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      Interindividual variability in average glucose‐glycated haemoglobin relationship in type 1 diabetes and implications for clinical practice

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          Abstract

          Aim

          Glycated haemoglobin (HbA1c) can fail to reflect average glucose levels, potentially compromising management decisions. We analysed variability in the relationship between mean glucose and HbA1c in individuals with diabetes.

          Materials and Methods

          Three months of continuous glucose monitoring and HbA1c data were obtained from 216 individuals with type 1 diabetes. Universal red blood cell glucose transporter‐1 Michaelis constant K M and individualized apparent glycation ratio (AGR) were calculated and compared across age, racial and gender groups.

          Results

          The mean age (range) was 30 years (8‐72) with 94 younger than 19 years, 78 between 19 and 50 years, and 44 were >50 years. The group contained 120 women and 96 men with 106 white and 110 black individuals. The determined K M value was 464 mg/dl and AGR was (mean ± SD) 72.1 ± 7 ml/g. AGR, which correlated with red blood cell lifespan marker, was highest in those aged >50 years at 75.4 ± 6.9 ml/g, decreasing to 73.2 ± 7.8 ml/g in 19‐50 years, with a further drop to 71.0 ± 5.8 ml/g in the youngest group ( p <0 .05). AGR differed between white and black groups (69.9 ± 5.8 and 74.2 ± 7.1 ml/g, respectively; p < .001). In contrast, AGR values were similar in men and women (71.5 ± 7.5 and 72.5 ± 6.6 ml/g, respectively; p = .27). Interestingly, interindividual AGR variation within each group was at least four‐fold higher than average for between‐group variation.

          Conclusions

          In this type 1 diabetes cohort, ethnicity and age, but not gender, alter the HbA1c‐glucose relationship with even larger interindividual variations found within each group than between groups. Clinical application of personalized HbA1c‐glucose relationships has the potential to optimize glycaemic care in the population with diabetes.

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          Most cited references46

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          SciPy 1.0: fundamental algorithms for scientific computing in Python

          SciPy is an open-source scientific computing library for the Python programming language. Since its initial release in 2001, SciPy has become a de facto standard for leveraging scientific algorithms in Python, with over 600 unique code contributors, thousands of dependent packages, over 100,000 dependent repositories and millions of downloads per year. In this work, we provide an overview of the capabilities and development practices of SciPy 1.0 and highlight some recent technical developments.
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            The Effect of Intensive Treatment of Diabetes on the Development and Progression of Long-Term Complications in Insulin-Dependent Diabetes Mellitus

            Long-term microvascular and neurologic complications cause major morbidity and mortality in patients with insulin-dependent diabetes mellitus (IDDM). We examined whether intensive treatment with the goal of maintaining blood glucose concentrations close to the normal range could decrease the frequency and severity of these complications. A total of 1441 patients with IDDM--726 with no retinopathy at base line (the primary-prevention cohort) and 715 with mild retinopathy (the secondary-intervention cohort) were randomly assigned to intensive therapy administered either with an external insulin pump or by three or more daily insulin injections and guided by frequent blood glucose monitoring or to conventional therapy with one or two daily insulin injections. The patients were followed for a mean of 6.5 years, and the appearance and progression of retinopathy and other complications were assessed regularly. In the primary-prevention cohort, intensive therapy reduced the adjusted mean risk for the development of retinopathy by 76 percent (95 percent confidence interval, 62 to 85 percent), as compared with conventional therapy. In the secondary-intervention cohort, intensive therapy slowed the progression of retinopathy by 54 percent (95 percent confidence interval, 39 to 66 percent) and reduced the development of proliferative or severe nonproliferative retinopathy by 47 percent (95 percent confidence interval, 14 to 67 percent). In the two cohorts combined, intensive therapy reduced the occurrence of microalbuminuria (urinary albumin excretion of > or = 40 mg per 24 hours) by 39 percent (95 percent confidence interval, 21 to 52 percent), that of albuminuria (urinary albumin excretion of > or = 300 mg per 24 hours) by 54 percent (95 percent confidence interval 19 to 74 percent), and that of clinical neuropathy by 60 percent (95 percent confidence interval, 38 to 74 percent). The chief adverse event associated with intensive therapy was a two-to-threefold increase in severe hypoglycemia. Intensive therapy effectively delays the onset and slows the progression of diabetic retinopathy, nephropathy, and neuropathy in patients with IDDM.
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              Clinical Targets for Continuous Glucose Monitoring Data Interpretation: Recommendations From the International Consensus on Time in Range

              Improvements in sensor accuracy, greater convenience and ease of use, and expanding reimbursement have led to growing adoption of continuous glucose monitoring (CGM). However, successful utilization of CGM technology in routine clinical practice remains relatively low. This may be due in part to the lack of clear and agreed-upon glycemic targets that both diabetes teams and people with diabetes can work toward. Although unified recommendations for use of key CGM metrics have been established in three separate peer-reviewed articles, formal adoption by diabetes professional organizations and guidance in the practical application of these metrics in clinical practice have been lacking. In February 2019, 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 this issue. This article summarizes the ATTD consensus recommendations for relevant aspects of CGM data utilization and reporting among the various diabetes populations.
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                Author and article information

                Contributors
                r.ajjan@leeds.ac.uk
                Journal
                Diabetes Obes Metab
                Diabetes Obes Metab
                10.1111/(ISSN)1463-1326
                DOM
                Diabetes, Obesity & Metabolism
                Blackwell Publishing Ltd (Oxford, UK )
                1462-8902
                1463-1326
                02 June 2022
                September 2022
                : 24
                : 9 ( doiID: 10.1111/dom.v24.9 )
                : 1779-1787
                Affiliations
                [ 1 ] Abbott Diabetes Care Alameda California USA
                [ 2 ] International Diabetes Center Park Nicollet, HealthPartners Minneapolis Minnesota USA
                [ 3 ] Leeds Institute of Cardiovascular and Metabolic Medicine University of Leeds Leeds UK
                Author notes
                [*] [* ] Correspondence

                Ramzi A. Ajjan, Leeds Institute of Cardiovascular and Metabolic Medicine, University of Leeds, Leeds LS2 9JT, UK.

                Email: r.ajjan@ 123456leeds.ac.uk

                Author information
                https://orcid.org/0000-0001-9446-8402
                https://orcid.org/0000-0002-9050-5584
                Article
                DOM14763
                10.1111/dom.14763
                9546041
                35546274
                346c7c2f-6643-4165-97cf-28763ecbd76e
                © 2022 Abbott Laboratories. Diabetes, Obesity and Metabolism published by John Wiley & Sons Ltd.

                This is an open access article under the terms of the http://creativecommons.org/licenses/by-nc-nd/4.0/ License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non‐commercial and no modifications or adaptations are made.

                History
                : 22 April 2022
                : 18 March 2022
                : 08 May 2022
                Page count
                Figures: 2, Tables: 2, Pages: 9, Words: 6072
                Funding
                Funded by: Abbott Diabetes Care , doi 10.13039/100011946;
                Categories
                Original Article
                Original Articles
                Custom metadata
                2.0
                September 2022
                Converter:WILEY_ML3GV2_TO_JATSPMC version:6.2.0 mode:remove_FC converted:07.10.2022

                Endocrinology & Diabetes
                age differences,apparent glycation ratio,gender differences,glucose‐hba1c relationship,kinetic modelling,racial differences,rbc lifespan

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