Evaluation of the Genetic Association Between Adult Obesity and Neuropsychiatric Disease
Bibliographic record
Abstract
Extreme obesity (EO) (BMI >50 kg/m2) is frequently associated with neuropsychiatric disease (NPD). As both EO and NPD are heritable central nervous system disorders, we assessed the prevalence of protein-truncating variants (PTVs) and copy number variants (CNVs) in genes/regions previously implicated in NPD in adults with EO (n = 149) referred for weight loss/bariatric surgery. We also assessed the prevalence of CNVs in patients referred to University College London Hospital (UCLH) with EO (n = 218) and obesity (O) (BMI 35–50 kg/m2; n = 374) and a Swedish cohort of participants from the community with predominantly O (n = 161). The prevalence of variants was compared with control subjects in the Exome Aggregation Consortium/Genome Aggregation Database. In the discovery cohort (high NPD prevalence: 77%), the cumulative PTV/CNV allele frequency (AF) was 7.7% vs. 2.6% in control subjects (odds ratio [OR] 3.1 [95% CI 2–4.1]; P < 0.0001). In the UCLH EO cohort (intermediate NPD prevalence: 47%), CNV AF (1.8% vs. 0.9% in control subjects; OR 1.95 [95% CI 0.96–3.93]; P = 0.06) was lower than the discovery cohort. CNV AF was not increased in the UCLH O cohort (0.8%). No CNVs were identified in the Swedish cohort with no NPD. These findings suggest that PTV/CNVs, in genes/regions previously associated with NPD, may contribute to NPD in patients with EO.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".