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. 2026 Mar 10;16:48. doi: 10.1186/s13568-026-02037-1

Microbial and metabolic profiles in autism spectrum disorder with atopic dermatitis in children

Xia Dong 1,#, Ting Zhang 2,#, Bin Tang 3, Qianwei Zeng 3, Zihan Hu 4, Ping Huang 5, Xia Xiong 6, Xiaohuan Wang 7, Wei Dong 8,✉, Yansen Cai 2,✉
PMCID: PMC13086995  PMID: 41806005

Abstract

Atopic dermatitis (AD), an inflammatory skin disease, exhibits increased incidence with autism spectrum disorders (ASD) in children. However, the mechanism underlying the ASD-AD comorbidity remains unclear. Here, we integrated the metagenomic and metabolomics analysis to characterize the compositions and functional profiles of gut microbiome in ASD children with AD. We found significant alteration in the composition of the intestinal microbial species between ASD-AD group and ASD group based on beta diversity analysis. LEfSe analysis showed tyzzerella_nexilis, eubacterium_sp_OM08_24 and clostridium_nexile_CAG348 were significantly increased in ASD children with AD. In addition, metabolite profiles showed that differentially expressed metabolites were mainly lipids and organic acids. Meanwhile, functional profiles showed that the pathway of cholesterol metabolism and biosynthesis of unsaturated fatty acids was abundant in ASD children with AD. Furthermore, the correlation analysis revealed that bacteroides_sp_CAG443, limosilactobacillus_mucosae had a positive correlation with traumatic acid and ricinoleic acid that were decreased in ASD-AD group, respectively. Eubacterium_ramulus and lachnospiraceae_bacterium were positively correlated with 11,14-eicosadienoic acid (EDA). Taken together, our results propose that altered gut microbiota regulates metabolites to affect the development of atopic dermatitis in ASD children.

Keywords: Autism spectrum disorders, Atopic dermatitis, Metagenome, Metabolome

Introduction

Atopic dermatitis (AD) is a chronic inflammatory disease with high prevalence in infants and children, affecting quality of life. Research have revealed that barrier abnormalities and immune response imbalance regulated by genetic factors and environmental factors contribute to dry skin, asthma and allergic rhinitis, and increase the risk of AD in children (Sroka-Tomaszewska et al. 2021; Murota et al. 2022; Schuler et al. 2024). AD children showed an elevated serum levels of IgE, which mediated hypersensitivity (Takahashi et al.2024). Moreover, different cytokines such as IL-4, IL-13 were activated to trigger inflammatory responses, leading to skin barrier dysfunction (Tsoi et al. 2019; Yang et al. 2024). Epidermis and immune response related genes mutations can impair protein synthesis and change metabolic processes to affect skin resistance to AD’s development (Suzuki et al. 2016; Cantarutti et al. 2022). Currently, a range of treatments were used for AD, including antibiotics, steroids, however, the etiologic of AD are complex, resulting in poor prevention and treatment of AD in children(Navarro-López et al. 2018; Conway et al. 2024), suggesting that novel therapeutic targets need to be identified.

In addition, studies have been shown the relationship between atopic dermatitis with neuropsychiatric symptoms such as depression, anxiety and autism spectrum disorder (ASD) (Zhang et al. 2023; Tsai et al. 2020). ASD is a heterogeneous neurodevelopmental disorder and ASD children has a higher co-occurrence probability with skin disorders, including AD with a maximum of 64.2%, owing to common risk factors such as genetic mutation and inflammation (Furue 2020). STAT6, GATA3, ADRB2 and miRNAs have been implicated in the pathogenesis of both AD and ASD (Yu et al. 2023; Khosrojerdi et al. 2024; Garrido-Torres et al. 2024; Yamaguchi et al. 2023). The pro-inflammatory cytokines have been shown to regulate the development of both diseases. In addition, maternal and neonatal vitamin D deficiency in patients with ASD is also associated with AD (Li et al. 2022), suggesting that there exists the common pathophysiological mechanism and shared treatment strategies.

Numerous studies have evidenced that intestinal microbiota and their derived metabolites play critical roles in disease development (McCarville et al. 2020). AD children had a lower diversity of gut microbial compared with healthy individuals (Fang et al. 2022). The abundances of staphylococcus aureus were significantly increased, while the relative abundances of streptococcus spp and halomonas was decreased in AD patient (Zheng et al. 2019). Microbiome-derived tryptophan metabolites such as indole can mediate IL-22 secretion to attenuate inflammation against AD (Zhang et al. 2024). IL-37, an anti-inflammatory cytokine can regulate intestinal bacterial diversity, intestinal metabolites and their medicated autophagy mechanism to ameliorate allergic inflammation (Hou et al. 2020), suggesting that gut microbiota and metabolites may play keys roles in the development of AD. Additionally, clostridium species were found lower abundance in children with ASD, and their derived metabolites such indole-3-propionic acid (IPA) and short-chain fatty acid (butyrate) increased the production of antioxidant and neuro-protectant molecules (Kandeel et al. 2020). Taken together, these data suggest that intestinal dysbiosis and gut microbiota-derived metabolites may participant in the development of ASD and AD, but the structure and function of gut microbiota and metabolites under the comorbidity between AD with ASD remain to be explored. Therefore, this study performed metagenomic and metabolomic sequencing to explore the association between gut microbiota and metabolites in ASD patients with AD, enhancing our understanding of the underlying mechanism on the co-occurrence of ASD and AD in children.

Materials and methods

Project inclusion and exclusion criteria

In this study, we enrolled ASD outpatients in Luzhou People’s Hospital, Affiliated Hospital of Southwest Medical University and Rehabilitation Service Center of Luzhou Disabled Persons Federation between December 2018 and February 2023. The inclusion criteria were as follows: (1) age between 3 and 12 years; (2) be diagnosed with ASD in Luzhou People’s Hospital based on the Diagnostic and Statistical Manual of Mental Disorder (Fourth Edition); (3) AD group: ASD children with AD; AD was diagnosed by a professional dermatologist in Luzhou People’s Hospital or Affiliated Hospital of Southwest Medical University; (4) Control group: ASD children without AD. Exclusion Criteria: (1) Patients with ASD or other populations who do not meet the above criteria. (2) Children with ASD diagnosed with chromosomal abnormalities such as X brittle syndrome or Down syndrome. The Affiliated Hospital of Southwest Medical University Ethics Committee approved this study, and all participants or their caregivers signed consent forms when enrolled in this clinical study.

Fecal sample collection

Before fecal collection, participants were required not to take any dose of antibiotics or any probiotics in a month. Fresh fecal sample was collected in 50 ml sterile centrifuge tubes and then transported to the laboratory in ice packs as soon as possible and stored in a − 80 °C refrigerator.

Metagenomics analysis

36 fecal samples frozen in liquid nitrogen were performed to m etagenomic analysis. Microbial DNA was extracted using Magnetic Soil and Stool DNA Kit (TIANGEN, China) and then was quantified to build libraries for shotgun sequencing on NovaSeq 6000 platform with 150 bp paired-end. Raw sequencing data was qualitied by deleting the adapter contamination and low-quality bases with FastQC and trimmomatic software to obtain the clean data. The clean data were assembled and annotated to obtain the abundance information on species. The complexity of species diversity was calculated based on the alpha and beta diversity indices, and the analysis of similarities (ANOSIM) and linear discriminant analysis effect size (LEfSe) were used to assess the variation between groups.

Metabolomics analysis

80 mg fecal sample was homogenized with 1 ml cold extraction solution (methanol/acetonitrile/H2O = 2:2:1). After vortexing, incubating and centrifuging, the supernatant was collected for nontargeted metabolomics analysis with LC–MS. The raw MS data were converted to MzXML files by using ProteoWizard MSConvert and were converted with the metabolomics data processing program metaX before Principal component analysis (PCA) and partial least squares discriminant analysis (PLS-DA) were performed. Differential metabolites were screened according to the default criteria: variable importance of projection (VIP) > 1, fold changes (FCs) > 2 or < 0.5, and P values < 0.05. MetaboAnalyst database (https://www.metaboanalyst.ca) and Kyoto Encyclopedia of Genes and Genomes (KEGG) database (https://www.genome.jp/KEGG/pathway.html) was used to analyse the function of differential metabolites.

The online tool MetaboAnalyst was employed for the enrichment analysis of differential metabolites in metabolic pathways, with selection criteria of the Impact Factor (IF) > 0.1 and P values < 0.05. The Kyoto Encyclopedia of Genes and Genomes (KEGG) database was used to identify the pathways associated with the differential metabolites (https://www.genome.jp/KEGG/pathway.html, accessed on 1 July 2024).

Results

The baseline clinical characteristics overview

A total of 36 children with ASD were recruited in our study between 2018 and 2023 with the disease assessment criteria, including 11 children with AD (ASD-AD group) and 25 children without AD (ASD group). There was no difference in the sex ration in ASD-AD group and ASD group, and the average age of each group was 6.05 and 5.40 years, respectively (P = 0.345). Additionally, a series of questionnaire surveys, including whether these ASD children have picky eating habits, allergies to specific foods/drugs, gastrointestinal disorders (GI) such as abdominal pain, diarrhea, and constipation, was conducted and the results showed that there was no difference between ASD-AD group and ASD group (Table 1).

Table 1.

The basic characteristics of the enrolled children

ASD-AD (n=11) ASD (n=25) P value
Sex ratio (F/M) 1/11 1/25 0.644
Age, year 6.05 (3~11) 5.40 (3.5~9) 0.345
Picky eaters 11/11 19/25 0.067
Allergies to specific foods/drugs 5/11 6/25 0.530
GI condition 6/11 7/25 0.506
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Alterations in diversity and functional of gut microbiota in ASD children with AD on metagenomic sequencing

To unravel gut microbiome features in ASD children with AD, shotgun metagenomic sequencing of faecal samples was performed to characterize the microbial community taxonomic composition and identify the intestinal microbiota associated with AD. A total of 3065 and 3687 genera in were found in ASD-AD group and ASD group, respectively (Fig. 1A), and there were no significant differences in the species richness, evenness and diversity of gut microbiome in two groups through analysing the alpha-diversity with shannon, simpson and invsimpson indices (Fig. 1B). In addition, the results showed that the relative abundances of Actinobacteria and Proteobacteria in the ASD-AD group were significantly higher than those in the ASD group, whereas Firmicutes and Bacteroidetes were most abundant in the ASD group at the phylum level (Fig. 1C). At the genus level, the relative abundances of Bifidobacterium in the ASD-AD group were significantly higher than those in the ASD group, and the relative abundances of Faecalibactrium, Roseburia and Prevotella were significantly decreased in the ASD-AD group (Fig. 1D). Moreover, beta diversity analysis showed that there were significant differences in the composition of the intestinal microorganisms between ASD-AD group and ASD group based with principal coordinate analysis (PCoA) at the phylum and species level, respectively (Fig. 1E and F). To further identify differential phylotypes, LEfSe analysis was performed and identified 19 differentially abundant species (LDA score > 2), in which Tyzzerella_nexilis, Eubacterium_sp_OM08_24 and Clostridium_nexile_CAG348 was significantly increased in ASD children with AD (Fig. 2A). Additionally, the relative abundance of the abundant species is presented in Fig. 2B.

Fig. 1.

Fig. 1

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The characteristics and compositions of gut microbiota between ASD-AD (A1A2C1) and ASD (BC2) groups. A Venn diagram of the gene number distribution between ASD-AD and ASD groups; B The alpha diversity analysis between two groups based on Simpson, Shannon and Invsimpson index; C The relative abundances of gut microbiota at the phylum level; D The relative abundances of gut microbiota at the species level; E Principal-coordinate analysis (PCoA) of phylum; F Principal-coordinate analysis (PCoA) of species

Fig. 2.

Fig. 2

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Differences in gut microbiota between ASD-AD (A1A2C1) and ASD (BC2) groups. A LEfSe analysis (LDA score > 2) identified significant difference of gut species between ASD-AS and ASD group. B The relative abundance of gut species detected in LEfSe analysis

Furthermore, to explore the functional characteristics of gut microbiota in ASD children with AD, gut microbiota was annotated and analyzed the functional composition between groups using the KEGG and eggNOG. Analysis of similarities (ANOSIM) and PCoA indicated significant function alteration of gut microbiota between ASD-AD group and ASD group at the KEGG Orthological (KO) level (Fig. 3A and B). Lefse analysis of KEGG functional profiles revealed that the activities of ABC transporters, nicotinate and nicotinamide metabolism, fatty acid degradation, RNA degradation, glucagon signalling pathway, lysine biosynthesis, peroxisome and insulin resistance were abundant in ASD-AD group (Fig. 3C). Conversely, modules such ribosome, oxidative phosphorylation, Fatty acid biosynthesis, Salmonella infection, NOD_like receptor signalling pathway, Biosynthesis of various secondary metabolites, fluid shear stress and atherosclerosis, parkinson disease, and protein processing in endoplasmic reticulum were prominent in ASD group (Fig. 3C). The distribution of functional genes of gut microbiota in the ASD-AD group was significantly changed from that in the ASD group based on ANOSIM and PCoA index with the eggNOG orthologous level (Fig. 4A and B). The functions of amino acid transport and metabolism carbohydrate transport and metabolism, inorganic ion transport and metabolism and defence mechanisms were more activated in the ASD-AD group, while transcription and secondary metabolites biosynthesis transport and catabolism were enriched in ASD group (Fig. 4C), suggesting that AS may impair gut ecosystem stability.

Fig. 3.

Fig. 3

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Functional characteristics of gut microbiota in ASD-AD (A1A2C1) with KEGG database. A ANOSIM analysis on KEGG Orthology (KO) level; B PCoA analysis; C clustering heatmap of KEGG tertiary metabolic pathway; D LEfSe analysis on KEGG tertiary metabolic pathway (LDA > 2.5)

Fig. 4.

Fig. 4

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Functional characteristics of gut microbiota in ASD-AD (A1A2C1) with eggNOG database. A ANOSIM analysis; B PCoA analysis; C LEfSe analysis

Alterations in metabolic patterns in ASD children with AD on untargeted metabolomics

To complement our microbial community profiling of the gut ASD children with AD, untargeted metabolomics were performed to reveal key metabolites and microbial metabolism pathways related with AD. After quality control and metabolites annotation, a total of 11 and 15 differentially expressed metabolites (DEMs) were screened in positive (Fig. 5A) and negative (Fig. 5B) ion models between ASD-AS and ASD groups according to the threshold values: the significance analysis with projection (VIP) values > 1, fold change ≥ 1.2 or ≤ 0.667, and P value < 0.05, respectively. The main metabolites enriched in the ASD-AD group is lipid and organic acids, including 11,14-eicosadienoic acid (EDA), 1-palmitoylglycerol, traumatic Acid, glycocholic acid, isopentenyl pyrophosphate,1,2-dimyristoyl-sn-glycero-3-phosphate, 11,14,17-eicosatrienoic acid, ricinoleic acid,13-hydroxy-9z,11e-octadecadienoic acid, oleic acid, methylphosphonic acid, orotate, propanoic acid, indole-3-butyric acid and demethoxycurcumin. Subsequently, KEGG enrichment analysis of DEMs represented that cholesterol metabolism and biosynthesis of unsaturated fatty acids were enriched (Fig. 5C). Furthermore, the correlation analysis of metabolizes revealed that demethoxycurcumin was negative with glycocholic acid, whereas 1,2-dimyristoyl-sn-glycero-3-phosphate (DMPA) was s positively correlated with multiple fatty acid, such as traumatic acid, octadecadienoic acid, indole-3-butyric acid and propanoic acid (Fig. 5D). Six DEMs for fatty acid pathway were observed between ASD-AS and ASD groups (Fig. 5E).

Fig. 5.

Fig. 5

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Differentially expressed metabolites analyses. A Volcano maps indicating the differential metabolites under positive ion model. B Volcano plot of differential metabolites using the negative ion model. C Bubble plot showing the KEGG enrichment pathways. D The correlation analysis between differentially expressed metabolites. E Violin plot showing quantitative abundance level of the individual metabolite

Integrative analysis reveals the associations between microbes and metabolites in ASD children with AD

To investigate the relationships between differential species and metabolites in ASD children with AD, spearman’s correlation analysis was calculated and there were the high correlations between differential metabolites and microbes. The levels of Clostridium, Ruminococcus and Anaeromassilibacillus were significantly positively correlated with the levels of propanoic acid that were decreased in ASD-AD group. Bacteroides_sp_CAG443 was significantly positively correlated with traumatic acid. Limosilactobacillus_mucosae were significantly positively correlated with ricinoleic acid and oleic acid that were reduced in ASD-AD group. Clostridium_sp_CAG299 was significantly positively correlated with 1,2-dimyristoyl-sn-glycero-3-phosphate and 4-Pregnen-17alpha,20beta-diol-3-one-20-sulfate that were decreased in ASD-AD group. In addition, Eubacterium_ramulus and lachnospiraceae_bacterium were positively correlated with 11,14-eicosadienoic acid (EDA) (Fig. 6).

Fig. 6.

Fig. 6

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Microbe-metabolite association analysis. A heatmap with spearman’s correlation analysis showed the relationships between metabolites and microbiota in ASD children with AD

Discussion

Atopic dermatitis (AD) is one of the most common skin disorders with chronic inflammatory. A large cross-sectional study found that 7% to 64.2% children with autism spectrum disorder (ASD) experienced AD and had a higher risk of developing AD. Additionally, Studies have elucidated that the pathogenesis of ASD and AD is complicated and multifactor, such as genetic factors, environmental factors and immune can influence the progression of ASD-AD comorbidity (Kasperkiewicz et al. 2018) . To explore the potential mechanism of the etiological association between ASD and AD, we integrated metagenomic and metabolomic data to identify the structural and functional characteristics of gut microbiome and metabolome in ASD children with AD.

Gut microbiota has been studied to associate with the occurrence and development of disease. In this study, Beta diversity index conformed gut microbiota dysregulation, and the composition of gut microbiota was significantly different in ASD children with AD. We found that Actinobacteria and Proteobacteria were the dominant phyla and Tyzzerella_nexilis, Eubacterium_sp_OM08_24 and Clostridium_nexile_CAG348 was significantly increased at the species level in ASD-AD group. An abundance of gut Proteobacteria has been reported to associate with autoimmune disorders and ASD patients, and ASD with allergy showed an increased relative abundance of gut Proteobacteria compared with those without allergy, indicating that AD may increase the abundance of gut Proteobacteria to affect host health (Kong et al. 2019). Additionally, Courtney et al. observed that Tyzzerella nexilis wase enriched in 1-year infant who would later develop AD. Clostridium species were also significantly increased in autistic patients (Hoskinson et al. 2024; Taniya et al. 2022).Moreover, the phyla Actinobacteria, Proteobacteria, Eubacterium and Clostridium metabolize choline-like substances to produce Trimethylamine (TMA) and trimethylamine N-oxide (TMAO) that have pro-inflammatory effect and are usually applied to build the model of atopic dermatitis(Yang et al. 2019; Ikarashi et al. 2020). The abundance of bacteroidete was significantly reduced in ASD-AD group compared to ASD group, which is consistent with previous research findings (Abrahamsson et al. 2012; Jakobsson et al. 2014). In addition, bacteroidete species can produce metabolite propionate that can alleviate atopic dermatitis by regulating ferroptosis, mitochondrial function and TRP channels(Kim et al. 2024; Xu et al. 2024; Xie et al. 2024), suggesting that intestinal microbiota-derived metabolites may be involved in the pathophysiology of AD. Additionally, studies indicate that gut microbiota dysbiosis is accompanied by changes in metabolites in fecal samples of patients with disease. Our metabolomic analysis identified some differentially expressed metabolites. Glycocholic acid is a primary glycine-conjugated bile acid and can be as a crucial biomarker for disease (Song et al. 2018; Amick et al. 2022). Previous study found that glycocholic acid was significantly decreased in serum of children with AD (Huang et al. 2014), which is consistent with our result. Owing that AD disease can induce inflammation and impair early-life immune homeostasis, glycocholic acid has been reported to show anti-inflammatory effects, suggesting that glycocholic acid may be involved in AD progression (Ge et al. 2023). Propanoic acid is a short-chain fatty acid secreted by the microbe and can reduces intestinal inflammation and protect against neurodegenerative disease (He et al. 2023; Wang et al. 2025).

In addition, studies have suggested that the active components, such as ginsenosides from Ginseng and melatonin can increase the production of propionic acid to inhibit AD-induced inflammatory response through G-protein-coupled receptor43 (GPR43)-mediated pathway (Li et al. 2024; Yang et al. 2025). Moreover, Bifidobacteria adolescentis treatments induced Lactobacillus-derived propionic acid to promote Treg differentiation and inhibit Th2 responses against DNFB-induced atopic dermatitis in mice (Fang et al. 2020), demonstrating that propionic acid may act as a promising therapeutic target for ASD-AD comorbidity. Furthermore, the correlation analysis combined with the LEfSe analysis showed a strong correlation between different gut microbiome and various acids. Eubacterium_ramulus and lachnospiraceae_bacterium were positively correlated with 1,14-eicosadienoic acid (EDA), which has been exhibited to have a higher binding affinity to anti-AD core targets by molecular docking (Khan et al. 2025). This study has several important limitations that should be considered when interpreting the findings. First, the modest sample size, particularly of the ASD-AD group (n = 11), limits statistical power and increases the risk of Type II errors. This reflects the real-world constraints of recruiting a well-phenotype cohort from a regional clinical setting. Second, AD severity was assessed clinically but not prospectively quantified using standardized instruments (e.g., SCORAD or EASI), which may affect cross-study comparability. Furthermore, our AD cohort specifically represents children with mild, stable disease (controlled for ≥ 1 year), and thus our results may not generalize to patients with active moderate-to-severe AD, in whom gut-brain-skin interactions could differ. Given these limitations, the findings—especially those derived from multivariate analyses—should be viewed as exploratory and hypothesis-generating. The reported microbial and metabolomic associations require validation in larger, independent, and ideally multi-centre cohorts that include patients across the full spectrum of AD severity. Despite these constraints, this study provides a foundational characterization of the gut ecosystem in a carefully defined subpopulation of children with ASD-AD comorbidity, offering preliminary targets for future mechanistic and translational research.

Conclusion

In this study, we performed metagenomic and metabolomic analyses to establish the association between gut microbiome and metabolites in ASD-AD comorbidity, and offers evidence of differences in the composition and function of gut microbiota and metabolites in ASD children with AD. These findings will provide novel insight for therapeutic approaches to control AD occurrence.

Author contributions

XD, TZ and YC designed this project, XD and TZ drafted and revised the manuscript, QZ collected samples, ZH, PH and XX collected and analysed the data, XW and WD revised the manuscript, YC suppled the funding and revised the manuscript. All authors have read and agreed to the published version of the manuscript.

Funding

This study was supported by Sichuan Science and Technology Program (2022YFS0631 and 2024NSFSC1641).

Data availability

The data presented in this study are available on Sciencedb (https://www.scidb.cn): 10.57760/sciencedb.28643.

Declarations

Competing interests

The authors declare no competing interests.

Footnotes

Publisher's Note

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

Xia Dong and Ting Zhang have contributed equally to the work.

Contributor Information

Wei Dong, Email: dongwei@swmu.edu.cn.

Yansen Cai, Email: cys_cys@aliyun.com.

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Data Availability Statement

The data presented in this study are available on Sciencedb (https://www.scidb.cn): 10.57760/sciencedb.28643.

Articles from AMB Express are provided here courtesy of Springer-Verlag

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