Publications by authors named "N C Schloot"

Introduction: We aimed to characterise and compare individuals diagnosed with type 1 diabetes (T1D), latent autoimmune diabetes in adults (LADA) and type 2 diabetes (T2D), in a real-world setting.

Methods: Anthropometric and clinical data from 36 959 people with diabetes diagnosed at age 30-70 years enrolled in the prospective diabetes patients follow-up (DPV) registry from 1995 to 2022 were analysed cross-sectionally at diagnosis and follow-up (≥6 months after diagnosis). LADA was defined as clinical diagnosis of T2D, positivity of ≥1 islet autoantibody and an insulin-free interval of ≥6 months upon diabetes diagnosis.

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Background: BMI variability has been associated with increased cardiovascular disease risk in individuals with type 2 diabetes, however comparison between clinical studies and real-world observational evidence has been lacking. Furthermore, it is not known whether BMI variability has an effect independent of HbA1c variability.

Methods: We investigated the association between BMI variability and 3P-MACE risk in the Harmony Outcomes trial (n = 9198), and further analysed placebo arms of REWIND (n = 4440) and EMPA-REG OUTCOME (n = 2333) trials, followed by real-world data from the Tayside Bioresource (n = 6980) using Cox regression modelling.

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Retatrutide is a novel triple agonist of the glucose-dependent insulinotropic polypeptide, glucagon-like peptide 1 and glucagon receptors. A 48-week phase 2 obesity study demonstrated weight reductions of 22.8% and 24.

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Introduction: Obesity is considered a poor lifestyle choice. 'Obesity' is not a sufficient definition for patients, any more than 'cancer' or 'arthritis' would be. A major obstacle is the lack of understanding of pathogenesis.

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Introduction: Effectively engaging people with type 2 diabetes (T2D) earlier in their health journeys is critical to prevent downstream complications. Digitally based diabetes programs are a growing component of care delivery that have the potential to engage individuals outside of traditional clinic-based settings and use personalized data to pair people to tailored diabetes self-management interventions. Knowing an individuals' diabetes empowerment and health-related motivation can help drive appropriate recommendations for personalized interventions.

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