Association study of polymorphisms in Insulin Induced Gene 2 (INSIG2) with antipsychotic‐induced weight gain in European and African‐American schizophrenia patients
Bibliographic record
Abstract
OBJECTIVE: Atypical antipsychotic drugs, in particular clozapine and olanzapine, influence cellular lipogenesis and are associated with metabolic side effects including weight gain. Insulin induced gene 2 (INSIG2) mediates feedback control of lipid synthesis and polymorphisms in the gene (rs17587100, rs10490624 and rs17047764) have been associated with antipsychotic induced weight gain. In this study we intended to replicate these findings in an independent patient population. METHODS: All three polymorphisms as well as an additional polymorphism (rs7566605) were genotyped in 154 patients who underwent treatment for chronic schizophrenia with one of four antipsychotics (clozapine, olanzapine, haloperidol or risperidone). Patients were evaluated for antipsychotic induced weight gain during treatment for up to 14 weeks. RESULTS: We did not observe any significant allelic, genotypic or haplotypic association of the polymorphisms with antipsychotic induced weight gain in the patients of European ancestry (p > 0.05). In the patients of African ancestry, no haplotypic association was observed but a trend of allelic association with the C allele of rs7566605 and genotypic association with the 'GC' genotype in rs17047764 was observed (p = 0.02; p(Bonferroni) = 0.225). CONCLUSION: We were unable to replicate significant associations in patients of European ancestry. However, we observed a marginal effect of the rs17047764 and rs7566605 in the African-American sample. Since the latter observations were generated in a relatively small sample set, further replication studies are warranted.
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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 | 0.001 |
| 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 | 0.000 |
| 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".