Objective: To determine whether the use of an inhaled insulin would improve HbA1c.
Methods: This study was performed in 20 type 2 diabetes mellitus (T2DM) participants with HbA1c values ≥7.5 (58) to ≤11.5% (102 mmol/mol) on a variety of glucose-lowering regimens. Prandial Technosphere insulin (TI) was rapidly titrated based on a treatment algorithm using postprandial blood glucose to calculate premeal doses. A 2-week baseline period was followed by 12 weeks of active treatment with TI. The primary outcome was change in HbA1c. Secondary outcomes included glucose time in range (time in range: 70-180 mg/dL) obtained by a blinded continuous glucose monitoring during the baseline period and at the end of 12 weeks. Goals were to assess how to rapidly and safely initiate TI intensification, determine dosing requirements, and establish an effective dose range in uncontrolled T2DM.
Results: Mean HbA1c decreased by -1.6% (-17 mmol/mol) from 9.0% (75 mmol/mol) at baseline to 7.4% (57 mmol/mol) at 12 weeks (P < .0001). Mean time in range increased from 42.2% to 65.7% (P < .0002). Mean prandial doses of TI were 18 or 19 units for all meals. Time below range was 1.1% baseline and 2.6% post treatment (P = .01).
Conclusion: Treatment with inhaled TI dosed using a simple algorithm improved glycemic control measured by both HbA1c and time in range, with low rates of hypoglycemia. These data add significantly to understanding TI in the management of T2DM patients for whom prandial insulin is a consideration.
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http://dx.doi.org/10.1016/j.eprac.2020.10.004 | DOI Listing |
J Diabetes Sci Technol
January 2025
Unit of Endocrine Diseases and Diabetology, Department of Medicine, ASST Papa Giovanni XXIII, Bergamo, Italy.
Aims: According to the 2023 International Consensus, glucose metrics derived from two-week-long continuous glucose monitoring (CGM) can be extrapolated up to 90 days before. However, no studies have focused on adults with type 1 diabetes (T1D) on multiple daily injections (MDIs) and with second-generation intermittently scanned CGM (isCGM) sensors in a real-world setting.
Methods: This real-world, retrospective study included 539 90-day isCGM data from 367 adults with T1D on MDI therapy.
Front Microbiol
December 2024
Department of Earth Sciences, University of Southern California, Los Angeles, CA, United States.
Microbial activity in the deep continental subsurface is difficult to measure due to low cell densities, low energy fluxes, cryptic elemental cycles and enigmatic metabolisms. Nonetheless, direct access to rare sample sites and sensitive laboratory measurements can be used to better understand the variables that govern microbial life underground. In this study, we sampled fluids from six boreholes at depths ranging from 244 m to 1,478 m below ground at the Sanford Underground Research Facility (SURF), a former goldmine in South Dakota, United States.
View Article and Find Full Text PDFFront Physiol
December 2024
Department of Sport and Exercise Science, Paris Lodron University Salzburg, Salzburg, Austria.
Introduction: Our recent meta-analyses have demonstrated that high-intensity interval training (HIIT) causes a range of mean changes in various measures and predictors of endurance and sprint performance in athletes. Here, we extend the analyses to relationships between mean changes of these measures and consider implications for understanding and improving HIIT that were not apparent in the previous analyses.
Methods: The data were mean changes from HIIT with highly trained endurance and elite other (mainly team sport) athletes in studies where two or more measures or predictors of performance were available.
J Clin Transl Endocrinol
December 2024
Division of Endocrinology Diabetes and Metabolism, Department of Medicine, University of Minnesota, Minneapolis, MN, USA.
Cystic fibrosis-related diabetes (CFRD) is the most common non-pulmonary comorbidity in people with cystic fibrosis (CF). Current guidelines recommend insulin therapy as the treatment of choice for people with CFRD. In the past, obesity and overweight were uncommon in individuals with CF.
View Article and Find Full Text PDFJAMIA Open
February 2025
Artificial Intelligence (AI) for Health Institute (AIHealth), Washington University in St Louis, St Louis, MO 63130, United States.
Objective: Extracorporeal membrane oxygenation (ECMO) is among the most resource-intensive therapies in critical care. The COVID-19 pandemic highlighted the lack of ECMO resource allocation tools. We aimed to develop a continuous ECMO risk prediction model to enhance patient triage and resource allocation.
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