Enrichment of pathogenic variants in genes associated with inborn errors of metabolism in psychiatric populations
Bibliographic record
Abstract
Many genetic conditions can mimic mental health disorders, with psychiatric symptoms that are difficult to treat with standard psychotropic medications. This study tests the hypothesis that psychiatric populations are enriched for pathogenic variants associated with selected inborn errors of metabolism (IEMs). Using next-generation sequencing, 2046 psychiatric patients were screened for pathogenic variants in genes associated with four IEMs, Niemann-Pick disease type C (NPC), Wilson disease (WD), homocystinuria (HOM), and acute intermittent porphyria (AIP). Among the 2046 cases, carrier rates of 0.83, 0.98, and 0.20%, for NPC, WD and HOM, and affected rates of 0.10 and 0.24% for NPC and AIP were seen, respectively. An enrichment of known and predicted pathogenic variants in the genes associated with NPC and AIP was found in the psychiatric cohort and especially in schizophrenia patients. The results of this study support that pathogenic variants in genes associated with IEMs are enriched in psychiatric populations. Underlying undiagnosed IEMs could account for the psychiatric symptomatology in a subset of psychiatric patients. Further studies are warranted to investigate the possibility that carriers for IEMs may have an increased risk for psychiatric disorders, particularly in the context of poor treatment response.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".