The dawn-phenomenon causes high fasting glucose values in IDDM patients during puberty. Even a bedtime injection of intermediate-acting insulin does not reliably suppress glucose rises during the morning hours. We therefore examined whether Semilente, an amorphous zinc insulin with kinetics different from NPH insulin, is better suited to alleviate the dawn-phenomenon in adolescent patients with long-standing diabetes. This prospective study included 15 adolescent patients (age 15.5 +/- 0.4 years; mean +/- SE) well beyond the remission phase of diabetes (mean duration: 7.5 +/- 0.8 years). On an inpatient basis, blood glucose profiles following bedtime injections of NPH or semilente insulin were compared, using a sequential cross-over design for intra-patient comparison. Fasting blood glucose was significantly lower following bedtime injections of Semilente (183 +/- 21 mg/dL [10.2 +/- 1.1 mmol/L]) compared to nights where NPH had been injected (235 +/- 22 mg/dL [13.1 +/- 1.2 mmol/L]). In addition, the morning postprandial blood glucose was significantly improved. The frequency of nocturnal hypoglycemia was not different, and the dose of Semilente insulin was slightly lower compared to the dose of NPH-insulin injected. For adolescent IDDM patients with suboptimal metabolic control due to a marked dawn-phenomenon, with high fasting glucose concentrations despite a bedtime injection of NPH insulin, bedtime injection of Semilente insulin may result in reduced fasting hyperglycemia on the next morning.
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http://dx.doi.org/10.1055/s-0029-1211468 | DOI Listing |
Kaohsiung J Med Sci
December 2024
Division of Endocrinology and Metabolism, Department of Internal Medicine, Chang Gung Memorial Hospital, Linkou, Taiwan.
The impacts of insulin degludec U100 (Deg-100) and insulin glargine U300 (Gla-300) on glycemic variability (GV) in patients with type 1 diabetes, as well as the impact of major nutrient components on GV in these patients, remain unclear. This was an observational, cross-sectional, retrospective study. Type 1 diabetes mellitus patients treated with either Deg-100 or Gla-300 as basal insulin were enrolled.
View Article and Find Full Text PDFTher Adv Endocrinol Metab
December 2023
Division of Pediatric Endocrinology, Department of Pediatrics, Hacettepe University Faculty of Medicine, Ankara, Turkey.
Background: Handling of the dawn phenomenon (DP) with multiple daily insulin injection (MDII) regimen is a real challenge.
Objective: We aimed to demonstrate the effectiveness of a dual-basal-insulin (a long-acting glargine and an intermediate-acting neutral protamine Hagedorn (NPH)) regimen for the management of DP in children with type 1 diabetes mellitus (T1DM). The primary efficacy outcome was to overcome morning hyperglycemia without causing hypoglycemia during the non-DP period of the night.
J Am Med Inform Assoc
December 2023
Artificial Intelligence for Medical Systems (AIMS) Lab, Department of Biomedical Engineering, Oregon Health & Science University, Portland, OR 97239, United States.
Objective: Nocturnal hypoglycemia is a known challenge for people with type 1 diabetes, especially for physically active individuals or those on multiple daily injections. We developed an evidential neural network (ENN) to predict at bedtime the probability and timing of nocturnal hypoglycemia (0-4 vs 4-8 h after bedtime) based on several glucose metrics and physical activity patterns. We utilized these predictions in silico to prescribe bedtime carbohydrates with a Smart Snack intervention specific to the predicted minimum nocturnal glucose and timing of nocturnal hypoglycemia.
View Article and Find Full Text PDFJ Diabetes Sci Technol
January 2025
Department of Electronic and Electrical Engineering, Imperial College London, London, UK.
Background: One of the biggest challenges for people with type 1 diabetes (T1D) using multiple daily injections (MDIs) is nocturnal hypoglycemia (NH). Recurrent NH can lead to serious complications; hence, prevention is of high importance. In this work, we develop and externally validate, device-agnostic Machine Learning (ML) models to provide bedtime decision support to people with T1D and minimize the risk of NH.
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