Distinct diet-microbiome associations in autism spectrum disorder

Domain: biology
Published: 2025-12-31
Editorial outcome: major_revision

Abstract
Autism spectrum disorder (ASD) is linked to both altered gut microbiota and unhealthy diets; however, the mechanistic connections remain elusive. In this study, we conducted a systematic analysis of fecal microbiome metagenomic data, paired with granular dietary assessments and phenotypic profiles, across a cohort of 818 children (462 with ASD, 356 without ASD; mean age = 8.4 years; 27.3% female). By integrating dietary indices, nutrient intake, and food additive exposures, we uncovered ASD-specific linkages to the microbiome. Poor dietary quality correlated with aggregated core autistic symptoms, gastro-intestinal complications, and atypical eating behaviors. Notably, children with ASD exhibited a more pronounced diet-microbiome interaction network compared to neurotypical peers, suggesting heightened microbial sensitivity to nutritional inputs. Furthermore, synthetic emulsifiers—specifically polysorbate-80 and carrageenan—were associated with disrupted microbial connectivity in ASD, a phenomenon attenuated in neurotypical children. Our findings elucidate the mechanistic links between dietary factors—particularly synthetic food additives—and microbiome dysregulation in ASD, urging a re-evaluation of dietary guidelines for ASD populations and laying the groundwork for personalized nutritional strategies.

Introduction
Autism spectrum disorder (ASD) is a neurodevelopmental condition marked by social communication challenges and restricted, repetitive behaviors1. Compared to neurotypical peers, children with ASD face a fivefold higher risk of feeding problems such as extreme food selectivity2, as well as a much higher rate of gastrointestinal (GI) disorders3–5. These issues may interact bidirectionally with core ASD traits (e.g., sensory sensitivities) and gut microbiome imbalances, creating a cycle where diet, microbiota, and neurodevelopment influence one another6,7. Studies have identified distinct microbial patterns in ASD, including shifts in Bacteroidetes and Clostridium species, as well as functional changes in pathways related to inflammation and neurotransmitter balance3,8. While these findings have sparked interest in microbiome-targeted therapies for ASD8,9, critical questions remain about how diet and gut microbes interact to shape neurodevelopmental outcomes.

Diet and medication are among the most powerful modulators of the gut microbiome10,11. In ASD, where diets are often limited (e.g., low in fiber, high in processed foods) and medications like antibiotics or psychotropics are commonly used, these factors may uniquely disrupt microbial balance12,13. However, the role of specific dietary components—such as synthetic additives (e.g., emulsifiers) or nutrient deficiencies—in driving ASD-associated dysbiosis remains poorly understood3,7. This gap hinders the creation of evidence-based nutritional guidelines for ASD, a population with highly individualized food preferences and microbiome profiles. Personalized approaches that account for diet-microbiome interactions are critical to advancing tailored interventions.

Here, we examine how dietary habits and medications influence the gut microbiome in 818 children with and without ASD by incorporating fecal metagenomics, detailed dietary surveys, and clinical data. Our findings elucidate ASD-specific links between diet and microbial dysbiosis, paving the way for microbiome-aware dietary guidance to improve gut health and mitigate behavioral challenges in children with ASD.

Results
Diet-driven gut microbial signatures and phenotypic correlates in ASD
Given the relevance of the Chinese Children's Healthy Dietary Index (CCDI) for characterizing general dietary quality in our study population, we first conducted dietary profiling in a cohort of 818 children (mean age = 8.4 years; 27.3% female; children without ASD [non-ASD] = 356, ASD = 462, Fig. 1A), categorized by tertiles of CCDI. The analysis revealed that individuals with poorer dietary patterns exhibited more severe autistic symptoms, increased medication use, GI complications, and challenges across multiple eating behaviors, including desire to drink, food enjoyment, fussiness, and satiety responsiveness (all p < 0.05, Table 1). Further dietary comparisons identified nutritional insufficiency and low dietary quality across multiple metrics, including the Alternative Healthy Eating Index (AHEI), Dietary Inflammatory Index (DII), sulfur-diet score, and healthy food diversity (HFD) index, alongside elevated polysorbate-80 exposure, all of which correlated with diminished CCDI scores (Supplementary Table S1).

Consistent with prior studies, gut microbiome alpha diversity (Shannon index: coef = 0.07, standard error [SE] = 0.03, p = 0.024; observed richness: coef = 9.05, SE = 3.49, p = 0.003) and dysbiosis score (Dissimilarities-based evaluation as detailed in the “Methods”, coef = −0.02, SE = 0.004, p < 0.001) differed significantly between children with ASD and those without (Fig. 1B, Supplementary Fig. 1A). A key finding was that the CCDI, among various continuous dietary indices, had a robust association with microbial dysbiosis in the ASD group (⍴ for Spearman correlation = −0.121, p = 0.009; t [degree of freedom = 461] = −0.258 for linear regression model adjusted by age, sex, GI conditions, and autistic symptoms, β = −0.0004, p = 0.042). In contrast, we did not observe this association in non-autistic peers (Fig. 1C, Supplementary Tables S2, 3 and Supplementary Fig. 1B, C). While the effect modification by ASD status did not attain statistical significance, the consistent pattern of association was verified in sensitivity analysis that excluded samples in accordance with the size and features of non-ASD (Supplementary Tables S2, 3).

To address the multidimensionality of eating behaviors, we derived principal components (PC1 and PC2) from the Children’s Eating Behavior Questionnaire (CEBQ) subscales, which collectively explained 60.9% of variance (PC1: 33.1%, PC2: 27.8%; Supplementary Fig. 1D), and adjusted for these in subsequent models. To quantify the influence of host factors on gut microbiota composition, we performed a multivariate PERMANOVA analysis through Bray-Curtis distances. Among phenotypic factors, dietary quality emerged as a key contributor to microbial taxonomic variation, explaining 1.57% of variance (p < 0.01)—second only to stool consistency (Bristol Stool Chart) and ahead of age, emulsifier exposure, and BMI (Fig. 1D). Medication use, in contrast, minimally influenced microbial composition, aligning with its negligible impact on functional pathway variance (Supplementary Fig. 1E). While effect sizes for these factors were comparable in children without ASD, most associations lost statistical significance, underscoring the ASD-specific interplay between diet, phenotype, and gut ecology (Fig. 1D).

ASD-specific diet-microbiome interactions
Building on the observed connections between diet, food additives, and microbial ecology, we systematically evaluated associations between dietary factors (indices, components, and food groups) and gut microbiome profiles within the ASD population. These associations were analyzed using MaAsLin2, adjusted for the age, sex, and GI conditions that were identified above. Significant microbial features were selected by ranking them based on the number of significant associations across the metadata panels, with the top 50 unique features retained for each panel. The coefficients, adjusted q-values, and prevalence for each microbial species are detailed in Supplementary Data 1, with statistical descriptions provided in the Description of Additional Supplementary Files. These microbial species (Fig. 2A), along with MetaCyc-based functional pathways and KEGG orthologs (KO) (Supplementary Figs. 2, 3), revealed distinct diet-microbe associations comping the non-ASD peers when conducting the same analysis procedure. Specifically, Firmicutes SGB4348, Megamonas funiformis, and Lacrimispora amygdalina emerged as indicators of healthier dietary patterns in ASD, characterized by higher CCDI scores (F. SGB4348: coef = 0.64, SE = 0.19, qval = 0.01 in ASD vs. coef = 0.18, SE = 0.28, qval = 0.79 in non-ASD; M. funiformis: coef = 0.56, SE = 0.17, qval = 0.01 in ASD vs. coef = 0.24, SE = 0.31, qval = 0.91 in non-ASD; L. amygdalina: coef = 0.72, SE = 0.19, qval = 0.01 in ASD vs. coef = 0.01, SE = 0.28, qval = 0.99 in non-ASD) and lower DII scores (F. SGB4348: coef = −0.49, SE = 0.19, qval = 0.09 in ASD vs. coef = −0.32, SE = 0.29, qval = 0.84 in non-ASD; M. funiformis: coef = −0.41, SE = 0.17, qval = 0.10 in ASD vs. coef = −0.38, SE = 0.25, qval = 0.51 in non-ASD; L. amygdalina: coef = −0.65, SE = 0.18, qval = 0.01 in ASD vs. coef = 0.24, SE = 0.23, qval = 0.69 in non-ASD). These associations were replicated in the ASD group using a sample-size matched sensitivity analysis (Supplementary Data 2). Consistently, the population-wide regression model with an interaction term (diet × ASD) indicated the effect modification of ASD status on dietary associations with the abundance of L. amygdalina and Anaerostipes hadrus (qval < 0.2, Supplementary Fig. 4, Supplementary Data 3). Notably, decreased abundance of L. amygdalina was specifically correlated with insufficient vitamin and mineral intake in the ASD group (|coef| > 0.5 and qval < 0.1), while Massilioclostridium coli and Escherichia coli exhibited inverse correlations with these dietary metrics (Supplementary Data 3). While these associations were modest overall, they were largely absent in non-ASD peers, and Venn analyses confirmed no overlap in diet-microbiome interaction patterns between groups, underscoring the ASD-specific nature of these relationships (Supplementary Fig. 5A, Supplementary Data 4).

The breadth of diet-microbe associations varied by dietary variable, showing stronger connections in ASD compared to their non-ASD peers, who exhibited negligible linkages. The dietary factors exhibiting the greatest magnitude of associations with microbial species—across nutritional components, dietary indices, and food groups—were protein intake (28 significant associations in total), CCDI scores (27 in total), and bean consumption (18 in total) (Fig. 2B, Supplementary Fig. 5B). By looking into specific nutrients in relation to microbial shifts, starch and magnesium emerged as the nutrients with the second-highest number of significant associations after protein intake, exhibiting 27 and 20 significant associations, respectively (Fig. 2B). Consistent results were observed at the compositional level in diet-microbiome associations measured using two complementary machine-learning approaches, each trained on relative microbial abundances to predict each dietary variable quantified by correlation coefficients (ρ) for regression and area-under-the-curves (AUCs) for classification (“Methods”). The random forest regression models demonstrated diet-associated species outperformed functional pathway-based predictions across 100 training/testing folds, where strong concordance was observed between predicted and actual values for protein intake (ρ > 0.35) and CCDI scores (Fig. 2B, Supplementary Fig. 5C). The random forest-based binary classification further achieved higher AUC performance in children with ASD compared to non-ASD (Fig. 2B). Notably, emulsifiers—particularly polysorbate-80 and carrageenan—demonstrated stronger associations with microbial features in the ASD group (Fig. 2B, Supplementary Fig. 6). Overall, we demonstrated that children with ASD exhibit distinct and stronger diet-gut microbiome associations compared to neurotypical peers.

Magnesium/protein enrichment and starch depletion in ASD
Given the prominence of microbial associations with CCDI scores and key nutrients (protein, starch, magnesium), we investigated gut microbial shifts in ASD children stratified by high intake of these dietary components. Notably, M. funiformis was enriched in ASD with elevated CCDI scores, high magnesium/protein intake, and reduced starch consumption, while four species—Cibionibacter quicibialis, Collinsella aerofaciens, Bacteroides nordii, and Vescimonas coprocola—were depleted in those with high starch but low protein intake (Fig. 3A). These trends attenuated in neurotypical peers (Supplementary Fig. 7A–D, Supplementary Data 5), with minimal overlap in microbial responses—a contrast that persisted against a sample-size-matched ASD cohort (Supplementary Data 6). The ASD-specific increase of M. funiformis in response to healthy dietary patterns collectively demonstrated the diet-by-ASD interaction (p for interaction < 0.05, Supplementary Data 6). Functional pathway analyses revealed reduced activity in Dihydroxy-6-naphthoate biosynthesis linked to high starch intake and suppressed fatty acid biosynthesis initiation (E. coli) associated with lower CCDI scores and magnesium intake (Fig. 3B, Supplementary Data 7). Concurrently, gene-set enrichment analysis based on KEGG orthology (KO) indicated enhanced microbial processing functions related to amino acid and cofactor biosynthesis (Supplementary Fig. 7E), suggesting a state of metabolic dysregulation in children with ASD linked to dietary imbalances. Collectively, these diet-microbe-functional perturbations highlight mechanistic pathways that may exacerbate challenges in ASD populations with suboptimal nutritional patterns.

To elucidate the role of microbiome in mediating these diet metrics associations in ASD, we performed mediation analyses. Among the specific nutritional components examined, Faecalibacterium prausnitzii exerted a beneficial mediating influence on the associations between protein/zinc intake and social communication (average causal mediation effect [ACME, 95% CI] = −0.125 [−0.245, −0.027], p for mediation = 0.006, proportion = 24.6%) and hyposensitivity (ACME = −0.004 [−0.009, −0.0005], p = 0.02, 15.3%) (Supplementary Fig. 8, Supplementary Data 8). Notably, Coprococcus eutactus emerged as the most prominent mediator, exhibiting the highest proportion of effect for several diet-restricted and repetitive behaviors (RRB) associations, including CCDI (ACME = 0.013 [0.001, 0.029], p = 0.02, 15.8%), DII (ACME = −0.098 [0.001, 0.019], p = 0.02, 19.9%), and bean consumption (ACME = 0.009 [−0.210, −0.011], p = 0.02, 35.4%).

Emulsifier-driven disruption of microbial ecological networks in ASD
Of particular interest were dietary emulsifiers, given their established potential to perturb gut microbiota and their notable contribution to the variance explained in microbial composition (Figs. 1D, 2B). Ruminococcus sp AF1328 emerged as a hallmark taxon inversely linked to polysorbate-80 and carrageenan exposure in ASD (Fig. 4A). Conversely, Collinsella SGB4121 and Bacteroides fragilis—species scarcely represented in neurotypical peers—were enriched with high carrageenan intake. While emulsifier exposures showed limited broad associations with microbial composition. Characterization of population-wide emulsifier exposure tertiles of the cohort revealed demonstrated ASD-specific microbial network instability, evident from significant topological alterations (Table 2) and a positive association between emulsifier levels and dysbiosis score (Supplementary Fig. 1C).

To ensure statistical robustness, we performed bootstrap analyses (n = 1000 iterations) for each network metric. In the highest exposure tertile, we observed fragmented microbial connectivity, marked by significant reductions in network degree, number of edges, and average degree (bias-corrected p < 0.01) (Fig. 4B, C, Table 2 and Supplementary Fig. 9A, B). The network connectivity, also as reflected by the clustering coefficient, was specifically decreased in the ASD group with higher polysorbate-80 intake (Table 2). Simulation attacks confirmed this fragility, as both random and targeted node removals disproportionately disrupted network integrity in ASD (Fig. 4D, E). We identified several taxa (e.g., Enterocloster bolteae, Hungatella hathewayi, and Clostridium symbiosum) as potential keystone species due to their high connecting degrees in the interaction network, displayed diminished interconnectivity in high-exposure ASD networks (Fig. 4B, C), while functional pathways critical for nutrient synthesis (e.g., erythronate and biotin biosynthesis) were suppressed (Supplementary Fig. 9C, D). KEGG enrichment analysis further revealed that polysorbate-80/carrageenan exposure concurrently downregulated the 2-oxocarboxylic acid metabolism and citrate cycle pathways (Supplementary Fig. 9E). Non-ASD children, in contrast, maintained stable network architecture and functional resilience across emulsifier exposure levels (Fig. 4F, G). These findings position synthetic emulsifiers as potent disruptors of microbial ecology in ASD, exacerbating network vulnerability and metabolic deficits already present in this population.

Diet- and medication-independent microbial biomarkers in ASD
These findings underscore the unique interplay between diet and gut ecology in ASD, prompting us to investigate whether dietary variables could influence the stability of previously identified microbial biomarkers for ASD diagnosis8. Reassuringly, ASD-linked microbial signatures—spanning taxonomic groups, functional pathways, and KO gene families—remained independent of dietary influences (Supplementary Fig. 10). Equally critical, systematic evaluation of medications (ADHD drugs, antipsychotics, melatonin, cyproheptadine) and nutritional supplements showed no meaningful interaction with these biomarkers in ASD models. This dual resilience to dietary and pharmacological confounders solidified the clinical utility of gut microbial markers in ASD, offering diagnostically robust insights unmarred by lifestyle or treatment variables.

Methods
Study cohort and descriptions
We initiated a prospective case-control study in Hong Kong, aiming at investigating the correlation between gut microbiota composition and autism symptoms and severity over time, as well as the validation of previously identified microbial markers of ASD, considering genetics and other confounding factors, including ASD-related symptoms and dietary information. Children with ASD were diagnosed by psychiatrists according to the fifth edition of the Diagnostic and Statistical Manual of Mental Disorders (DSM-5) and were recruited from the Child and Adolescent Psychiatric Clinic of the New Territory East Cluster (NTEC) of the Hospital Authority in Hong Kong from December 2021 to December 2023. Non-autistic children, without first-degree relatives diagnosed with autism, by screening negative on the Autism-Spectrum Quotient-10 (AQ-10) and absence of psychiatric disorder according to the DISC-5, were recruited from the community during the same period. Both groups included children under the age of 12, alongside exclusion criteria that comprised a known history of intellectual disability, psychosis, depression, and neurological disorders. No statistical method was used to predetermine sample size. Demographic details and several questionnaires used for the assessments of the severity of autistic symptoms, GI conditions, medication history, and dietary intake, were obtained from parents’ report8. Biological measurements were also conducted at the baseline. A total of 818 children (ASD = 462 and non-ASD = 356, 27.3% females, age range: 3–12 years) of Chinese ethnicity, with complete dietary and gut metagenomics data, were included in the current study. The non-ASD group exhibited no abnormal behaviors following ASD screening and did not have any comorbidities. The study protocol adhered to the principles outlined in the Declaration of Helsinki and received ethics approval from the Joint Committee on Clinical Research Ethics, CUHK-New Territories East Hospital Cluster (CUHK-NTEC CRE, Ref.: 2021.550). Written informed consent was obtained from the parents or caregivers of the participants.

Dietary questionnaires and assessments
To comprehensively evaluate dietary associations with the gut microbiome, we treated multiple dietary metrics as continuous measures, including indices (CCDI, AHEI, DII, sulfur-diet score, HFD index, vitamin insufficiency, and mineral insufficiency), individual nutrient and food additive intakes, and food group consumptions—collectively constituting a holistic dietary profiling.

Participants’ habitual dietary intake was evaluated using a validated food frequency questionnaire (FFQ) encompassing 250 food items, with consumption frequencies recorded over a three-month period. Nutrient intake was derived using the Food Processor Nutrition Analysis and Fitness Software (version 8.0, ESHA Research, Salem, USA). All dietary data were energy-adjusted to amounts per 1000 kcal of total daily energy, except for macronutrients, which were expressed as percentages of total energy (%en)33. Multiple indices were calculated to characterize diet quality and pro-inflammatory potential, with detailed compositional criteria provided in Supplementary Tables. Reflecting overall dietary balance, the CCDI (range: 0–140) is a measure specifically tailored to assess the dietary patterns of Chinese children, with higher scores indicating superior overall dietary quality34. The dietary diversity, a core component of the CCDI, is strongly connected to the gut microbiome, reflecting its value in capturing diet-microbiome relationships35. DII was used as a validated measure of the pro-inflammatory potential of an individual’s diet36,37. The HFD index, which was created by multiplying individual food consumptions by weighted health values and a Simpson’s index score of all ingested items in accordance with the German Nutrition Society rules, is a useful tool for offering more insight into the variety of healthy diets35,38. The HFD index (range: 0–1) reflects dietary variety, with higher values denoting greater diversity. Adapted from the Harvard AHEI-201039, this index demonstrated robust associations with gut microbial composition in prior studies. Each component was scored against dietary recommendations, with elevated scores signifying improved diet quality35. Each component was scored against dietary recommendations, with elevated scores signifying improved diet quality39. Methodological details, including food transformation algorithms and scoring adjustments, are provided in the Supplementary Materials. Furthermore, micronutrient insufficiency status was quantified through vitamin insufficiency and mineral insufficiency, representing the cumulative count of vitamins and minerals, respectively, with intake levels below the Chinese Dietary Reference Intakes (DRIs) as defined by the national standard WS/T 578 40–42.

Exposures to food additive data were measured using a validated food additive questionnaire43, which quantified annual intakes (in grams) and then normalized on a per-body weight basis (g/kg bw per year), as is the usual procedure in toxicology44. There were three types of emulsifiers/thickeners: polysorbate-80 [P80], carboxymethylcellulose [CMC], and carrageenan [CRN], with the sum reflecting total exposures as these components were commonly used in mixed-use30.

Phenotype data measurements
Systematic phenotypic assessment protocols were detailed in our prior study8. In brief, we implemented standardized instruments to evaluate core clinical domains, including the Chinese-validated Social Responsiveness Scale, 2nd edition (SRS-2)45 and the 33-item Sensory Experiences Questionnaire (SEQ)46, to evaluate the overall severity of ASD symptoms and sensory responsiveness. The Children’s Eating Behavior Questionnaire (CEBQ) provided a multidimensional assessment of problems in responsiveness to food, less enjoyment, less satiety responsiveness, food fussiness, emotional overeating, emotional undereating, and desire to drinks47. Principal component analysis (PCA) was performed on CEBQ subscales to address the multidimensionality of eating behaviors (Supplementary Fig. 1B). Stool consistency was classified using the validated Bristol Stool Chart (7-type scale), serving as a proxy for bowel function48. Sociodemographic characteristics (parental education, employment status, household composition) were parent-reported. Continuous covariates were discretized into quartiles. Binary coding (yes/no) was applied for comorbidities, GI symptoms, familial medical history, and medication usage.

Fecal DNA sequencing and data analyses
To minimize potential batch effects during sample processing and sequencing, fecal samples were processed using a standard protocol8. Following preservative removal, microbial DNA was extracted using the Qiagen DNeasy PowerSoil Pro kit per the manufacturer’s protocol. Post-quality assessment (Qubit 2.0, agarose gel electrophoresis, Agilent 2100), libraries were prepared with Illumina DNA Prep (M) Tagmentation through end repair, A-tailing, purification, and PCR amplification. Sequencing was performed on an Illumina NovaSeq system (150 bp paired-end). The ZymoBIOMICS Microbial Community Standards (Standards D6300/D6306) served as positive controls throughout.

Raw sequenced data were quality filtered with Trimmomatic (v.39) to eliminate adapters, low-quality sequences (quality score < 20), and reads shorter than 50 base pairs. The remaining reads were then mapped to several mammalian genomes obtained from the UCSC Genome Browser, as well as bacterial plasmids and complete plastomes from the NCBI RefSeq database. Bowtie 2 (v.2.4.2) was used for mapping, and potential host- and laboratory-associated contaminant reads were removed using KneadData v.0.6. To expedite data processing, GNU parallel (v.3.0) facilitated parallel analysis jobs. Taxonomic classification for metagenomic reads was performed using Kraken 2 (v.2.1.2) with an adoption of k-mer-based algorithms. To obtain precise estimates of taxonomic abundance, particularly at the species and genus levels, Bracken (v.2.5.0) was utilized, building on the results from Kraken 2. The read counts for each species were then converted into relative abundances for subsequent analysis49. Furthermore, microbiome functional pathways were profiled using HUMAnN (v.3.0) and transformed into relative abundance before analysis.

Features with ultra-low prevalence (<10%) were originally filtered out50,51. Calculations of diversity richness (observed species and Shannon diversity) were performed using phyloseq (v1.24.2) and vegan (v2.6–4) in R. Considering that microbial data are sparse with a non-normal distribution, relevant statistics using relative abundance were performed using the ggpubr (v.0.6.0) package (https://github.com/kassambara/ggpubr). A dysbiosis score, calculated using Bray–Curtis dissimilarities to the gut microbiome compositions of the non-ASD group, was employed to assess the dysbiotic levels in autistic children52. Initially, a reference set was created from samples of non-ASD subjects. The dysbiosis score for each sample was defined as the median Bray–Curtis dissimilarity to this reference set, excluding samples from the same subject. To identify samples that significantly diverged from the reference, we set the threshold for the dysbiosis score at the 90th percentile of scores from non-ASD samples52.

Calculation of microbial features associated with diet and medications use
The correlations of diversity richness and gut dysbiosis score with dietary components within different groups were performed using Spearman’s rank test, Kendall correlation method, and generalized regression model with adjustments of age, sex, body mass index, Bristol Stool Chart, GI conditions, and autistic symptoms. To address the issues of non-independence, centered-log-ratio transformation was conducted in the variance component and association analyses using the compositions (v2.0–5) R package. To evaluate the proportions of variance in microbiome composition or MetaCyc microbial functional pathways attributed to phenotypes, we applied permutational multivariate analysis of variance (PERMANOVA), derived from the adonis function in the vegan package of R (v.2.6–4). This approach focused on beta-diversity, employing the Bray–Curtis distance matrix generated from the relative abundances of microbial species, with 9999 permutations conducted. MaAsLin2 (v1.4.0, huttenhower.sph.harvard.edu/maaslin2) analysis was performed on microbial functionality and bacterial species linking to dietary factors after adjustments for age, sex, and GI conditions. The latter was selected over the Bristol Stool Chart due to its stronger association with overall microbial community variation. To evaluate whether diet-microbiome associations differed between groups, we fitted a regression model with an interaction term (microbe ~ diet + ASD + diet × ASD + age + sex + GI conditions). In addition to the default functional profiling using the HUMAnN database with MetaCyc summed pathway analysis, we further conducted KEGG Orthology (KO) analysis to enhance the mechanistic interpretation of microbial metabolic potential and functional capabilities. To ensure sufficient statistical power for within-group comparisons of ASD and non-ASD participants, we applied a 1:1 nearest-neighbor matching algorithm using the MatchIt (v3.7.2) package in R, matching on age, sex, and GI conditions.

Machine learning regression and binary classification models were employed to examine the relationships between actual dietary factors and microbiome-based dietary predictions, by generating correlation results and AUC values, respectively. AUC values were calculated using the pROC package (v.1.18.5) in R. We used random forest classification and regression on both species-level taxonomic relative abundance and functional pathways (RF [v2.2] R package, ntree 1000, using default mtry), as the random forest models were demonstrated to have strong robustness and suitability in relation to microbiome abundance data51. For both the regression and classification tasks, the Random Forest method was applied five times, with 10 training/testing folds was implemented.

Microbial connection network and stability
The connection network among fecal bacterial species were calculated using the Sparcc method. The correlation matrix generated by the Sparcc package (v4.4.1). The topological features of the co-occurrence networks were generated by the iGraph package (v4.4.1) and visualized with node sizes proportional to relative abundances. To assess differences in microbial connection network, we compared ASD and non-ASD groups within tertiles of food emulsifier exposure, which were calculated across all participants to establish consistent risk strata. We assessed the significance of differences in topological features—such as reduced average node degree and clustering coefficient, which reflect ecological instability—using a non-parametric bootstrap procedure to compute bias-corrected p-values, with significance letters indicating p < 0.05 after multiple comparison adjustment53. Those nodes with the criteria: qval < 0.005 and |correlation coefficient| > 0.5 were colored based on the groups of modules, while the remaining nodes were in gray. The placement of the colored nodes was performed using the Fruchterman-Reingold layout, which utilizes a force-directed algorithm. We also applied the MDiNE model fitted by the Hamiltonian Monte Carlo method (1000 iterations) to estimate changes in the fecal microbial network topology within the groups54.

Statistical analysis
Statistical evaluations were conducted using R v.4.4.1. Participants were stratified into tertiles based on their CCDI scores. Between-group differences were assessed using Kruskal-Wallis tests for continuous variables and Pearson’s Chi-squared tests for categorical variables, respectively. The tests were two-tailed, and exact p-values along with confidence intervals are reported. All the statistically significant diet-microbiome associations were further adjusted for multiple comparisons using false discovery rate (FDR) correction (Benjamini-Hochberg procedure). For each metadata panel, microbial species were ranked by the number of significant associations observed; then, the top 50 uniquely associated species per panel were retained for downstream analysis. In the association heatmaps, asterisks denote statistically significant associations (FDR-adjusted qval < 0.1).

Ethics statement
This research complies with ethics regulations, with protocol approved by the Joint Chinese University of Hong Kong-New Territories East Cluster Clinical Research Ethics Committee (The Joint CUHK-NTEC CREC). Written consent was obtained from the children’s parents.

Reporting summary
Further information on research design is available in the Nature Portfolio Reporting Summary linked to this article.

Data availability
The metagenomic sequencing data used in this study have been published before and were available in the NCBI Sequence Read Archive database under accession code PRJNA9436878. Processed dietary and microbial data are deposited in Zenodo (https://zenodo.org/) under the identifier No. 1773081855. The full results of the diet-microbiome association analyses, including all exact p-values, are available in the Supplementary Data. The corresponding legends can be found in the Description of Additional Supplementary Files. Participant metadata cannot be made publicly available via repositories as outlined in the patient consent form to protect participant privacy. Requests for sharing metadata, including dietary profiles, can be submitted with a written proposal to the corresponding author (Prof. Siew C. Ng) at siewchienng@cuhk.edu.hk. The proposal should detail the intended use of the data. The data management team, composed of scientists and clinicians, will review these requests based on scientific merit and ethical considerations, including patient consent, to avoid any misuse or misinterpretation. Data sharing will be undertaken if the proposed projects have a sound scientific rationale or potential patient benefit. Data recipients are required to enter a formal data sharing agreement, which describes the conditions for release and requirements for data transfer, storage, archiving, and publication. Since the data management meeting is held monthly, please anticipate a response within two working months. Data access is typically granted for 12 months under a Data Use Agreement that prohibits participant re-identification and third-party data transfer.

Code availability
Scripts for data analysis steps in this manuscript are publicly available on Zenodo: https://zenodo.org/records/1773081855.

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "Wu et al. aimed to determine how the relationship between dietary intake and the gut microbiome differed between children (3-12 years-old) with and without autism spectrum disorder (ASD) through conducting extensive dietary surveys, phenotypic data measurements such as evaluation of ASD and GI symptom severity, and gut microbiome profiling via shotgun metagenomics sequencing of fecal samples. The size of the cohort (n=818) and breadth of data is impressive, and the research aim is both important and novel. However, the statistical analysis and presentation of the data in this current version of the manuscript hinders adequate interpretation of the data by the reader. We recommend major revisions.\nLine [80]: “Diet-driven gut microbial signatures and phenotypic correlates in ASD”\nIn this section, it is stated that CCDI is significantly correlated with more severe autistic symptoms and GI complications. However, there are other diet metrics discussed in this manuscript that have ASD-specific diet-microbiome interactions: protein intake, bean consumption, P80, carrageenan, starch, and magnesium. Do these diet metrics correlate with severity of autistic symptoms or GI complications in the entire population, or specifically in the ASD population? Are the abundances of the microbial taxa or functions that were associated with these dietary metrics additionally correlated with severity of autistic symptoms or GI complications? Mediation and/or moderation analysis can be completed to determine if the microbiome mediates or moderates the association between diet and severity of autistic symptoms or GI complications.\nIn the current version of this manuscript, there are a lot of diet-microbiome interaction findings in ASD versus TD populations, but it is hard to determine if any of these findings are clinically relevant. It is important to demonstrate which dietary components, beyond just the dietary indices, exacerbate and also which dietary components alleviate the severity of autistic symptoms and GI complications both independently and by acting through the microbiome.\nLine [108]: “While effect sizes for these factors were comparable in TD children, most associations lost statistical significance, underscoring the ASD-specific interplay between diet, phenotype, and gut ecology (Fig. 1D).”\nThere are two main issues with this statement. First, the sample size for the ASD population (n=462) is over 100 participants larger than the sample size for the TD population (n=356). Therefore, the loss in statistical significance could be due to the lower statistical power in the TD calculation comparted to the ASD calculation. Second, the TD population could be more variable than the ASD population, because even though the TD population has an absence of neurological disorders, the TD population may not be completely “healthy” and may exhibit other morbidities relevant to microbiome dysbiosis such as gastrointestinal disorders. We recommend the following changes:\n1) Effect sizes should be provided in the manuscript text for all important statistical findings. Results with similar effect sizes between groups should be interpreted with caution.\n2) More phenotypic information should be provided on the TD population in order to understand its inherent variability.\na) Is there any overlap between the behavioral outcomes (SRS-2 and SEQ) in the ASD versus TD populations? If numbers permit, it may be best to remove patients in the TD population who were not diagnosed with ASD but had abnormal SRS-2 or SEQ scores to reduce population variability.\nb) How many in the TD population versus the ASD population had irregular stools on the Bristol Stool Scale or had GI symptoms? If numbers permit, it would be interesting to do a sub-analysis on the ASD and TD populations without GI complications, and the ASD and TD populations with GI complications, to determine how ASD impacts diet-microbiome interactions independently of GI complications, or how diet-microbiome interactions during GI complications are different in TD children and children with ASD.\n3) As part of the limitations section, the difference in numbers between the TD and ASD populations and impact on statistics should be stated. Also, it should be mentioned that although the TD population was screened for neurological disorders, this population may contain children with other types of disorders. If you have comorbidity data, it would be relevant to state if any comorbidities were substantially present in the TD population.\n[Line 127] “The breadth of diet-microbe associations varied by dietary variable: in ASD, protein intake (n=28), CCDI scores (n=27), and bean consumption (n=18) exerted the strongest effects on microbial species, whereas non-ASD children showed negligible linkages.”\nThe total n for these analyses is quite low. Considering it was stated that 818 children were in the study, what is the reason for these low n? Was it a lack of completion of dietary surveys, a lack of fecal samples collected, or a lack of fecal samples sent out for shotgun metagenomics sequencing? An effort should be made to increase these numbers if possible. If not, interpretations from analyses with such low n should be made with caution, and the limitations of interpreting these analyses should be stated in the discussion section.\n[Line 133]: “Moreover, predictive modeling using diet-associated species outperformed functional pathway-based predictions (Supplementary Fig. 3C), with random forest models demonstrating strong concordance between predicted and actual values for protein intake (ρ > 0.35) and CCDI scores.”\nThese findings are unusual. One would expect a microbiome function to be tied to diet-microbiome interactions, which would yield important mechanistic clues. Two reasons on why these findings occurred that need to be addressed:\n1. Due to the low n, these species level findings could be spurious, as there is much more variance in the presence/absence of individual species across individuals whereas the microbiome functions are much more conserved. For all findings involving microbial species, the number of individuals who had the species present in their gut microbiome should be stated.\n2. The default HUMAnN database is the BioCyc/MetaCyc summed pathway abundances. The important microbiome functions may not be represented in this database. Better results may be yielded through the use of additional databases such as KEGG, GO, and CAZyme. It may also be better to conduct analyses on the individual orthologies rather than the summed pathways, and to subsequently complete gene-set enrichment analysis as a follow-up step.\nLine [142]: “Given the prominence of microbial associations with CCDI scores and key nutrients (protein, starch, magnesium), we investigated gut microbial shifts in ASD children stratified by high intake of these dietary components.”\nThe microbial associations with the key nutrients (starch and magnesium) are not clearly stated in the previous sections, so this sentence is unclear to the reader.\nLine [222] “Children with ASD exhibited pronounced ecological fragility in response to emulsifier exposure, marked by fragmented microbial networks and loss of keystone taxa.”\nChildren with ASD also had lower microbiome alpha diversity than TD children. In this discussion section, we suggest that the authors link the findings of pronounced ecological fragility and fragmented microbial networks to the diminished alpha diversity, as it is known that lower diversity leads to higher interdependence between species-species relationships and species-substrate relationships within the microbial community.\nLine [233]: “Limitations”\nWe think it is important to state as a limitation that the studied populations were relatively ethnically and culturally homogenous, and that as future work the study should be repeated in populations with different dietary patterns to increase diversity and validate the generalizability of this study’s findings. Further, it should be stated if any of the dietary indices, dietary components, or food groups had low variability across the studied populations. For example, if the vast majority of the ASD population had low dark green vegetable intake or the TD population had high fish intake, this study would be limited in its ability to observe how the microbiome differs by dark green vegetable intake in the ASD population or fish intake in the TD population.", "reviewer_2": "The work does not align with established literature. For example, Zafar and Habib (2021) found lactobacillus and prevotella to be greatly decreased in the gastrointestinal tract of children with autism. The authors do not even look for these bacterial species in their study and yet, they claim to have analyzed \"the top 50 microbial species.\"\nThe authors do not consider the impact of dietary heavy metals on gut microbiota when numerous studies indicate heavy metal exposures are involved in autism. Mangalam et al (2017) found a decline in Prevotella in response to heavy metal mixtures in the gut.\nThe study design has gaps in it which render the results of the study highly questionable in terms of their utility. The authors do not look at synthetic food additives which contain heavy metals (e.g., petroleum-based food colors). The methodology is not sound. I find this to be a poorly designed study because the authors clearly did not do a thorough literature review before they designed their study.", "reviewer_3": "Thank you for the opportunity to review Wu et al.’s manuscript.\nThe authors perform a metagenomics analysis of a large cohort accompanied by deep phenotyping information. As a starting point, they perform an important replication of previous work finding that poor dietary quality is associated with degree of autism symptoms and measures of microbiome diversity. In this well-powered dataset, they also find associations between poor dietary quality and gastrointestinal complications. The authors then go further, performing a variety of following analyses to characterise dietary-microbiome relationships.\nA major strength of the paper is the depth of phenotyping information collected, particularly with respect to dietary measures, and how the authors have carefully explored potential confounders. I particularly like the data-driven analyses that have been performed in Section 2.2.\nMy questions largely relate to the logical flow of the paper which I believe could be improved to take the reader along on the journey. I often feel unsure what the “deep-dive” analyses (e.g., Section 2.3, 2.4) are being directed by, which can give the impression of cherry-picking (even if it is not). I also believe that the analyses need more statistical testing (rather than the largely qualitative presentation of results at current), plus technical and specific details to be included in the main text (examples below).\nMajor points:\n• For Table 1, please justify why tertiles are used (and not quartiles, quintiles, etc.)? It would be better still if there were known cutoffs for CCDI (rather than being driven by the current data) that you could use to divide. Were tertiles used throughout the analysis (as is suggested in Methods line 403), or just for Figure 1? Please clarify – I believe that CCDI should be kept as a continuous variable in analyses or information is lost unnecessarily.\n• Given there are various systematic differences shown in Table 1, please articulate what covariates have been included in each analysis, including in the main text.\n• It is not immediately clear to me how the specific dietary measures shown in Fig.2A and Fig.2B were chosen. Fig. 2B in particular seems to be a subset of Fig.2A. How were these chosen? Were the dietary variables in Fig.2A also a subset and if so how were they chosen?\n• For the Fig.2B analysis, is it possible that the difference in classification performance could be due to there being ~100 more people in the ASD group vs TD?\n• In Section 2.3 Magnesium/protein enrichment and starch depletion in ASD – I am unclear in what way the protein, starch and magnesium associations are “prominent” (at least when going off Figure 2 where these variables do not stand out). Is this shown in a plot or table somewhere? If so, it would be helpful if this is referenced (and also should probably be in a main text figure). Without this clarity, I feel I am missing the relevance of this section.\n• Similarly in Section 2.4, please point to what analysis led you to choose to focus on emulsifier exposures? Again, I think this should be in a main text figure.\n• In Section 2.4, it would be help logical flow to prime the reader more to the relevance of network analyses and why you have used them.\n• In Section 2.4, line 165, “dysbiosis” is mentioned. But what is the definition of dysbiosis here? Probably better to mention as ecological instability if some technical definition of dysbiosis is not met.\n• In Section 2.4, please provide more specific metrics of the network differences with statistical testing if possible. At currently, the main text is entirely qualitative which I do not think is sufficient, and I believe Table 2 is unreferenced. Eg. I am not very familiar with network analyses (but the audience may also not be) but I am not immediately convinced that there is a difference between the ASD vs nonASD networks. Eg. “Neurotypical children, in contrast, maintained stable network architecture and functional resilience across emulsifier exposure levels”. Please provide statistical tests to support such claims.\n• In Section 2.4, how is a keystone species defined?\n• For Figure 4 A, what is the x-axis label? For B/C, I’d suggest putting ASD and nonASD labels on the plots to make it easier to understand. What does “high exposures” mean here? For D/E, I would suggest including the nonASD results from SF6, if a key claim the authors are making is that ASD and nonASD networks are fundamentally different.\n• For Section 2.5, I do not know what specific analyses have actually been performed. Could this please be clarified?\n• In general, it would be helpful for the reader if the main text included some more details of methods than how the manuscript is currently written. Otherwise, the reader is required to flip back and forth between Methods and Results to make a critical assessment. Eg. For the analysis resulting in Fig.1D, could the authors please specify the model/analysis used in the main text rather than just the Methods (I believe this is PERMANOVA). For the Fig.2B analysis, could the authors please elaborate beyond “machine learning”. Etc.\nMinor points:\n• Figure 2B could be improved by having the rectangle/circle described within the figure key (rather than just the legend).\n• Is there a reason why carrageenan is abbreviated to CRN? It would make it easier to read if spelt out in full and wouldn’t increase word count.\n• There seems to be mention of both non-ASD and TD. Would suggest consistency unless this means something distinct.\n• Reference 7 is cited as Johnson and Howell 2021 Cell Metabolism. However, this is commentary rather than the original primary analysis article which is Yap et al. 2021 Cell – I would suggest referencing the latter article (which to be transparent, I acknowledge is mine) as it was the first to empirically support dietary hypotheses of microbiome differences in autism.\nKind regards,\nChloe Yap", "reviewer_4": "I co-reviewed this manuscript with one of the reviewers who provided the listed reports. This is part of the Nature Communications initiative to facilitate training in peer review and to provide appropriate recognition for Early Career Researchers who co-review manuscripts."}, "author_response": "POINT-BY-POINT REPLY TO EDITORS AND REVIEWERS\nDear reviewers,\nThank you very much for the valuable comments to our manuscript. We have provided a point-\nby-point response to the reviewers’ comments, together with a tracked and clean version of the\nrevised manuscript. All changes are highlighted in yellow.\nWe appreciate the opportunity to resubmit and hope you find this version acceptable for\npublication.\nYours sincerely\nSiew Ng on behalf of co-authors\nReviewers' Comments:\nReviewer #1:\nRemarks to the Author:\nWu et al. aimed to determine how the relationship between dietary intake and the gut\nmicrobiome differed between children (3-12 years-old) with and without autism spectrum\ndisorder (ASD) through conducting extensive dietary surveys, phenotypic data measurements\nsuch as evaluation of ASD and GI symptom severity, and gut microbiome profiling via shotgun\nmetagenomics sequencing of fecal samples. The size of the cohort (n=818) and breadth of data\nis impressive, and the research aim is both important and novel. However, the statistical analysis\nand presentation of the data in this current version of the manuscript hinders adequate\ninterpretation of the data by the reader. We recommend major revisions.\nIn this section, it is stated that CCDI is significantly correlated with more severe autistic\nsymptoms and GI complications. However, there are other diet metrics discussed in this\nmanuscript that have ASD-specific diet-microbiome interactions: protein intake, bean\nconsumption, P80, carrageenan, starch, and magnesium. Do these diet metrics correlate with\nseverity of autistic symptoms or GI complications in the entire population, or specifically in the\nASD population? Are the abundances of the microbial taxa or functions that were associated\nwith these dietary metrics additionally correlated with severity of autistic symptoms or GI\ncomplications? Mediation and/or moderation analysis can be completed to determine if the\nmicrobiome mediates or moderates the association between diet and severity of autistic\nsymptoms or GI complications.\nResponse: We sincerely thank the reviewer for the positive comments. We categorized the\nChinese Children Dietary Index (CCDI) rather than other dietary metrics to evaluate its\nassociation with the gut microbiota 1. We employed this approach because the CCDI reflects\noverall dietary quality and is specifically tailored to the dietary patterns and requirements of\nChinese children, making it highly relevant to our study population. Furthermore, as supported\nby previous research, closer connections of healthy dietary index and diversity with the gut\nmicrobiota highlighted the importance of using a holistic dietary index—rather than focusing\non isolated nutrients or single foods 2,3. We have revised the manuscript to clearly articulate the\nsignificance of the CCDI in the initial analysis (line 82-82, 381-385).\nLine 82: Given the relevance of the Chinese Children Healthy Dietary Index (CCDI) for\ncharacterizing general dietary quality in our study population, we first conducted dietary\nprofiling in a cohort of 818 children\nLine 381: Reflecting overall dietary balance, the CCDI (range: 0–140) is a measure\nspecifically tailored to assess the dietary patterns of Chinese children, with higher scores\nindicating superior overall diet quality 34. The dietary diversity, a core component of the CCDI,\nis strongly connected to the gut microbiome, reflecting its value in capturing diet-microbiome\nrelationships.\nWe thank the reviewer for raising the important point regarding other diet metrics potentially\ncorrelated with autistic severity. We indeed found dietary intake and nutritional insufficiencies\ndiffer markedly between children with ASD and their non-ASD peers and correlate specifically\nwith core symptoms within the ASD group. As shown in the following Figure, while consistent\nassociations were observed between poor dietary quality (e.g., lower CCDI scores, pro-\ninflammatory diet) and hypersensitivity in ASD, specific nutrient-symptom associations often\ndiffered between ASD and non-ASD groups. Given the disease-specific dietary patterns and\nthe potential for biased effect estimates in combined analyses, we analyzed the ASD and non-\nASD groups separately instead of pooling these phenotypically distinct populations. This\napproach served as the methodological basis for the mediation analyses presented in the revised\nmanuscript (line 191-196, Supplementary Fig. S7, Supplementary Table S8).\nFigure Heatmap for the associations between diet and autistic phenotypes in children\nwith/without ASD. The multivariable linear regression model was adjusted for age, sex, BMI,\nmother’s education, and GI—covariates previously identified to confound dietary intake.\nTo determine whether the diet-associated microbial species were additionally correlated with\nthe severity of autistic symptoms, we conducted comprehensive mediation analyses within the\nASD population (line 191-196) and included a discussion of their mechanistic implications\n(line 281-286). GI conditions were treated as a confounder in the mediation analysis rather than\nas an outcome parallel to autistic symptoms. The complete results are available in\nSupplementary Table S8, with statistically significant mediation models visualized in\nSupplementary Fig. S7. Our analyses identified that intake of protein, zinc, and trans-fatty acids\n(TFA) was significantly linked to autistic symptoms, with these relationships being partially\nmediated by variations in gut microbial composition. Specifically, Faecalibacterium prausnitzii\nemerged as a potential beneficial mediator, attenuating the associations between dietary\ncomponents and ASD symptomatology. Conversely, reduced abundances of\nFirmicutes_SGB14874 and Firmicutes _SGB71883, observed in the context of higher TFA and\nlower zinc intake, were significantly associated with more severe ASD symptoms, highlighting\ntheir role in the adverse diet-microbiome-symptom pathway.\nLine 191: To elucidate the role of microbiome in mediating these diet metrics associations in\nASD, we performed mediation analyses. Among the specific nutritional components examined,\nFaecalibacterium prausnitzii exerted a beneficial mediating influence on the associations\nbetween protein/zinc intake and ASD symptomatology (Supplementary Fig. 7; Supplementary\nTable S8). Notably, Coprococcus eutactus dominated with the highest mediation proportion\neffects across all diet-ASD associations.\nLine 281: Mediation analysis revealed that Faecalibacterium prausnitzii mediated the\nbeneficial effects of protein and zinc intake on ASD symptoms, likely through its anti-\ninflammatory and butyrate-producing properties 26. Coprococcus eutactus exhibited the\nstrongest mediation effect, suggesting a key role in gut-brain communication and potential as a\nprobiotic target for dietary interventions 27,28. These findings highlight the possibility of\nmicrobiota-directed therapies for ASD.\n[1] Duan, R., Qiao, T., Chen, Y. et al. The overall diet quality in childhood is prospectively\nassociated with the timing of puberty. Eur J Nutr. 2021. 60, 2423–2434. Doi: 10.1007/s00394-\n020-02425-8\n[2] Asnicar F, Berry SE, Valdes AM, et al. Microbiome connections with host metabolism and\nhabitual diet from 1,098 deeply phenotyped individuals. Nat Med. 2021, 27(2):321-332. doi:\n10.1038/s41591-020-01183-8.\n[3] Bowyer, R.C.E., Jackson, M.A., Pallister, T. et al. Use of dietary indices to control for diet\nin human gut microbiota studies. Microbiome. 2018. 6, 77. doi: 10.1186/s40168-018-0455-y\nIn the current version of this manuscript, there are a lot of diet-microbiome interaction findings\nin ASD versus TD populations, but it is hard to determine if any of these findings are clinically\nrelevant. It is important to demonstrate which dietary components, beyond just the dietary\nindices, exacerbate and also which dietary components alleviate the severity of autistic\nsymptoms and GI complications both independently and by acting through the microbiome.\nResponse: Thank you for this insightful comment. In response, we have now conducted\ncomprehensive mediation analyses, adjusting for age, sex, and GI conditions, to identify\nspecific microbial taxa that significantly mediate the relationships between dietary factors and\nASD severity (Supplementary Fig. S7 and Table S8). This analysis screened all diet-associated\nmicrobial features to prioritize those with the greatest potential clinical relevance as mediators.\nThe results revealed several promising candidates; for instance, Faecalibacterium prausnitzii\nappeared to modulate the impact of diet on symptom severity, suggesting their potential as\ntherapeutic targets. We have expanded the Discussion (line 191-196, 281-286) to incorporate\nthese findings and emphasize their clinical implications.\nLine [108]: “While effect sizes for these factors were comparable in TD children, most\nassociations lost statistical significance, underscoring the ASD-specific interplay between diet,\nphenotype, and gut ecology (Fig. 1D).”\nThere are two main issues with this statement. First, the sample size for the ASD population\n(n=462) is over 100 participants larger than the sample size for the TD population (n=356).\nTherefore, the loss in statistical significance could be due to the lower statistical power in the\nTD calculation comparted to the ASD calculation. Second, the TD population could be more\nvariable than the ASD population, because even though the TD population has an absence of\nneurological disorders, the TD population may not be completely “healthy” and may exhibit\nother morbidities relevant to microbiome dysbiosis such as gastrointestinal disorders. We\nrecommend the following changes:\n1) Effect sizes should be provided in the manuscript text for all important statistical findings.\nResults with similar effect sizes between groups should be interpreted with caution.\nResponse: Thank you for your feedback regarding the comparison of effect sizes between the\nASD and non-ASD populations. We have supplemented the manuscript with a formal\ncomparison of effect sizes and species prevalences across all groups to ensure transparent and\nclear presentation (Supplementary Table S4). We acknowledge the important points that\nstatistical power depends on sample size and that we cautiously compared effect sizes between\nthe ASD and non-ASD groups. As highlighted in the revision (line 133-142), typical dietary\nmetrics—CCDI, DII, and vitamin/mineral insufficiency—demonstrated stronger effect sizes on\nFirmicutes SGB4348 and Megamonas funiformis, along with significant q-values in ASD. We\nalso included the sensitivity analysis using a 1:1-matched ASD subgroup (n = 356) with an\nequal number of non-ASD participants (Supplementary Table S3, line 104-106). Notably, even\nwith this reduced sample size, CCDI exhibited more significant associations with microbial\nfeatures in the ASD group, reinforcing the validity and clinical relevance of our initial results.\nLine 133: Specifically, Firmicutes SGB4348 and Megamonas funiformis emerged as indicators\nof healthier dietary patterns in ASD, characterized by higher CCDI scores (F.SGB4348: coef =\n0.64, qval = 0.01 in ASD vs. coef = 0.18, qval = 0.79 in non-ASD; M.funiformis: coef = 0.56,\nqval = 0.01 in ASD vs. coef = 0.24, qval = 0.91 in non-ASD) and lower DII scores (F.SGB4348:\ncoef = -0.49, qval=0.09 in ASD vs. coef = -0.32, qval = 0.84 in non-ASD; M.funiformis: coef\n= -0.41, qval=0.10 in ASD vs. coef = -0.38, qval = 0.51 in non-ASD). The decreased abundance\nof F.SGB4348 also indicated stronger correlations influenced by insufficient vitamin and\nmineral intake remarkably observed in ASD (|coef| > 0.5 and qval < 0.1, Supplementary Table\nS4).\n2) More phenotypic information should be provided on the TD population in order to\nunderstand its inherent variability.\na) Is there any overlap between the behavioral outcomes (SRS-2 and SEQ) in the ASD versus\nTD populations? If numbers permit, it may be best to remove patients in the TD population\nwho were not diagnosed with ASD but had abnormal SRS-2 or SEQ scores to reduce population\nvariability.\nResponse: We appreciate the Reviewer’s insightful suggestion regarding the importance of\nreducing heterogeneity within the non-ASD by accounting for abnormal behaviors. We would\nlike to clarify, however, that the non-ASD participants in our cohort were carefully\ncharacterized to minimize heterogeneity: all scored within the normal ranges on the Social\nResponsiveness Scale (SRS) and the Sensory Experience Questionnaire (SEQ), and were free\nof neurodevelopmental and psychiatric comorbidities, as detailed in the demographic table\nbelow. This suggests a relatively homogeneous comparison group suitable for identifying\nASD-specific associations. Furthermore, to address the potential confounding effect of GI\nsymptoms—which were already adjusted for in our primary models—we added a sensitivity\nanalysis by excluding the small subset of non-ASD individuals with GI complications (line\n258-263, Supplementary Fig. S10). Please attend the following characteristic table for\nreference. We have supplemented this information in the main text to elaborate on the\npotential bias due to variability.\nLine 307: The absence of neurodevelopmental and psychiatric comorbidities in the non-ASD\ngroup minimized the heterogeneity of the population and solidified our non-significant\nfindings (Supplementary Table S9).\nLine 310: Despite data variability of diet was sufficient for the primary analyses, the limited\nrange in certain micronutrients (e.g., B vitamins) may have reduced sensitivity for detecting\nsubtler associations (Supplementary Table S9).\nSupplementary Table S9:\nASD Non-ASD p value\nN=462 N=356\nAge 8.00 (6.00-10.0) 9.00 (8.00-10.0) <0.001\nMale 395 (85.5%) 200 (56.2%) <0.001\nBMI 16.0 (14.6-18.1) 16.3 (14.8-18.7) 0.056\nFamily medical history 93 (20.1%) 19 (5.34%) <0.001\nComorbidity (yes) 259 (56.1%) 0 (0.00%) <0.001\nMedication 150 (32.5%) 0 (0.00%) <0.001\nGI symptoms 92 (19.9%) 19 (5.34%) <0.001\nBristol stool chart 0.050\ntype 1 25 (5.41%) 11 (3.09%)\ntype 2 67 (14.5%) 45 (12.6%)\ntype 3 100 (21.6%) 84 (23.6%)\ntype 4 160 (34.6%) 146 (41.0%)\ntype 5 29 (6.28%) 31 (8.71%)\ntype 6 9 (1.95%) 1 (0.28%)\ntype 7 1 (0.22%) 0 (0.00%)\nAutistic symptoms\nSRS_RRB 62.0 (55.0-71.0) 46.0 (43.0-50.0) <0.001\nSRS_SCI 68.0 (61.0-75.0) 53.0 (49.0-58.0) <0.001\nSEQ_hyper 2.07 (1.79-2.50) 1.57 (1.36-1.86) <0.001\nSEQ_hypo 1.67 (1.33-2.17) 1.33 (1.00-1.50) <0.001\nEating behaviors (CEBQ) 0.17 (-0.42-0.79) -0.26 (-0.82-0.37) <0.001\nb) How many in the TD population versus the ASD population had irregular stools on the\nBristol Stool Scale or had GI symptoms? If numbers permit, it would be interesting to do a sub-\nanalysis on the ASD and TD populations without GI complications, and the ASD and TD\npopulations with GI complications, to determine how ASD impacts diet-microbiome\ninteractions independently of GI complications, or how diet-microbiome interactions during GI\ncomplications are different in TD children and children with ASD.\nResponse: We thank the reviewer for this insightful suggestion. We agree that this is a crucial\nquestion for disentangling the specific effects of ASD from those of co-occurring GI\nconditions. We therefore adjusted for GI conditions in all original association analyses in the\nmain text. We have conducted additional sub-analyses stratifying both the ASD and non-ASD\npopulations by the presence of GI complications and have incorporated a discussion of these\nresults and their implications into the revised manuscript (line 263–268 and 314–316). To\nensure statistical power given the low number of non-ASD children with GI symptoms (n =\n19), we defined GI complications broadly to include both parent-reported GI symptoms and\nirregular stool types (Bristol Stool Scale types other than 3 or 4). The results, visualized in\nSupplementary Fig. S10, revealed that the most significant and consistent diet-microbiome\ninteractions were observed in ASD children without GI complications. This pattern suggests\nthat GI comorbidities may alter or obscure microbial responses to dietary intake, highlighting\nthe importance of considering GI status when designing microbiome-targeted dietary\ninterventions for children with ASD.\nLine 263: This critical question—regarding how heterogeneity in GI conditions modulates\ndiet-microbiome interactions—prompted a focused sub-analysis within the ASD population,\nstratified by the presence of GI symptoms and irregular Bristol Stool types. It revealed that\ndietary indices exerted a more pronounced influence on microbial features in ASD children\nwithout GI complications (Supplementary Fig. 10), suggest that GI health appears to be a\nprerequisite for robust diet-microbiome interactions 20.\nLine 314: Meanwhile, our findings positioned GI health as a potential effect modifier of diet-\nmicrobiome interactions, necessitating careful consideration in subgroups to avoid biased\ninterpretations.\n3) As part of the limitations section, the difference in numbers between the TD and ASD\npopulations and impact on statistics should be stated. Also, it should be mentioned that\nalthough the TD population was screened for neurological disorders, this population may\ncontain children with other types of disorders. If you have comorbidity data, it would be\nrelevant to state if any comorbidities were substantially present in the TD population.\nResponse: We appreciate your suggestion to include this clarification in the limitations\nsection. Regarding comorbidity data, we have reviewed our study's metadata, which indicates\nthat there are no substantial comorbidities present in the non-ASD children, as shown in the\nsupplementary table S9 in response to your previous comment. This information was clearly\narticulated in the revised manuscript (line 303-309).\nLine 303: However, limitations warrant caution: (i) The cross-sectional design precludes\ncausal inference, and the discrepant sample sizes between ASD and non-ASD groups may\ninfluence statistics. Nevertheless, sensitivity analyses using matched subgroup sizes\nconsistently confirmed the distinct diet-microbiome associations observed in ASD. The\nabsence of neurodevelopmental and psychiatric comorbidities in the non-ASD group\nminimized the heterogeneity of the population and solidified our non-significant findings\n(Supplementary Table S9).\n[Line 127] “The breadth of diet-microbe associations varied by dietary variable: in ASD,\nprotein intake (n=28), CCDI scores (n=27), and bean consumption (n=18) exerted the\nstrongest effects on microbial species, whereas non-ASD children showed negligible\nlinkages.”\nThe total n for these analyses is quite low. Considering it was stated that 818 children were in\nthe study, what is the reason for these low n? Was it a lack of completion of dietary surveys,\na lack of fecal samples collected, or a lack of fecal samples sent out for shotgun metagenomics\nsequencing? An effort should be made to increase these numbers if possible. If not,\ninterpretations from analyses with such low n should be made with caution, and the\nlimitations of interpreting these analyses should be stated in the discussion section.\nResponse: We thank the reviewer for this valuable feedback and apologize for any\nmisunderstanding caused by our initial description. We would like to clarify that all\nassociation analyses utilized the full sample size. The values n = 28, n = 27, and n = 18 refer\nto the number of microbial species found to be significantly associated with dietary\ncomponents—not the sample size of the study population. We have revised the relevant\nsection in the Results (line 148–156) to prevent potential misinterpretation.\nLine 148: The breadth of diet-microbe associations varied by dietary variable, showing\nstronger connections in ASD compared to their non-ASD peers, who exhibited negligible\nlinkages. The dietary factors that exerted the most substantial effects on microbial species—\nacross nutritional components, dietary indices, and food groups—were protein intake (28\nsignificant associations in total), CCDI scores (27 in total), and bean consumption (18 in\ntotal) (Fig. 2B; Supplementary Fig. 4B). By looking into specific nutrients in relation to\nmicrobial shifts, starch and magnesium emerged as the nutrients with the second-highest\nnumber of significant associations after protein intake, exhibiting 27 and 20 significant\nassociations, respectively (Fig. 2B).\n[Line 133]: “Moreover, predictive modeling using diet-associated species outperformed\nfunctional pathway-based predictions (Supplementary Fig. 3C), with random forest models\ndemonstrating strong concordance between predicted and actual values for protein intake (ρ >\n0.35) and CCDI scores.”\nThese findings are unusual. One would expect a microbiome function to be tied to diet-\nmicrobiome interactions, which would yield important mechanistic clues. Two reasons on\nwhy these findings occurred that need to be addressed:\n1. Due to the low n, these species level findings could be spurious, as there is much more\nvariance in the presence/absence of individual species across individuals whereas the\nmicrobiome functions are much more conserved. For all findings involving microbial species,\nthe number of individuals who had the species present in their gut microbiome should be\nstated.\nResponse: We thank the reviewer for raising this important point. To clarify, all correlation\nanalyses between diet and microbial species were conducted using the complete dataset,\nwherein full dietary information was available for all participants, as stipulated in the\nMethods. No samples were excluded due to missing dietary metrics. In direct response to this\ncomment, we have now included Supplementary Table S4, which specifies the number of\nindividuals in which each bacterial species was detected. We also confirm that downstream\nassociation analyses were restricted to microbial species with a detection prevalence\nexceeding 10% of samples (line 443), thereby reducing the risk of spurious associations\narising from low-prevalence taxa. Furthermore, we have revised the wording in the relevant\nResults section to more explicitly frame these findings as exploratory and to highlight the\nnecessity for validation in larger, independent cohorts. Thank you for helping improve the\nclarity and rigor of our manuscript.\n2. The default HUMAnN database is the BioCyc/MetaCyc summed pathway abundances. The\nimportant microbiome functions may not be represented in this database. Better results may\nbe yielded through the use of additional databases such as KEGG, GO, and CAZyme. It may\nalso be better to conduct analyses on the individual orthologies rather than the summed\npathways, and to subsequently complete gene-set enrichment analysis as a follow-up step.\nResponse: We appreciate your insight regarding the limitations of the HUMAnN database. In\nresponse, we have included KEGG orthology (KO) analyses to enhance the mechanistic\nunderstanding of the findings. Among the dietary metrics, healthy dietary indices, especially\nCCDI score, exhibited strong associations with predicted gene functions as the heatmap\nshown (Supplementary Fig. S3). The subsequent gene-set enrichment analysis further\ndemonstrated significant contributions of animo-acids, particularly Phenylalanine, tyrosine\nand tryptophan biosynthesis, involved in the diet-microbiome interaction. We have included\ndetailed descriptions in the main text (line 184-188, 222-224, Supplementary Fig. S6E, Fig.\nS8E).\nLine 184: Concurrently, gene-set enrichment analysis based on KEGG orthology (KO)\nindicated enhanced microbial processing functions related to amino acid and cofactor\nbiosynthesis (Supplementary Fig. 6E), suggesting a state of metabolic dysregulation in\nchildren with ASD linked to dietary imbalances.\nLine 222: KEGG enrichment analysis further revealed that polysorbate-80/carrageenan\nexposure concurrently downregulated the 2-oxocarboxylic acid metabolism and citrate cycle\npathways (Supplementary Fig. 8E).\nLine 274: Complementary KEGG enrichment analysis linked CCDI-associated orthologs to\ncofactor biosynthesis (e.g., thiamine metabolism), suggesting its role in the improved ASD-\nrelated outcomes 23.\nLine [142]: “Given the prominence of microbial associations with CCDI scores and key\nnutrients (protein, starch, magnesium), we investigated gut microbial shifts in ASD children\nstratified by high intake of these dietary components.”\nThe microbial associations with the key nutrients (starch and magnesium) are not clearly\nstated in the previous sections, so this sentence is unclear to the reader.\nResponse: Thank you for your valuable comment. We apologize for the oversight regarding\nthe logic flow, as the key nutrients were not clearly stated in the previous section. The\nassociations of these nutrients (starch and magnesium) with microbial shifts were identified as\nsignificantly related to the microbiome, exhibiting the second-highest numbers of significant\nassociations after protein intake. We have revised the wording in the manuscript to clarify\nthese connections for the reader (line 150-156).\nLine 150: The dietary factors that exerted the most substantial effects on microbial species—\nacross nutritional components, dietary indices, and food groups—were protein intake (28\nsignificant associations in total), CCDI scores (27 in total), and bean consumption (18 in\ntotal) (Fig. 2B; Supplementary Fig. 4B). By looking into specific nutrients in relation to\nmicrobial shifts, starch and magnesium emerged as the nutrients with the second-highest\nnumber of significant associations after protein intake, exhibiting 27 and 20 significant\nassociations, respectively (Fig. 2B).\nLine [222] “Children with ASD exhibited pronounced ecological fragility in response to\nemulsifier exposure, marked by fragmented microbial networks and loss of keystone taxa.”\nChildren with ASD also had lower microbiome alpha diversity than TD children. In this\ndiscussion section, we suggest that the authors link the findings of pronounced ecological\nfragility and fragmented microbial networks to the diminished alpha diversity, as it is known\nthat lower diversity leads to higher interdependence between species-species relationships and\nspecies-substrate relationships within the microbial community.\nResponse: We thank the reviewer for this constructive suggestion. We agree that the\nfragmented microbial networks in children with ASD are closely linked to their reduced alpha\ndiversity. As illustrated in supplementary Figure S1C, higher exposure to P80 and CRN\nappeared strongly correlated with decreased alpha diversity and elevated dysbiosis index—a\nrelationship that was especially observed in the ASD group. We have incorporated this\nperspective into the Discussion section (line 290–293) to more thoroughly contextualize how\ndietary emulsifier exposure may contribute to diversity loss and exacerbate microbial network\ninstability in ASD.\nLine 290: The significant loss of alpha diversity and the exacerbated dysbiosis associated with\nincreased exposure to carrageenan/polysorbate-80 in autistic children (Supplementary Fig.\n1C) offered a plausible mechanism for the pronounced network instability observed\nspecifically in ASD following the dietary disturbance.\nLine [233]: “Limitations”\nWe think it is important to state as a limitation that the studied populations were relatively\nethnically and culturally homogenous, and that as future work the study should be repeated in\npopulations with different dietary patterns to increase diversity and validate the generalizability\nof this study’s findings. Further, it should be stated if any of the dietary indices, dietary\ncomponents, or food groups had low variability across the studied populations. For example, if\nthe vast majority of the ASD population had low dark green vegetable intake or the TD\npopulation had high fish intake, this study would be limited in its ability to observe how the\nmicrobiome differs by dark green vegetable intake in the ASD population or fish intake in the\nTD population.\nResponse: We sincerely thank the reviewers for this constructive comment. We have now\nextensively revised the Discussion section to emphasize that the relatively ethnically and\nculturally homogenous populations may limit the generalizability of our findings. We have\nincluded a call for future studies to validate our results in more diverse cohorts (line 313-314).\nWe also acknowledged concerns regarding dietary metrics with low variability. A review of\nour metadata (included in Supplementary Table S9) indicates that variability is similar across\nboth ASD and non-ASD populations. We have added a sentence stating that while variability\nwas sufficient to detect the reported associations, the restricted range in some micronutrients,\nsuch as vitamin B groups, might have limited our power to detect more subtle relationships\ninvolving them (line 310-312).\nLine 310: Despite data variability of diet was sufficient for the primary analyses, the limited\nrange in certain micronutrients (e.g., B vitamins) may have reduced sensitivity for detecting\nsubtler associations (Supplementary Table S9). As dietary habits are culturally embedded, our\nfindings require validation in more diverse populations to ensure generalizability.\nLine 313: As dietary habits are culturally embedded, our findings require validation in more\ndiverse populations to ensure generalizability.\nPart of the Supplementary Table S9:\nASD Non-ASD p value\nCharacteristics\nn=462 n=356\nDietary variables\nAHEI 53.9 (20.6-81.4) 54.7 (26.8-86.3) 0.059\nCCDI 74.3 (40.5-111) 79.0 (34.5-116) <0.001\nDII 0.295 (-4.09-4.19) -0.220 (-4.27-4.30) 0.022\nSulfurdiet score 0.28 (-4.85-6.47) -0.06 (-4.17-3.96) <0.001\nHFD 0.46 (0.20-0.69) 0.48 (0.26-0.76) 0.003\nMineral insufficiency 5.00 (1.00-12.0) 4.00 (0-12.0) 0.698\nVitamin insufficiency 7.00 (1.00-10.0) 7.00 (1.00-10.0) 0.861\nPolysorbate-80 7.91 (0.36-40.9) 6.38 (0.20-33.9) <0.001\nCarboxymethylcellulouse 16.2 (0-90.6) 12.2 (0.08-55.0) <0.001\nCarrageenan 9.96 (0.28-57.6) 7.85 (0.48-40.6) <0.001\nEmulsifiers 35.1 (1.28-179) 27.4 (1.28-95.4) <0.001\nProtein (% of total energy) 14.9 (7.82-27.5) 16.5 (9.92-27.5) <0.001\nFat (% of total energy) 30.6 (18.0-52.7) 29.7 (16.6-45.4) 0.600\nCarbohydrate (% of total energy) 54.3 (17.9-73.9) 54.2 (29.4-72.1) 0.652\nSFA (% of total energy) 10.1 (4.30-16.4) 10.3 (4.41-19.2) <0.001\nTFA (% of total energy) 0.17 (0.01-1.93) 0.15 (0-1.21) 0.181\nPUFA (% of total energy) 4.77 (1.57-11.5) 4.08 (1.55-8.84) <0.001\nMUFA (% of total energy) 7.72 (0.61-21.8) 7.05 (0.767-15.1) 0.007\nn3/n6 0.12 (0.04-0.42) 0.13 (0.07-0.29) 0.136\nMonosaccharide (g/1000 kcal) 6.71 (0-55.8) 7.96 (0-35.2) 0.016\nDisaccharide (g/1000 kcal) 3.77 (0-26.9) 4.91 (0-24.5) 0.004\nStarch (g/1000 kcal) 76.7 (15.8-136) 74.2 (32.0-139) 0.033\nFiber (g/1000 kcal) 0.78 (0.09-11.30) 0.89 (0.19-4.51) 0.002\nAdded Sugar (g/1000kcal) 2.15 (0-50.9) 3.67 (0-27.8) <0.001\nCalcium (mg/1000 kcal) 267 (96.9-2170) 288 (78.8-972) 0.182\nPhosphorus (mg/1000 kcal) 463 (189-1170) 495 (241-840) <0.001\nSodium (mg/1000 kcal) 1190 (308-2700) 1290 (421-2770) 0.009\nPotassium (mg/1000 kcal) 980 (464-2210) 1050 (507-2990) <0.001\nMagnesium (mg/1000 kcal) 102 (41.8-184) 107 (40.2-236) 0.001\nChromium (mg/1000 kcal) 2.69 (0.618-14.0) 2.69 (0.417-10.5) 0.884\nCopper (mg/1000 kcal) 0.433 (0.15-1.53) 0.467 (0.18-1.49) <0.001\nIodine (mcg/1000 kcal) 33.6 (0.28-195) 35.5 (1.54-193) 0.073\nIron (mg/1000 kcal) 5.10 (1.96-26.4) 5.45 (2.05-17.4) 0.048\nMolybdenum (mcg/1000 kcal) 6.72 (0-33.1) 7.69 (0-47.8) 0.002\nSelenium (mcg/1000 kcal) 44.5 (13.4-274) 48.7 (19.6-112) 0.014\nZinc (mg/1000 kcal) 4.10 (1.61-12.1) 4.34 (2.04-11.6) 0.020\nVitamin B1 (mg/1000 kcal) 0.43 (0.145-1.56) 0.437 (0.12-1.23) 0.993\nVitamin B2 (mg/1000 kcal) 0.57 (0.16-3.21) 0.57 (0.12-1.68) 0.198\nVitamin B3 (mg/1000 kcal) 6.32 (1.34-17.9) 6.68 (1.47-14.1) 0.014\nPantothenic acid (mg/1000 kcal) 2.01 (0.72-8.36) 2.10 (0.61-4.42) 0.045\nVitamin B6 (mg/1000 kcal) 0.31 (0.06-1.24) 0.30 (0.08-1.11) 0.614\nVitamin B12 (mg/1000 kcal) 0.292 (0-2.96) 0.328 (0-4.08) 0.662\nBiotin (mcg/1000 kcal) 9.21 (1.26-57.9) 10.2 (1.22-43.3) 0.003\nVitamin C (mg/1000 kcal) 40.3 (1.65-254) 43.3 (0.94-235) 0.088\nVitamin D (mcg/1000 kcal) 1.62 (0.12-11.4) 1.77 (0.04-8.25) 0.423\nFolate (mcg/1000 kcal) 89.1 (17.4-323) 96.2 (11.2-297) <0.001\nVitamin K (mcg/1000 kcal) 29.5 (2.32-342) 34.3 (0.01-350) 0.042\nCaffeine (mg/1000 kcal) 0.0262 (0-11.8) 0.956 (0-23.4) <0.001\nChlorophyll (mg/1000 kcal) 128 (25.8-453) 141 (42.0-747) <0.001\nFruit (g/1000 kcal) 113 (0-710) 145 (0-1490) <0.001\nVegetable (g/1000 kcal) 145 (0-1050) 194 (0-1830) <0.001\nSugarjam (g/1000 kcal) 4.65 (0-175) 4.29 (0-77.1) 0.105\nProcessed meat (g/1000 kcal) 18.8 (0-266) 18.0 (0-163) 0.265\nFried food (g/1000 kcal) 37.3 (0-180) 40.4 (0-178) 0.741\nDark green vegetables (g/1000 kcal) 20.0 (0-166) 28.6 (0-480) <0.001\nDairy (g/1000 kcal) 66.5 (0-1000) 81.1 (0-773) 0.420\nBeans (g/1000 kcal) 15.4 (0-271) 22.1 (0-196) 0.015\nMacronutrients were presented as percentages of total energy intake. SRS = the Social Responsiveness\nScale; SEQ = the Sensory experiences questionnaire; CCDI= the Chinese Children Healthy Dietary Index;\nAFEI = Alternative Healthy Eating Index; DII = Dietary Inflammatory Index; HFD = healthy food\ndiversity index; SFA = saturated fatty acids; MUFA = monounsaturated fatty acids; PUFA =\npolyunsaturated fatty acids; TFA = trans-fatty acids.\nReviewer #2 (Remarks to the Author):\nThe work does not align with established literature. For example, Zafar and Habib (2021) found\nlactobacillus and prevotella to be greatly decreased in the gastrointestinal tract of children with\nautism. The authors do not even look for these bacterial species in their study and yet, they\nclaim to have analyzed \"the top 50 microbial species.\"\nResponse: We thank the reviewer for highlighting this important point regarding alignment\nwith the existing literature. We appreciate the opportunity to clarify the focus and\nmethodology of our study. Our work specifically investigated the diet-microbiome interaction\nin the context of autism, rather than focusing solely on case-control taxonomic differences.\nWe identified \"the top 50 microbial species\" that, while not classified as ASD-specific\naccording to case-control analyses, demonstrated significant interactions with dietary factors.\nThe primary aim was to identify how dietary factors shape the gut microbiota in children with\nASD and whether these relationships differ from those in neurotypical children. Although\nwell-characterized taxa such as Lactobacillus and Prevotella, which are depleted in ASD, are\ncertainly of interest, their responses to dietary factors were not prominently observed in our\nstudy, limiting their relevance for interpreting the diet-microbiome interaction.\nThe authors do not consider the impact of dietary heavy metals on gut microbiota when\nnumerous studies indicate heavy metal exposures are involved in autism. Mangalam et al\n(2017) found a decline in Prevotella in response to heavy metal mixtures in the gut.\nThe study design has gaps in it which render the results of the study highly questionable in\nterms of their utility. The authors do not look at synthetic food additives which contain heavy\nmetals (e.g., petroleum-based food colors). The methodology is not sound. I find this to be a\npoorly designed study because the authors clearly did not do a thorough literature review\nbefore they designed their study.\nResponse: We thank the reviewer for raising this critical point. We appreciate the reviewer's\ncomments regarding the impact of dietary heavy metals on gut microbiota and the potential\ngaps in our study design. While we acknowledge the importance of heavy metals, their presence\nas contaminants in foods is not a direct dietary component under voluntary consumption. Our\naim is to identify modifiable dietary components that directly affect the gut microbiome—with\nthe ultimate goal of informing microbiome-targeted dietary interventions. We would like to\nclaim very low risk of heavy metal exposure obtained from food additives, since the validated\nassessment tool on nine types of food additives commonly consumed in Hong Kong 1,2\ndemonstrating artificial stabilizers should be the most serious problem less likely contained\nheavy metal. Recent literature also highlights the elevated risks associated with emulsifiers in\nHong Kong's children, demonstrating a closer relationship with neurodevelopmental disorders\nand gut microbiota 3. This supports our decision to concentrate on these stabilizers. Moreover,\nit is reported that in highly developed regions such as Hong Kong 4,5, heavy metal exposure\nwould be generally well-regulated and relatively low compared with other regions.\nOur own ancillary analysis of other additives, including titanium dioxide (which may contain\ntrace metals), revealed no significant between-group differences or substantial variation in\nexposure levels. This further supports the notion that heavy metals are unlikely to be the\nprimary driver in our cohort.\nLastly, we fully agree that environmental pollutants including heavy metals warrant further\ninvestigation. We have now explicitly addressed this point in the Limitations and Future\nDirections section (line 316-321), emphasizing the need for future studies to integrate multi-\npollutant models and expand contaminant monitoring in dietary ASD research.\nLine 316: Emulsifier effects may be compounded by unmeasured additives, and dietary heavy\nmetals, as a risk factor, may also interact with the gut microbiome. Future studies specifically\ndesigned to incorporate biomarker-based assessments or utilize detailed dietary databases with\ncomprehensive additive composition are urgently needed to elucidate the role of these\nsubstances.\n[1] Trakman GL, Lin W, Wilson-O'Brien AL, Stanley A, Hamilton AL, Tang W, Or L, Ching\nJ, Morrison M, Yu J, Ng SC, Kamm MA. Development and Validation of Surveys to Estimate\nFood Additive Intake. Nutrients. 2020;12(3):812. doi: 10.3390/nu12030812.\n[2] Loayza JJJ, Kang S, Schooth L, et al. Effect of food additives on key bacterial taxa and the\nmucosa-associated microbiota in Crohn's disease. The ENIGMA study. Gut Microbes.\n2023;15(1):2172670. doi: 10.1080/19490976.2023.2172670.\n[3] Liu Y, Wang S, Xu W, et al. The impact of food additive intake and altered gut\nmicrobiome on perinatal health: data from 3 regions in China from the MOMMY COHORT.\nGastroenterology. 2024; 166 (5):S-272 - S-273. Doi: 10.1016/S0016-5085(24)01111-9.\n[4] Hou, W., Hu, R., Cui, Z. et al. Heavy metals in urban and rural indoor dust in plateau\nhomes: levels, sources, and health risks. Aerosol Sci Eng (2025).\nhttps://doi.org/10.1007/s41810-025-00308-1\n[5] Luo L, Wang L, Li Y, Cao H, Guo Y, Liao X. Urban-rural inequality in soil heavy metal\nhealth risks: Insights from Baoding, China. Ecotoxicol Environ Saf. 2025; 300:118458. doi:\n10.1016/j.ecoenv.2025.118458.\nReviewer #3 (Remarks to the Author):\nThank you for the opportunity to review Wu et al.’s manuscript.\nThe authors perform a metagenomics analysis of a large cohort accompanied by deep\nphenotyping information. As a starting point, they perform an important replication of\nprevious work finding that poor dietary quality is associated with degree of autism symptoms\nand measures of microbiome diversity. In this well-powered dataset, they also find\nassociations between poor dietary quality and gastrointestinal complications. The authors then\ngo further, performing a variety of following analyses to characterise dietary-microbiome\nrelationships.\nA major strength of the paper is the depth of phenotyping information collected, particularly\nwith respect to dietary measures, and how the authors have carefully explored potential\nconfounders. I particularly like the data-driven analyses that have been performed in Section\n2.2.\nMy questions largely relate to the logical flow of the paper which I believe could be improved\nto take the reader along on the journey. I often feel unsure what the “deep-dive” analyses\n(e.g., Section 2.3, 2.4) are being directed by, which can give the impression of cherry-picking\n(even if it is not). I also believe that the analyses need more statistical testing (rather than the\nlargely qualitative presentation of results at current), plus technical and specific details to be\nincluded in the main text (examples below).\nThanks Yap. We much appreciate your thoughtful feedback and constructive suggestions. We\nwould like to address all the comments and response one by one, particularly to enhance the\nclarity of our deep-dive sections.\nMajor points:\n• For Table 1, please justify why tertiles are used (and not quartiles, quintiles, etc.)? It would\nbe better still if there were known cutoffs for CCDI (rather than being driven by the current\ndata) that you could use to divide. Were tertiles used throughout the analysis (as is suggested\nin Methods line 403), or just for Figure 1? Please clarify – I believe that CCDI should be kept\nas a continuous variable in analyses or information is lost unnecessarily.\nResponse: Thank you for your comments. The choice to use tertiles in Table 1, rather than\nquartiles or quintiles, is primarily driven by the study sample size, offering a practical\napproach to illustrate various characteristics and phenotypic information while maintaining\nenough participants in each group for meaningful comparisons. Since current literature on\nCCDI has not proven well-defined cutoffs, we believe tertiles-based clarification is supported\nby many literature 1,2. The most important feature of Table 1 is that it shows a significant\nassociation between the CCDI and ASD status itself when comparing with demographic\nfactors. We would like to clarify in the main text that the remainder of our analyses retained\nCCDI as a continuous variable, which has been stated in the revision to improve clarity (line\n98-100, line 368-369).\nLine 98: A key finding was that the CCDI, among various continuous dietary indices, had a\nrobust association with Shannon diversity that was specific to the ASD group\nLine 368: To comprehensively evaluate dietary associations with the gut microbiome, we\ntreated multiple dietary metrics as continuous measures, including indices (CCDI, AHEI, DII,\nsulfur-diet score, HFD index, vitamin insufficiency, and mineral insufficiency), individual\nnutrient and food additive intakes, and food group consumptions—collectively constituting a\nholistic dietary profiling.\n[1] Duan, R., Qiao, T., Chen, Y. et al. The overall diet quality in childhood is prospectively\nassociated with the timing of puberty. Eur J Nutr. 2021. 60, 2423–2434. Doi:\n10.1007/s00394-020-02425-8\n[2] Meng L, Wang Y, Li T, et al. Dietary diversity and food variety in Chinese children aged\n3⁻17 years: are they negatively associated with dietary micronutrient inadequacy? Nutrients.\n2018. 10(11):1674. doi: 10.3390/nu10111674.\n• Given there are various systematic differences shown in Table 1, please articulate what\ncovariates have been included in each analysis, including in the main text.\nResponse: We thank the reviewer for this critical comment. We have now explicitly stated\nthe included covariates in the main text and Methods section (line 98-102, 126-127, 457-458).\nAge, sex, and GI conditions were adjusted in all analyses examining microbial species and\ndietary metrics. Additional variables—including BMI, Bristol Stool Chart, and autistic\nbehaviors—were further adjusted for in the linear regression models evaluating associations\nbetween dietary indices and microbial features.\nLine 98: A key finding was that the CCDI, among various continuous dietary indices, had a\nrobust association with Shannon diversity that was specific to the ASD group (p for Kendall\ncorrelation = 0.049; p for Pearson correlation = 0.006; p for linear regression model adjusted\nby age, sex, Bristol Stool Chart, GI conditions, and autistic symptoms = 0.010).\nLine 126: These associations were analyzed using MaAsLin2, adjusted for the age, sex, and\nGI conditions that identified above.\nLine 457: and generalized regression model with adjustments of age, sex, body mass index,\nBristol Stool Chart, GI conditions, and autistic symptoms.\n• It is not immediately clear to me how the specific dietary measures shown in Fig.2A and\nFig.2B were chosen. Fig. 2B in particular seems to be a subset of Fig.2A. How were these\nchosen? Were the dietary variables in Fig.2A also a subset and if so how were they chosen?\nResponse: We thank the reviewer for raising this important point and apologize for any\nconfusion caused by our initial description. To clarify, all correlation analyses between diet\nand microbial species were performed using the full available dataset, for which complete\ndietary information was available for all participants, as described in the Methods. The values\nn = 28, n = 27, and n = 18 referred to the number of microbial species that were significantly\nassociated with dietary components—not the sample size of the study population. We have\nclarified this in the results (line 148-156) to prevent misunderstanding.\nLine 148: The breadth of diet-microbe associations varied by dietary variable, showing\nstronger connections in ASD compared to their non-ASD peers, who exhibited negligible\nlinkages. The dietary factors that exerted the most substantial effects on microbial species—\nacross nutritional components, dietary indices, and food groups—were protein intake (28\nsignificant associations in total), CCDI scores (27 in total), and bean consumption (18 in\ntotal) (Fig. 2B; Supplementary Fig. 4B). By looking into specific nutrients in relation to\nmicrobial shifts, starch and magnesium emerged as the nutrients with the second-highest\nnumber of significant associations after protein intake, exhibiting 27 and 20 significant\nassociations, respectively (Fig. 2B).\n• For the Fig.2B analysis, is it possible that the difference in classification performance could\nbe due to there being ~100 more people in the ASD group vs TD?\nResponse: Thank you for this comment. We fully acknowledge the important point regarding\nthe influence of sample size on statistical power. To further substantiate our findings, we\nconducted a sensitivity analysis using a 1:1-matched ASD subgroup (n = 356) with an equal\nnumber of non-ASD participants (Supplementary Table S3, line 104-106). Notably, even with\nreduced sample size, CCDI showed strengthened associations with microbial features in the\nASD group, further supporting the robustness of our initial observations. We have also\nincorporated the descriptions into the main text, accompanied by a discussion of the potential\nlimitations they may introduce (line 303-307).\nLine 104: The findings remained consistent in the sensitivity analysis conducted after\nexcluding samples in accordance with the size and features of non-ASD (Supplementary\nTable S3).\nLine 303: (i) The cross-sectional design precludes causal inference, and the discrepant sample\nsizes between ASD and non-ASD groups may influence statistics. Nevertheless, sensitivity\nanalyses using matched subgroup sizes consistently confirmed the distinct diet-microbiome\nassociations observed in ASD.\n• In Section 2.3 Magnesium/protein enrichment and starch depletion in ASD – I am unclear in\nwhat way the protein, starch and magnesium associations are “prominent” (at least when\ngoing off Figure 2 where these variables do not stand out). Is this shown in a plot or table\nsomewhere? If so, it would be helpful if this is referenced (and also should probably be in a\nmain text figure). Without this clarity, I feel I am missing the relevance of this section.\nResponse: Thank you for this comment and for highlighting the need for greater clarity\nregarding the selection of magnesium/protein enrichment and starch depletion for section 2.3.\nThese specific dietary factors were prioritized based on our initial screening of diet-\nmicrobiome associations, as they demonstrated the most substantial influence on the gut\nmicrobiota (the greatest number of significant associations with microbial species). We have\namended Figure 2B to clearly highlight the numbers of microbial species that were\nsignificantly associated with each dietary variable. In addition, we have revised the Results\nsection (line 150–156) to more explicitly state that magnesium, protein, and starch were\nselected based on the strength and number of their associations with microbial features, and to\nclarify their contextual importance within central energy-metabolism pathways. These\nmodifications ensure that the rationale for focusing on these particular nutrients and visually\nsupported within the manuscript.\nLine 150: The dietary factors that exerted the most substantial effects on microbial species—\nacross nutritional components, dietary indices, and food groups—were protein intake (28\nsignificant associations in total), CCDI scores (27 in total), and bean consumption (18 in\ntotal) (Fig. 2B; Supplementary Fig. 4B). By looking into specific nutrients in relation to\nmicrobial shifts, starch and magnesium emerged as the nutrients with the second-highest\nnumber of significant associations after protein intake, exhibiting 27 and 20 significant\nassociations, respectively (Fig. 2B).\n• Similarly in Section 2.4, please point to what analysis led you to choose to focus on\nemulsifier exposures? Again, I think this should be in a main text figure.\nResponse: Thank you for this question, which allows us to clarify the rationale behind our\nfocus on dietary emulsifiers. Our decision was motivated by a convergence of prior evidence\nand our own analytical findings: First, as noted in the introduction (line 67-69), emulsifiers\nare widely recognized in the literature as having a potent and direct impact on gut, making\nthem a target for investigation in the context of ASD. Second, our own data supported this\nfocus. As shown in Figure 1, emulsifier exposure emerged as a notable contributor to the\nvariance of microbial compositions. More importantly, the nature of its association with\nmicrobial features differed markedly between ASD and non-ASD children (Supplementary\nFig. 1C). This suggests the presence of ASD may amplify susceptibility to the microbial\nperturbations linked to emulsifiers. Finally, the predictive modeling results presented in\nFigure 2B provided additional support. The relatively high accuracy with which gut microbial\nprofiles could predict emulsifier intake implies the existence of a strong and reproducible\nmicrobial signature associated with this exposure. We have now clarified the rationale before\nstarting off the Section 2.4 (line 199-201).\nLine 199: Of particular interest were dietary emulsifiers, given their established potential to\nperturb gut microbiota and their notable contribution to the variance explained in microbial\ncomposition (Fig. 1D and Fig 2B).\n• In Section 2.4, it would be help logical flow to prime the reader more to the relevance of\nnetwork analyses and why you have used them.\nResponse: We appreciate your suggestion. To improve logical flow and prime the reader for\nthe relevance of the subsequent analysis, we have added an introductory sentence at the\nbeginning of Section 2.4 (line 199-201). This addition clarifies that although dietary\nemulsifiers showed limited broad associations with individual microbial taxa, their significant\ncontribution to the overall variance in microbial composition (Fig. 1D) — combined with\nprior evidence of their biofilm-disrupting and pro-inflammatory effects — motivated us to\nexamine their influence on microbial co-occurrence network\n(https://onlinelibrary.wiley.com/doi/10.1111/all.15825\nhttps://www.nature.com/articles/s41467-025-62397-3).\nLine 199: Of particular interest were dietary emulsifiers, given their established potential to\nperturb gut microbiota and their notable contribution to the variance explained in microbial\ncomposition (Fig. 1D and Fig 2B).\n• In Section 2.4, line 165, “dysbiosis” is mentioned. But what is the definition of dysbiosis\nhere? Probably better to mention as ecological instability if some technical definition of\ndysbiosis is not met.\nResponse: Thank you for highlighting this point. In response, we have revised the wording as\n\"ecological instability\" (line 208) to better capture the intended meaning without confusion on\ntechnical-defined “dysbiosis” that may cause.\n• In Section 2.4, please provide more specific metrics of the network differences with\nstatistical testing if possible. At currently, the main text is entirely qualitative which I do not\nthink is sufficient, and I believe Table 2 is unreferenced. Eg. I am not very familiar with\nnetwork analyses (but the audience may also not be) but I am not immediately convinced that\nthere is a difference between the ASD vs nonASD networks. Eg. “Neurotypical children, in\ncontrast, maintained stable network architecture and functional resilience across emulsifier\nexposure levels”. Please provide statistical tests to support such claims.\nResponse: Thank you very much for your valuable suggestions. While the topological\nproperties we analyzed (e.g., connectivity, modularity) are well-established metrics for\nassessing ecological network stability 1, we agree that direct statistical comparisons between\ngroups are essential to robustly support our conclusions. To address this point, we have now\nperformed a Bootstrap Test to quantitatively compare network metrics between the ASD and\nnon-ASD groups, thereby providing statistical evidence for the observed structural differences\n(described in Methods, line 484–489). Additionally, our original data on simulation attacks\nplots (figure 4D-E) also supported the stability of network architecture since random or\ntargeted node removals could significantly disrupted network integrity in ASD compared to\nnon-ASD. We have revised the wordings and made the conclusions more readable in the main\ntext (line 207-212).\nLine 207: Stratifying children by emulsifier exposure levels (categorized into tertiles)\nrevealed stark network ecological instability exclusively in ASD. To ensure statistical\nrobustness, we performed bootstrap analyses (n = 1000 iterations) for each network metric.\nIn the highest exposure tertile, we observed fragmented microbial connectivity, marked by\nsignificant reductions in network degree, number of edges, and average degree (bias-corrected\np-values < 0.01)\nLine 484: The topological features of the co-occurrence networks were generated by the\niGraph package (v4.4.1) and visualized with node sizes proportional to relative abundances.\nWe implemented a nonparametric bootstrap procedure and computed bias-corrected p-values\nfor the statistical differences of each topological feature, wuth letters indicating significance\nafter multiple comparisons (p < 0.05).\n[1] Kajihara, K.T., Hynson, N.A. Networks as tools for defining emergent properties of\nmicrobiomes and their stability. Microbiome. 2024. 12, 184. doi: 10.1186/s40168-024-01868-\nz\n• In Section 2.4, how is a keystone species defined?\nResponse: We thank the reviewer for their insightful feedback regarding the use of the term\n\"keystone species.\" We acknowledge that our initial usage was methodologically imprecise 1.\nIn our study, we intended to highlight taxa that act as highly connected hubs—demonstrating\nhigh degree centrality within the co-occurrence network—rather than to invoke the stricter\necological definition often associated with the term. To prevent any potential confusion, we\nhave revised the wording in the manuscript (line 216–219) to describe these taxa.\nLine 216: We identified several taxa (e.g., Enterocloster bolteae, Hungatella hathewayi, and\nClostridium symbiosum) as potential keystone species due to their high connecting degrees in\nthe interaction network,\n[1] Boyse, E., Robinson, K.P., Carr, I.M., et al. Inferring species interactions from co-\noccurrence networks with environmental DNA metabarcoding data in a coastal marine food\nweb. Mol Ecol. 2025. 34: e17701. doi: 10.1111/mec.17701\n• For Figure 4 A, what is the x-axis label? For B/C, I’d suggest putting ASD and nonASD\nlabels on the plots to make it easier to understand. What does “high exposures” mean here?\nFor D/E, I would suggest including the nonASD results from SF6, if a key claim the authors\nare making is that ASD and nonASD networks are fundamentally different.\nResponse: We thank the reviewer for their suggestions. Regarding Figure 4A, we have\nupdated the figure caption to explicitly indicate that the x-axis represents correlation\ncoefficients derived from MaAsLin2 analyses.\nIn response to the query concerning the term “high exposure,” we have added clarification in\nthe Results section (line 207–210). As standardized limits for emulsifier intake are lacking,\nwe categorized the overall study population into tertiles based on estimated dietary emulsifier\nconsumption, with the highest tertile designated as the “high exposure” group. This\nmethodology was previously detailed in the Table 2 legend. Furthermore, we appreciate the\nsuggestion to incorporate comparative findings from the non-ASD group. We have now\nexpanded the Results to include relevant comparisons (Fig. 4).\nLine 207: Stratifying children by emulsifier exposure levels (categorized into tertiles)\nrevealed stark network ecological instability exclusively in ASD. To ensure statistical\nrobustness, we performed bootstrap analyses (n = 1000 iterations) for each network metric.\nIn the highest exposure tertile, we observed fragmented microbial connectivity,\nFig. 4\n• For Section 2.5, I do not know what specific analyses have actually been performed. Could\nthis please be clarified?\nResponse: We appreciate your comment and request for clarification on the specific analyses\npreviously performed. The primary goal of this section is to align our findings with the\npreviously published study (https://www.nature.com/articles/s41564-024-01739-1),\nemphasizing that different medications and nutritional supplements did not substantially\ninfluence on our previously identified biomarkers (species, genes and pathways) that\ndemonstrated stability for ASD diagnosis. This supports the notion that the interactions\nbetween food and microbial communities are independent of these identified biomarkers. We\nalso propose that future research should explore whether these biomarkers impact the overall\nmicrobiome community niche, which may, in turn, influence the microbiome-diet\ninteractions. We believe this perspective opens up exciting avenues for further microbiome-\ntargeted strategies. Thank you for the opportunity to elaborate on this important aspect of our\nstudy (line 327-329).\nLine 327: Whether those ASD-specific biomarkers influence the overall microbiome\ncommunity niche, which may in turn affect microbiome-diet interactions, opens up exciting\navenues for exploration.\n• In general, it would be helpful for the reader if the main text included some more details of\nmethods than how the manuscript is currently written. Otherwise, the reader is required to flip\nback and forth between Methods and Results to make a critical assessment. Eg. For the\nanalysis resulting in Fig.1D, could the authors please specify the model/analysis used in the\nmain text rather than just the Methods (I believe this is PERMANOVA). For the Fig.2B\nanalysis, could the authors please elaborate beyond “machine learning”. Etc.\nResponse: Thank you for your constructive suggestions on including more methodological\ndetail in the main text. We have revised the manuscript accordingly. We have now explicitly\nstated in the main text that the statistical analysis performed was a PERMANOVA (Adonis\ntest) based on Bray-Curtis dissimilarities (line 110-112). Regarding the machine learning\nanalysis in Fig. 2B: We have elaborated in the Results section to specify that we used a\nRandom Forest classifier (line 158-162). Other findings with necessary methods explanations\nneeded have also been added in the main text to improve the reader's experience and the\nclarity of our findings (line 95-98, 126, 208-210).\nLine 95: Consistent with prior studies, gut microbiome alpha diversity (Shannon index, p =\n0.024; observed richness, p = 0.003) and dysbiosis score (Dissimilarities-based evaluation as\ndetailed in the Methods, p = 0.001) differed significantly between children with ASD and\nthose without\nLine 110: To quantify the influence of host factors on gut microbiota composition, we\nperformed a multivariate PERMANOVA analysis through Bray-Curtis distances.\nLine 158: using two complementary machine-learning approaches, each trained on relative\nmicrobial abundances to predict each dietary variable quantified by correlation coefficients\n(ρ) for regression and area-under-the-curves (AUCs) for classification (Methods). The random\nforest regression models demonstrated diet-associated species outperformed functional\npathway-based predictions across 100 training/testing folds,\nLine 208: To ensure statistical robustness, we performed bootstrap analyses (n = 1000\niterations) for each network metric.\nMinor points:\n• Figure 2B could be improved by having the rectangle/circle described within the figure key\n(rather than just the legend).\n• Is there a reason why carrageenan is abbreviated to CRN? It would make it easier to read if\nspelt out in full and wouldn’t increase word count.\n• There seems to be mention of both non-ASD and TD. Would suggest consistency unless this\nmeans something distinct.\n• Reference 7 is cited as Johnson and Howell 2021 Cell Metabolism. However, this is\ncommentary rather than the original primary analysis article which is Yap et al. 2021 Cell – I\nwould suggest referencing the latter article (which to be transparent, I acknowledge is mine)\nas it was the first to empirically support dietary hypotheses of microbiome differences in\nautism.\nResponse: Thank you for your thoughtful comments and suggestions. Below are our\nresponses to your specific points: To enhance clarity, we have polished Figure 2B to\nincorporate the change you suggested.\nWe appreciate your feedback regarding the abbreviation \"CRN\" for carrageenan and have\nspelled it out in full throughout the manuscript.\nRegarding consistency in terminology, thank you for pointing out the mention of both non-\nASD and TD (typically developing). We have replaced TD with non-ASD and ensured\nconsistency in our terminology.\nWe appreciate your point regarding the Reference citation and have replaced the citation.\nThank you very much for your significant contributions to this research field.\nFig. 2\n7. Yap CX, et al. Autism-related dietary preferences mediate autism-gut microbiome\nassociations. Cell 184(24), 5916-5931.e17; 10.1016/j.cell.2021.10.015 (2021).\nPOINT-BY-POINT REPLY TO EDITORS AND REVIEWERS\nDear reviewers,\nWe are deeply grateful to the reviewers for their continued engagement and for providing\nadditional insightful feedback, which has further helped us strengthen our work. We have\nprovided a point-by-point response to the reviewers’ comments, together with a tracked and\nclean version of the revised manuscript. All changes are highlighted in yellow.\nWe appreciate the opportunity to resubmit and hope you find this version acceptable for\npublication.\nYours sincerely\nSiew Ng on behalf of co-authors\nReviewers' Comments:\nReviewer #1:\nRemarks to the Author:\nThe authors have done an excellent job with this revision. The findings and manuscript are\nmuch stronger having incorporated responses to all of the critiques. The results from their newly\nincorporated mediation analysis are particularly interesting.\nResponse: We are grateful for the reviewer's feedback and are delighted that they find the new\nmediation analysis interesting; the previous suggestions were instrumental in this improvement.\nReviewer #3:\nRemarks to the Author:\n1. Thank you very much for the comprehensive replies and additional analyses completed by\nWu et al. My major remaining concern (apologies for only identifying one instance of this\nin my last review) relate to the validity of running separate analyses for ASD and nonASD\ngroups, and then inferring differences based on a direct comparison of p-\nvalues between\nthe groups. In essence this is a subgroup analysis of which an overview of pitfalls is\nprovided here: https://www.nejm.org/doi/10.1056/NEJMsr077003. The p-value\ndifferences identified in these analyses may simply relate to differences in power between\nthe groups, and the coefficients could just relate to different spread of data between groups.\nThe correct analysis needed to identify whether there truly is a significantly different\ndiet/microbiome relationship between the groups is to run a regression with an interaction\nterm. Eg. microbe ~ diet + group + diet*group + covariates. If the diet*group term is\nsignificant, then you can conclude that the groups have innately differently diet-\nmicrobiome associations. These analyses apply for all cases in which ASD and nonASD\ngroups have been compared as subgroups.\nResponse: We appreciate the reviewer for highlighting this limitation in our initial approach\nand for proposing the explicit solution. We acknowledge the concern on limited sample size in\nthe between-group analysis and have now performed a comprehensive re-analysis of all diet-\nmicrobiome associations using the interaction model (microbe ~ diet + group + diet*group +\ncovariates) to examine the significance of interaction effect (line 142-147, line 186-191). We\nhave now added a new supplementary table S6 and Figure S4 detailing the results (coefficient,\nadjusted q-value) for all significant interaction terms. Supplementary Table S9 also\ndemonstrated significant diet-by-ASD interaction for certain microbial species, especially\nMegamonas funiformis, which was consistently increased in the ASD group with higher CCDI\nscore and high magnesium intake. These results were updated in the Section 2.3. We hope this\nnew analysis has substantially strengthened our conclusions.\nGiven that our primary objective was to identify ASD-specific associations that are clinically\nrelevant for this group, we did not prioritize examination of general diet-microbiome\nassociations. Considering the distinct dietary patterns between ASD and non-ASD, pooled data\nwith different spreads may distort associations due to between-group clustering, potentially\nobscuring associations that are unique to the ASD. To address the concern about sample size\ndisparity, we conducted a 1:1 matched sensitivity analysis (n=356 per group, Supplementary\nTable S2-3, Table S9) to confirm that diet-microbiome associations remain significant in the\nASD group.\nBelow are details for revision throughout the manuscript:\nTable S5: Sensitivity analysis for the diet-microbiome associations for autism spectrum\ndisorder (ASD)-specific species in the 1:1 matched-ASD group (n=356)\nTable S6: Effect sizes and adjusted q-values for the diet-microbiome associations incorporated\nwith the interaction term (diet*ASD) across the study population\nTable S9: Sensitivity analysis for the alterations of microbial species associated with diet, and\ndiet-by-autism spectrum disorder (ASD) interaction after 1:1-sample size matching. The low\nintake group is used as the reference compared to the high intake group.\nLine 142: These associations were replicated in the ASD group using a sample-size matched\nsensitivity analysis (Supplementary Table S5). Consistently, the population-wide regression\nmodel with an interaction term (ASD*diet) indicated that dietary indices showed substantial\ninteractions with ASD status in relation to the abundance of L. amygdalina and Anaerostipes\nhadrus (qval < 0.2, Supplementary Fig. 4; Supplementary Table S6). Notably, decreased\nabundance of L. amygdalina was specifically correlated with insufficient vitamin and mineral\nintake in the ASD group (|coef| > 0.5 and qval < 0.1), while Massilioclostridium coli and\nEscherichia coli exhibited inverse correlations with these dietary metrics (Supplementary Table\nS4 and Table S5).\nLine 186: These trends attenuated in neurotypical peers (Supplementary Fig. 7A-D; Table S8),\nwith minimal overlap in microbial responses—a contrast that persisted against a sample-size-\nmatched ASD cohort (Supplementary Table S9). The ASD-specific increase of M. funiformis in\nresponse to healthy dietary patterns collectively demonstrated the diet-by-ASD interaction (p\nfor interaction < 0.05, Supplementary Table S9).\nLine 320: (i) The cross-sectional design precludes causal inference, and the discrepant sample\nsizes between ASD and non-ASD groups may influence statistics. Nevertheless, sensitivity\nanalyses using matched subgroup sizes consistently confirmed the distinct diet-microbiome\nassociations observed in ASD and, meanwhile, cemented an interaction model to support an\nASD-specific microbial response.\nLine 490: To evaluate whether diet-microbiome associations differed between groups, we fitted\na regression model with an interaction term (microbe ~ diet + ASD + ASD*diet + age + sex +\nGI conditions).\nSupplementary Fig. 4: Interaction effects of autism spectrum disorder (ASD) status on diet-\nmicrobiome associations. Heatmap displays the identified diet-associated microbial species and\ntheir association coefficients with dietary metrics. Coefficients derived from the pooled sample\n(children with and without ASD) are indicated by circles, and significant coefficients for the\nASD-by-diet interaction term are indicated by rectangular. Significant interactions (qval < 0.2)\nare marked with “+” or “-” corresponding to the effect direction (see Supplementary Table S5\nfor details). Analyses were adjusted for age, sex, and gastrointestinal conditions using\nMaAsLin2.\n2. Relatedly, I appreciate the inclusion of Supplementary Table 3, but for it to be interpretable,\nthere needs to be a side-by-side comparison of 1) full-ASD group coefficients, 2) subset\nn=356 ASD group coefficients, 3) non-ASD group coefficients. It also needs to be\nexpanded (probably as separate Supplementary Tables) as sensitivity analyses to stress-\ntest other key results (eg. Firmicutes SGB4348, M funiformis, magnesium/protein-\nmicrobiome results, ideally the network results as well).\nResponse: We sincerely thank the reviewer for this additional suggestion. To address it we\nhave expanded supplementary Table S2 and S3 to include results from both the non-ASD group\nand the general population, thereby improving the interpretability and comparability of the data\n(line 102-106). This revision allowed readers to clearly observe that the correlation between\nhealthy dietary index (CCDI) and gut dysbiosis remained more pronounced in the sample size-\nmatched ASD group compared to the non-ASD. We have also extended sensitivity analyses to\nkey findings (Supplementary Tables S6 and S9). Firmicutes SGB4348 and M. funiformis were\nconsistently associated with healthy dietary indices in the sample size-matched ASD cohort (n\n= 356, line 189-191). Furthermore, Supplementary Table S9 reinforced that M. funiformis was\nsubstantially influenced by a diet-by-ASD interaction. These updates have been summarized in\nthe revised Results section. Regarding the network results, since all children were stratified into\ntertiles without separating ASD and non-ASD, we believe this could ensure a fair comparison.\nBelow are details for revision throughout the manuscript:\nTable S2: Correlations between various dietary indices and gut microbial features in the general\npopulation and those with or without autism spectrum disorder (ASD)\nTable S3: Sensitivity analysis of diet–microbiome correlations in a 1:1 matched cohort of\nchildren with and without autism spectrum disorder (ASD)\nTable S6: Effect sizes and adjusted q-values for the diet-microbiome associations incorporated\nwith the interaction term (diet*ASD) across the study population\nTable S9: Sensitivity analysis for the alterations of microbial species associated with diet, and\ndiet-by-autism spectrum disorder (ASD) interaction after 1:1-sample size matching. The low\nintake group is used as the reference compared to the high intake group\nLine 102: In contrast, this association was markedly attenuated in non-autistic peers (Fig. 1C;\nSupplementary Table S2-3; Supplementary Fig. 1B-C). The findings remained consistent in the\nsensitivity analysis conducted after excluding samples in accordance with the size and features\nof non-ASD (Supplementary Table S3).\nLine 142: These associations were replicated in the ASD group using a sample-size matched\nsensitivity analysis (Supplementary Table S5). Consistently, the population-wide regression\nmodel with an interaction term (ASD*diet) indicated that dietary indices showed substantial\ninteractions with ASD status in relation to the abundance of L. amygdalina and Anaerostipes\nhadrus (qval < 0.2, Supplementary Fig. 4; Supplementary Table S6). Notably, decreased\nabundance of L. amygdalina was specifically correlated with insufficient vitamin and mineral\nintake in the ASD group (|coef| > 0.5 and qval < 0.1), while Massilioclostridium coli and\nEscherichia coli exhibited inverse correlations with these dietary metrics (Supplementary Table\nS4 and Table S5).\nLine 186: These trends attenuated in neurotypical peers (Supplementary Fig. 7A-D; Table S8),\nwith minimal overlap in microbial responses—a contrast that persisted against a sample-size-\nmatched ASD cohort (Supplementary Table S9). The ASD-specific increase of M. funiformis in\nresponse to healthy dietary patterns collectively demonstrated the diet-by-ASD interaction (p\nfor interaction < 0.05, Supplementary Table S9).\n# Specific comments\n3. Paragraph starting line 94: could you please include coefficients and standard errors\n(currently just p-values)\nResponse: We thank the reviewer for pointing this out. We have revised the paragraph to\ninclude the requested coefficients and standard errors for all reported associations (line 94).\nThis addition provides greater transparency regarding the effect sizes and the precision of our\nestimates.\nBelow are details for revision throughout the manuscript:\nLine 94: Consistent with prior studies, gut microbiome alpha diversity (Shannon index: coef =\n0.07, standard error [SE] = 0.03, p = 0.024; observed richness: coef = 9.05, SE = 3.49, p = 0.003)\nand dysbiosis score (Dissimilarities-based evaluation as detailed in the Methods, coef = -0.02,\nSE = 0.004, p < 0.001) differed significantly between children with ASD and those without.\n4. Section 2.2: Apologies I missed this in the initial review – my (now) understanding of the\nanalysis in this section is that the authors performed analyses within the ASD and non-\nASD groups separately and then compare the q-value between the groups to identify\n“ASD-specific diet-microbiome interactions”. However, to me, a disparity in q-value\nseems more likely to reflect a difference in power between the groups: the ASD group is\nlarger and also has a greater spread of results (which would make the |coefficient|\nlarger). Thus, I believe the correct analysis is to run the analysis with ASD+nonASD together\nand including ASD/non-ASD status as an explanatory variable. Then I suggest reporting the\ncoefficient for ASD/non-ASD status and identifying ASD-specific diet-microbiome\ninteractions where the q-value for the ASD/non-ASD term meets threshold (eg. line 134-\n146). I would expect these results to add more robustness to the idea that ASD and non-\nASD diet-microbiome associations are in some way distinct.\nResponse: We thank the reviewer for this critical insight and for proposing a more statistically\nrobust analytical approach. As suggested in response to the aforementioned subgroup pitfalls,\nwe have added a comprehensive re-analysis by including the diet * ASD interaction term across\nthe board (line 142-147, line 186-191, line 324-325). The results, which confirm several\nsignificant ASD-specific diet-microbiome interactions, are now reported in the revised\nmanuscript (Supplementary Table S6). Considering sample-size confounding the results, to\nfurther support microbial changes in response to diet were ASD-specific, we conducted sample\nsize-matched sensitivity analyses (Supplementary table S2-3, Table S5, Table S9). The key\nfinding on M. funiformis was reinforced by a significant diet-by-ASD interaction (p for\ninteraction <0.05, Supplementary Table S9). We believe this new analysis has substantially\nstrengthened our conclusions regarding the distinct nature of these associations in ASD.\nBelow are details for revision throughout the manuscript:\nTable S2: Correlations between various dietary indices and gut microbial features in the general\npopulation and those with or without autism spectrum disorder (ASD)\nTable S3: Sensitivity analysis of diet–microbiome correlations in a 1:1 matched cohort of\nchildren with and without autism spectrum disorder (ASD)\nTable S6: Effect sizes and adjusted q-values for the diet-microbiome associations incorporated\nwith the interaction term (diet*ASD) across the study population\nTable S9: Sensitivity analysis for the alterations of microbial species associated with diet, and\ndiet-by-autism spectrum disorder (ASD) interaction after 1:1-sample size matching. The low\nintake group is used as the reference compared to the high intake group.\nLine 142: These associations were replicated in the ASD group using a sample-size matched\nsensitivity analysis (Supplementary Table S5). Consistently, the population-wide regression\nmodel with an interaction term (ASD*diet) indicated that dietary indices showed substantial\ninteractions with ASD status in relation to the abundance of L. amygdalina and Anaerostipes\nhadrus (qval < 0.2, Supplementary Fig. 4; Supplementary Table S6). Notably, decreased\nabundance of L. amygdalina was specifically correlated with insufficient vitamin and mineral\nintake in the ASD group (|coef| > 0.5 and qval < 0.1), while Massilioclostridium coli and\nEscherichia coli exhibited inverse correlations with these dietary metrics (Supplementary Table\nS4 and Table S5).\nLine 186: These trends attenuated in neurotypical peers (Supplementary Fig. 7A-D; Table S8),\nwith minimal overlap in microbial responses—a contrast that persisted against a sample-size-\nmatched ASD cohort (Supplementary Table S9). The ASD-specific increase of M. funiformis in\nresponse to healthy dietary patterns collectively demonstrated the diet-by-ASD interaction (p\nfor interaction < 0.05, Supplementary Table S9).\nLine 320: (i) The cross-sectional design precludes causal inference, and the discrepant sample\nsizes between ASD and non-ASD groups may influence statistics. Nevertheless, sensitivity\nanalyses using matched subgroup sizes consistently confirmed the distinct diet-microbiome\nassociations observed in ASD and, meanwhile, cemented an interaction model to support an\nASD-specific microbial response.\n5. Line 124: It would be helpful to briefly justify why Bristol Stool Chart was not also\nincluded as a covariate\nResponse: We appreciate the reviewer's suggestion. The Bristol Stool Chart was not included\nas a separate covariate because it captures information that is largely encompassed by the\nbroader GI symptom score already in our model (Figure 1D, line 114-115). PERMANOVA\nanalysis indicated that the contribution of GI symptoms was more important that the Bristol\nStool Chart. Including both would represent a conceptual and statistical overlap. We have\nincorporated a brief explanation regarding the covariate selection in the Methods section.\nBelow are details for revision throughout the manuscript:\nLine 488: The latter was selected over the Bristol Stool Chart due to its stronger association\nwith overall microbial community variation.\n6. Line 139: “The decreased abundance of F.SGB4348 also indicated stronger correlations\ninfluenced by insufficient vitamin and mineral intake remarkably observed in ASD” – I’m\nnot sure what “remarkably observed” means here\nResponse: We thank the reviewer for prompting us to clarify this point. Our original phrasing\nintended to highlight that the correlation between reduced F.SGB4348 abundance and\ninsufficient vitamin/mineral intake was a distinctive feature specific to ASD. However,\nfollowing the reviewer's suggestion to implement an interaction model and sensitivity analyses,\nwe have re-evaluated our findings. This led us to amend the manuscript to feature the strong\nand consistent result for L. amygdalina, although the F.SGB4348 result remained significant.\nBelow are details for revision throughout the manuscript:\nLine 147: Notably, decreased abundance of L. amygdalina was specifically correlated with\ninsufficient vitamin and mineral intake in the ASD group (|coef| > 0.5 and qval < 0.1), while\nMassilioclostridium coli and Escherichia coli exhibited inverse correlations with these dietary\nmetrics (Supplementary Table S4 and Table S5).\n7. Reply to Figure 2B analysis classification performance: Thank you for this information. It\nwould also help in the manuscript to state how you chose the matched subgroup.\nResponse: We appreciate the reviewer's valuable feedback on this point. The matched\nsubgroup was selected using the MatchIt package in R. A 1:1 nearest-neighbor matching\nalgorithm was applied based on the covariates of age, sex, and GI condition status. This\nprocedure ensured that the ASD and non-ASD groups were balanced on these key demographic\nfactors, thereby strengthening the robustness of our comparative analyses. We have now\nclarified in the manuscript:\nLine 495: To ensure sufficient statistical power for within-group comparisons of ASD and non-\nASD participants, we applied a 1:1 nearest-neighbor matching algorithm using the MatchIt\npackage in R, matching on age, sex, and GI conditions.\n8. Line 149, 167 – “exerted the most substantial effects”, “outsized role in modulating …”\nimplies causality which I don’t think can be determined from this analysis. Suggest\nrewording.\nResponse: We thank the reviewer for this valuable comment and for catching this\noverstatement. We have carefully revised the text to correct this and ensure our interpretations\nare precise. The changes have been made as follows:\nLine 157: The dietary factors exhibiting the greatest magnitude of associations with microbial\nspecies—\nLine 174: Notably, emulsifiers—particularly polysorbate-80 and carrageenan—demonstrated\nstronger associations with microbial features in the ASD group (Fig. 2B; Supplementary Fig.\n6).\nIt revealed that dietary indices exerted a more pronounced association with microbial features\nin ASD children without GI complications (Supplementary Fig. 11),\nLine 342: Overall, our findings position the ASD gut microbiome as a factor both engaged in\nan interplay with diet and associated with dietary responses,\n9. Line 179: Again, unclear whether the attenuated effect size could be a power issue / from\nthere being less spread in the data. Again, suggest analysis with all individuals and extract\ncoefficient for ASD/non-ASD status as an explanatory variable. Then for species\nassociated with both the relevant dietary measures and ASD group status, can do GSEA\nanalysis.\nResponse: We appreciate the reviewer's emphasis on analytical rigor. After careful\nconsideration, we have prioritized analytical approaches to focus on the core aim: uncovering\nASD-specific microbial responses to diet-- a question raising on diet-by-ASD interaction.\nAs suggested, we have implemented the following in our revised manuscript: We supplemented\na unified model including a diet*ASD_status interaction term. We conducted a sensitivity\nanalysis using a 1:1-matched ASD subgroup. This analysis robustly demonstrates that the\nstronger diet-microbe associations in the ASD group are not merely an artifact of sample size\nor data spread, but a replicable and clinically relevant finding. While GSEA is a powerful tool\nfor identifying pre-defined gene sets associated with a single phenotype (such as ASD status),\nits falls outside the scope of our present study. We appreciate this insightful perspective and\nhave incorporated a discussion of it as a valuable direction for future research.\nBelow are details for revision throughout the manuscript:\nLine 104: The findings remained consistent in the sensitivity analysis conducted after excluding\nsamples in accordance with the size and features of non-ASD (Supplementary Table S3).\nLine 142: These associations were replicated in the ASD group using a sample-size matched\nsensitivity analysis (Supplementary Table S5). Consistently, the population-wide regression\nmodel with an interaction term (ASD*diet) indicated that dietary indices showed substantial\ninteractions with ASD status in relation to the abundance of L. amygdalina and Anaerostipes\nhadrus (qval < 0.2, Supplementary Fig. 4; Supplementary Table S6).\nLine 322: Nevertheless, sensitivity analyses using matched subgroup sizes consistently\nconfirmed the distinct diet-microbiome associations observed in ASD and, meanwhile,\ncemented an interaction model to support an ASD-specific microbial response.\nLine 339: (iv) Finally, microbial network analyses warrant mechanistic explorations into\nmicrobiome-directed signals and gene-enrichment functions, ultimately requiring validation in\ngnotobiotic models.\n10. Paragraph starting line 190: please add statistics from mediation analysis.\nResponse: We thank the reviewer for this suggestion. We have now added the detailed statistics\nfrom the mediation analysis (including the average causal mediation effect with proportion) to\nthe paragraph (line 202-211).\nLine 202: Among the specific nutritional components examined, Faecalibacterium prausnitzii\nexerted a beneficial mediating influence on the associations between protein/zinc intake and\nsocial communication (average causal mediation effect [ACME]= -0.125, p for mediation =\n0.006, proportion = 24.6%) and hyposensitivity (ACME = -0.004, p = 0.02, 15.3%)\n(Supplementary Fig. 7; Supplementary Table S8). Notably, Coprococcus eutactus emerged as\nthe most prominent mediator, exhibiting the highest proportion of effect for several diet-\nrestricted and repetitive behaviors (RRB) associations, including CCDI (ACME = 0.013, p =\n0.02, 15.8%), DII (ACME = -0.098, p = 0.02, 19.9%), and bean consumption (ACME = 0.009,\np = 0.02, 35.4%).\n11. Line 206: Please clarify whether children were split into tertiles when aggregated together\nor within the ASD/non-ASD groups. I believe that the former is required to make a fair\ncomparison.\nResponse: We are grateful to the reviewer for prompting us to clarify this methodological point.\nAs suggested, we have revised the Methods (line 514-516) to explicitly state that children were\nsplit into tertiles based on pooled data from the entire cohort. This methodological choice was\nalso stated in the Results section (line 221).\nBelow are details for revision throughout the manuscript:\nLine 221: Characterization of population-wide emulsifier exposure tertiles of the cohort\nrevealed demonstrated ASD-specific microbial network instability,\nLine 514: To assess differences in microbial connection network, we compared ASD and non-\nASD groups within tertiles of food emulsifier exposure, which were calculated across all\nparticipants to establish consistent risk strata.\n12. Reply to Section 2.4 “dysbiosis” vs “ecological instability”: please also define exactly\nwhat is meant by ecological instability or whatever metric it is you use (apologies my\ncomment before was unclear)\nResponse: We sincerely thank the reviewer for this clarifying comment and apologize for the\nlack of precision in our initial description. We have revised the manuscript to clarify that\n\"ecological instability\" is operationalized through the comparative analysis of network\ntopology between groups, rather than by a defined cutoff (line 516-519). Specifically, in the\nrevised Section 2.4, we now define it as a state characterized by a breakdown of topological\nstructure, manifesting as a significant reduction in both the average node degree (indicating\nfewer microbial interactions) and the clustering coefficient (reflecting a loss of modular\norganization). The Methods section has been updated to cite the basis for these metrics and to\ndescribe the bootstrapping approach used for statistical comparison (line 516-519).\nAdditionally, we have recalled the previous Supplementary Fig. 1C, reflecting high exposure\nto polysorbate-80 was associated with increased dysbiosis in ASD (line 223-225).\nBelow are details for revision throughout the manuscript:\nLine 221: Characterization of population-wide emulsifier exposure tertiles of the cohort\nrevealed demonstrated ASD-specific microbial network instability, evident from significant\ntopological alterations (Table 2) and a positive association between emulsifier levels and\ndysbiosis score (Supplementary Fig. 1C). To ensure statistical robustness, we performed\nbootstrap analyses (n = 1000 iterations) for each network metric. In the highest exposure tertile,\nwe observed fragmented microbial connectivity, marked by significant reductions in network\ndegree, number of edges, and average degree (bias-corrected p-values < 0.01) (Fig. 4B-C; Table\n2; Supplementary Fig. 9A-B).\nLine 516: We assessed the significance of differences in topological features—such as reduced\naverage node degree and clustering coefficient, which reflect ecological instability—using a\nnon-parametric bootstrap procedure to compute bias-corrected p-values, with significance\nletters indicating p < 0.05 after multiple comparison adjustment.\nReviewer #4:\nRemarks to the Author:\nI co-reviewed this manuscript with one of the reviewers who provided the listed reports. This\nis part of the Nature Communications initiative to facilitate training in peer review and to\nprovide appropriate recognition for Early Career Researchers who co-review manuscripts.\nResponse: We truly appreciate you sharing your expertise and insightful comments by co-\nreviewing with other reviewers. We have rigorously addressed all your suggestions. Most\nnotably, we have bolstered the statistical rigor by implementing unified models with interaction\nterms to formally test our core hypothesis, and we have provided comprehensive sensitivity\nanalyses. Thank you again for your invaluable contribution.\nPOINT-BY-POINT REPLY TO EDITORS AND REVIEWERS\nDear reviewers,\nWe wish to express our gratitude to the reviewers for their ongoing guidance and insightful\ncomments, which have been instrumental in enhancing this work. We have provided a point-\nby-point response to the reviewers’ comments, together with a tracked and clean version of the\nrevised manuscript. All changes are highlighted in yellow.\nWe appreciate the opportunity to resubmit and hope you find this version acceptable for\npublication.\nYours sincerely\nSiew Ng on behalf of co-authors\nReviewers' Comments:\nReviewer #3:\nRemarks to the Author:\n1. Thank you very much to the authors for the additional analyses that have been performed.\nIt is much appreciated.\nLine 102-104: This analysis comparing effects between groups should include both ASD\nand non-autistic peers in a regression of: CCDI ~ Shannon diversity + ASDgroup +\nShannon diversity*ASDgroup + covariates. You can then claim that the ASDgroup has a\nsignificantly stronger association if the interaction term is significant.\nResponse: Thank you for this insightful comment regarding the use of an interaction model.\nWe have now added this as a supplementary analysis (Table S2), while prioritizing the ASD-\nspecific analysis that guided by the distinct focus of our research question. Our aim was to\ndelineate the structure of diet-microbiome-phenotype relationships specifically within the\nASD population, which is more straightforward by describing the relationship network within\nthe ASD group. Finding no statistical significance of interaction may cause by limited sample\nsize, insufficient variation in one or both of the predictor variables to show a clear interaction,\nor unmodeled nonlinear relationships\n(https://besjournals.onlinelibrary.wiley.com/doi/10.1111/2041-210X.13714). For example,\npotential correlation between GI conditions and ASD status could inflate variance, making it\ndifficult to disentangle their unique effects and potentially obscuring the significance of their\ninteraction. Therefore, we employed a 1:1 matched cohort and sensitivity analyses to facilitate\na more clinically interpretable assessment of findings specific to ASD.\nTable S2: Relationships between various dietary indices and gut microbial features specific to\nautism spectrum disorder (ASD) and the effect modification of ASD across the study population\n2. Sections 2.2 and 2.3 (relevant sections: lines 136-152; lines 184-193) Thank you for\nperforming these analyses with the population-wide regression model + interaction term.\nThe Supplementary Tables are very helpful and useful. However, I believe that the order\nof analyses should be the other way around. They are currently presented as 1) subgroup\nanalysis 2) population analysis. However, I believe it should be 1) population analysis\n(ie. to test the question “Is there a significant ASDgroup difference in dietary feature X?”)\n2) subgroup analysis to test the magnitude of the difference.\nResponse: We appreciate the reviewer's suggestion regarding the order of results. We believe\nthis may help to clarify the primary narrative of our study, while the manuscript was designed\nwith a specific, clinically driven focus: to directly identify and characterize associations that\nare most salient and relevant within the ASD population. This priority for discovery within\nthe ASD group guided our analytical sequence. Presenting the ASD group analysis first\nallows us to immediately introduce the reader to the core clinical landscape of our findings—\nthe specific dietary-microbiome relationships that are most pertinent to ASD children. The\nsubsequent population-wide analysis with interaction terms then serves a crucial, but\ncomplementary, role. This sequence—from discovery within the target population to\nconfirmation of its specificity—creates a narrative flow that best serves our primary objective.\nAs noted, the limited sample size and other complex factors may attenuate the statistical\npower to detect interactions, which is precisely why we complemented it with additional,\nrobust methods like 1:1 nearest-neighbor matching analysis.\nTo enhance the logical coherence of our story, we have revised the main text to better articulate\nthe rationale behind the sequence of our results:\nLine 103: In contrast, we did not observe this association in non-autistic peers (Fig. 1C;\nSupplementary Table S2-3; Supplementary Fig. 1B-C). While the effect modification by ASD\nstatus did not attain statistical significance, the consistent pattern of association was verified in\nsensitivity analysis that excluded samples in accordance with the size and features of non-ASD\n(Supplementary Table S2-3).\nLine 123: Building on the observed connections between diet, food additives, and microbial\necology, we systematically evaluated associations between dietary factors (indices,\ncomponents, and food groups) and gut microbiome profiles within the ASD population.\nLine 133: revealed distinct diet-microbe associations comping the non-ASD peers when\nconducting the same analysis procedure.\nLine 147: Consistently, the population-wide regression model with an interaction term (diet ×\nASD) indicated the effect modification of ASD status on dietary associations with the\nabundance of L. amygdalina and Anaerostipes hadr\n3. Paragraph starting line 203: please indicate whether the p-values reported are with\nmultiple testing correction.\nResponse: We thank the reviewer for raising this issue. We apologize for the unclear\npresentation of the results and have revised to clarify that significant p-values reported were\nadjusted by multiple testing correction (updated in Source Data file, line 213-214)."}, {"round": 2, "reviewer_comments": {"reviewer_1": "The authors have done an excellent job with this revision. The findings and manuscript are much stronger having incorporated responses to all of the critiques. The results from their newly incorporated mediation analysis are particularly interesting.", "reviewer_3": "Thank you very much for the comprehensive replies and additional analyses completed by Wu et al.\nMy major remaining concern (apologies for only identifying one instance of this in my last review) relate to the validity of running separate analyses for ASD and nonASD groups, and then inferring differences based on a direct comparison of p- values between the groups. In essence this is a subgroup analysis of which an overview of pitfalls is provided here: https://www.nejm.org/doi/10.1056/NEJMsr077003. The p-value differences identified in these analyses may simply relate to differences in power between the groups, and the coefficients could just relate to different spread of data between groups.\nThe correct analysis needed to identify whether there truly is a significantly different diet/microbiome relationship between the groups is to run a regression with an interaction term. Eg. microbe ~ diet + group + diet*group + covariates. If the diet*group term is significant, then you can conclude that the groups have innately differently diet-microbiome associations. These analyses apply for all cases in which ASD and nonASD groups have been compared as subgroups.\nRelatedly, I appreciate the inclusion of Supplementary Table 3, but for it to be interpretable, there needs to be a side-by-side comparison of 1) full-ASD group coefficients, 2) subset n=356 ASD group coefficients, 3) non-ASD group coefficients. It also needs to be expanded (probably as separate Supplementary Tables) as sensitivity analyses to stress-test other key results (eg. Firmicutes SGB4348, M funiformis, magnesium/protein-microbiome results, ideally the netwrk results as well).\n# Specific comments\nParagraph starting line 94: could you please include coefficients and standard errors (currently just p-values)\nSection 2.2: Apologies I missed this in the initial review – my (now) understanding of the analysis in this section is that the authors performed analyses within the ASD and nonASD groups separately, and then compare the q-value between the groups to identify “ASD-specific diet-microbiome interactions”. However, to me, a disparity in q-value seems more likely to reflect a difference in power between the groups: the ASD group is larger, and also has a greater spread of results (which would make the |coefficient| larger). Thus, I believe the correct analysis is to run the analysis with ASD+nonASD together, and including ASD/non-ASD status as an explanatory variable. Then I suggest reporting the coefficient for ASD/nonASD status, and identifying ASD-specific diet-microbiome interactions where the q-value for the ASD/nonASD term meets threshold (eg. line 134-146). I would expect these results to add more robustness to the idea that ASD and nonASD diet-microbiome associations are in some way distinct.\nLine 124: It would be helpful to briefly justify why Bristol Stool Chart was not also included as a covariate\nLine 139: “The decreased abundance of F.SGB4348 also indicated stronger correlations influenced by insufficient vitamin and mineral intake remarkably observed in ASD” – I’m not sure what “remarkably observed” means here\nReply to Figure 2B analysis classification performance: Thank you for this information. It would also help in the mansucript to state how you chose the matched subgroup.\nLine 149, 167 – “exerted the most substantial effects”, “outsized role in modulating …” implices causality which I don’t think can be determined from this analysis. Suggest rewording.\nLine 179: Again, unclear whether the attenuated effect size could be a power issue / from there being less spread in the data. Again, suggest analysis with all individuals and extract coefficient for ASD/non-ASD status as an explanatory variable. Then for species associated with both the relevant dietary measures and ASD group status, can do GSEA analysis.\nParagraph starting line 190: please add statistics from mediation analysis.\nLine 206: Please clarify whether children were split into tertiles when aggregated together or within the ASD/non-ASD groups. I believe that the former is required to make a fair comparison.\nReply to Section 2.4 “dysbiosis” vs “ecological instability”: please also define exactly what is meant by ecological instability or whatever metric it is you use (apologies my comment before was unclear)", "reviewer_4": "I co-reviewed this manuscript with one of the reviewers who provided the listed reports. This is part of the Nature Communications initiative to facilitate training in peer review and to provide appropriate recognition for Early Career Researchers who co-review manuscripts."}, "author_response": "POINT-BY-POINT REPLY TO EDITORS AND REVIEWERS\nDear reviewers,\nThank you very much for the valuable comments to our manuscript. We have provided a point-\nby-point response to the reviewers’ comments, together with a tracked and clean version of the\nrevised manuscript. All changes are highlighted in yellow.\nWe appreciate the opportunity to resubmit and hope you find this version acceptable for\npublication.\nYours sincerely\nSiew Ng on behalf of co-authors\nReviewers' Comments:\nReviewer #1:\nRemarks to the Author:\nWu et al. aimed to determine how the relationship between dietary intake and the gut\nmicrobiome differed between children (3-12 years-old) with and without autism spectrum\ndisorder (ASD) through conducting extensive dietary surveys, phenotypic data measurements\nsuch as evaluation of ASD and GI symptom severity, and gut microbiome profiling via shotgun\nmetagenomics sequencing of fecal samples. The size of the cohort (n=818) and breadth of data\nis impressive, and the research aim is both important and novel. However, the statistical analysis\nand presentation of the data in this current version of the manuscript hinders adequate\ninterpretation of the data by the reader. We recommend major revisions.\nIn this section, it is stated that CCDI is significantly correlated with more severe autistic\nsymptoms and GI complications. However, there are other diet metrics discussed in this\nmanuscript that have ASD-specific diet-microbiome interactions: protein intake, bean\nconsumption, P80, carrageenan, starch, and magnesium. Do these diet metrics correlate with\nseverity of autistic symptoms or GI complications in the entire population, or specifically in the\nASD population? Are the abundances of the microbial taxa or functions that were associated\nwith these dietary metrics additionally correlated with severity of autistic symptoms or GI\ncomplications? Mediation and/or moderation analysis can be completed to determine if the\nmicrobiome mediates or moderates the association between diet and severity of autistic\nsymptoms or GI complications.\nResponse: We sincerely thank the reviewer for the positive comments. We categorized the\nChinese Children Dietary Index (CCDI) rather than other dietary metrics to evaluate its\nassociation with the gut microbiota 1. We employed this approach because the CCDI reflects\noverall dietary quality and is specifically tailored to the dietary patterns and requirements of\nChinese children, making it highly relevant to our study population. Furthermore, as supported\nby previous research, closer connections of healthy dietary index and diversity with the gut\nmicrobiota highlighted the importance of using a holistic dietary index—rather than focusing\non isolated nutrients or single foods 2,3. We have revised the manuscript to clearly articulate the\nsignificance of the CCDI in the initial analysis (line 82-82, 381-385).\nLine 82: Given the relevance of the Chinese Children Healthy Dietary Index (CCDI) for\ncharacterizing general dietary quality in our study population, we first conducted dietary\nprofiling in a cohort of 818 children\nLine 381: Reflecting overall dietary balance, the CCDI (range: 0–140) is a measure\nspecifically tailored to assess the dietary patterns of Chinese children, with higher scores\nindicating superior overall diet quality 34. The dietary diversity, a core component of the CCDI,\nis strongly connected to the gut microbiome, reflecting its value in capturing diet-microbiome\nrelationships.\nWe thank the reviewer for raising the important point regarding other diet metrics potentially\ncorrelated with autistic severity. We indeed found dietary intake and nutritional insufficiencies\ndiffer markedly between children with ASD and their non-ASD peers and correlate specifically\nwith core symptoms within the ASD group. As shown in the following Figure, while consistent\nassociations were observed between poor dietary quality (e.g., lower CCDI scores, pro-\ninflammatory diet) and hypersensitivity in ASD, specific nutrient-symptom associations often\ndiffered between ASD and non-ASD groups. Given the disease-specific dietary patterns and\nthe potential for biased effect estimates in combined analyses, we analyzed the ASD and non-\nASD groups separately instead of pooling these phenotypically distinct populations. This\napproach served as the methodological basis for the mediation analyses presented in the revised\nmanuscript (line 191-196, Supplementary Fig. S7, Supplementary Table S8).\nFigure Heatmap for the associations between diet and autistic phenotypes in children\nwith/without ASD. The multivariable linear regression model was adjusted for age, sex, BMI,\nmother’s education, and GI—covariates previously identified to confound dietary intake.\nTo determine whether the diet-associated microbial species were additionally correlated with\nthe severity of autistic symptoms, we conducted comprehensive mediation analyses within the\nASD population (line 191-196) and included a discussion of their mechanistic implications\n(line 281-286). GI conditions were treated as a confounder in the mediation analysis rather than\nas an outcome parallel to autistic symptoms. The complete results are available in\nSupplementary Table S8, with statistically significant mediation models visualized in\nSupplementary Fig. S7. Our analyses identified that intake of protein, zinc, and trans-fatty acids\n(TFA) was significantly linked to autistic symptoms, with these relationships being partially\nmediated by variations in gut microbial composition. Specifically, Faecalibacterium prausnitzii\nemerged as a potential beneficial mediator, attenuating the associations between dietary\ncomponents and ASD symptomatology. Conversely, reduced abundances of\nFirmicutes_SGB14874 and Firmicutes _SGB71883, observed in the context of higher TFA and\nlower zinc intake, were significantly associated with more severe ASD symptoms, highlighting\ntheir role in the adverse diet-microbiome-symptom pathway.\nLine 191: To elucidate the role of microbiome in mediating these diet metrics associations in\nASD, we performed mediation analyses. Among the specific nutritional components examined,\nFaecalibacterium prausnitzii exerted a beneficial mediating influence on the associations\nbetween protein/zinc intake and ASD symptomatology (Supplementary Fig. 7; Supplementary\nTable S8). Notably, Coprococcus eutactus dominated with the highest mediation proportion\neffects across all diet-ASD associations.\nLine 281: Mediation analysis revealed that Faecalibacterium prausnitzii mediated the\nbeneficial effects of protein and zinc intake on ASD symptoms, likely through its anti-\ninflammatory and butyrate-producing properties 26. Coprococcus eutactus exhibited the\nstrongest mediation effect, suggesting a key role in gut-brain communication and potential as a\nprobiotic target for dietary interventions 27,28. These findings highlight the possibility of\nmicrobiota-directed therapies for ASD.\n[1] Duan, R., Qiao, T., Chen, Y. et al. The overall diet quality in childhood is prospectively\nassociated with the timing of puberty. Eur J Nutr. 2021. 60, 2423–2434. Doi: 10.1007/s00394-\n020-02425-8\n[2] Asnicar F, Berry SE, Valdes AM, et al. Microbiome connections with host metabolism and\nhabitual diet from 1,098 deeply phenotyped individuals. Nat Med. 2021, 27(2):321-332. doi:\n10.1038/s41591-020-01183-8.\n[3] Bowyer, R.C.E., Jackson, M.A., Pallister, T. et al. Use of dietary indices to control for diet\nin human gut microbiota studies. Microbiome. 2018. 6, 77. doi: 10.1186/s40168-018-0455-y\nIn the current version of this manuscript, there are a lot of diet-microbiome interaction findings\nin ASD versus TD populations, but it is hard to determine if any of these findings are clinically\nrelevant. It is important to demonstrate which dietary components, beyond just the dietary\nindices, exacerbate and also which dietary components alleviate the severity of autistic\nsymptoms and GI complications both independently and by acting through the microbiome.\nResponse: Thank you for this insightful comment. In response, we have now conducted\ncomprehensive mediation analyses, adjusting for age, sex, and GI conditions, to identify\nspecific microbial taxa that significantly mediate the relationships between dietary factors and\nASD severity (Supplementary Fig. S7 and Table S8). This analysis screened all diet-associated\nmicrobial features to prioritize those with the greatest potential clinical relevance as mediators.\nThe results revealed several promising candidates; for instance, Faecalibacterium prausnitzii\nappeared to modulate the impact of diet on symptom severity, suggesting their potential as\ntherapeutic targets. We have expanded the Discussion (line 191-196, 281-286) to incorporate\nthese findings and emphasize their clinical implications.\nLine [108]: “While effect sizes for these factors were comparable in TD children, most\nassociations lost statistical significance, underscoring the ASD-specific interplay between diet,\nphenotype, and gut ecology (Fig. 1D).”\nThere are two main issues with this statement. First, the sample size for the ASD population\n(n=462) is over 100 participants larger than the sample size for the TD population (n=356).\nTherefore, the loss in statistical significance could be due to the lower statistical power in the\nTD calculation comparted to the ASD calculation. Second, the TD population could be more\nvariable than the ASD population, because even though the TD population has an absence of\nneurological disorders, the TD population may not be completely “healthy” and may exhibit\nother morbidities relevant to microbiome dysbiosis such as gastrointestinal disorders. We\nrecommend the following changes:\n1) Effect sizes should be provided in the manuscript text for all important statistical findings.\nResults with similar effect sizes between groups should be interpreted with caution.\nResponse: Thank you for your feedback regarding the comparison of effect sizes between the\nASD and non-ASD populations. We have supplemented the manuscript with a formal\ncomparison of effect sizes and species prevalences across all groups to ensure transparent and\nclear presentation (Supplementary Table S4). We acknowledge the important points that\nstatistical power depends on sample size and that we cautiously compared effect sizes between\nthe ASD and non-ASD groups. As highlighted in the revision (line 133-142), typical dietary\nmetrics—CCDI, DII, and vitamin/mineral insufficiency—demonstrated stronger effect sizes on\nFirmicutes SGB4348 and Megamonas funiformis, along with significant q-values in ASD. We\nalso included the sensitivity analysis using a 1:1-matched ASD subgroup (n = 356) with an\nequal number of non-ASD participants (Supplementary Table S3, line 104-106). Notably, even\nwith this reduced sample size, CCDI exhibited more significant associations with microbial\nfeatures in the ASD group, reinforcing the validity and clinical relevance of our initial results.\nLine 133: Specifically, Firmicutes SGB4348 and Megamonas funiformis emerged as indicators\nof healthier dietary patterns in ASD, characterized by higher CCDI scores (F.SGB4348: coef =\n0.64, qval = 0.01 in ASD vs. coef = 0.18, qval = 0.79 in non-ASD; M.funiformis: coef = 0.56,\nqval = 0.01 in ASD vs. coef = 0.24, qval = 0.91 in non-ASD) and lower DII scores (F.SGB4348:\ncoef = -0.49, qval=0.09 in ASD vs. coef = -0.32, qval = 0.84 in non-ASD; M.funiformis: coef\n= -0.41, qval=0.10 in ASD vs. coef = -0.38, qval = 0.51 in non-ASD). The decreased abundance\nof F.SGB4348 also indicated stronger correlations influenced by insufficient vitamin and\nmineral intake remarkably observed in ASD (|coef| > 0.5 and qval < 0.1, Supplementary Table\nS4).\n2) More phenotypic information should be provided on the TD population in order to\nunderstand its inherent variability.\na) Is there any overlap between the behavioral outcomes (SRS-2 and SEQ) in the ASD versus\nTD populations? If numbers permit, it may be best to remove patients in the TD population\nwho were not diagnosed with ASD but had abnormal SRS-2 or SEQ scores to reduce population\nvariability.\nResponse: We appreciate the Reviewer’s insightful suggestion regarding the importance of\nreducing heterogeneity within the non-ASD by accounting for abnormal behaviors. We would\nlike to clarify, however, that the non-ASD participants in our cohort were carefully\ncharacterized to minimize heterogeneity: all scored within the normal ranges on the Social\nResponsiveness Scale (SRS) and the Sensory Experience Questionnaire (SEQ), and were free\nof neurodevelopmental and psychiatric comorbidities, as detailed in the demographic table\nbelow. This suggests a relatively homogeneous comparison group suitable for identifying\nASD-specific associations. Furthermore, to address the potential confounding effect of GI\nsymptoms—which were already adjusted for in our primary models—we added a sensitivity\nanalysis by excluding the small subset of non-ASD individuals with GI complications (line\n258-263, Supplementary Fig. S10). Please attend the following characteristic table for\nreference. We have supplemented this information in the main text to elaborate on the\npotential bias due to variability.\nLine 307: The absence of neurodevelopmental and psychiatric comorbidities in the non-ASD\ngroup minimized the heterogeneity of the population and solidified our non-significant\nfindings (Supplementary Table S9).\nLine 310: Despite data variability of diet was sufficient for the primary analyses, the limited\nrange in certain micronutrients (e.g., B vitamins) may have reduced sensitivity for detecting\nsubtler associations (Supplementary Table S9).\nSupplementary Table S9:\nASD Non-ASD p value\nN=462 N=356\nAge 8.00 (6.00-10.0) 9.00 (8.00-10.0) <0.001\nMale 395 (85.5%) 200 (56.2%) <0.001\nBMI 16.0 (14.6-18.1) 16.3 (14.8-18.7) 0.056\nFamily medical history 93 (20.1%) 19 (5.34%) <0.001\nComorbidity (yes) 259 (56.1%) 0 (0.00%) <0.001\nMedication 150 (32.5%) 0 (0.00%) <0.001\nGI symptoms 92 (19.9%) 19 (5.34%) <0.001\nBristol stool chart 0.050\ntype 1 25 (5.41%) 11 (3.09%)\ntype 2 67 (14.5%) 45 (12.6%)\ntype 3 100 (21.6%) 84 (23.6%)\ntype 4 160 (34.6%) 146 (41.0%)\ntype 5 29 (6.28%) 31 (8.71%)\ntype 6 9 (1.95%) 1 (0.28%)\ntype 7 1 (0.22%) 0 (0.00%)\nAutistic symptoms\nSRS_RRB 62.0 (55.0-71.0) 46.0 (43.0-50.0) <0.001\nSRS_SCI 68.0 (61.0-75.0) 53.0 (49.0-58.0) <0.001\nSEQ_hyper 2.07 (1.79-2.50) 1.57 (1.36-1.86) <0.001\nSEQ_hypo 1.67 (1.33-2.17) 1.33 (1.00-1.50) <0.001\nEating behaviors (CEBQ) 0.17 (-0.42-0.79) -0.26 (-0.82-0.37) <0.001\nb) How many in the TD population versus the ASD population had irregular stools on the\nBristol Stool Scale or had GI symptoms? If numbers permit, it would be interesting to do a sub-\nanalysis on the ASD and TD populations without GI complications, and the ASD and TD\npopulations with GI complications, to determine how ASD impactsmicrobiome\ninteractions independently of GI complications, or how diet-microbiome interactions during GI\ncomplications are different in TD children and children with ASD.\nResponse: We thank the reviewer for this insightful suggestion. We agree that this is a crucial\nquestion for disentangling the specific effects of ASD from those of co-occurring GI\nconditions. We therefore adjusted for GI conditions in all original association analyses in the\nmain text. We have conducted additional sub-analyses stratifying both the ASD and non-ASD\npopulations by the presence of GI complications and have incorporated a discussion of these\nresults and their implications into the revised manuscript (line 263–268 and 314–316). To\nensure statistical power given the low number of non-ASD children with GI symptoms (n =\n19), we defined GI complications broadly to include both parent-reported GI symptoms and\nirregular stool types (Bristol Stool Scale types other than 3 or 4). The results, visualized in\nSupplementary Fig. S10, revealed that the most significant and consistent diet-microbiome\ninteractions were observed in ASD children without GI complications. This pattern suggests\nthat GI comorbidities may alter or obscure microbial responses to dietary intake, highlighting\nthe importance of considering GI status when designing microbiome-targeted dietary\ninterventions for children with ASD.\nLine 263: This critical question—regarding how heterogeneity in GI conditions modulates\ndiet-microbiome interactions—prompted a focused sub-analysis within the ASD population,\nstratified by the presence of GI symptoms and irregular Bristol Stool types. It revealed that\ndietary indices exerted a more pronounced influence on microbial features in ASD children\nwithout GI complications (Supplementary Fig. 10), suggest that GI health appears to be a\nprerequisite for robust diet-microbiome interactions 20.\nLine 314: Meanwhile, our findings positioned GI health as a potential effect modifier of diet-\nmicrobiome interactions, necessitating careful consideration in subgroups to avoid biased\ninterpretations.\n3) As part of the limitations section, the difference in numbers between the TD and ASD\npopulations and impact on statistics should be stated. Also, it should be mentioned that\nalthough the TD population was screened for neurological disorders, this population may\ncontain children with other types of disorders. If you have comorbidity data, it would be\nrelevant to state if any comorbidities were substantially present in the TD population.\nResponse: We appreciate your suggestion to include this clarification in the limitations\nsection. Regarding comorbidity data, we have reviewed our study's metadata, which indicates\nthat there are no substantial comorbidities present in the non-ASD children, as shown in the\nsupplementary table S9 in response to your previous comment. This information was clearly\narticulated in the revised manuscript (line 303-309).\nLine 303: However, limitations warrant caution: (i) The cross-sectional design precludes\ncausal inference, and the discrepant sample sizes between ASD and non-ASD groups may\ninfluence statistics. Nevertheless, sensitivity analyses using matched subgroup sizes\nconsistently confirmed the distinct diet-microbiome associations observed in ASD. The\nabsence of neurodevelopmental and psychiatric comorbidities in the non-ASD group\nminimized the heterogeneity of the population and solidified our non-significant findings\n(Supplementary Table S9).\n[Line 127] “The breadth of diet-microbe associations varied by dietary variable: in ASD,\nprotein intake (n=28), CCDI scores (n=27), and bean consumption (n=18) exerted the\nstrongest effects on microbial species, whereas non-ASD children showed negligible\nlinkages.”\nThe total n for these analyses is quite low. Considering it was stated that 818 children were in\nthe study, what is the reason for these low n? Was it a lack of completion of dietary surveys,\na lack of fecal samples collected, or a lack of fecal samples sent out for shotgun metagenomics\nsequencing? An effort should be made to increase these numbers if possible. If not,\ninterpretations from analyses with such low n should be made with caution, and the\nlimitations of interpreting these analyses should be stated in the discussion section.\nResponse: We thank the reviewer for this valuable feedback and apologize for any\nmisunderstanding caused by our initial description. We would like to clarify that all\nassociation analyses utilized the full sample size. The values n = 28, n = 27, and n = 18 refer\nto the number of microbial species found to be significantly associated with dietary\ncomponents—not the sample size of the study population. We have revised the relevant\nsection in the Results (line 148–156) to prevent potential misinterpretation.\nLine 148: The breadth of diet-microbe associations varied by dietary variable, showing\nstronger connections in ASD compared to their non-ASD peers, who exhibited negligible\nlinkages. The dietary factors that exerted the most substantial effects on microbial species—\nacross nutritional components, dietary indices, and food groups—were protein intake (28\nsignificant associations in total), CCDI scores (27 in total), and bean consumption (18 in\ntotal) (Fig. 2B; Supplementary Fig. 4B). By looking into specific nutrients in relation to\nmicrobial shifts, starch and magnesium emerged as the nutrients with the second-highest\nnumber of significant associations after protein intake, exhibiting 27 and 20 significant\nassociations, respectively (Fig. 2B).\n[Line 133]: “Moreover, predictive modeling using diet-associated species outperformed\nfunctional pathway-based predictions (Supplementary Fig. 3C), with random forest models\ndemonstrating strong concordance between predicted and actual values for protein intake (ρ >\n0.35) and CCDI scores.”\nThese findings are unusual. One would expect a microbiome function to be tied to diet-\nmicrobiome interactions, which would yield important mechanistic clues. Two reasons on\nwhy these findings occurred that need to be addressed:\n1. Due to the low n, these species level findings could be spurious, as there is much more\nvariance in the presence/absence of individual species across individuals whereas the\nmicrobiome functions are much more conserved. For all findings involving microbial species,\nthe number of individuals who had the species present in their gut microbiome should be\nstated.\nResponse: We thank the reviewer for raising this important point. To clarify, all correlation\nanalyses between diet and microbial species were conducted using the complete dataset,\nwherein full dietary information was available for all participants, as stipulated in the\nMethods. No samples were excluded due to missing dietary metrics. In direct response to this\ncomment, we have now included Supplementary Table S4, which specifies the number of\nindividuals in which each bacterial species was detected. We also confirm that downstream\nassociation analyses were restricted to microbial species with a detection prevalence\nexceeding 10% of samples (line 443), thereby reducing the risk of spurious associations\narising from low-prevalence taxa. Furthermore, we have revised the wording in the relevant\nResults section to more explicitly frame these findings as exploratory and to highlight the\nnecessity for validation in larger, independent cohorts. Thank you for helping improve the\nclarity and rigor of our manuscript.\n2. The default HUMAnN database is the BioCyc/MetaCyc summed pathway abundances. The\nimportant microbiome functions may not be represented in this database. Better results may\nbe yielded through the use of additional databases such as KEGG, GO, and CAZyme. It may\nalso be better to conduct analyses on the individual orthologies rather than the summed\npathways, and to subsequently complete gene-set enrichment analysis as a follow-up step.\nResponse: We appreciate your insight regarding the limitations of the HUMAnN database. In\nresponse, we have included KEGG orthology (KO) analyses to enhance the mechanistic\nunderstanding of the findings. Among the dietary metrics, healthy dietary indices, especially\nCCDI score, exhibited strong associations with predicted gene functions as the heatmap\nshown (Supplementary Fig. S3). The subsequent gene-set enrichment analysis further\ndemonstrated significant contributions of animo-acids, particularly Phenylalanine, tyrosine\nand tryptophan biosynthesis, involved in the diet-microbiome interaction. We have included\ndetailed descriptions in the main text (line 184-188, 222-224, Supplementary Fig. S6E, Fig.\nS8E).\nLine 184: Concurrently, gene-set enrichment analysis based on KEGG orthology (KO)\nindicated enhanced microbial processing functions related to amino acid and cofactor\nbiosynthesis (Supplementary Fig. 6E), suggesting a state of metabolic dysregulation in\nchildren with ASD linked to dietary imbalances.\nLine 222: KEGG enrichment analysis further revealed that polysorbate-80/carrageenan\nexposure concurrently downregulated the 2-oxocarboxylic acid metabolism and citrate cycle\npathways (Supplementary Fig. 8E).\nLine 274: Complementary KEGG enrichment analysis linked CCDI-associated orthologs to\ncofactor biosynthesis (e.g., thiamine metabolism), suggesting its role in the improved ASD-\nrelated outcomes 23.\nLine [142]: “Given the prominence of microbial associations with CCDI scores and key\nnutrients (protein, starch, magnesium), we investigated gut microbial shifts in ASD children\nstratified by high intake of these dietary components.”\nThe microbial associations with the key nutrients (starch and magnesium) are not clearly\nstated in the previous sections, so this sentence is unclear to the reader.\nResponse: Thank you for your valuable comment. We apologize for the oversight regarding\nthe logic flow, as the key nutrients were not clearly stated in the previous section. The\nassociations of these nutrients (starch and magnesium) with microbial shifts were identified as\nsignificantly related to the microbiome, exhibiting the second-highest numbers of significant\nassociations after protein intake. We have revised the wording in the manuscript to clarify\nthese connections for the reader (line 150-156).\nLine 150: The dietary factors that exerted the most substantial effects on microbial species—\nacross nutritional components, dietary indices, and food groups—were protein intake (28\nsignificant associations in total), CCDI scores (27 in total), and bean consumption (18 in\ntotal) (Fig. 2B; Supplementary Fig. 4B). By looking into specific nutrients in relation to\nmicrobial shifts, starch and magnesium emerged as the nutrients with the second-highest\nnumber of significant associations after protein intake, exhibiting 27 and 20 significant\nassociations, respectively (Fig. 2B).\nLine [222] “Children with ASD exhibited pronounced ecological fragility in response to\nemulsifier exposure, marked by fragmented microbial networks and loss of keystone taxa.”\nChildren with ASD also had lower microbiome alpha diversity than TD children. In this\ndiscussion section, we suggest that the authors link the findings of pronounced ecological\nfragility and fragmented microbial networks to the diminished alpha diversity, as it is known\nthat lower diversity leads to higher interdependence between species-species relationships and\nspecies-substrate relationships within the microbial community.\nResponse: We thank the reviewer for this constructive suggestion. We agree that the\nfragmented microbial networks in children with ASD are closely linked to their reduced alpha\ndiversity. As illustrated in supplementary Figure S1C, higher exposure to P80 and CRN\nappeared strongly correlated with decreased alpha diversity and elevated dysbiosis index—a\nrelationship that was especially observed in the ASD group. We have incorporated this\nperspective into the Discussion section (line 290–293) to more thoroughly contextualize how\ndietary emulsifier exposure may contribute to diversity loss and exacerbate microbial network\ninstability in ASD.\nLine 290: The significant loss of alpha diversity and the exacerbated dysbiosis associated with\nincreased exposure to carrageenan/polysorbate-80 in autistic children (Supplementary Fig.\n1C) offered a plausible mechanism for the pronounced network instability observed\nspecifically in ASD following the dietary disturbance.\nLine [233]: “Limitations”\nWe think it is important to state as a limitation that the studied populations were relatively\nethnically and culturally homogenous, and that as future work the study should be repeated in\npopulations with different dietary patterns to increase diversity and validate the generalizability\nof this study’s findings. Further, it should be stated if any of the dietary indices, dietary\ncomponents, or food groups had low variability across the studied populations. For example, if\nthe vast majority of the ASD population had low dark green vegetable intake or the TD\npopulation had high fish intake, this study would be limited in its ability to observe how the\nmicrobiome differs by dark green vegetable intake in the ASD population or fish intake in the\nTD population.\nResponse: We sincerely thank the reviewers for this constructive comment. We have now\nextensively revised the Discussion section to emphasize that the relatively ethnically and\nculturally homogenous populations may limit the generalizability of our findings. We have\nincluded a call for future studies to validate our results in more diverse cohorts (line 313-314).\nWe also acknowledged concerns regarding dietary metrics with low variability. A review of\nour metadata (included in Supplementary Table S9) indicates that variability is similar across\nboth ASD and non-ASD populations. We have added a sentence stating that while variability\nwas sufficient to detect the reported associations, the restricted range in some micronutrients,\nsuch as vitamin B groups, might have limited our power to detect more subtle relationships\ninvolving them (line 310-312).\nLine 310: Despite data variability of diet was sufficient for the primary analyses, the limited\nrange in certain micronutrients (e.g., B vitamins) may have reduced sensitivity for detecting\nsubtler associations (Supplementary Table S9). As dietary habits are culturally embedded, our\nfindings require validation in more diverse populations to ensure generalizability.\nLine 313: As dietary habits are culturally embedded, our findings require validation in more\ndiverse populations to ensure generalizability.\nPart of the Supplementary Table S9:\nASD Non-ASD p value\nCharacteristics\nn=462 n=356\nDietary variables\nAHEI 53.9 (20.6-81.4) 54.7 (26.8-86.3) 0.059\nCCDI 74.3 (40.5-111) 79.0 (34.5-116) <0.001\nDII 0.295 (-4.09-4.19) -0.220 (-4.27-4.30) 0.022\nSulfurdiet score 0.28 (-4.85-6.47) -0.06 (-4.17-3.96) <0.001\nHFD 0.46 (0.20-0.69) 0.48 (0.26-0.76) 0.003\nMineral insufficiency 5.00 (1.00-12.0) 4.00 (0-12.0) 0.698\nVitamin insufficiency 7.00 (1.00-10.0) 7.00 (1.00-10.0) 0.861\nPolysorbate-80 7.91 (0.36-40.9) 6.38 (0.20-33.9) <0.001\nCarboxymethylcellulouse 16.2 (0-90.6) 12.2 (0.08-55.0) <0.001\nCarrageenan 9.96 (0.28-57.6) 7.85 (0.48-40.6) <0.001\nEmulsifiers 35.1 (1.28-179) 27.4 (1.28-95.4) <0.001\nProtein (% of total energy) 14.9 (7.82-27.5) 16.5 (9.92-27.5) <0.001\nFat (% of total energy) 30.6 (18.0-52.7) 29.7 (16.6-45.4) 0.600\nCarbohydrate (% of total energy) 54.3 (17.9-73.9) 54.2 (29.4-72.1) 0.652\nSFA (% of total energy) 10.1 (4.30-16.4) 10.3 (4.41-19.2) <0.001\nTFA (% of total energy) 0.17 (0.01-1.93) 0.15 (0-1.21) 0.181\nPUFA (% of total energy) 4.77 (1.57-11.5) 4.08 (1.55-8.84) <0.001\nMUFA (% of total energy) 7.72 (0.61-21.8) 7.05 (0.767-15.1) 0.007\nn3/n6 0.12 (0.04-0.42) 0.13 (0.07-0.29) 0.136\nMonosaccharide (g/1000 kcal) 6.71 (0-55.8) 7.96 (0-35.2) 0.016\nDisaccharide (g/1000 kcal) 3.77 (0-26.9) 4.91 (0-24.5) 0.004\nStarch (g/1000 kcal) 76.7 (15.8-136) 74.2 (32.0-139) 0.033\nFiber (g/1000 kcal) 0.78 (0.09-11.30) 0.89 (0.19-4.51) 0.002\nAdded Sugar (g/1000kcal) 2.15 (0-50.9) 3.67 (0-27.8) <0.001\nCalcium (mg/1000 kcal) 267 (96.9-2170) 288 (78.8-972) 0.182\nPhosphorus (mg/1000 kcal) 463 (189-1170) 495 (241-840) <0.001\nSodium (mg/1000 kcal) 1190 (308-2700) 1290 (421-2770) 0.009\nPotassium (mg/1000 kcal) 980 (464-2210) 1050 (507-2990) <0.001\nMagnesium (mg/1000 kcal) 102 (41.8-184) 107 (40.2-236) 0.001\nChromium (mg/1000 kcal) 2.69 (0.618-14.0) 2.69 (0.417-10.5) 0.884\nCopper (mg/1000 kcal) 0.433 (0.15-1.53) 0.467 (0.18-1.49) <0.001\nIodine (mcg/1000 kcal) 33.6 (0.28-195) 35.5 (1.54-193) 0.073\nIron (mg/1000 kcal) 5.10 (1.96-26.4) 5.45 (2.05-17.4) 0.048\nMolybdenum (mcg/1000 kcal) 6.72 (0-33.1) 7.69 (0-47.8) 0.002\nSelenium (mcg/1000 kcal) 44.5 (13.4-274) 48.7 (19.6-112) 0.014\nZinc (mg/1000 kcal) 4.10 (1.61-12.1) 4.34 (2.04-11.6) 0.020\nVitamin B1 (mg/1000 kcal) 0.43 (0.145-1.56) 0.437 (0.12-1.23) 0.993\nVitamin B2 (mg/1000 kcal) 0.57 (0.16-3.21) 0.57 (0.12-1.68) 0.198\nVitamin B3 (mg/1000 kcal) 6.32 (1.34-17.9) 6.68 (1.47-14.1) 0.014\nPantothenic acid (mg/1000 kcal) 2.01 (0.72-8.36) 2.10 (0.61-4.42) 0.045\nVitamin B6 (mg/1000 kcal) 0.31 (0.06-1.24) 0.30 (0.08-1.11) 0.614\nVitamin B12 (mg/1000 kcal) 0.292 (0-2.96) 0.328 (0-4.08) 0.662\nBiotin (mcg/1000 kcal) 9.21 (1.26-57.9) 10.2 (1.22-43.3) 0.003\nVitamin C (mg/1000 kcal) 40.3 (1.65-254) 43.3 (0.94-235) 0.088\nVitamin D (mcg/1000 kcal) 1.62 (0.12-11.4) 1.77 (0.04-8.25) 0.423\nFolate (mcg/1000 kcal) 89.1 (17.4-323) 96.2 (11.2-297) <0.001\nVitamin K (mcg/1000 kcal) 29.5 (2.32-342) 34.3 (0.01-350) 0.042\nCaffeine (mg/1000 kcal) 0.0262 (0-11.8) 0.956 (0-23.4) <0.001\nChlorophyll (mg/1000 kcal) 128 (25.8-453) 141 (42.0-747) <0.001\nFruit (g/1000 kcal) 113 (0-710) 145 (0-1490) <0.001\nVegetable (g/1000 kcal) 145 (0-1050) 194 (0-1830) <0.001\nSugarjam (g/1000 kcal) 4.65 (0-175) 4.29 (0-77.1) 0.105\nProcessed meat (g/1000 kcal) 18.8 (0-266) 18.0 (0-163) 0.265\nFried food (g/1000 kcal) 37.3 (0-180) 40.4 (0-178) 0.741\nDark green vegetables (g/1000 kcal) 20.0 (0-166) 28.6 (0-480) <0.001\nDairy (g/1000 kcal) 66.5 (0-1000) 81.1 (0-773) 0.420\nBeans (g/1000 kcal) 15.4 (0-271) 22.1 (0-196) 0.015\nMacronutrients were presented as percentages of total energy intake. SRS = the Social Responsiveness\nScale; SEQ = the Sensory experiences questionnaire; CCDI= the Chinese Children Healthy Dietary Index;\nAFEI = Alternative Healthy Eating Index; DII = Dietary Inflammatory Index; HFD = healthy food\ndiversity index; SFA = saturated fatty acids; MUFA = monounsaturated fatty acids; PUFA =\npolyunsaturated fatty acids; TFA = trans-fatty acids.\nReviewer #2 (Remarks to the Author):\nThe work does not align with established literature. For example, Zafar and Habib (2021) found\nlactobacillus and prevotella to be greatly decreased in the gastrointestinal tract of children with\nautism. The authors do not even look for these bacterial species in their study and yet, they\nclaim to have analyzed \"the top 50 microbial species.\"\nResponse: We thank the reviewer for highlighting this important point regarding alignment\nwith the existing literature. We appreciate the opportunity to clarify the focus and\nmethodology of our study. Our work specifically investigated the diet-microbiome interaction\nin the context of autism, rather than focusing solely on case-control taxonomic differences.\nWe identified \"the top 50 microbial species\" that, while not classified as ASD-specific\naccording to case-control analyses, demonstrated significant interactions with dietary factors.\nThe primary aim was to identify how dietary factors shape the gut microbiota in children with\nASD and whether these relationships differ from those in neurotypical children. Although\nwell-characterized taxa such as Lactobacillus and Prevotella, which are depleted in ASD, are\ncertainly of interest, their responses to dietary factors were not prominently observed in our\nstudy, limiting their relevance for interpreting the diet-microbiome interaction.\nThe authors do not consider the impact of dietary heavy metals on gut microbiota when\nnumerous studies indicate heavy metal exposures are involved in autism. Mangalam et al\n(2017) found a decline in Prevotella in response to heavy metal mixtures in the gut.\nThe study design has gaps in it which render the results of the study highly questionable in\nterms of their utility. The authors do not look at synthetic food additives which contain heavy\nmetals (e.g., petroleum-based food colors). The methodology is not sound. I find this to be a\npoorly designed study because the authors clearly did not do a thorough literature review\nbefore they designed their study.\nResponse: We thank the reviewer for raising this critical point. We appreciate the reviewer's\ncomments regarding the impact of dietary heavy metals on gut microbiota and the potential\ngaps in our study design. While we acknowledge the importance of heavy metals, their presence\nas contaminants in foods is not a direct dietary component under voluntary consumption. Our\naim is to identify modifiable dietary components that directly affect the gut microbiome—with\nthe ultimate goal of informing microbiome-targeted dietary interventions. We would like to\nclaim very low risk of heavy metal exposure obtained from food additives, since the validated\nassessment tool on nine types of food additives commonly consumed in Hong Kong 1,2\ndemonstrating artificial stabilizers should be the most serious problem less likely contained\nheavy metal. Recent literature also highlights the elevated risks associated with emulsifiers in\nHong Kong's children, demonstrating a closer relationship with neurodevelopmental disorders\nand gut microbiota 3. This supports our decision to concentrate on these stabilizers. Moreover,\nit is reported that in highly developed regions such as Hong Kong 4,5, heavy metal exposure\nwould be generally well-regulated and relatively low compared with other regions.\nOur own ancillary analysis of other additives, including titanium dioxide (which may contain\ntrace metals), revealed no significant between-group differences or substantial variation in\nexposure levels. This further supports the notion that heavy metals are unlikely to be the\nprimary driver in our cohort.\nLastly, we fully agree that environmental pollutants including heavy metals warrant further\ninvestigation. We have now explicitly addressed this point in the Limitations and Future\nDirections section (line 316-321), emphasizing the need for future studies to integrate multi-\npollutant models and expand contaminant monitoring in dietary ASD research.\nLine 316: Emulsifier effects may be compounded by unmeasured additives, and dietary heavy\nmetals, as a risk factor, may also interact with the gut microbiome. Future studies specifically\ndesigned to incorporate biomarker-based assessments or utilize detailed dietary databases with\ncomprehensive additive composition are urgently needed to elucidate the role of these\nsubstances.\n[1] Trakman GL, Lin W, Wilson-O'Brien AL, Stanley A, Hamilton AL, Tang W, Or L, Ching\nJ, Morrison M, Yu J, Ng SC, Kamm MA. Development and Validation of Surveys to Estimate\nFood Additive Intake. Nutrients. 2020;12(3):812. doi: 10.3390/nu12030812.\n[2] Loayza JJJ, Kang S, Schooth L, et al. Effect of food additives on key bacterial taxa and the\nmucosa-associated microbiota in Crohn's disease. The ENIGMA study. Gut Microbes.\n2023;15(1):2172670. doi: 10.1080/19490976.2023.2172670.\n[3] Liu Y, Wang S, Xu W, et al. The impact of food additive intake and altered gut\nmicrobiome on perinatal health: data from 3 regions in China from the MOMMY COHORT.\nGastroenterology. 2024; 166 (5):S-272 - S-273. Doi: 10.1016/S0016-5085(24)01111-9.\n[4] Hou, W., Hu, R., Cui, Z. et al. Heavy metals in urban and rural indoor dust in plateau\nhomes: levels, sources, and health risks. Aerosol Sci Eng (2025).\nhttps://doi.org/10.1007/s41810-025-00308-1\n[5] Luo L, Wang L, Li Y, Cao H, Guo Y, Liao X. Urban-rural inequality in soil heavy metal\nhealth risks: Insights from Baoding, China. Ecotoxicol Environ Saf. 2025; 300:118458. doi:\n10.1016/j.ecoenv.2025.118458.\nReviewer #3 (Remarks to the Author):\nThank you for the opportunity to review Wu et al.’s manuscript.\nThe authors perform a metagenomics analysis of a large cohort accompanied by deep\nphenotyping information. As a starting point, they perform an important replication of\nprevious work finding that poor dietary quality is associated with degree of autism symptoms\nand measures of microbiome diversity. In this well-powered dataset, they also find\nassociations between poor dietary quality and gastrointestinal complications. The authors then\ngo further, performing a variety of following analyses to characterise dietary-microbiome\nrelationships.\nA major strength of the paper is the depth of phenotyping information collected, particularly\nwith respect to dietary measures, and how the authors have carefully explored potential\nconfounders. I particularly like the data-driven analyses that have been performed in Section\n2.2.\nMy questions largely relate to the logical flow of the paper which I believe could be improved\nto take the reader along on the journey. I often feel unsure what the “deep-dive” analyses\n(e.g., Section 2.3, 2.4) are being directed by, which can give the impression of cherry-picking\n(even if it is not). I also believe that the analyses need more statistical testing (rather than the\nlargely qualitative presentation of results at current), plus technical and specific details to be\nincluded in the main text (examples below).\nThanks Yap. We much appreciate your thoughtful feedback and constructive suggestions. We\nwould like to address all the comments and response one by one, particularly to enhance the\nclarity of our deep-dive sections.\nMajor points:\n• For Table 1, please justify why tertiles are used (and not quartiles, quintiles, etc.)? It would\nbe better still if there were known cutoffs for CCDI (rather than being driven by the current\ndata) that you could use to divide. Were tertiles used throughout the analysis (as is suggested\nin Methods line 403), or just for Figure 1? Please clarify – I believe that CCDI should be kept\nas a continuous variable in analyses or information is lost unnecessarily.\nResponse: Thank you for your comments. The choice to use tertiles in Table 1, rather than\nquartiles or quintiles, is primarily driven by the study sample size, offering a practical\napproach to illustrate various characteristics and phenotypic information while maintaining\nenough participants in each group for meaningful comparisons. Since current literature on\nCCDI has not proven well-defined cutoffs, we believe tertiles-based clarification is supported\nby many literature 1,2. The most important feature of Table 1 is that it shows a significant\nassociation between the CCDI and ASD status itself when comparing with demographic\nfactors. We would like to clarify in the main text that the remainder of our analyses retained\nCCDI as a continuous variable, which has been stated in the revision to improve clarity (line\n98-100, line 368-369).\nLine 98: A key finding was that the CCDI, among various continuous dietary indices, had a\nrobust association with Shannon diversity that was specific to the ASD group\nLine 368: To comprehensively evaluate dietary associations with the gut microbiome, we\ntreated multiple dietary metrics as continuous measures, including indices (CCDI, AHEI, DII,\nsulfur-diet score, HFD index, vitamin insufficiency, and mineral insufficiency), individual\nnutrient and food additive intakes, and food group consumptions—collectively constituting a\nholistic dietary profiling.\n[1] Duan, R., Qiao, T., Chen, Y. et al. The overall diet quality in childhood is prospectively\nassociated with the timing of puberty. Eur J Nutr. 2021. 60, 2423–2434. Doi:\n10.1007/s00394-020-02425-8\n[2] Meng L, Wang Y, Li T, et al. Dietary diversity and food variety in Chinese children aged\n3⁻17 years: are they negatively associated with dietary micronutrient inadequacy? Nutrients.\n2018. 10(11):1674. doi: 10.3390/nu10111674.\n• Given there are various systematic differences shown in Table 1, please articulate what\ncovariates have been included in each analysis, including in the main text.\nResponse: We thank the reviewer for this critical comment. We have now explicitly stated\nthe included covariates in the main text and Methods section (line 98-102, 126-127, 457-458).\nAge, sex, and GI conditions were adjusted in all analyses examining microbial species and\ndietary metrics. Additional variables—including BMI, Bristol Stool Chart, and autistic\nbehaviors—were further adjusted for in the linear regression models evaluating associations\nbetween dietary indices and microbial features.\nLine 98: A key finding was that the CCDI, among various continuous dietary indices, had a\nrobust association with Shannon diversity that was specific to the ASD group (p for Kendall\ncorrelation = 0.049; p for Pearson correlation = 0.006; p for linear regression model adjusted\nby age, sex, Bristol Stool Chart, GI conditions, and autistic symptoms = 0.010).\nLine 126: These associations were analyzed using MaAsLin2, adjusted for the age, sex, and\nGI conditions that identified above.\nLine 457: and generalized regression model with adjustments of age, sex, body mass index,\nBristol Stool Chart, GI conditions, and autistic symptoms.\n• It is not immediately clear to me how the specific dietary measures shown in Fig.2A and\nFig.2B were chosen. Fig. 2B in particular seems to be a subset of Fig.2A. How were these\nchosen? Were the dietary variables in Fig.2A also a subset and if so how were they chosen?\nResponse: We thank the reviewer for raising this important point and apologize for any\nconfusion caused by our initial description. To clarify, all correlation analyses between diet\nand microbial species were performed using the full available dataset, for which complete\ndietary information was available for all participants, as described in the Methods. The values\nn = 28, n = 27, and n = 18 referred to the number of microbial species that were significantly\nassociated with dietary components—not the sample size of the study population. We have\nclarified this in the results (line 148-156) to prevent misunderstanding.\nLine 148: The breadth of diet-microbe associations varied by dietary variable, showing\nstronger connections in ASD compared to their non-ASD peers, who exhibited negligible\nlinkages. The dietary factors that exerted the most substantial effects on microbial species—\nacross nutritional components, dietary indices, and food groups—were protein intake (28\nsignificant associations in total), CCDI scores (27 in total), and bean consumption (18 in\ntotal) (Fig. 2B; Supplementary Fig. 4B). By looking into specific nutrients in relation to\nmicrobial shifts, starch and magnesium emerged as the nutrients with the second-highest\nnumber of significant associations after protein intake, exhibiting 27 and 20 significant\nassociations, respectively (Fig. 2B).\n• For the Fig.2B analysis, is it possible that the difference in classification performance could\nbe due to there being ~100 more people in the ASD group vs TD?\nResponse: Thank you for this comment. We fully acknowledge the important point regarding\nthe influence of sample size on statistical power. To further substantiate our findings, we\nconducted a sensitivity analysis using a 1:1-matched ASD subgroup (n = 356) with an equal\nnumber of non-ASD participants (Supplementary Table S3, line 104-106). Notably, even with\nreduced sample size, CCDI showed strengthened associations with microbial features in the\nASD group, further supporting the robustness of our initial observations. We have also\nincorporated the descriptions into the main text, accompanied by a discussion of the potential\nlimitations they may introduce (line 303-307).\nLine 104: The findings remained consistent in the sensitivity analysis conducted after\nexcluding samples in accordance with the size and features of non-ASD (Supplementary\nTable S3).\nLine 303: (i) The cross-sectional design precludes causal inference, and the discrepant sample\nsizes between ASD and non-ASD groups may influence statistics. Nevertheless, sensitivity\nanalyses using matched subgroup sizes consistently confirmed the distinct diet-microbiome\nassociations observed in ASD.\n• In Section 2.3 Magnesium/protein enrichment and starch depletion in ASD – I am unclear in\nwhat way the protein, starch and magnesium associations are “prominent” (at least when\ngoing off Figure 2 where these variables do not stand out). Is this shown in a plot or table\nsomewhere? If so, it would be helpful if this is referenced (and also should probably be in a\nmain text figure). Without this clarity, I feel I am missing the relevance of this section.\nResponse: Thank you for this comment and for highlighting the need for greater clarity\nregarding the selection of magnesium/protein enrichment and starch depletion for section 2.3.\nThese specific dietary factors were prioritized based on our initial screening of diet-\nmicrobiome associations, as they demonstrated the most substantial influence on the gut\nmicrobiota (the greatest number of significant associations with microbial species). We have\namended Figure 2B to clearly highlight the numbers of microbial species that were\nsignificantly associated with each dietary variable. In addition, we have revised the Results\nsection (line 150–156) to more explicitly state that magnesium, protein, and starch were\nselected based on the strength and number of their associations with microbial features, and to\nclarify their contextual importance within central energy-metabolism pathways. These\nmodifications ensure that the rationale for focusing on these particular nutrients and visually\nsupported within the manuscript.\nLine 150: The dietary factors that exerted the most substantial effects on microbial species—\nacross nutritional components, dietary indices, and food groups—were protein intake (28\nsignificant associations in total), CCDI scores (27 in total), and bean consumption (18 in\ntotal) (Fig. 2B; Supplementary Fig. 4B). By looking into specific nutrients in relation to\nmicrobial shifts, starch and magnesium emerged as the nutrients with the second-highest\nnumber of significant associations after protein intake, exhibiting 27 and 20 significant\nassociations, respectively (Fig. 2B).\n• Similarly in Section 2.4, please point to what analysis led you to choose to focus on\nemulsifier exposures? Again, I think this should be in a main text figure.\nResponse: Thank you for this question, which allows us to clarify the rationale behind our\nfocus on dietary emulsifiers. Our decision was motivated by a convergence of prior evidence\nand our own analytical findings: First, as noted in the introduction (line 67-69), emulsifiers\nare widely recognized in the literature as having a potent and direct impact on gut, making\nthem a target for investigation in the context of ASD. Second, our own data supported this\nfocus. As shown in Figure 1, emulsifier exposure emerged as a notable contributor to the\nvariance of microbial compositions. More importantly, the nature of its association with\nmicrobial features differed markedly between ASD and non-ASD children (Supplementary\nFig. 1C). This suggests the presence of ASD may amplify susceptibility to the microbial\nperturbations linked to emulsifiers. Finally, the predictive modeling results presented in\nFigure 2B provided additional support. The relatively high accuracy with which gut microbial\nprofiles could predict emulsifier intake implies the existence of a strong and reproducible\nmicrobial signature associated with this exposure. We have now clarified the rationale before\nstarting off the Section 2.4 (line 199-201).\nLine 199: Of particular interest were dietary emulsifiers, given their established potential to\nperturb gut microbiota and their notable contribution to the variance explained in microbial\ncomposition (Fig. 1D and Fig 2B).\n• In Section 2.4, it would be help logical flow to prime the reader more to the relevance of\nnetwork analyses and why you have used them.\nResponse: We appreciate your suggestion. To improve logical flow and prime the reader for\nthe relevance of the subsequent analysis, we have added an introductory sentence at the\nbeginning of Section 2.4 (line 199-201). This addition clarifies that although dietary\nemulsifiers showed limited broad associations with individual microbial taxa, their significant\ncontribution to the overall variance in microbial composition (Fig. 1D) — combined with\nprior evidence of their biofilm-disrupting and pro-inflammatory effects — motivated us to\nexamine their influence on microbial co-occurrence network\n(https://onlinelibrary.wiley.com/doi/10.1111/all.15825\nhttps://www.nature.com/articles/s41467-025-62397-3).\nLine 199: Of particular interest were dietary emulsifiers, given their established potential to\nperturb gut microbiota and their notable contribution to the variance explained in microbial\ncomposition (Fig. 1D and Fig 2B).\n• In Section 2.4, line 165, “dysbiosis” is mentioned. But what is the definition of dysbiosis\nhere? Probably better to mention as ecological instability if some technical definition of\ndysbiosis is not met.\nResponse: Thank you for highlighting this point. In response, we have revised the wording as\n\"ecological instability\" (line 208) to better capture the intended meaning without confusion on\ntechnical-defined “dysbiosis” that may cause.\n• In Section 2.4, please provide more specific metrics of the network differences with\nstatistical testing if possible. At currently, the main text is entirely qualitative which I do not\nthink is sufficient, and I believe Table 2 is unreferenced. Eg. I am not very familiar with\nnetwork analyses (but the audience may also not be) but I am not immediately convinced that\nthere is a difference between the ASD vs nonASD networks. Eg. “Neurotypical children, in\ncontrast, maintained stable network architecture and functional resilience across emulsifier\nexposure levels”. Please provide statistical tests to support such claims.\nResponse: Thank you very much for your valuable suggestions. While the topological\nproperties we analyzed (e.g., connectivity, modularity) are well-established metrics for\nassessing ecological network stability 1, we agree that direct statistical comparisons between\ngroups are essential to robustly support our conclusions. To address this point, we have now\nperformed a Bootstrap Test to quantitatively compare network metrics between the ASD and\nnon-ASD groups, thereby providing statistical evidence for the observed structural differences\n(described in Methods, line 484–489). Additionally, our original data on simulation attacks\nplots (figure 4D-E) also supported the stability of network architecture since random or\ntargeted node removals could significantly disrupted network integrity in ASD compared to\nnon-ASD. We have revised the wordings and made the conclusions more readable in the main\ntext (line 207-212).\nLine 207: Stratifying children by emulsifier exposure levels (categorized into tertiles)\nrevealed stark network ecological instability exclusively in ASD. To ensure statistical\nrobustness, we performed bootstrap analyses (n = 1000 iterations) for each network metric.\nIn the highest exposure tertile, we observed fragmented microbial connectivity, marked by\nsignificant reductions in network degree, number of edges, and average degree (bias-corrected\np-values < 0.01)\nLine 484: The topological features of the co-occurrence networks were generated by the\niGraph package (v4.4.1) and visualized with node sizes proportional to relative abundances.\nWe implemented a nonparametric bootstrap procedure and computed bias-corrected p-values\nfor the statistical differences of each topological feature, wuth letters indicating significance\nafter multiple comparisons (p < 0.05).\n[1] Kajihara, K.T., Hynson, N.A. Networks as tools for defining emergent properties of\nmicrobiomes and their stability. Microbiome. 2024. 12, 184. doi: 10.1186/s40168-024-01868-\nz\n• In Section 2.4, how is a keystone species defined?\nResponse: We thank the reviewer for their insightful feedback regarding the use of the term\n\"keystone species.\" We acknowledge that our initial usage was methodologically imprecise 1.\nIn our study, we intended to highlight taxa that act as highly connected hubs—demonstrating\nhigh degree centrality within the co-occurrence network—rather than to invoke the stricter\necological definition often associated with the term. To prevent any potential confusion, we\nhave revised the wording in the manuscript (line 216–219) to describe these taxa.\nLine 216: We identified several taxa (e.g., Enterocloster bolteae, Hungatella hathewayi, and\nClostridium symbiosum) as potential keystone species due to their high connecting degrees in\nthe interaction network,\n[1] Boyse, E., Robinson, K.P., Carr, I.M., et al. Inferring species interactions from co-\noccurrence networks with environmental DNA metabarcoding data in a coastal marine food\nweb. Mol Ecol. 2025. 34: e17701. doi: 10.1111/mec.17701\n• For Figure 4 A, what is the x-axis label? For B/C, I’d suggest putting ASD and nonASD\nlabels on the plots to make it easier to understand. What does “high exposures” mean here?\nFor D/E, I would suggest including the nonASD results from SF6, if a key claim the authors\nare making is that ASD and nonASD networks are fundamentally different.\nResponse: We thank the reviewer for their suggestions. Regarding Figure 4A, we have\nupdated the figure caption to explicitly indicate that the x-axis represents correlation\ncoefficients derived from MaAsLin2 analyses.\nIn response to the query concerning the term “high exposure,” we have added clarification in\nthe Results section (line 207–210). As standardized limits for emulsifier intake are lacking,\nwe categorized the overall study population into tertiles based on estimated dietary emulsifier\nconsumption, with the highest tertile designated as the “high exposure” group. This\nmethodology was previously detailed in the Table 2 legend. Furthermore, we appreciate the\nsuggestion to incorporate comparative findings from the non-ASD group. We have now\nexpanded the Results to include relevant comparisons (Fig. 4).\nLine 207: Stratifying children by emulsifier exposure levels (categorized into tertiles)\nrevealed stark network ecological instability exclusively in ASD. To ensure statistical\nrobustness, we performed bootstrap analyses (n = 1000 iterations) for each network metric.\nIn the highest exposure tertile, we observed fragmented microbial connectivity,\nFig. 4\n• For Section 2.5, I do not know what specific analyses have actually been performed. Could\nthis please be clarified?\nResponse: We appreciate your comment and request for clarification on the specific analyses\npreviously performed. The primary goal of this section is to align our findings with the\npreviously published study (https://www.nature.com/articles/s41564-024-01739-1),\nemphasizing that different medications and nutritional supplements did not substantially\ninfluence on our previously identified biomarkers (species, genes and pathways) that\ndemonstrated stability for ASD diagnosis. This supports the notion that the interactions\nbetween food and microbial communities are independent of these identified biomarkers. We\nalso propose that future research should explore whether these biomarkers impact the overall\nmicrobiome community niche, which may, in turn, influence the microbiome-diet\ninteractions. We believe this perspective opens up exciting avenues for further microbiome-\ntargeted strategies. Thank you for the opportunity to elaborate on this important aspect of our\nstudy (line 327-329).\nLine 327: Whether those ASD-specific biomarkers influence the overall microbiome\ncommunity niche, which may in turn affect microbiome-diet interactions, opens up exciting\navenues for exploration.\n• In general, it would be helpful for the reader if the main text included some more details of\nmethods than how the manuscript is currently written. Otherwise, the reader is required to flip\nback and forth between Methods and Results to make a critical assessment. Eg. For the\nanalysis resulting in Fig.1D, could the authors please specify the model/analysis used in the\nmain text rather than just the Methods (I believe this is PERMANOVA). For the Fig.2B\nanalysis, could the authors please elaborate beyond “machine learning”. Etc.\nResponse: Thank you for your constructive suggestions on including more methodological\ndetail in the main text. We have revised the manuscript accordingly. We have now explicitly\nstated in the main text that the statistical analysis performed was a PERMANOVA (Adonis\ntest) based on Bray-Curtis dissimilarities (line 110-112). Regarding the machine learning\nanalysis in Fig. 2B: We have elaborated in the Results section to specify that we used a\nRandom Forest classifier (line 158-162). Other findings with necessary methods explanations\nneeded have also been added in the main text to improve the reader's experience and the\nclarity of our findings (line 95-98, 126, 208-210).\nLine 95: Consistent with prior studies, gut microbiome alpha diversity (Shannon index, p =\n0.024; observed richness, p = 0.003) and dysbiosis score (Dissimilarities-based evaluation as\ndetailed in the Methods, p = 0.001) differed significantly between children with ASD and\nthose without\nLine 110: To quantify the influence of host factors on gut microbiota composition, we\nperformed a multivariate PERMANOVA analysis through Bray-Curtis distances.\nLine 158: using two complementary machine-learning approaches, each trained on relative\nmicrobial abundances to predict each dietary variable quantified by correlation coefficients\n(ρ) for regression and area-under-the-curves (AUCs) for classification (Methods). The random\nforest regression models demonstrated diet-associated species outperformed functional\npathway-based predictions across 100 training/testing folds,\nLine 208: To ensure statistical robustness, we performed bootstrap analyses (n = 1000\niterations) for each network metric.\nMinor points:\n• Figure 2B could be improved by having the rectangle/circle described within the figure key\n(rather than just the legend).\n• Is there a reason why carrageenan is abbreviated to CRN? It would make it easier to read if\nspelt out in full and wouldn’t increase word count.\n• There seems to be mention of both non-ASD and TD. Would suggest consistency unless this\nmeans something distinct.\n• Reference 7 is cited as Johnson and Howell 2021 Cell Metabolism. However, this is\ncommentary rather than the original primary analysis article which is Yap et al. 2021 Cell – I\nwould suggest referencing the latter article (which to be transparent, I acknowledge is mine)\nas it was the first to empirically support dietary hypotheses of microbiome differences in\nautism.\nResponse: Thank you for your thoughtful comments and suggestions. Below are our\nresponses to your specific points: To enhance clarity, we have polished Figure 2B to\nincorporate the change you suggested.\nWe appreciate your feedback regarding the abbreviation \"CRN\" for carrageenan and have\nspelled it out in full throughout the manuscript.\nRegarding consistency in terminology, thank you for pointing out the mention of both non-\nASD and TD (typically developing). We have replaced TD with non-ASD and ensured\nconsistency in our terminology.\nWe appreciate your point regarding the Reference citation and have replaced the citation.\nThank you very much for your significant contributions to this research field.\nFig. 2\n7. Yap CX, et al. Autism-related dietary preferences mediate autism-gut microbiome\nassociations. Cell 184(24), 5916-5931.e17; 10.1016/j.cell.2021.10.015 (2021).\nPOINT-BY-POINT REPLY TO EDITORS AND REVIEWERS\nDear reviewers,\nWe are deeply grateful to the reviewers for their continued engagement and for providing\nadditional insightful feedback, which has further helped us strengthen our work. We have\nprovided a point-by-point response to the reviewers’ comments, together with a tracked and\nclean version of the revised manuscript. All changes are highlighted in yellow.\nWe appreciate the opportunity to resubmit and hope you find this version acceptable for\npublication.\nYours sincerely\nSiew Ng on behalf of co-authors\nReviewers' Comments:\nReviewer #1:\nRemarks to the Author:\nThe authors have done an excellent job with this revision. The findings and manuscript are\nmuch stronger having incorporated responses to all of the critiques. The results from their newly\nincorporated mediation analysis are particularly interesting.\nResponse: We are grateful for the reviewer's feedback and are delighted that they find the new\nmediation analysis interesting; the previous suggestions were instrumental in this improvement.\nReviewer #3:\nRemarks to the Author:\n1. Thank you very much to the authors for the additional analyses that have been performed.\nIt is much appreciated.\nLine 102-104: This analysis comparing effects between groups should include both ASD\nand non-autistic peers in a regression of: CCDI ~ Shannon diversity + ASDgroup +\nShannon diversity*ASDgroup + covariates. You can then claim that the ASDgroup has a\nsignificantly stronger association if the interaction term is significant.\nResponse: Thank you for this insightful comment regarding the use of an interaction model.\nWe have now added this as a supplementary analysis (Table S2), while prioritizing the ASD-\nspecific analysis that guided by the distinct focus of our research question. Our aim was to\ndelineate the structure of diet-microbiome-phenotype relationships specifically within the\nASD population, which is more straightforward by describing the relationship network within\nthe ASD group. Finding no statistical significance of interaction may cause by limited sample\nsize, insufficient variation in one or both of the predictor variables to show a clear interaction,\nor unmodeled nonlinear relationships\n(https://besjournals.onlinelibrary.wiley.com/doi/10.1111/2041-210X.13714). For example,\npotential correlation between GI conditions and ASD status could inflate variance, making it\ndifficult to disentangle their unique effects and potentially obscuring the significance of their\ninteraction. Therefore, we employed a 1:1 matched cohort and sensitivity analyses to facilitate\na more clinically interpretable assessment of findings specific to ASD.\nTable S2: Relationships between various dietary indices and gut microbial features specific to\nautism spectrum disorder (ASD) and the effect modification of ASD across the study population\n2. Sections 2.2 and 2.3 (relevant sections: lines 136-152; lines 184-193) Thank you for\nperforming these analyses with the population-wide regression model + interaction term.\nThe Supplementary Tables are very helpful and useful. However, I believe that the order\nof analyses should be the other way around. They are currently presented as 1) subgroup\nanalysis 2) population analysis. However, I believe it should be 1) population analysis\n(ie. to test the question “Is there a significant ASDgroup difference in dietary feature X?”)\n2) subgroup analysis to test the magnitude of the difference.\nResponse: We appreciate the reviewer's suggestion regarding the order of results. We believe\nthis may help to clarify the primary narrative of our study, while the manuscript was designed\nwith a specific, clinically driven focus: to directly identify and characterize associations that\nare most salient and relevant within the ASD population. This priority for discovery within\nthe ASD group guided our analytical sequence. Presenting the ASD group analysis first\nallows us to immediately introduce the reader to the core clinical landscape of our findings—\nthe specific dietary-microbiome relationships that are most pertinent to ASD children. The\nsubsequent population-wide analysis with interaction terms then serves a crucial, but\ncomplementary, role. This sequence—from discovery within the target population to\nconfirmation of its specificity—creates a narrative flow that best serves our primary objective.\nAs noted, the limited sample size and other complex factors may attenuate the statistical\npower to detect interactions, which is precisely why we complemented it with additional,\nrobust methods like 1:1 nearest-neighbor matching analysis.\nTo enhance the logical coherence of our story, we have revised the main text to better articulate\nthe rationale behind the sequence of our results:\nLine 103: In contrast, we did not observe this association in non-autistic peers (Fig. 1C;\nSupplementary Table S2-3; Supplementary Fig. 1B-C). While the effect modification by ASD\nstatus did not attain statistical significance, the consistent pattern of association was verified in\nsensitivity analysis that excluded samples in accordance with the size and features of non-ASD\n(Supplementary Table S2-3).\nLine 123: Building on the observed connections between diet, food additives, and microbial\necology, we systematically evaluated associations between dietary factors (indices,\ncomponents, and food groups) and gut microbiome profiles within the ASD population.\nLine 133: revealed distinct diet-microbe associations comping the non-ASD peers when\nconducting the same analysis procedure.\nLine 147: Consistently, the population-wide regression model with an interaction term (diet ×\nASD) indicated the effect modification of ASD status on dietary associations with the\nabundance of L. amygdalina and Anaerostipes hadr\n3. Paragraph starting line 203: please indicate whether the p-values reported are with\nmultiple testing correction.\nResponse: We thank the reviewer for raising this issue. We apologize for the unclear\npresentation of the results and have revised to clarify that significant p-values reported were\nadjusted by multiple testing correction (updated in Source Data file, line 213-214)."}]

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