Neurodevelopmental disorders (NDDs) have been linked to changes in the gut microbiome, but the exact nature of these alterations is not fully understood. This research seeks to explore the variations in both the diversity and composition of the gut microbiota in individuals diagnosed with NDDs.
Methods
We conducted a systematic literature search up to April 2025. Meta-analyses using STATA 16.0 evaluated alpha diversity, beta diversity, and relative abundance between individuals with NDDs and healthy controls.
Results
No significant alpha diversity differences were found between NDD and control groups. Beta diversity analysis revealed distinct microbial communities across autism spectrum disorder (ASD), attention-deficit/hyperactivity disorder (ADHD), and tic disorder (TD) subgroups. At the family level, NDDs showed increased Peptostreptococcaceae (SMD = 0.47; 95% CI: 0.05 to 0.90). Genus-level analysis demonstrated reduced Escherichia/Shigella (SMD = −0.39; 95% CI: −0.59 to −0.19) and Roseburia (SMD = −0.39; 95% CI: −0.78 to 0), alongside elevated Eubacterium (SMD = 0.33; 95% CI: 0.20–0.47) in NDDs.
Conclusion
This study highlights the complex changes in gut microbiota in NDDs, particularly significant differences at the beta diversity, family, and genus levels. However, the results are constrained by research heterogeneity and small sample sizes. To better elucidate these associations, larger, more standardized studies are required.
gut microbiotadysbiosisneurodevelopmental disordershumanssystematic review and meta-analysisThe author(s) declare that no financial support was received for the research and/or publication of this article.section-at-acceptanceMicroorganisms in Vertebrate Digestive SystemsIntroduction
Neurodevelopmental disorders (NDDs) comprise a group of conditions characterized by impaired brain development and function, leading to cognitive, emotional, and behavioral disturbances. Affecting approximately 10% of children worldwide (Dash et al., 2022; Morris-Rosendahl and Crocq, 2020; Thapar et al., 2017), NDDs represent a heterogeneous collection of conditions including autism spectrum disorder (ASD), attention-deficit/hyperactivity disorder (ADHD), tic disorders (TD), intellectual disability, and developmental coordination disorder (Lukens and Eyo, 2022; American Psychiatric Association, 2013). Their etiology involves complex interactions between genetic, environmental, and epigenetic factors (Lord et al., 2018; Dall’Aglio et al., 2018; Thapar et al., 2013). In recent years, the gut-brain axis has emerged as a key pathophysiological mechanism and research focus in NDDs.
The gut microbiota constitutes a complex ecosystem of microorganisms residing in the human gastrointestinal tract (Clarke et al., 2014). This community plays crucial roles in nutrient metabolism, bioactive compound production, and the regulation of host physiology, including immune function, neural activity, and intestinal barrier integrity (Cryan et al., 2019; De Theije et al., 2014; Borre et al., 2014). The gut-brain axis provides a bidirectional communication network between gut microbes and the central nervous system, enabling microbial influence on brain function and behavior (Chernikova et al., 2021; Goncalves et al., 2024). This connection is particularly relevant in NDDs, where patients frequently experience both gastrointestinal symptoms and neuropsychiatric manifestations.
ASD, among the most prevalent NDDs, demonstrates substantial gut microbiota alterations compared to neurotypical individuals. Multiple investigations have revealed distinct differences in microbial community structure and composition. Kang et al. (2013) reported reduced abundance of Prevotella and Coprococcus in children with ASD, particularly those presenting gastrointestinal symptoms. Bezawada et al. (2020) documented elevated levels of Clostridium and Desulfovibrio, which may promote neuroinflammation through toxin production. Some studies suggest that interventions such as prebiotics, probiotics, or fecal microbiota transplantation, which restore gut microbial balance, may lead to improvements in neurofunctional and behavioral symptoms (Martinez-Gonzalez and Andreo-Martinez, 2020; Tan et al., 2021; Song et al., 2022).
In ADHD, considerable gut microbiota differences relative to healthy controls have been consistently observed. These include reduced microbial diversity and altered taxonomic profiles, notably imbalances in the Firmicutes/Bacteroidetes ratio (Prehn-Kristensen et al., 2018). Increased Lactobacillus abundance has been associated with ADHD symptomatology, potentially through modulation of neurotransmitter systems (e.g., serotonin, dopamine) and gut-brain axis-mediated neuroinflammation (Bull-Larsen and Mohajeri, 2019; Aarts et al., 2017). Furthermore, Aresti-Sanz et al. (2021) demonstrated that ADHD medications such as methylphenidate can modify gut microbiota composition, adding complexity to microbiome study interpretations. Dietary interventions including vitamin D3 supplementation and probiotic administration have yielded modest symptomatic benefits, reinforcing the gut-brain connection in ADHD (Abhishek et al., 2024).
Although research is still limited, emerging evidence suggests that the gut microbiota may modulate the neurobiological mechanisms underlying TD. TD shows differences in gut microbiota composition compared to healthy controls (Xi et al., 2021; Wang et al., 2022). Dysbiosis may exacerbate neuroinflammation or affect dopamine signaling, potentially playing a role in TD pathogenesis (Altaib et al., 2021; Kanaan et al., 2017; Nikolaus et al., 2022; De Jong et al., 2016). Xi et al. (2021) identified increased Escherichia/Shigella in TD patients, possibly linked to dopamine dysregulation. Geng et al. (2023) reported elevated pro-inflammatory bacteria (e.g., Bacteroides), supporting the neuroinflammation hypothesis in TD. Many Tourette syndrome (TS) patients also experience gastrointestinal symptoms, further supporting the link between gut microbiota and TD.
Although previous studies have documented alterations in gut microbiota within individual NDDs, a quantitative meta-analysis that concurrently integrates cohorts from ASD, ADHD, and TD is still lacking. The present study was therefore designed to address this gap by systematically consolidating and statistically analyzing gut microbiome variations across these three major NDDs. Our objectives are to identify robust, clinically relevant microbial biomarkers and to provide informed recommendations for future research in this rapidly evolving field.
This study is registered with PROSPERO, https://www.crd.york.ac.uk/prospero/, under the ID number CRD42024585913.
MethodsSearch strategy
This systematic review and meta-analysis was conducted in accordance with PRISMA guidelines (Page et al., 2021). On April 8, 2025, we performed a comprehensive literature search across multiple databases including PubMed, Embase, Cochrane Library, Web of Science, Scopus, and PsycINFO. The complete search strategy is provided in Supplementary Table S1.
Inclusion and exclusion criteria
Eligibility for study inclusion was determined according to the following predefined parameters: (1) Case–control study design; (2) Study populations comprising patients with clinically diagnosed NDDs (ASD, ADHD, or TD); (3) Comparative analyses of gut microbiota composition between NDD patients and healthy controls; (4) Reporting of quantitative gut microbial diversity metrics (alpha- or beta-diversity indices) and/or relative abundance data; (5) Microbiota profiling using fecal samples. Studies were excluded according to the following criteria: (1) Animal model or in vitro studies; (2) Investigations analyzing non-fecal samples (e.g., blood, urine) or reporting only microbial metabolites without microbiota composition data; (3) Literature reviews, meta-analyses, case reports, conference abstracts, or editorials; (4) Non-English publications.
Data extraction
Two investigators (HY and AW) independently extracted data using a standardized form. Extracted information included publication details, participant demographics, clinical characteristics, and methodological parameters. We also documented whether studies accounted for dietary factors or the use of probiotics and antibiotics. Primary outcomes encompassed gut microbiota characteristics, including community-level alpha/beta diversity and taxonomic composition (from phylum to genus levels). Any discrepancies between reviewers were resolved through discussion with a third investigator (JY). Corresponding authors were contacted for additional data when necessary.
Study quality was assessed using the Newcastle-Ottawa Scale (NOS), which evaluates three domains (selection, comparability, exposure) across eight items. The maximum achievable scores were 4, 2, and 3 points per domain, respectively. Studies scoring ≥7 points were considered high quality.
Statistical analysis
Statistical analyses were conducted in STATA 16.0. Microbial community characteristics were evaluated through alpha-diversity, beta-diversity, and hierarchical taxonomic profiling (phylum to genus). Data transformation from medians (IQR) to means (SD) was performed using established computational methods.1
Numerical data were extracted from graphical representations using GetData Graph Digitizer software (version 2.26) and Adobe Acrobat’s measurement tool when required. Continuous variables were expressed as standardized mean differences (SMD) with 95% confidence intervals (CI) to evaluate effect sizes and between-study variability.
The I2 statistic was used to assess heterogeneity of effect sizes, with values categorized as low (25%), moderate (50%), or high (75%) heterogeneity. Sensitivity analyses were conducted to evaluate result robustness and identify potential sources of heterogeneity. Publication bias was assessed using Egger’s regression test and funnel plot inspection. Statistical significance was defined as p < 0.05 for all analyses.
ResultsSearch results
Our systematic search identified 7,059 records from multiple databases: PubMed (n = 2,628), Scopus (n = 1,516), Web of Science (n = 1,436), Embase (n = 750), PsycINFO (n = 498), and Cochrane Library (n = 231). Following removal of 2,538 duplicates, we screened 4,521 records based on title and abstract. Of these, 205 full-text articles were assessed for eligibility. We excluded 79 studies that did not assess gut microbiota, 22 lacking control groups, 31 with insufficient data, 18 review articles or meta-analyses, 7 utilizing non-fecal samples, and 3 focusing on non-target disorders (Figure 1). The final analysis included 45 case–control studies published between 2011 and 2025. Figure 1 illustrates the study selection process, and the PRISMA checklist is provided in Supplementary Table S2.
Flow diagram for selection of studies (PRISMA flow diagram).
Flowchart of the PRISMA method used in the study selection process. It begins with 7,059 records identified and no additional sources. After removing 2,538 duplicates, 4,521 records are screened. Out of these, 4,316 are excluded for various reasons such as irrelevance and intervention reviews. 205 full-text articles are assessed for eligibility, with 160 excluded. Reasons for exclusion include lack of data and inappropriate samples. Finally, 45 studies are included in both qualitative synthesis and meta-analysis.Characteristics of included studies
The 45 included studies comprised 2,767 NDD patients and 1,611 age-matched neurotypical controls. A pooled analysis of all participants revealed the following ranges across the individual studies: age, 2–33 years; male proportion, 37.5–100%; and BMI, 14.7–24.7 kg/m2 (Table 1). The majority of the research was carried out in China, representing 26 studies (57.8%) (Wang et al., 2022; Zhang et al., 2018; Sun et al., 2019; Ma et al., 2019; Niu et al., 2019; Zou et al., 2020; Ding et al., 2020; Chen et al., 2020; Cao et al., 2021; Wan et al., 2022; Ye et al., 2021; Huang et al., 2021; Chen et al., 2021; Ding et al., 2021; Chen et al., 2022; Deng et al., 2022; He et al., 2023; Wang et al., 2023; Zhao et al., 2023; Mendive Dubourdieu and Guerendiain, 2023; Pang et al., 2023; Yitik Tonkaz et al., 2023; Xu and Zhang, 2023; Li et al., 2023; Jiang et al., 2018; Wang et al., 2020; Wan et al., 2020; Bao et al., 2023). Italy (Strati et al., 2017; Coretti et al., 2018; Chiappori et al., 2022) and Thailand (Bhusri et al., 2025; Panpetch et al., 2024; Boonchooduang et al., 2025) each contributed 3 studies (6.7%). Spain (Plaza-Diaz et al., 2019; Richarte et al., 2021) and the Netherlands (Aarts et al., 2017; Szopinska-Tokov et al., 2020) provided 2 studies each (4.4%), with single studies from Australia (Wang et al., 2011), America (Kang et al., 2013), India (Pulikkan et al., 2018), Russia (Kovtun et al., 2020), Denmark (Bundgaard-Nielsen et al., 2023), Uruguay (Mendive Dubourdieu and Guerendiain, 2023), Turkey (Yitik Tonkaz et al., 2023), Germany (Prehn-Kristensen et al., 2018), and Israel (Steckler et al., 2024) (2.2% each).
Characteristics of the studies included in the meta-analysis.
Study
Disorder
Country
Definitionof disorder
SampleSize, n
Mean Age, years
Male, %
Mean BMI
DietaryAssessment
ProbioticsUsage
AntibioticsUsage
Sequencing
Diversity Assessments
Wang et al. (2011)
ASD
Australia
DSM-IV
P: 23HC: 9
P: 10.3HC: 9.5
P: 91.3HC: 44.4
–
–
–
–
qPCR
α: not measuredβ: not measured
Kang et al. (2013)
ASD
America
ADI-Revised, ADOS
P: 20HC: 20
P: 6.7HC: 8.3
P: 90.0HC: 85.0
–
YES
NO
–
Pyrosequencing
α: Chao1, Shannon, PDβ: UniFrac (weighted)
Strati et al. (2017)
ASD
Italy
DSM-5
P: 40HC: 40
P: 11.1HC: 9.2
P: 77.5HC: 70.0
–
YES
NO
NO
16S rRNAV3-V5
α: not measuredβ: UniFrac (weighted & unweighted), Bray-Curtis
ASD, autism spectrum disorder; ADHD, attention deficit hyperactivity disorder; TD, tic disorder; BMI body mass index; P, patient; HC healthy control; DSM, Diagnostic and Statistical Manual of Mental Disorders; ADI-Revised, Autism Diagnostics Interview – Revised; ADOS, Autism Diagnostics Observation Schedule; ICD, International Classification of Diseases; qPCR, quantitative polymerase chain reaction; PD, phylogenetic diversity; Observed sp., observed species; ACE, abundance-based coverage estimator; Inv. Simpson, inverse Simpson.
Microbiota analyses primarily focused on phylum, family, and genus levels, encompassing diverse bacterial taxa. Bacteroidetes and Firmicutes represented the most abundant phyla in children’s gut microbiota, followed by Actinobacteria. Substantial methodological variations in stool processing and composition analysis were observed across studies (detailed in Table 1; Supplementary Table S3). Dietary factors were evaluated in 17 studies (37.8%), while probiotic use was not reported in 16 studies (35.6%), and antibiotic use was not mentioned in 11 studies (24.4%).
Comprehensive meta-analysis results for bacterial classifications across taxonomic levels are presented in Supplementary Table S4. Forest plots for phylum-level analyses are displayed in the main figures, while non-significant findings for family and genus levels are available in Supplementary Figures S1, S2.
Study quality assessment
Quality assessment using the Newcastle-Ottawa Scale (NOS) classified 44 studies as high quality and 1 study as moderate quality (Supplementary Table S5). Egger’s test results for publication bias are summarized in Supplementary Table S6. Sensitivity analysis demonstrated that pooled effect estimates for all key outcomes remained consistent and were not substantially influenced by any individual study (Supplementary Figure S3).
Alpha diversity
We evaluated 10 different indices measuring richness (Chao1, observed species, abundance coverage estimator, Goods coverage), evenness (Shannon evenness, J Index), combined richness/evenness (Shannon, Simpson, inverse Simpson), and biodiversity (phylogenetic diversity). The most frequently reported indices were Shannon, Chao1, Simpson, abundance coverage estimator (ACE), and observed species.
A quantitative meta-analysis was conducted on the alpha diversity indices for NDDs and control groups, as shown in Figure 2. Eighteen studies reported the Chao1 index (Prehn-Kristensen et al., 2018; Aarts et al., 2017; Wang et al., 2022; Ma et al., 2019; Zou et al., 2020; Ding et al., 2020; Ye et al., 2021; Chen et al., 2021; Ding et al., 2021; He et al., 2023; Wang et al., 2023; Xu and Zhang, 2023; Li et al., 2023; Jiang et al., 2018; Wang et al., 2020; Wan et al., 2020; Panpetch et al., 2024; Bao et al., 2023), 32 studies reported the Shannon index (Prehn-Kristensen et al., 2018; Aarts et al., 2017; Wang et al., 2022; Pulikkan et al., 2018; Zhang et al., 2018; Coretti et al., 2018; Ma et al., 2019; Zou et al., 2020; Ding et al., 2020; Chen et al., 2020; Ye et al., 2021; Huang et al., 2021; Chen et al., 2021; Ding et al., 2021; Chen et al., 2022; Chiappori et al., 2022; He et al., 2023; Bundgaard-Nielsen et al., 2023; Wang et al., 2023; Zhao et al., 2023; Pang et al., 2023; Xu and Zhang, 2023; Li et al., 2023; Bhusri et al., 2025; Jiang et al., 2018; Szopinska-Tokov et al., 2020; Wang et al., 2020; Wan et al., 2020; Steckler et al., 2024; Panpetch et al., 2024; Boonchooduang et al., 2025; Bao et al., 2023), 14 studies reported the Simpson index (Wang et al., 2022; Ye et al., 2021; Chen et al., 2021; Chen et al., 2022; He et al., 2023; Wang et al., 2023; Pang et al., 2023; Xu and Zhang, 2023; Li et al., 2023; Bhusri et al., 2025; Jiang et al., 2018; Wang et al., 2020; Wan et al., 2020; Bao et al., 2023), 9 studies reported the ACE (Wang et al., 2022; Ma et al., 2019; Zou et al., 2020; Chen et al., 2021; Ding et al., 2021; He et al., 2023; Wang et al., 2023; Xu and Zhang, 2023; Li et al., 2023; Jiang et al., 2018), and 19 studies reported Observed Species (Prehn-Kristensen et al., 2018; Pulikkan et al., 2018; Coretti et al., 2018; Ding et al., 2020; Chen et al., 2020; Wan et al., 2022; Ye et al., 2021; Chen et al., 2021; Deng et al., 2022; Chiappori et al., 2022; He et al., 2023; Bundgaard-Nielsen et al., 2023; Wang et al., 2023; Zhao et al., 2023; Xu and Zhang, 2023; Bhusri et al., 2025; Szopinska-Tokov et al., 2020; Steckler et al., 2024; Boonchooduang et al., 2025). No significant differences were observed. The funnel plot in Supplementary Figure S4 indicated no signs of publication bias.
Forest plots of alpha diversity in the gut microbiota of patients with neurodevelopmental disorders compared with healthy controls. (A) Chao1; (B) Observed species; (C) Abundance coverage estimator; (D) Simpson; (E) Shannon. ASD, autism spectrum disorder; ADHD, attention-deficit/hyperactivity disorder; TD, tic disorder.
Five forest plots labeled A to E displaying meta-analysis results for different diversity indices across studies. Each plot shows the effect size, confidence interval, and weight for studies on ASD, ADHD, and TD. A: Chao1, B: Observed species, C: Abundance coverage estimator, D: Simpson, E: Shannon. Heterogeneity notes are included.Beta diversity
Beta diversity assessments revealed significant compositional differences in gut microbiota between NDD patients and healthy controls, with distinct disorder-specific patterns. The most consistent findings emerged in ASD, where 16 of 33 studies (48.5%) reported significant differences using various diversity metrics (Kang et al., 2013; Strati et al., 2017; Pulikkan et al., 2018; Coretti et al., 2018; Zou et al., 2020; Chen et al., 2020; Wan et al., 2022; Ye et al., 2021; Huang et al., 2021; Deng et al., 2022; He et al., 2023; Bundgaard-Nielsen et al., 2023; Wang et al., 2023; Zhao et al., 2023; Pang et al., 2023; Li et al., 2023), while 3 studies (9.1%) showed non-significant results (Chen et al., 2021; Ding et al., 2021; Chen et al., 2022) and 4 studies (12.1%) exhibited metric-dependent variations (Zhang et al., 2018; Ma et al., 2019; Ding et al., 2020; Chen et al., 2022). For ADHD, only 2 of 10 studies (20%) identified significant differences (Prehn-Kristensen et al., 2018; Szopinska-Tokov et al., 2020) compared to 5 negative reports (50%) (Jiang et al., 2018; Wang et al., 2020; Richarte et al., 2021; Steckler et al., 2024; Panpetch et al., 2024), with 1 study (10%) showing inconsistent results (Boonchooduang et al., 2025). Preliminary evidence from 2 studies suggested microbial alterations in TD (Wang et al., 2022; Bao et al., 2023). These disorder-specific patterns, potentially influenced by methodological heterogeneity (diversity metrics, analytical approaches) and cohort characteristics, underscore the importance of considering NDD subtypes when evaluating gut microbiome perturbations. Detailed methodology and results for beta-diversity analyses are provided in Supplementary Table S7.
Microbial composition
Relative abundance of gut microbiota was evaluated in 28 of 45 studies. Combined effect sizes across phylum, family, and genus categories are presented in Supplementary Table S4. Figure 3 illustrates gut microbiota changes in ADHD, ASD, and TD patients compared to controls, revealing considerable within-disorder variability that merits further investigation.
Changes in the relative abundance of microbial taxa across diagnostic categories. (A) Level: phylum; (B) Level: family; (C) Level: genus. ASD, autism spectrum disorder; ADHD, attention-deficit/hyperactivity disorder.
Table showing microbial changes at three taxonomic levels: phylum, family, and genus, labeled A, B, and C respectively. Colors indicate increase (red), decrease (blue), no difference (yellow), and not examined or reported (gray) in ASD, ADHD, and TD groups.
At the phylum level, analysis of 10 studies investigating Actinobacteria revealed no significant overall difference between NDD patients and controls (Kang et al., 2013; Prehn-Kristensen et al., 2018; Aarts et al., 2017; Wang et al., 2022; Coretti et al., 2018; Plaza-Diaz et al., 2019; Niu et al., 2019; Ding et al., 2020; Szopinska-Tokov et al., 2020; Wang et al., 2020). However, subgroup analysis demonstrated a significant increase in Actinobacteria in ADHD (SMD = 0.39; 95% CI: 0.06 to 0.72; p = 0.020; I2 = 36.0%), contrasting with significantly lower levels in TD (SMD = −0.90; 95% CI: −1.50 to −0.31; p = 0.003; I2 = 0). Analysis of 11 studies each for Bacteroidetes (Kang et al., 2013; Prehn-Kristensen et al., 2018; Aarts et al., 2017; Zhang et al., 2018; Coretti et al., 2018; Plaza-Diaz et al., 2019; Niu et al., 2019; Ding et al., 2020; Yitik Tonkaz et al., 2023; Szopinska-Tokov et al., 2020; Wang et al., 2020) and Firmicutes (Kang et al., 2013; Prehn-Kristensen et al., 2018; Aarts et al., 2017; Wang et al., 2022; Zhang et al., 2018; Plaza-Diaz et al., 2019; Niu et al., 2019; Ding et al., 2020; Yitik Tonkaz et al., 2023; Szopinska-Tokov et al., 2020; Wang et al., 2020) revealed no significant overall differences, though Firmicutes was significantly elevated in the TD subgroup (SMD = 0.87; 95% CI: 0.28 to 1.47; p = 0.004; I2 = 0). No significant differences were observed for Proteobacteria and Verrucomicrobia (Figure 4).
Forest plots of gut microbiota at the phylum level in patients with neurodevelopmental disorders compared with healthy controls. (A) Actinobacteria; (B) Bacteroidetes; (C) Verrucomicrobia; (D) Proteobacteria; (E) Firmicutes. ASD, autism spectrum disorder; ADHD, attention-deficit/hyperactivity disorder; TD, tic disorder.
Forest plots for studies on the relative abundance of bacterial phyla in mental disorders are shown. Panels A through E depict Actinobacteria, Bacteroidetes, Verrucomicrobia, Proteobacteria, and Firmicutes, respectively. Each plot displays effect sizes with confidence intervals and weights for individual studies concerning Autism Spectrum Disorder (ASD), Attention Deficit Hyperactivity Disorder (ADHD), and typically developing (TD) groups. Subgroup and overall effects are indicated with heterogeneity statistics and confidence intervals. Notes at the base of each panel explain statistical tests used.
At the family level, a preliminary meta-analysis of only four studies assessing Peptostreptococcaceae suggested a significant increase in patients (SMD = 0.47; 95% CI: 0.05 to 0.90; p = 0.028; I2 = 68.7%), with subgroup analysis indicating a potential elevation in ADHD (SMD = 0.30; 95% CI: 0.02 to 0.58; p = 0.033; I2 = 0). However, this finding should be interpreted with caution due to the limited number of contributing studies (Figure 5).
Forest plots of gut microbiota at the family and genus level in patients with neurodevelopmental disorders compared with healthy controls. (A) Peptostreptococcaceae; (B) Escherichia/Shigella; (C) Eubacterium; (D) Roseburia. ASD, autism spectrum disorder; ADHD, attention-deficit/hyperactivity disorder.
Four forest plots labeled A to D display meta-analysis data on Peptostreptococcaceae, Escherichia/Shigella, Eubacterium, and Roseburia. Each plot includes effect sizes and confidence intervals for studies on ASD and ADHD. Subgroup effects and heterogeneity statistics are provided for clarity, highlighting differences and aggregating overall effects with visual markers like diamonds indicating pooled effects.
At the genus level, analysis of 6 studies demonstrated a significant decrease in Escherichia/Shigella in NDD patients (SMD = −0.39; 95% CI: −0.59 to −0.19; p < 0.001; I2 = 33.7%), particularly pronounced in ASD (SMD = −0.48; 95% CI: −0.70 to −0.26; p < 0.001; I2 = 0). Conversely, 5 studies revealed a significant increase in Eubacterium (SMD = 0.33; 95% CI: 0.20 to 0.47; p < 0.001; I2 = 34.8%) (Kang et al., 2013; Strati et al., 2017; Plaza-Diaz et al., 2019; Mendive Dubourdieu and Guerendiain, 2023; Li et al., 2023). Analysis of 6 studies investigating Roseburia showed a significant decrease in patients (SMD = −0.39; 95% CI: −0.78 to 0; p = 0.049; I2 = 69.0%) (Kang et al., 2013; Prehn-Kristensen et al., 2018; Strati et al., 2017; Niu et al., 2019; Mendive Dubourdieu and Guerendiain, 2023; Pang et al., 2023), with significant reduction in ASD (SMD = −0.46; 95% CI: −0.89 to −0.04; p = 0.033; I2 = 71.6%). Supplementary material analysis revealed extensive heterogeneity in study-level findings across ADHD, ASD, and TD (Supplementary Figure S5).
Discussion
This systematic review and meta-analysis reveal significant alterations in gut microbiota composition among individuals with NDDs, reinforcing the crucial role of the gut-brain axis in these disorders. The substantial variability in study designs and demographic characteristics reflects the complex involvement of gut microbiota in NDD pathogenesis. Our findings provide comprehensive insights into microbial diversity and structural changes across ASD, ADHD, and TD. Despite considerable methodological heterogeneity, we identified consistent patterns that merit further investigation.
In our meta-analysis of alpha diversity indices, we observed no significant differences between NDD patients and healthy controls for the most commonly used indices, such as Chao1, Shannon, Simpson, and ACE. This suggests that global gut microbiota diversity, as measured by alpha diversity indices, may not be substantially altered in NDD patients. However, our findings indicate heterogeneous results within different NDD subtypes, such as ASD, ADHD, and TD. The gut microbiome’s alpha diversity in individuals with ASD shows inconsistent patterns when compared to healthy controls. Some studies reported higher richness and diversity in ASD patients (Zhai et al., 2019; Kang et al., 2018), while others found lower diversity (Wu et al., 2020; Liu et al., 2019), with some showing no significant difference (Strati et al., 2017; Carissimi et al., 2019). Similarly, ADHD studies demonstrated both decreased diversity (Prehn-Kristensen et al., 2018; Steckler et al., 2024), and no significant difference (Bundgaard-Nielsen et al., 2023; Wan et al., 2020; Bundgaard-Nielsen et al., 2020). Our study, with its large sample size and broader range of NDDs, did not find consistent evidence of reduced diversity, suggesting that alpha diversity may not be a reliable biomarker across all NDDs. It is noteworthy that certain alpha diversity indices, such as ACE, exhibited significant heterogeneity. This variability may be attributed to specific study factors (e.g., patient characteristics or differences in microbiota analysis methodologies), which could influence the results.
The meta-analysis of beta diversity showed more varied results. Seven studies observed no notable differences between patients and controls, while others identified distinct microbial clustering in individuals with ASD, ADHD, and TD, suggesting that specific NDDs may be associated with unique gut microbiota profiles. These findings are consistent with previous studies that reported altered beta diversity in ASD, ADHD, and TD (Bundgaard-Nielsen et al., 2023; Steckler et al., 2024; Yap et al., 2021). However, the assessment methods for beta diversity (such as principal coordinate analysis (PCoA) or other distance metrics) may contribute to variability in the consistency of these results. Our findings also suggest that diagnostic categories may exert a greater influence on microbiota composition than a general NDD diagnosis. For instance, in ASD and TD, the microbiota differences between patients and controls were more pronounced, possibly reflecting more consistent and robust identification of microbiota dysbiosis in these disorders (Chen et al., 2024; Xie et al., 2022). In contrast, the results for ADHD were less clear and more variable, possibly due to greater heterogeneity in their pathophysiology and microbiota composition.
This meta-analysis investigated gut microbiome abundance at the phylum, family, and genus levels and found no notable variations in Actinobacteria, Bacteroidetes, Firmicutes, Proteobacteria, or Verrucomicrobia between individuals with NDDs and healthy controls at the phylum level. However, subgroup analysis within NDDs revealed a significant increase in Actinobacteria at the genus level in patients with ADHD, while a significant decrease was observed in patients with TD (Aarts et al., 2017; Wang et al., 2022). The role of Actinobacteria may vary among different types of NDDs. The increase in Actinobacteria in ADHD patients may relate to neuroimmune dysregulation, which leads to behavioral abnormalities. Neuroimmune dysregulation could affect neuroinflammation and neurotransmitter signaling, thereby influencing gut-brain axis communication pathways (Chen et al., 2021; Abdel-Haq et al., 2019; Zhou et al., 2022). Our analysis also found a significant increase in Firmicutes in patients with TD. Short-chain fatty acids (SCFAs) modulate neuroinflammation, support brain function, and promote gut health. The increase in Firmicutes may influence the gut-brain axis through SCFA modulation, leading to corresponding clinical manifestations (Den Besten et al., 2013; Boukthir et al., 2010; De Magistris et al., 2010). No notable variations occurred in Bacteroidetes, Proteobacteria, and Verrucomicrobia between individuals with NDDs and healthy controls, implying that these particular phyla might exhibit greater stability across NDDs.
At the family level, our analysis revealed a significant elevation in Peptostreptococcaceae abundance among individuals with NDDs, with the most pronounced increase observed in ADHD (Jiang et al., 2018; Richarte et al., 2021). This finding corroborates growing evidence suggesting gut microbial dysbiosis as a potential contributor to neuropsychiatric pathogenesis (Szopinska-Tokov et al., 2020). Furthermore, Ruminococcaceae levels demonstrated a specific association with core symptoms of inattention, highlighting potential microbiota-behavior relationships in NDD (Tang et al., 2022). An elevated abundance of Peptostreptococcaceae may adversely affect the nervous system through immune-inflammatory pathways. Specifically, these bacteria can initiate pro-inflammatory responses that stimulate intestinal epithelial cells to release cytokines, including IL-6 and TNF-α. The resulting local inflammation weakens intestinal barrier function, permitting microbial products such as lipopolysaccharides to enter systemic circulation and induce low-grade systemic inflammation. This inflammatory state can traverse the blood–brain barrier, activate microglia, and promote neuroinflammation, ultimately impairing neuronal function and synaptic plasticity. These processes are considered integral to NDD pathophysiology (Palanivelu et al., 2024; Efremova et al., 2024). Moreover, certain clostridial species produce phenolic compounds such as phenol and p-cresol, which demonstrate neurotoxicity and may disrupt dopamine and norepinephrine metabolism in ADHD (Zhang et al., 2025). The between-study heterogeneity in the overall analysis may reflect diagnostic heterogeneity across NDDs or methodological variations in microbiota assessment. These results suggest Peptostreptococcaceae as a potential microbial marker warranting further investigation in NDDs, particularly regarding its role in modulating gut-brain communication through metabolic and immune pathways.
At the genus level, our analysis showed a notable decrease in the abundance of Escherichia/Shigella in patients compared to the control group, aligning with the results of Zou et al. (2020) regarding gut dysbiosis in ASD patients. The decrease of Escherichia/Shigella in the gut of NDD patients, especially ASD patients, may relate to decreased resistance to pathogenic microorganisms (Wang et al., 2020). Strati et al. (2017) also reported that in ASD patients, the abundance of Escherichia/Shigella was associated with gastrointestinal symptoms. Our study suggests that the significant decrease in the abundance of Escherichia/Shigella in ASD patients supports the hypothesis that alterations in these genera may contribute to the development of ASD. In contrast, we found a significant increase in the abundance of Eubacterium in patients, which aligns with the findings of Mendive Dubourdieu and Guerendiain (2023)Eubacterium, a key producer of SCFAs particularly butyrate, plays a crucial role in dietary fiber fermentation. While butyrate contributes to gut homeostasis by energizing colonocytes, strengthening the intestinal barrier, and exerting anti-inflammatory effects, elevated levels may exert paradoxical neurobehavioral effects. Evidence suggests that excess butyrate from specific microbial sources can influence neurodevelopment through epigenetic regulation of gene expression or direct interference with mitochondrial function (Dinan and Cryan, 2017; Srikantha and Mohajeri, 2019). A recent study using ASD patient-derived intestinal organoids demonstrated that metabolites from specific Eubacterium strains modulate neuronal activity, providing direct evidence for their role in gut-brain communication (Li et al., 2023). Consequently, the increased abundance of Eubacterium observed in NDDs may represent an adaptive response to dietary or gastrointestinal alterations, potentially influencing neuroinflammatory processes through SCFA-mediated pathways. Notably, our study demonstrated a marked decrease in Roseburia, consistent with reports by Kang et al. (2013). The decreased abundance of Roseburia leads to reduced butyrate levels in the gut, which may compromise intestinal barrier integrity. Insufficient butyrate supply impairs colonocyte energy metabolism, downregulates tight junction protein expression, and increases intestinal permeability, thereby facilitating the entry of neuroactive or pro-inflammatory substances into systemic circulation. Concurrently, diminished anti-inflammatory activity due to butyrate deficiency disinhibits pro-inflammatory signaling pathways such as NF-κB, potentially amplifying neuroinflammatory responses in the central nervous system (Niu et al., 2019; Guevara-Ramirez et al., 2025). Thus, the depletion of Roseburia likely represents a key factor driving the pathophysiology of NDDs. These findings emphasize the important role of gut microbiota composition in neurodevelopmental disorders and suggest that specific microbial taxa could serve as potential therapeutic targets for intervention.
The consistent microbial alterations identified in our study, particularly the enrichment of Peptostreptococcaceae and depletion of butyrate-producing Roseburia, provide a compelling rationale for microbiome-targeted interventions in NDDs. Probiotic supplementation with specific strains has demonstrated efficacy in improving both gastrointestinal and behavioral symptoms in children with ASD and ADHD (Tan et al., 2021; Novau-Ferre et al., 2025). Furthermore, prebiotic interventions, such as galacto-oligosaccharides, can modulate gut microbiota composition and improve attentional set-shifting performance (Gronier et al., 2018). Dietary strategies, including Mediterranean-style diets rich in fermentable fibers, may also help restore microbial balance and support gut-brain axis function (Park et al., 2024; Young et al., 2022). However, future interventions should account for the substantial heterogeneity observed across NDDs by adopting personalized approaches based on individual microbial profiles and should be validated through larger, well-designed clinical trials to establish optimal formulations and treatment durations.
This meta-analysis has several limitations. First, an substantial imbalance exists in the distribution of studies across different NDDs. Research on ASD constitutes the majority of included studies, while studies focusing on ADHD and particularly TD remain limited. This skewed distribution may compromise the generalizability of our findings across the entire spectrum of NDDs. Secondly, probiotics and antibiotics can significantly affect microbiota composition, but some studies did not report the use of these agents. Lastly, the limited number of studies for certain outcomes restricted both the precision of our estimates and the exploration of heterogeneity sources through subgroup analyses, highlighting the need for larger cohorts in future research.
Conclusion
In summary, this meta-analysis demonstrates significant alterations in the gut microbiota of individuals with NDDs, with distinct microbial profiles emerging across different disorder subtypes. While patients with NDDs showed no significant differences in alpha diversity compared to healthy controls, we identified substantial variations in beta diversity and microbial composition at multiple taxonomic levels.
The consistent pattern of dysbiosis, characterized by a trend toward increased Peptostreptococcaceae based on preliminary evidence alongside decreased Escherichia/Shigella and Roseburia, suggests these taxa may serve as potential microbial markers for NDDs. Microbiome-targeted interventions, including probiotic supplementation and dietary modifications, represent promising approaches for alleviating clinical symptoms in affected individuals. However, future large-scale, longitudinal studies are necessary to elucidate the causal relationships between gut microbiota and NDD pathophysiology and to develop personalized therapeutic strategies.
Data availability statement
The original contributions presented in the study are included in the article/Supplementary material, further inquiries can be directed to the corresponding author/s.
Author contributions
HY: Formal analysis, Writing – review & editing, Data curation, Writing – original draft, Investigation, Visualization. AW: Data curation, Investigation, Writing – original draft. JY: Writing – review & editing, Investigation, Data curation. RL: Supervision, Writing – review & editing. YY: Conceptualization, Writing – review & editing, Supervision.
Acknowledgments
We thank all the authors from the studies included in this meta-analysis for their prior work and data sharing.
Conflict of interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
Generative AI statement
The authors declare that no Gen AI was used in the creation of this manuscript.
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Supplementary material
The Supplementary material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fmicb.2025.1650212/full#supplementary-material
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Edited by: Gabriele Deidda, Queen Mary University of London–Malta campus, Malta
Reviewed by: Richa Dwivedi, Meharry Medical College, United States
Roghayeh Afifirad, Tehran University of Medical Sciences, Iran