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Background No studies have assessed if 2‐week of continuous glucose monitoring (CGM) data provide good estimation of long‐term glycemic control and glucose variability (GV) in pediatric patients with type 1 diabetes (T1D) as in adults. Methods Six hundred fifty‐four T1D pediatric patients were enrolled and 12‐weeks of CGM data, before HbA1c measurement, were collected. Metrics of glycemic control and GV in incremental sampling periods were calculated. The agreement between metrics calculated in the sampling periods and the full 12‐week period was assessed with correlation analysis (R2), median relative absolute difference (RAD) or absolute difference in the entire study populations and subjects stratified by age, pubertal status, insulin therapy (MDI,CSII), type of CGM (intermittently scanned [isCGM], real‐time [rtCGM]), and HbA1c level. Results Correlations with metrics of the full 12‐week period improved by extending the sampling periods. R2 values close to 0.90 using 4‐week period were significantly higher than 2‐week period, particularly for coefficient of variation, mean glucose SD, percentage of time below the range <70 mg/dL. A significant difference was found comparing the median RAD of 2‐ and 4‐week, especially for mean glucose and coefficient of variation. Similar results were obtained analyzing subjects according to age and pubertal status, whereas in patients with HbA1c ≤7%, using rtCGM and CSII significant correlations were found for 2‐week period. Conclusions In T1D pediatric subjects, 4‐week CGM data better reflects long‐term glycemic control and GV in MDI and isCGM users. The 2‐week period may be acceptably accurate in CSII and rtCGM users, especially in those with good glycometabolic control.

Several studies suggest that, together with glucose variability, the variability of other risk factors, as blood pressure, plasma lipids, heart rate, body weight, and serum uric acid, might play a role in the development of diabetes complications. Moreover, the variability of each risk factor, when contemporarily present, may have additive effects. However, the question is whether variability is causal or a marker. Evidence shows that the quality of care and the attainment of the target impact on the variability of all risk factors. On the other hand, for some of them causality may be considered. Although specific studies are still lacking, it should be useful checking the variability of a risk factor, together with its magnitude out of the normal range, in clinical practice. This can lead to an improvement of the quality of care, which, in turn, could further hesitate in an improvement of risk factors variability.

AimsCVD is the main cause of morbidity and mortality in individuals with diabetes. It is currently unclear whether daily glucose variability contributes to CVD. Therefore, we investigated whether glucose variability is associated with arterial measures that are considered important in CVD pathogenesis.MethodsWe included participants of The Maastricht Study, an observational population-based cohort, who underwent at least 48 h of continuous glucose monitoring (CGM) (n = 853; age: 59.9 ± 8.6 years; 49% women, 23% type 2 diabetes). We studied the cross-sectional associations of two glucose variability indices (CGM-assessed SD [SDCGM] and CGM-assessed CV [CVCGM]) and time in range (TIRCGM) with carotid–femoral pulse wave velocity (cf-PWV), carotid distensibility coefficient, carotid intima–media thickness, ankle–brachial index and circumferential wall stress via multiple linear regression.ResultsHigher SDCGM was associated with higher cf-PWV after adjusting for demographics, cardiovascular risk factors and lifestyle factors (regression coefficient [B] per 1 mmol/l SDCGM [and corresponding 95% CI]: 0.413 m/s [0.147, 0.679], p = 0.002). In the model additionally adjusted for CGM-assessed mean sensor glucose (MSGCGM), SDCGM and MSGCGM contributed similarly to cf-PWV (respective standardised regression coefficients [st.βs] and 95% CIs of 0.065 [−0.018, 0.167], p = 0.160; and 0.059 [−0.043, 0.164], p = 0.272). In the fully adjusted models, both higher CVCGM (B [95% CI] per 10% CVCGM: 0.303 m/s [0.046, 0.559], p = 0.021) and lower TIRCGM (B [95% CI] per 10% TIRCGM: −0.145 m/s [−0.252, −0.038] p = 0.008) were statistically significantly associated with higher cf-PWV. Such consistent associations were not observed for the other arterial measures.ConclusionsOur findings show that greater daily glucose variability and lower TIRCGM are associated with greater aortic stiffness (cf-PWV) but not with other arterial measures. If corroborated in prospective studies, these results support the development of therapeutic agents that target both daily glucose variability and TIRCGM to prevent CVD.

Abstract Context The interaction of glycated hemoglobin A1c (HbA1c) and glycemic variability in relation to diabetes-related outcomes remains unknown. Objective To evaluate the relationship between HbA1c and all-cause mortality across varying degrees of glycemic variability in patients with type 2 diabetes. Design, Setting, and Patients This was a prospective study conducted in a single referral center. Data of 6090 hospitalized patients with type 2 diabetes was analyzed. Glucose coefficient of variation [coefficient of variation (CV)] was obtained as the measure of glycemic variability by using continuous glucose monitoring for 3 days. Cox proportional hazards regression models were used to estimate hazard ratios and 95% CIs for all-cause mortality. Results During a median follow-up of 6.8 years, 815 patients died. In patients with the lowest and middle tertiles of glucose CV, HbA1c ≥ 8.0% was associated with 136% (95% CI 1.46-3.81) and 92% (95% CI 1.22-3.03) higher risks of all-cause mortality, respectively, as compared with HbA1c 6.0%-6.9%, after adjusting for confounders. However, a null association of HbA1c with mortality was found in patients with the highest tertile of glucose CV. Conclusions HbA1c may not be a robust marker of all-cause mortality in patients with high degree of glycemic variability. New metrics of glycemic control may be needed in these individuals to achieve better diabetes management.

Abstract Context Sleep disruption is associated with worse glucose metabolic control and altered gut microbiota in animal models. Objective We aimed to evaluate the possible links among rapid eye movement (REM) sleep duration, continuous glucose levels, and gut microbiota composition. Methods This observational, prospective, real-life, cross-sectional case-control study included 118 (60 with obesity), middle-aged (39.1-54.8 years) healthy volunteers recruited at a tertiary hospital. Glucose variability and REM sleep duration were assessed by 10-day continuous glucose monitoring (CGM) (Dexcom G6) and wrist actigraphy (Fitbit Charge 3), respectively. The coefficient of variation (CV), interquartile range (IQR), and SD of glucose variability was assessed and the percentage of time in range (% TIR), at 126-139 mg/dL (TIR2), and 140-199 mg/dL (TIR3) were calculated. Shotgun metagenomics sequencing was applied to study gut microbiota taxonomy and functionality. Results Increased glycemic variability (SD, CV, and IQR) was observed among subjects with obesity in parallel to increased % TIR2 and % TIR3. REM sleep duration was independently associated with % TIR3 (β = −.339; P < .001) and glucose variability (SD, β = −.350; P < .001). Microbial taxa from the Christensenellaceae family (Firmicutes phylum) were positively associated with REM sleep and negatively with CGM levels, while bacteria from Enterobacteriacea family and bacterial functions involved in iron metabolism showed opposite associations. Conclusion Decreased REM sleep duration was independently associated with a worse glucose profile. The associations of species from Christensenellaceae and Enterobacteriaceae families with REM sleep duration and continuous glucose values suggest an integrated picture of metabolic health.

Over the past two decades there has been a substantial rise in the adoption of diabetes therapeutic technology among children, adolescents and younger adults with type 1 diabetes, and its use is now also advocated for older individuals. Older people with diabetes are more prone to experience hypoglycaemia because of numerous predisposing factors and are at higher risk of hypoglycaemic events requiring third-party assistance as well as other adverse sequelae. Hypoglycaemia may also have long-term consequences, including cognitive impairment, frailty and disability. Diabetes in older people is often characterised by marked glucose variability related to age-associated changes such as variable appetite and levels of physical activity, comorbidities and polypharmacotherapy. Preventing hypoglycaemia and mitigating glucose excursions may have considerable positive impacts on physical and cognitive function and general well-being and may even prevent or improve frailty. Technology for older people includes continuous glucose monitoring systems, insulin pumps, automated insulin delivery systems and smart insulin pens. Clinical trials and real-world studies have shown that older people with diabetes benefit from technology in terms of glucose management, reductions in hypoglycaemic events, emergency department attendance and hospital admissions, and improvement in quality of life. However, ageing may bring physical impairments and other challenges that hinder the use of technology. Healthcare professionals should identify older adults with diabetes who may benefit from therapeutic technology and then adopt an individualised approach to education and follow-up for individuals and their caregivers. Future research should explore the impact of diabetes technology on outcomes relevant to older people with diabetes. Graphical Abstract

A growing body of evidence points to the role of glucose variability (GV) in the development of the microvascular and macrovascular complications of diabetes. In this review, we summarize data on GV-induced biochemical, cellular and molecular events involved in the pathogenesis of diabetic complications. Current data indicate that the deteriorating effect of GV on target organs can be realized through oxidative stress, glycation, chronic low-grade inflammation, endothelial dysfunction, platelet activation, impaired angiogenesis and renal fibrosis. The effects of GV on oxidative stress, inflammation, endothelial dysfunction and hypercoagulability could be aggravated by hypoglycemia, associated with high GV. Oscillating hyperglycemia contributes to beta cell dysfunction, which leads to a further increase in GV and completes the vicious circle. In cells, the GV-induced cytotoxic effect includes mitochondrial dysfunction, endoplasmic reticulum stress and disturbances in autophagic flux, which are accompanied by reduced viability, activation of apoptosis and abnormalities in cell proliferation. These effects are realized through the up- and down-regulation of a large number of genes and the activity of signaling pathways such as PI3K/Akt, NF-κB, MAPK (ERK), JNK and TGF-β/Smad. Epigenetic modifications mediate the postponed effects of glucose fluctuations. The multiple deteriorative effects of GV provide further support for considering it as a therapeutic target in diabetes.

« Variability in glucose homoeostasis » is a better description than « glycaemic variability » as it encompasses two categories of dysglycaemic disorders: i) the short-term daily glucose fluctuations and ii) long-term weekly, monthly or quarterly changes in either HbA1c, fasting or postprandial plasma glucose. Presently, the relationship between the \"variability in glucose homoeostasis\" and diabetes complications has never been fully clarified because studies are either observational or limited to retrospective analysis of trials not primarily designed to address this issue. Despite the absence of definitive evidence from randomized controlled trials (RCTs), it is most likely that acute and long-term glucose homoeostasis \"cycling\", akin to weight and blood pressure \"cycling\" in obese and hypertensive individuals, are additional risk factors for diabetes complications in the presence of sustained ambient hyperglycaemia. As hypoglycaemic events are strongly associated with short- and long-term glucose variability, two relevant messages can be formulated. Firstly, due consideration should be given to avoid within-day glucose fluctuations in excess of 36% (coefficient of variation) at least for minimizing the inconvenience and dangers associated with hypoglycaemia. Secondly, it seems appropriate to consider that variability in glucose homoeostasis is not only associated with cardiovascular events but is also a causative risk factor via hypoglycaemic episodes as intermediary step. Untangling the\" Gordian Knot\", to provide confirmation about the impact of variability in glucose homoeostasis and diabetes complications remains a daunting prospect.

High glycemic variability (GV) is common in critically-ill patients; however, its association with development of chronic critical illness (CCI) and mortality remains unclear. The aim of this study is to determine the effect of GV, on the development of CCI and mortality in critically-ill COVID-19 patients. This study is a retrospective observational study including adult critically-ill COVID-19 patients consecutively admitted to the Medical ICU between 20th March 2020 and 15th June 2021 who had at least three blood glucose measurements on admission day. CCI was defined as ICU length of stay ≥14 days, cardiovascular SOFA score ≥ 1, and other SOFA parameters ≥2 on day 14. In each patient, the mean and SD of blood glucose concentration during ICU stay were calculated. To evaluate variability, the coefficient of variability (GluCV = GluSD / GluMEAN) and mean glucose difference (MGD) (absolute max-min) were calculated for each patient. We also recorded mean, SD, and maximum blood glucose concentrations in the first 7 days of ICU stay. 397 confirmed COVID-19 patients were admitted to the ICU during the study period. Among them, 157 patients had at least three blood glucose samples on admission day. Mean age was 65.1 ± 14.8, mean APACHE II was 17.6 ± 7.6, mean admission SOFA score was 5.5 ± 3.3. 44.6% of the patients developed CCI. 43.9% patients died in the ICU. On the first day of ICU admission, mean glucose level was 165.9 ± 60.1 mg/dL, SD was 30.4 ± 40.1, CV was 17.1 ± 17.1%, and MGD was 50.1 ± 54.6 mg/dL. First week mean glucose level was 165.4 ± 46.9 mg/dL, SD was 47.2 ± 29.5, CV was 27.2 ± 13.8%, and MGD was 156.9 ± 109.6 mg/dL. Comparisons of glycemic variability parameters between patients with CCI and without CCI were not different (p > 0.05 for all). MGD in the first day was higher in patients who died compared to survived patients (60.4 ± 70.1 vs 42.1 ± 45.4, respectively; p = 0.027). First week glycemic variability parameters were higher in patients who died compared to patients who survived: SD 55.5 ± 32.4 vs 40.5 ± 25.3, p = 0.003; CV30.5 ± 15.7 vs 24.6 ± 11.5, p = 0.026; MGD 191.9 ± 119.1 vs 129.5 ± 93.5, p < 0.001. Glucose variability was not found to be associated with the development of CCI but higher glucose variability was associated with ICU mortality in critically-ill COVID-19 patients.

Aims and data synthesis Glucose variability (GV) is increasingly considered an additional index of glycemic control. Growing evidence indicates that GV is associated with diabetic vascular complications, thus being a relevant point to address in diabetes management. GV can be measured using various parameters, but to date, a gold standard has not been identified. This underscores the need for further studies in this field also to identify the optimal treatment. Conclusions We reviewed the definition of GV, the pathogenetic mechanisms of atherosclerosis, and its relationship with diabetic complications.