Aims To investigate the association between day‐to‐day fasting self‐monitored blood glucose (SMBG) variability and risk of hypoglycaemia in type 1 (T1D) and type 2 diabetes (T2D), and to compare day‐to‐day fasting SMBG variability between treatments with insulin degludec (degludec) and insulin glargine 100 units/mL (glargine U100). Materials and Methods Data were retrieved from two double‐blind, randomized, treat‐to‐target, two‐period (32 weeks each) crossover trials of degludec vs glargine U100 in T1D (SWITCH 1, n = 501) and T2D (SWITCH 2, n = 720). Available fasting SMBGs were used to determine the standard deviation (SD) of day‐to‐day fasting SMBG variability for each patient and the treatment combination. The association between day‐to‐day fasting SMBG variability and overall symptomatic, nocturnal symptomatic and severe hypoglycaemia was analysed for the pooled population using linear regression, with fasting SMBG variability included as a three‐level factor defined by population tertiles. Finally, day‐to‐day fasting SMBG variability was compared between treatments. Results Linear regression showed that day‐to‐day fasting SMBG variability was significantly associated with overall symptomatic, nocturnal symptomatic and severe hypoglycaemia risk in T1D and T2D ( P < 0.05). Day‐to‐day fasting SMBG variability was significantly associated ( P < 0.01) with all categories of hypoglycaemia risk, with the exception of severe hypoglycaemia in T2D when analysed within tertiles. Degludec was associated with 4% lower day‐to‐day fasting SMBG variability than glargine U100 in T1D ( P = 0.0082) and with 10% lower day‐to‐day fasting SMBG variability in T2D ( P < 0.0001). Conclusions Higher day‐to‐day fasting SMBG variability is associated with an increased risk of overall symptomatic, nocturnal symptomatic and severe hypoglycaemia. Degludec has significantly lower day‐to‐day fasting SMBG variability vs glargine U100.
Aims: The ability to differentiate patient populations with type 2 diabetes at high risk of severe hypoglycaemia could impact clinical decision making. The aim of this study was to develop a risk score, using patient characteristics, that could differentiate between populations with higher and lower 2-year risk of severe hypoglycaemia among individuals at increased risk of cardiovascular disease. Materials and methods: Two models were developed for the risk score based on data from the DEVOTE cardiovascular outcomes trials. The first, a data-driven machinelearning model, used stepwise regression with bidirectional elimination to identify risk factors for severe hypoglycaemia. The second, a risk score based on known clinical risk factors accessible in clinical practice identified from the data-driven model, included: insulin treatment regimen; diabetes duration; sex; age; and glycated haemoglobin, all at baseline. Both the data-driven model and simple risk score were evaluated for discrimination, calibration and generalizability using data from DEVOTE, and were validated against the external LEADER cardiovascular outcomes trial dataset.
Background: There is a need to validate time-in-range (TIR; percentage of time with plasma glucose between 70 and 180 mg/dL (3.9-10.0 mmol/L) as a surrogate endpoint for long-term clinical outcomes. Methods: We used data from patients with 8-point glucose profiles (8pp) from the double-blind cardiovascular outcomes trial, DEVOTE (NCT01959529). In total, 7637 patients with T2D and either established CVD or at high risk for CVD were included in the trial. The primary endpoint in DEVOTE was time to first MACE. The 8pp were collected at 1 year, 2 years and end-of-trial. Median length of follow-up was 2 years. For 5644 patients, 8pps with at least 7 points existed. Among the 681 major adverse cardiovascular events (MACEs) in DEVOTE, 360 were among patients with 8pps. Individual TIR was derived as the proportion of the 8pp within range. A Cox model was used to estimate the association between derived TIR and time to first MACE. Hazard ratios (HR) were estimated for patients with TIR>70% vs. TIR≤70%, and for TIR>70% and TIR 50 −70% vs. TIR≤50%. Results: Derived TIR was >70% for 65% of the patients. Estimated rate of first MACE was lower for TIR >70% and TIR 50-70% vs. TIR≤50% (Figure) and for TIR>70% vs. TIR≤70% (HR: 0.74 [0.60;0.91]95% CI; p<0.01). The associations were maintained when analyses were adjusted for baseline characteristics. Conclusions: Derived TIR was associated with rate of first MACE for T2D patients in DEVOTE. Disclosure R.M. Bergenstal: Consultant; Self; Ascensia Diabetes Care, Johnson & Johnson. Other Relationship; Self; Abbott, Dexcom, Inc., Hygieia, Lilly Diabetes, Medtronic, Novo Nordisk A/S, Onduo, Roche Diabetes Care, Sanofi, UnitedHealth Group. E. Hachmann-Nielsen: Employee; Self; Novo Nordisk A/S. K. Kvist: Employee; Self; Novo Nordisk A/S. J.B. Buse: Consultant; Self; Cirius Therapeutics, CSL Behring, Neurimmune. Research Support; Self; American Diabetes Association, National Institutes of Health, Novo Nordisk A/S, Patient-Centered Outcomes Research Institute, Sanofi, Tolerion, Inc., vTv Therapeutics. Stock/Shareholder; Self; Mellitus Health, Pendulum Therapeutics, PhaseBio Pharmaceuticals, Inc., Stability Health. Other Relationship; Self; ADOCIA, AstraZeneca, Dance Biopharm Holdings, Inc., Eli Lilly and Company, MannKind Corporation, NovaTarg Therapeutics, Novo Nordisk A/S, Senseonics, Inc, vTv Therapeutics, Zafgen, Inc. Funding Novo Nordisk A/S
Aims: To undertake a post-hoc analysis, utilizing a hypoglycaemia risk score based on DEVOTE trial data, to investigate if a high risk of severe hypoglycaemia was associated with an increased risk of cardiovascular events, and whether reduced rates of severe hypoglycaemia in patients identified as having the highest risk affected the risk of cardiovascular outcomes. Materials and Methods: The DEVOTE population was divided into quartiles according to patients' individual hypoglycaemia risk scores. For each quartile, the observed incidence and rate of severe hypoglycaemia, major adverse cardiovascular event (MACE) and all-cause mortality were determined to investigate whether those with the highest risk of hypoglycaemia were also at the greatest risk of MACE and all-cause mortality. In addition, treatment differences within each risk quartile [insulin degludec (degludec) vs. insulin glargine 100 units/mL (glargine U100)] in terms of severe hypoglycaemia, MACE and all-cause mortality were investigated. Results: Patients with the highest risk scores had the highest rates of severe hypoglycaemia, MACE and all-cause mortality. Treatment ratios between degludec and glargine U100 in the highest risk quartile were 95% confidence interval (CI) 0.56 (0.39; 0.80) (severe hypoglycaemia), 95% CI 0.76 (0.58; 0.99) (MACE) and 95% CI 0.77 (0.55; 1.07) (all-cause mortality). Conclusions: The risk score demonstrated that a high risk of severe hypoglycaemia was associated with a high incidence of MACE and all-cause mortality and that, in this high-risk group, those treated with degludec had a lower incidence of MACE. These observations support the hypothesis that hypoglycaemia is a risk factor for cardiovascular events. *Affiliation at the time of the trial.
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