What Is the Influence of HIV Serostatus on HbA1c? A Prospective Analysis Using Continuous Glucose Monitoring.

OBJECTIVE: Type 2 diabetes (T2D) is reported to be more common in people living with HIV (PLWH). Clinical guidelines recommend screening for diabetes in PLWH, but there is no agreed method due to studies reporting HbA1c is falsely low in PLWH. These studies were performed in the early HIV era when participants were taking older preparations of antiretroviral therapy that are rarely used today. We aimed to investigate whether HIV serostatus influences HbA1c. RESEARCH DESIGNS AND METHODS: We conducted a prospective cohort study of PLWH and age- and sex-matched HIV-negative participants who were purposely recruited from clinics in Brighton, U.K. Each participant wore a Dexcom G6 continuous glucose monitor (CGM) for up to 10 days, had glucose measured during an oral glucose tolerance test, and fructosamine and paired HbA1c were measured. We performed regression analysis to assess the influence of HIV on HbA1c and used a separate model for CGM glucose, venous glucose, and fructosamine. In addition, we included predictor variables used in previous studies that explored HbA1c discrepancy. RESULTS: We recruited 60 PLWH (90% men, 50% with T2D, mean ± SD age 57 ± 10.7 years, 100% undetectable viral load) and 48 people without HIV (92% men, 30% with T2D, mean age 57.7 ± 8.9 years). We found that HIV serostatus did not have a significant influence on HbA1c within the regression models. CONCLUSIONS: We performed a comprehensive assessment of glycemia to assess whether HIV serostatus influences HbA1c. We did not find any strong evidence that HIV serostatus influenced HbA1c. The results of our study support incorporating HbA1c into routine clinical blood work in PLWH.

Ward monitoring 4.0: real-time metabolic insights from continuous glucose monitoring into perioperative organ dysfunction.

The now-routine clinical deployment of continuous glucose monitoring has demonstrated benefit in real-world settings. We make the case that continuous glucose monitoring can help re-examine, at scale, the role that (stress) hyperglycaemia plays in fuelling organ dysfunction after tissue trauma. Provided robust perioperative data do emerge, well-established continuous glucose monitoring technology could soon help transform the perioperative landscape.

The relationship between islet autoantibody status and the genetic risk of type 1 diabetes in adult-onset type 1 diabetes.

AIMS/HYPOTHESIS: The reason for the observed lower rate of islet autoantibody positivity in clinician-diagnosed adult-onset vs childhood-onset type 1 diabetes is not known. We aimed to explore this by assessing the genetic risk of type 1 diabetes in autoantibody-negative and -positive children and adults. METHODS: We analysed GAD autoantibodies, insulinoma-2 antigen autoantibodies and zinc transporter-8 autoantibodies (ZnT8A) and measured type 1 diabetes genetic risk by genotyping 30 type 1 diabetes-associated variants at diagnosis in 1814 individuals with clinician-diagnosed type 1 diabetes (1112 adult-onset, 702 childhood-onset). We compared the overall type 1 diabetes genetic risk score (T1DGRS) and non-HLA and HLA (DR3-DQ2, DR4-DQ8 and DR15-DQ6) components with autoantibody status in those with adult-onset and childhood-onset diabetes. We also measured the T1DGRS in 1924 individuals with type 2 diabetes from the Wellcome Trust Case Control Consortium to represent non-autoimmune diabetes control participants. RESULTS: The T1DGRS was similar in autoantibody-negative and autoantibody-positive clinician-diagnosed childhood-onset type 1 diabetes (mean [SD] 0.274 [0.034] vs 0.277 [0.026], p=0.4). In contrast, the T1DGRS in autoantibody-negative adult-onset type 1 diabetes was lower than that in autoantibody-positive adult-onset type 1 diabetes (mean [SD] 0.243 [0.036] vs 0.271 [0.026], p<0.0001) but higher than that in type 2 diabetes (mean [SD] 0.229 [0.034], p<0.0001). Autoantibody-negative adults were more likely to have the more protective HLA DR15-DQ6 genotype (15% vs 3%, p<0.0001), were less likely to have the high-risk HLA DR3-DQ2/DR4-DQ8 genotype (6% vs 19%, p<0.0001) and had a lower non-HLA T1DGRS (p<0.0001) than autoantibody-positive adults. In contrast to children, autoantibody-negative adults were more likely to be male (75% vs 59%), had a higher BMI (27 vs 24 kg/m2) and were less likely to have other autoimmune conditions (2% vs 10%) than autoantibody-positive adults (all p<0.0001). In both adults and children, type 1 diabetes genetic risk was unaffected by the number of autoantibodies (p>0.3). These findings, along with the identification of seven misclassified adults with monogenic diabetes among autoantibody-negative adults and the results of a sensitivity analysis with and without measurement of ZnT8A, suggest that the intermediate type 1 diabetes genetic risk in autoantibody-negative adults is more likely to be explained by the inclusion of misclassified non-autoimmune diabetes (estimated to represent 67% of all antibody-negative adults, 95% CI 61%, 73%) than by the presence of unmeasured autoantibodies or by a discrete form of diabetes. When these estimated individuals with non-autoimmune diabetes were adjusted for, the prevalence of autoantibody positivity in adult-onset type 1 diabetes was similar to that in children (93% vs 91%, p=0.4). CONCLUSIONS/INTERPRETATION: The inclusion of non-autoimmune diabetes is the most likely explanation for the observed lower rate of autoantibody positivity in clinician-diagnosed adult-onset type 1 diabetes. Our data support the utility of islet autoantibody measurement in clinician-suspected adult-onset type 1 diabetes in routine clinical practice.

Islet Autoantibody Level Distribution in Type 1 Diabetes and Their Association With Genetic and Clinical Characteristics.

CONTEXT: The importance of the autoantibody level at diagnosis of type 1 diabetes (T1D) is not clear. OBJECTIVE: We aimed to assess the association of glutamate decarboxylase (GADA), islet antigen-2 (IA-2A), and zinc transporter 8 (ZnT8A) autoantibody levels with clinical and genetic characteristics at diagnosis of T1D. METHODS: We conducted a prospective, cross-sectional study. GADA, IA-2A, and ZnT8A were measured in 1644 individuals with T1D at diagnosis using radiobinding assays. Associations between autoantibody levels and the clinical and genetic characteristics for individuals were assessed in those positive for these autoantibodies. We performed replication in an independent cohort of 449 people with T1D. RESULTS: GADA and IA-2A levels exhibited a bimodal distribution at diagnosis. High GADA level was associated with older age at diagnosis (median 27 years vs 19 years, P = 9 × 10-17), female sex (52% vs 37%, P = 1 × 10-8), other autoimmune diseases (13% vs 6%, P = 3 × 10-6), and HLA-DR3-DQ2 (58% vs 51%, P = .006). High IA-2A level was associated with younger age of diagnosis (median 17 years vs 23 years, P = 3 × 10-7), HLA-DR4-DQ8 (66% vs 50%, P = 1 × 10-6), and ZnT8A positivity (77% vs 52%, P = 1 × 10-15). We replicated our findings in an independent cohort of 449 people with T1D where autoantibodies were measured using enzyme-linked immunosorbent assays. CONCLUSION: Islet autoantibody levels provide additional information over positivity in T1D at diagnosis. Bimodality of GADA and IA-2A autoantibody levels highlights the novel aspect of heterogeneity of T1D. This may have implications for T1D prediction, treatment, and pathogenesis.

A game for all shapes and sizes? Changes in anthropometric and performance measures of elite professional rugby union players 1999-2018.

Background: Rugby union player size has increased since the game turned professional in 1995. Changes in physical and performance capability over this period have yet to be fully described. Hypothesis: Increases in player momentum would result from changes in body mass. Methods: Within-player rates of change in anthropometric and kinetic variables with season played were sampled in three successively studied professional rugby union club cohorts playing at the highest level of European competition between 1999 and 2019. Data comprised 910 seasons of observation for 291 elite male players. Most players had 2, 3 or 4 seasons of observation. Mixed-effects modelling distinguished changes independent of position played, club and international status. Results: With each season played, player body mass, fat-free mass and maximum speed increased significantly, while per cent fat decreased. The mean maximal velocity of a rugby player in 1999 was 8.2 (±0.18) m/s, which in 2019 had risen to 9.1 (±0.10) m/s. Player's momentum in 2019 was 14% more than those playing in 1999. In the Front Five, momentum increased in this period by >25%, mainly driven by greater running speed, disproving our hypothesis. Conclusions: The momentum of players, particularly forwards, increased markedly over 20 seasons of professional rugby. The resulting forces generated in collisions are thus significantly greater, although these may be mitigated by better player conditioning. Proactive regulation to address player safety may be required to address the changing nature of anthropometric measures and physical performance, minimising injury rates and potential long-term sequelae.

Identifying Continuous Glucose Monitoring Data Using Machine Learning.

Background and Aims: The recent increase in wearable devices for diabetes care, and in particular the use of continuous glucose monitoring (CGM), generates large data sets and associated cybersecurity challenges. In this study, we demonstrate that it is possible to identify CGM data at an individual level by using standard machine learning techniques. Methods: The publicly available REPLACE-BG data set (NCT02258373) containing 226 adult participants with type 1 diabetes (T1D) wearing CGM over 6 months was used. A support vector machine (SVM) binary classifier aiming to determine if a CGM data stream belongs to an individual participant was trained and tested for each of the subjects in the data set. To generate the feature vector used for classification, 12 standard glycemic metrics were selected and evaluated at different time periods of the day (24 h, day, night, breakfast, lunch, and dinner). Different window lengths of CGM data (3, 7, 15, and 30 days) were chosen to evaluate their impact on the classification performance. A recursive feature selection method was employed to select the minimum subset of features that did not significantly degrade performance. Results: A total of 40 features were generated as a result of evaluating the glycemic metrics over the selected time periods (24 h, day, night, breakfast, lunch, and dinner). A window length of 15 days was found to perform the best in terms of accuracy (86.8% ± 12.8%) and F1 score (0.86 ± 0.16). The corresponding sensitivity and specificity were 85.7% ± 19.5% and 87.9% ± 17.5%, respectively. Through recursive feature selection, a subset of 9 features was shown to perform similarly to the 40 features. Conclusion: It is possible to determine with a relatively high accuracy if a CGM data stream belongs to an individual. The proposed approach can be used as a digital CGM "fingerprint" or for detecting glycemic changes within an individual, for example during intercurrent illness.

An In Silico Head-to-Head Comparison of the Do-It-Yourself Artificial Pancreas Loop and Bio-Inspired Artificial Pancreas Control Algorithms.

BACKGROUND: User-developed automated insulin delivery systems, also referred to as do-it-yourself artificial pancreas systems (DIY APS), are in use by people living with type 1 diabetes. In this work, we evaluate, in silico, the DIY APS Loop control algorithm and compare it head-to-head with the bio-inspired artificial pancreas (BiAP) controller for which clinical data are available. METHODS: The Python version of the Loop control algorithm called PyLoopKit was employed for evaluation purposes. A Python-MATLAB interface was created to integrate PyLoopKit with the UVa-Padova simulator. Two configurations of BiAP (non-adaptive and adaptive) were evaluated. In addition, the Tandem Basal-IQ predictive low-glucose suspend was used as a baseline algorithm. Two scenarios with different levels of variability were used to challenge the algorithms on the adult (n = 10) and adolescent (n = 10) virtual cohorts of the simulator. RESULTS: Both BiAP and Loop improve, or maintain, glycemic control when compared with Basal-IQ. Under the scenario with lower variability, BiAP and Loop perform relatively similarly. However, BiAP, and in particular its adaptive configuration, outperformed Loop in the scenario with higher variability by increasing the percentage time in glucose target range 70-180 mg/dL (BiAP-Adaptive vs Loop vs Basal-IQ) (adults: 89.9% ± 3.2%* vs 79.5% ± 5.3%* vs 67.9% ± 8.3%; adolescents: 74.6 ± 9.5%* vs 53.0% ± 7.7% vs 55.4% ± 12.0%, where * indicates the significance of P < .05 calculated in sequential order) while maintaining the percentage time below range (adults: 0.89% ± 0.37% vs 1.72% ± 1.26% vs 3.41 ± 1.92%; adolescents: 2.87% ± 2.77% vs 4.90% ± 1.92% vs 4.17% ± 2.74%). CONCLUSIONS: Both Loop and BiAP algorithms are safe and improve glycemic control when compared, in silico, with Basal-IQ. However, BiAP appears significantly more robust to real-world challenges by outperforming Loop and Basal-IQ in the more challenging scenario.

DR15-DQ6 remains dominantly protective against type 1 diabetes throughout the first five decades of life.

AIMS/HYPOTHESIS: Among white European children developing type 1 diabetes, the otherwise common HLA haplotype DR15-DQ6 is rare, and highly protective. Adult-onset type 1 diabetes is now known to represent more overall cases than childhood onset, but it is not known whether DR15-DQ6 is protective in older-adult-onset type 1 diabetes. We sought to quantify DR15-DQ6 protection against type 1 diabetes as age of onset increased. METHODS: In two independent cohorts we assessed the proportion of type 1 diabetes cases presenting through the first 50 years of life with DR15-DQ6, compared with population controls. In the After Diabetes Diagnosis Research Support System-2 (ADDRESS-2) cohort (n = 1458) clinician-diagnosed type 1 diabetes was confirmed by positivity for one or more islet-specific autoantibodies. In UK Biobank (n = 2502), we estimated type 1 diabetes incidence rates relative to baseline HLA risk for each HLA group using Poisson regression. Analyses were restricted to white Europeans and were performed in three groups according to age at type 1 diabetes onset: 0-18 years, 19-30 years and 31-50 years. RESULTS: DR15-DQ6 was protective against type 1 diabetes through to age 50 years (OR < 1 for each age group, all p < 0.001). The following ORs for type 1 diabetes, relative to a neutral HLA genotype, were observed in ADDRESS-2: age 5-18 years OR 0.16 (95% CI 0.08, 0.31); age 19-30 years OR 0.10 (0.04, 0.23); and age 31-50 years OR 0.37 (0.21, 0.68). DR15-DQ6 also remained highly protective at all ages in UK Biobank. Without DR15-DQ6, the presence of major type 1 diabetes high-risk haplotype (either DR3-DQ2 or DR4-DQ8) was associated with increased risk of type 1 diabetes. CONCLUSIONS/INTERPRETATION: HLA DR15-DQ6 confers dominant protection from type 1 diabetes across the first five decades of life.

Increasing glycaemia is associated with a significant decline in HDL cholesterol in women with prediabetes in two national populations.

Internationally, studies have shown associations between lipids and glycemia; however, whether the link varies by gender and population has been rarely examined. We investigated relationships between glycemia and HDL- and Non-HDL-cholesterol and their modification by gender. We undertook a cross-sectional analysis from the National Health Examination Survey for Thailand (NHES-Thailand) and the Health Survey for England (HS-England) in adults aged 18-75 year. Glycaemia was assessed by FPG in Thailand and by HbA1c in the UK. In population- and gender-stratified analyses, the relationships between glycemia and lipids were explored. A total of 15,145 Thai and 3484 UK adults with blood measurement were included. The prevalences of prediabetes were: in NHES-Thailand, 16% (SE = 0.004), based on FPG (5.6 to < 7.0 mmol/L) and in HS-England, 19% (0.007) based on HbA1c (39 to < 48 mmol/mol). Increasingly abnormal glucose homeostasis was associated with increasing age, adiposity, SBP, proportion of antihypertensive and lipid-lowering agent use and with decreasing HDL-cholesterol. Independent of age, adiposity, smoking, alcohol, physical activity, and lipid and BP lowering drug use, increasing glycemia was associated with decreasing HDL-cholesterol specifically in women with prediabetes (NHES-Thailand, beta-coefficient - 0.07 (95% CI - 0.15, - 0.001) p = 0.04 and HS-England, - 0.03 (- 0.04, - 0.006) p = 0.01). In both populations, among those with prediabetes, increasing glycaemia is associated with an adverse, significant decline in HDL cholesterol, specifically in women. These adverse effects are apparent in widely-differing international populations.

Adverse outcomes in COVID-19 and diabetes: a retrospective cohort study from three London teaching hospitals.

INTRODUCTION: Patients with diabetes mellitus admitted to hospital with COVID-19 have poorer outcomes. However, the drivers of poorer outcomes are not fully elucidated. We performed detailed characterization of patients with COVID-19 to determine the clinical and biochemical factors that may be drivers of poorer outcomes. RESEARCH DESIGN AND METHODS: This is a retrospective cohort study of 889 consecutive inpatients diagnosed with COVID-19 between March 9 and April 22, 2020 in a large London National Health Service Trust. Unbiased multivariate logistic regression analysis was performed to determine variables that were independently and significantly associated with increased risk of death and/or intensive care unit (ICU) admission within 30 days of COVID-19 diagnosis. RESULTS: 62% of patients in our cohort were of non-white ethnic background and the prevalence of diabetes was 38%. 323 (36%) patients met the primary outcome of death/admission to the ICU within 30 days of COVID-19 diagnosis. Male gender, lower platelet count, advancing age and higher Clinical Frailty Scale (CFS) score (but not diabetes) independently predicted poor outcomes on multivariate analysis. Antiplatelet medication was associated with a lower risk of death/ICU admission. Factors that were significantly and independently associated with poorer outcomes in patients with diabetes were coexisting ischemic heart disease, increasing age and lower platelet count. CONCLUSIONS: In this large study of a diverse patient population, comorbidity (ie, diabetes with ischemic heart disease; increasing CFS score in older patients) was a major determinant of poor outcomes with COVID-19. Antiplatelet medication should be evaluated in randomized clinical trials among high-risk patient groups.

Roux-en-Y Gastric Bypass Increases Glycemic Variability and Time in Hypoglycemia in Patients With Obesity and Prediabetes or Type 2 Diabetes: A Prospective Cohort Study.

OBJECTIVE: Roux-en-Y gastric bypass (RYGB) is an established treatment for type 2 diabetes and obesity. The study objective was to establish RYGB's effects on glycemic variability (GV) and hypoglycemia. RESEARCH DESIGN AND METHODS: This was a prospective observational study of 10 participants with obesity and prediabetes or type 2 diabetes who underwent RYGB. Patients were studied before RYGB (Pre) and 1 month, 1 year, and 2 years postsurgery with continuous glucose measurement (CGM). A mixed-meal test (MMT) was conducted at Pre, 1 month, and 1 year. RESULTS: After RYGB, mean CGM decreased (at 1 month, 1 year, and 2 years), and GV increased (at 1 year and 2 years). Five of the 10 participants had a percent time in range (%TIR) <3.0 mmol/L (54 mg/dL) greater than the international consensus target of 1% at 1 or 2 years. Peak glucagon-like peptide-1 (GLP-1) and glucagon area under the curve during MMT were positively and negatively associated, respectively, with contemporaneous %TIR <3.0 mmol/L. CONCLUSIONS: Patients undergoing RYGB are at risk for development of postbariatric hypoglycemia due to a combination of reduced mean glucose, increased GV, and increased GLP-1 response.

Glucose management for exercise using continuous glucose monitoring (CGM) and intermittently scanned CGM (isCGM) systems in type 1 diabetes: position statement of the European Association for the Study of Diabetes (EASD) and of the International Society for Pediatric and Adolescent Diabetes (ISPAD) endorsed by JDRF and supported by the American Diabetes Association (ADA).

Physical exercise is an important component in the management of type 1 diabetes across the lifespan. Yet, acute exercise increases the risk of dysglycaemia, and the direction of glycaemic excursions depends, to some extent, on the intensity and duration of the type of exercise. Understandably, fear of hypoglycaemia is one of the strongest barriers to incorporating exercise into daily life. Risk of hypoglycaemia during and after exercise can be lowered when insulin-dose adjustments are made and/or additional carbohydrates are consumed. Glycaemic management during exercise has been made easier with continuous glucose monitoring (CGM) and intermittently scanned continuous glucose monitoring (isCGM) systems; however, because of the complexity of CGM and isCGM systems, both individuals with type 1 diabetes and their healthcare professionals may struggle with the interpretation of given information to maximise the technological potential for effective use around exercise (i.e. before, during and after). This position statement highlights the recent advancements in CGM and isCGM technology, with a focus on the evidence base for their efficacy to sense glucose around exercise and adaptations in the use of these emerging tools, and updates the guidance for exercise in adults, children and adolescents with type 1 diabetes. Graphical abstract.

Glucose management for exercise using continuous glucose monitoring (CGM) and intermittently scanned CGM (isCGM) systems in type 1 diabetes: position statement of the European Association for the Study of Diabetes (EASD) and of the International Society for Pediatric and Adolescent Diabetes (ISPAD) endorsed by JDRF and supported by the American Diabetes Association (ADA).

Physical exercise is an important component in the management of type 1 diabetes across the lifespan. Yet, acute exercise increases the risk of dysglycaemia, and the direction of glycaemic excursions depends, to some extent, on the intensity and duration of the type of exercise. Understandably, fear of hypoglycaemia is one of the strongest barriers to incorporating exercise into daily life. Risk of hypoglycaemia during and after exercise can be lowered when insulin-dose adjustments are made and/or additional carbohydrates are consumed. Glycaemic management during exercise has been made easier with continuous glucose monitoring (CGM) and intermittently scanned continuous glucose monitoring (isCGM) systems; however, because of the complexity of CGM and isCGM systems, both individuals with type 1 diabetes and their healthcare professionals may struggle with the interpretation of given information to maximise the technological potential for effective use around exercise (ie, before, during and after). This position statement highlights the recent advancements in CGM and isCGM technology, with a focus on the evidence base for their efficacy to sense glucose around exercise and adaptations in the use of these emerging tools, and updates the guidance for exercise in adults, children and adolescents with type 1 diabetes.

Loss of association between HbA1c and vascular disease in older adults with type 1 diabetes.

AIMS: Robust evidence supports intensive glucose control in those with recently diagnosed type 1 diabetes to reduce the risk of developing micro- and macrovascular complications. Data to support longitudinal glycaemic targets is lacking. We aimed to explore if longer duration of diabetes and greater age might reduce the impact of glycaemia on the risk of vascular complications. RESEARCH AND DESIGN METHODS: Data for adults age 20 years or more, was extracted from a clinical database of people with type 1 diabetes cared for at a London teaching hospital. The presence or absence of micro- and macro-vascular complications was recorded. Multivariable logistic regression analysis was performed using HbA1c as independent variable, diabetes duration and age as continuous variable and obesity, hypertension, hypercholesterolaemia, low HDL cholesterol and hypertriglyceridaemia as categorical variables. RESULTS: Data from 495 patients was used. HbA1c above 60 mmol/mol (7.6%) was associated with increased microvascular complications in patients aged 20-44 years, independent of age and duration of diabetes. In older people with T1DM duration of diabetes was the major risk factor. CONCLUSIONS: Our study suggests that increased age and greater duration of diabetes reduce the impact of glycaemia on the risk of vascular complications. Intensive blood glucose management in patients aged ≥45 years may have limited benefits in terms of reducing the risk of complications although this does not dismiss the benefits of good glycaemic control in older people with T1DM.

Hyperglycemia recognised in early pregnancy is phenotypically type 2 diabetes mellitus not gestational diabetes mellitus: a case control study.

Objective: Gestational diabetes mellitus is defined as "diabetes recognized in the second or third trimester that is not clearly overt diabetes". Evidence relating to women with hyperglycemia early in pregnancy is limited. We aimed to evaluate women diagnosed with hyperglycemia early in pregnancy (eGDM) and compared them to those with pregestational established type 2 diabetes mellitus (T2DM) and gestational diabetes diagnosed routinely at 24-28-week gestation (rtGDM) to determine if the length of exposure to hyperglycemia adversely affected outcomes.Methods: Forty consecutive women with eGDM who attended a multidisciplinary antenatal clinic were reviewed. Two separate BMI-matched control groups were identified, recognized pregestational T2DM (n = 80) and rtGDM (n = 80). Baseline demographics and outcomes were compared.Results: A higher proportion of women in the eGDM and T2DM group required insulin and the incidence of hypertensive disorders was similarly increased compared with the rtGDM group (88.6, 77.0 versus 8.1%, p < .001 and 42.5%, 37.5 versus 12.5% p < .001, respectively). The proportion of infants born small for gestational age varied (eGDM 11.1%, T2DM 13.0%, and rtGDM 2.5%, p=.049). Postpartum, 7.5% of eGDM women were diagnosed with T2DM versus 1.3% in the rtGDM group (p<.001).Conclusions: These novel data demonstrate that the length of exposure to glucose adversely affects materno-foetal outcomes independent of maternal adiposity.