Relationship between body weight change and glycaemic control with tirzepatide treatment in people with type 2 diabetes: A post hoc assessment of the <scp>SURPASS</scp> clinical trial programme
Bibliographic record
Abstract
AIM: To assess the relationship between HbA1c and body weight reductions with tirzepatide treatment (5, 10 or 15 mg). MATERIALS AND METHODS: HbA1c and body weight data at 40 weeks (SURPASS-1, -2 and -5) and 52 weeks (SURPASS-3 and -4) were analysed by trial. RESULTS: Across the SURPASS clinical trials, HbA1c reductions from baseline were observed in 96%-99%, 98%-99% and 94%-99% of participants treated with tirzepatide 5, 10 and 15 mg, respectively. Moreover, 87%-94%, 88%-95% and 88%-97% of participants, respectively, experienced weight loss associated with HbA1c reductions. Statistically significant associations (correlation coefficients ranging from 0.1438 to 0.3130 across studies; P ≤ .038) between HbA1c and body weight changes were observed with tirzepatide in SURPASS-2, -3, -4 (all doses) and -5 (tirzepatide 5 mg only). CONCLUSIONS: In this post hoc analysis, consistent reductions in both HbA1c and body weight were observed in most participants treated with tirzepatide at doses of 5, 10 or 15 mg. A statistically significant but modest association between HbA1c and body weight change was observed in SURPASS-2, SURPASS-3 and SURPASS-4, suggesting that both weight-independent and weight-dependent mechanisms are responsible for the tirzepatide-induced improvement in glycaemic control.
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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 | 0.007 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".