Diabetes Care[JOURNAL]

Young KG, McGovern AP, Hopkins R … +11 more , Jansz TT, Cardoso PM, Holman RR, Pearson ER, Hattersley AT, Jones AG, Docherty K, Sattar N, Shields BM, Dennis JM, MASTERMIND Consortium

Diabetes Care · 2026 Jun · PMID 42160591 · Full text

OBJECTIVE: Sodium-glucose cotransporter 2 inhibitors (SGLT2i) reduce heart failure (HF) risk in type 2 diabetes (T2D) and are recommended for patients with T2D who have atherosclerotic cardiovascular disease (ASCVD), HF,... OBJECTIVE: Sodium-glucose cotransporter 2 inhibitors (SGLT2i) reduce heart failure (HF) risk in type 2 diabetes (T2D) and are recommended for patients with T2D who have atherosclerotic cardiovascular disease (ASCVD), HF, or chronic kidney disease (CKD). However, most individuals with T2D do not have these conditions, and current guidelines for this group do not indicate which individuals may benefit most from SGLT2i. We aimed to develop and validate a model to predict the individual-level HF benefit of SGLT2i in individuals with T2D without ASCVD, HF, or CKD. RESEARCH DESIGN AND METHODS: We developed the SGLT2i Absolute Benefit Response (SABRE) model, combining absolute HF risk from the validated QDiabetes-HF model with the SGLT2i-associated hazard ratio (HR) for HF hospitalization from a trial meta-analysis (HR 0.63) to estimate individual 5-year HF benefit. Model components and predictions were validated using U.K. primary care data with linked hospital and death records from 2013 to 2020. RESULTS: Among 57,368 SGLT2i initiators and 111,673 comparator (dipeptidyl peptidase 4 inhibitor or sulfonylurea) initiators, SGLT2i use was associated with a 30% lower risk of new-onset HF (HR 0.70 [95% CI 0.63-0.78]), consistent with trial evidence. Relative HF benefit did not vary by baseline absolute HF risk (P = 0.82). The SABRE model-predicted 5-year absolute HF benefit with SGLT2i ranged from <0.1% to 14.1% (median 1.0% [interquartile range 0.6-1.8%]) and calibrated well against observed HF outcomes. SABRE provided more targeted HF prevention than current guidelines in those with T2D without ASCVD, HF, or CKD. CONCLUSIONS: The SABRE model is an easily deployed clinical prediction model integrating trial evidence and allowing more precise targeting of SGLT2i for primary HF prevention in T2D.

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Comment on Sattar et al. Weight Reduction With Tirzepatide Varied Meaningfully by Baseline HbA1c Category in Adults With Overweight or Obesity and Type 2 Diabetes in SURMOUNT-2. Diabetes Care 2025;48:e136-e137.

Corrao S

Diabetes Care · 2026 Jun · PMID 42160590 · Publisher ↗

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Comparing Approaches for Deriving Diabetes Care Cascades to Inform Policy: A Cross-sectional Analysis Using National Data From 88 Countries.

Teufel F, Theilmann M, Marcus ME … +36 more , Sulola MA, Guwatudde D, Quintana HK, Banegas JR, Soniwala A, Kim S, Aryal K, Bahendeka S, Bicaba B, Damasceno A, Farzadfar F, Houehanou C, Howitt C, Karki K, Lunet N, Martins J, Mayige MT, Mwangi KJ, Quesnel-Crooks S, Roa Rodríguez RG, Rodríguez-Artalejo F, Moghaddam SS, Sibai AM, Sturua L, Tsabedze L, Zhumadilov Z, Bärnighausen T, Geldsetzer P, Atun R, Vollmer S, Manne-Goehler J, Flood D, Gregg EW, Davies JI, Ali MK, Varghese JS

Diabetes Care · 2026 Jun · PMID 42160589 · Publisher ↗

OBJECTIVE: Care cascade indicators are widely used to monitor national diabetes control efforts. However, diabetes definitions used to derive care cascades vary across studies, which may markedly affect results and subse... OBJECTIVE: Care cascade indicators are widely used to monitor national diabetes control efforts. However, diabetes definitions used to derive care cascades vary across studies, which may markedly affect results and subsequent policy decisions. Here, we examine the magnitude of resultant differences between approaches. RESEARCH DESIGN AND METHODS: We analyzed nationally representative, cross-sectional data of 800,348 individuals aged ≥25 years from 88 countries in 2008-2021. We used two different diabetes definitions: elevated biomarkers (glycated hemoglobin [HbA1c] ≥6.5%; fasting plasma glucose ≥7.0 mmol/L; or random plasma glucose ≥11.1 mmol/L) or self-reported diagnosis ("diagnosis-based definition") versus elevated biomarkers or self-reported treatment ("treatment-based definition"). Care cascade estimates included 1) proportions of individuals with diabetes who were diagnosed, and proportions of individuals with diagnosed diabetes who 2) received treatment and 3) attained glycemic control. We benchmarked results against World Health Organization (WHO) diabetes targets. RESULTS: Diabetes prevalence was 12.9% (95% CI 12.1-13.8) applying the diagnosis-based definition and 11.3% (95% CI 10.5-12.1) with the treatment-based definition. Using the diagnosis-based rather than treatment-based diabetes definition to derive care cascades consistently increased the percentages of those who attain diagnosis and control stages but decreased percentages of those receiving treatment. Across countries, median differences between approaches were 11.3% (interquartile range [IQR] 5.1-24.7) for diabetes diagnosis, 21.6% (IQR 14.5-37.8) for treatment, and 16.4% (IQR 7.8-26.8) for control. The WHO 80% glycemic control target was met by 22% versus 6% of countries when using the diagnosis-based versus treatment-based definition, respectively. CONCLUSIONS: Care cascade estimates diverged substantially and consistently across diabetes definitions, skewing policy implications in predictable ways. Harmonizing diabetes performance metrics may improve decision-making and facilitate cross-country comparisons.

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Food Coloring Additives and Incidence of Type 2 Diabetes in the NutriNet-Santé Prospective Cohort.

Shah S, Hasenböhler A, Javaux G … +20 more , Payen de la Garanderie M, Szabo de Edelenyi F, Yvroud P, Agaësse C, De Sa A, Huybrechts I, Pierre F, Audebert M, Coumoul X, Julia C, Kesse-Guyot E, Allès B, Deschamps V, Hercberg S, Chassaing B, Cosson E, Tatulashvili S, Deschasaux-Tanguy M, Srour B, Touvier M

Diabetes Care · 2026 Jun · PMID 42157365 · Full text

OBJECTIVE: To investigate potential association between exposure to food coloring additives and type 2 diabetes incidence. RESEARCH DESIGN AND METHODS: The study followed 108,723 participants (79.2% female, mean age 42.5... OBJECTIVE: To investigate potential association between exposure to food coloring additives and type 2 diabetes incidence. RESEARCH DESIGN AND METHODS: The study followed 108,723 participants (79.2% female, mean age 42.5 [SD 14.6] years) from the French NutriNet-Santé cohort (2009-2023). Dietary data were assessed using repeated 24-h dietary records, including industrial food brands. Cumulative time-dependent exposure to food additives was evaluated through multiple composition databases and ad hoc laboratory assays in food matrices. Associations between exposures to food coloring additives (sex-specific tertiles if proportion of exposed participants was more than two-thirds, or nonexposed/lower/higher exposed based on sex-specific median otherwise) and type 2 diabetes incidence were assessed using multivariable Cox proportional hazards models. RESULTS: There were 1,131 incident type 2 diabetes cases diagnosed (median follow-up, 8.05 years). After false discovery rate correction, intakes of the following colors were associated with higher type 2 diabetes incidence: total food coloring additives (hazard ratio [HR]higher vs. non/lower consumers 1.38 [95% CI 1.17-1.63], P = 0.0002), total caramel (1.43 [1.21-1.67], P = 0.0002), plain caramel (1.46 [1.26-1.70], P = 0.0002), sulfite ammonia caramel (1.30 [1.07-1.59], P = 0.007), total carotene (1.27 [1.08-1.48], P = 0.007), carotenoids (1.39 [1.19-1.62], P = 0.0002), β-carotene (1.44 [1.23-1.68], P = 0.0002), paprika-capsanthin-capsorubin (1.26 [1.08-1.46], P = 0.004), lutein (1.20 [1.02-1.40], P = 0.0002), curcumin (1.49 [1.29-1.73], P = 0.0002), cochineal-carminic acid-carmines (1.27 [1.10-1.48], P = 0.003), and anthocyanins (1.40 [1.17-1.68], P = 0.0002). CONCLUSIONS: Several positive associations were observed between exposure to natural and synthetic food coloring additives and type 2 diabetes incidence. Further studies are needed to gain insights into underlying mechanisms, and if confirmed, call for reevaluation of food coloring additives to protect consumer health.

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Sleep Optimization to Improve Glycemic Targets in Adults With Type 1 Diabetes: A Randomized Controlled Parallel Intervention Trial.

Martyn-Nemeth P, Duffecy J, Steffen AD … +15 more , Baron KG, Quinn L, Burke L, Withington MHC, Loiacono B, Takgbajouah M, Irsheed GA, Perez RI, Park M, Saleh AH, Mihailescu D, Hong SJ, Pratuangtham S, Kessler J, Reutrakul S

Diabetes Care · 2026 Jul · PMID 42149583 · Full text

OBJECTIVE: To investigate the effects of a sleep intervention (Sleep-Opt) compared with an attention control intervention (Healthy Living) on glycemic targets and psychological outcomes in adults with type 1diabetes (T1D... OBJECTIVE: To investigate the effects of a sleep intervention (Sleep-Opt) compared with an attention control intervention (Healthy Living) on glycemic targets and psychological outcomes in adults with type 1diabetes (T1D). RESEARCH DESIGN AND METHODS: Adults with T1D and short (<6.5 h/night) or irregular (variability of sleep duration ≥1 h) sleep were randomly assigned to either the Sleep-Opt (n = 73) or Healthy Living control (n = 71) intervention, both of which were remotely delivered in eight sessions for 12 weeks. Primary (A1C, continuous glucose monitoring [CGM], and objective sleep) and secondary (psychological and subjective sleep) outcomes were collected at baseline and 6, 12, and 24 weeks, with 12 weeks being a primary end point. RESULTS: A1C and CGM parameters at 12 weeks indicated no significant differences between groups. Sleep duration increased at 6 weeks in both groups, but no significant differences were observed between groups at any time point. Sleep-Opt participants had reduced diabetes distress (median difference -0.18 [95% CI -0.35, -0.01]) and improved subjective sleep quality (-0.96 [-0.17, -0.23]) compared with the Healthy Living group at 6 weeks but not at other time points. A significant interaction was found suggesting the effect of the intervention depended on baseline A1C level. At 12 weeks, the Sleep-Opt group with baseline A1C ≥7% had a lower A1C level than the Healthy Living group (marginal mean -0.32% [95% CI -0,64%, -0.005%]; -3.5 [95% CI -7.0, -0.1] mmol/mol; P = 0.047). CONCLUSIONS: Sleep-Opt did not improve glycemic targets or sleep parameters, although sleep improved in both intervention groups. Glycemic benefits were observed in participants with suboptimal A1C, suggesting a possible role of sleep intervention in this patient group.

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