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Medical Journal News
Stepwise dual antiplatelet therapy de-escalation in patients after drug coated balloon angioplasty (REC-CAGEFREE II): multicentre, randomised, open label, assessor blind, non-inferiority trial
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Food additive mixtures and type 2 diabetes incidence: Results from the NutriNet-Santé prospective cohort
by Marie Payen de la Garanderie, Anaïs Hasenbohler, Nicolas Dechamp, Guillaume Javaux, Fabien Szabo de Edelenyi, Cédric Agaësse, Alexandre De Sa, Laurent Bourhis, Raphaël Porcher, Fabrice Pierre, Xavier Coumoul, Emmanuelle Kesse-Guyot, Benjamin Allès, Léopold K. Fezeu, Emmanuel Cosson, Sopio Tatulashvili, Inge Huybrechts, Serge Hercberg, Mélanie Deschasaux-Tanguy, Benoit Chassaing, Héloïse Rytter, Bernard Srour, Mathilde Touvier
BackgroundMixtures of food additives are daily consumed worldwide by billions of people. So far, safety assessments have been performed substance by substance due to lack of data on the effect of multiexposure to combinations of additives. Our objective was to identify most common food additive mixtures, and investigate their associations with type 2 diabetes incidence in a large prospective cohort.
Methods and FindingsParticipants (n = 108,643, mean follow-up = 7.7 years (standard deviation (SD) = 4.6), age = 42.5 years (SD = 14.6), 79.2% women) were adults from the French NutriNet-Santé cohort (2009–2023). Dietary intakes were assessed using repeated 24h-dietary records, including industrial food brands. Exposure to food additives was evaluated through multiple food composition databases and laboratory assays. Mixtures were identified through nonnegative matrix factorization (NMF), and associations with type 2 diabetes incidence were assessed using Cox models adjusted for potential socio-demographic, anthropometric, lifestyle and dietary confounders. A total of 1,131 participants were diagnosed with type 2 diabetes. Two out of the five identified food additive mixtures were associated with higher type 2 diabetes incidence: the first mixture included modified starches, pectin, guar gum, carrageenan, polyphosphates, potassium sorbates, curcumin, and xanthan gum (hazard ratio (HR)per an increment of 1SD of the NMF mixture score = 1.08 [1.02, 1.15], p = 0.006), and the other mixture included citric acid, sodium citrates, phosphoric acid, sulphite ammonia caramel, acesulfame-K, aspartame, sucralose, arabic gum, malic acid, carnauba wax, paprika extract, anthocyanins, guar gum, and pectin (HR = 1.13 [1.08,1.18], p < 0.001). No association was detected for the three remaining mixtures: HR = 0.98 [0.91, 1.06], p = 0.67; HR = 1.02 [0.94, 1.10], p = 0.68; and HR = 0.99 [0.92, 1.07], p = 0.78. Several synergistic and antagonist interactions between food additives were detected in exploratory analyses. Residual confounding as well as exposure or outcome misclassifications cannot be entirely ruled out and causality cannot be established based on this single observational study.
ConclusionsThis study revealed positive associations between exposure to two widely consumed food additive mixtures and higher type 2 diabetes incidence. Further experimental research is needed to depict underlying mechanisms, including potential synergistic/antagonist effects. These findings suggest that a combination of food additives may be of interest to consider in safety assessments, and they support public health recommendations to limit nonessential additives.
Trial RegistrationThe NutriNet-Santé cohort is registered at clinicaltrials.gov (NCT03335644). https://clinicaltrials.gov/study/NCT03335644.
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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
by Heather Wightman, Elaine Butterly, Lili Wei, Ryan McChrystal, Naveed Sattar, Amanda Adler, David Phillippo, Sofia Dias, Nicky Welton, Andrew Clegg, Miles Witham, Kenneth Rockwood, David A. McAllister, Peter Hanlon
BackgroundThe 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 findingsWe 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.
ConclusionsFrailty 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.