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Glucose variability (GV)-the degree of fluctuation in glucose levels over a certain period of time-is emerging as an important parameter of dynamic glycemic control. Repeated glycemic oscillations have been reported to be the link to diabetes complications. This prospective observational study aims to: (1) identify multilevel risk factors (personal and social-built environmental factors) associated with high GV; (2) identify "within-person predictors" of high GV leveraging the intra-person data to inform future personalized diabetes interventions; and (3) examine which lifestyle factors either mediate or moderate the relationship between emotional well-being and GV among diverse adults with type 2 diabetes (T2D). We will recruit 200 adults with T2D from the community. All participants will complete baseline surveys assessing demographics, lifestyle, social-built environmental, and clinical factors. Real-time dynamic glucose levels will be measured using continuous glucose monitoring (CGM). Sleep, physical activity, diet/eating, and emotional well-being will be measured with an actigraphy device and a real-time self-report tool (ecological momentary assessment [EMA]) across 14 days. Two 24-h dietary recall data will be collected by online video calls. Generalized linear models, multilevel models, and structural equation models will be developed to achieve the study aims. The findings from the study will identify high-risk groups of high GV who would benefit from CGM to improve diabetes outcomes and inform the future development of personalized just-in-time interventions targeting lifestyle behaviors with an increased understanding of GV and by supporting healthcare providers' clinical decisions.

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AIMS: To assess the effects of IDegLira on glucometric indices deriving from intermittently scanned Continuous Glucose Monitoring (isCGM) in patients with type 2 diabetes (T2D). METHODS: Retrospective, observational, cohort, multi-center, "pre - post" study. All adults consecutively identified in the medical records who started treatment with IDegLira, and for whom an isCGM report before and after the initiation of IDegLira was available were included in the study. Time in range (TIR) represented the primary endpoint. Additional glucometric indices, insulin doses and body weight were also assessed. RESULTS: Overall, 87 patients were included by 5 diabetes centers [mean age 70.2 ± 11.0 years, mean duration of T2D 15.5 ± 9.6 years; BMI 29.4 ± 5.4 kg/m2, baseline HbA1c 9.1 ± 2.1%, 33% insulin naïve, 20.7% treated with basal-oral therapy (BOT), and 46% treated with multiple daily injections of insulin (MDI)]. After an average of 1.7 weeks from IDegLira initiation, TIR significantly increased from 56.8 ± 23.5% to 81.3 ± 13.5% (p < 0.0001), TAR decreased from 42.3 ± 24.2% to 17.1 ± 13.6% (p < 0.0001), while TBR remained steadily low (from 1.3 ± 2.3% to 1.4 ± 2.6%; p = 0.62). Estimated HbA1c decreased from 9.1 ± 2.1% to 6.7 ± 0.6% (p < 0.0001) and percentage of patients with a blood glucose coefficient of variation ≥ 36% dropped from 33.2 to 13.8% (p = 0.0005). In patients on MDI, the reduction in the total insulin dose was substantial (from 55.8 ± 31.2 IU to 27.2 ± 12.3 U). CONCLUSIONS: In T2D patients with poor metabolic control, either insulin naïve or treated with BOT or MDI, the introduction of IDegLira produces a significant increase in the time spent in good metabolic control and a marked reduction in glycemic fluctuations.

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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.

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In this three-year retrospective study, data from 51 patients with type 1 or type 2 diabetes mellitus (DM), receiving a minimum of 3-4 insulin injections per day and self-monitoring their blood glucose (SMBG) four times a day, were derived from our internal medicine residency primary care clinic. The patients were equipped with a continuous glucose monitoring (CGM) device that shared 24-hour glucose data with the clinic. They were assigned to members of our CGM team, which included internal medicine or transitional year medical residents who functioned under the supervision of a board-certified endocrinologist. The residents, in consultation with our endocrinologist, assessed the patients' glucose management data and adjusted their treatment regimens biweekly by calling the patients, and monthly by seeing the patients in the clinic. Significant results from the study include a reduction in HbA1c from 9.9% to 7.6%, an average blood glucose decrement from 242 mg/dL to 169 mg/dL, a reduction in the incidence of mild hypoglycemia from below 70 mg/dL to 54 mg/dL, from 4.68% to 0.76% per day, and a more pronounced hypoglycemia with glucose less than 54 mg/dL from 3.1% per day to 0.2% per day. We observed a significant increase in the time in the range of the blood glucose from 33% to 67% per day. Furthermore, 9.5% of the patients in this study eventually discontinued their daily insulin injections and continued treatment with oral diabetic medications with or without the use of injectable GLP-1 receptors once a week. Our study affirms that CGM devices significantly improve glycemic control compared to SMBG, supporting its efficacy in optimizing glycemic control in real-world clinical practice. The results imply that this can be accomplished in internal medicine residency clinics and not exclusively in specialized endocrine clinics. As far as we know, this is the first study of its kind in a residency clinic in the USA. This study confirms the benefits of widening the application of CGM in DM, along with the challenges that must be overcome to realize the evidence-based benefits of this technology. CGM needs to become a part of routine monitoring for type 1 and type 2 DM.

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Objective: After fully lifting coronavirus disease 2019 (COVID-19) pandemic control measures in mainland China in 12/2022, the incidence of COVID-19 has increased markedly, making it difficult to meet the general time-in-range (TIR) requirement. We investigated a more clinically practical TIR threshold and examined its association with the prognosis of COVID-19 patients with type 2 diabetes(T2D). Research design and methods: 63 T2D patients complicated with COVID-19 were evaluated. Patients were divided into favorable outcome group and adverse outcome group according to whether achieving composite endpoint (a >20-day length of stay, intensive care unit admission, mechanical ventilation use, or death). TIR, the time-below-range (TBR) and the time-above-range (TAR) were calculated from intermittently scanned continuous glucose monitoring. Logistic regression analysis and other statistical methods were used to analyze the correlation between glucose variability and prognosis to establish the appropriate reference range of TIR. Results: TIR with thresholds of 80 to 190 mg/dL was significantly associated with favorable outcomes. An increase of 1% in TIR is connected with a reduction of 3.70% in the risk of adverse outcomes. The Youden index was highest when the TIR was 54.73%, and the sensitivity and specificity were 58.30% and 77.80%, respectively. After accounting for confounding variables, our analysis revealed that threshold target ranges (TARs) ranging from 200 mg/dL to 230 mg/dL significantly augmented the likelihood of adverse outcomes. Conclusion: The TIR threshold of 80 to 190 mg/dL has a comparatively high predictive value of the prognosis of COVID-19. TIR >54.73% was associated with a decreased risk of adverse outcomes. These findings provide clinically critical insights into possible avenues to improve outcomes for COVID-19 patients with T2D.

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Background: The nutritional status of the patients before critical illness and nutrition support given during the critical illness play an important role in the recovery. We aimed to evaluate the nutritional prescription and its effect on ICU mortality. Materials and methods: This was a prospective observational study conducted after institutional ethical committee approval (IEC 94/2018, CTRI/2018/06/014625) in a case-mixed (medical and surgical) ICU. Patients admitted to the ICU were enrolled within 24 hours of admission. The amount of calories and proteins prescribed and received by the patients was collected for 7 days. The primary outcome was ICU mortality. Results: A total of 100 patients were included. The mean age was 48.63 (16.25) years, and 62% were males. The acute physiology and chronic health evaluation (APACHE II), sequential organ failure assessment (SOFA), and modified Nutric (mNUTRIC) scores were comparable between the two groups. The ICU mortality was 30%. The calorie and protein deficits were comparable between survivors and non-survivors. Among the secondary outcomes, a significant time effect (p = 0.013) and interaction effect (p = 0.004) were noted for maximum glucose levels. The glucose variability calculated by coefficient of variation (CV) was significantly higher in non-survivors than survivors (p = 0.031). Conclusion: The calorie and protein deficits did not affect ICU mortality. The maximum glucose variability and CV were significant parameters associated with ICU mortality. How to cite this article: Havaldar AA, Selvam S. Nutritional Prescription in ICU Patients: Does it Matter? Indian J Crit Care Med 2024;28(7):657-661.

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BACKGROUND: Elevated blood glucose (BG) variability has been reported as an independent risk factor for poor prognosis in a variety of diseases. This study aimed to investigate the association between BG variability and clinical outcomes in patients with spontaneous cerebellar hemorrhage (SCH) undergoing surgical operation. METHODS: This retrospective cohort study of the consecutive patients admitted to the department of Neurosurgery, the Affiliated Hospital of Qingdao University between January 2014 and June 2022 with the diagnosis of SCH underwent surgical intervention. BG analysis was continuously and routinely performed. BG variability was represented by the standard deviation (SD) of the serial measurements within the first 7 days. The general characteristics, imageological information, blood glucose level, and surgical information were reviewed and compared through medical records. RESULTS: A total of 115 patients (65 male and 50 female) were enrolled. Out of all 115 patients, the overall clinical outcomes according to the modified Rankin Scale (mRS) were poor (mRS 3-6) in 31 patients (26.96%) and good (mRS 0-2) in 84 patients (73.04%). Twelve of the 115 patients died during hospitalization, and the mortality rate was 10.43%. Multivariate logistic regression analysis showed that SD of BG (odds ratio (OR), 4.717; 95% confidence interval (CI), 1.054-21.115; P = 0.043), GCS (OR, 0.563; 95% CI, 0.330-0.958; P = 0.034), and hematoma volume (OR, 1.395; 95% CI, 1.118-1.748; P = 0.003) were significant predictors. The area under the ROC curve of SD of BG was 0.911 (95% CI, 0.850-0.973; P < 0.001) with a sensitivity and specificity of 90.3% and 83.3%, respectively, and the cut-off value was 1.736. CONCLUSIONS: High BG Variability is independently correlated with the 6-month poor outcomes in patients with SCH undergoing surgical operation.

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The efficacy of glucagon-like peptide-1 receptor agonists (GLP1-RA) in type 2 diabetes mellitus is well-established. GLP1-RAs are not approved for use in type 1 diabetes mellitus (T1DM). A 34-year-old woman with a 23-year history of T1DM presented for review for weight gain (weight 63 kg, BMI 26.9 kg/m2) and increased HbA1c (8.3%) and glycemic variability. Subcutaneous semaglutide (1 mg weekly) was commenced. After two months, there was decrease in weight by 12 kg, body fat percent by 15%, visceral fat by 7%, and a reduction in insulin dose, glycemic variability, and HbA1c. Semaglutide could be an important adjunct to insulin treatment in T1DM.

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BACKGROUND: A variety of metrics are used to describe glycemic variation, some of which may be difficult to comprehend or require complex strategies for smoothing of the glucose curve. We aimed to describe a new metric named time with rapid change of glucose (TRC), which is presented as percentage of time, similar to time above range (TAR), time in range (TIR), and time below range (TBR). METHOD: We downloaded glucose data for 90 days from 159 persons with type 1 diabetes using the Abbott Freestyle Libre version 1. We defined TRC as the proportion of time (%) with an absolute rate of change of glucose > 1.5 mmol/L/15 minutes (1.8mg/dL/min) corresponding to a minimum rate of change for glucose in the 3.9-10.0 mmol/L (70-180 mg/dL) range within 1 hour. TRC is related to the other glucose variability metrics: CV within day (CVw) and mean amplitude of glycemic excursion (MAGE). RESULTS: The more than 1.27 million glucose rates were t-location scale distributed with SD 0.91 mmol/L/15 min (1.1 mg/dL/15 min). The median TRC was 6.9% (IQR 4.5%-9.5%). The proportion of TRC with positive slope was 3.9% (2.6%-5.3%) and significantly higher than the proportion with negative slope 2.8% (1.5%-4.4%) P < .001. TRC correlated with CVw and MAGE (Spearman's correlation coefficient .56 and .65, respectively, P < .001). CONCLUSION: TRC is proposed as an easily perceived metric to compare the performance of hybrid or fully automated closed-loop insulin delivery systems to obtain glucose homeostasis.

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Introduction: Hypoglycemia has been associated with cardiovascular events, and glucose variability has been suggested to be associated with increased cardiovascular risk. Therefore, in this study, we examined the effect on proteomic cardiovascular risk protein markers of (i) mild iatrogenic hypoglycemia and (ii) severe iatrogenic hypoglycemia followed by rebound hyperglycemia. Methods: Two iatrogenic hypoglycemia studies were compared; firstly, mild hypoglycemia in 18 subjects (10 type 2 diabetes (T2D), 8 controls; blood glucose to 2.8 mmoL/L (50 mg/dL) for 1 h), and secondly, severe hypoglycemia in 46 subjects (23 T2D, 23 controls; blood glucose to <2.2 mmoL/L (<40 mg/dL) transiently followed by intravenous glucose reversal giving rebound hyperglycemia). A SOMAscan assay was used to measure 54 of the 92 cardiovascular protein biomarkers that reflect biomarkers involved in inflammation, cellular metabolic processes, cell adhesion, and immune response and complement activation. Results: Baseline to euglycemia showed no change in any of the proteins measured in the T2D cohort. With severe hypoglycemia, the study controls showed an increase in Angiopoietin 1 (ANGPT1) (p < 0.01) and Dickkopf-1 (DKK1) (p < 0.01), but no changes were seen with mild hypoglycemia. In both the mild and severe hypoglycemia studies, at the point of hypoglycemia, T2D subjects showed suppression of Brother of CDO (BOC) (p < 0.01). At 1 h post-hypoglycemia, the changes in ANGPT1, DKK1, and BOC had resolved, with no additional protein biomarker changes despite rebound hyperglycemia from 1.8 ± 0.1 to 12.2 ± 2.0 mmol/L. Conclusions: Proteomic biomarkers of cardiovascular disease showed changes at hypoglycemia that resolved within 1 h following the hypoglycemic event and with no changes following hyperglycemia rebound, suggesting that any cardiovascular risk increase is due to the hypoglycemia and not due to glucose fluctuation per se.

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Brain function is highly altered by glucose toxicity related to diabetes. High consumption of sugar in normal conditions is suspected to affect as well brain integrity. The present study investigates the possible effects of short-term exposure to high sugar diet on brain redox homeostasis in healthy mice. Male adult healthy mice were divided into two groups: control (CG) and sugar-exposed group (SG), that was exposed continually to 10% of glucose in drinking water for 7 days and 20% sucrose pellets food. Behavior, blood glucose variability and cerebral cortex oxidative stress biomarkers were measured at the end of exposure. Animals exposed to the high sugar diet expressed a significant increase in blood glucose levels and high glucose variability compared to control. These animals expressed as well anxiolytic behavior as revealed by the plus maze test. Exposure to the sugar diet altered redox homeostasis in the brain cortex as revealed by an increase in lipid peroxidation and the activity of antioxidant enzymes superoxide dismutase (SOD) and glutathione-s-transferase (GST). On the other hand, catalase (CAT) activity was decreased, and reduced glutathione (GSH) level was not altered compared to control. Further studies are required to understand the mechanisms trigging oxidative stress (OS) in the brain in response to short term exposure to high sugar diet and glucose fluctuations.

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OBJECTIVES: Glucose fluctuations are more harmful than persistent hyperglycemia for chronic complications of diabetes. However, the relationship between cognition and glucose fluctuations in patients with acute ischemic stroke (AIS) complicated by type 2 diabetes mellitus (T2DM) remains unclear. We aimed to evaluate the association between short-term glucose fluctuations and cognition in patients with AIS complicated by T2DM. DESIGN: A cohort study with a 2-year follow-up. SETTING AND PARTICIPANTS: We included 554 patients with mild AIS (mean age: 62 years; 170 females and 384 males). MEASUREMENTS: Glucose variability (GV) was evaluated using glycated hemoglobin (HbA1c), stress hyperglycemia (SHR), standard deviation of blood glucose (SDBG), mean postprandial blood glucose (MPBG), mean amplitude of glycemic excursion (MAGE), and time in range (TIR). We evaluated the relationship between GV, fasting blood glucose (FBG) and cognition during the acute phase using linear regression analysis. We evaluated the relationship between GV, FBG and the occurrence of post-stroke cognitive impairment (PSCI) using a logistic regression model. Mediation analyses were fitted to explore whether the relationships of HbA1c with cognition were mediated by cerebral small vessel disease (CSVD). RESULTS: A clear pattern of age-related GV was observed. Higher SHR in middle-aged participants; higher HbA1c, and lower TIR in older participants; and higher MAGE, MPBG, and SDBG across a broad age range (50-80 years) were associated with cognitive impairment in the acute phase of AIS. Higher SHR and SDBG together with lower TIR in middle-aged participants, higher HbA1c in older participants, and higher FBG, MPBG, and MAGE across a broad age range (50-80 years) were associated with the occurrence of PSCI. The association between HbA1c and cognition was partially mediated (proportion: 7-16%) by CSVD. CONCLUSIONS: Short-term glucose fluctuations are associated with cognition and a higher risk of PSCI in patients with AIS complicated by T2DM. CSVD might play an important role in the relationship between short-term glucose fluctuations and cognition.

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PURPOSE: To analyze the time in tight range (TITR), and its relationship with other glucometric parameters in patients with type 1 diabetes (T1D) treated with advanced hybrid closed-loop (AHCL) systems. METHODS: A prospective observational study was conducted on pediatric and adult patients with T1D undergoing treatment with AHCL systems for at least 3 months. Clinical variables and glucometric parameters before and after AHCL initiation were collected. RESULTS: A total of 117 patients were evaluated. Comparison of metabolic control after AHCL initiation showed significant improvements in HbA1c (6.9 ± 0.9 vs. 6.6 ± 0.5%, p < 0.001), time in range (TIR) (68.2 ± 11.5 vs. 82.5 ± 6.9%, p < 0.001), TITR (43.7 ± 10.8 vs. 57.3 ± 9.7%, p < 0.001), glucose management indicator (GMI) (6.9 ± 0.4 vs. 6.6 ± 0.3%, p < 0.001), time below range (TBR) 70-54 mg/dl (4.3 ± 4.5 vs. 2.0 ± 1.4%, p < 0.001), and time above range (TAR) > 180 mg/dl (36.0 ± 7.6 vs. 15.1 ± 6.4%, p < 0.001). Coefficient of variation (CV) also improved (36.3 ± 5.7 vs. 30.6 ± 3.7, p < 0.001), while time between 140-180 mg/dl remained unchanged. In total, 76.3% achieved TITR > 50% (100% pediatric). Correlation analysis between TITR and TIR and GRI showed a strong positive correlation, modified by glycemic variability. CONCLUSIONS: AHCL systems achieve significant improvements in metabolic control (TIR > 70% in 93.9% patients). The increase in TIR was not related to an increase in TIR 140-180 mg/dl. Despite being closely related to TIR, TITR allows for a more adequate discrimination of the achieved control level, especially in a population with good initial metabolic control. The correlation between TIR and TITR is directly influenced by the degree of glycemic variability.

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Purpose: This study aimed to investigate the glycometabolism, fat mass, and lean mass in primary aldosteronism (PA) during disease progression. Patients and Methods: Patients diagnosed with PA and healthy controls (HCs) were enrolled. A flash glucose monitoring system (FGMS) and dual-energy X-ray absorptiometry (DEXA) were used to measure glucose variability and glucose target rate along with fat mass and lean mass. Comparative analysis of FGMS- or DEXA-derived parameters along with correlation analyses between these parameters and PA progression were performed. Results: Increased glucose variability and poor glucose target rate, along with an increased proportion of truncal fat mass, and decreased proportion of appendicular lean mass, were identified in PA group compared to those in HCs. Plasma aldosterone concentration was positively correlated with glucose variability and poor glucose target rate. Plasma renin concentration was positively correlated with the proportion of truncal fat mass and lean mass, and negatively correlated with the proportion of appendicular fat mass. Aldosterone-to-renin ratio was negatively correlated with the proportion of truncal fat mass and lean mass, and positively correlated with the proportion of appendicular fat mass. Conclusion: Patients with PA presented significant differences in glycometabolism, fat mass, and lean mass compared with HCs, and these alterations correlated with PA progression.

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Background: Computer simulators of human metabolism are powerful tools to design and validate new diabetes treatments. However, these platforms are often limited in the diversity of behaviors and glycemic conditions they can reproduce. Replay methodologies leverage field-collected data to create ad hoc simulation environments representative of real-life conditions. After formal validations of our method in prior publications, we demonstrate its capacity to reproduce a recent clinical trial. Methods: Using the replay methodology, an ensemble of replay simulators was generated using data from a randomized crossover clinical trial comparing the hybrid closed loop (HCL) and fully closed loop (FCL) control modalities in automated insulin delivery (AID), creating 64 subject/modality pairs. Each virtual subject was exposed to the alternate AID modality to compare the simulated versus observed glycemic outcomes. Equivalence tests were performed for time in, below, and above range (TIR, TBR, and TAR) and high and low blood glucose indices (HBGI and LBGI) considering equivalence margins corresponding to clinical significance. Results: TIR, TAR, LBGI, and HBGI showed statistical and clinical equivalence between the original and the simulated data; TBR failed the equivalence test. For example, in the HCL mode, simulated TIR was 84.89% versus an observed 84.31% (P = 0.0170, confidence interval [CI] [-3.96, 2.79]), and for FCL mode, TIR was 76.58% versus 77.41% (P = 0.0222, CI [-2.54, 4.20]). Conclusion: Clinical trial data confirm the prior in silico validation of the UVA replay method in predicting the glycemic impact of modified insulin treatments. This in vivo demonstration justifies the application of the replay method to the personalization and adaptation of treatment strategies in people with type 1 diabetes.

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BACKGROUND AND AIMS: The Q-Score is a single-number composite metric that is constructed based on the following components: central glycemic tendency, hyperglycemia, hypoglycemia, and intra- and interday variability. Herein, we refined the Q-Score for the screening and analysis of short-term glycemic control using continuous glucose monitoring (CGM) profiles. METHODS: Continuous glucose monitoring profiles were obtained from noninterventional, retrospective cross-sectional studies. The upper limit of the Q-Score component hyperglycemia' that is, the time above target range (TAR), was adjusted from 8.9 to 10 mmol/L (n = 1562 three-day-sensor profiles). A total of 302 people with diabetes mellitus treated with intermittent CGM for ≥14 days were enrolled. The time to stability was determined via correlation-based analysis. RESULTS: There was a strong correlation between the Q-Scores of the two TARs, that is, 8.9 and 10 mmol/L (Q-ScoreTAR10 = -0.03 + 1.00 Q-ScoreTAR8.9, r = .997, p < .001). The times to stability of the Q-Score and TIR were 10 and 12 days, respectively. The Q-Score was correlated with fructosamine concentrations, the glucose management indicator (GMI), the time in range (TIR), and the glycemic risk index (GRI) (r = .698, .887, -.874, and .941), respectively. The number of Q-Score components above the target increased as the TIR decreased, from two (1.7 ± 0.9) in CGM profiles with a TIR between 70% and 80% to four (3.9 ± 0.5) in the majority of the CGM profiles with a TIR below 50%. A conversion matrix between the Q-Score and glycemic indices was developed. CONCLUSIONS: The Q-Score is a tool for assessing short-term glycemic control. The Q-Score can be translated into clinician opinion using the GRI.

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BACKGROUND: The aim of this study was to describe the clinical characteristics of people with diabetic foot ulcer (DFU) according to glucose variability (GV) and to investigate the relationship between GV and DFU outcome in a population with type 2 diabetes (T2D) and DFU. METHODS: This is a retrospective study of 300 individuals aged 64.3 years (181 males) treated for DFU in a tertiary-care center with a regular follow-up for 6 months. Laboratory measurements and clinical assessments were collected at baseline. According to the coefficient of variation (CV) cut-off (≥36%), people were divided into two groups (low and high GV). RESULTS: Compared with low GV group (n = 245), high GV group (n = 55) had significant longer duration of diabetes [low vs high GV, mean ± Standard Deviation (SD), 17.8 ± 11.8 vs 22.4 ± 10.8, P = 0.012], higher levels of glycated haemoglobin [median (IQR), 7.4 (6.6, 8.8) vs 8.2 (7.0, 9.6), P = 0.010] and urinary albumin excretion [25.2 (11.9, 77.0) vs 48.0 (23.2, 106.0), P = 0.031]. Moreover, 10 days self-monitoring of blood glucose-derived glycemic metrics were significantly different between groups. No differences among clinical features were found. The multiple logistic regression analysis identified CV and SD as negative predictors of healing. CONCLUSIONS: In a population of people with T2D and DFU treated in a tertiary-care center, individuals with high GV had a 3-fold higher risk of healing failure, as compared with those with low GV. CV and SD were related to poor healing within 6 months follow-up.

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Background: Although studies have shown that glycemic variability is positively associated with an increased risk of cardiovascular disease, few studies have compared hemoglobin A1c (HbA1c) and fasting plasma glucose (FPG) variability with adverse cardiovascular events in patients with type 2 diabetes mellitus (T2DM). Methods: This was a post hoc analysis of the Action to Control Cardiovascular Risk in Diabetes (ACCORD) study. Cox proportional hazards models were used to explore the relationship between HbA1c or FPG variability and the incidence of major adverse cardiovascular events (MACEs). Results: In total, 9,547 patients with T2DM were enrolled in this study. During the median 4.6 ± 1.5 years follow-up period, 907 patients developed MACEs. The risk of MACEs increased in the HbA1c variability group in each higher quartile of HbA1c variability (P < 0.01). Compared with those in the first quartile of HbA1c variability, patients in the fourth quartile had a hazard ratio of 1.37 (Model 2, 95% confidence interval: 1.13-1.67) for MACEs. Higher FPG variability was not associated with a higher risk of MACEs in patients with T2DM (P for trend=0.28). A U-shaped relationship was observed between HbA1c and FPG variability, and MACEs. Glucose control therapy modified the relationship between HbA1c and MACEs; participants with higher HbA1c variability receiving intensive glucose control were more likely to develop MACEs (P for interaction <0.01). Conclusion: In adults with T2DM, the relationship between glycemic variability evaluated using HbA1c and FPG was U-shaped, and an increase in HbA1c variability rather than FPG variability was significantly associated with MACEs. The relationship between HbA1c variability and MACEs was affected by the glucose control strategy, and a higher HbA1c variability was more strongly associated with MACEs in patients receiving an intensive glucose control strategy.

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Continuous glucose monitoring (CGM) has become the standard of care in diabetes management with the recent advances in technology and accessibility in the last decade. An International Consensus was established to define CGM metrics and its goals in diabetes care. The 2019 International Consensus suggested 14 days of CGM sampling for the assessment of CGM metrics stating the limitations that may occur for hypoglycemia and glycemic variability metrics. Since then, several studies assessed the correlation between CGM metrics and duration of the sampling period. This review summarized the studies that investigated the relationship between 14-day CGM sampling to 90-day CGM data in >70% CGM users for all CGM metrics and highlighted possible solutions for more accurate CGM sampling durations in type 1 diabetes (T1D). Accumulating evidence showed that 14-day CGM sampling correlates well with 90-day CGM data for mean glucose, time in 70-180 mg/dL, and hyperglycemia metrics; however, it correlates weakly for hypoglycemia and glycemic variability metrics. In the studies included in this review, in adults with T1D, minimum sampling duration was 14 days for mean glucose, time in 70-180 mg/dL, and time in hyperglycemia (>180 and >250 mg/dL); however, minimum sampling duration varied between 21 to 30 days for time <70 mg/dL, 30 to 35 days for time <54 mg/dL, and 28 to 35 days for coefficient of variation. Longer than 14 days of CGM, sampling was required to properly assess hypoglycemia and glycemic variability in T1D.

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