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Aims/hypothesis: The aim of this work was to investigate the association between macronutrient intakes and continuous glucose monitoring (CGM) metrics in individuals with type 1 diabetes.
Methods: In 470 individuals with type 1 diabetes of the GUTDM1 cohort (65% female, median age 40 [IQR 28-53] years, median diabetes duration 15 [IQR 6-29] years), we used logistic regression to establish associations between macronutrient intakes and the CGM metrics time in range (TIR, time spent between 3.9-10.0 mmol/l blood glucose, optimally set at ≥70%) and time below range (TBR, <3.9 mmol/l blood glucose, optimally set at <4%). ORs were expressed per 1 SD intake of nutrient and were adjusted for other macronutrient intakes, age, sex, socioeconomic status, BMI, duration of type 1 diabetes, pump use, insulin dose and alcohol intake.
Results: The median (IQR) TIR was 67 (51-80)% and TBR was 2 (1-4)%; the mean ± SD energy intake was 6879±2001 kJ, fat intake 75±31 g, carbohydrate intake 162±63 g, fibre intake 20±9 g and protein intake 70±24 g. A higher fibre intake and a lower carbohydrate intake were associated with higher odds of having a TIR≥70% (OR [95% CI] 1.64 [1.22, 2.24] and 0.67 [0.51, 0.87], respectively), whereas solely a higher carbohydrate intake was associated with TBR<4% (OR 1.34 [95% CI 1.02, 1.78]).
Conclusions/interpretation: A higher fibre intake is independently associated with a higher TIR. A higher carbohydrate intake is associated with less time spent in hypoglycaemia, a lower TIR and a higher time above range. These findings warrant confirmatory (interventional) investigations and may impact current nutritional guidelines for type 1 diabetes.
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http://dx.doi.org/10.1007/s00125-024-06213-5 | DOI Listing |
Diabetes Res Clin Pract
September 2025
Health Education Department, and Department of Endocrinology and Diabetes, Diabetes Treatment Center, Prince Sultan Military Medical City, Riyadh, Saudi Arabia.
Background: Despite advances, glycemic control in people with type 2 diabetes (PwT2D) treated with oral antidiabetic medications (ADMs) often remains suboptimal. Continuous glucose monitoring (CGM) has shown promise in diabetes management, offering real-time insights into glucose trends. This study evaluates the impact of transitioning from conventional self-monitoring of blood glucose (SMBG) to CGM on glycemic outcomes and self-management in PwT2D receiving oral ADMs.
View Article and Find Full Text PDFDiabetes Technol Ther
September 2025
Diabetes Technol Ther
September 2025
Department of Genetics, Stanford University, Stanford, California, USA.
Continuous glucose monitoring (CGM) devices provide real-time actionable data on blood glucose levels, making them essential tools for effective glucose management. Integrating blood glucose data with food log data is crucial for understanding how dietary choices impact glucose levels. Despite their utility, many CGM applications lack integration with other external services, such as food trackers, and do not generate useful glycemic variability (GV) metrics or advanced visualizations.
View Article and Find Full Text PDFDiabetes Metab J
September 2025
Department of Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea.
This study evaluated whether a stage 4 smart insulin pen (SIP) provides superior glycemic control compared with a traditional insulin pen (TIP) in individuals with intensively insulin-treated diabetes. Forty-two adults with continuous glucose monitoring (CGM), multiple daily insulin injections, and no prior SIP use were included. After diabetes self-management education (DSME), the SIP group (n=21) initiated SIP, whereas the TIP group (n=21) continued their usual regimens.
View Article and Find Full Text PDFBMJ Open
September 2025
Department of Endocrinology and Nephrology, Nordsjællands Hospital, Hillerød, Capital Region of Denmark, Denmark.
Introduction: Continuous glucose monitoring (CGM) provides real-time glucose data for people with diabetes. However, detailed knowledge of its use in daily life remains limited. We aim to investigate the interaction between people with type 1 diabetes (T1D) and their CGM data and the impact of the interaction on glycaemia and diabetes distress.
View Article and Find Full Text PDF