Visit-to-visit HbA1c variability and risk of potentially avoidable hospitalisations in adults with type 2 diabetes receiving outpatient care at a tertiary hospital.

AIMS: This study aims to investigate the relationship between long-term visit-to-visit within-person HbA1c variability and hospitalisation outcomes in adults with type 2 diabetes (T2D). METHODS: We conducted a cohort study at a tertiary hospital in Singapore involving people aged 21 to 101 years with T2D who had ≥3 HbA1c tests over 2 years. HbA1c variability was assessed using coefficient of variation (CV), variability independent of the mean (VIM) and HbA1c variability score (HVS). A 1-year follow-up was performed after the last HbA1c measurement to identify all-cause and potentially avoidable hospitalisations (PAH), categorised as overall, acute, chronic and diabetes composites. RESULTS: The study included 14 923 patients (mean age: 62.9 ± 12.9 years; 55% male). The median HbA1c variability was 8.6% CV (IQR: 5.1-14.3). Higher quartiles of HbA1c variability were associated with greater risks of PAH and all-cause hospitalisations, independent of glycaemic control. Compared to Q1, for example, the risk ratios and 95% confidence intervals for diabetes-related PAH based on HbA1c CV were as follows: Q2, 1.32 (0.93-1.88); Q3, 1.65 (1.18-2.31) and Q4, 2.16 (1.54-3.03). For all-cause hospitalisations, they were as follows: Q2, 0.97 (0.90-1.05); Q3, 1.08 (1.00-1.17) and Q4, 1.16 (1.07-1.26). When stratified by glycaemic control, elevated risk of PAH persisted even in those with optimal glycaemic control. Consistent findings were observed using HbA1c VIM and HVS measures. CONCLUSIONS: In individuals receiving care at specialist outpatient clinics of a tertiary hospital, HbA1c variability is associated with a higher risk of PAH. Comprehensive diabetes management strategies addressing both glycaemic control and variability may offer benefits.

The Relation of Diabetes Complications to a New Interpretation of Glycaemic Variability from Continuous Glucose Monitoring in People with Type 1 Diabetes.

INTRODUCTION: Microvascular and macrovascular complications in type 1 diabetes (T1D) may be linked to endothelial stress due to glycaemic variability. Continuous glucose monitoring systems (CGMs) provide new opportunities to quantify this variability, utilising the amplitude of glucose change summated over time. The aim of this study was to examine whether this determination of glucose variability (GV) is associated with microvascular clinical sequelae. METHODS: Continuous glucose monitoring values were downloaded for 89 type 1 diabetes mellitus (T1D) individuals for up to 18 months from 2021 to 2023. Data for patient demographics was also taken from the patient record which included Sex, Date of Birth, and Date of Diagnosis. The recorded laboratory glycated haemoglobin (HbA1c) test results were also recorded. The glucose management index (GMI) was calculated from average glucose readings for 18 months using the formula GMI (%) = (0.82-(Average glucose/100)). This was then adjusted to give GMI (mmol/mol) = 10.929 * (GMI (%) - 2.15). Average Glucose Fluctuation (AGF) was calculated by adding up the total absolute change value between all recorded results over 18 months and dividing by the number of results minus one. The % Above Critical Threshold (ACT) was calculated by summing the total number of occurrences for each result value. A cumulative 95% limit was then applied to identify the glucose value that only 5% of results exceeded in the overall population. Using this value, we estimated the percentage of total tests that were above the Critical Threshold (ACT). RESULTS: Results for the 89 individuals (44 men and 45 women) were analysed over 18 months. The mean age of participants was 43 years and the mean duration of diabetes was 18 years. A total of 3.22 million readings were analysed, giving an average of 10.3 mmol/L blood glucose. Those with the largest change in glucose from reading to reading, summated over time, showed the greatest change in eGFR of 3.12 ml/min/1.73 m2 (p = 0.007). People with a higher proportion of glucose readings > 18 mmol/L showed a fall in eGFR of 2.8 ml/min/1.73 m2 (p = 0.009) and experienced higher rates of sight-threatening retinopathy (44% of these individuals) (p = 0.01) as did 39% of individuals in the highest tertile of glucose levels (p = 0.008). CONCLUSION: Those individuals with T1D in the highest tertile of reading-to-reading glucose change showed the greatest change in eGFR. Those with a higher proportion of glucose readings > 18 mmol/L also showed a fall in eGFR and experienced higher rates of sight-threatening retinopathy, as did people with higher mean glucose. Discussions with T1D individuals could reflect on how the percentage recorded glucose above a critical level and degree of change in glucose are important in avoiding future tissue complications.

Effects of glucagon-like peptide-1 receptor agonists on glycated haemoglobin and continuous glucose monitoring metrics as adjunctive therapy to insulin in adults with type 1 diabetes: A meta-analysis of randomized controlled trials.

AIMS: To conduct a meta-analysis of randomized controlled trials (RCTs) to evaluate the effect of glucagon-like peptide-1 receptor agonists (GLP-1RAs) on glycated haemoglobin (HbA1c) and continuous glucose monitoring (CGM) metrics as an adjunct to insulin therapy in adults with type 1 diabetes mellitus (T1D). METHODS: A systematic literature search was conducted through Medline (via PubMed), Cochrane Library, and Google Scholar up to 27 May 2024. Dual-independent study selection, data extraction, and quality assessment were performed. Results were summarized using random-effects meta-analysis. RESULTS: Six RCTs were identified, involving a total of 378 individuals with T1D. The use of GLP-1RAs in addition to standard insulin therapy was associated with a significant reduction in HbA1c (mean difference [MD] -0.21%, 95% confidence interval [CI] -0.36 to -0.06; p = 0.007) and a similar time in range (TIR) compared to placebo (MD -0.22%, 95% CI -2.39 to 1.95; p = 0.84). GLP-1RA therapy resulted in a significantly higher time below range (MD 1.13%, 95% CI 0.50 to 1.76; p < 0.001) and a lower time above range compared with placebo (MD -1.83%, 95% CI -2.51 to -1.15; p < 0.001). Nonsignificant differences were noted for the secondary outcomes, including the mean amplitude of glucose excursion, continuous overall net glycaemic action for 60 min, mean daily glucose, coefficient of variation, and mean standard deviation of weekly glucose levels. CONCLUSION: Our findings suggest that, in individuals with T1D, add-on therapy with GLP-1RAs does not confer significant benefits in terms of CGM metrics and is associated with a longer time below the target glycaemic range.

The association of glycaemic risk factors and diabetes duration with risk of heart failure in people with type 2 diabetes: A systematic review and meta-analysis.

AIMS: To conduct a systematic review in order to better understand the association of glycaemic risk factors and diabetes duration with risk of heart failure (HF) in individuals with type 2 diabetes (T2D). METHODS: We identified longitudinal studies investigating the association of glycaemic factors (glycated haemoglobin [HbA1c], HbA1c variability, and hypoglycaemia) and diabetes duration with HF in individuals with T2D. Hazard ratios and odds ratios were extracted and meta-analysed using a random-effects model where appropriate. Risk of bias assessment was carried out using a modified Newcastle-Ottawa Scale. Egger's test along with the trim-and-fill method were used to assess and account for publication bias. RESULTS: Forty studies representing 4 102 589 people met the inclusion criteria. The risk of developing HF significantly increased by 15% for each percentage point increase in HbA1c, by 2% for each additional year of diabetes duration, and by 43% for having a history of severe hypoglycaemia. Additionally, variability in HbA1c levels was associated with a 20%-26% increased risk of HF for each unit increase in the metrics of variability (HbA1c standard deviation, coefficient of variation, and average successive variability). All included studies scored high in the risk of bias assessment. Egger's test suggested publication bias, with trim-and-fill analyses revealing a significant 14% increased risk of HF per percentage point increase in HbA1c. CONCLUSIONS: Glycaemic risk factors and diabetes duration significantly contribute to the heightened risk of HF among individuals with T2D. A reduction in risk of HF is anticipated with better management of glycaemic risk factors.

The Association of HbA1c Variability with 12 Week and 12 Month Outcomes on Diabetes Related Foot Ulcer Healing.

INTRODUCTION: This study aimed to determine the relationship between HbA1c variability and foot ulcer healing at 12 weeks and 12 months. METHODS: Using National Diabetic Foot Care Audit (NDFA) and hospital records, demographics, baseline ulcer characteristics and healing outcomes for subjects presenting with a foot ulcer between 2017-2022 were collected at 12 weeks and 12 months. Subjects had diabetes duration > 3 years and ≥ 3 HbA1c recordings in the 5 years prior to presentation. RESULTS: At 12 weeks, factors associated with an active ulcer were presence on hind foot (adjusted odds ratios) (2.1 [95% CI 1.3-3.7]), ischaemia (2.1 [95% CI:1.4-3.2]), area > 1 cm2 (2.7 [95% CI:1.7-4.2]) and diabetes duration > 24 years vs 3-10 (AOR 2.0 [95% CI 1.2-3.5]). After adjustment, HbA1c variability 6-10 mmol/mol and > 14.5 mmol/mol had AOR of 1.76 (95% CI 1.1-2.8; p = 0.0192) and 1.5 (95% CI 0.9-2.6; p = 0.1148) of an active ulcer at 12 weeks vs variability < 6 mmol/mol. At 12 months, ischaemia (AOR 2.4 [95% CI 1.5-3.8]) and diabetes duration > 24 years vs 3-10 years (AOR 3.3 [95% CI 1.7-6.4] were significant factors. HbA1c variability was not significant at 12 months. CONCLUSION: In keeping with the national NDFA data, in our cohort ulcer characteristics, but not HbA1c variability, were the key factors associated with ulcer healing at 12 weeks and 12 months.

Diabetes complications occur more frequently when glucose control is not as good as it could be. For a long time, HbA_1c, or glycated haemoglobin, has been used as a measure of how well someone's diabetes has been controlled. However, another way of looking at diabetes control is to look at the changes of HbA_1c over time­this is called glycaemic variability. Diabetes-related foot disease is one of the most feared complications of the condition, and our group has previously shown in a small study that glycaemic variability was associated with ulcer healing at 12 weeks­with lower variability leading to better healing. However, it did not consider other variables known to be associated with not being alive and ulcer free at 12 months. In the UK, data are collected as part of the National Diabetes Footcare Audit (NDFA). This dataset collects a lot of information on new foot ulcers and their outcomes 12 weeks later. We have used our centre's data to look at factors not included in the NDFA dataset­in particular glycaemic variability­to determine whether this influences ulcer outcomes at 12 weeks, but also at 12 months. We found that low glycaemic variability is associated with greater chances of healing but that the greatest association is the presence of poor blood flow and diabetes duration.

Potential Benefits of Continuous Glucose Monitoring for Predicting Vascular Outcomes in Type 2 Diabetes: A Rapid Review of Primary Research.

(1) Background: Chronic hyperglycaemia is a cause of vascular damage and other adverse clinical outcomes in type 2 diabetes mellitus (T2DM). Emerging evidence suggests a significant and independent role for glycaemic variability (GV) in contributing to those outcomes. Continuous glucose monitoring (CGM) provides valuable insights into GV. Unlike in type 1 diabetes mellitus, the use of CGM-derived GV indices has not been widely adopted in the management of T2DM due to the limited evidence of their effectiveness in predicting clinical outcomes. This study aimed to explore the associations between GV metrics and short- or long-term vascular and clinical complications in T2DM. (2) Methods: A rapid literature review was conducted using the Cochrane Library, MEDLINE, and Scopus databases to seek high-level evidence. Lower-quality studies such as cross-sectional studies were excluded, but their content was reviewed. (3) Results: Six studies (five prospective cohort studies and one clinical trial) reported associations between GV indices (coefficient of variation (CV), standard deviation (SD), Mean Amplitude of Glycaemic Excursions (MAGE), Time in Range (TIR), Time Above Range (TAR), and Time Below Range (TBR)), and clinical complications. However, since most evidence came from moderate to low-quality studies, the results should be interpreted with caution. (4) Conclusions: Limited but significant evidence suggests that GV indices may predict clinical compilations in T2DM both in the short term and long term. There is a need for longitudinal studies in larger and more diverse populations, longer follow-ups, and the use of numerous CGM-derived GV indices while collecting information about all microvascular and macrovascular complications.

HbA1c variability is independently associated with progression of diabetic kidney disease in an urban multi-ethnic cohort of people with type 1 diabetes.

AIMS/HYPOTHESIS: The role of HbA1c variability in the progression of diabetic kidney disease is unclear, with most studies to date performed in White populations and limited data on its role in predicting advanced kidney outcomes. Our aim was to evaluate if long-term intra-individual HbA1c variability is a risk factor for kidney disease progression (defined as an eGFR decline of ≥50% from baseline with a final eGFR of <30 ml/min per 1.73 m2) in an ethnically heterogeneous cohort of people with type 1 diabetes with a preserved eGFR ≥45 ml/min per 1.73 m2 at baseline. METHODS: Electronic health record data from people attending outpatient clinics between 2004 and 2018 in two large university hospitals in London were collected. HbA1c variability was assessed using three distinct methods: (1) SD of HbA1c (SD-HbA1c); (2) visit-adjusted SD (adj-HbA1c): SD-HbA1c/√n/(n-1), where n is the number of HbA1c measurements per participant; and (3) CV (CV-HbA1c): SD-HbA1c/mean-HbA1c. All participants had six or more follow-up HbA1c measurements. The eGFR was measured using the Chronic Kidney Disease Epidemiology Collaboration equation and clinical/biochemical results from routine care were extracted from electronic health records. RESULTS: In total, 3466 participants (50% female, 78% White, 13% African Caribbean, 3% Asian and 6% of mixed heritage or self-reporting as 'other') were followed for a median (IQR) of 8.2 (4.2-11.6) years. Of this cohort, 249 (7%) showed kidney disease progression. Higher HbA1c variability was independently associated with a higher risk of kidney disease progression, with HRs (95% CIs) of 7.76 (4.54, 13.26), 2.62 (1.75, 3.94) and 5.46 (3.40, 8.79) (lowest vs highest HbA1c variability quartile) for methods 1-3, respectively. Increasing age, baseline HbA1c, systolic BP and urinary albumin/creatinine ratio were also associated with kidney disease progression (p<0.05 for all). African Caribbean ethnicity was associated with an increased risk of kidney disease progression (HR [95% CI] 1.47 [1.09, 1.98], 1.76 [1.32, 2.36] and 1.57 [1.17, 2.12] for methods 1-3, respectively) and this effect was independent of glycaemic variability and other traditional risk factors. CONCLUSIONS/INTERPRETATION: We observed an independent association between HbA1c variability, evaluated using three distinct methods, and significant kidney disease progression in a multi-ethnic type 1 diabetes cohort. Further studies are needed to elucidate the mechanisms that may explain our results and evaluate if HbA1c variability is a modifiable risk factor for preventing diabetic kidney disease progression.

Heterogeneity of glycaemic phenotypes in type 1 diabetes.

AIMS/HYPOTHESIS: Our study aims to uncover glycaemic phenotype heterogeneity in type 1 diabetes. METHODS: In the Study of the French-speaking Society of Type 1 Diabetes (SFDT1), we characterised glycaemic heterogeneity thanks to a set of complementary metrics: HbA1c, time in range (TIR), time below range (TBR), CV, Gold score and glycaemia risk index (GRI). Applying the Discriminative Dimensionality Reduction with Trees (DDRTree) algorithm, we created a phenotypic tree, i.e. a 2D visual mapping. We also carried out a clustering analysis for comparison. RESULTS: We included 618 participants with type 1 diabetes (52.9% men, mean age 40.6 years [SD 14.1]). Our phenotypic tree identified seven glycaemic phenotypes. The 2D phenotypic tree comprised a main branch in the proximal region and glycaemic phenotypes in the distal areas. Dimension 1, the horizontal dimension, was positively associated with GRI (coefficient [95% CI]) (0.54 [0.52, 0.57]), HbA1c (0.39 [0.35, 0.42]), CV (0.24 [0.19, 0.28]) and TBR (0.11 [0.06, 0.15]), and negatively with TIR (-0.52 [-0.54, -0.49]). The vertical dimension was positively associated with TBR (0.41 [0.38, 0.44]), CV (0.40 [0.37, 0.43]), TIR (0.16 [0.12, 0.20]), Gold score (0.10 [0.06, 0.15]) and GRI (0.06 [0.02, 0.11]), and negatively with HbA1c (-0.21 [-0.25, -0.17]). Notably, socioeconomic factors, cardiovascular risk indicators, retinopathy and treatment strategy were significant determinants of glycaemic phenotype diversity. The phenotypic tree enabled more granularity than traditional clustering in revealing clinically relevant subgroups of people with type 1 diabetes. CONCLUSIONS/INTERPRETATION: Our study advances the current understanding of the complex glycaemic profile in people with type 1 diabetes and suggests that strategies based on isolated glycaemic metrics might not capture the complexity of the glycaemic phenotypes in real life. Relying on these phenotypes could improve patient stratification in type 1 diabetes care and personalise disease management.

Cross-sectional association of continuous glucose monitoring-derived metrics with cerebral small vessel disease in older adults with type 2 diabetes.

AIM: To examine cross-sectional associations between continuous glucose monitoring (CGM)-derived metrics and cerebral small vessel disease (SVD) in older adults with type 2 diabetes. MATERIALS AND METHODS: In total, 80 patients with type 2 diabetes aged ≥70 years were analysed. Participants underwent CGM for 14 days. From the CGM data, we derived mean sensor glucose, percentage glucose coefficient of variation, mean amplitude of glucose excursion, time in range (TIR, 70-180 mg/dl), time above range (TAR) and time below range metrics, glycaemia risk index and high/low blood glucose index. The presence of cerebral SVD, including lacunes, microbleeds, enlarged perivascular spaces and white matter hyperintensities, was assessed, and the total number of these findings comprised the total cerebral SVD score (0-4). Ordinal logistic regression analyses were performed to examine the association of CGM-derived metrics with the total SVD score. RESULTS: The median SVD score was 1 (interquartile range 0-2). Higher hyperglycaemic metrics, including mean sensor glucose, TAR >180 mg/dl, TAR >250 mg/dl, and high blood glucose index and glycaemia risk index, were associated with a higher total SVD score. In contrast, a higher TIR (per 10% increase) was associated with a lower total SVD score (odds ratio 0.73, 95% confidence interval 0.56-0.95). Glycated haemoglobin, percentage glucose coefficient of variation, mean amplitude of glucose excursions, time below range and low blood glucose index were not associated with total cerebral SVD scores. CONCLUSIONS: The hyperglycaemia metrics and TIR, derived from CGM, were associated with cerebral SVD in older adults with type 2 diabetes.

Intra-pancreatic fat is associated with continuous glucose monitoring metrics.

AIM: To investigate the relationship of fat in the pancreas with time spent in different glycaemic ranges. METHODS: Abdominal magnetic resonance imaging at 3.0 Tesla was used to quantify fat in the pancreas as both continuous [i.e. intra-pancreatic fat deposition (IPFD)] and binary (i.e. fatty change of the pancreas vs. normal pancreas) variables. Dexcom G6 devices were used to collect continuous glucose monitoring data every 5 min over a continuous 7-day period. Time above range (TAR), time in range (TIR) and time below range were computed. Statistical models were built to adjust for age, sex, body composition, and other covariates in linear regression analysis and analysis of covariance. RESULTS: In total, 38 individuals were studied. IPFD was significantly associated with TAR (p = .036) and TIR (p = .042) after adjustment for covariates. For every 1% increase in IPFD, there was a 0.3 unit increase in TAR and a decrease in TIR. Individuals with fatty change of the pancreas, when compared with those with normal pancreas, had significantly higher TAR (p = .034) and lower TIR (p = .047) after adjustment for covariates. Neither IPFD (p = .805) nor fatty change of the pancreas (p = .555) was associated with time below range after adjustment for covariates. CONCLUSION: Increased fat in the pancreas is associated with excessive glycaemic variability. Fatty change of the pancreas may contribute to heightening the risk of cardiovascular diseases.

A meta-analysis of the relationship between glycaemic variability and the mortality of patients with heart failure.

Recent findings indicate that fluctuations in blood glucose could potentially increase the risk of unfavourable outcomes in individuals with cardiovascular conditions. The objective of the research was to assess the correlation between glycaemic variability (GV) and the mortality of patients with heart failure (HF) through a comprehensive review and meta-analysis. Longitudinal follow-up studies comparing the mortality risk between HF patients with higher and lower GV were identified by searching Medline, Embase, Web of Science, and Cochrane Library databases. The results were combined using a random-effects model that accounted for the potential variability. The meta-analysis included nine cohort studies involving 76 843 patients diagnosed with HF, out of which 35 853 patients died within a follow-up period of up to 86 months. The combined findings indicated that a significant increase in GV was linked to an elevated risk of mortality in patients with HF during the follow-up period (RR 2.18, 95% CI 1.61 to 2.96, P < 0.001, I2 = 83%). The relationship between GV and mortality in HF patients was not significantly influenced by the patients' diabetic status (diabetic or non-diabetic), type of GV (acute or long-term GV), study design (prospective or retrospective), country of the study (Asian or non-Asian), follow-up durations, or the scores of study quality (P-values for subgroup differences all >0.05). A high GV could be a risk factor of mortality of patients with HF.

Early treatment with dipeptidyl-peptidase 4 inhibitors reduces glycaemic variability and delays insulin initiation in type 2 diabetes: A propensity score-matched cohort study.

AIMS: To examine whether early treatment intensification using dipeptidyl-peptidase 4 inhibitors (DPP4i) delays insulin initiation in Chinese patients diagnosed with type 2 diabetes for less than 5 years. MATERIALS AND METHODS: In a territory-wide prospective cohort study, patients with type 2 diabetes initiating DPP4i at diabetes duration <2 years (early intensification) and 3-5 years (late intensification) were matched using 1:1 propensity-score matching (n = 908 in each arm). We used Cox regression to compare the risk of insulin initiation between the two groups. We explored the interactive and mediation effects of glycated haemoglobin (HbA1c) variability score (HVS), defined as the percentage of HbA1c varying by ≥0.5% compared with preceding values. RESULTS: Of 1816 patients (60.7% men, mean age 54.4 ± 11.9 years), 92.4% and 71.9% were treated with metformin and sulphonylureas respectively at DPP4i initiation. Early DPP4i intensification [hazard ratio (HR) 0.71, (95% CI 0.58-0.68)] and low HVS (<50%) (HR = 0.40, 0.33-0.50) were associated with delayed insulin initiation during a median 4.08 years of follow-up. Early intensification with low HVS had the lowest risk versus late intensification with high HVS (HR = 0.30, 0.22-0.40) (pinteraction  = 0.013). HVS mediated 19.5% of the total effect of early DPP4i intensification on delaying insulin initiation. The late and early intensification groups had similar HbA1c at month 0 (8.4 ± 1.3% vs. 8.4 ± 1.5%) and month 3 (7.6 ± 1.2% vs. 7.6 ± 1.3%) after DPP4i initiation. By month 12, HbA1c in the late intensification group deteriorated (7.9 ± 1.4%) but remained stable in the early intensification group (7.6 ± 1.4%, p = 0.001) with persistent between-group difference over 72 months (8.2 ± 1.7% vs. 7.7 ± 1.6%, p = 0.001). CONCLUSIONS: In type 2 diabetes, early DPP4i intensification delayed insulin initiation, partially explained by reduced glycaemic variability.

What is Glycaemic Variability and which Pharmacological Treatment Options are Effective? A Narrative Review.

Glycated haemoglobin is currently used for diagnosis and follow-up of diabetes mellitus. However, it has important limitations; as it only reflects the average glycaemia over the last 3 months, it does not allow the identification of crucial events, such as episodes of hypoglycaemia and hyperglycaemia. Strict control of hyperglycaemia can result in severe hypoglycaemia that can be life threatening and can have important sequelae. Recently, the concept of glycaemic variability has been developed to provide information about the magnitude of glycaemic excursions and the duration of these fluctuations. This new approach has the potential to improve outcomes, decrease the risk of hypoglycaemia, and decrease cardiovascular risk. This review describes the most commonly prescribed non-insulin anti-diabetic drugs for diabetes management, their mechanism of action, and the existing evidence about their effectiveness in improving glycaemic variability and diabetes control.

Key indices of glycaemic variability for application in diabetes clinical practice.

Near normal glycaemic control in diabetes consists to target daily glucose fluctuations and quarterly HbA1c oscillations in addition to overall glucose exposure. Consequently, the prerequisite is to define simple, and mathematically undisputable key metrics for the short- and long-term variability in glucose homeostasis. As the standard deviations (SD) of either glucose or HbA1c are dependent on their means, the coefficient of variation (CV = SD/mean) should be applied instead as it that avoids the correlation between the SD and mean values. A CV glucose of 36% is the most appropriate threshold between those with stable versus labile glucose homeostasis. However, when near normal mean glucose concentrations are achieved a lower CV threshold of <27 % is necessary for reducing the risk for hypoglycaemia to a minimal rate. For the long-term variability in glucose homeostasis, a CVHbA1c < 5 % seems to be a relevant recommendation for preventing adverse clinical outcomes.

Use of Continuous Glucose Monitoring System in Children with Type 1 Diabetes Mellitus in a Resource Limited Setting.

Background: Regular self-monitoring of blood glucose (SMBG) remains the mainstay method for diabetes monitoring. The major limitation of SMBG is poor compliance and it only provides a snapshot of glucose values at that point of time. Continuous glucose monitors (CGMs) are non-invasive devices which measure subcutaneous interstitial glucose for every five minutes and provide glucose variability throughout the day. Aim and Objective: To assess the effectiveness of intermittent continuous blood glucose monitoring in comparison with SMBG on the percentage reduction in HbA1c level in children with type 1 diabetes mellitus (DM). Methods: Children diagnosed with type 1 DM of age group 3-18 years were enlisted into the study. Participants were randomised to the study arm (CGMs+SMBG) or the control arm (SMBG alone). Subjects in the study group were given CGM along with regular SMBG for 14 days. The control group was asked to perform SMBG. HbA1c levels were measured in both groups after three months of intervention. Results: There were 62 children in each group. After three months, in the intervention group HbA1c level dropped from 11.23% ± 1.53% (Mean ± SD) to 10.14% ± 1.99%, in control group HbA1c level dropped from 11.62% ± 1.62% to 11.32% ± 1.57%. The fall in HbA1c level in intervention group is significant (p value -0.01). Conclusion: In a resource-limited setting, intermittent use of CGMs atleast once every two to three months will help in understanding the factors influencing glucose variation throughout the day and, with appropriate therapeutic modifications, will aid in achieving optimal glycaemic control.

Faster-acting insulin aspart reduces glycaemic variability in sensor-augmented pump treated type 1 diabetes patients.

AIM: To evaluate the effect of faster aspart over glycaemic variability in type 1 diabetes (T1D) patients treated with sensor-augmented pump (SAP) in a real-world scenario. METHODS: Observational study with SAP-treated adult T1D patients treated with faster aspart for three months. The primary endpoint was the mean amplitude of glucose excursions (MAGE). RESULTS: Fifty patients were treated with faster aspart. Eleven patients (23%) withdrew during the follow-up mainly due to worsening of diabetes control (9 patients). Mean age was 41.2 yrs. (range 21-59) and T1D duration 22.4±10.0 yrs. Mean SAP treatment duration was 3.6±3.1 yrs. We detected a reduction of -7.0 (95% CI -1.1, -12.9; p=0.021) in MAGE at the end of the study. Other glycemic variability indices were also improved: standard deviation of mean interstitial glucose (-3mg/dl; 95% CI, -1, -5; p=0.01), CONGA4 (-2.2; 95% CI -0.3, -4.2; p=0.029), CONGA6 (-2.6; 95% CI -0.6, -4.6; p=0.011), GRADE (-0.5; 95% CI -0.1, -0.9; p=0.022), HBGI (-0.7; 95% CI -0.2, -1.3; p=0.013), J-index (-2.9; 95% CI -0.7, -5.0; p=0.011) and MODD (-5.7; 95% CI -1.7, -9.7; p=0.006). A slight reduction in mean glucose management indicator was also detected (-0.14%; 95% CI, -0.02, -0.27; -1.4mmol/mol; 95% CI -0.1, -3.3; p=0.03). CONCLUSIONS: In SAP-treated T1D patients, faster aspart insulin was associated with reduced glycaemic variability, but also a high percentage of dropouts due to worsened glycaemic control. NCT04233203.

Effects of metformin on glycaemic variability in combination with insulin in overweight/obese patients with type 1 diabetes.

BACKGROUND: The prevalence of overweight and obesity in type 1 diabetes mellitus (T1DM) individuals is increasing. Overweight people with T1DM may be insulin resistant. Glycaemic variability (GV) is an emerging measure of glycaemic control. The aim of this study is to investigate whether metformin, in adjunct to insulin, would have any favourable effect on GV. METHODS: This was a multi-centre, open-label randomised crossover study. Twenty-four overweight/obese T1DM patients aged ⩾18 years old with HbA1c ⩾ 7.0% (53 mmol/mol) were recruited and randomised into two study arms. For first 6-week, one arm remained on standard of care (SOC), the other arm received metformin, adjunctive to SOC. After 2-week washout, patients crossed over and continued for another 6 weeks. Glycaemic variability, other glycaemic parameters and metabolic profile were monitored. RESULTS: There were significant reduction in metformin group for GV: mean (0.18 ± 1.73 vs -0.95 ± 1.24, p = 0.014), %CV (-15.84 (18.92) vs -19.08 (24.53), p = 0.044), glycemic risk assessment of diabetes equation (-0.69 (3.83) vs -1.61 (3.61), p = 0.047), continuous overlapping net glycaemic action (0.25 ± 1.62 vs -0.85 ± 1.22, p = 0.013), J-index (-0.75 (21.91) vs -7.11 (13.86), p = 0.034), time in range (1.13 ± 14.12% vs 10.83 ± 15.47%, p = 0.032); changes of systolic blood pressure (2.78 ± 11.19 mmHg vs -4.30 ± 9.81 mmHg, p = 0.027) and total daily dose (TDD) insulin (0.0 (3.33) units vs -2.17 (11.45) units, p = 0.012). Hypoglycaemic episodes were not significant in between groups. CONCLUSION: Metformin showed favourable effect on GV in overweight/obese T1DM patients and reduction in systolic blood pressure, TDD insulin, fasting venous glucose and fructosamine.

Beyond hyperglycemia: glycaemic variability as a prognostic factor after acute ischemic stroke.

INTRODUCTION: Glycaemic variability (GV) refers to variations in blood glucose levels, and may affect stroke outcomes. This study aims to assess the effect of GV on acute ischaemic stroke progression. METHODS: We performed an exploratory analysis of the multicentre, prospective, observational GLIAS-II study. Capillary glucose levels were measured every 4 hours during the first 48 hours after stroke, and GV was defined as the standard deviation of the mean glucose values. The primary outcomes were mortality and death or dependency at 3 months. Secondary outcomes were in-hospital complications, stroke recurrence, and the impact of the route of insulin administration on GV. RESULTS: A total of 213 patients were included. Higher GV values were observed in patients who died (n = 16; 7.8%; 30.9 mg/dL vs 23.3 mg/dL; p = 0.05). In a logistic regression analysis adjusted for age and comorbidity, both GV (OR = 1.03; 95% CI, 1.003-1.06; p = 0.03) and stroke severity (OR = 1.12; 95% CI, 1.04-1.2; p = 0.004) were independently associated with mortality at 3 months. No association was found between GV and the other outcomes. Patients receiving subcutaneous insulin showed higher GV than those treated with intravenous insulin (38.95 mg/dL vs 21.34 mg/dL; p < 0.001). CONCLUSIONS: High GV values during the first 48 hours after ischaemic stroke were independently associated with mortality. Subcutaneous insulin may be associated with higher VG levels than intravenous administration.

Chronic hyperglycaemia increases the vulnerability of the hippocampus to oxidative damage induced during post-hypoglycaemic hyperglycaemia in a mouse model of chemically induced type 1 diabetes.

AIMS/HYPOTHESIS: Chronic hyperglycaemia and recurrent hypoglycaemia are independently associated with accelerated cognitive decline in type 1 diabetes. Recurrent hypoglycaemia in rodent models of chemically induced (streptozotocin [STZ]) diabetes leads to cognitive impairment in memory-related tasks associated with hippocampal oxidative damage. This study examined the hypothesis that post-hypoglycaemic hyperglycaemia in STZ-diabetes exacerbates hippocampal oxidative stress and explored potential contributory mechanisms. METHODS: The hyperinsulinaemic glucose clamp technique was used to induce equivalent hypoglycaemia and to control post-hypoglycaemic glucose levels in mice with and without STZ-diabetes and Nrf2-/- mice (lacking Nrf2 [also known as Nfe2l2]). Subsequently, quantitative proteomics based on stable isotope labelling by amino acids in cell culture and biochemical approaches were used to assess oxidative damage and explore contributory pathways. RESULTS: Evidence of hippocampal oxidative damage was most marked in mice with STZ-diabetes exposed to post-hypoglycaemic hyperglycaemia; these mice also showed induction of Nrf2 and the Nrf2 transcriptional targets Sod2 and Hmox-1. In this group, hypoglycaemia induced a significant upregulation of proteins involved in alternative fuel provision, reductive biosynthesis and degradation of damaged proteins, and a significant downregulation of proteins mediating the stress response. Key differences emerged between mice with and without STZ-diabetes following recovery from hypoglycaemia in proteins mediating the stress response and reductive biosynthesis. CONCLUSIONS/INTERPRETATION: There is a disruption of the cellular response to a hypoglycaemic challenge in mice with STZ-induced diabetes that is not seen in wild-type non-diabetic animals. The chronic hyperglycaemia of diabetes and post-hypoglycaemic hyperglycaemia act synergistically to induce oxidative stress and damage in the hippocampus, possibly leading to irreversible damage/modification to proteins or synapses between cells. In conclusion, recurrent hypoglycaemia in sub-optimally controlled diabetes may contribute, at least in part, to accelerated cognitive decline through amplifying oxidative damage in key brain regions, such as the hippocampus. DATA AVAILABILITY: The datasets generated during and/or analysed during the current study are available in ProteomeXchange, accession no. 1-20220824-173727 ( www.proteomexchange.org ). Additional datasets generated during and/or analysed during the present study are available from the corresponding author upon reasonable request.