Association between metformin and physical activity with glucose control in adults with type 2 diabetes
Bibliographic record
Abstract
Abstract Objective To examine the combined association between metformin use and physical activity on HbA1c in adults with type 2 diabetes. Research Design and Methods Adults with type 2 diabetes from NHANES continuous survey (1999‐2018, n = 6447) were classified as active and inactive based on self‐reported engagement in moderate‐to‐vigorous or vigorous physical activity (MVPA or VigPA) and metformin use over the last month. Results There was a significant negative main effect of metformin usage on HbA1c levels, independent of whether individuals engaged in modest levels of MVPA or VigPA. Moreover, there was a higher prevalence of metformin users with a HbA1c < 6.5% than non‐metformin users with no differences by activity status (36.1%‐39.5% versus 24.9%‐29.7%, respectively). There was a significantly lower HbA1c level (P = .007) and trend for a higher odds of having a HbA1c that achieved the clinical target of <7% (OR, 95% CI = 1.2, 1.0‐1.4, P = .06) in the MVPA than non‐MVPA group for only those not using metformin. For those using metformin, there was no difference in HbA1c levels by either MVPA or VigPA (both P > .05). Conclusions There appears to be independent benefits of metformin and regular physical activity on glucose control, but the impact of these two treatments are not necessarily additive. Based on this analyses, the benefit of physical activity on HbA1c levels in type 2 diabetes is likely more apparent in those not taking metformin, as compared to those who are.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.002 | 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".