Reduced Weight Gain with Pioglitazone vs Vildagliptin in CREBRF rs373863828 A-allele Carriers: Insights from the WORTH Trial
Bibliographic record
Abstract
Background/Objectives: This subgroup analysis of a randomised, open-label, two-period crossover trial in Aotearoa New Zealand (February 2019 to March 2020) assessed whether the glucose-lowering effects of vildagliptin, vs pioglitazone varied by the CREBRF (p.Arg457Gln) rs373863828 genotype. Methods: Adults with type 2 diabetes and HbA1c > 58 mmol/mol (> 7.5%) received either pioglitazone (30 mg) or vildagliptin (50 mg) for 16 weeks, then switched medications for another 16 weeks. Differences in HbA1c between treatments (pioglitazone vs vildagliptin) were tested for an interaction with CREBRF rs373863828 A-allele carrier status and controlling for baseline HbA1c using linear mixed models. Secondary endpoints included weight, systolic blood pressure, and diabetes treatment satisfaction. Results: Participants with the AA/AG genotype had a higher baseline weight than those with the GG genotype (121.4 kg vs 106.6 kg, respectively; p < 0.01). No significant difference in achieved HbA1c was found based on A-allele carrier status (0.43 mmol/mol; 95% CI − 4.83, 5.69; p =0.87). Among Māori and Pacific participants with the A-allele, a smaller weight difference was observed after pioglitazone vs vildagliptin compared to those with the GG genotype (interaction effect − 1.66 kg; 95% CI − 3.27, − 0.05; p =0.04). Conclusion: CREBRF rs373863828 A-allele carriers show a similar HbA1c-lowering response to pioglitazone vs vildagliptin compared to non-carriers but exhibit less weight gain with pioglitazone, despite having significantly higher baseline weights. Keywords: pioglitazone, vildagliptin, CREBRF, pharmacogenetics, precision medicine, stratified drug response
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".