Role of Ethnicity in Antipsychotic-Induced Weight Gain and Tardive Dyskinesia: Genes or Environment?
Bibliographic record
Abstract
AIM: This study explored the role of self-reported ethnicity and genetic ancestry on antipsychotic (AP)-induced weight gain and tardive dyskinesia (TD) in schizophrenia. PATIENTS & METHODS: Ethnicity was determined by self-report as well as Structure analysis of 190 SNPs selected from HapMap3, genotyped using a customized Illumina BeadChip. Age, gender, baseline weight and AP response using Brief Psychiatric Rating Scale were assessed. Multivariate regression models for AP-induced weight gain and TD, based on the Abnormal Involuntary Movement Scale were constructed. RESULTS: African-American ethnicity (self-report, p = 0.021 and Structure analysis, p = 0.042) predicted AP-induced weight gain but not TD (self-report, p = 0.408 and Structure analysis, p = 0.714). CONCLUSION: Self-reported African-American ethnicity seemed to better predict AP-induced weight gain in schizophrenia compared with genetic ancestry, suggesting a possible role of environmental in addition to genetic factors. Future larger studies are needed to clarify specific gene-environment mechanisms mediating the effect of ethnicity on AP-induced weight gain.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| 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".