Submitted:
14 April 2026
Posted:
15 April 2026
You are already at the latest version
Abstract
Keywords:
1. Introduction
1.1. Heterogeneity of Autism Spectrum Disorder and the Rationale for a Metabolic Subtype
1.2. The Expanded Concept of the Gut–Brain Axis and the Pathophysiological Significance of Gut-Derived Metabolites
1.3. Research Trends on Short-Chain Fatty Acids and the Scope of This Review
2. Physiological Roles of Gut Microbiota and Short-Chain Fatty Acids
2.1. Composition of the Gut Microbiota and Mechanisms of Short-Chain Fatty Acid Production
2.2. Physiological Functions of Major Short-Chain Fatty Acids and Gut–Immune–Neural Homeostasis
2.3. SCFA Signaling Mechanisms: Receptor-Mediated Pathways and Epigenetic Regulation
2.4. Developmental Stage– and Context-Dependent Actions of SCFAs
3. Gut Microbiota and Short-Chain Fatty Acid Imbalance in Autism Spectrum Disorder
3.1. Alterations in Gut Microbiota Composition in Individuals with ASD and Their Functional Implications
3.2. Clinical Heterogeneity of Reported Short-Chain Fatty Acid Alterations in Individuals with ASD
3.3. Selective Associations Between Propionate Alterations and ASD Phenotypes
4. Neuro-pathophysiological Implications of Short-Chain Fatty Acids with a Focus on Propionate
4.1. Absorption, Systemic Distribution, and Central Nervous System Accessibility of Propionate
4.3. Mitochondrial Dysfunction and Energy Metabolic Stress
4.4. Distinguishing Direct Effects from Axis-Mediated Indirect Effects
5. Retrograde Signaling via the Gut–Brain Axis
5.1. Conceptual Framework of Gut Metabolite–Based Bottom-Up Signaling
5.2. Signal Amplification Mediated by Intestinal Barrier Dysfunction and Systemic Inflammation
5.3. Integration of Neuro–Immune–Metabolic Pathways and Central Nervous System Responses
5.4. Limitations of Preclinical Evidence and Clinical Implications
6. Gut-Derived Metabolites and ASD: Integration of Clinical Evidence, Boundaries of Interpretation, and a Research Roadmap for Precision Interventions
6.1. Integrative Interpretation of Clinical Evidence: Signals Are Present, but Not Universal Markers
6.2. Guarding Against Premature Causal Inference: Reframing “Limitations” as Design Requirements
6.3. Therapeutic Implications of the Metabolic ASD Concept: Defining the Target and the Endpoints, Not the Magnitude of Effect
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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| Study | Model | Sample Type | Age/Stage | ASD Characteristics | ASD patients | ||
|---|---|---|---|---|---|---|---|
| Acetate | Propionate | Butyrate | |||||
| Wang et al., 2019 [47] | Human | Feces | Children | ASD vs TD controls | ↔ | ↔ | ↔ |
| Liu et al., 2019 [48] | ↓ | NS | ↓ | ||||
| Study | Model | Sample type | Increased taxa | Decreased taxa | Functional implication |
|---|---|---|---|---|---|
| Liu et al., 2019 [48] | Human (ASD) | Feces | Propionate- and acetate-producing taxa; Desulfovibrio (G) | Butyrate-associated taxa; Ruminococcaceae (F), Eubacterium (G), Lachnospiraceae (F), Erysipelotrichaceae (F) | Altered SCFA-related microbial balance |
| Li et al., 2024 [97] | Human (ASD) | Fusobacteria (P), Firmicutes (P), Verrucomicrobia (P), | Lentisphaerae (P), Bacteroidetes (P), Euryarchaeota (P), Patescibacteria (P) | Altered microbial community structure | |
| Lou et al., 2022 [98] | Human (ASD) |
Bifidobacterium (G), Veillonella (G), Enterobacteriaceae (F), Lachnospiraceae (F) |
Clostridium (G), Veillonella ratti (S) |
Early-life cohort-specific microbial deviation | |
| Iglesias-Vázquez et al., 2020 [100] | Human (ASD) | Bacteroides (G), Parabacteroides (G), Clostridium (G), Faecalibacterium (G), Phascolarctobacterium (G) | Coprococcus (G), Bifidobacterium (G) | Context-dependent lack of a consistent ASD microbial signature | |
| Kang et al., 2013 [102] | Human (ASD) | NR | Prevotella (G), Coprococcus (G), unclassified Veillonellaceae (F) | Disrupted microbial interactions | |
| Coretti et al., 2018 [104] | Human (ASD) | Bacteroidetes (P), Proteobacteria (P) | Actinobacteria (P) | Pro-inflammatory microbial shift |
| Source of heterogeneity | Categories / examples | Impact on SCFA readouts | Implications for interpretation |
|---|---|---|---|
| Sample source | Feces vs plasma/serum | Captures luminal fermentation output vs absorbed/systemically available SCFAs, influenced by host metabolism (e.g., hepatic clearance) | Fecal and circulating SCFAs are not interchangeable |
| GI symptom stratification | Constipation, diarrhea, abdominal pain | Alters intestinal transit time, fermentation kinetics, absorption efficiency, and stool water content | Null findings may reflect signal dilution in unstratified cohorts |
| Developmental stage | Infancy, childhood, adolescence | Age-dependent microbiota maturation and diet transitions shift baseline SCFA profiles | Age mismatch limits cross-study comparability |
| Diet and fiber intake | High vs low fermentable fiber; dietary patterns | Directly modulates microbial substrate availability and SCFA production | Dietary control is critical for stronger causal inference |
| Medication exposure | Antibiotics, probiotics/prebiotics, laxatives, psychotropics, PPIs | Reshapes microbial composition and metabolic output; may interact with GI status | Medication history should be systematically captured and adjusted for |
| Analytical workflow / platform | GC-FID, GC–MS, LC–MS/MS (with derivatization and internal standards) | Differences in sensitivity, specificity, and quantification range across platforms | Methodological heterogeneity contributes substantially to variability |
| Reporting units / normalization | Absolute concentration vs relative proportion; wet vs dry weight; different unit scales | Limits quantitative comparability and may invert apparent group differences | Directional interpretation is frequently more appropriate than magnitude alone |
| Experimental design / model | Human cohorts vs rodent models; exposure-based vs endogenous metabolism | Exposure paradigms may not recapitulate endogenous SCFA dynamics in humans | Animal findings should not be directly generalized to human ASD |
| Developmental stage | SCFA-related context | Biological processes potentially affected | Observed or reported outcomes | Representative references |
|---|---|---|---|---|
| Pregnancy (maternal) | Circulating maternal SCFAs reflecting gut fermentation | Maternal metabolic–immune milieu influencing fetal neurodevelopmental susceptibility | Associations with offspring neurodevelopmental measures in cohort studies | [93] |
| Prenatal period (maternal immune activation) | SCFA–immune crosstalk potentially intersecting with maternal inflammation | Immune signaling at the maternal–fetal interface | Increased vulnerability to neurodevelopmental alterations in offspring | [14,95] |
| Early postnatal (infancy) | Infant gut fermentation–derived SCFAs with limited systemic availability | Microglial maturation and synaptic pruning (preclinical support) | Atypical early developmental trajectories in early-life microbiome studies | [90,92] |
| Childhood | Diet-modulated gut and plasma metabolomic profiles | Systemic metabolic shifts linked to microbiota modulation | Context-dependent associations with autism-related traits | [107,115] |
| Juvenile (experimental animal models) | Experimental SCFA exposure (e.g., propionate/PPA) | Neuroinflammation and mitochondrial stress pathways | ASD-like behavioral phenotypes in specific paradigms | [1,94] |
| Biomarker domain | Candidate markers | Measurement source | Biological rationale |
|---|---|---|---|
| Gut-derived metabolites | Relative SCFA patterns (acetate, propionate, butyrate) | Feces / plasma | Reflects microbial fermentation patterns and systemic availability |
| Gastrointestinal integrity | Lipopolysaccharide-binding protein (LBP) | Serum | Indicates barrier-related metabolite translocation |
| Immune activation | Representative inflammatory cytokines (e.g., IL-6, IL-17A) | Serum | Captures inflammatory tone linked to metabolic signaling |
| Oxidative stress / redox imbalance | Redox-related markers | Blood | Reflects oxidative imbalance relevant to neuronal vulnerability |
| Mitochondrial / energy metabolism | Energy metabolism–related indicators | Blood | Reflects altered cellular energy metabolism |
| Microbiota structure | Relative depletion of fermentative taxa | Fecal sequencing | Reflects reduced SCFA-producing capacity |
| Developmental timing | Perinatal/early-life exposures | Clinical history | Identifies sensitive developmental windows |
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