Stool metabolite profiling revealed altered organization of tryptophan- and catecholamine-linked metabolic associations in ASD and identified a small metabolite panel with exploratory discriminative potential.
Abstract
Background Gut-brain axis dysregulation and microbiome-linked metabolic alterations have been implicated in autism spectrum disorder (ASD), but the contribution of gut-derived neuroactive metabolites remains incompletely characterized. Methods We conducted a cross-sectional case-control study of 59 participants (32 ASD, 27 controls) and quantified 18 stool metabolites related to catecholamine synthesis, inhibitory neurotransmission, and tryptophan-linked NAD+-precursor metabolism using targeted liquid chromatography-tandem mass spectrometry. Group differences were assessed using fold-change analysis and linear models adjusted for age and sex. Random forest models evaluated classification performance, and within-group Spearman correlations were used to examine metabolic relationships. Results Norepinephrine showed the largest increase in ASD, whereas dopamine and tetrahydrobiopterin exhibited nominal group differences that did not remain significant after correction for multiple testing. A three-metabolite panel comprising tetrahydrobiopterin, γ-aminobutyric acid, and kynurenine showed exploratory discrimination between groups (area under the receiver operating characteristic curve = 0.750, 95% confidence interval 0.622–0.878), but this performance requires external validation. Correlation analysis revealed conserved bile acid coupling in both groups. In controls, tryptophan was positively associated with kynurenine, whereas this relationship was not observed in ASD. Instead, ASD samples showed broader associations between tryptophan and metabolites linked to neurotransmission and NAD+-precursor metabolism. Conclusion Stool metabolite profiling revealed altered organization of tryptophan- and catecholamine-linked metabolic associations in ASD and identified a small metabolite panel with exploratory discriminative potential. These findings provide a foundation for future studies examining gut-derived neuroactive metabolites in ASD and their relationship to gut-brain axis biology.
Ass associations between urinary tryptophan-pathway metabolites and essential/toxic trace elements in children with ASD and healthy controls are investigated, suggesting cofactor-dependent modulation in ASD.
Jusko Osredkar, Kristina Kumer, Maja Jekovec Vrhovšek et al.· International Journal of Try...· 0 citations
Background Alzheimer’s disease (AD) is the leading cause of dementia, with early diagnosis remaining constrained by the invasiveness and limited accessibility of current biomarkers. The gut–brain axis has emerged as a critical communication network linking intestinal homeostasis with central nervous system function, yet the diagnostic utility of gut-microbiota-associated circulating metabolites in AD remains largely unexplored. Methods In this case-control study, 85 participants (37 AD patients and 48 cognitively normal controls) were enrolled. Targeted plasma metabolomics using four complementary UPLC-MS/MS assays covering 375 endogenous metabolites was integrated with fecal metagenomic sequencing in 72 participants with paired samples. Differential metabolites were screened using the Mann–Whitney U test, orthogonal partial least squares discriminant analysis, and fold-change thresholds. Gut-microbiota-associated plasma metabolites were defined through Spearman correlation with linear discriminant analysis effect size-identified differential bacterial species. A diagnostic panel was constructed using least absolute shrinkage and selection operator logistic regression with stability selection, and its performance was evaluated by support vector machine with repeated cross-validation, with age and sex evaluated as covariates. Methods We identified 27 differential plasma metabolites enriched in tricarboxylic acid cycle and amino acid metabolic pathways. Correlation analysis identified 10 gut-microbiota-associated plasma metabolites significantly associated with gut microbial alterations. A three-metabolite panel (citric acid, 3-hydroxyoctanoic acid, and glyoxylic acid) achieved a cross-validated area under the curve of 0.865, with a modest sensitivity of 62.2% and specificity of 100.0%, positioning it as a confirmatory auxiliary tool rather than a broad screening instrument. The panel score correlated negatively with Mini-Mental State Examination scores (r = −0.544, P < 0.001), and integration with clinical cognitive assessment yielded an area under the curve of 0.978. Conclusion These findings potentially advance the translational understanding of the gut–brain metabolic axis in neurodegeneration and support the exploratory clinical utility of microbiota-linked peripheral metabolic signatures as non-invasive auxiliary diagnostic tools for AD, awaiting external validation in larger, multi-center cohorts.
Jiao Chen, Xuhui Chen, Weiwei Yu et al.· Frontiers in Pharmacology· 0 citations
INTRODUCTION
Obesity is highly prevalent in schizophrenia (SCZ) and contributes to unfavorable clinical and cognitive outcomes, yet its underlying metabolic mechanisms remain unclear. We applied non-targeted metabolomics to characterize body mass index (BMI) related metabolic signatures in chronic SCZ.
METHODS
A total of 347 chronic SCZ patients were stratified into high BMI (n = 187) and low BMI (n = 160) groups. The Positive and Negative Symptom Scale (PANSS) and the Repeatable Battery for the Assessment of Neuropsychological Status (RBANS) scales were used to evaluate psychopathology and cognition. Fasting serum samples were collected and analyzed using non-targeted metabolomics via ultra-high-performance liquid chromatography-high-resolution mass spectrometry. Multivariate and univariate statistical approaches, pathway enrichment analyses, receiver operating characteristic (ROC) analyses, and linear regression models were applied.
RESULTS
Fifty-eight metabolites differed significantly between high and low BMI groups (primarily involving 12 amino acids and 8 fatty acids). Pathway analyses revealed significant perturbations in Arginine biosynthesis (p < 0.001), Nicotinate and nicotinamide metabolism (p = 0.008) and Phenylalanine metabolism (p = 0.037). Panels combining differential amino acids and fatty acids demonstrated moderate discriminative performance for high BMI status (AUC > 0.7). Moreover, C-Mannosyl Valine and C-Mannosyl Phenylalanine were associated with cognitive impairment in the low BMI group (all p < 0.05), whereas 2-Aminoheptanoic acid were associated with negative symptoms severity in the high BMI group (p < 0.001).
CONCLUSION
Distinct metabolic profiles characterize BMI heterogeneity in SCZ and are differentially associated with cognitive impairment and negative symptoms.
Objective This exploratory pilot study aimed to investigate the associations of anger-related irritability symptoms with gut microbiota and circulating metabolites in patients with type 2 diabetes mellitus (T2DM) using multi-omics analysis. Methods We conducted a cross-sectional study, in which T2DM patients were categorized into a self-reported irritable T2DM group (IDM, n = 29) and a self-reported non-irritable DM group (NIDM, n = 28) based on Visual Analog Scale (VAS) scores, with a healthy control group (HC, n = 30) also established. Fecal 16S rRNA gene sequencing and UPLC-MS/MS-based untargeted metabolomic profiling of blood samples were performed. Differences in microbial community structure between groups were analyzed using alpha and beta diversity metrics and linear discriminant analysis effect size (LEfSe) analysis. Differentially abundant genera were identified by MaAsLin2 with adjustment for confounders. Exploratory metabolite markers were screened based on thresholds for p < 0.05, variable importance in projection (VIP) > 1, |log2 fold change (FC)| > 1. Associations between differentially abundant genera and exploratory metabolite markers were explored using Spearman correlation analysis. Functional prediction was conducted based on differentially represented KEGG orthology (KO) and clusters of orthologous groups (COG) entries to identify altered metabolic pathways. Results Both beta diversity and LEfSe analyses revealed differences in gut microbial community structure and potential discriminatory taxa among the three groups. Five differentially abundant genera and twelve exploratory metabolite markers were identified between IDM and NIDM. A combined model incorporating microbial and metabolomic markers demonstrated superior diagnostic performance (AUC = 0.872) compared with models using either type of marker alone. Twelve statistically significant microbiota–metabolite associations were found in Spearman analysis. Functional prediction analysis indicated enrichment of bile acid and tryptophan metabolism pathways. Conclusion VAS-defined irritability symptoms were associated with specific gut microbial and metabolomic alterations in T2DM, providing exploratory evidence for future studies on emotion-related metabolic dysregulation.
Xueli Bao, Ye-Xin Chen, Tunan Ding et al.· Frontiers in Microbiology· 0 citations
This study provides suggestive evidence that elevated adenine and proline may be potential risk factors for ASD and suggests possible involvement of the mitochondrial–Hippo–microtubule pathway, and proposes benzo[a]pyrene as a candidate environmental toxicant that may perturb CSF metabolism.
Dan Zhao, Jun-Zhi Guo, Ying Zhang et al.· Genes· 0 citations
Autism spectrum disorder (ASD) has been associated with gut microbiome and metabolic alterations, but reported biomarkers are inconsistent and often inadequately account for family and shared environment. We analysed 620 children, including 334 with ASD and 286 neurotypical siblings, with a median age of 6.0 years (IQR 4.0–8.0). Faecal and urine samples were collected monthly up to eight times. After quality control, 869 microbiome, 754 faecal metabolome and 787 urine metabolome samples were analysed using 16S rRNA sequencing, NMR and LC–MS metabolomics. Mixed models accounted for family, repeated sampling and biological sex. Family membership explained 46.1% of variation across microbiome and metabolome profiles, compared with 8.0% attributable to ASD. After family adjustment, ASD explained only 0.1% to 0.3% of microbiome beta diversity. No stable taxonomic biomarkers were identified, and microbial classification was poor (AUROC <0.75). Urinary metabolomics identified elevated 5-hydroxy-L-tryptophan and altered phenylalanine metabolism. Structural equation modelling found no direct associations between selected taxa and metabolites. These findings argue against a universal ASD microbiome and indicate that family context contributes more strongly than diagnosis to microbial and metabolic variation.
Ashley G. Bell, Kiana A. West, Gary Frost et al.· bioRxiv· 0 citations
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