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Article

Gut Microbiota and Autism Spectrum Disorders: Neurodevelopmental, Behavioral, and Gastrointestinal Interactions

by
Zuzanna Lewandowska-Pietruszka
1,2,
Magdalena Figlerowicz
1 and
Katarzyna Mazur-Melewska
1,*
1
Poznan University of Medical Sciences, Department of Infectious Diseases and Child Neurology, 60-572 Poznan, Poland
2
Poznan University of Medical Sciences, Doctoral School, 60-812 Poznan, Poland
*
Author to whom correspondence should be addressed.
Nutrients 2025, 17(17), 2781; https://doi.org/10.3390/nu17172781
Submission received: 7 August 2025 / Revised: 21 August 2025 / Accepted: 25 August 2025 / Published: 27 August 2025
(This article belongs to the Special Issue The Impact of Gut Microbiota and Their Metabolites on Chronic Diseases)
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Abstract

Background: Autism spectrum disorder (ASD) is a complex neurodevelopmental condition characterized by social communication deficits, repetitive behaviors, and frequent gastrointestinal comorbidities. Emerging research suggests gut microbiota alterations contribute to ASD symptoms and gastrointestinal dysfunction, but detailed microbial profiles and clinical correlations remain underexplored. Methods: This study analyzed gut microbiota in 45 children aged 2–18 years diagnosed with ASD. Stool samples underwent 16S rRNA gene sequencing. Clinical assessments included ASD diagnostic subtype, adaptive functioning using the Vineland Adaptive Behavior Scale, gastrointestinal symptoms as per the Rome IV criteria, dietary patterns, and demographic variables. Statistical analyses correlated microbiota profiles with clinical features. Results: Gut microbiota composition was significantly influenced by delivery mode, age, sex, and diet. Vaginally delivered children had higher beneficial SCFA-producing bacteria, whereas Cesarean section was linked to increased pathogenic Clostridiales. High-calorie and protein-rich diets correlated with shifts toward pro-inflammatory taxa. Microbial diversity and specific genera correlated with adaptive behavior domains (communication, socialization, motor skills) and severity of gastrointestinal symptoms. Both pro-inflammatory and anti-inflammatory bacteria variably impacted neurodevelopmental outcomes. Conclusions: Gut microbiota composition in children with ASD is shaped by multifactorial influences and connected to neurobehavioral and gastrointestinal phenotypes. The findings of this study support the potential of microbiota-targeted interventions to ameliorate ASD-associated symptoms and improve quality of life.

1. Introduction

Autism spectrum disorder (ASD) is a multi-etiological disorder impacting the neurological development of individuals. The World Health Organization estimates that approximately 1 in 100 children globally exhibits symptoms of autism; however, prevalence data from developing countries may be underreported [1,2]. ASD is defined by its core symptoms, including differences in social and communicative function, stereotypical patterns of behavior, and selective interests [3]. Alongside the core symptoms, patients often present with comorbidities. These include neuropsychological conditions such as intellectual disability, anxiety, affective disorders, sleep disturbances, and epilepsy. Somatic conditions are also common, particularly physical disabilities and gastrointestinal symptoms [4]. The latter are observed in up to 70% of patients and can lead to a significant reduction in quality of life [5].
A growing body of evidence supports the role of abnormalities in the intestinal microbiome in core and associated symptoms [6,7]. The results remain equivocal; however, there are several patterns observed in multiple studies on microbiota composition in patients with ASD. Children with ASD present with lower abundance of Bacteroidetes, alongside higher abundance of Firmicutes and Pseudomonadota [8]. The bacteria from genera Prevotella, Roseburia, Ruminococcus, Megasphaera, and Catenibacterium were hypothesized to be potential biomarkers for ASD [8,9,10]. A lower Bacteroidetes to Firmicutes ratio and higher abundance of Desulfovibrio, Bacteroides, and Clostridium were observed in patients with more severe core symptoms. A lower Bacteroidetes to Firmicutes ratio, typically observed in patients with ASD, seems to correlate with gastrointestinal and behavioral symptoms [8,11,12]. Moreover, diet composition, including elimination diets or food selectivity (FS), has a visible impact on microbiota composition in patients with ASD [8,13,14,15].
Furthermore, microbiota composition can influence hormonal responses, while hormonal stress response leads to differences in microbiota composition [16,17,18]. Abnormalities in the release of one of the main stress hormones, cortisol, were previously observed in children with ASD. Patients presented with an abnormally low basal level of cortisol and an atypical hormonal response to stressors. A common phenomenon was a flattened diurnal cortisol release slope [19].
We proposed that children with ASD may exhibit unique gut microbiota profiles, linked to both gastrointestinal and behavioral characteristics, and that these microbial patterns are associated with dietary habits. The results of this study could also serve as an initial step toward developing an interventional microbiota method of reducing the gastrointestinal or affective symptoms reported by patients as being difficult to bear, potentially leading to an increase in quality of life.
The specific objectives of this study were to: (1) define the intestinal microbiota composition in children with ASD through 16S rRNA gene sequencing; (2) examine associations between microbiome composition, dietary patterns, food preferences, and gastrointestinal symptoms; (3) assess correlations between gut microbial profiles and adaptive functioning; and (4) identify microbial markers with potential predictive value for functional outcomes in ASD.

2. Materials and Methods

2.1. Participants

Participants were enrolled by the research team from the Department of Infectious Diseases and Child Neurology at Poznan University of Medical Sciences between 2020 and 2023. Eligibility criteria were: (1) a confirmed clinical ASD diagnosis, i.e., established by a multidisciplinary team composed of psychologists and psychiatrists, in line with ICD-10 guidelines, and (2) age ranging from 2 to 18 years. Initially, 234 children’s guardians expressed interest in being included in the research group.
Exclusion criteria comprised: (1) inability or refusal to complete all required questionnaires and/or provide biological samples within the study timeline; (2) epilepsy diagnosis, as confirmed by a pediatric neurologist; (3) confirmed organic gastrointestinal disorders, as assessed by a pediatric gastroenterologist; (4) use within the past three months of medications likely to alter gut microbiota, including antibiotics, probiotics, or acid-suppressing agents; (5) history of fecal microbiota transplantation; and (6) currently following an elimination diet due to conditions such as celiac disease, lactose intolerance, or allergies, as validated by a specialist.
Exclusions were applied as follows: 59 for incomplete participation or unwillingness to comply with study requirements; 18 owing to epilepsy; 15 due to gastrointestinal disorders; 68 due to recent pharmacological treatments; and 29 because of elimination diets. After these exclusions, the final cohort comprised 45 patients, including 7 female and 38 male participants. Informed consent was obtained from legal guardians for the collection of clinical data and biological samples.

2.2. The Evaluation of Medical Background

Diagnosis as per ICD-10 (F84.0 versus other subtypes of ASD), age, sex, mode of delivery, and coexisting conditions were stated by guardians and their children on a personal questionnaire.
The patients’ functioning was measured using the Vineland Adaptive Behavior Scale, Third Edition (VABS), with scores examined in relation to the primary domains of socialization, communication, daily living skills, and motor abilities. Additionally, subdomains such as receptive, expressive, and written communication, daily living skills, socialization, and gross and fine motor skills were evaluated, enabling the identification of groups based on their functional levels.
A questionnaire was administered to guardians to evaluate symptoms of functional gastrointestinal disorders, in accordance with the Rome IV Criteria.
Parents were asked to keep a detailed dietary log over a period of seven days, noting the child’s food intake, which included five main meals as well as any snacks and drinks consumed. When relevant, brand names were specified. A standardized paper form was provided for each participant for manual completion. In addition to recording the types of food, caregivers estimated portion sizes and documented the timing of each eating occasion [20,21]. Using this information, the researchers calculated calorie and fluid consumption, along with the nutritional breakdown of fats (saturated and unsaturated), sugars (simple and complex), and proteins. Based on their dietary profiles, participants were categorized into six groups according to their intake patterns: low-calorie diet (LCD), defined by calorie consumption not surpassing standard age- and sex-specific recommendations; high-calorie diet (HCD), with intake above typical levels; protein-based diet (PBD), where protein made up more than 20–30% of daily energy; and carbohydrate-based diet (ChBD).

2.3. Collecting and Storing Stool Samples

The lead researcher collected stool specimens individually from each child in an interior environment. Just before collecting, the child’s vital measurements were recorded, and information regarding recent exposure to sick individuals was gathered. Sterile stool collection containers (F.L. Medical s.r.l., Torreglia, Italy) were used for sampling. All specimens were transferred to a specialized laboratory freezer (ARCTIKO Ltd., Salisbury, UK) and stored at −80 °C within one hour after collection. To protect patient privacy and reduce bias, each sample was anonymized by the investigator through the use of a unique, coded identifier.

2.4. The Analysis of Microbiome Composition

Genomic DNA extraction was performed by skilled laboratory staff using the Auto-Pure Mini system (Allsheng, Hangzhou, China) along with MagnifiQ reagents (A&A Biotechnology, Gdańsk, Poland). DNA libraries were created via PCR amplification specifically targeting the V3–V4 regions of the 16S rRNA gene. Microbial community profiling was then carried out using Next-Generation Sequencing (NGS) of bacterial 16S rRNA genes on the Illumina MiSeq platform (Illumina, San Diego, CA, USA). All DNA samples that met the quality standards required for 16S rRNA gene sequencing were included in the study; no samples were excluded due to insufficient yield or degradation. The same bioinformatics workflow was applied uniformly to all samples.
Sequence quality control was conducted through the DADA2 method implemented within QIIME 2. During denoising, forward and reverse reads were truncated at 260 base pairs, with the first 10 bases trimmed from forward reads and the first 20 bases from reverse reads. A maximum of two expected errors per read was allowed, and reads were trimmed at the initial base where the quality score dropped below 2. Chimeric sequences were identified and removed using a consensus-based approach. Only sequence variants with a total frequency of at least 10 across the dataset were preserved for further analysis.
To eliminate non-bacterial sequences, taxonomic filtering was applied: features assigned to mitochondria, chloroplasts, Archaea, or Eukaryota were excluded. Taxonomic classification used QIIME 2′s classify-consensus-blast against the SILVA 138-99 reference database.
All experimental work was carried out at the certified SANPROBI Research and Development Centre in Szczecin, Poland, under the guidance of expert scientists associated with Pomeranian Medical University, Szczecin.

2.5. Statistics and Biostatistics

Data analyses were performed by a skilled biostatistician using “R” version 4.3.2 (released on 30 October 2023, ucrt). Relationships between different microbiota compositions and influencing factors were examined through Spearman’s and Winsorized Pearson’s correlation methods. Multivariate relationships within the microbiota data were explored using Pearson’s correlation. The choice between Pearson’s and Spearman’s correlation tests was guided by the assessment of bivariate normality with the Shapiro–Wilk test. A p-value threshold of ≤0.05 was applied to determine statistical significance.

2.6. Ethical Approval

All procedures involving human participants were performed in full compliance with the ethical principles outlined by the relevant institutional and/or national research ethics committees and were consistent with the 1964 Declaration of Helsinki, including its subsequent revisions and comparable international guidelines [22]. The study protocol underwent formal review and received approval from the Bioethics Committee of Poznań University of Medical Sciences (approval no. KB 1138/19; 5 December 2019). Prior to study participation, written informed consent was obtained from all individuals and/or their legal representatives, ensuring voluntary enrollment and adherence to ethical requirements.

3. Results

3.1. Gut Microbiota Sequencing

The analysis of the gut microbiota using stool DNA samples exposed the existence of microbial communities that encompassed 11 phyla, 16 classes, 42 orders, 83 families, 223 genera, and 363 species. Consequently, associations between the microbiota diversity and various clinical, dietary, and physiological characteristics of the study population were examined in subsequent analyses. Researchers found several statistically significant links between the types of microbes in a child’s gut and their health and nutrition. Robust correlations (r > 0.5 or r < −0.5) were regarded as potentially biologically relevant. The composition of the microbiota varied substantially among participants; nonetheless, certain patterns were evident in most children. The predominant bacteria belonged to the Clostridium class, particularly the genera Bacilli and Subdoligranulum, and Actinobacteria, mainly the Bifidobacterium genus. Although there is no established consensus on the normal Firmicutes to Bacteroidetes ratio, it was notably low across all participants compared to existing data (mean 0.04 ± 0.14). Further analyses explored the relationships between microbiota diversity at various taxonomic levels and a range of clinical, dietary, and physiological factors in the study group, including the presence of functional gastrointestinal disorders (FGID), dietary habits and food selectivity (FS), and participants’ performance across multiple functional domains.

3.2. The Structure of the Group

The cohort consisted of 45 children with ASD, with a marked male predominance (n = 38, 84%) compared to females (n = 7, 16%), which aligns with the known higher prevalence of ASD in males. The participants were stratified by age into three categories: preschoolers (2–6 years; n = 12), school children (6–14 years; n = 28), and adolescents (15–18 years; n = 5). Regarding perinatal factors, vaginal delivery was reported in 69% of the cases (n = 31), while Cesarean section occurred in 31% (n = 14) of cases. A detailed analysis of the structure of the group is shown in Table 1.
There were notable disparities in the gut microbiota composition between the different participant subgroups. The statistically significant differences are detailed in Table 2 and illustrated in Figure 1, Figure 2, Figure 3 and Figure 4.
Micrococcales abundance was negatively correlated with age (r = −0.47), while age exhibited a negative correlation with Rothia abundance (r = −0.47). Table 3 displays all the correlations.

3.3. Diet and Food Selectivity

The analysis of the data on diet composition is shown in Table 1.
ChBD was declared by the parents of 50% (n = 6) of preschoolers, 25% (n = 7) of school children, and only one adolescent. Most of them (n = 12, 86%) were boys and did not present with symptoms of FGID (n = 8, 57%).
HCD was consumed by all of the preschoolers (n = 12), 32% (n = 9) of the school children, and only one adolescent. The majority of them (n = 19, 86%) were boys and did not present with symptoms of FGID (n = 14, 63%).
Most of the children with FS were boys (n = 13, 81%) of preschool age (n = 10, 63%). Twenty-five percent (n = 4) of them also presented with FGID symptoms. Sixty-nine percent (n = 11) of the children with FS had HCD, while the numbers following a protein-based and ChBD were balanced.
Gut microbiota compositions differed due to diet differences. The statistically important differences are detailed in Table 2 and illustrated in Figure 5, Figure 6 and Figure 7.
The ratio between saturated fat and total fat correlated negatively with Peptostreptococcales/Tissierellales abundance (r = −0.46). The mean consumption of complex carbohydrates correlated positively with the Clostridiales (r = 0.5) and Peptostreptococcales/Tissierellales (r = 0.47) levels.
At the genus level, the ratio between saturated fat and total fat consumption positively correlated with Leuconostoc (r = 0.49) abundance, while it correlated negatively with Eubacterium coprostanoligenes (r = −0.46), Eubacterium hallii (r = −0.59), and Dorea (r = −0.6) abundance. On the other hand, the unsaturated fat to total fat intake ratio (r = 0.55) and mean fat consumption to weight ratio (r = 0.47) correlated positively with Dielma abundance. The simple carbohydrates to total carbohydrates intake ratio positively correlated with Epulopiscium (r = 0.53) and Weissella (r = 0.51) abundance and negatively with Flavonifractor abundance (r = −0.5). Complex carbohydrates intake correlated positively with Romboutsia level (r = 0.52). The ratios between the energy obtained from carbohydrates to total energy consumed and between the energy gained from carbohydrates to total carbohydrates intake both correlated positively with Epulopiscium (r = 0.5) and Klebsiella (r = 0.66) abundance, with the latter correlating positively also with the Weissella level (r = 0.51). Dielma abundance was positively correlated with mean protein intake to weight ratio (r = 0.52).
The saturated fat to total fat intake ratio positively correlated with Enterobacter cloacae (r = 0.68), Leuconostoc citreum (r = 0.53), Leuconostoc lactis (r = 0.56), Staphylococcus epidermidis (r = 0.46), and Streptococcus gallolyticus (r = 0.66) abundance. The ratio between kilocalories from carbohydrates and mean total carbohydrates intake positively correlated with Clostridum perfingens abundance (r = 0.62).
Table 3 contains a list of all correlations.

3.4. Functional Gastrointestinal Disorders

The data on the prevalence of FGID in the research group are shown in Table 1. The group presenting with any FGID consisted of four preschoolers (33%), 11 school children (39%), and three adolescents (60%), mostly boys (n = 16, 89%). Significant differences were observed in the constitution of the gut microbiota depending on the occurrence of FGID. The statistically important differences are detailed in Table 2 and illustrated in Figure 8, Figure 9, Figure 10 and Figure 11.

3.5. Vineland Adaptive Behavioral Scale Results

All patients were assessed for their functioning across four domains, i.e., communication, daily living skills, socialization, and motor skills, and specific subdomains were further explored. We compared three groups of children—those with low, adequate, or high scores in each domain and subdomain—against microbiota abundance at the different taxa levels, emphasizing statistically significant distinctions. Additionally, we examined the correlations between gut microbiota constitution and different factors, including age, dietary preferences, weight, and the V-scores from the VABS assessment.

3.5.1. Domain: Communication

Thirty-one children received a low or moderately low result in the total communication domain. Four participants received an adequate result, while 10 received a high or moderately high result. Statistically important differences are detailed in Table 2 and illustrated in Figure 12.
Subdomain: Receptive Communication
In the receptive communication subdomain, 10 participants demonstrated high or moderately high scores, whereas 27 exhibited low or moderately low performance. Among the higher-scoring group, seven were from the middle group and three were older boys. FGID were present in three individuals, and FS was reported in another three. Most followed PBD (n = 9) and LCD (n = 8). The lower-scoring subgroup included 11 preschoolers, 15 school children, and one adolescent. FGID were identified in 10 participants, and FS was observed in 11. The majority followed PBD (n = 17) and HCD (n = 15) dietary patterns. Statistically important differences are detailed in Table 2 and illustrated in Figure 13. V-score of receptive communication positively correlated with Flavonifractor abundance (r = 0.49). Table 3 displays all the correlations.
Subdomain: Expressive Communication
In the expressive communication subdomain, 10 children (seven boys and one girl) achieved high or moderately high scores, whereas 24 children obtained low or moderately low scores. Among those with higher scores, eight belonged to the school children group and two were older adolescents. Two of these participants presented with FGID, and three with FS. The majority adhered to PBD (n = 9) or LCD (n = 8).
Children with lower scores were predominantly boys (n = 20). This group comprised eight preschoolers, 14 school-aged children, and two adolescents. Nine participants presented with one or more FGIDs, and 11 with FS. Most of them followed PBD (n = 14), and half also adhered to LCD.
Statistically important differences are detailed in Table 2 and illustrated in Figure 14.
Subdomain: Writing Skills
Within the writing skills subdomain, 5 participants attained scores in the high or moderately high range, whereas 32 demonstrated weaker performance. The higher-achieving group consisted exclusively of boys from the middle age category; two of these children presented with FGID and one also with FS. Three followed PBD, while every child in this subgroup adhered to LCD.
Among those with lower outcomes, all 16 were school boys. Nine presented with FGID and 8 with FS. Dietary assessments revealed that 15 children followed PBD, and 12 also reported adherence to LCD.
The statistically significant differences are detailed in Table 2 and illustrated in Figure 15. V-score for writing skills negatively correlated with the abundance of Anaerostipes (r = −0.51). Table 3 contains a list of all correlations.

3.5.2. Domain: Daily Living Skills

In the total daily living skills domain, 27 children obtained low or moderately low scores. Ten participants achieved results within the adequate range, while 8 demonstrated high or moderately high performance. Statistically significant differences are presented in Table 2 and graphically depicted in Figure 16.
Subdomain: Personal Skills
Eight participants achieved high or moderately high scores in the personal skills subdomain, while 27 scored low or moderately low. All of the high scorers were boys, including six school-aged children and two adolescents. Among them, five had been diagnosed with childhood autism. Two had FGID, and three had FS. Most of these participants consumed PBD (n = 6) and LCD (n = 7). The lower-scoring group consisted mainly of boys (n = 20), with eight preschoolers, 14 school-aged children, and two adolescents. Six in this group had diagnoses other than childhood autism. Nine had FGID, and 11 had FS. The majority followed PBD (n = 14), and 11 also consumed LCD. Statistically significant differences are presented in Table 2 and illustrated in Figure 17. The V-score in the personal skills subdomain showed a positive correlation with Romboutsia abundance (r = 0.5). Table 3 displays all the correlations.
Subdomain: Domestic Skills
Seven children (all boys) achieved high or moderately high scores in the domestic skills subdomain, while 27 children (24 boys and three girls) scored low or moderately low. Among the high scorers, five were from the intermediate age group and two were older children. Two of these children had one or more FGIDs, and two had FS. Most followed PBD (n = 5) and LCD (n = 7). The lower-scoring group included seven preschoolers, 17 school-aged children, and three adolescents. Eight of them had diagnoses other than childhood autism. Eleven had any FGID, and 13 had FS. The majority of this group consumed PBD (n = 18) and HCD (n = 14). Statistically significant differences are presented in Table 2 and depicted in Figure 18. The V-score in the domestic skills subdomain showed a positive correlation with Intestinibacter abundance (r = 0.5).
Subdomain: Community Skills
Eleven children scored high or moderately high in the community skills subdomain, while 26 scored low or moderately low. Among the higher scorers, two were preschoolers, seven were school-aged children, and two were adolescents. Nine of these children were boys, and five had been diagnosed with childhood autism. Six presented with one or more FGIDs, and two also had FS. Nine followed PBD, and nine adhered to LCD. Of the lower-scoring group, ten were preschoolers, 14 were school-aged children, and two were adolescents, with 22 boys in total. Nineteen children in this group were diagnosed with childhood autism. Nine had any FGID, and 12 had FS. Fifteen participants consumed PBD, while 16 followed HCD. The statistically significant differences are detailed in Table 2 and illustrated in Figure 19.

3.5.3. Domain: Socialization

Thirty children received a low or moderately low result in total in the socialization domain. Eleven participants received an adequate result, while four received a high or moderately high result. The statistically important differences are detailed in Table 2 and illustrated in Figure 20.
Subdomain: Interpersonal Skills
Six participants scored high or moderately high in the interpersonal skills subdomain, while 28 scored low or moderately low. All of the high scorers were boys, including one preschooler, three school-aged children, and two adolescents. Three of them were diagnosed with childhood autism. Three presented with one or more FGIDs, and two had FS. Half of these participants followed PBD (n = 3), and most consumed LCD (n = 5). Among the lower-scoring group, most were boys (n = 23). Eleven were preschoolers, 14 were school-aged children, and three were adolescents. Ten had diagnoses other than childhood autism. Twelve presented with one or more FGIDs, and twelve had FS. The majority consumed PBD (n = 19) and HCD (n = 16).
The statistically important differences are detailed in Table 2 and illustrated in Figure 21.
Subdomain: Play and Leisure Skills
Five children (all boys) achieved high or moderately high scores in the play and leisure skills subdomain, while 30 children (25 boys and five girls) scored low or moderately low. Among the high scorers, three were school children, while two were adolescents. Two had been diagnosed with childhood autism. One presented with one or more FGIDs, and one had FS. All of them followed PBD and LCD. Of the lower-scoring group, 11 were preschoolers, 16 were school-aged children, and three were adolescents. Ten had diagnoses other than childhood autism. Thirteen presented with one or more FGIDs, and 13 had FS. The majority consumed PBD (n = 19) and HCD (n = 17).
The statistically important differences are detailed in Table 2 and illustrated in Figure 22.
Subdomain: Coping Skills
Four children scored high or moderately high in the coping skills subdomain, while 30 scored low or moderately low. Among the higher scorers, two were preschoolers and two were adolescents. All were boys. Two had been diagnosed with childhood autism. Two presented with one or more FGIDs, and one also had FS. All followed PBD, with three adhering to LCD. Of the lower-scoring participants, 10 were preschoolers, 17 were school-aged children, and three were adolescents. Twenty-four were boys. Nineteen were diagnosed with childhood autism. Thirteen had one or more FGIDs, and 12 had FS. Twenty-one participants consumed a PBD, while 17 followed an LCD. Statistically significant differences are detailed in Table 2 and illustrated in Figure 23. The V-score in coping skills showed a positive correlation with F0332 abundance (r = 0.5). Table 3 contains a list of all correlations.

3.5.4. Domain: Motor Skills

Thirteen children scored low or moderately low in the motor skills domain. Ten participants achieved an adequate result, while 13 scored high or moderately high. According to the Vineland Adaptive Behavioral Score scoring method, children aged 10 years or older were not assessed in the motor skills domain. The statistically important differences are detailed in Table 2 and illustrated in Figure 24.
Subdomain: Large Muscle Skills
Seventeen children (14 boys and three girls) scored high or moderately high in the large muscle skills subdomain, while 11 (eight boys and three girls) scored low or moderately low. Among the high scorers, six were preschoolers and 11 were school-aged children. Thirteen were diagnosed with childhood autism. Seven presented with one or more FGIDs, and seven had FS. Ten of these children followed PBD, and 13 adhered to HCD. In the lower-scoring group, six were preschoolers and five were school-aged children. Five had diagnoses other than childhood autism. Two presented with one or more FGIDs, and five had FS. The majority consumed PBD (n = 8) and HCD (n = 7). Statistically significant differences are detailed in Table 2 and illustrated in Figure 25.
Subdomain: Small Muscle Skills
Eight children scored high or moderately high in the small muscle skills subdomain, while 20 scored low or moderately low. Among the higher scorers, one was a preschooler and seven were school-aged children. Six were boys, and six had been diagnosed with childhood autism. Two presented with one or more FGIDs, and three also had FS. Half of them followed LCD, and seven consumed PBD. Of the lower-scoring participants, eight were preschoolers and 12 were school-aged children. Sixteen were boys, and 14 had been diagnosed with childhood autism. Six presented with one or more FGIDs, while nine had FS. Eleven participants followed PBD, and 11 adhered to HCD. Statistically significant differences are detailed in Table 2 and illustrated in Figure 26.
The V-score in fine motor skills showed positive correlations with Clostridiales (r = 0.53) and Peptostreptococcales/Tissierellales (r = 0.57) abundance, as well as with Romboutsia abundance (r = 0.55). All correlations are summarized in Table 3.

4. Discussion

4.1. The Structure of the Study Group and Microbiota Composition

The composition of gut microbiota in individuals with ASD appears to be influenced by a variety of host-related factors, including age, sex, mode of delivery, diagnosis subtype, and dietary habits. The following analysis outlines key taxonomic differences associated with these variables, highlighting potential links between microbial profiles and clinical or neurodevelopmental characteristics.
Patients delivered via Cesarean section exhibited a higher relative abundance of Clostridiales, and at the genus level, Clostridium sensu stricto, which includes pathogenic species such as Clostridium perfringens—a microorganism previously implicated in post-Cesarean dysbiosis [23]. An increased prevalence of Clostridiales was also observed in the middle subgroup compared to older children, suggesting a less mature gut microbiota composition.
Notably, Sellimonas, a genus found to be elevated in individuals with schizophrenia, was also enriched in patients born via Cesarean section [24]. In contrast, vaginally delivered individuals showed higher levels of Faecalitalea, a genus known for short-chain fatty acid (SCFA) production and immunoregulatory properties [25]. These findings underscore the critical role of delivery mode in shaping early gut microbiota development.
Micrococcales and Rothia, both negatively correlated with age and recognized for their potential pathogenicity in humans [26,27,28], further support the hypothesis of microbiota immaturity in younger individuals.
Beyond Clostridiales, the middle subgroup demonstrated increased abundance of genera associated with neuropsychiatric conditions, including Subdoligranulum, which was previously found to be elevated in patients with major depressive disorder (MDD), and Olsenella, which has been linked to both MDD and insomnia [29,30,31]. Conversely, Lachnospiraceae, commonly reduced in Alzheimer’s disease (AD) [24], and Romboutsia, decreased in MDD [32], were found in lower abundance in this age group. Adlercreutzia, recognized for its anti-inflammatory properties [33], was also detected.
Adolescents presented with elevated levels of pathogenic taxa, such as Klebsiella and Clostridium perfringens [23,34], possibly reflective of their typical dietary patterns characterized by increased consumption of highly processed carbohydrate-rich foods and reduced fiber intake [35]. Nonetheless, their microbiota also showed greater representation of probiotic species [36,37,38], indicative of increased microbial maturity.
Sex-based differences were also apparent. Peptococcales, which were more abundant in boys, have been associated with mucin degradation [39], whereas girls exhibited higher levels of Rhodospirillales, known for their anti-inflammatory effects [40]. Other immunomodulatory taxa included Eubacterium ruminantium and Lachnospiraceae UCG-010, which—despite their regulatory roles—have also been associated with neurological and respiratory pathologies [41,42,43]. Similarly, Ruminococcus gnavus, enriched in males, and Lautropia, also predominantly found in boys, are taxa with known pathogenic potential [44,45].
In contrast, Coprobacillus, more prevalent in female participants, has been linked to a lower risk of irritable bowel syndrome (IBS) [46], while Lachnospiraceae FCS020, identified in boys, has been implicated in constipation among children with ASD [47]. Odoribacter, which has been described in neuropsychiatric microbiota profiles, was found to be reduced in Rett syndrome (RS) and increased in AD. Meanwhile, Marvinbryantia, which was more common in boys, has been reported to be decreased in AD [48]. Lactobacillus fermentum, which was detected across groups, is known for its anti-inflammatory properties and role in promoting neurological recovery post-stroke [49,50].
These findings suggest that although both sexes harbored microbial communities comprising taxa with pro- and anti-inflammatory as well as neuromodulatory potential, beneficial species appeared more frequently in girls, potentially contributing to sex-specific phenotypic presentations in ASD.
However, this divergence was not clearly reflected when comparing children diagnosed with childhood autism versus those with other neurodevelopmental disorders. Both diagnostic groups shared a proinflammatory microbiota profile [51,52,53,54]. Of note, Ruminococcus torques, predominantly observed in children with childhood autism, has shown ambiguous associations with cognitive impairment and dementia in prior studies [48,55,56].

4.2. The Influence of Diet on Gut Microbiota Composition

HCD was associated with an increased abundance of Staphylococcales, which is known for its pathogenic potential [57], and Saccharimonadales, which was previously observed to be elevated in patients with AD or mild cognitive impairment [58]. Similarly, Anaerostipes levels are elevated in Parkinson’s disease (PD) and RS [59,60]. In contrast, Blautia is decreased in MDD [61], while Slackia, noted for its anti-inflammatory properties, is reduced in lumbar degenerative spondylolisthesis [62]. Weissella, which is found in higher abundance among patients on LCD, is recognized for its beneficial health effects [63]. Notably, in our study, HCD was consumed by a majority of patients with food selectivity, consistent with the known tendency of individuals with ASD and food selectivity to experience weight abnormalities [64].
Among patients without food selectivity, Peptostreptococcales/Tissierellales were more abundant. This order has been described as potentially promoting increased cortical gray matter volume and was found in higher levels in individuals without insomnia or MDD symptoms [65]. Its abundance negatively correlated with the ratio of saturated fat to total fat intake, while positively correlating with complex carbohydrates consumption. Interestingly, both complex carbohydrate intake and the absence of food selectivity also correlated with Clostridiales, an order noted for its potential pathogenicity. DTU089, which was more prevalent in patients without food selectivity, was previously observed to decrease in patients with IBS following a starch- and sucrose-reduced diet [66]. Conversely, Coprococcus has been associated with individuals not experiencing or experiencing mild IBS symptoms [46]. Barnesiella, which decreased in RS [24], and Eubacterium coprostanoligenes, which has been linked to cognitive impairment unrelated to AD and colorectal cancer risk, were also noted [67,68]. Lachnospiraceae NK4A136, which has been reported to have a causal association with puerperal sepsis [69], contrasted with Turicibacter, which exerted potentially beneficial effects on lipid and bile acids metabolism in a murine model [70]. These findings imply that food selectivity may not be a primary driver of overall microbiota composition.
Eubacterium coprostanoligenes, which negatively correlates with saturated fat intake, demonstrated a hypocholesterolemic effect in a rabbit model [71]. Eubacterium hallii similarly showed a negative correlation with total cholesterol level in humans and improved insulin sensitivity in mice [72,73]. Dorea, which is more abundant in PD, multiple sclerosis (MS), and IBS patients [74,75], is also elevated in overweight and obese women consuming moderately high protein diets [76]. Leuconostoc citreum and Leuconostoc lactis, which are positively correlated with protein intake, are recognized for their anti-inflammatory and antibacterial properties [77,78]. In contrast, saturated fat consumption correlated positively with species known for proinflammatory and pro-oncogenic activity [52,53,57,79,80]. Notably, Dielma showed a positive correlation with the ratio of unsaturated fat to total fat intake, mean fat consumption relative to weight, and high-protein diet. This genus is elevated in patients with post-stroke sleep disturbance [81], although it decreases in male adults on high-protein diets [76] and is reduced in cases of malnutrition associated with esophageal neoplasms [82].
Patients with high protein intake also had elevated levels of Eubacterium brachy, a genus linked to exacerbation of atopic dermatitis [83] but paradoxically associated with a lower risk of insomnia characterized by difficulty falling asleep [31]. Additionally, Lactobacillus curvatus, described as neuroimmune modulator, reduces the risk of age-related memory deficits in a murine model [61].
Higher consumption of simple carbohydrates and the proportion of energy derived from them correlated positively with Epulopiscium, which has been previously linked to impaired cognitive functions [84], as well as with Weissella, a proposed probiotic genus. Simple carbohydrate intake correlated negatively with Flavonifractor, a genus known to induce oxidative stress and which is more abundant in bipolar disorder (BD) patients [24,85]. Energy from carbohydrates also showed a positive correlation with Klebsiella, recognized for its pro-inflammatory characteristics [34], while total kilocalorie intake from carbohydrates correlated with Clostridium perfingens, a known agent of intestinal diseases [86].

4.3. The Role of Gut Microbiota in Functional Gastrointestinal Disorders

The occurrence of FGID symptoms in patients with ASD represents a significant clinical challenge, as it substantially lowers quality of life—not only due to the symptoms themselves, but also because of associated social stigma, depression, and anxiety [87,88]. Several bacteria genera that were found to be more abundant in ASD patients, including Oscillibacter, Holdemania, Eubacterium coprostanoligenes, or Enterobacter, have also been linked to MDD [30]. Oscillibacter is reduced in RS, while Lachnospira is decreased in AD [24,89]. Papillibacter, which correlates positively with Mini-Mental Test Examination scores and negatively with neuropsychiatric symptoms of AD [90], is less abundant in these patients but increased in PD [91]. Dielma has been observed at elevated levels in individuals experiencing post-stroke sleep disturbances [81]. Importantly, neuropsychiatric disorders can exacerbate gastrointestinal symptoms.
Lachnospiraceae UCG.004 has been described as being protective against gastroesophageal reflux disease [92]. Although exopolysaccharides produced by Leuconostoc mesenteroides partially convert to SCFA, which is beneficial for intestinal health, certain polysaccharides like dextran may contribute to FGID through their osmotic effects [93].
Staphylococcus has been implicated in constipation, particularly among individuals consuming high-protein and high-fat diets [94]; however, some evidence suggests that early-life colonization with Staphylococcus may reduce constipation risk in infants [95]. Clostridium innocuum, despite its known association with diarrhea [96], was found to be more abundant in children experiencing constipation. Moreover, Eubacterium hallii, which has been postulated as a next-generation probiotic, was also more abundant in the individuals with constipation and bloating [97]. In contrast, SCFA-producing bacteria such as Agathobacter and Lactococcus lactis were more common in children without constipation. Agathobacter is depleted in Crohn’s disease, suggesting anti-inflammatory influence [98], while Lactococcus lactis has demonstrated efficacy as a probiotic for mild constipation in adults [99].
Oxalobacter, which is more abundant in patients without functional diarrhea or bloating, has a role in oxalate degradation—a key factor in kidney stone formation [100]—though the relationship between oxalate and diarrhea remains unclear. Conversely, Enterobacter contributes to FGID symptoms through hydrogen sulfide production in the colon, which metabolizes into sulfuric acid, damaging cells and causing immune, secretory, and motility disturbances that increase visceral sensitivity [101], potentially leading to diarrhea. Family XII UCG.001 has been associated with neuroendocrine neoplasms [102]. While Leuconostoc lactis and Leuconostoc citreum exhibit anti-inflammatory and antibacterial properties [77,78], their abundance was paradoxically higher in participants with diarrhea. Interestingly, individuals without diarrhea showed higher levels of Enterobacter cloacae, a species known for its proinflammatory effects [52,53]. Lactobacillus brevis, which has been used successfully as a probiotic to reduce the pro-inflammatory activity of several bacteria species [37,103], also produces γ-aminobutyric acid (GABA), a crucial inhibitory neurotransmitter involved in neuropsychiatric disorders [61]. By contrast, Lactobacillus oligofermentans has not yet been characterized for its probiotic potential.
The Bacilli class, observed in greater abundance in participants without bloating, includes both pathogenic species such as Bacillus cereus and species with probiotic properties that alleviate gastrointestinal symptoms [104]. This class is typically elevated in children with ASD [105]. Eubacterium eligens, on the other hand, is usually reduced in ASD patients compared to typically developing ones [106]. Both species that were more abundant in children without bloating are recognized for their probiotic potential [36,107]. Additionally, Lactobacillus delbrueckii has been proposed as a psychobiotic candidate due to its anxiolytic activity, as demonstrated in a fish model [108]. Eubacterium coprostanoligenes, found in higher levels in participants reporting bloating, has been previously linked with gastrointestinal symptoms in ASD patients.

4.4. The Influence of Microbiota Composition on Communication Skills

Communication skills in patients showed strong associations with the abundance of SCFA producers and neuroimmune modulators, whereas lower communication scores related to higher levels of proinflammatory and potentially pathogenic bacteria.
Proteobacteria, which are generally elevated in individuals with ASD [8], were observed in higher-functioning patients. Similarly, improved communication abilities, particularly in the receptive domain, were linked to increased levels of SCFA-producing bacteria such as those in the Bacilli class [109]. SCFAs are known to regulate neuroimmune responses in the central nervous system (CNS), potentially reducing neuroinflammation and protecting against neurodegeneration. Additionally, some Bacilli members modulate neurodevelopment by influencing neurotransmitter production [110]. The receptive communication subdomain was also positively associated with Veillonellales/Selenomonadales, another SCFA-producing group whose role in ASD remains ambiguous; some studies have reported decreased abundance in ASD children, while others found increased levels in those with severe symptoms [60,111].
Interestingly, patients with better communication scores also had elevated levels of other SCFA producers with mixed impacts on health and neuromodulation. For instance, Peptostreptococcales/Tissierellales, which can be harmful, are abundant in PD and in individuals under chronic stress [24], while Tyzzerella is elevated in MS [112]. Flavonifractor, enriched in children with high expressive and receptive communication skills and positively linked to receptive abilities, is known to induce oxidative stress and is elevated in schizophrenia, MDD, and BD [24,86]. Yet, it may have protective effects in vascular diseases by reducing arterial stiffness [113]. Eisenbergiella and Hungatella, also increased here, are linked to MS [114]; Hungatella’s association with depressive symptoms is thought to involve stimulating GABA production [29]. On the other hand, its abundance is elevated in infants succeeding at the Point and Gaze test, indicative of further language development [115].
The genus Enterococcus has demonstrated a positive influence on infant neural rhythm tracking—an important factor in communication development [115]—and was found reduced in ASD but elevated in PD and RS [24]. Other genera noted include Enorma (increased in spinal muscular atrophy (SMA)), Anaerococcus (elevated in schizophrenia), and Gemella (typically decreased in ASD) [116,117]. Among receptive communication high scorers, pro-inflammatory genera like Dialister and CAG.352, and pro-allergic GCA.90006657 were more abundant [51,62]. Intestinibacter, which is potentially harmful, has been linked to neurodevelopmental disorders [118]. Higher expressive communication was associated with increased Eubacterium abundance. While Eubacterium coprostanoligenes is pro-inflammatory that exacerbates gastrointestinal symptoms in ASD, Eubacterium brachy appears beneficial, alleviating insomnia [31,119].
Bifidobacterium animalis stands out as a neuroimmunomodulatory species, showing benefits in neurological conditions, including ASD murine models [120,121]. Lactococcus garvieae enhances intestinal mucosal integrity [122], and Streptococcus salivarius produces bacteriocins contributing to antimicrobial activity [123].
Oscillospirales, which were more abundant in children with adequate expressive communication, shows an ambiguous role in the CNS, i.e., they are decreased in PD and RS but increased in anti-N-methyl-D-aspartate (anti-NMDAR) encephalitis and in male rodent models of autism [124,125,126]. Ruminiclostridium—a SCFA producer linked to alleviating rhythm disruptions [127,128]—was enriched in adequate and high-scoring patients in communication subdomains. Conversely, Dielma, elevated in individuals with post-stroke sleep disorders [81], was linked to adequate communication scores but lower writing skills. Phascolarctobacterium is typically higher in ASD [8], while Lachnospiraceae, another SCFA producer, is increased in AD [89]. Paraprevotella was positively associated with cognitive functioning in AD [129]. Catenisphaera, which was more abundant in adequate writing scorers, is decreased in violent women [130].
Oscillibacter, elevated in adequate-scoring children, was reduced in RS but increased in MDD and is especially related to negative affect [24,30,131]. MDD patients also show increased Faecalibacterium—an SCFA producer linked to anti-inflammatory effects—whose abundance is reduced in RS, AD, multi-system atrophy (MSA), PD, MS, and schizophrenia [24,132]. Romboutsia, considered pro-inflammatory, is paradoxically decreased in MDD [32,133]. Faecalitalea, which was more abundant in adequate receptive communicators, produces SCFAs but may increase in inflammatory bowel diseases, indicating a complex role in gut homeostasis [25,134]. Dolosigranulum, which was elevated in those with adequate expressive skills, has potential probiotic properties [135].
Leuconostoc species, known for immunomodulatory capacity, were more abundant across adequate scoring groups [77,78,136]. In writing subdomain adequate scorers, Lactobacillus fermentum—an SCFA producer—alleviates gastrointestinal inflammation and post-stroke neurological symptoms [49,50]. Bifidobacterium bifidum may impede neurodegeneration progression [128], while Streptococcus sobrinus, although pro-inflammatory in oral health, is less characteristic in gut microbiota [137].
Clostridia, associated with lower overall communication functioning, are elevated in ASD—especially in severe cases—and in RS, MSA, and MDD, though reduced in PD and MS [8,24]. Notably, Clostridia was also positively associated with neural rhythm tracking [115], possibly explaining their higher abundance in those with better receptive and expressive scores.
Other bacteria linked to lower communication skills often include opportunistic or proinflammatory species that are capable of disrupting neural signaling and acting as pathobionts [24,138,139,140,141,142,143]. Their ability to trigger systemic inflammation may impair neurodevelopment and communication. Clostridium, Pseudomonas, Enterococcus, and Haemophilus species enriched in low scorers have known proinflammatory and infection-related roles [140,144,145,146,147]. Intriguingly, Rhizobiales, Pseudomonadales, and Enterococcus were more abundant in children with adequate writing skills, despite being linked to lower overall communication scores; Enterococcus notably also participates in MS pathophysiology [147]. Erysipelotrichaceae UCG.003, which is associated with Western diet consumption, was elevated in RS [24,96], though some taxa like Defluviitaleaceae UCG-011 were decreased in MDD [61]. Collinsella, which was more abundant in poorer writing performers, was elevated in ASD [66], whereas Blautia was decreased in MDD [61]. Ruminococcus gauvreauii, a SCFA producer linked to prosocial behavior [148], was increased in low-writing scorers. Anaerostipes, which was negatively correlated with writing skills, was elevated in PD and RS [59,60].

4.5. Microbiota Composition and Daily Living Skills

Daily living skills exhibited a complex pattern of associations with both pro- and anti-inflammatory bacteria, alongside genera linked to neuropsychiatric disorders.
Gammaproteobacteria, which has been found in higher abundance among patients with strong daily living skills—including domestic ones—have also been reported as elevated in PD [149]. Negativicutes, which are more prevalent in patients with adequate scores compared to low scorers, were previously observed predominantly in typically developing children versus those with ASD [150].
Interestingly, Lactobacillales, known for their probiotic activity [37,38], were more abundant in low-scoring patients, including those with reduced personal and domestic skills. Propionibacteriales, which were higher in children with adequate personal skills than in those with higher skills, are recognized for their potential to cause infections [139].
Lower scores in daily living skills were linked to an increased abundance of pro-inflammatory bacteria such as Pseudomonas, Ruminiclostridium, Granulicatella, and Parvimonas [140,151,152]. Additionally, these patients exhibited higher levels of Oxalobacter—a genus involved in oxalate degradation [100]—and Anaerostipes, Collinsella, and Butyricicoccus, genera linked to various neurological conditions, such as PD, MD, RS, and schizophrenia [24,59,60,74]. Moreover, Collinsella and Ruminococcus were previously observed to be more abundant in children with ASD [60], with the latter being simultaneously decreased in RS [24]. Most species associated with low scoring were pro-inflammatory, except for Lactococcus garvieae, Lactobacillus fermentum, and Odoribacter splanchnicus, which have anti-inflammatory or probiotic properties despite being abundant in children with poorer domestic skills [50,122,153].
Adequate daily living skill scores were associated with a mix of pro-inflammatory genera—such as Ruminiclostridium, Sarcina, Staphylococcus, Eubacterium siraeum, Haemophilus, and NK4A214 [57,69,140,154,155,156]—and genera prevalent in neurological disorders, including Anaerostipes [59]. Simultaneously, beneficial genera like Dolosigranulum, Butyricimonas, Agathobacter, and Coprococcus were also more abundant [46,98,135,157]. Some taxa, like Oscillibacter showed mixed influence; while being reduced in RS and known for anti-inflammatory effects in the gut, they were elevated in MDD [24,30,46]. Clostridium sensu stricto contains both beneficial and harmful strains and exhibits altered abundance in neuropsychiatric disorders [24]. Lachnospiraceae, noted for anti-oncogenic intestinal effects and decreased in PD, was paradoxically correlated with constipation in ASD [47,89,158]. Barnesiella and Odoribacter, which were more abundant in children with adequate community skills scores, were decreased in RS [24], while Parabacteroides levels were reduced in MS but increased in PD [74]. Alistipes, which was more abundant in adequate scorers in community skills, is typically reduced in children with ASD [66]. Most species elevated in adequate community skills were anti-inflammatory [50,123,153], with the exception of Streptococcus sobrinus [137].
Higher daily living skill scores were also linked to several pro-inflammatory genera, including Dialister, CAG.352, Intestinibacter, Romboutsia, Flavonifractor, Senegalimassilia, Colidextribacter, and Escherichia/Shigella; the latter was also increased in RS and associated with anxiety [24,62,85,133,159,160,161,162]. Romboutsia was decreased in MDD [32], whereas Colidextribacter was reduced in PD [163]. Interestingly, despite its pro-inflammatory nature, Intestinibacter positively correlated with domestic skills, and Romboutsia with personal skills. Children with stronger personal skills showed higher abundances of Eubacterium brachy—known to alleviate insomnia—and Eubacterium coprostanoligenes, considered pro-inflammatory [31,119]. GCA.900066575 appears to have an anti-inflammatory role in food allergies [51].
Several bacterial genera that were more abundant in participants with higher daily living skills scores have likewise been associated with other neuropsychiatric disorders. Tyzzerella has been associated with MS progression and psychoneurological symptoms in head and neck cancer patients [112,164]. Papillibacter is decreased in AD but elevated in PD [90,91], while Anaerococcus is more abundant in schizophrenia [117]. Veillonella, which was enriched in those with higher personal skills, is typically decreased in children with ASD [150]. Enterococcus, previously discussed, is linked to improved neural rhythm tracking and was elevated in RS patients but reduced in ASD [24,114]. In contrast, Hungatella, connected to MDD and MS, was found to be decreased in children who scored higher on the Point and Gaze test, which measures joint attention [29,114,115].

4.6. The Role of Microbiota in the Development of Socialization Skills

The socialization domain showed associations with both pro- and anti-inflammatory bacteria.
Patients with lower socialization scores had higher abundances of Clostridia and Desulfovibrionales, taxa previously linked to more severe ASD symptoms [8,61]. Clostridia were also elevated in RS, MSA, and MDD [24]. Low scorers in the interpersonal skills subdomain exhibited increased levels of pro-inflammatory genera such as Ruminiclostridium, as well as bacteria commonly found in neurological disorders, including Anaerostipes, Butyricicoccus, and Collinsella [24,59,74]. In contrast, Barnesiella, which was reduced in RS [24], was lower in these low-scoring patients. Interestingly, both poorer and adequate performers showed higher levels of Lactococcus garvieae and Lactobacillus fermentum, which protect intestinal mucosa and promote SCFA-mediated recovery post-stroke [51,122], alongside a paradoxical increase in the pro-inflammatory Streptococcus sobrinus [165].
Children with slightly better socialization outcomes, scoring in the adequate range, had increased abundances of potentially pathogenic Enterobacterales [101,166], as well as Veillonellales/Selenomonadales, which are typically decreased in patients with ASD [150]. Adequate scorers in coping skills also had elevated Bacilli, known to produce neurotransmitters such as dopamine and noradrenaline, a bacterial class typically increased in ASD [105,110]. These individuals additionally showed higher levels of Saccharimonadia, associated with cognitive impairments [58], and more of the potentially protective Monoglobales compared to high scorers [167].
At the genus level, adequate scorers displayed higher abundances of the pro-inflammatory Dialister alongside the anti-inflammatory Coprococcus [46,62]. Enterococcus, which has been linked to improved neural rhythm tracking and was typically decreased in ASD but elevated in PD and RS, was also more abundant [24,115]. Subdoligranulum, associated with MDD, and Hungatella—linked to depression and also elevated in these adequate scorers in coping skills—were noted [29]. Other genera enriched in this group included Eubacterium brachy, which is known for reducing insomnia risk, and Weissella, which has been recognized for its probiotic potential [31,63]. Ruminococcus torques, which was more abundant in children with adequate interpersonal and coping skills, was increased in PD and ASD but decreased in Alzheimer’s disease (AD) [48,58,168]. Veillonella, which was higher in adequate-scorers in the play and leisure and in the coping skills subdomains, and Actinomyces, which were higher in adequate-performers in the coping skills subdomain, are typically decreased in children with ASD [150]. Butyricicoccus was linked to MS and PD [74]. Odoribacter was previously observed to be decreased in RS [24], with O. splanchnicus, which was observed in adequate scorers in the play and leisure skills subdomain, known for its probiotic potential [153]. Children with adequate coping skills also carried more immunomodulatory Leuconostoc citreum and Leuconostoc lactis [77,78,136]. However, adequate scorers in the interpersonal and the play and leisure skills also carried pro-inflammatory Streptococcus mutans and Streptococcus sobrinus [169].
High-scoring children in socialization exhibited greater abundances of Bilophila, which is typically reduced in ASD but raised in MS and schizophrenia, Olsenella, which is increased in MDD, and Oscillibacter, which is decreased in RS [24,30,60]. Clostridia, which was elevated in high scorers in coping skills and linked to severe ASD symptoms as well as other neuropsychiatric disorders, showed positive associations with neural rhythm tracking [24,115]. Burkholderiales, which is known to be more abundant in ASD, and Defluviivitaleaceae, which is decreased in MDD, were also observed [170,171]. Oscillibacter appeared to be linked to MDD but was reduced in RS [24,30], while Dorea was more abundant in MS and PD [74]. Several pro-inflammatory genera—including Parasutterella, Ruminococcus gnavus, CAG.352, Rhizobiales, Pseudomonadales, Eubacterium brachy, Dialister, Enterobacter, Flavonifractor, Varibaculum, and Propionibacteriales—were elevated [24,44,52,53,62,83,85,139,140,141,172,173]. Lachnospiraceae and Clostridium innocuum demonstrated ambiguous roles in neurological disorders and inflammation [47,91,96,158], whereas the significance of Oxalobacter in ASD remains unclear [100]. Ruminiclostridium, which produces beneficial SCFAs and helps alleviate rhythm disruptions, was also noted [127,128]. Overall, higher socialization scores were linked to greater abundances of several pro-inflammatory species [144,145,146,156,174], except for Lactococcus garvieae, Streptococcus salivarius, and Leuconostoc citreum, all of which are associated with anti-inflammatory or probiotic functions [78,122,175].

4.7. Microbiota and the Development of Motor Skills

Motor skills showed associations with both protective and pro-inflammatory bacteria, highlighting the complex role of microbiota in neurological function.
The Monoglobales order, known for its protective potential, was more abundant in patients with adequate rather than high scores in the motor skills domain [167]. Adequate fine motor skills were linked to higher levels of Negativicutes, a group previously found to be reduced in ASD [150]. Enhanced fine motor performance was also associated with increased abundance of Peptostreptococcales/Tissierellales, an order connected to greater cortical gray matter volume and absence of insomnia or depression [65], which may be important for motor development. At the genus level, children exhibiting better motor skills showed elevated levels of protective bacteria such as Ruminococcus gauvreauii, Coprococcus, and Eubacterium hallii [46,97,148]. However, some pro-inflammatory taxa, like Eubacterium siraeum [155], were also more abundant in this group. Intriguingly, certain genera with protective attributes had conflicting associations with neuropsychiatric disorders; for example, Eubacterium ventriosum was increased in MDD but decreased in AD [29,48]. Similarly, Lachnospiraceae and Ruminococcus torques were reduced in AD, while R. torques was elevated in ASD [48,168]. Romboutsia, a pro-inflammatory genus elevated in adequate-functioning patients with gross motor skills and in high-functioning patients with fine motor skills, is known to be increased in MDD [32,133]. Additionally, Frisingicoccus negatively correlated with anxiety and motor deficits in a PD rodent model and was elevated in SMA [116,157].
Patients with high motor skills scores exhibited greater abundances of anti-inflammatory bacteria, including Eubacterium coprostanoligenes and members of Bifidobacteriales—especially Bifidobacterium bifidum [119,176]—as well as pro-inflammatory Intestinibacter [118]. Notably, high-functioning individuals, particularly those with superior fine motor skills, correlated with increased Clostridiales abundance. While Clostridiales were linked to more severe ASD symptoms and dysbiosis in RS, MSA, and MDD, they were also associated with enhanced neural rhythm tracking, a mechanism essential for fine motor skill development [8,24,61,115]. Fine motor abilities further correlated with higher levels of anti-inflammatory taxa, such as Butyricimonas, Lactobacillus salivarius, Lactococcus garvieae, and Bifidobacterium bifidum [122,123,157,176], as well as SCFA-producing bacteria, such as Ruminiclostridia [127,128]. Simultaneously, several pro-inflammatory bacteria—including Eubacterium coprostanoligenes, Parasutterella, Romboutsia, Intestinibacter, and Streptococcus anginosus [31,118,133,172,177]—were enriched among individuals excelling in fine motor skills, alongside neuropsychiatric disorder-associated genera like Ruminococcus torques and Sarcina [55,168,178].
Interestingly, gross motor skills appeared to improve with increased abundances of pro-inflammatory taxa such as Flavonifractor and Staphylococcus [24,57,85], with the exception of Bifidobacterium, which is generally protective [176]. TM7x, notable for its highly reduced genome and parasitic lifestyle on other bacteria [179], was also more abundant in patients exhibiting better gross motor function, illustrating the intricate microbial interactions influencing host physiology.

4.8. Limitations

This study offers new understanding of the intricate relationship between gut microbiota and behavioral, dietary, and gastrointestinal factors in children with ASD. However, some limitations must be considered. First, the cross-sectional nature of the study prevented establishing any cause-and-effect relationship between microbial composition and clinical characteristics. Long-term studies are necessary to clarify whether the microbial patterns observed are a result or a cause of ASD-related symptoms. Second, despite careful selection of the cohort, the sample size was relatively small, especially for subgroup analyses based on sex, age, diet, or adaptive functioning. This constraint may affect the broader applicability of the results and highlights the importance of validating these findings in larger and more diverse groups.

5. Conclusions

This study highlights the significant influence of delivery mode, diet, age, and sex on gut microbiota composition in children with neurodevelopmental and neuropsychiatric disorders. The complex interplay between microbial communities and clinical symptoms—such as communication, motor skills, socialization, and gastrointestinal function—suggests a strong gut–brain–immune connection. Both pro-inflammatory and anti-inflammatory bacterial genera contribute variably to neurological outcomes, indicating that microbial balance is critical in symptom severity and development. These findings underscore the importance of considering microbiota-targeted interventions as part of comprehensive care strategies for affected individuals.

Author Contributions

Conceptualization, Z.L.-P., M.F. and K.M.-M.; methodology, Z.L.-P., M.F. and K.M.-M.; software, Z.L.-P. and K.M.-M.; validation, Z.L.-P., M.F. and K.M.-M.; formal analysis, Z.L.-P., M.F. and K.M.-M.; investigation, Z.L.-P., M.F. and K.M.-M.; resources, Z.L.-P., M.F. and K.M.-M.; data curation, Z.L.-P. and K.M.-M.; writing—original draft preparation, Z.L.-P. and K.M.-M.; writing—review and editing, Z.L.-P. and K.M.-M.; visualization, Z.L.-P. and K.M.-M.; supervision, M.F. and K.M.-M.; project administration, Z.L.-P. and K.M.-M.; funding acquisition, Z.L.-P., M.F. and K.M.-M. All authors have read and agreed to the published version of the manuscript.

Funding

The research was co-founded by Doctoral School of Poznan University of Medical Sciences under the Large Research Grant (SDUM-GB23/03/21).

Institutional Review Board Statement

All procedures involving human participants were conducted in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Declaration of Helsinki and its later amendments or comparable ethical standards [15]. The study protocol was reviewed and approved by the Bioethics Committee of Poznań University of Medical Sciences, approval number KB 1138/19 on 5 December 2019.

Informed Consent Statement

Informed consent was obtained from all subjects and/or their legal guardians involved in the study.

Data Availability Statement

The raw data supporting the conclusions of this article will be made available by the authors on request due to privacy of the patients, including regional GDPR regulations.

Acknowledgments

The authors would like to thank Szymon Melewski, for his support in conducting the statistical analysis of the data.

Conflicts of Interest

The authors declare no conflict of interest.

Abbreviations

The following abbreviations are used in this manuscript:
ADAlzheimer’s disease
ASD Autism Spectrum Disorder
ChBDcarbohydrate-based diet
CNScentral nervous system
FGIDFunctional Gastrointestinal Disorder
FSfood selectivity
GABAγ-aminobutyric acid
HCDhigh-calorie diet
IBSirritable bowel syndrome
ICD-10International Statistical Classification of Diseases and Related Health Problems—10th Revision
LCDlow-calorie diet
MDDmajor depressive disorder
MSmultiple sclerosis
MSAmultiple system atrophy
PBDprotein-based diet
PDParkinson’s disease
RSRett syndrome
SCFAshort-chain fatty acid
SMAspinal muscular atrophy
VABSVineland Adaptive Behavior Scales

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Nutrients 17 02781 g001
Figure 1. Statistically significant differences among age groups (* p ≤ 0.05, ** p ≤ 0.01, *** p ≤ 0.001, NS: not significant).
Figure 1. Statistically significant differences among age groups (* p ≤ 0.05, ** p ≤ 0.01, *** p ≤ 0.001, NS: not significant).
Nutrients 17 02781 g001
Nutrients 17 02781 g002
Figure 2. Statistically significant differences between the two diagnosis groups (* p ≤ 0.05, ** p ≤ 0.01).
Figure 2. Statistically significant differences between the two diagnosis groups (* p ≤ 0.05, ** p ≤ 0.01).
Nutrients 17 02781 g002
Nutrients 17 02781 g003
Figure 3. Statistically meaningful differences between the two sex groups (* p ≤ 0.05, ** p ≤ 0.01).
Figure 3. Statistically meaningful differences between the two sex groups (* p ≤ 0.05, ** p ≤ 0.01).
Nutrients 17 02781 g003
Nutrients 17 02781 g004
Figure 4. Statistically significant differences between the two modes of delivery groups (* p ≤ 0.05, ** p ≤ 0.01).
Figure 4. Statistically significant differences between the two modes of delivery groups (* p ≤ 0.05, ** p ≤ 0.01).
Nutrients 17 02781 g004
Nutrients 17 02781 g005
Figure 5. Statistically meaningful differences between the two food selectivity groups (* p ≤ 0.05, ** p ≤ 0.01).
Figure 5. Statistically meaningful differences between the two food selectivity groups (* p ≤ 0.05, ** p ≤ 0.01).
Nutrients 17 02781 g005
Nutrients 17 02781 g006
Figure 6. Statistically significant differences between two diet subtypes—carbohydrate-based diet vs. protein-based diet (* p ≤ 0.05).
Figure 6. Statistically significant differences between two diet subtypes—carbohydrate-based diet vs. protein-based diet (* p ≤ 0.05).
Nutrients 17 02781 g006
Nutrients 17 02781 g007
Figure 7. Statistically significant differences between two diet subtypes—high-calorie diet vs. low-calorie diet (* p ≤ 0.05, ** p ≤ 0.01).
Figure 7. Statistically significant differences between two diet subtypes—high-calorie diet vs. low-calorie diet (* p ≤ 0.05, ** p ≤ 0.01).
Nutrients 17 02781 g007
Nutrients 17 02781 g008
Figure 8. Statistically relevant differences between the two functional gastrointestinal disorder groups (* p ≤ 0.05, ** p ≤ 0.01).
Figure 8. Statistically relevant differences between the two functional gastrointestinal disorder groups (* p ≤ 0.05, ** p ≤ 0.01).
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Nutrients 17 02781 g009
Figure 9. Statistically meaningful differences between the two groups (* p ≤ 0.05, ** p ≤ 0.01).
Figure 9. Statistically meaningful differences between the two groups (* p ≤ 0.05, ** p ≤ 0.01).
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Nutrients 17 02781 g010
Figure 10. Statistically significant differences between the two groups (* p ≤ 0.05).
Figure 10. Statistically significant differences between the two groups (* p ≤ 0.05).
Nutrients 17 02781 g010
Nutrients 17 02781 g011
Figure 11. Statistically relevant differences between the two groups (* p ≤ 0.05, ** p ≤ 0.01).
Figure 11. Statistically relevant differences between the two groups (* p ≤ 0.05, ** p ≤ 0.01).
Nutrients 17 02781 g011
Nutrients 17 02781 g012aNutrients 17 02781 g012b
Figure 12. Statistically significant differences between the two groups in the communication domain (* p ≤ 0.05, ** p ≤ 0.01, NS: not significant).
Figure 12. Statistically significant differences between the two groups in the communication domain (* p ≤ 0.05, ** p ≤ 0.01, NS: not significant).
Nutrients 17 02781 g012aNutrients 17 02781 g012b
Nutrients 17 02781 g013
Figure 13. Statistically meaningful differences between the two groups in the receptive communication subdomain (* p ≤ 0.05, ** p ≤ 0.01, NS: not significant).
Figure 13. Statistically meaningful differences between the two groups in the receptive communication subdomain (* p ≤ 0.05, ** p ≤ 0.01, NS: not significant).
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Nutrients 17 02781 g014
Figure 14. Statistically relevant differences between the two groups in the expressive communication subdomain (* p ≤ 0.05, ** p ≤ 0.01, *** p ≤ 0.001, NS: not significant).
Figure 14. Statistically relevant differences between the two groups in the expressive communication subdomain (* p ≤ 0.05, ** p ≤ 0.01, *** p ≤ 0.001, NS: not significant).
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Nutrients 17 02781 g015
Figure 15. Statistically significant differences between the two groups in the writing skills subdomain (* p ≤ 0.05, ** p ≤ 0.01, *** p ≤ 0.001, NS: not significant).
Figure 15. Statistically significant differences between the two groups in the writing skills subdomain (* p ≤ 0.05, ** p ≤ 0.01, *** p ≤ 0.001, NS: not significant).
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Nutrients 17 02781 g016
Figure 16. Statistically relevant differences between the two groups in the daily living skills domain (* p ≤ 0.05, ** p ≤ 0.01, NS: not significant).
Figure 16. Statistically relevant differences between the two groups in the daily living skills domain (* p ≤ 0.05, ** p ≤ 0.01, NS: not significant).
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Nutrients 17 02781 g017
Figure 17. Statistically significant differences between the two groups in the personal skills subdomain (* p ≤ 0.05, ** p ≤ 0.01, NS: not significant).
Figure 17. Statistically significant differences between the two groups in the personal skills subdomain (* p ≤ 0.05, ** p ≤ 0.01, NS: not significant).
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Nutrients 17 02781 g018
Figure 18. Statistically meaningful differences between the two groups in the domestic skills subdomain (* p ≤ 0.05, ** p ≤ 0.01, NS: not significant).
Figure 18. Statistically meaningful differences between the two groups in the domestic skills subdomain (* p ≤ 0.05, ** p ≤ 0.01, NS: not significant).
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Nutrients 17 02781 g019
Figure 19. Statistically relevant differences between the two groups in the community skills subdomain (* p ≤ 0.05, ** p ≤ 0.01, NS: not significant).
Figure 19. Statistically relevant differences between the two groups in the community skills subdomain (* p ≤ 0.05, ** p ≤ 0.01, NS: not significant).
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Nutrients 17 02781 g020
Figure 20. Statistically significant differences between the two groups in the socialization domain (* p ≤ 0.05, ** p ≤ 0.01, NS: not significant).
Figure 20. Statistically significant differences between the two groups in the socialization domain (* p ≤ 0.05, ** p ≤ 0.01, NS: not significant).
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Nutrients 17 02781 g021
Figure 21. Statistically relevant differences between the two groups in the interpersonal skills subdomain (* p ≤ 0.05, ** p ≤ 0.01, NS: not significant).
Figure 21. Statistically relevant differences between the two groups in the interpersonal skills subdomain (* p ≤ 0.05, ** p ≤ 0.01, NS: not significant).
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Nutrients 17 02781 g022
Figure 22. Statistically meaningful differences between the two groups in the play and leisure skills subdomain (* p ≤ 0.05, ** p ≤ 0.01, NS: not significant).
Figure 22. Statistically meaningful differences between the two groups in the play and leisure skills subdomain (* p ≤ 0.05, ** p ≤ 0.01, NS: not significant).
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Nutrients 17 02781 g023
Figure 23. Statistically relevant differences between the two groups in the coping skills subdomain (* p ≤ 0.05, ** p ≤ 0.01, *** p ≤ 0.001, NS: not significant).
Figure 23. Statistically relevant differences between the two groups in the coping skills subdomain (* p ≤ 0.05, ** p ≤ 0.01, *** p ≤ 0.001, NS: not significant).
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Nutrients 17 02781 g024
Figure 24. Statistically relevant differences between the two groups in the motor skills domain (* p ≤ 0.05, ** p ≤ 0.01, NS: not significant).
Figure 24. Statistically relevant differences between the two groups in the motor skills domain (* p ≤ 0.05, ** p ≤ 0.01, NS: not significant).
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Nutrients 17 02781 g025
Figure 25. Statistically significant differences between the two groups in the large muscle skills subdomain (* p ≤ 0.05, NS: not significant).
Figure 25. Statistically significant differences between the two groups in the large muscle skills subdomain (* p ≤ 0.05, NS: not significant).
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Nutrients 17 02781 g026
Figure 26. Statistically relevant differences between the two groups in the small muscle skills subdomain (* p ≤ 0.05, ** p ≤ 0.01, NS: not significant).
Figure 26. Statistically relevant differences between the two groups in the small muscle skills subdomain (* p ≤ 0.05, ** p ≤ 0.01, NS: not significant).
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Table 1. Detailed data analysis of the structure of the group, diet, and functional gastrointestinal disorders (FGID).
Table 1. Detailed data analysis of the structure of the group, diet, and functional gastrointestinal disorders (FGID).
Total Number of Participants: n = 45
SexFemalen = 7
Malen = 38
Age
(distribution: 7.9 ± 3.35)		2–6 yearsn = 12
6–14 yearsn = 28
15–18 yearsn = 5
Diagnosis according to ICD-10Childhood autismn = 30 (67%)
Other diagnosisn = 15 (33%)
Mode of deliveryVaginal deliveryn = 31 (69%)
Cesarean sectionn = 14 (31%)
Gastrointestinal symptomsThe presence of any FGIDn = 18 (40%)
Functional constipationn = 8 (18%)
Functional diarrhean = 9 (20%)
Functional bloatingn = 7 (16%)
DietType of diet (macroelements)Protein-based: n = 31 (6%)
Carbohydrate-based: n = 14 (31%)
Type of diet (calorie intake)Low-calorie diet: n = 23 (51%)
High-calorie diet: n = 12 (49%)
Food selectivityn = 16 (36%)
Mean daily protein consumption (g/day)90.05 ± 31.06
Mean protein intake normalized to body weight
(g/kg BW)		3.81 ± 1.71
Mean daily carbohydrate consumption (g/day)251.02 ± 62.66
Percentage of energy from carbohydrates (%)51.41 ± 9.31
Mean proportion of simple carbohydrates in total carbohydrate intake (%)41.31 ± 7.82
Mean daily fat consumption (g/day)79.51 ± 31.32
Mean fat intake normalized to body weight (g/kg BW)3.3 ± 1.7
Fraction of unsaturated fatty acids within total fat intake (%)59.89 ± 5.30
Table 2. Differences in gut microbiota across patient groups.
Table 2. Differences in gut microbiota across patient groups.
FeatureBacteriap-Value ≤
Class
high-calorie dietSaccharimonadia0.05
no bloatingBacilli0.05
communication—expressive skills (adequate > low)Clostridia0.05
communication—expressive skills (high > low)Negativicutes0.05
communication—writing skills (low > high)Coriobacteria0.05
communication—total (high > adequate)Bacilli0.05
communication—total (high > low)Alphaproteobacteria0.05
Gammaproteobacteria0.05
daily living skills—domestic skills (adequate > low)Negativicutes0.05
daily living skills—domestic skills (low > high)Gammaproteobacteria0.05
daily living skills—community skills (adequate > low)Negativicutes0.01
daily living skills—total (high > low)Gammaproteobacteria0.05
daily living skills—total (high > adequate)Gammaproteobacteria0.05
socialization—coping skills (adequate > low)Bacilli0.05
Saccharimonadia0.05
socialization—coping skills (high > low)Saccharimonadia0.05
using large muscles (high > low)Actinobacteria0.05
using small muscles (adequate > low)Negativicutes0.05
Order
age (school children > adolescents)Clostridiales0.05
sex: femaleRhodospirillales0.05
sex: malePeptococcales0.05
Caesarean’s sectionClostridiales0.05
high-calorie dietSaccharimonadales0.05
Staphylococcales0.01
no food selectivityPeptostreptococcales/Tissierellales0.05
communication—expressive skills (adequate > low)Clostridiales0.001
Oscillospirales0.05
Peptostreptococcales/Tissierellales0.05
communication—expressive skills (high > low)Clostridia0.05
Peptostreptococcales/Tissierellales0.01
Veillonellales/Selenomonadales0.05
communication—receptive skills (high > low)Clostridia0.01
Peptostreptococcales/Tissierellales0.05
communication—receptive skills (high > adequate)Veillonellales/Selenomonadales0.05
communication—writing skills (low > high)Coriobacteriales0.05
communication—writing skills (low > adequate)Lachnospirales0.05
communication—writing skills (adequate > low)Pseudomonadales0.001
Rhizobiales0.001
communication—total (low > adequate)Actinomycetales0.05
Lactobacillales0.05
communication—total (low > high)Clostridia0.05
Propionibacteriales0.05
Pseudomonadales0.05
Rhizobiales0.05
communication—total (adequate > low)Acidaminococcales0.05
Clostridia0.05
communication—total (adequate > high)Acidaminococcales0.05
communication—total (high > low)Peptostreptococcales/Tissierellales0.05
communication—total (high > adequate)Lactobacillales0.05
daily living skills—personal skills (adequate > low)Clostridiales0.01
daily living skills—personal skills (high > low)Enterobacterales0.05
Peptostreptococcales/Tissierellales0.01
Veillonellales/Selenomonadales0.05
daily living skills—domestic skills (low > adequate)Clostridia0.05
ClostridiaLachnospirales0.05
Pseudomonadales0.05
Rhizobiales0.05
daily living skills—domestic skills (adequate > low)Clostridiales0.01
Veillonellales/Selenomonadales0.05
daily living skills—domestic skills (high > low)Enterobacterales0.05
Peptostreptococcales/Tissierellales0.05
Veillonellales/Selenomonadales0.05
daily living skills—community skills (adequate > low)Clostridia0.01
Veillonellales/Selenomonadales0.05
daily living skills—community skills (adequate > high)Pasteurellales0.05
daily living skills—community skills (high > low)Enterobacterales0.05
daily living skills—total (low > high)Lachnospirales0.05
Pseudomonadales0.05
Rhizobiales0.05
daily living skills—total (adequate > low)Clostridia0.05
Clostridiales0.05
daily living skills—total (high > low)Enterobacterales0.05
Peptostreptococcales/Tissierellales0.05
Veillonellales/Selenomonadales0.05
socialization—interpersonal skills (low > adequate)Clostridia0.05
socialization—interpersonal skills (adequate > low)Veillonellales/Selenomonadales0.05
socialization—interpersonal skills (adequate > high)Monoglobales0.05
socialization—interpersonal skills (high > low)Veillonellales/Selenomonadales0.05
socialization—play and leisure skills (adequate > low)Clostridia0.05
Veillonellales/Selenomonadales0.05
socialization—play and leisure skills (high > low)Pseudomonadales0.01
Rhizobiales0.01
socialization—coping skills (adequate > low)Actinomycetales0.05
Saccharimonadales0.05
socialization—coping skills (high > low)Burkholderiales0.05
Clostridia0.05
Propionibacteriales0.001
Saccharimonadales0.05
socialization—coping skills (high > adequate)Burkholderiales0.05
socialization—total (low > adequate)Clostridia0.05
socialization—total (low > high)Desulfovibrionales0.01
socialization—total (adequate > low)Enterobacterales0.05
Veillonellales/Selenomonadales0.01
socialization—total (adequate > high)Desulfovibrionales0.01
using large muscles (high > low)Bifidobacteriales0.05
using small muscles (adequate > low)Clostridiales0.01
Peptostreptococcales/Tissierellales0.05
using small muscles (adequate > high)Monoglobales0.05
using small muscles (high > low)Clostridia0.05
Clostridiales0.01
Peptostreptococcales/Tissierellales0.05
motor skills—total (adequate > low)Monoglobales0.05
motor skills—total (high > low)Clostridiales0.05
motor skills—total (high > adequate)Clostridiales0.01
Genus
age (preschoolers > school children)Enterorhabdus0.05
age (school children > preschoolers)Adlercreutzia0.05
Olsenella0.05
Lachnospira0.05
Subdoligranulum0.05
Romboutsia0.05
age (school children > adolescents)Clostridium sensu stricte0.05
Lachnospiraceae NK4A1360.05
age (adolescents > school children)Klebsiella0.01
Lactobacillus0.05
Olsenella0.001
diagnosis: childhood autismRuminococcus torques0.01
GCA.9000665750.05
diagnosis: otherEnterorhabdus0.05
sex: femaleEubacterium ruminantium0.05
Coprobacillus0.05
Odoribacter0.05
UCG0100.05
sex: maleRuminococcus gravus0.05
Lachnospiraceae FCS0200.05
Lautropia0.05
Peptococcus0.05
Marvinbryantia0.05
vaginal deliveryFaecalitalea0.05
Caesarian’s sectionEubacterium eligens0.05
F03320.05
Sellimonas0.05
Clostridium sensu stricte0.05
high protein dietEubacterium brachy0.05
low-calorie dietWeissella0.05
high-calorie dietAnaerostipes0.05
Blautia0.05
Slackia0.05
Staphylococcus0.05
TM7x0.05
no food selectivityClostridium sensu stricte0.05
Eubacterium coprostanoligenes0.05
Barnesiella0.05
Coprococcus0.05
DTU0890.01
Lachnospiraceae NK4A1360.05
Turicibacter0.05
FGIDEnterobacter0.05
F03320.01
Holdemania0.05
Oscillibacter0.05
Clostridium innocuum0.05
Staphylococcus0.05
no FGIDLachnospiraceae UCG0040.05
bloatingEubacterium coprostanoligenes0.05
no bloatingDielma0.05
Oxalobacter0.05
Eubacterium eligens0.05
diarrhoeaEnterobacter0.05
Family XII UCG0010.05
Oscillibacter0.05
Papillibacter0.05
F03320.05
no diarrhoeaOxalobacter0.05
constipationClostridium innocuum0.05
Oscillibacter0.05
Staphylococcus0.01
Lachnospira0.05
no constipationAgathobacter0.05
Lactococcus0.05
communication—expressive skills (adequate > low)Clostridium sensu stricto0.001
Dolosigranulum0.05
Faecalibacterium0.05
Lachnospiraceae UCG0030.05
Romboutsia0.05
communication—expressive skills (high > low)CAG3520.05
Eubacterium brachy0.05
Eubacterium coprostanoligenes0.05
Dialister0.01
Enterococcus0.05
Flavonifractor0.05
Incertae sedis0.05
Intestinibacter0.05
Romboutsia0.05
Ruminiclostridium0.05
Veillonella0.05
communication—receptive skills (low > adequate)Erysipelotrichaceae UCG0030.05
communication—receptive skills (adequate > low)GCA.9000665750.01
Tyzzerella0.05
communication—receptive skills (adequate > high)Faecalitalea0.05
communication—receptive skills (high > low)CAG3520.05
Eisenbergiella0.05
Enorma0.05
Enterococcus0.01
Flavonifractor0.01
GCA.9000665750.01
Hungatella0.05
Intestinibacter0.05
Ruminiclostridium0.01
Tyzzerella0.05
communication—receptive skills (high > adequate)Dialister0.05
Insertae sedis0.05
communication—writing skills (low > adequate)Blautia0.05
communication—writing skills (low > high)Ruminococcus gauvreauli0.05
Collinsella0.05
Dielma0.05
communication—writing skills (adequate > low)Catenisphaera0.05
Lachnospiraceae UCG.0030.05
Pseudochrobactrum0.001
Pseudomonas0.001
Tyzzerella0.01
communication—writing skills (high > low)Hungatella0.05
communication—total (low > adequate)Actinomyces0.05
Gemella0.05
Granullicatella0.05
Streptococcus0.05
communication—total (low > high)Defluviitaleaceae UCG.0110.05
GCA.9000665750.05
Pseudochrobactrum0.05
Pseudomonas0.05
Pseudopropionibacterium0.05
Ruminiclostridium0.05
communication—total (adequate > low)Dielma0.05
Lachnospiraceae NK4A1360.05
Oscillibacter0.05
Paraprevotella0.05
Phascolarctobacterium0.05
Ruminiclostridium0.05
communication—total (adequate > high)Phascolarctobacterium0.05
communication—total (high > low)Anaerococcus0.05
CAG.3520.01
Eisenbergiella0.01
Enorma0.05
Enterococcus0.05
Flavonifractor0.05
Gemella0.05
Hungatella0.05
Tyzzerella0.05
communication—total (high > adequate)Granullicatella0.05
Streptococcus0.05
daily living skill personal (low > high)Collinsella0.05
daily living skill personal (adequate > low)Clostidium sensu stricto0.05
Veillonella0.01
Dolosigranulum0.05
Lachnospiraceae FCS0200.05
Sarcina0.05
daily living skill personal (high > low)CAG.3520.05
Eubacterium brachy0.05
Eubacterium coprostanoligenes0.05
Dialister0.05
Flavonifractor0.05
Incertae sedis0.05
Intestinibacter0.05
Romboutsia0.05
Veillonella0.05
daily living skill personal (high > adequate)Anaerococcus0.05
CAG.3520.05
Colidextribacter0.05
GCA.9000665750.05
daily living skill domestic (low > adequate)Ruminiclostridium0.05
daily living skill domestic (low > high)Anaerostipes0.05
Butyricicoccus0.05
Lachnospiraceae UCG.0030.05
Oxalobacter0.05
Pseudochrobacter0.05
Pseudomonas0.05
daily living skill domestic (adequate > low)Butyricimonas0.05
Clostridium sensu stricto0.01
Coprococcus0.05
DTU0890.05
Intestinibacter0.05
Veillonella0.05
daily living skill domestic (high > low)Dialister0.05
Enterococcus0.05
Escherichia/Shigella0.05
Hungatella0.05
Incertae sedis0.05
Intestinibacter0.05
Papillibacter0.05
Tyzzerella0.01
daily living skills—community skills (low > adequate)Ruminococcus gnavus0.05
daily living skills—community skills (low > high)Granullicatella0.05
Parvimonas0.05
daily living skill community (adequate > low)Barnesiella0.05
Eubacterium siraeum0.05
Coprococcus0.05
Dialister0.01
Lachnospiraceae FCS0200.01
NK4A2140.05
Odoribacter0.05
Oscillibacter0.01
Parabacteroides0.05
Ruminiclostridium0.01
Sarcina0.05
Turicibacter0.05
UCG.0030.05
daily living skill community (adequate > high)Agathobacter0.05
Allistipes0.01
Barnesiella0.05
Ruminococcus gauvreaui0.05
Haemophilus0.05
Parabacteroides0.05
UCG.0030.05
daily living skills—community skills (high > low)Escherichia/Shigella0.05
Hungatella0.05
Incertae sedis0.05
Senegalimassilia0.05
Tyzzerella0.05
daily living skills—community skills (high > adequate)Ruminococcus gnavus0.05
daily living skills—total (low > high)Anaerostipes0.05
Butyricicoccus0.05
Lachnospiraceae UCG.0030.05
Oxalobacter0.05
Pseudochrobactrum0.05
Pseudomonas0.05
daily living skill total (adequate > low)Butyricimonas0.05
Clostidium sensu stricto0.05
Oscillibacter0.05
Ruminiclostridium0.05
Staphylococcus0.05
daily living skill total (adequate > high)Anaerostipes0.05
daily living skills—total (high > low)Dialister0.05
Enterococcus0.05
Escherichia/Shigella0.05
Flavonifractor0.05
Hungatella0.05
Papillibacter0.05
Tyzzerella0.01
socialization—interpersonal skills (low > adequate)Ruminiclostridium0.05
socialization—interpersonal skills (low > high)Anaerostipes0.05
Barnesiella0.05
Butyricicoccus0.05
Collinsella0.05
socialization—interpersonal skills (adequate > low)Ruminococcus torques0.05
Coprococcus0.05
Dialister0.05
Lachnospiraceae FCS0200.05
Odoribacter0.05
Subdoligranulum0.01
socialization—interpersonal skills (adequate > high)Barnesiella0.05
Butyricicoccus0.05
Lachnospiraceae NK4A1360.05
Monoglobus0.05
socialization—interpersonal skills (high > low)Ruminococcus gnavus0.05
F00320.05
Lachnospiraceae UCG.0030.05
Oxalobacter0.05
Veillonella0.01
socialization—interpersonal skills (high > adequate)Clostidium innocuum0.05
Ruminococcus gnavus0.05
socialization—play and leisure skills (adequate > low)Barnesiella0.05
Odoribacter0.05
Ruminiclostridium0.05
UCG.0030.05
Veillonella0.05
socialization—play and leisure skills (high > low)CAG.3520.01
Dialister0.05
Enorma0.05
Pseudochrobactrum0.01
Pseudomonas0.01
socialization—coping skills (adequate > low)Actinomyces0.05
Eubacterium brachy0.01
F03320.05
Hungatella0.05
Ruminococcus torques0.05
TM7x0.01
Veillonella0.05
Weissella0.01
socialization—coping skills (high > low)Eubacterium brachy0.01
Defluviitaleaceae UCG.0110.001
Dialister0.05
Dorea0.05
Enorma0.001
Enterobacter0.05
F03320.001
Flavonifractor0.05
Oscillibacter0.05
Parasutterella0.01
Pseudopropionibacterium0.001
Ruminiclostridium0.05
TM7x0.01
Varibaculum0.05
socialization—coping skills (high > adequate)Parasutterella0.05
socialization—total (low > high)Enorma0.05
socialization—total (adequate > low)Clostridium sensu stricto0.05
Dialister0.05
Enterobacter0.05
Enterococcus0.05
Lachnospira0.05
Subdoligranulum0.05
socialization—total (high > low)Bilophila0.01
F03320.05
Oscillibacter0.05
Parasutterella0.05
socialization—total (high > adequate)Bilophila0.01
Olsenella0.01
using large muscles (adequate > low)Romboutsia0.05
using large muscles (high > low)Bifidobacterium0.05
using large muscles (high > adequate)Flavonifractor0.05
Staphylococcus0.05
TM7x0.05
using small muscles (adequate > low)Clostridium sensu stricto0.05
Coprococcus0.05
Dolosigranulum0.05
Lachnospiraceae FCS0200.05
Romboutsia0.05
Veillonella0.01
using small muscles (adequate > high)Monoglobus0.05
using small muscles (high > low)Butyricimonas0.05
Clostridium sensu stricto0.05
Eubacterium coprostanoligenes0.05
Ruminococcus torques0.05
Incertae sedis0.05
Intestinibacter0.05
Parasutterella0.05
Romboutsia0.05
Ruminiclostridium0.05
Sarcina0.01
UCG.0050.05
Veillonella0.05
using small muscles (high > adequate)Butyricimonas0.05
Sarcina0.05
motor skills—total (adequate > low)Eubacterium siraeum0.01
Eubacterium ventriosum0.05
Ruminococcus gauvreauii0.01
Ruminococcus torques0.01
Coprococcus0.05
Frisingicoccus0.05
Lachnospiraceae FCS0200.05
Monoglobus0.05
motor skills—total (high > low)Clostridium sensu stricto0.05
Eubacterium coprostanoligenes0.05
Intestinibacter0.05
Lachnospiraceae FCS0200.05
motor skills—total (high > adequate)Clostridium sensu stricto0.01
Intestinibacter0.01
Species
age (adolescents > school children)Clostridum perfingens0.01
Lactobacillus paracasei0.01
Lactobacillus sakei0.05
Pediococcus pentosaceus0.01
diagnosis: childhood autismStreptococcus mutans0.05
sex: femaleLactobacillus fermentum0.01
Streptococcus sobrinus0.01
high-protein dietLactobacillus curvatus0.05
FGIDLeuconostoc mesenteroides0.05
diarrhoeaLeuconostoc lactis0.05
Leuconostoc citreum0.05
no diarrhoeaEnterobacter cloacae0.05
Lactobacillus oligofermentans0.05
Lactobacillus brevis0.05
bloatingEubacterium hallii0.05
no bloatingLactobacillus delbrueckii0.05
Pediococcus pentosaceus0.05
constipationEubacterium hallii0.05
no constipationLactococcus lactis0.05
communication—expressive skills (high > low)Lactococcus garvieae0.05
communication—receptive skills (adequate > low)Leuconostoc citreum0.05
Leuconostoc lactis0.05
communication—receptive skills (high > low)Lactococcus garvieae0.01
communication—writing skills (low > high)Clostridium tertium0.05
communication—writing skills (adequate > low)Enterococcus casseliflavus0.001
Enterococcus faecalis0.001
Lactobacillus fermentum0.05
Pseudomonas spp.0.001
Streptococcus sobrinus0.05
communication—writing skills (adequate > high)Bifidobacterium bifidum0.05
communication—total (low > high)Clostridium tertium0.05
Enterococcus casseliflavus0.05
Enterococcus faecalis0.05
Haemophilus pittmaniae0.05
Pseudomonas spp.0.05
communication—total (high > low)Bifidobacterium animalis0.05
Lactococcus garvieae0.01
communication—total (high > adequate)Streptococcus salivarius0.01
daily living skill personal (adequate > low)Odoribacter splanchnicus0.05
daily living skill personal (high > low)Lactococcus garvieae0.05
daily living skill domestic (low > adequate)Staphylococcus epidermidis0.01
Streptococcus gallolyticus0.05
Streptococcus sobrinus0.05
daily living skill domestic (low > high)Clostridium tertium0.05
Enterococcus casseliflavus0.05
Enterococcus faecalis0.05
Lactococcus garvieae0.05
Pseudomonas spp.0.05
daily living skill domestic (adequate > low)Lactobacillus fermentum0.05
Odoribacter splanchnicus0.01
daily living skill community (adequate > low)Lactobacillus fermentum0.01
Lactobacillus salivarius0.05
Odoribacter splanchnicus0.01
Streptococcus sobrinus0.01
daily living skill total (low > high)Clostridium tertium0.05
Enterococcus casseliflavus0.05
Enterococcus faecalis0.05
Lactococcus garvieae0.05
Pseudomonas spp.0.05
socialization—interpersonal skills (low > adequate)Lactobacillus fermentum0.05
Streptococcus sobrinus0.05
socialization—interpersonal skills (adequate > low)Streptococcus mutans0.05
socialization—interpersonal skills (high > low)Clostridium tertium0.05
Lactococcus garvieae0.05
Staphylococcus haemolyticus0.05
socialization—play and leisure skills (adequate > low)Lactobacillus fermentum0.05
Lactococcus garvieae0.05
Odoribacter splanchnicus0.05
Streptococcus sobrinus0.05
socialization—play and leisure skills (high > low)Enterococcus casseliflavus0.01
Enterococcus faecalis0.01
Pseudomonas spp.0.01
socialization—coping skills (adequate > low)Leuconostoc citreum0.01
Leuconostoc lactis0.01
socialization—coping skills (high > low)Haemophilus pittmaniae0.001
Lactococcus garvieae0.001
Leuconostoc citreum0.001
Streptococcus salivarius0.05
socialization—total (low > adequate)Lactococcus garvieae0.05
using large muscles (adequate > low)Streptococcus mutans0.05
using small muscles (high > low)Bifidobacterium bifidum0.01
Lactobacillus salivarius0.05
Lactococcus garvieae0.05
Streptococcus anginosus0.05
motor skills—total (adequate > low)Eubacterium hallii0.05
motor skills—total (high > adequate)Bifidobacterium bifidum0.05
FGID—functional gastrointestinal disorders.
Table 3. Table of correlations among various features and gut microbiota composition.
Table 3. Table of correlations among various features and gut microbiota composition.
FeatureBacteriaCorrelation (r)
Order
ageMicrococcales−0.47
fat saturated (mean)/fat total (mean)Peptostreptococcales/Tissierellales−0.46
complex carbohydrates (mean)Clostridiales0.5
Peptostreptococcales/Tissierellales0.47
daily living skills—personal skills (v-score)Peptostreptococcales/Tissierellales0.5
using small muscles (v-score)Clostridiales0.53
Peptostreptococcales/Tissierellales0.57
Genus
ageRothia−0.47
fat saturated (mean)/fat total (mean)Eubacterium coprostanoligenes−0.46
Eubacterium hallii−0.59
Dorea−0.6
Leucostonoc0.49
fat unsaturated (mean)/fat total (mean)Dielma0.55
fat (mean)/weight (kg)Dielma0.47
simple carbohydrates (mean)/carbohydrates total (mean)Epulopiscium0.53
Flavonifractor−0.5
Weissella0.51
complex carbohydrates (mean)Romboutsia0.52
kcal from carbohydrates/total carbohydrates (mean)Epulopiscium0.5
Klebsiella0.66
Weissella0.51
kcal from carbohydrates/total kcalEpulopiscium0.5
Klebsiella0.66
protein (mean)/weight (kg)Dielma0.52
communication—receptive skills (v-score)Flavonifractor0.49
communication—writing skills (v-score)Anaerostipes−0.51
daily living skills—personal skills (v-score)Romboutsia0.5
daily living skills—domestic skills (v-score)Intestinibacter0.5
socialization—coping skills (v-score)F00320.5
using small muscles (v-score)Romboutsia0.55
Species
fat saturated (mean)/fat total (mean)Enterobacter cloacae0.68
Leuconostoc citreum0.53
Leuconostoc lactis0.56
Staphylococcus epidermidis0.46
Streptococcus gallolyticus0.66
kcal from carbohydrates/total carbohydrates (mean)Clostridium perfingens0.62
Statistical methods applied to the data in Table 3: Pearson’s correlation coefficient was used for all correlations, with the exception of those involving mean complex carbohydrates and age, where Spearman’s rank correlation coefficient was employed.
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Lewandowska-Pietruszka, Z.; Figlerowicz, M.; Mazur-Melewska, K. Gut Microbiota and Autism Spectrum Disorders: Neurodevelopmental, Behavioral, and Gastrointestinal Interactions. Nutrients 2025, 17, 2781. https://doi.org/10.3390/nu17172781

AMA Style

Lewandowska-Pietruszka Z, Figlerowicz M, Mazur-Melewska K. Gut Microbiota and Autism Spectrum Disorders: Neurodevelopmental, Behavioral, and Gastrointestinal Interactions. Nutrients. 2025; 17(17):2781. https://doi.org/10.3390/nu17172781

Chicago/Turabian Style

Lewandowska-Pietruszka, Zuzanna, Magdalena Figlerowicz, and Katarzyna Mazur-Melewska. 2025. "Gut Microbiota and Autism Spectrum Disorders: Neurodevelopmental, Behavioral, and Gastrointestinal Interactions" Nutrients 17, no. 17: 2781. https://doi.org/10.3390/nu17172781

APA Style

Lewandowska-Pietruszka, Z., Figlerowicz, M., & Mazur-Melewska, K. (2025). Gut Microbiota and Autism Spectrum Disorders: Neurodevelopmental, Behavioral, and Gastrointestinal Interactions. Nutrients, 17(17), 2781. https://doi.org/10.3390/nu17172781

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