Gut microbiota in young adults with high-functioning autism spectrum disorder and its performance as diagnostic biomarkers

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Diagnosing ASD in adults presents unique challenges and there are currently no specific biomarkers for this condition. Most existing studies on gut microbiota in ASD are conducted in children; however, the composition of the gut microbiota in children differs significantly from that of adults. This study aimed to study young adults with high-functioning ASD on their gut microbiota. Using metagenomic sequencing, we evaluated the gut microbiota in 45 adults with high-functioning ASD and 45 matched healthy controls. Adjusting for sociodemographic information, dietary habits, and clinical data, we observed a distinct microbiota profile between adults with ASD and controls, with their autistic symptom severity strongly correlating to microbial diversity (correlation coefficient = -0.351, p-value <0.001). Despite a similar dietary pattern, the ASD group exhibited more gastrointestinal symptoms than the healthy controls. An internally validated machine-learning predictive model that combines the Autism Spectrum Quotient questionnaire score and microbial features could achieve an area under the receiver operating characteristic curve (AUC) of 0.955 in diagnosing ASD in adults. This study evaluates the gut microbiota in adult ASD and highlights its potential as a non-invasive biomarker to enhance diagnosis of ASD in this population group. Health sciences/Biomarkers/Diagnostic markers Biological sciences/Psychology/Human behaviour Gut microbiota autism adult biomarker diagnostic tool Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Autism spectrum disorder (ASD) is a complex neurodevelopmental condition characterized by difficulties in social communication and interaction, as well as the manifestation of repetitive and restrictive patterns of behavior, activities, or interests ( 1 ). Globally, the prevalence of ASD has been reported to range from 0.01–4.36%, with a median prevalence of 1% ( 2 ). In the United States, it has been estimated recently that the overall ASD prevalence is 2.76% (one in 36 children), which is higher than the previous estimates during 2000 to 2018 ( 3 ). ASD is associated with considerable impact and economic burden on the families and public health. For example, the lifetime cost for an individual with ASD in China is $ 2.65 million United States Dollars (USD) for those without intellectual disability (ID) and $ 4.61 million USD for those with comorbid ID ( 4 ). The exact pathogenesis of ASD is still not understood, although it is widely considered to result from the interactions between genetic and environmental factors ( 5 ). Hundreds of genes have been identified to contribute to the significant deficits in social cognition and communication in ASD, but these genetic factors account for only 10–20% of ASD cases ( 6 ). Various environmental factors, such as gestational diabetes and low birth weight, are reported to be associated with ASD ( 7 ). Recently, gut microbiota has been identified to play an important role in the gut-brain axis and may contribute to the development of ASD. The gut-brain axis involves a bidirectional connection with the central nervous system, and occurs through several mechanisms, such as the autonomic nervous system, enteric nervous system, neurotransmitters and hormones ( 8 ). Various studies have observed an altered gut microbiota composition in persons with ASD ( 9 – 11 ). These observations are supported by interventional studies, where fecal microbiota transfer from ASD donors into germ-free mice has been shown to induce autistic behaviors ( 12 ). In human, microbiota transfer therapy has been found to mitigate symptoms of ASD ( 13 ). In addition, a recent study has demonstrated strong correlations between ASD symptoms and urinary metabolites, such as 3-hydroxyhippuric acid and homovanillic acid, following fecal microbiota transplantation ( 14 ), These findings further implicate gut microbiota in the metabolic disturbances associated with ASD. Most studies on the gut microbiota to date have focused on children with ASD. A recent systematic review in this area reveals that only one study has assessed the microbiota in adult individuals with ASD and has only examined the bacterial microbiota ( 9 ). A recent study has applied metagenomic sequencing to study the multi-kingdom microbiota, but has only included children aged 1–13 years ( 15 ). Since the gut microbiota is correlated with age ( 16 ), the differences in the composition of gut microorganisms between children and adults may not be consistent. Adults with ASD exhibit different clinical presentations compared to children. Individuals with ASD who do not have intellectual impairment are considered as having high-functioning ASD ( 17 ) or ASD without accompanying intellectual impairment ( 1 ). Though ASD traits usually start in the early developmental period, symptoms may not become fully apparent until adulthood for high-functioning ASD ( 18 ). Diagnosing ASD in adult individuals, especially high-functioning ASD, presents several challenges, such as inaccurate recall of childhood history, behavioral overlap with other mental disorders, and learned coping strategies to mask certain ASD symptoms ( 19 ). There are currently no reliable biomarkers to effectively diagnose ASD in adults, and sometimes ASD among adults can be even misdiagnosed as other psychiatric conditions ( 20 ). Therefore, it is worthwhile to explore the gut microbiota as potential biomarkers for diagnosing ASD among adults. The present study aimed to comprehensively investigate the gut microbiome and their functional pathways among young adults with high-functioning ASD and evaluate their potential application as a diagnostic tool for adults with ASD. We hypothesized that ASD adults would have an altered microbial composition, compared to the healthy controls, and the different microbial composition could facilitate ASD diagnosis in adults. Methods Study participants Persons with ASD aged between 21 and 40 years old were recruited from the Adult Neurodevelopmental Service (ANDS) at the Institute of Mental Health (IMH). IMH is the only tertiary psychiatric hospital in Singapore. ANDS provides both inpatient and outpatient services for adults with ASD and/or ID, and its new case clinic has the capacity to review 319 patients over a two-year period (21). The ASD diagnosis was made according to the Diagnostic and Statistical Manual of Mental Disorders Fifth Edition (DSM-5) by qualified psychiatrists. Healthy controls with similar gender, age range and ethnicity were recruited from the community, and they were required to have no self-reported history of ASD or other mental health conditions. All participants were recruited from October 2023 to April 2024. Recruitment efforts included distributing flyers and advertisements targeted at hospital staff to encourage referrals and participant enrollment. Written informed consent was obtained from all the participants. All study procedures were performed in accordance with the relevant laws and institutional guidelines and were approved by the National Healthcare Group Domain Specific Review Board. Exclusion criteria for both ASD group and control group included having intellectual disability; receiving any formulated probiotic or prebiotic supplements; and being treated with any antibiotics within one month of the study. Sample size The sample size was calculated a priori using G* Power software (22). In a previous study exploring the relationship of gut microbiota and autism (10), the alpha diversity index, estimated by Chao1 index, was 79.8 (Standard Deviation, SD=19.3) in the ASD group and 92.6 (SD=20.2) in the control group. Based on the differences in this ecological index from this study, 41 participants in the ASD group and another 41 in the control group were needed to achieve a power of 80% with alpha error of 0.05. Assuming a drop-out rate of 10%, the present study aimed to recruit 45 participants in each group. Questionnaires Demographics and clinical information. Social demographics, including age, gender, ethnicity, body mass index (BMI), marital status, education level and monthly household income, were collected for both ASD group and control group. The Autism Spectrum Quotient was used to quantify ASD traits in participants. The Autism Spectrum Quotient is a widely used tool in clinical practice and research to quantify ASD symptoms (23). It has 50 items with a maximum score of 50. It has acceptable high sensitivity and specificity. At a cut off score of 26, it has sensitivity of 0.95, specificity of 0.52, positive predictive value of 0.84, and negative predictive value of 0.78 (24). The questionnaire has been used in Asian countries, such as Japan (25). The Functional Gastrointestinal Checklist (FGI Checklist) was used to assess gastrointestinal symptoms of participants. The FGI checklist was initially developed to measure both upper and lower gastrointestinal symptoms (26). It assesses 20 common gastrointestinal symptoms, such as regurgitating, heartburn, epigastric pain, diarrhea, and abdominal distension. The Diet Screener questionnaire was used in this study to explore the dietary habits in participants. This 37-item questionnaire was developed to assess dietary patterns in a multi-ethnic population in Singapore (27). It evaluates average food intake of 37 food/beverage items over the preceding year. Each item is rated on a 10-point scale ranging from “never or rarely” to “6 or more times a day”. The items are categorized into the following seven groups: vegetables, fruit, nuts/legumes, whole grains, low fat dairy, red and processed meat, and sweetened beverages. Based on the scores in each of the seven groups, each participant is assigned a score between 1 and 5 corresponding to the intake quintile they belong to, with reverse scoring applied for meat and sweetened beverages. Subsequently, these seven quintile scores are added together to calculate the total value, which is the Dietary Approaches to Stop Hypertension (DASH) score. The detailed description on the calculation of the DASH score can be found in a previous study (28). Sample collection, DNA extraction and sequencing Approximately 200-300mg of stool samples were collected from each participant using a stool collection kit. Preservative media (Nuclei acid preservative, cat: 28330, Norgen Biotek Corp, Ontario, Canada) were included in the stool collection tubes to allow safe transportation of microbial DNA at room temperature. Stool samples in preservative media were delivered to the laboratory to be stored at -80 °C refrigerators until further processing. DNA was extracted from the stool samples with the QIAamp PowerFecal Pro DNA Kit (QIAGEN, Germany), following the manufacturer instructions. The DNA concentration and A260/280 ratio were measured with a NanoDrop Spectrophotometer for quality control. The extracted DNA was then sent for shotgun metagenomic sequencing on the Illumina NovSeq 6000 platform (Novogene, Singapore). Bioinformatics and statistical analysis Raw sequencing reads were first quality filtered and trimmed using Trimmomatic ver 0.39 (29). To remove host associated reads, filtered reads were mapped against the human reference genome (hg38) using bowtie version 2.4.5 (30). Read pairs with any read mate aligned to the reference genome were discarded for further analysis. For profiling the distribution of microbial species, taxonomic classification of cleaned reads resolved at strain level was carried out using MetaPhlAn version 4 (31), a marker-gene based metagenomic taxonomic profiler that is based on the marker genes identified from over one million prokaryotic and metagenome-assembled genomes. The profiles of microbial functional pathways (e.g. MetaCyc, KO gene family) were obtained using HUMAnN ver3.0 (32). To account for the difference in library size, mapped read counts were converted into copies per million (CPM) values prior to downstream analyses. For the abundance estimation of fungus and virus, cleaned reads were mapped against the NCBI Fungi and refseq viral databases respectively using Kraken2 version 2.1 (33) after removing bacterial reads by mapping cleaned reads against the unified human gastrointestinal genome (UHGG version 2.1) database (34). Sequencing data were analyzed using R (version 4.4.1). Alpha diversity metrics, including Simpson, Shannon, and Chao indices were calculated with the “vegan” package (35). Beta diversity was assessed using PERMANOVA (adonis) in the “vegan” package. Both alpha and beta diversity analyses were adjusted for age, gender, ethnicity, BMI, FGI Checklist score and DASH score. The differences in microbiota abundance and functions between the ASD and control groups were evaluated by the “phyloseq” (36) and “DESeq2” packages (37). Heatmap plots were generated using the “pheatmap” package and volcano plots were created by the “ggplot2” and “ggrepel” packages. Network plots were generated using the “igraph”, “ggraph”, and “reshape2” packages. Corrections for multiple hypothesis testing were applied using the false discovery rate (FDR) approach. The random forest model in Python was used to identify specific microbial taxa and functions that were different between the ASD and control groups. Receiver operating characteristic (ROC) curve analysis was performed to assess the predictive power of the biomarkers selected from the model, followed by the area under the curve (AUC) calculation. Mean AUC value based on 100 performances was calculated to provide a robust estimate of the model’s predictive capability. Results Demographics and clinical characteristics of the participants A total of 45 individuals with ASD and 45 control subjects were recruited for this study. The two groups were comparable in terms of gender and BMI distributions. In the ASD group, there were 41 males and 4 females, with a median age of 23 years (interquartile range [IQR]= 4) and a median BMI of 26.0 kg/m 2 (IQR = 8.77). The control group included 40 males and 5 females, with a median age of 25 years IQR = 4) and median BMI of 24.5 kg/m 2 (IQR = 6.23). Most participants were ethnic Chinese and resided in government flats. The demographic information is summarized in Table 1 . The ASD group scored higher on the Autism Spectrum Quotient questionnaire (median = 30, IQR = 10) compared to the control group (median = 18, IQR = 11, p-value < 0.001), indicating greater autistic traits in the ASD group. The FGI Checklist scores were also higher in the ASD group (median = 4, IQR = 8) than the control group (median = 2, IQR = 4, p-value < 0.001), revealing more gastrointestinal symptoms among participants with ASD. However, the DASH scores were similar between the ASD group (mean = 20.53, SD = 3.68) and the control group (mean = 20.53, SD = 4.65), with no significant difference between the intake of the seven food groups that were evaluated. Table 1. Demographic information of the study population ASD group (n=45) Control group (n=45) P value Age (years) Median (IQR) 23 (4) 25 (4) 0.004 Gender Male Female 41 4 40 5 1.000 Race Chinese Malay Indian Others 42 1 2 0 37 4 2 2 0.268 BMI (kg/m 2 ) Mean (SD) 26.0 (8.77) 24.5 (6.23) 0.696 Housing type 1-3 room government flat 4-5 room government flat private flat or landed property 5 25 15 2 29 14 0.466 Gut microbial diversity between ASD and control groups A total of 993 bacterial species were identified across all participants. Alpha diversity analysis showed that within-subject microbial richness was significantly lower in the ASD group than the control group, as indicated by the Chao1 index (140 ± 46.1 (mean ± SD) in the ASD group and 168 ± 54.8 in the control group, p = 0.025), although Simpson (0.925 ± 0.0341 in the ASD group and 0.918 ± 0.0531 in the control group, p = 0.335) and Shannon (3.31 ± 0.390 in the ASD group and 3.40 ± 0.503 in the control group, p = 0.548) indices did not show significant differences ( Figure 1 ). Furthermore, Autism Spectrum Quotient scores were significantly correlated with the Chao1 index (correlation coefficient = -0.351, p-value <0.001) ( Figure S1 in the supplementary material), suggesting individuals with more ASD symptoms had lower microbial richness. Beta diversity analysis revealed that the microbial composition differed significantly between two groups, as shown by the principal coordinate analysis (PCoA) of Bray-Curtis Distance (p-value = 0.001) ( Figure 1 ). Abundance of microbial species between ASD and control groups Of the 993 bacterial species identified, 94 were present in ≥ 10% of samples and exhibited distinct profiles between ASD and control groups ( Figure 2 ). Species such as Anaerostipes hadrus, Lactobacillus gasseri, Weissella confusa, and Eubacterium limosum were enriched in the ASD group. In contrast, species like Prevotella copri clade A, and Ruminococcaceae bacterium were more prevalent in the control group . There were no clear patterns between the distribution of bacterial species and gastrointestinal symptoms, but overall, the gastrointestinal symptoms, measured by FGI Checklist scores, were more severe in the ASD group ( Figure 2 ). The interactions between those bacterial species in the ASD group and control group are presented in Figure 3 . The ASD group exhibited a distinct microbial interaction pattern when compared to the control group. The top 50 bacterial species with the largest log2 fold change are highlighted in the volcano plot in Figure S2 of the supplementary material. Across all samples, a total of 50 fungal species were identified. Notably, species like Meyerozyma caribbica and Mucor circinelloides were more prevalent in the ASD group whereas Dictyocoela roeselum and Podosphaera cerasi were more abundant in the control group ( Figure S3 ). A total of 40 viral species were found across all samples. Some viruses, such as Carjivirus hominis and Oengusvirus oengus , were more abundant in the ASD group. In contrast, viruses, like Aurodevirus hiberniae and Toutatisvirus toutatis, were more commonly seen in the control group. Figure S4 illustrates the differences in viral species distribution between the ASD and control groups. Among the 174 MetaCyc pathways in all the samples, 24 differential pathways were identified. Pathways, such as inosine-5'-phosphate biosynthesis III (PWY-7234) and thiamine diphosphate salvage IV (PWY-7356) were positively associated with ASD, while pathways, such as pyrimidine ribonucleotides de novo biosynthesis (PWY0-162) and geranylgeranyl diphosphate biosynthesis II (via MEP) (PWY-5121) showed negative associations with ASD ( Figure S5 in the supplementary material). Microbial biomarkers for ASD diagnosis The potential of microbial biomarkers for ASD diagnosis was investigated using a random forest model. Recognizing that dataset splits and parameter selection can substantially influence model performance, the experimental design incorporated robust validation. Specifically, the dataset was randomly split into 70% training and 30% validation sets, and this process was repeated 100 times. For each split, a 3-fold cross-validation was performed on the training set to identify the optimal model parameters. After tuning, the model was retrained on the entire training set and evaluated on the validation set, ensuring a thorough and reliable assessment of its predictive performance. The associations between numbers of features selected and mean AUC values were presented in Figure S6 in the supplementary material. To achieve a good balance between AUC values and number of features, a total of 25 optimal features ( Table S1 of the supplementary material), including 20 bacterial species, 3 fungal species, and 2 microbial pathways, were included in the prediction model. When only these 25 microbial markers were included, the model achieved a mean AUC of 0.8076. In contrast, when only the autism spectrum quotient scores were used, the mean AUC was 0.9112. Notably, by combining both the microbiota markers and Autism Spectrum Quotient scores, the mean AUC improved to 0.9551 ( Figure 4 ). Discussion To the best of our knowledge, this is the first metagenomic study to explore the gut microbiota in young adults with high-functioning ASD. Our findings demonstrate that the microbiota composition in adults with ASD is significantly different from that of healthy controls, with the richness of the bacterial species being strongly correlated with the severity of ASD symptoms. The microbial features in the gut microbiota, including bacteria, fungi, viruses and functional pathways could be useful in identifying adults with ASD. In this study, certain bacterial species differed between ASD and control groups. Though most of those species are not consistently reported in other studies, some of the findings are similar as those in several studies performed in children. For example, ASD group exhibited depleted abundance of Prevotella copri and increased levels of Eubacterium limosum . These are consistent with previous studies which focused on children with ASD ( 38 , 39 ). Prevotella copri , a predominant species in the Prevotella genus, is commonly found in the human gastrointestinal tract ( 40 ) and capable of generating short-chain fatty acids (SCFAs) that protect the mucosal barrier and reduce the risk of inflammation ( 41 ). While Eubacterium limosum has been reported to ameliorate colonic inflammation, its metabolism in gastrointestinal tract remains poorly understood ( 42 ). We also found that several bacteria in the Ruminococcaceae family, such as Ruminococcaceae bacterium ,and Ruminococcaceae bacterium AM07 -15 were reduced in abundance in the ASD group. Our results align with another study in children showing an inverse association between the abundance of butyrate-producing Ruminococcaceae and ASD ( 43 ). Since gut microbiota can be modified, for example, through dietary interventions ( 44 ), these findings could open the door to developing intervention strategies aimed at reducing specific bacteria to support adults with ASD. Interestingly, this study revealed that several bacteria with potential probiotic properties, such as Anaerostipes hadrus, Weissella confusa and Lactobacillus gasseri , were increased in the ASD group. To the best of our knowledge, these findings have not been reported in other studies. Anaerostipes hadrus is known for its butyrate producing capacity and contains biotin synthesis genes that could regulate inflammation and immunity ( 45 ), making it a potentially beneficial microorganism ( 46 ). However, it is also reported that Anaerostipes hadrus can affect the availability of long-chain free fatty acids in the portal circulation, contributing to hepatic fibrosis( 47 ). Weissella confusa, often isolated from fermented foods, has been proposed as a potential probiotic ( 48 , 49 ). The probiotic Lactobacillus gasseri can offer various health benefits, including maintenance of gut homeostasis and immunomodulation, as supported by genomic and empirical evidence ( 50 ). There could be a potential link between beneficial bacteria and a reduction in ASD symptoms. In this study, patients with ASD were classified as having mild or high-functioning ASD. The presence of these beneficial bacteria may contribute to a lower risk of developing more severe ASD symptoms. This study also identified the difference in fungi and viruses as well as microbial pathway in ASD, expanding beyond previous studies that focused primarily on bacteria ( 9 ). With the advancement of metagenomic sequencing technologies, other microbial communities could also be analyzed in depth to provide more insight in this area. In the current study, the fungus Mucor circinelloides was found to be much more common in the ASD group. It is a naturally growing mold that can lead to potentially fatal infections in individuals with compromised immune systems ( 51 ). Most viruses identified in this study, such as Toutatisvirus toutatis and Aurodevirus hiberniae , are bacteriophages associated with the gut microbiome, which may interact with gut. Certain pathways were found to differ between ASD and control groups. For example, the inosine-5'-phosphate biosynthesis III was more prevalent in the ASD group. This pathway has also been found to be more abundant in the gut microbiota of smokers ( 52 ). Our understanding of the relationship between gut microbiota and ASD remains limited, and the functions of these microorganisms are not yet fully understood. The observation of reduced bacterial diversity in the gut microbiota of individuals with ASD aligns with broader microbiome research, which often links lower microbial diversity to various health conditions. In this study, we found that individuals with ASD had variations in microbial richness, with a significant correlation between the diversity of bacterial species in the gut and the severity of ASD symptoms. This finding is consistent with studies on children with ASD ( 10 , 53 ) and suggests the bacterial metabolites may play a role in the development of ASD symptoms. The change in the microbial composition in persons with ASD is commonly referred to as “dysbiosis” which is characterized by an imbalance in the microbiota, along with alterations in the metabolic activities and functional composition ( 54 ). While gut microbiota symbiosis supports the maintenance of normal host physiology ( 55 ), dysbiosis has been associated with various other conditions, including depression, anxiety and Parkinson’s disease ( 56 ). In this study, we developed prediction models to aid in the diagnosis of ASD in adults. The first model, incorporating gut microbiota data and questionnaire responses, achieved an AUC of 0.9551, while the second model, based solely on gut microbiota data, attained an AUC of 0.8076.Although ASD can be diagnosed as early as in children aged two years, many individuals remain undiagnosed until adulthood ( 19 ). These undiagnosed adults face increased risks of emotional and functional challenges due to unrecognized ASD symptoms ( 19 ). Currently, there is a lack of validated diagnostic tools for adults, as most of the tools are exclusively designed for children ( 57 ). Diagnostic tools created for children are not always suitable for use in adults ( 58 ). Various biomarkers have been explored to diagnose ASD, such as C-reactive protein, oxytocin, iron and zinc, but the accuracy is not ideal ( 59 ). A recent study investigated gut microbiota markers as standalone diagnostic tools for ASD in children, without incorporating additional questionnaires, and achieved an AUC of 0.91. ( 15 ). Our study extends this to gut microbiota in adults, supporting the potential application of the gut microbiota as diagnostic biomarkers for high-functioning ASD. While this study sheds new light on the altered gut microbiota composition in adults with high-functioning ASD, there are some limitations. First, the sample size in this study was small, and our findings need to be validated in larger sample of adults with ASD. Second, we did not evaluate our prediction model using samples from an independent cohort, and to our knowledge, there is no public data available for metagenomics sequencing data for adults with ASD. Finally, as this was a cross-sectional study, the causal relationship between the observed differences in gut microbiota composition and ASD cannot be established and verified. Future longitudinal studies are necessary to examine the lifetime trajectory of ASD, its interaction with gut microbiota, and to determine whether microbial changes influence clinical symptoms over time. In summary, our study reveals the altered gut microbiota composition in adults with high-functioning ASD and underscores the potential application of gut microbiota as non-invasive diagnostic tools. These findings have significant clinical implications for improving diagnostic processes to more effectively and efficiently identify high-functioning ASD in adults. Furthermore, they open avenues for developing potential intervention strategies, including gut microbiota modulation, to better support individuals with ASD. Declarations Acknowledgements The authors would like to thank the Adult Neurodevelopmental Service team of the Institute of Mental Health for their efforts in recruitment of participants for this study. Data availability Data are made available from the corresponding author upon request. Competing interests The authors declare no competing interests. Funding The present work was funded by the grant of the NHG-LKCMedicine Clinician-Scientist Preparatory Programme Plus. Author Contributions JY, MS, and SHW conceived and designed the experiments. JY, ACKC performed the experiments. JY, XX, RZ, SKN and XF analyzed the data. JY took the lead in drafting the initial manuscript. MS and SHW critically revised the manuscript, supported and supervised this project. All authors contributed to the manuscript and endorsed the final manuscript. References Diagnostic and Statistical Mannual of Mental Disorders, Fifth Edition, Text Revision. Washington DC: American Psychiatric Association; 2022. Zeidan J, Fombonne E, Scorah J, Ibrahim A, Durkin MS, Saxena S, et al. Global prevalence of autism: A systematic review update. Autism Res. 2022;15(5):778-90. 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Integrating taxonomic, functional, and strain-level profiling of diverse microbial communities with bioBakery 3. Elife. 2021;10. Wood DE, Lu J, Langmead B. Improved metagenomic analysis with Kraken 2. Genome Biol. 2019;20(1):257. Almeida A, Nayfach S, Boland M, Strozzi F, Beracochea M, Shi ZJ, et al. A unified catalog of 204,938 reference genomes from the human gut microbiome. Nat Biotechnol. 2021;39(1):105-14. Oksanen J SG, Blanchet F, Kindt R, Legendre P, Minchin P, O'Hara R, Solymos P, Stevens M, Szoecs E, Wagner H, Barbour M, Bedward M, Bolker B, Borcard D, Carvalho G, Chirico M, De Caceres M, Durand S, Evangelista H, FitzJohn R, Friendly M, Furneaux B, Hannigan G, Hill M, Lahti L, McGlinn D, Ouellette M, Ribeiro Cunha E, Smith T, Stier A, Ter Braak C, Weedon J. vegan: Community Ecology Package. 2024;R package version 2.6-6.1. McMurdie PJ, Holmes S. phyloseq: an R package for reproducible interactive analysis and graphics of microbiome census data. PLoS One. 2013;8(4):e61217. Love MI, Huber W, Anders S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 2014;15(12):550. Wan Y, Zuo T, Xu Z, Zhang F, Zhan H, Chan D, et al. Underdevelopment of the gut microbiota and bacteria species as non-invasive markers of prediction in children with autism spectrum disorder. Gut. 2022;71(5):910-8. Zhang M, Chu Y, Meng Q, Ding R, Shi X, Wang Z, et al. A quasi-paired cohort strategy reveals the impaired detoxifying function of microbes in the gut of autistic children. Sci Adv. 2020;6(43). Abdelsalam NA, Hegazy SM, Aziz RK. The curious case of Prevotella copri. Gut microbes. 2023;15(2):2249152. Bedarf JR, Hildebrand F, Coelho LP, Sunagawa S, Bahram M, Goeser F, et al. Functional implications of microbial and viral gut metagenome changes in early stage L-DOPA-naive Parkinson's disease patients. Genome medicine. 2017;9(1):39. Kanauchi O, Fukuda M, Matsumoto Y, Ishii S, Ozawa T, Shimizu M, et al. Eubacterium limosum ameliorates experimental colitis and metabolite of microbe attenuates colonic inflammatory action with increase of mucosal integrity. World journal of gastroenterology. 2006;12(7):1071-7. Liu S, Li E, Sun Z, Fu D, Duan G, Jiang M, et al. Altered gut microbiota and short chain fatty acids in Chinese children with autism spectrum disorder. Sci Rep. 2019;9(1):287. Simpson HL, Campbell BJ. Review article: dietary fibre-microbiota interactions. Aliment Pharmacol Ther. 2015;42(2):158-79. Tian B, Yao JH, Lin X, Lv WQ, Jiang LD, Wang ZQ, et al. Metagenomic study of the gut microbiota associated with cow milk consumption in Chinese peri-/postmenopausal women. Frontiers in microbiology. 2022;13:957885. Hu X, Li H, Zhao X, Zhou R, Liu H, Sun Y, et al. Multi-omics study reveals that statin therapy is associated with restoration of gut microbiota homeostasis and improvement in outcomes in patients with acute coronary syndrome. Theranostics. 2021;11(12):5778-93. Ali RO, Quinn GM, Umarova R, Haddad JA, Zhang GY, Townsend EC, et al. Longitudinal multi-omics analyses of the gut-liver axis reveals metabolic dysregulation in hepatitis C infection and cirrhosis. Nat Microbiol. 2023;8(1):12-27. Ayeni FA, Sanchez B, Adeniyi BA, de Los Reyes-Gavilan CG, Margolles A, Ruas-Madiedo P. Evaluation of the functional potential of Weissella and Lactobacillus isolates obtained from Nigerian traditional fermented foods and cow's intestine. International journal of food microbiology. 2011;147(2):97-104. Lee KW, Park JY, Jeong HR, Heo HJ, Han NS, Kim JH. Probiotic properties of Weissella strains isolated from human faeces. Anaerobe. 2012;18(1):96-102. Selle K, Klaenhammer TR. Genomic and phenotypic evidence for probiotic influences of Lactobacillus gasseri on human health. FEMS microbiology reviews. 2013;37(6):915-35. Vellanki S, Navarro-Mendoza MI, Garcia A, Murcia L, Perez-Arques C, Garre V, et al. Mucor circinelloides: Growth, Maintenance, and Genetic Manipulation. Current protocols in microbiology. 2018;49(1):e53. Yan S, Ma Z, Jiao M, Wang Y, Li A, Ding S. Effects of Smoking on Inflammatory Markers in a Healthy Population as Analyzed via the Gut Microbiota. Frontiers in cellular and infection microbiology. 2021;11:633242. Zhao Y, Wang Y, Meng F, Chen X, Chang T, Huang H, et al. Altered Gut Microbiota as Potential Biomarkers for Autism Spectrum Disorder in Early Childhood. Neuroscience. 2023;523:118-31. Cocean AM, Vodnar DC. Exploring the gut-brain Axis: Potential therapeutic impact of Psychobiotics on mental health. Prog Neuropsychopharmacol Biol Psychiatry. 2024;134:111073. Vernocchi P, Del Chierico F, Putignani L. Gut Microbiota Metabolism and Interaction with Food Components. Int J Mol Sci. 2020;21(10). Socala K, Doboszewska U, Szopa A, Serefko A, Wlodarczyk M, Zielinska A, et al. The role of microbiota-gut-brain axis in neuropsychiatric and neurological disorders. Pharmacol Res. 2021;172:105840. Howlin P, Moss P. Adults with autism spectrum disorders. Canadian journal of psychiatry Revue canadienne de psychiatrie. 2012;57(5):275-83. Fusar-Poli L, Brondino N, Rocchetti M, Panisi C, Provenzani U, Damiani S, et al. Diagnosing ASD in Adults Without ID: Accuracy of the ADOS-2 and the ADI-R. J Autism Dev Disord. 2017;47(11):3370-9. Lin P, Zhang Q, Sun J, Li Q, Li D, Zhu M, et al. A comparison between children and adolescents with autism spectrum disorders and healthy controls in biomedical factors, trace elements, and microbiota biomarkers: a meta-analysis. Frontiers in psychiatry. 2023;14:1318637. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5753373","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":399615119,"identity":"0b735959-0d93-4d39-a8b1-99194d64e05d","order_by":0,"name":"Sunny H Wong","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA6ElEQVRIiWNgGAWjYHAD5gMQ+gDxWtgSIKpJ0MJjQJwW+Rm5xx7zMByWM+df8+3xxzYGOb4bCcwfv+DRYnAjL90YqMXYcsbb7QYH2xiMJW8ksEnL4NMikWMmzcNwO3HDjbPbJIBagIwENmYJvA6DaznzDKSlHqiF+TM+LQw3YFrO97CBtCQY3EhgkPyAz2Fn3phJzjH4b2xwg81M4sw5CcOZZx62SeOzRL49x0ziTUWanMH5w88kKsps5PmOJx/++AOfHiBgAsYIA4NEAogN8gRjAzMPAS2MYDP5D6CLjIJRMApGwSiAAAC/6k/lKTF//wAAAABJRU5ErkJggg==","orcid":"","institution":"Nanyang Technological University","correspondingAuthor":true,"prefix":"","firstName":"Sunny","middleName":"H","lastName":"Wong","suffix":""},{"id":399615120,"identity":"4cb48815-a371-4038-b145-ee2c6b50c616","order_by":1,"name":"Jiangbo Ying","email":"","orcid":"https://orcid.org/0000-0001-8503-8240","institution":"Institute of Mental Health","correspondingAuthor":false,"prefix":"","firstName":"Jiangbo","middleName":"","lastName":"Ying","suffix":""},{"id":399615121,"identity":"4a817bd7-f066-435a-85e3-0d0578397d52","order_by":2,"name":"Xinran Xu","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Xinran","middleName":"","lastName":"Xu","suffix":""},{"id":399615122,"identity":"29247f44-25c9-49ab-bda1-bf850d1d0184","order_by":3,"name":"Ruwen Zhou","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Ruwen","middleName":"","lastName":"Zhou","suffix":""},{"id":399615123,"identity":"96a159c2-a94e-400a-a5bb-1ad56ad6f73b","order_by":4,"name":"Arthur C K Chung","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Arthur","middleName":"C K","lastName":"Chung","suffix":""},{"id":399615124,"identity":"50ab849e-32fd-46cb-9b80-923de843d0ba","order_by":5,"name":"Siu Kin Ng","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Siu","middleName":"Kin","lastName":"Ng","suffix":""},{"id":399615125,"identity":"b3c68ef1-cee4-4b8d-82b8-f4043c9eded8","order_by":6,"name":"Xiuyi Fan","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Xiuyi","middleName":"","lastName":"Fan","suffix":""},{"id":399615126,"identity":"002bc8bc-178b-4e4e-a2bf-9fb59a898a8a","order_by":7,"name":"Mythily Subramaniam","email":"","orcid":"https://orcid.org/0000-0003-4530-1096","institution":"Research Division, Institute of Mental Health (IMH), Singapore","correspondingAuthor":false,"prefix":"","firstName":"Mythily","middleName":"","lastName":"Subramaniam","suffix":""}],"badges":[],"createdAt":"2025-01-02 17:22:11","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5753373/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5753373/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":73677063,"identity":"73a0c892-f91a-4655-8036-9cc1e4ff0160","added_by":"auto","created_at":"2025-01-13 13:16:34","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":110543,"visible":true,"origin":"","legend":"\u003cp\u003eGut microbial diversity between ASD and control groups. Alpha diversity indices at species level measured by Chao1 index (p = 0.025), Simpson index (p = 0.335) and Shannon index (p = 0.548). Beta diversity measured by the principal coordinate analysis of Bray-Curtis Distance. Each point represents samples and the circles surrounding the points indicate 80% confidence interval. P values were adjusted for age, gender, ethnicity, BMI, FGI Checklist score and DASH score. **: p-value \u0026lt; 0.01, *: p-value \u0026lt; 0.05, ns: p-value ≥ 0.05 or not significant.\u003c/p\u003e","description":"","filename":"Figure1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5753373/v1/6557fd4157740ebe9b06fc4c.jpg"},{"id":73675748,"identity":"e4074a06-64f3-40d3-b9dc-df82f70657fe","added_by":"auto","created_at":"2025-01-13 13:08:29","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":510309,"visible":true,"origin":"","legend":"\u003cp\u003eHeatmap depicts abundances of 94 bacterial species which appeared in ≥ 10% samples and had distinct profiles between ASD and control groups.\u003c/p\u003e","description":"","filename":"Figure2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5753373/v1/bc5844b839e371fef3b61a1d.jpg"},{"id":73675824,"identity":"b8d62e87-0d1e-4770-b770-5bd15f5808e2","added_by":"auto","created_at":"2025-01-13 13:08:35","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":354718,"visible":true,"origin":"","legend":"\u003cp\u003eNetwork visualization of bacterial species which appeared in ≥ 15% samples in the Control group and ASD group. Node color represents group and node size (degree) indicates the number of direct interactions for a given microbe. Color depth and width of each line reflect the strength of interaction (edge weight).\u003c/p\u003e","description":"","filename":"Figure3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5753373/v1/46ca8c4d690817c2bda623fa.jpg"},{"id":73675763,"identity":"15c822eb-42e6-40bb-98c9-b36748a67623","added_by":"auto","created_at":"2025-01-13 13:08:30","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":235581,"visible":true,"origin":"","legend":"\u003cp\u003eRandom forest performance analysis. The ROC curves were generated based on three datasets: gut microbiota data combined with Autism Spectrum Quotient questionnaire data (red curve), gut microbiota data alone (blue curve), and Autism Spectrum Quotient questionnaire data alone (yellow curve).\u003c/p\u003e","description":"","filename":"Figure4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5753373/v1/fab29bc94527190ef8cf19bb.jpg"},{"id":78888605,"identity":"78a21545-c1e6-4167-872c-52e1f8536c6d","added_by":"auto","created_at":"2025-03-20 10:04:26","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1727619,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5753373/v1/064379ad-77e5-4823-9f1d-ffb8ba4a90b4.pdf"},{"id":73675794,"identity":"dd65505a-c51c-4c43-bd59-9a665296dbb3","added_by":"auto","created_at":"2025-01-13 13:08:33","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":405994,"visible":true,"origin":"","legend":"Supplemental Material","description":"","filename":"supplementarymaterial20241220a.docx","url":"https://assets-eu.researchsquare.com/files/rs-5753373/v1/4d478a979b9ac4e0a9b55f09.docx"}],"financialInterests":"The authors have declared there is \u003cb\u003eNO\u003c/b\u003e conflict of interest to disclose","formattedTitle":"Gut microbiota in young adults with high-functioning autism spectrum disorder and its performance as diagnostic biomarkers","fulltext":[{"header":"Introduction","content":"\u003cp\u003eAutism spectrum disorder (ASD) is a complex neurodevelopmental condition characterized by difficulties in social communication and interaction, as well as the manifestation of repetitive and restrictive patterns of behavior, activities, or interests (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). Globally, the prevalence of ASD has been reported to range from 0.01\u0026ndash;4.36%, with a median prevalence of 1% (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). In the United States, it has been estimated recently that the overall ASD prevalence is 2.76% (one in 36 children), which is higher than the previous estimates during 2000 to 2018 (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e). ASD is associated with considerable impact and economic burden on the families and public health. For example, the lifetime cost for an individual with ASD in China is \u003cspan\u003e$\u003c/span\u003e2.65\u0026nbsp;million United States Dollars (USD) for those without intellectual disability (ID) and \u003cspan\u003e$\u003c/span\u003e4.61\u0026nbsp;million USD for those with comorbid ID (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe exact pathogenesis of ASD is still not understood, although it is widely considered to result from the interactions between genetic and environmental factors (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e). Hundreds of genes have been identified to contribute to the significant deficits in social cognition and communication in ASD, but these genetic factors account for only 10\u0026ndash;20% of ASD cases (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e). Various environmental factors, such as gestational diabetes and low birth weight, are reported to be associated with ASD (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e). Recently, gut microbiota has been identified to play an important role in the gut-brain axis and may contribute to the development of ASD. The gut-brain axis involves a bidirectional connection with the central nervous system, and occurs through several mechanisms, such as the autonomic nervous system, enteric nervous system, neurotransmitters and hormones (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e). Various studies have observed an altered gut microbiota composition in persons with ASD (\u003cspan additionalcitationids=\"CR10\" citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e). These observations are supported by interventional studies, where fecal microbiota transfer from ASD donors into germ-free mice has been shown to induce autistic behaviors (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e). In human, microbiota transfer therapy has been found to mitigate symptoms of ASD (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e). In addition, a recent study has demonstrated strong correlations between ASD symptoms and urinary metabolites, such as 3-hydroxyhippuric acid and homovanillic acid, following fecal microbiota transplantation (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e), These findings further implicate gut microbiota in the metabolic disturbances associated with ASD.\u003c/p\u003e \u003cp\u003eMost studies on the gut microbiota to date have focused on children with ASD. A recent systematic review in this area reveals that only one study has assessed the microbiota in adult individuals with ASD and has only examined the bacterial microbiota (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e). A recent study has applied metagenomic sequencing to study the multi-kingdom microbiota, but has only included children aged 1\u0026ndash;13 years (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e). Since the gut microbiota is correlated with age (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e), the differences in the composition of gut microorganisms between children and adults may not be consistent.\u003c/p\u003e \u003cp\u003eAdults with ASD exhibit different clinical presentations compared to children. Individuals with ASD who do not have intellectual impairment are considered as having high-functioning ASD (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e) or ASD without accompanying intellectual impairment (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). Though ASD traits usually start in the early developmental period, symptoms may not become fully apparent until adulthood for high-functioning ASD (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e). Diagnosing ASD in adult individuals, especially high-functioning ASD, presents several challenges, such as inaccurate recall of childhood history, behavioral overlap with other mental disorders, and learned coping strategies to mask certain ASD symptoms (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e). There are currently no reliable biomarkers to effectively diagnose ASD in adults, and sometimes ASD among adults can be even misdiagnosed as other psychiatric conditions (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e). Therefore, it is worthwhile to explore the gut microbiota as potential biomarkers for diagnosing ASD among adults.\u003c/p\u003e \u003cp\u003eThe present study aimed to comprehensively investigate the gut microbiome and their functional pathways among young adults with high-functioning ASD and evaluate their potential application as a diagnostic tool for adults with ASD. We hypothesized that ASD adults would have an altered microbial composition, compared to the healthy controls, and the different microbial composition could facilitate ASD diagnosis in adults.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e\u003cem\u003eStudy participants\u003c/em\u003e\u003c/p\u003e\n\n\u003cp\u003ePersons with ASD aged between 21 and 40 years old were recruited from the Adult Neurodevelopmental Service (ANDS) at the Institute of Mental Health (IMH). IMH is the only tertiary psychiatric hospital in Singapore. ANDS provides both inpatient and outpatient services for adults with ASD and/or ID, and its new case clinic has the capacity to review 319 patients over a two-year period (21). The ASD diagnosis was made according to the Diagnostic and Statistical Manual of Mental Disorders Fifth Edition (DSM-5) by qualified psychiatrists. Healthy controls with similar gender, age range and ethnicity were recruited from the community, and they were required to have no self-reported history of ASD or other mental health conditions. All participants were recruited from October 2023 to April 2024. Recruitment efforts included distributing flyers and advertisements targeted at hospital staff to encourage referrals and participant enrollment. Written informed consent was obtained from all the participants. All study procedures were performed in accordance with the relevant laws and institutional guidelines and were approved by the National Healthcare Group Domain Specific Review Board.\u003c/p\u003e\n\n\u003cp\u003eExclusion criteria for both ASD group and control group included having intellectual disability; receiving any formulated probiotic or prebiotic supplements; and being treated with any antibiotics within one month of the study. \u003c/p\u003e\n\n\u003cp\u003e\u003cem\u003eSample size\u003c/em\u003e\u003c/p\u003e\n\n\u003cp\u003eThe sample size was calculated \u003cem\u003ea priori\u003c/em\u003e using G* Power software (22). In a previous study exploring the relationship of gut microbiota and autism (10), the alpha diversity index, estimated by Chao1 index, was 79.8 (Standard Deviation, SD=19.3) in the ASD group and 92.6 (SD=20.2) in the control group. Based on the differences in this ecological index from this study, 41 participants in the ASD group and another 41 in the control group were needed to achieve a power of 80% with alpha error of 0.05. Assuming a drop-out rate of 10%, the present study aimed to recruit 45 participants in each group. \u003c/p\u003e\n\n\u003cp\u003eQuestionnaires \u003c/p\u003e\n\n\u003cp\u003eDemographics and clinical information. Social demographics, including age, gender, ethnicity, body mass index (BMI), marital status, education level and monthly household income, were collected for both ASD group and control group. \u003c/p\u003e\n\n\u003cp\u003eThe Autism Spectrum Quotient was used to quantify ASD traits in participants. The Autism Spectrum Quotient is a widely used tool in clinical practice and research to quantify ASD symptoms (23). It has 50 items with a maximum score of 50. It has acceptable high sensitivity and specificity. At a cut off score of 26, it has sensitivity of 0.95, specificity of 0.52, positive predictive value of 0.84, and negative predictive value of 0.78 (24). The questionnaire has been used in Asian countries, such as Japan (25). \u003c/p\u003e\n\n\u003cp\u003eThe Functional Gastrointestinal Checklist (FGI Checklist) was used to assess gastrointestinal symptoms of participants. The FGI checklist was initially developed to measure both upper and lower gastrointestinal symptoms (26). It assesses 20 common gastrointestinal symptoms, such as regurgitating, heartburn, epigastric pain, diarrhea, and abdominal distension. \u003c/p\u003e\n\n\u003cp\u003eThe Diet Screener questionnaire was used in this study to explore the dietary habits in participants. This 37-item questionnaire was developed to assess dietary patterns in a multi-ethnic population in Singapore (27). It evaluates average food intake of 37 food/beverage items over the preceding year. Each item is rated on a 10-point scale ranging from \u0026ldquo;never or rarely\u0026rdquo; to \u0026ldquo;6 or more times a day\u0026rdquo;. The items are categorized into the following seven groups: vegetables, fruit, nuts/legumes, whole grains, low fat dairy, red and processed meat, and sweetened beverages. Based on the scores in each of the seven groups, each participant is assigned a score between 1 and 5 corresponding to the intake quintile they belong to, with reverse scoring applied for meat and sweetened beverages. Subsequently, these seven quintile scores are added together to calculate the total value, which is the Dietary Approaches to Stop Hypertension (DASH) score. The detailed description on the calculation of the DASH score can be found in a previous study (28). \u003c/p\u003e\n\n\u003cp\u003e\u003cem\u003eSample collection, DNA extraction and sequencing\u003c/em\u003e\u003c/p\u003e\n\n\u003cp\u003eApproximately 200-300mg of stool samples were collected from each participant using a stool collection kit. Preservative media (Nuclei acid preservative, cat: 28330, Norgen Biotek Corp, Ontario, Canada) were included in the stool collection tubes to allow safe transportation of microbial DNA at room temperature. Stool samples in preservative media were delivered to the laboratory to be stored at -80 \u0026deg;C refrigerators until further processing. DNA was extracted from the stool samples with the QIAamp PowerFecal Pro DNA Kit (QIAGEN, Germany), following the manufacturer instructions. The DNA concentration and A260/280 ratio were measured with a NanoDrop Spectrophotometer for quality control. The extracted DNA was then sent for shotgun metagenomic sequencing on the Illumina NovSeq 6000 platform (Novogene, Singapore).\u003c/p\u003e\n\n\u003cp\u003eBioinformatics and statistical analysis\u003c/p\u003e\n\n\u003cp\u003eRaw sequencing reads were first quality filtered and trimmed using Trimmomatic ver 0.39 (29). To remove host associated reads, filtered reads were mapped against the human reference genome (hg38) using bowtie version 2.4.5 (30). Read pairs with any read mate aligned to the reference genome were discarded for further analysis. For profiling the distribution of microbial species, taxonomic classification of cleaned reads resolved at strain level was carried out using MetaPhlAn version 4 (31), a marker-gene based metagenomic taxonomic profiler that is based on the marker genes identified from over one million prokaryotic and metagenome-assembled genomes. The profiles of microbial functional pathways (e.g. MetaCyc, KO gene family) were obtained using HUMAnN ver3.0 (32). To account for the difference in library size, mapped read counts were converted into copies per million (CPM) values prior to downstream analyses. For the abundance estimation of fungus and virus, cleaned reads were mapped against the NCBI Fungi and refseq viral databases respectively using Kraken2 version 2.1 (33) after removing bacterial reads by mapping cleaned reads against the unified human gastrointestinal genome (UHGG version 2.1) database (34).\u003c/p\u003e\n\n\u003cp\u003eSequencing data were analyzed using R (version 4.4.1). Alpha diversity metrics, including Simpson, Shannon, and Chao indices were calculated with the \u0026ldquo;vegan\u0026rdquo; package (35). Beta diversity was assessed using PERMANOVA (adonis) in the \u0026ldquo;vegan\u0026rdquo; package. Both alpha and beta diversity analyses were adjusted for age, gender, ethnicity, BMI, FGI Checklist score and DASH score. The differences in microbiota abundance and functions between the ASD and control groups were evaluated by the \u0026ldquo;phyloseq\u0026rdquo; (36) and \u0026ldquo;DESeq2\u0026rdquo; packages (37). Heatmap plots were generated using the \u0026ldquo;pheatmap\u0026rdquo; package and volcano plots were created by the \u0026ldquo;ggplot2\u0026rdquo; and \u0026ldquo;ggrepel\u0026rdquo; packages. Network plots were generated using the \u0026ldquo;igraph\u0026rdquo;, \u0026ldquo;ggraph\u0026rdquo;, and \u0026ldquo;reshape2\u0026rdquo; packages. Corrections for multiple hypothesis testing were applied using the false discovery rate (FDR) approach.\u003c/p\u003e\n\n\u003cp\u003eThe random forest model in Python was used to identify specific microbial taxa and functions that were different between the ASD and control groups. Receiver operating characteristic (ROC) curve analysis was performed to assess the predictive power of the biomarkers selected from the model, followed by the area under the curve (AUC) calculation. Mean AUC value based on 100 performances was calculated to provide a robust estimate of the model\u0026rsquo;s predictive capability. \u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cem\u003eDemographics and clinical characteristics of the participants\u0026nbsp;\u003c/em\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eA total of 45 individuals with ASD and 45 control subjects were recruited for this study. The two groups were comparable in terms of gender and BMI distributions. In the ASD group, there were 41 males and 4 females, with a median age of 23 years (interquartile range [IQR]= 4) and a median BMI of 26.0 kg/m\u003csup\u003e2\u003c/sup\u003e (IQR = 8.77). The control group included 40 males and 5 females, with a median age of 25 years IQR = 4) and median BMI of 24.5 kg/m\u003csup\u003e2\u0026nbsp;\u003c/sup\u003e(IQR = 6.23). Most participants were ethnic Chinese and resided in government flats. The demographic information is summarized in \u003cstrong\u003eTable 1\u003c/strong\u003e.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe ASD group scored higher on the Autism Spectrum Quotient questionnaire (median = 30, IQR = 10) compared to the control group (median = 18, IQR = 11, p-value \u0026lt; 0.001), indicating greater autistic traits in the ASD group. The FGI Checklist scores were also higher in the ASD group (median = 4, IQR = 8) than the control group (median = 2, IQR = 4, p-value \u0026lt; 0.001), revealing more gastrointestinal symptoms among participants with ASD. However, the DASH scores were similar between the ASD group (mean = 20.53, SD = 3.68) and the control group (mean = 20.53, SD = 4.65), with no significant difference between the intake of the seven food groups that were evaluated. \u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"601\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"4\" valign=\"top\" style=\"width: 601px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTable 1.\u0026nbsp;\u003c/strong\u003eDemographic information of the study population\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 216px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003eASD group (n=45)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 150px;\"\u003e\n \u003cp\u003eControl group (n=45)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 97px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;P value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 216px;\"\u003e\n \u003cp\u003eAge (years)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; Median (IQR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e23 (4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 150px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e25 (4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 97px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 216px;\"\u003e\n \u003cp\u003eGender\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; Male\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; Female\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e41\u003c/p\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 150px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e40\u003c/p\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 97px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 216px;\"\u003e\n \u003cp\u003eRace\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; Chinese\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; Malay\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; Indian\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; Others\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e42\u003c/p\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 150px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e37\u003c/p\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 97px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.268\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 216px;\"\u003e\n \u003cp\u003eBMI (kg/m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; Mean (SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e26.0 (8.77)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 150px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e24.5 (6.23)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 97px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.696\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 216px;\"\u003e\n \u003cp\u003eHousing type\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; 1-3 room government flat\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; 4-5 room government flat\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; private flat or landed property\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003cp\u003e25\u003c/p\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 150px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003cp\u003e29\u003c/p\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 97px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.466\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cem\u003eGut microbial diversity between ASD and control groups\u003c/em\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eA total of 993 bacterial species were identified across all participants. Alpha diversity analysis showed that within-subject microbial richness was significantly lower in the ASD group than the control group, as indicated by the Chao1 index (140 \u0026plusmn; 46.1 (mean \u0026plusmn; SD) in the ASD group and 168 \u0026plusmn; 54.8 in the control group, p = 0.025), although Simpson (0.925 \u0026plusmn; 0.0341 in the ASD group and 0.918 \u0026plusmn; 0.0531 in the control group, p = 0.335) and Shannon (3.31 \u0026plusmn; 0.390 in the ASD group and 3.40 \u0026plusmn; 0.503 in the control group, p = 0.548) indices did not show significant differences (\u003cstrong\u003eFigure 1\u003c/strong\u003e). Furthermore, Autism Spectrum Quotient scores were significantly correlated with the Chao1 index (correlation coefficient =\u0026nbsp;-0.351, p-value \u0026lt;0.001) (\u003cstrong\u003eFigure S1\u0026nbsp;\u003c/strong\u003ein the supplementary material), suggesting individuals with more ASD symptoms had lower microbial richness. Beta diversity analysis revealed that the microbial composition differed significantly between two groups, as shown by the principal coordinate analysis (PCoA) of Bray-Curtis Distance (p-value = 0.001) (\u003cstrong\u003eFigure 1\u003c/strong\u003e).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eAbundance of microbial species between ASD and control groups\u003c/em\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eOf the 993 bacterial species identified, 94 were present in \u0026ge; 10% of samples and exhibited distinct profiles between ASD and control groups (\u003cstrong\u003eFigure 2\u003c/strong\u003e). Species such as \u003cem\u003eAnaerostipes hadrus,\u0026nbsp;\u003c/em\u003e\u003cem\u003eLactobacillus gasseri,\u003c/em\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003cem\u003eWeissella confusa,\u0026nbsp;\u003c/em\u003eand \u003cem\u003eEubacterium limosum\u003c/em\u003e were enriched in the ASD group. In contrast, species like \u003cem\u003ePrevotella copri clade A,\u0026nbsp;\u003c/em\u003eand \u003cem\u003eRuminococcaceae bacterium\u003c/em\u003e were more prevalent in the control group\u003cem\u003e.\u003c/em\u003e There were no clear patterns between the distribution of bacterial species and gastrointestinal symptoms, but overall, the gastrointestinal symptoms, measured by FGI Checklist scores, were more severe in the ASD group (\u003cstrong\u003eFigure 2\u003c/strong\u003e). The interactions between those bacterial species in the ASD group and control group are presented in \u003cstrong\u003eFigure 3\u003c/strong\u003e. The ASD group exhibited a distinct microbial interaction pattern when compared to the control group. The top 50 bacterial species with the largest log2 fold change are highlighted in the volcano plot in \u003cstrong\u003eFigure S2\u0026nbsp;\u003c/strong\u003eof the supplementary material. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAcross all samples, a total of 50 fungal species were identified. Notably, species like \u003cem\u003eMeyerozyma caribbica\u0026nbsp;\u003c/em\u003eand \u003cem\u003eMucor circinelloides\u003c/em\u003e were more prevalent in the ASD group whereas \u003cem\u003eDictyocoela roeselum\u003c/em\u003e and\u003cem\u003e\u0026nbsp;Podosphaera cerasi\u0026nbsp;\u003c/em\u003ewere more abundant in the control group (\u003cstrong\u003eFigure S3\u003c/strong\u003e). A total of 40 viral species were found across all samples. Some viruses, such as \u003cem\u003eCarjivirus hominis\u003c/em\u003e and \u003cem\u003eOengusvirus oengus\u003c/em\u003e, were more abundant in the ASD group. In contrast, viruses, like \u003cem\u003eAurodevirus hiberniae\u003c/em\u003e and \u003cem\u003eToutatisvirus toutatis,\u003c/em\u003e were more commonly seen in the control group. \u003cstrong\u003eFigure S4\u003c/strong\u003e illustrates the differences in viral species distribution between the ASD and control groups.\u003c/p\u003e\n\u003cp\u003eAmong the 174 MetaCyc pathways in all the samples, 24 differential pathways were identified. Pathways, such as inosine-5\u0026apos;-phosphate biosynthesis III (PWY-7234) and thiamine diphosphate salvage IV (PWY-7356) were positively associated with ASD, while pathways, such as pyrimidine ribonucleotides de novo biosynthesis (PWY0-162) and geranylgeranyl diphosphate biosynthesis II (via MEP) (PWY-5121) showed negative associations with ASD (\u003cstrong\u003eFigure S5\u0026nbsp;\u003c/strong\u003ein the supplementary material).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eMicrobial biomarkers for ASD diagnosis\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe potential of microbial biomarkers for ASD diagnosis was investigated using a random forest model. Recognizing that dataset splits and parameter selection can substantially influence model performance, the experimental design incorporated robust validation. Specifically, the dataset was randomly split into 70% training and 30% validation sets, and this process was repeated 100 times. For each split, a 3-fold cross-validation was performed on the training set to identify the optimal model parameters. After tuning, the model was retrained on the entire training set and evaluated on the validation set, ensuring a thorough and reliable assessment of its predictive performance.\u003c/p\u003e\n\u003cp\u003eThe associations between numbers of features selected and mean AUC values were presented in Figure S6 in the supplementary material. To achieve a good balance between AUC values and number of features, a total of 25 optimal features (\u003cstrong\u003eTable S1\u003c/strong\u003e of the supplementary material), including 20 bacterial species, 3 fungal species, and 2 microbial pathways, were included in the prediction model. When only these 25 microbial markers were included, the model achieved a mean AUC of 0.8076. In contrast, when only the autism spectrum quotient scores were used, the mean AUC was 0.9112. Notably, by combining both the microbiota markers and Autism Spectrum Quotient scores, the mean AUC improved to 0.9551 (\u003cstrong\u003eFigure 4\u003c/strong\u003e).\u0026nbsp;\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eTo the best of our knowledge, this is the first metagenomic study to explore the gut microbiota in young adults with high-functioning ASD. Our findings demonstrate that the microbiota composition in adults with ASD is significantly different from that of healthy controls, with the richness of the bacterial species being strongly correlated with the severity of ASD symptoms. The microbial features in the gut microbiota, including bacteria, fungi, viruses and functional pathways could be useful in identifying adults with ASD.\u003c/p\u003e \u003cp\u003eIn this study, certain bacterial species differed between ASD and control groups. Though most of those species are not consistently reported in other studies, some of the findings are similar as those in several studies performed in children. For example, ASD group exhibited depleted abundance of \u003cem\u003ePrevotella copri\u003c/em\u003e and increased levels of \u003cem\u003eEubacterium limosum\u003c/em\u003e. These are consistent with previous studies which focused on children with ASD (\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e). \u003cem\u003ePrevotella copri\u003c/em\u003e, a predominant species in the \u003cem\u003ePrevotella\u003c/em\u003e genus, is commonly found in the human gastrointestinal tract (\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e) and capable of generating short-chain fatty acids (SCFAs) that protect the mucosal barrier and reduce the risk of inflammation (\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e). While \u003cem\u003eEubacterium limosum\u003c/em\u003e has been reported to ameliorate colonic inflammation, its metabolism in gastrointestinal tract remains poorly understood (\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e). We also found that several bacteria in the \u003cem\u003eRuminococcaceae\u003c/em\u003e family, such as \u003cem\u003eRuminococcaceae bacterium\u003c/em\u003e,and \u003cem\u003eRuminococcaceae bacterium AM07 -15\u003c/em\u003e were reduced in abundance in the ASD group. Our results align with another study in children showing an inverse association between the abundance of butyrate-producing \u003cem\u003eRuminococcaceae\u003c/em\u003e and ASD (\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e). Since gut microbiota can be modified, for example, through dietary interventions (\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e), these findings could open the door to developing intervention strategies aimed at reducing specific bacteria to support adults with ASD.\u003c/p\u003e \u003cp\u003eInterestingly, this study revealed that several bacteria with potential probiotic properties, such as \u003cem\u003eAnaerostipes hadrus, Weissella confusa\u003c/em\u003e and \u003cem\u003eLactobacillus gasseri\u003c/em\u003e, were increased in the ASD group. To the best of our knowledge, these findings have not been reported in other studies. \u003cem\u003eAnaerostipes hadrus\u003c/em\u003e is known for its butyrate producing capacity and contains biotin synthesis genes that could regulate inflammation and immunity (\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e), making it a potentially beneficial microorganism (\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e). However, it is also reported that \u003cem\u003eAnaerostipes hadrus\u003c/em\u003e can affect the availability of long-chain free fatty acids in the portal circulation, contributing to hepatic fibrosis(\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e). \u003cem\u003eWeissella\u003c/em\u003e confusa, often isolated from fermented foods, has been proposed as a potential probiotic (\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e, \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e). The probiotic \u003cem\u003eLactobacillus gasseri\u003c/em\u003e can offer various health benefits, including maintenance of gut homeostasis and immunomodulation, as supported by genomic and empirical evidence (\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e). There could be a potential link between beneficial bacteria and a reduction in ASD symptoms. In this study, patients with ASD were classified as having mild or high-functioning ASD. The presence of these beneficial bacteria may contribute to a lower risk of developing more severe ASD symptoms.\u003c/p\u003e \u003cp\u003eThis study also identified the difference in fungi and viruses as well as microbial pathway in ASD, expanding beyond previous studies that focused primarily on bacteria (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e). With the advancement of metagenomic sequencing technologies, other microbial communities could also be analyzed in depth to provide more insight in this area. In the current study, the fungus \u003cem\u003eMucor circinelloides\u003c/em\u003e was found to be much more common in the ASD group. It is a naturally growing mold that can lead to potentially fatal infections in individuals with compromised immune systems (\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e). Most viruses identified in this study, such as \u003cem\u003eToutatisvirus toutatis\u003c/em\u003e and \u003cem\u003eAurodevirus hiberniae\u003c/em\u003e, are bacteriophages associated with the gut microbiome, which may interact with gut. Certain pathways were found to differ between ASD and control groups. For example, the inosine-5'-phosphate biosynthesis III was more prevalent in the ASD group. This pathway has also been found to be more abundant in the gut microbiota of smokers (\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e). Our understanding of the relationship between gut microbiota and ASD remains limited, and the functions of these microorganisms are not yet fully understood.\u003c/p\u003e \u003cp\u003eThe observation of reduced bacterial diversity in the gut microbiota of individuals with ASD aligns with broader microbiome research, which often links lower microbial diversity to various health conditions. In this study, we found that individuals with ASD had variations in microbial richness, with a significant correlation between the diversity of bacterial species in the gut and the severity of ASD symptoms. This finding is consistent with studies on children with ASD (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e) and suggests the bacterial metabolites may play a role in the development of ASD symptoms. The change in the microbial composition in persons with ASD is commonly referred to as \u0026ldquo;dysbiosis\u0026rdquo; which is characterized by an imbalance in the microbiota, along with alterations in the metabolic activities and functional composition (\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e). While gut microbiota symbiosis supports the maintenance of normal host physiology (\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e), dysbiosis has been associated with various other conditions, including depression, anxiety and Parkinson\u0026rsquo;s disease (\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn this study, we developed prediction models to aid in the diagnosis of ASD in adults. The first model, incorporating gut microbiota data and questionnaire responses, achieved an AUC of 0.9551, while the second model, based solely on gut microbiota data, attained an AUC of 0.8076.Although ASD can be diagnosed as early as in children aged two years, many individuals remain undiagnosed until adulthood (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e). These undiagnosed adults face increased risks of emotional and functional challenges due to unrecognized ASD symptoms (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e). Currently, there is a lack of validated diagnostic tools for adults, as most of the tools are exclusively designed for children (\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e). Diagnostic tools created for children are not always suitable for use in adults (\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e). Various biomarkers have been explored to diagnose ASD, such as C-reactive protein, oxytocin, iron and zinc, but the accuracy is not ideal (\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e). A recent study investigated gut microbiota markers as standalone diagnostic tools for ASD in children, without incorporating additional questionnaires, and achieved an AUC of 0.91. (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e). Our study extends this to gut microbiota in adults, supporting the potential application of the gut microbiota as diagnostic biomarkers for high-functioning ASD.\u003c/p\u003e \u003cp\u003eWhile this study sheds new light on the altered gut microbiota composition in adults with high-functioning ASD, there are some limitations. First, the sample size in this study was small, and our findings need to be validated in larger sample of adults with ASD. Second, we did not evaluate our prediction model using samples from an independent cohort, and to our knowledge, there is no public data available for metagenomics sequencing data for adults with ASD. Finally, as this was a cross-sectional study, the causal relationship between the observed differences in gut microbiota composition and ASD cannot be established and verified. Future longitudinal studies are necessary to examine the lifetime trajectory of ASD, its interaction with gut microbiota, and to determine whether microbial changes influence clinical symptoms over time.\u003c/p\u003e \u003cp\u003eIn summary, our study reveals the altered gut microbiota composition in adults with high-functioning ASD and underscores the potential application of gut microbiota as non-invasive diagnostic tools. These findings have significant clinical implications for improving diagnostic processes to more effectively and efficiently identify high-functioning ASD in adults. Furthermore, they open avenues for developing potential intervention strategies, including gut microbiota modulation, to better support individuals with ASD.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors would like to thank the Adult Neurodevelopmental Service team of the Institute of Mental Health for their efforts in recruitment of participants for this study. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eData are made available from the corresponding author upon request.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe present work was funded by the grant of the NHG-LKCMedicine Clinician-Scientist Preparatory Programme Plus.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eJY, MS, and SHW conceived and designed the experiments. JY, ACKC performed the experiments. JY, XX, RZ, SKN and XF analyzed the data. JY took the lead in drafting the initial manuscript. MS and SHW critically revised the manuscript, supported and supervised this project. All authors contributed to the manuscript and endorsed the final manuscript.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eDiagnostic and Statistical Mannual of Mental Disorders, Fifth Edition, Text Revision. Washington DC: American Psychiatric Association; 2022.\u003c/li\u003e\n\u003cli\u003eZeidan J, Fombonne E, Scorah J, Ibrahim A, Durkin MS, Saxena S, et al. Global prevalence of autism: A systematic review update. Autism Res. 2022;15(5):778-90.\u003c/li\u003e\n\u003cli\u003eMaenner MJ, Warren Z, Williams AR, Amoakohene E, Bakian AV, Bilder DA, et al. Prevalence and Characteristics of Autism Spectrum Disorder Among Children Aged 8 Years - Autism and Developmental Disabilities Monitoring Network, 11 Sites, United States, 2020. 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Frontiers in psychiatry. 2023;14:1318637.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Gut microbiota, autism, adult, biomarker, diagnostic tool ","lastPublishedDoi":"10.21203/rs.3.rs-5753373/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5753373/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eAlthough autism spectrum disorder (ASD) starts in the early developmental period, symptoms may not become fully apparent until adulthood in patients with high-functioning ASD. Diagnosing ASD in adults presents unique challenges and there are currently no specific biomarkers for this condition. Most existing studies on gut microbiota in ASD are conducted in children; however, the composition of the gut microbiota in children differs significantly from that of adults. This study aimed to study young adults with high-functioning ASD on their gut microbiota. Using metagenomic sequencing, we evaluated the gut microbiota in 45 adults with high-functioning ASD and 45 matched healthy controls. Adjusting for sociodemographic information, dietary habits, and clinical data, we observed a distinct microbiota profile between adults with ASD and controls, with their autistic symptom severity strongly correlating to microbial diversity (correlation coefficient = -0.351, p-value \u0026lt;0.001). Despite a similar dietary pattern, the ASD group exhibited more gastrointestinal symptoms than the healthy controls. An internally validated machine-learning predictive model that combines the Autism Spectrum Quotient questionnaire score and microbial features could achieve an area under the receiver operating characteristic curve (AUC) of 0.955 in diagnosing ASD in adults. This study evaluates the gut microbiota in adult ASD and highlights its potential as a non-invasive biomarker to enhance diagnosis of ASD in this population group.\u003c/p\u003e","manuscriptTitle":"Gut microbiota in young adults with high-functioning autism spectrum disorder and its performance as diagnostic biomarkers","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-01-13 13:08:10","doi":"10.21203/rs.3.rs-5753373/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"6c98bb45-7eec-4f9c-a93f-2f3b808c5075","owner":[],"postedDate":"January 13th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":42582218,"name":"Health sciences/Biomarkers/Diagnostic markers"},{"id":42582219,"name":"Biological sciences/Psychology/Human behaviour"}],"tags":[],"updatedAt":"2025-03-20T09:56:19+00:00","versionOfRecord":[],"versionCreatedAt":"2025-01-13 13:08:10","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5753373","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5753373","identity":"rs-5753373","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}