Neurometabolic profiles of autism spectrum disorder patients with genetic variants in specific neurotransmission and synaptic genes
Bibliographic record
Abstract
Abstract Autism Spectrum Disorder (ASD) is a neurodevelopmental condition characterized by impaired social interaction, and restricted and repetitive patterns of behavior. ASD presents as a clinical spectrum, with variable levels of severity and multiple co-occurring conditions. The etiology of ASD may involve hundreds of genes and there is evidence that neurotransmitter and synaptic (NS) pathways are implicated. Proton Magnetic Resonance Spectroscopy ( 1 H-MRS) has made it possible to study the concentration of brain neurometabolites and compare their levels in the brains of ASD and control individuals. We integrated genetic variants in NS genes with 1 H-MRS analysis, and identified 12 predicted damaging variants (PDVs) in 12 NS genes in 10 ASD individuals, most mapping to genes involved in Gamma-aminobutyric acid (GABA) and glutamate pathways. Total creatine (tCr) and total N-acetyl aspartate (tNAA), markers of bioenergetics and neuronal metabolism, respectively, were lower in ASD patients with genetic alterations in NS genes compared to a control group without ASD. We conclude that PDVs in NS genes that are important for the regulation of glutamate or involved in GABAergic functions are associated with neurometabolic alterations, and that dysfunction in glutamatergic and/or GABAergic pathways may be implicated as these pathways are linked to the metabolic measures altered in cases.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".