Measurement of glycated hemoglobin (HbA) has been the traditional method for assessing glycemic control. However, it does not reflect intra- and interday glycemic excursions that may lead to acute events (such as hypoglycemia) or postprandial hyperglycemia, which have been linked to both microvascular and macrovascular complications. Continuous glucose monitoring (CGM), either from real-time use (rtCGM) or intermittently viewed (iCGM), addresses many of the limitations inherent in HbA testing and self-monitoring of blood glucose. Although both provide the means to move beyond the HbA measurement as the sole marker of glycemic control, standardized metrics for analyzing CGM data are lacking. Moreover, clear criteria for matching people with diabetes to the most appropriate glucose monitoring methodologies, as well as standardized advice about how best to use the new information they provide, have yet to be established. In February 2017, the Advanced Technologies & Treatments for Diabetes (ATTD) Congress convened an international panel of physicians, researchers, and individuals with diabetes who are expert in CGM technologies to address these issues. This article summarizes the ATTD consensus recommendations and represents the current understanding of how CGM results can affect outcomes.
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http://dx.doi.org/10.2337/dc17-1600 | DOI Listing |
Front Clin Diabetes Healthc
January 2025
Department of Endocrinology and Diabetes, Birmingham Women's and Children's NHS Foundation Trust, Birmingham, United Kingdom.
Background: The UK National Paediatric Diabetes Audit (NPDA) data reports disparities in Haemoglobin A1c (HbA1c) levels among children and young people (CYP) with Type 1 Diabetes (T1D), with higher levels in those of Black ethnic background and lower socioeconomic status who have less access to technology. We investigate HbA1c differences in a T1D cohort with higher than national average technology uptake where > 60% come from an ethnic minority and/or socioeconomically deprived population.
Design & Methods: Retrospective cross-sectional study investigating the influence of demographic factors, technology use, and socioeconomic status (SES) on glycaemic outcomes.
PeerJ
January 2025
Faculty of Fisheries, Muğla Sıtkı Koçman University, Muğla, Türkiye.
This study aims to determine and compare the reference values of the haematological and biochemical blood parameters of two fish species collected from the Gökova Bay (Muğla, South-Western of Türkiye): the non-native and invasive Randall's threadfin bream, and the native Common pandora, . Both species inhabit the same environment and compete for resources. Blood samples were collected from a total of 100 fish samples (50 and 50 ) which were caught from a depth of 30 to 60 meters between February 2023 and July 2024.
View Article and Find Full Text PDFFront Endocrinol (Lausanne)
January 2025
Department of Metabolic Diseases, Jagiellonian University Medical College, Krakow, Poland.
Background: Continuous glucose monitoring (CGM) improves glycemic control and quality of life. Data on glycemic indices and fear of hypoglycemia (FoH) in newly diagnosed T1DM patients are limited.
Aim: To assess the impact of initiating intermittently scanned CGM (isCGM) within 1-6 months of diagnosis on glycemic control and FoH in adults with T1DM.
Acta Biochim Biophys Sin (Shanghai)
January 2025
CAS Key Laboratory of Synthetic Biology, CAS Center for Excellence in Molecular Plant Sciences, Chinese Academy of Sciences (CAS), Shanghai 200032, China.
U32 is an industrial strain capable of producing therapeutically useful rifamycin SV. In early days of fermentation studies, nitrate was found to increase the yield of rifamycin along with globally, affecting both carbon and nitrogen metabolism in favor of antibiotic biosynthesis; thus, the (NSE) hypothesis was proposed. Although GlnR is likely the master regulator of the pleotropic effect of NSE, the global metabolism affected by NSE has never been systematically examined.
View Article and Find Full Text PDFJ Clin Res Pediatr Endocrinol
January 2025
Narasaraopeta Engineering College, Computer Science and Engineering, India.
Objective: The honeymoon phase in Type 1 Diabetes (T1D) presents a temporary improvement in glycemic control, complicating insulin management. This study aims to develop and validate a machine learning-driven method for accurately detecting this phase to optimize insulin therapy and prevent adverse outcomes.
Methods: Data from pediatric T1D patients aged 6-17 years, including continuous glucose monitoring (CGM) data, Glucose Management Indicator (GMI) reports, HbA1c values, and patient medical history, were used to train machine learning models.
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