Common genetic variants near <i>SLC2A2</i> and glycemic response to glimepiride in the GRADE comparative effectiveness clinical trial
Bibliographic record
Abstract
ABSTRACT Optimizing second-line therapy for type 2 diabetes is challenging due to interindividual variability in response. We conducted a pharmacogenomic genome-wide association study (GWAS) in the Glycemia Reduction Approaches in Type 2 Diabetes: A Comparative Effectiveness (GRADE) Study to identify genetic predictors of glycemic response to insulin glargine, glimepiride, liraglutide, and sitagliptin, when added to metformin in a diverse population. We identified 21 genome-wide significant loci associated with treatment response. rs1905505, a non-coding variant near SLC2A2 , the gene encoding the glucose transporter GLUT2, was enriched in Africans/African Americans and conferred a 36% increased risk of treatment failure on glimepiride ( p =4.83×10). Carriers had impaired β-cell function, evidenced by a lower C-peptide index during OGTT, and diminished glucose-lowering response to an acute sulfonylurea challenge. Genetic manipulation in zebrafish confirmed that slc2a2 disruption attenuates the glucose-lowering effect of glimepiride. In conclusion, genetic variation influences glycemic response to medications, with SLC2A2 emerging as a key determinant of sulfonylurea response. Clinical Trial registration number: NCT01794143
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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) | 0.007 | 0.001 |
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".