Genetic variants of increased waist circumference in psychosis
Bibliographic record
Abstract
OBJECTIVE: We examined whether established metabolic risk genetic variants in the population confer a risk for increased waist circumference in patients with schizophrenia spectrum disorders and also an association with schizophrenia spectrum disorders irrespective of waist circumference. PATIENTS AND METHODS: We analyzed the association in (i) a case-case model in which patients with schizophrenia spectrum disorder with increased waist circumference (≥80 cm for women and ≥94 cm for men) (n=534) were compared with patients with normal waist circumference (<80 cm for women; <94 cm for men) (n=124), and in (ii) a case-control model in which schizophrenia spectrum disorder patients with increased waist circumference or irrespective of waist circumference were compared with population-derived controls (n=494) adjusted for age, sex, fasting glucose, smoking, and family history of diabetes. RESULTS: Genetic variants in five genes (MIA3, MRAS, P2RX7, CAMKK2, and SMAD3) were associated with increased waist circumference in patients with schizophrenia spectrum disorder (P<0.046). Genetic variants in three other genes (PPARD, MNTR1B, and NOTCH2) were associated with increased waist circumference in patients when compared with control individuals (P<0.037). Genetic variants in the PPARD, MNTR1B, NOTCH2, and HNF1B were nominally associated with schizophrenia spectrum disorder irrespective of waist circumference (P<0.027). No differences in waist circumference between specific psychosis diagnoses were detected. CONCLUSION: Increased waist circumference in patients with schizophrenia spectrum disorder may be explained, in part, by increased metabolic risk gene burden, and it indicates a shared genetic susceptibility to metabolic disorder and psychosis per se. Along these lines, common metabolic risk genetic variants confer a risk for increased waist circumference in patients with schizophrenia spectrum disorders.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".