Prevalence of Creatine Deficiency Syndromes in Children With Nonsyndromic Autism
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVE: Creatine deficiency may play a role in the neurobiology of autism and may represent a treatable cause of autism. The goal of the study was to ascertain the prevalence of creatine deficiency syndromes (CDSs) in children with autism spectrum disorder (ASD). METHODS: In a prospective multicenter study, 443 children were investigated after a confirmed diagnosis of ASD. Random spot urine screening for creatine metabolites (creatine, guanidinoacetate, creatinine, and arginine) with liquid chromatography-tandem mass spectrometry and second-tier testing with high-performance liquid chromatography methodology was followed by recall testing in 24-hour urines and confirmatory testing by Sanger-based DNA sequencing of GAMT, GATM, and SLC6A8 genes. Additional diagnostic tests included plasma creatine metabolites and in vivo brain proton magnetic resonance spectroscopy. The creatine metabolites in spot urine in the autism group were compared with 128 healthy controls controlled for age. RESULTS: In 443 subjects with ASD investigated for CDS, we had 0 events (event: 0, 95% confidence interval 0-0.0068), therefore with 95% confidence the prevalence of CDS is <7 in 1000 children with ASD. The autism and control groups did not vary in terms of creatine metabolites (P > .0125) in urine. CONCLUSION: Our study revealed a very low prevalence of CDS in children with nonsyndromic ASD and no obvious association between creatine metabolites and autism. Unlike our study population, we expect more frequent CDS among children with severe developmental delay, speech impairment, seizures, and movement disorders in addition to impairments in social communication, restricted interests, and repetitive behaviors.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".