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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., (© 2024 by the American Diabetes Association.)

Aims: People with recently-diagnosed type 1 diabetes mellitus (T1D) may undergo a transient period of glycaemic control with less exogenous insulin. Identification of predictors of this 'remission' could inform a better understanding of glycaemic control., Methods: Participants in the ADDRESS-2 study were included who had 1 or 2 assessments of remission status (coincident insulin dose and HbA1c measurement, with remission defined by ≤0.4 units insulin/kg-body-weight/day with HbA1c < 53 mmol/mol). Demographic and clinical presentation characteristics were compared according to remission status and predictors of remission were explored by logistic regression analysis., Results: 1470 first and 469 second assessments of remission status were recorded within 12 months of diagnosis of T1D. Step increases in the probability of remission were identified at age-at-diagnosis 20 years and 3 months after diagnosis (both p < 0.001). Among those aged < 20 years, remission was associated with male gender (p = 0.02), no ketoacidosis (p = 0.02) and fewer than 2 symptoms at presentation (p = 0.004). None of these characteristics predicted remission in those aged ≥ 20 years. In the subgroup with two assessments, transition to remission was independently associated with first remission assessment in months 1-2 post-diagnosis (p = 0.01), with age-at-diagnosis ≥ 20 years (p = 0.01) and, in those aged < 20 years, with an early HbA1c of <57 mmol/mol. Adiposity, ethnicity, autoantibody status and other autoimmune disease were unrelated to remission., Conclusions: For those diagnosed before 20 years of age, males, ketoacidosis-free, with fewer symptoms and low early HbA1c were more likely to experience remission, but remission was most likely in anyone aged ≥ 20 at diagnosis., (Copyright © 2019 Elsevier B.V. All rights reserved.)

Purpose: Exposure to high altitude has been shown to enhance both glucose and lipid utilization depending on experimental protocol. In addition, high and low blood glucose levels have been reported at high altitude. We hypothesized that gradual ascent to high altitude results in changes in glucose levels in healthy young adults., Methods: Twenty-five adult volunteers, split into two teams, took part in the British Services Dhaulagiri Medical Research Expedition completing 14 d of trekking around the Dhaulagiri circuit in Nepal reaching a peak altitude of 5300 m on day 11 of the trek. Participants wore blinded continuous glucose monitors (CGM) throughout. Blood samples for C-peptide, proinsulin, and triacylglycerides were taken at sea level (United Kingdom) and in acclimatization camps at 3600, 4650, and 5120 m. Energy intake was determined from food diaries., Results: There was no difference in time spent in hypoglycemia stratified by altitude. Nocturnal CGM readings (2200-0600 h) were chosen to reduce the short-term effect of physical activity and food intake and showed a significant (P < 0.0001) increase at 3600 m (5.53 ± 0.22 mmol·L), 4650 m (4.77 ± 0.30 mmol·L), and 5120 m (4.78 ± 0.24 mmol·L) compared with baseline altitude 1100 m (vs 4.61 ± 0.25 mmol·L). Energy intake did not differ by altitude. Insulin resistance and beta-cell function, calculated by homeostatic model assessment, were reduced at 3600 m compared with sea level., Conclusions: We observed a significant increase in nocturnal CGM glucose at 3600 m and greater despite gradual ascent from 1100 m. Taken with the changes in insulin resistance and beta-cell function, it is possible that the stress response to high altitude dominates exercise-enhanced insulin sensitivity, resulting in relative hyperglycemia.

Aims: Variation in cardiometabolic risk in prediabetes and any impacts of ethnicity on such variation have been little studied. In an ethnically diverse dataset, selected according to a high-risk HbA1c-based definition of prediabetes, we have investigated relationships between glycaemia and cardiometabolic risk factors and the influence of ethnicity on these relationships., Methods: We undertook a cross-sectional analysis of baseline data from a diabetes prevention study in the UK and a chronic care clinic in Thailand, selected for people without diabetes (fasting plasma glucose <7.0 mmol/l) with HbA1c 6.0-6.4% (42-47 mmol/mol). Thai (n=158) and UK White (n=600), South Asian (n=112), Black (n=70) and other/mixed (n=103) groups were distinguished and measurements included fasting plasma glucose (FPG), blood pressure (BP), lipids and insulin resistance-related risk factors (IRFs)., Results: Independently of individual characteristics including ethnicity, only systolic BP was weakly associated with FPG (beta coefficient 1.76 (95%CI 0.10-3.42), p 0.03) and only LDL-c with IFG (FPG 5.6 to <7) (adjusted -0.14 (-0.27, -0.003) p 0.04). There were no significant independent associations with cardiometabolic risk factors when categories of impaired fasting glucose (FPG ≥ 6.1 to <7.0 mmol/L) were considered. Relative to White, South Asian ethnicity was independently associated with lower systolic and diastolic BP, Black with lower triglycerides, cholesterol/HDL-c ratio and having 2 or more IRFs, and Thai with lower cholesterol/HDL-c ratio and all three non-white ethnicities with lower total and LDL cholesterol., Conclusion: In high-risk HbA1c-defined prediabetes additional measurement of FPG will add little to evaluation of cardiometabolic risk. Additionally, UK Whites tend to have the most adverse cardiometabolic profile of any ethnic group., (Copyright © 2017 Elsevier B.V. All rights reserved.)

Objective: To report the impact of continuous glucose monitoring (CGM) on glycemic variability (GV) indices, factors predictive of change, and to correlate variability with conventional markers of glycemia., Methods: Data from the JDRF study of CGM in participants with type 1 diabetes were used. Participants were randomized to CGM or self-monitored blood glucose (SMBG). GV indices at baseline, at 26 weeks in both groups, and at 52 weeks in the control group were analyzed. The associations of demographic and clinical factors with change in GV indices from baseline to 26 weeks were evaluated., Results: Baseline data were available for 448 subjects. GV indices were all outside normative ranges (P < 0.001). Intercorrelation between GV indices was common and, apart from coefficient of variation (CV), low blood glucose index (LBGI), and percentage of glycemic risk assessment diabetes equation score attributable to hypoglycemia (%GRADE hypoglycemia ), all indices correlate positively with HbA1c. There was strong correlation between time spent in hypoglycemia, and CV, LBGI, and %GRADE hypoglycemia , but not with HbA1c. A significant reduction in all GV indices, except lability index and mean absolute glucose change per unit time (MAG), was demonstrated in the intervention group at 26 weeks compared with the control group. Baseline factors predicting a change in GV with CGM include baseline HbA1c, baseline GV, frequency of daily SMBG, and insulin pump use., Conclusions: CGM reduces most GV indices compared with SMBG in people with type 1 diabetes. The strong correlation between time spent in hypoglycemia and CV, LBGI, and %GRADE hypoglycemia highlights the value of these metrics in assessing hypoglycemia as an adjunct to HbA1c in the overall assessment of glycemia.

Background: Glycemic variability has been proposed as a contributing factor in the development of diabetes complications. Multiple measures exist to calculate the magnitude of glycemic variability, but normative ranges for subjects without diabetes have not been described. For treatment targets and clinical research we present normative ranges for published measures of glycemic variability., Methods: Seventy-eight subjects without diabetes having a fasting plasma glucose of <120 mg/dL (6.7 mmol/L) underwent up to 72 h of continuous glucose monitoring (CGM) with a Medtronic Minimed (Northridge, CA) CGMS(®) Gold device. Glycemic variability was calculated using EasyGV(©) software (available free for non-commercial use at www.easygv.co.uk ), a custom program that calculates the SD, M-value, mean amplitude of glycemic excursions (MAGE), average daily risk ratio (ADRR), Lability Index (LI), J-Index, Low Blood Glucose Index (LBGI), High Blood Glucose Index (HBGI), continuous overlapping net glycemic action (CONGA), mean of daily differences (MODD), Glycemic Risk Assessment in Diabetes Equation (GRADE), and mean absolute glucose (MAG)., Results: Eight CGM traces were excluded because there were inadequate data. From the remaining 70 traces, normative reference ranges (mean±2 SD) for glycemic variability were calculated: SD, 0-3.0; CONGA, 3.6-5.5; LI, 0.0-4.7; J-Index, 4.7-23.6; LBGI, 0.0-6.9; HBGI, 0.0-7.7; GRADE, 0.0-4.7; MODD, 0.0-3.5; MAGE-CGM, 0.0-2.8; ADDR, 0.0-8.7; M-value, 0.0-12.5; and MAG, 0.5-2.2., Conclusions: We present normative ranges for measures of glycemic variability in adult subjects without diabetes for use in clinical care and academic research.