2834 Frailty in randomised controlled trials of glucose-lowering therapies for type 2 diabetes
Bibliographic record
Abstract
Abstract Background The representation of frailty in type 2 diabetes trials is unclear. This study used individual patient data (IPD) from trials of newer glucose-lowering therapies to quantify frailty and assess the association between frailty and efficacy and adverse events. Method We analysed IPD from 34 trials of SGLT2 inhibitors, GLP1 receptor agonists and DDP4 inhibitors. Frailty was quantified using a cumulative deficit frailty index (FI). For each trial, we quantified the distribution of frailty; assessed interactions between frailty and treatment efficacy (HbA1c and major adverse cardiovascular events [MACE], pooled using random-effects network meta-analysis); and associations between frailty and withdrawal, adverse events, and hypoglycaemic episodes. Findings Trial participants numbered 25,208. Mean age 53·8 to 74·2 years. Using FI > 0·24 to indicate frailty, median prevalence was 1·9% (IQR 0·8% to 6·1%). Prevalence was higher in trials of older people and people with renal impairment. For SGLT2i and GLP1ra, there was a small attenuation in efficacy on HbA1c with increasing frailty (0·07%-point and 0·14%-point smaller reduction, respectively, per 0·1-point increase in FI). Findings for MACE had high uncertainty (few events). A 0·1-point increase in the FI was associated with more adverse events (incidence rate ratio, IRR 1·43, 95% confidence interval 1·34 to 1·53), treatment-related adverse events (1·35, 1·22 to 1·50), serious adverse events (2·04, 1·80 to 2·30), hypoglycaemia (1·18, 1·04 to 1·34), MACE (hazard ratio 3·02, 2·49 to 3·68) and withdrawal (odds ratio 1·45, 1·30 to 1·62). Interpretation Frailty is associated very modest attenuation of treatment efficacy for glycaemic outcomes and with greater incidence of both adverse events and MACE. Frailty was rare in most trials. While these findings support calls to relax HbA1c-based targets in people living with frailty, they also highlight the need for inclusion of people living with frailty in trials as the absolute balance of risks and benefits remains uncertain.
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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 | 0.127 | 0.221 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.017 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".