Frailty in randomized controlled trials of glucose-lowering therapies for type 2 diabetes: An individual participant data meta-analysis of frailty prevalence, treatment efficacy, and adverse events
Notice bibliographique
Résumé
BACKGROUND: The representation of frailty in type 2 diabetes trials is unclear. This study used individual participant data from trials of newer glucose-lowering therapies to quantify frailty and assess the association between frailty and efficacy and adverse events. METHODS AND FINDINGS: We analysed IPD from 34 trials of sodium-glucose cotransporter 2 (SGLT2) inhibitors, glucagon-like peptide-1 (GLP1) receptor agonists, and dipeptidyl peptidase 4 (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. Trial participants numbered 25,208. Mean age across the included trials ranged from 53.8 to 74.2 years. Using a cut-off of FI > 0.2 to indicate frailty, median prevalence was 9.5% (IQR 2.4%-15.4%). Applying a higher threshold of FI > 0.3, median prevalence was 0.5% (IQR 0.1%-1.5%). Prevalence was higher in trials of older people and people with renal impairment however, even in these higher risk populations, people with FI > 0.4 were generally absent. For SGLT2 inhibitors and GLP1 receptor agonists, there was a small attenuation in efficacy on HbA1c with increasing frailty (0.08%-point and 0.14%-point smaller reduction, respectively, per 0.1-point increase in FI), below the level of clinical significance. Findings for the effect of treatment on MACE (and whether this varied by frailty) had high uncertainty, with few events occurring in trial follow-up. A 0.1-point increase in the FI was associated with more all-cause adverse events regardless of treatment allocation (incidence rate ratio, IRR 1.44, 95% CI 1.35-1.54, p < 0.0001), adverse events judged to the possibly or probably related to treatment (1.36, 1.23, to 1.49, p < 0.0001), serious adverse events (2.09, 1.85, to 2.36, p < 0.0001), hypoglycaemia (1.21, 1.06, to 1.38, p = 0.012), baseline risk of MACE (hazard ratio 3.01, 2.48, to 3.67, p < 0.0001) and with withdrawal from the trial (odds ratio 1.41, 1.27, to 1.57, p < 0.0001). The main limitation was that the large cardiovascular outcome trials did not include data on functional status and so we were unable to assess frailty in these larger trials. CONCLUSIONS: Frailty was uncommon in these trials, and participants with a high degree of frailty were rarely included. Frailty is associated very modest attenuation of treatment efficacy for glycaemic outcomes and with greater incidence of both adverse events and MACE independent of treatment allocation. While these findings are compatible with 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. This would require changes to trial processes to facilitate the explicit assessment of frailty and support the participation of people living with frailty. Such changes are important as the absolute balance of risks and benefits remains uncertain among those with higher degrees of frailty, who are largely excluded from trials.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,009 | 0,023 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,013 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».