The role of genetic variation across IL-1β, IL-2, IL-6, and BDNF in antipsychotic-induced weight gain
Why this work is in the frame
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Bibliographic record
Abstract
OBJECTIVES: Antipsychotics with high weight gain-inducing propensities influence the expression of immune and neurotrophin genes, which have been independently related to obesity indices. Thus, we investigated whether variants in the genes encoding interleukin (IL)-1β, IL-2, and IL-6 and brain-derived neurotrophic factor (BDNF) Val66Met are associated with antipsychotic-induced weight gain (AIWG). METHODS: Nineteen polymorphisms were genotyped using Taqman(®) assays in 188 schizophrenia patients on antipsychotic treatment for up to 14 weeks. Mean weight change (%) from baseline was compared across genotypic groups using analysis of covariance (ANCOVA). Epistatic effects between cytokine polymorphisms and BDNF Val66Met were tested using Model-Based Multifactor Dimensionality Reduction. RESULTS: In European patients, IL-1β rs16944*GA (P = 0.013, Pcorrected = 0.182), IL-1β rs1143634*G (P = 0.001, Pcorrected = 0.014), and BDNF Val66Met (Val/Val, P = 0.004, Pcorrected = 0.056) were associated with greater AIWG, as were IL-1β rs4849127*A (P = 0.049, Pcorrected = 0.784), and IL-1β rs16944*GA (P = 0.012, Pcorrected = 0.192) in African Americans. BDNF Val66Met interacted with both IL-1β rs13032029 (Val/Met+ TT, PPerm = 0.029), and IL-6 rs2069837 (Val/Val+ AA, PPerm = 0.021) in Europeans, in addition to IL-1β rs16944 (Val/Val+ GA, PPerm = 0.006) in African Americans. CONCLUSIONS: SNPs across IL-1β and BDNF Val66Met may influence AIWG. Replication of these findings in larger, independent samples is warranted.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it