Inborn errors of metabolism associated with psychosis: literature review and case–control study using exome data from 5090 adult individuals
Bibliographic record
Abstract
A literature review was conducted, using the computerized "Online Mendelian Inheritance in Man" (OMIM) and PubMed, to identify inborn errors of metabolism (IEM) in which psychosis may be a predominant feature or the initial presenting symptom. Different combinations of the following keywords were searched using OMIM: "psychosis", "schizophrenia", or "hallucinations" and "metabolic", "inborn error of metabolism", "inborn errors of metabolism", "biochemical genetics", or "metabolic genetics". The OMIM search generated 126 OMIM entries, 40 of which were well known IEM. After removing IEM lacking evidence in PubMed for an association with psychosis, 29 OMIM entries were identified. Several of these IEM are treatable. They involve different small organelles (lysosomes, peroxisomes, mitochondria), iron or copper accumulation, as well as defects in other met-abolic pathways (e.g., defects leading to hyperammonemia or homocystinemia). A clinical checklist summarizing the key features of these conditions and a guide to clinical approach are provided. The genes corresponding to each of these con-ditions were identified. Whole exome data from 2545 adult cases with schizophrenia and 2545 unrelated controls, accessed via the Database of Genotypes and Phenotypes (dbGaP), were analyzed for rare functional variants in these genes. The odds ratio of having a rare functional variant in cases versus controls was calculated for each gene. Eight genes are significantly associated with schizophrenia (p < 0.05, OR >1) using an unselected group of adult patients with schizophrenia. Increased awareness of clinical clues for these IEM will optimize referrals and timely metabolic interventions.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.023 | 0.021 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".