The role of genetic variation across IL-1β, IL-2, IL-6, and BDNF in antipsychotic-induced weight gain
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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) | 0.001 | 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".