Pharmacogenetics of Metformin for Medication-Induced Weight Gain in Autism Spectrum Disorder
Bibliographic record
Abstract
Objectives: We recently found that metformin attenuated weight gain due to mixed dopamine and serotonin receptor antagonists, commonly termed atypical antipsychotics, in children and adolescents with autism spectrum disorder (ASD). Previous studies have found that genetic variation predicts response to metformin in diabetes. In this study, we aimed to assess whether response to metformin for weight gain in this population is associated with variants in five genes previously implicated in metformin response in diabetes. Methods: Youth with ASD who experienced significant weight gain while taking mixed receptor antagonist medications were randomly assigned to metformin or placebo for 16 weeks, followed by open-label metformin treatment for 16 weeks. In the 53 participants with available DNA samples, we used a linear, mixed model analysis to assess response in the first 16 weeks of metformin treatment, whether in the randomized or open-label period, based upon genotypes at polymorphisms in five genes previously associated with metformin response in diabetes: ATM, SLC2A2, MATE1, MATE2, and OCT1. Results: In the primary analysis, both ATM and OCT1 showed significant effects of genotype on change in body mass index z-scores, the primary outcome measure, during the first 16 weeks of treatment with metformin. No other polymorphism showed a significant difference. Conclusion: As has been shown for metformin treatment in diabetes, genetic variation may predict response to metformin for weight gain in youth with ASD treated with mixed receptor antagonists. Further work is needed to replicate these findings and evaluate whether they can be used prospectively to improve outcomes.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| 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) | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".