The dysbiosis of gut microbiota in major depressive disorder and comorbidity with overweight/obesity: unraveling biomarkers and metabolic pathways from a microbial perspective

Abstract

Background

Major depressive disorder (MDD), a leading global public health concern, exhibits high comorbidity with overweight/obesity. While emerging evidence implicates shared gut microbial dysregulation in both conditions, the identification of specific microbial biomarkers and their interaction with metabolic pathways in comorbid MDD-obesity remains poorly characterized. Our study investigated gut microbiota signatures in patients with MDD, and evaluated their modulation by overweight/obesity comorbidity.

Method

A total of 53 patients with MDD and 92 healthy controls (HCs) underwent 16 S rRNA gene sequencing. The KEGG function analysis was performed with potential differential microbiota. Correlation analysis related to bacterial taxa, biochemical parameters, and clinical indexes was aimed to indicate the specific metabolic dysregulation and gut microbiota imbalance in MDD, and whether these biomarkers were affected by the comorbidity of overweight/obesity.

Results

Sixty-three bacterial genera showed differential abundance between MDD and HCs. Notably, Succinivibrio, Megamonas, and Bifidobacterium demonstrate significant distinctions between overweight-MDD and normal weight-MDD. The significantly altered metabolic pathways between MDD and HC primarily involve the biosynthesis and metabolism of long-chain unsaturated fatty acids. Within the MDD group, BMI demonstrated pairwise negative correlations with Bifidobacterium and total bilirubin (TBIL), while positively associating with Fusobacterium and alanine transaminase (ALT).

Conclusion

This study provides novel insights into the interplay between gut microbiota dysbiosis and metabolic perturbations in MDD. Despite intriguing associations between BMI and specific microbial taxa within the MDD group, the preliminary nature of these associations necessitates validation through larger cohorts, multi-omics approaches, and longitudinal designs.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12888-025-07388-w.

Introduction

Major depressive disorder (MDD) is a common and debilitating mental illness that is characterized by persistent low mood, loss of interest, and impaired cognitive and physical functioning. Between 2005 and 2015, there was an 18.4% rise in the overall estimated count of individuals with MDD [1]. Due to the impact of the COVID-19 pandemic, the global number of MDD patients increased by 53 million in 2020 [2, 3]. MDD is also associated with elevated mortality risks, especially from suicide and cardiovascular diseases [4, 5].

One of the critical challenges in treating MDD is the high prevalence of comorbid illnesses, particularly obesity and metabolic disorders. Epidemiological evidence point out the potential bidirectional MDD-obesity associations [6, 7], mediated through complex multifactorial mechanisms, including but not limited to genetic, environmental, behavioral, and physiological factors. Gut microbiota dysbiosis is defined as an imbalance in the microbial communities of the gastrointestinal tract, which can impact the metabolism, immunology, and neuroendocrine control of the host [8]. This change may also upset the delicate balance of the host’s neurotransmitters (e.g., serotonin synthesis), systemic inflammation, and adipose tissue signaling [9, 10], thereby establishing a dynamic link between gastrointestinal and mental health [11, 12], and even exacerbating the relationship between MDD and overweight/obesity.

A review by Bear et al. and a randomized controlled study by Uemura et al. demonstrate that gut microbiota modulation has the potential both to alleviate depressive symptoms and to improve metabolic disorders [13, 14]. An experiment using mice elucidated the specific influence of unsaturated alginate oligosaccharides on obesity-related metabolic abnormalities, highlighting the potential of targeted therapies to alter the gut microbiota [15]. An investigation of the gut microbiome in human obesity provides critical insights to the discussion on the gut-brain axis and metabolic health by resolving whether changes in gut microbiota associated with obesity are due to compositional or functional changes [16]. Additionally, a retrospective cohort study of 1,914 Chinese individuals highlights diagnostic challenges in obesity-related metabolic disorders due to microbial heterogeneity [17]. [17]. Collectively, these studies provide robust evidence that gut microbiota modulation holds therapeutic potential not only for metabolic disorders but also for their psychiatric comorbidities, including depression and obesity. The gut-brain axis emerges as a critical bidirectional pathway warranting mechanistic investigation [18] and serving as a potentia targets for developing precision therapies to promote mental and metabolic health in mood disorders and related conditions [19]. As stated above, the interplay between gut microbiota and metabolic pathways is of particular relevance in MDD and obesity, where disruptions in metabolic homeostasis are hallmark features.

Extant research has delineated pathways underlying gut microbiota dysregulation in MDD and the additional impact of metabolic issues. Elevated levels of specific microbial communities in MDD patients are closely linked to alterations in neurotransmitter synthesis pathways such as serotonin and tryptophan [20]. Research targeting various neuropsychiatric disorders has suggested that changes in neuroactive molecules serve as intermediate pathways, participating in the impact of host genetics on the pathogenesis of MDD through the gut microbiota [21]. In addition to neurotransmitter pathways, immune pathways [22], and energy regulation-related metabolic pathways [23] also contribute to alterations in the gut microbiota in MDD patients. Additionally, obesity comorbidity may amplify these effects with gut microbiota in MDD patients via endocrine dysregulation, adipose tissue inflammation [24], and insulin resistance [25] pathways.

Currently, numerous studies have explored microbiota biomarkers in patients with MDD, yet the mechanistic relevance of these changes remains unclear. Our study aims to integrate specific alterations in the gut microbiota of MDD patients with functional annotation analyses to identify gut microbiota biomarkers associated with MDD. Additionally, we seek to investigate the distinct role of comorbid obesity in this context.

Method

Ethics statement

The authors assert that all procedures related to this work comply with the ethical standards of the relevant national and institutional committees on human experimentation guidelines and with the Helsinki Declaration of 1975, as revised in 2013. The study protocol received initial independent ethical review by the Medical Ethics Committee of The Ninth People’s Hospital of Chongqing (Approval no. IRB-2021-016) during the design phase. Subsequently, prior to participant recruitment and data collection, mandatory implementation approval was obtained from the Medical Ethics Committee of Zhumadian Second People’s Hospital (Approval no. 2021-006-02). Prior to data collection, each individual or their legal guardian was obtained and signed the informed consent.

Participants

A total of 53 patients with MDD and 92 healthy controls (HCs) were recruited from Zhumadian Second People’s Hospital, Henan Province. Diagnoses of MDD patients were confirmed through clinical assessment by trained psychiatrists using the Diagnostic and Statistical Manual of Mental Disorders, 5th Edition (DSM-5), followed by structured verification of active major depressive episode (MDE) status using the Mini International Neuropsychiatric Interview (MINI 5.0). All MDD patients had a Hamilton Depression Scale (HAMD)−24 score of no less than 20.

Inclusion criteria for both groups included: (1) aged 18–60 years; (2) primary school education or higher; (3) no antipsychotic medication use within one month prior to enrollment; (4) absence of visual, hearing, or intellectual disabilities. HC eligibility requirements additionally included: (1) normal test results on routine laboratory tests (blood, urine, stool), hepatic/renal function panels, fasting blood glucose, chest X-ray, and electrocardiogram; (2) demographic matching (age, sex, residence) with MDD group; (3) no history of DSM-5 defined mental illness, and a HAMD-24 score of less than 8. Exclusion criteria comprised: (1) diagnosis of organic brain disease or neurological disorders; (2) history of gastrointestinal diseases; (4) history of substance abuse, such as alcohol and tobacco; (5) current pregnancy or breastfeeding women; (6) yogurt consumption within 7 h before sampling.

Basic and clinical information collection

Demographic and clinical characteristics of participants were systematically collected, including but not limited to age, sex, height, weight, marital status, drinking and smoking history. Depression symptoms were measured with HAMD-24, which is a clinical relevance scale with quantifies depression severity across 24 items (e.g., mood, guilt, insomnia). Higher scores of HAMD-24 reflect greater depressive symptom burden, and total score ≥ 20 indicates moderate-severe depression. Anxiety symptoms was measured by the Generalized Anxiety Disorder Scale (GAD-7), which screens anxiety severity via 7-item Likert scale (0–21 range), with a higher score indicating a greater severity of self-reported anxiety symptoms [26]. The presence of anhedonia was collected by Snaith-Hamilton Pleasure Scale (SHAPS). It assesses anhedonia through 14-item self-report (e.g., food, hobbies, social enjoyment). Higher scores of SHAPS denote reduced capacity to experience pleasure [27]. The Clinical Global Impression-Severity (CGI-S) scale (1–7 points) assesses baseline illness severity, where higher scores indicate greater symptom burden. The Clinical Global Impression-Improvement (CGI-I) scale (1–7 points) evaluates post-treatment symptom change relative to baseline, with lower scores denoting greater improvement. General adiposity was defined based on BMI using cut-off points according to the guidelines for prevention and control of overweight and obesity in Chinese adults. Overweight is defined as a body mass index (BMI) of 24 kg/m² or higher.

Stool and plasma sample collection and detection

A total of 53 blood samples were collected from individuals with MDD. Venous blood was obtained during clinical examination visits and transferred into anticoagulant-treated tubes, followed by gentle inversion to ensure homogenous mixing. Samples were centrifuged at 3,000 rpm for 10 min to isolate plasma. The supernatant was aliquoted into Eppendorf tubes and stored at −80 °C.

Fecal samples were collected from all participants after questionnaire completion and immediately frozen at −80 °C. The MicroLocker YM-F02A system was utilized for standardized fecal sample collection and short-term ambient temperature storage prior to freezing.

DNA extraction and sequencing

The DNA extraction procedures of a total of 145 fecal samples were as follows. We retrieved 500 ug portions of each stool sample. The DNA was then purified using the TruSeqTM DNA Sample Prep Kit (Illumina). After extraction, the quality of the extracted DNA was assessed using 1% agarose gel electrophoresis. Subsequently, PCR amplification was performed using TransStart Fastpfu DNA Polymerase, followed by the construction of an Illumina library for sequencing.

After sample splitting, the paired-end (PE) reads obtained from Illumina sequencing were subjected to the following steps. Both ends of the reads underwent quality control and filtering based on sequencing quality. Low-quality reads are removed. The overlapping regions between paired-end reads were identified, and the reads were concatenated (merged) to obtain optimized data after quality control. The processed and optimized data were finally obtained using sequence denoising methods (DADA2), resulting in amplicon Amplicon sequence variants (ASVs) and their corresponding abundance.

Statistical analysis

General characteristics analysis

The general characteristics analysis was conducted using SPSS 28.0 (IBM Corp., Armonk, NY, USA). Continuous variables were described as mean ± standard error, and categorical variables as percentages. Differences in continuous variables were assessed using independent-samples t-test, while gender differences were evaluated with chi-square test.

Community diversity and taxonomic analysis

α-Diversity was calculated using the Shannon index, with group differences analyzed via Mann-Whitney U test. β-Diversity was computed using Bray-Curtis dissimilarity and visualized through principal coordinate analysis (PCoA). Multivariate analysis of variance was employed to examine inter-group differences. The MDD group was stratified into mdd-ow (overweight) and mdd-nw (normal weight) based on BMI. Taxonomic annotation of ASVs was performed, followed by abundance quantification per sample. The Mann-Whitney U test was performed to access the differential bacterial species at the genus level. This test was performed between MDD group and HC group as well as between mdd-ow group and mdd-nw group.

Correlation network analysis and KEGG function analysis

Spearman’s rank correlation coefficients were calculated among bacterial species, clinical data and biochemical indicators. Only when the correlation between two factors reached 0.2, a corresponding edge was established between the two nodes. Based on rules mentioned above, we created a network graph where each variable was represented as a node, and significant correlations were depicted as an edge connecting the nodes. Network visualization was implemented using ggplot2 package in R 4.3.1. KEGG pathway annotation was performed, with MDD-HC differences assessed via Mann-Whitney U test. All diversity, taxonomic, and functional analyses were conducted on Majorbio Cloud Platform (cloud.majorbio.com).

Results

Demographic and clinical characteristics

Demographic and clinical characteristics are presented in Table 1. No significant differences were observed between the two groups in age (p = 0.42) or BMI (p = 0.07). However, the difference in gender between the two groups has reached a significant level (p = 0.04).

Table 1.

Descriptive data of included subjects in the study

Variable	MDD	HC	p-value
Subject, n	53	92
Age, years, Mean (S.D.)	41.08 (15.68)	42.17 (6.51)	0.42
Gender, F/M	29/24	65/27	0.04
BMI, kg/m2, Mean (S.D.)	22.82(4.20)	23.96(2.14)	0.07
Overweight (BMI ≥ 24 kg/m2), n (%)	19 (36%)	42 (46%)
Normal weight (BMI < 24 kg/m2), n (%)	34 (64%)	50 (54%)
Clinical characteristics
Frist episode, yes/no	26/27
SHAPS, Mean (S.D.)	37.51(4.91)	18.33(3.56)	< 0.0001
GAD-7, Mean (S.D.)	13.04(3.36)	0.55(0.94)	< 0.0001
CGI-S, Mean (S.D.)	5.94(0.54)	NA	NA
CGI-I, Mean (S.D.)	3.51(1.14)	NA	NA
HAMD, Mean (S.D.)	26.17(6.17)	2.11(1.62)	< 0.0001

MDD Major depression disorder, HC Health control, S.D Standard deviation, BMI Body mass index, SHAPS, Snaith-Hamilton Pleasure Scale, GAD-7 Generalized Anxiety Disorder-7, CGI-S Clinical Global Impression-severity of illness, CGI-I Clinical Global Impression-global improvement, HAMD Hamilton’s Depression Scale

Microbiome community profiling

The α-diversity and β-diversity

The results of α-diversity between the MDD group and HC group is presented in Fig. 1a. The α-diversity between the mdd-ow group and mdd-nw group is visualized in Fig. 1b. Both results failed to reach the statistically significant threshold (Mann–Whitney U test).

Fig. 1.

Results of Community Diversity. a The histogram of Shannon index in MDD group and HC group; b mdd_ow group and mdd_nw group; c, d β-diversity analysis with principal coordinate analysis (PCoA) based on Bray-Curtis distance and permutational multivariate analysis of variance (PERMANOVA). Both comparisons in a and b did not reach statistical significance (Mann–Whitney U test). A significant separation was observed between MDD and HC groups in c (PCoA analysis; R² = 0.0411; F = 6.9788; p = 0.001.) The difference between mdd_ow group and mdd_nw group d did not reach the statistically significant threshold (Mann–Whitney U test). Note: Error bars in panels a and b represent standard errors.

The β-diversity results are shown in Fig. 1c and d. A significant difference was observed between the MDD and HC groups (PCoA analysis; R² = 0.04; F = 6.98; p < 0.01.) The difference between mdd_ow group and mdd_nw group did not reach the statistically significant threshold (Mann–Whitney U test).

Taxonomic results

Following diversity analysis, abundance changes of identified taxonomic units are reported in Fig. 2. In the MDD vs. HC comparison,, 61 species showed statistically significant abundance differences (Supplementary Table 1). After FDR correction, 58 species remained significant (FDR-corrected p-value < 0.05). Figure 2a displays the top 10 most abundant species from Supplementary Table 1. We excluded Prevotella due to its high abundance to manifest other species in the plot. Wilcoxon tests revealed significant decreases in Alloprevotella (p = 0.003), Subdoligranulum (p = 0.022), and Klebsiella (p = 0.026) in the MDD group versus HC, while Bacteroides, Escherichia-Shigella, and Bifidobacterium were elevated (p < 0.001). Succinivibrio also increased in MDD (p = 0.022).

Fig. 2.

Taxonomic analysis results. a Genus-level bar plot comparing the MDD and HC groups using Wilcoxon rank-sum test (after excluding Prevotella for visualization clarity); bTGenus-level bar plot comparing the mdd-ow group and mdd-nw group using Wilcoxon rank-sum test. Note: Error bars in panel (b) represent standard errors

The microbial composition at the genus level exhibited significant differences between the mdd-ow and mdd-nw groups, encompassing 11 distinct genera (Supplementary Table 2). Seven genera showed significant differences (Fig. 2b), with increased abundance of Megamonas (p = 0.047), Succinivibrio (p = 0.043), Paracoccus (p = 0.019), and Sphingobacterium (p = 0.019) in the mdd-ow group, whereas Bifidobacterium (p = 0.014), Cloacibacillus (p = 0.016), and Holdemania (p = 0.016) were reduced. It is noteworthy that Shuttleworthia was exclusively present in the mdd-ow group.

In exploring common microbial signatures shared between depression and obesity, we identified four genera as overlapping differential taxa in both the MDD and Overweight groups. Among these, only three genera exhibited consistent directional changes in both groups (Supplementary Tables 1 and 2). These genera included Succinivibrio, g__norank_f__Selenomonadaceae and g__unclassified_o__Saccharimonadales, suggesting a potential shared microbial profile associated with the co-occurrence of MDD and overweight.

Correlation between bacteria, biochemical and clinical indexes

Due to the absence of biochemical data from a healthy control group, our study sample was limited to 53 participants from the MDD group. No significant pairwise correlations were detected among depression severity (HAMD scores), biochemical parameters, and microbial taxa in the circular interaction network (Fig. 3 and Supplemental Table 3).

Fig. 3.

Circular interaction network of microbial-clinical-biochemical correlations. Node color codes: green = bacterial taxa, blue = clinical parameters, purple = biochemical indices. Legend terms: “cluster” = node classification; “linesize” = edge thickness scaled to r; “survival-impact” = minimum correlation coefficient per edge; “class” = p-value categories. Note: We assigned the line thickness to the correlation coefficient between the two variables at both ends of each line and named it “linesize”. To make each line clearer and more observable, we multiplied this assignment by three times for display

BMI demonstrated significant negative correlations with Bifidobacterium (r = −0.33, p < 0.001) and total bilirubin (TBIL, r = −0.33, p < 0.001), and positive correlations with Fusobacterium (r = 0.35, p = 0.01) and alanine transaminase (ALT, r = 0.35, p < 0.001). This result highlights potential links between overweight/obesity and specific microbial taxa.

In the clinical parameters, we only observed a positive correlation between DBIL and depression severity. Furthermore, the depression severity demonstrated positive associations with the abundance of Romboutsia and Collinsella genera, while showing a negative correlation with the abundance of Lachnoclostridium genus.

Functional prediction analysis

According to the KEGG functional annotation analysis, notable differences in MetaCyc metabolic pathways were observed between the MDD and HC groups (Supplementary Fig. 1). To identify key metabolic pathway differences capable of distinguishing MDD patients, we performed Mann-Whitney U tests and selected pathways with p < 0.001 alongside the top 7 expression values (Fig. 4).

Fig. 4.

Result of functional prediction. The histogram of function abundance associated with MDD group and HC group. Note: All the error bars represent the standard deviations

We identified significant alterations in two carbohydrate metabolism pathways and five lipid metabolism pathways between the MDD and HC groups. Specifically, pathways such as mannose and heptose biosynthesis, mycolate biosynthesis, (5Z)-dodecenoate biosynthesis I, palmitoleate biosynthesis I, stearate biosynthesis II, and oleate biosynthesis IV showed a significant decrease in expression in the MDD group. Additionally, the peptidoglycan maturation pathway exhibited a significant increase in expression in the MDD group. These findings underscore the potential relevance of specific metabolic pathways in distinguishing individuals with MDD.

Discussion

We found significant differences in relevant microbial communities mainly characterized by fatty acid metabolism between MDD patients and healthy controls. Notably, we observed associations between gut dysbiosis, biochemical alterations, and overweight status in a MDD subgroup.

Our study observed significant changes in the microbial composition of individuals with MDD, including the presence of genera such as Prevotella, Bacteroides, Escherichia-Shigella, and Bifidobacterium, which aligns with previous research on the gut microbiota in anxiety and depression [28, 29]. We also found a dysregulation in fatty acid pathways in the MDD group. Both short-chain fatty acids (SCFAs) and long-chain polyunsaturated fatty acids [30, 31] may modulate inflammatory processes in depression and obesity. All of the potential gut microbiota biomarkers showed SCFA-related metabolic linkages, supporting the well-documented reduction of SCFA-producing bacteria in depression and anxiety [28].

Experimental evidence in animal models suggests that the antibiotic rifaximin can improve fatty acid metabolism and depressive-like behavior in mice by altering the proportion of SCFA-producing bacteria [32]. High dietary fiber intervention could positively correlate with increased Lactobacillus and SCFA levels alongside improvement in depressive-like behavior [33]. These findings may collectively implicate that fatty acid metabolism plays a crucial role in the gut microbiota biomarkers of MDD patients and the course of depression [34]. Thus, improving the proportion of SCFA-producing bacteria may have potential effect to prevent the onset of depression. Supplementation with probiotics such as Lactobacillus and Bifidobacterium, as well as intake of short-chain fatty acids, may also helpful to alter the composition of gut microbiota and exert therapeutic effects on psychological disorders such as depression and anxiety [35–38].The potential causal mechanisms and therapeutic strategy require longitudinal validation.

In MDD-overweight comorbidity, 7 taxa differentiated subgroups statistically, including enriched Succinivibrio, Megamonas, Paracoccus and Sphingobacterium; and a decrease in Bifidobacterium, Holdemania and Cloacibacillus. A separate study shows MDD patients’ BMI exhibits negative pairwise correlations within between Bifidobacterium and TBIL. Bifidobacterium has been found to be negatively correlated with obesity and the severity of depression in both human and animal studies [39, 40]. Increasing the intake of Bifidobacterium may also reduce the severity of depression and obesity by improving the composition of gut microbiota [41].

Several previously studies have reported an association between total bilirubin and mental disorders, indicating a correlation with relevant gut microbiota [42, 43]. Additionally, its intervention effects extend to mitigating obesity induced by a high-fat diet through the modulation of short-chain fatty acid metabolism [44–46]. Moreover, animal experiments, such as the study by Alam et al., demonstrate that probiotics containing Bifidobacterium, in combination with local herbs, can improve bilirubin levels and induce weight loss through satiety effects [47].

A noteworthy finding in our study was the marked increase in the abundance of Succinivibrio, a genus not widely reported in previous MDD studies but previously linked to schizophrenia in several Chinese case-control studies [48, 49]. Greater severity of schizophrenia symptoms was also positively correlated with the abundance of the genus Succinivibrio [50]. Wang et al. further demonstrated in a Chinese cohort that in individuals with schizophrenia, type 2 diabetes may affect the composition of their gut microbiota [51], thereby reinforcing the association of gut microbiota with schizophrenia and its potential role in metabolic comorbidity. Furthermore, our investigation also revealed a statistical increase in Succinivibrio in the mdd-ow population, extending its potential disease associations. Beyond its recognized role as a producer of SCFAs, Succinivibrio abundance was negatively associated with oxylipins, the oxidation products of polyunsaturated fatty acids(PUFAs) [52, 53]. Succinivibrio may influence PUFA biosynthesis and oxidation pathways in mental health and comorbidity with overweight/obesity. Our findings and previous evidence suggest potential involvement of Succinivibrio in lipid metabolism regulation, highlighting its context-dependent biological effects across metabolic and neuropsychiatric conditions. Furthermore, future studies evaluating Succinivibrio as a potential biomarker for psychiatric disorders should also investigate whether its associations differ across distinct diagnostic categories (e.g., major depressive disorder, schizophrenia, and bipolar disorder).

Our findings indicate that BMI in patients with MDD exhibited positive pairwise correlations with Fusobacterium and ALT. Consistent with prior reports, elevated Fusobacterium has been documented in patients with liver and bile diseases as well as in obese populations [54–56]. Although the sequence of metabolic alterations and changes in gut microbiota composition among obese individuals remains unclear, these modifications may contribute to the development of liver diseases and alterations in biomarkers such as secondary bile acids, bilirubin, ALT, and AST [45]. While our study did not assess bile acids due to limited data, existing evidence highlights bidirectional interactions between between liver and bile diseases and metabolic dysregulation [57, 58].

In light of the evidence at hand, it’s plausible that the Bifidobacterium and Succinivibrio genera may exert an influence on the of trajectory MDD and overweight/obesity. This could potentially occur through modulating the metabolic levels of short-chain fatty acids or bilirubin. As for the Fusobacterium genus, it might have a correlation with the BMI levels of overweight/obese individuals, possibly by interacting with liver biochemical indicators such as ALT and AST. Subsequent research could compare the dynamics of Bifidobacterium and Succinivibrio genera in MDD patients with vs. without obesity to validate comorbid-specific biomarkers, as well as explore targeted modulation of Fusobacterium-liver axis interventions. These differential microbial patterns may inform future precision nutrition strategies, such as dietary modifications targeting specific bacterial taxa or probiotic supplementation protocols tailored to metabolic subgroups.

It is imperative to acknowledge several limitations inherent in our study. Firstly, in our study, the total sample size was relatively small, particularly within subgroups (e.g., mdd-ow vs. mdd-nw), which may limit statistical power. Secondly, as a cross-sectional study, we are limited in our ability to establish causal relationships between gut microbiota variations and the observed metabolic markers. Statements suggesting therapeutic implications or pathophysiological mechanisms, such as those involving SCFA metabolism or bilirubin pathways, should be more cautious or supported with stronger evidence. Additionally, biochemical data are only available for the MDD group, precluding comparison with controls and limiting the interpretation of associations. Moreover, the overweight classification relied exclusively on BMI rather than other measures of central adiposity or metabolic dysfunction, which may not fully capture the metabolic diversity of this population. Lastly, short-term dietary intake, probiotic supplementation and longitudinal lifestyle patterns were not quantitatively evaluated, which may potentially confound microbiome observations. Longitudinal investigations are warranted to elucidate the dynamic interplay between gut microbiota composition and metabolic dysregulations over time.

Conclusions

Herein our findings reveal the interplay between gut microbiota dysbiosis and metabolic perturbations in MDD, with overweight-associated dysbiosis patterns observed in the MDD subgroup. While enterotype-biochemical stratification may inform future therapeutic development, the cross-sectional nature of this study precludes causal interpretations between dysbiosis and clinical outcomes. In keeping with mechanistically-informed target therapeutics, future research should prioritize longitudinal validation of MDD-overweitght/obesity enterotypes with larger cohorts, multi-omics approaches, and longitudinal designs or pre-clinical studies. Moreover, future research should attempt to identify whether a subpopulation of MDD exists on the basis of MDD and obesity and enterotype/biochemistry and whether this subgroup is likely to benefit from interventions that correct underlying the dysbiosis patterns.

Authors’ contributions

Zhan L and Cao B conceived and designed the study; Lu CX and Cao B collected the data and performed the statistical analysis; Lu CX, and Yan L contributed to the discussion; All authors have read and approved the final version of this article.

Funding

This work was sponsored by the National Natural Science Foundation of China (32300926). The funding agents had no role in the design and conduct of the study, including the collection, management, analysis, and interpretation of the data and the preparation, review, or approval of the manuscript.

Data availability

The data that support the findings of this study are available from the corresponding author upon reasonable request.

Declarations

Ethics approval and consent to participate

The authors assert that all procedures related to this work comply with the ethical standards of the relevant national and institutional committees on human experimentation guidelines and with the Helsinki Declaration of 1975, as revised in 2013. The study protocol received initial independent ethical review by the Medical Ethics Committee of The Ninth People’s Hospital of Chongqing (Approval no. IRB-2021-016) during the design phase. Subsequently, prior to participant recruitment and data collection, mandatory implementation approval was obtained from the Medical Ethics Committee of Zhumadian Second People’s Hospital (Approval no. 2021-006-02). Prior to data collection, each individual or their legal guardian was obtained and signed the informed consent.

Consent for publication

Written informed consent for publication was obtained from all participants.

Competing interests

Dr. Roger S. McIntyre has received research grant support from CIHR/GACD/National Natural Science Foundation of China (NSFC) and the Milken Institute; speaker/consultation fees from Lundbeck, Janssen, Alkermes, Neumora Therapeutics, Boehringer Ingelheim, Sage, Biogen, Mitsubishi Tanabe, Purdue, Pfizer, Otsuka, Takeda, Neurocrine, Neurawell, Sunovion, Bausch Health, Axsome, Novo Nordisk, Kris, Sanofi, Eisai, Intra-Cellular, NewBridge Pharmaceuticals, Viatris, Abbvie and Atai Life Sciences. Dr. S. Roger McIntyre is a CEO of Braxia Scientific Corp. Kayla M Teopiz has received fees from Braxia Scientific corp. All other authors have no conflicts of interest to declare.