbiota disturbance in MDD may contribute to the disorder’s pathogenesis, potentially by triggering a systemic inflammatory state and reducing the production of beneficial metabolites such as SCFAs. A meta-analysis reported elevated levels of plasma inflammatory factors in MDD patients, which were significantly correlated with depressive symptoms, supporting the presence of a systemic inflammatory state in MDD [23]. Another study conducted in adolescent MDD patients revealed significantly increased intestinal mucosal permeability compared to HC [25]. Therefore, together with our findings, these results support the “leaky gut” hypothesis. This model proposes that gut microbiota dysbiosis in MDD patients impairs intestinal mucosal barrier function, thus allowing bacteria and their harmful metabolites to enter the bloodstream and induce a systemic inflammatory state [26]. Ortega et al. also found that the level of SCFAs in MDD patients was significantly lower than that in HC [27]. Acetic, propionic, and butyric acids, as major microbial metabolites, influence host metabolism and brain function through multiple mechanisms, including energy supply, maintenance of the intestinal barrier, involvement in lipid metabolism and neurotransmitter synthesis, and immune regulation [28]. We hypothesized that the changes in the composition and structure of the microbiota in MDD may reduce SCFAs production, which in turn compromises intestinal barrier function and exacerbates systemic inflammation. This inflammatory environment may further disrupt the balance of microbiota and inhibit the growth of beneficial bacteria, ultimately establishing a vicious cycle.

Among the various clinical phenotypes of MDD, patients with suicidal ideation often experience more severe depressive symptoms and a worse prognosis, making this subgroup a focus of our research [5]. To further explore gut microbiota differences among the three groups, we performed LEfSe analysis to identify the representative microbiota. The HC group showed a higher abundance of Bacteroides, Roseburia, Dialister, Parabacteroides, and Sutterella. Bacteroides and Roseburia are associated with higher production of SCFAs such as acetic, propionic, and butyric acids [29, 30]. As key microbial metabolites, SCFAs not only serve as an energy source for colonocytes but also modulate immune cell function through mechanisms such as inhibition of histone deacetylase (HDAC) and activation of G protein-coupled receptors (GPCRs), thereby suppressing inflammatory pathways and attenuating inflammatory responses [31]. The abundance of Pilimelia and Haemophilus was higher in the SC0 group. Haemophilus is an opportunistic pathogen that, under conditions of bacterial dysbiosis, can provoke intestinal inflammation and damage the intestinal mucosal barrier. Its overgrowth has been linked to inflammatory bowel diseases, intestinal tumors, and metabolic disorders [32–34]. Consistent with this, a study in patients with schizophrenia reported a positive correlation between Haemophilus abundance and both negative psychiatric symptoms and depressive symptoms [35]. The SC1 group had higher abundance of Bifidobacterium, Blautia, and Dorea. Lu et al. also found that Bifidobacterium was significantly increased in depression model mice, and caused hippocampal mitochondrial dysfunction by inducing an increase in the production of ceramide in the host, which further led to depression-like behavior [36]. At present, the results of studies on the changes in the abundance of Blautia in MDD patients are different. Liu et al. suggested that Blautia is a probiotic [37]. Consistent with our findings, Cao et al. also reported an increased abundance of Blautia in MDD patients [38]. These discrepancies may be attributable to species-level diversity within the genus, as different species can exert distinct biological effects. In our exploratory analyses, we observed a trend toward increased abundance of Veillonellaceae and Blautia in the SC1 group, while the Haemophilus appeared more abundant in the SC0 group (none met the threshold of FDR-corrected p < 0.05). These results tentatively suggest that, after controlling for depression severity, the differences in microbial composition between the SC0 and SC1 groups are no longer significant. Only a few gut microbial taxa may be associated with suicidal ideation. However, this conclusion is only preliminary and lacks statistical significance, thus requiring further validation. Given the limited matched sample size (N = 15 pairs), there is currently insufficient evidence to support these microbial features as biomarkers of suicidal ideation independent of depression severity. Future studies with larger samples and multi-omics data such as metabolomics are needed to better examine these potential associations. We further observed associations between these differential microbiota and clinical measures of depression and suicidal ideation. Bacteroides, Roseburia, and Sutterella were negatively correlated with both BSI and HAMD scores. In contrast, Blautia was positively correlated with BSI and HAMD scores. Supporting these findings, a study analyzing colonic fecal samples from deceased suicide cases also reported significantly elevated abundances of Blautia and Dorea, which were positively correlated with inflammation-related metabolic pathways, though that study did not include further evidence such as plasma metabolite data [39].

Blautia is an anaerobic bacterium that is widely found in the gut of mammals. Blautia has traditionally been regarded as a beneficial microbiota, but an increasing amount of evidence indicates that it specifically accumulates in various disease ecology, suggesting that the function of this genus may be species specific and dependent on the host microenvironmental state. Previous studies have considered that Blautia acts as a probiotic through the production of acetic acid and activation of GPR41/GPR43 receptors, leading to improved metabolism and reduced inflammation [40]. Richard et al. and Wei et al. similarly observed a reduced abundance of Blautia in patients with inflammatory bowel disease and colitis-associated cancer. They suggested that Blautia exerts anti-inflammatory effects and helps maintain intestinal mucosal integrity, and that its reduction may aggravate disease processes [41, 42]. However, recent research on Blautia has revealed its association with enhanced activity in pro-inflammatory pathways and upregulated endotoxin synthesis, indicating a potential pro-inflammatory role [43, 44]. Sheikh et al. transplanted fecal microbiota from patients with inflammatory bowel disease into germ-free mice and found that the inflammatory response of the mice was related to the continuous increase in Blautia abundance [45]. Accumulating evidence shows a higher abundance of Blautia in patients with MDD. Chung et al. reported an increase in Blautia abundance in MDD patients [46], while Zhang et al. demonstrated that Blautia may contribute to cognitive impairment in MDD through activation of the tryptophan-kynurenine metabolic pathway [44]. Furthermore, Kozhakhmetov et al. observed elevated Blautia levels in individuals who died by suicide, which positively correlated with inflammation-related metabolic pathways [39]. Based on the above findings, we speculate that Blautia may influence brain function through immune-inflammatory mechanisms, potentially promoting the development and progression of MDD and suicidal ideation. Therefore, we conducted a mediation analysis to explore the potential associations among Blautia, systemic inflammation, and suicidal ideation. When controlling for demographic variables, CRP played a significant partial mediating role between Blautia and suicidal ideation. However, after additionally controlling for HAMD scores, which did not differ significantly between the SC0 and SC1 groups, the previously observed mediation pathway became nonsignificant. This indicates that the potential effect of Blautia is mediated or confounded by depression severity, rather than representing an independent influence. MDD is closely linked to gut dysbiosis, systemic inflammation, and elevated suicide risk, depression severity itself constitutes a proximal and strong risk factor for suicidal ideation. The initial model may have captured shared variance among depression severity, Blautia, CRP, and suicidal ideation. Controlling for HAMD scores removed this shared effect, thereby leaving the potential independent contribution of Blautia undetectable in this analysis. The limited sample size in this study, especially after controlling for a strong covariate, resulted in low statistical power. Therefore, the present study does not support an independent pathway from Blautia to suicidal ideation that operates separately from depression severity. Future research using longitudinal or interventional designs with larger samples is needed to examine the causal role of Blautia and to determine whether its observed association with suicidal ideation is fully explained by depression severity.

This study characterized the gut microbiota and plasma metabolites in MDD patients with suicidal ideation and revealed an association between Blautia and the severity of suicidal ideation. However, several limitations should be acknowledged. First, the relatively small sample size and lenient multiple comparison correction strategy in microbiome analysis may increase the risk of type I and type II errors in detecting microbiota differences in MDD patients. Second, due to the cross-sectional design, the present results can only demonstrate correlations between gut microbiota, plasma metabolites, and suicidal ideation in MDD, rather than establish causality. Third, due to the possible presence of undetected subclinical suicidal ideation in the SC0 group of this study, together with the lack of collected information on dietary habits, smoking history, physical activity levels, and other metabolic diseases, the results may be affected by these confounding factors. Finally, with 16S rRNA sequencing offering only genus level resolution and no overall significant differences in β diversity, the relative abundance variations of certain microbial genera may not represent true biological differences, thus collectively constraining accurate interpretation of the microbiome-suicidal ideation association. Future research needs to increase sample size while using rigorous multiple comparison correction, adopt a longitudinal design, and employ metagenomic sequencing to more reliably reveal the association between gut microbiota and suicidal ideation in MDD patients.

Conclusion

This study exploratorily found that MDD patients with suicidal ideation showed preliminary characteristics in gut microbiota diversity, composition, and plasma levels of CRP and SCFAs, and that CRP may play a partial mediating role in the relationship between Blautia and suicidal ideation. These findings were confounded by depression severity, and the underlying mechanisms require further investigation.

Electronic supplementary material

Below is the link to the electronic supplementary material.

Abbreviations

MDD

Major depressive disorder

CRP

C-reactive protein

SCFAs

Short-chain fatty acids

HC

Healthy controls

HAMD-24

Hamilton Depression Scale-24

BSI-CV

Beck Scale for Suicide Ideation-Chinese Version

16S rRNA

16S ribosomal RNA

ELISA

Enzyme-linked immunosorbent assay

FDR

False discovery rate

GC-MS

Targeted gas chromatography-mass spectrometry

MGB

Microbiota-gut-brain

ENS

Enteric nervous system

CNS

Central nervous system

HPA

Hypothalamic-pituitary-adrenal

DSM-V

Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition

ASV

Amplicon sequence variant

PCoA

Principal Coordinates Analysis

LEfSe

Linear discriminant analysis effect size

BMI

Body mass index

ANOVA

One-way analysis of variance

SD

Standard deviation

HDAC

Histone deacetylase

GPCRs

G protein-coupled receptors

Author contributions

Ning Sun and Penghong Liu designed the experiment; Fengtao Guo, Aixia Zhang and Chunxia Yang collected the participants clinical data, blood samples, and stool samples; Jinrui Xing and Chuhan Qu were responsible for processing the specimens; Fei Li and Ziwei Jiang completed the supplementary clinical data collection, data analysis, and manuscript writing. All authors reviewed the manuscript.

Funding

This study was supported by the National Natural Science Foundation of China (82571754, 82471558), the Central Guiding Local Technology Development Funding Projects (YDZJSX2024B009), the Shanxi Youth Scientific Research Project (202303021222347). Shanxi Province Higher Education “Billion Project” Science and Technology Guidance Project, Key Laboratory of Cellular Physiology (Shanxi Medical University), Ministry of Education, China.

Data availability

The datasets used and analysed during the current study are available from the corresponding author on reasonable request.

Declarations

Ethics approval and consent to participate

This study was conducted in accordance with the Declaration of Helsinki, approved by the Medical Ethics Committee of the First Hospital of Shanxi Medical University (ethics number: K-K004). All participants signed a written informed consent after a detailed description of the research objectives and procedures.

Consent for publication

Not applicable.

Competing interests

The authors declare that they have no competing interests.