Rapid Improvement in Weight, Body Composition, and Glucose Variability With Semaglutide in Type 1 Diabetes.

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.

Nutritional Prescription in ICU Patients: Does it Matter?

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.

Effect of Hypoglycemia and Rebound Hyperglycemia on Proteomic Cardiovascular Risk Biomarkers.

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.

Time With Rapid Change of Glucose.

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.

High sugar consumption for seven days in adult mice increased blood glucose variability, induced an anxiolytic effect and triggered oxidative stress in cerebral cortex.

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.

Association between short-term glucose fluctuations and cognition in patients with acute ischemic stroke complicated by type 2 diabetes mellitus.

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.

Association of Aldosterone, Renin, and Aldosterone to Renin Ratio with Metabolic Profile in Primary Aldosteronism.

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.

Utility of time in tight range (TITR) in evaluating metabolic control in pediatric and adult patients with type 1 diabetes in treatment with advanced hybrid closed-loop systems.

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.

Q-Score Complements the Time in Range in the Evaluation of Short-Term Glycemic Control.

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.

Validation of the UVA Simulation Replay Methodology Using Clinical Data: Reproducing a Randomized Clinical Trial.

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.

Short-term glucose variability as a determinant of the healing rate of diabetic foot ulcer: A retrospective study.

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.

Glycemic variability evaluated by HbA1c rather than fasting plasma glucose is associated with adverse cardiovascular events.

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.

Time in Range, Time in Tight Range, and Average Glucose Relationships Are Modulated by Glycemic Variability: Identification of a Glucose Distribution Model Connecting Glycemic Parameters Using Real-World Data.

Background: Time in range (TIR), time in tight range (TITR), and average glucose (AG) are used to adjust glycemic therapies in diabetes. However, TIR/TITR and AG can show a disconnect, which may create management difficulties. We aimed to understand the factors influencing the relationships between these glycemic markers. Materials and Methods: Real-world glucose data were collected from self-identified diabetes type 1 and type 2 diabetes (T1D and T2D) individuals using flash continuous glucose monitoring (FCGM). The effects of glycemic variability, assessed as glucose coefficient of variation (CV), on the relationship between AG and TIR/TITR were investigated together with the best-fit glucose distribution model that addresses these relationships. Results: Of 29,164 FCGM users (16,367 T1D, 11,061 T2D, and 1736 others), 38,259 glucose readings/individual were available. Comparing low and high CV tertiles, TIR at AG of 150 mg/dL varied from 80% ± 5.6% to 62% ± 6.8%, respectively (P < 0.001), while TITR at AG of 130 mg/dL varied from 65% ± 7.5% to 49% ± 7.0%, respectively (P < 0.001). In contrast, higher CV was associated with increased TIR and TITR at AG levels outside the upper limit of these ranges. Gamma distribution was superior to six other models at explaining AG and TIR/TITR interactions and demonstrated nonlinear interplay between these metrics. Conclusions: The gamma model accurately predicts interactions between CGM-derived glycemic metrics and reveals that glycemic variability can significantly influence the relationship between AG and TIR with opposing effects according to AG levels. Our findings potentially help with clinical diabetes management, particularly when AG and TIR appear mismatched.

Limitations of 14-Day Continuous Glucose Monitoring Sampling for Assessment of Hypoglycemia and Glycemic Variability in Type 1 Diabetes.

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.

Thrombotic risk assessed by PAI-1 in patients with COVID-19: The influence of hyperglycemia and diabetes mellitus.

OBJECTIVE: To assess thrombotic risk with PAI-1 levels in patients with COVID-19, to evaluate PAI-1 differences between hyperglycemic and/or Type 2 Diabetes Mellitus (T2DM) versus non-hyperglycemic patients, and to analyze the association of plasminogen activator inhibitor-1 (PAI-1) with hyperglycemia and T2DM. METHODS: A cross-sectional study carried out in 181 patients hospitalized for COVID-19. Two groups were formed: the patients with hyperglycemia at admission and/or previously diagnosed T2DM group and the non-hyperglycemic group. Fibrinolysis was assessed by measuring PAI-1 levels by ELISA. RESULTS: The mean age was 59.4±16.1 years; 55.8% were male 54.1% of patients presented obesity, 38.1% had pre-existing T2DM and 50.8% had admission hyperglycemia and/or pre-existing T2DM. The patients with admission hyperglycemia and/or preexisting T2DM had higher PAI-1 compared with non-hyperglycemic patients [197.5 (128.8-315.9) vs 158.1 (113.4-201.4) ng/mL; p=0.031]. The glucose levels showed a positive correlation with PAI-1 levels (r=0.284, p=0.041). A multivariate logistic regression analysis showed association of PAI-1 level and hyperglycemia and pre-existing T2DM with severity of COVID-19. CONCLUSION: Patients hospitalized for COVID-19 infection with preexisting T2DM or hyperglycemia detected during their hospitalization presented a greater increase in PAI-1 levels, which suggests that hyperglycemia contributes directly to the hypercoagulable state and probably a worse outcome from the patients.

Continuous Glucose Monitoring Systems in Noninsulin-Treated People with Type 2 Diabetes: A Systematic Review and Meta-Analysis of Randomized Controlled Trials.

Introduction: Continuous glucose monitoring (CGM) has shown favorable outcomes in patients with type 2 diabetes (T2D) who are on insulin therapy. However, the efficacy of CGM in managing glucose levels in noninsulin-treated people with T2D remains controversial. Methods: PubMed, Cochrane, and Embase were searched for randomized controlled trials (RCTs) comparing CGM to self-monitoring of blood glucose (SMBG) in people with T2D not using insulin. We computed weighted mean differences (WMDs) and standard mean differences (SMD) for continuous outcomes, with 95% confidence intervals (CIs). Heterogeneity was assessed using I2 statistics. Statistical analyses were performed using R version 4.2.3. Results: We included six RCTs comprising 407 noninsulin-treated people with T2D of whom 228 were randomized to CGM. Diabetes duration ranged from 5.4 to 13.9 years. The mean age was 57.9 years and the mean body mass index was 30.8 kg/m2. Four trials used real-time CGM (rt-CGM) and two intermittent scanning CGM (is-CGM). Compared with SMBG, CGM significantly reduced the glycated hemoglobin level (WMD -0.31%; 95% CI -0.42 to -0.21; I2 = 0%), glucose level (WMD -11.16 mg/dL; 95% CI -19.94 to -2.39; I2 = 0%), time in hypoglycemia level 2 (WMD -0.28%; 95% CI -0.52 to -0.03; I2 = 91%), glucose time >180 mg/dL (WMD -7.75%; 95% CI -12.04 to -3.45; I2 = 0%), and the standard deviation of glucose variation (WMD -4.00 mg/dL; 95% CI -6.86 to -1.14; I2 = 0%). CGM also increased time in range (WMD 8.63%; 95% CI 4.54-12.71; I2 = 0%) and treatment satisfaction (SMD 0.79; 95% CI 0.54-1.05; I2 = 0%). Conclusion: In this meta-analysis, rt-CGM and is-CGM were associated with improvement in glycemic control in people with T2D not using insulin when compared to SMBG.

Comparison of Effects of Injectable Semaglutide and Dulaglutide on Oxidative Stress and Glucose Variability in Patients with Type 2 Diabetes Mellitus: A Prospective Preliminary Study.

INTRODUCTION: Recent trials have shown that glucagon-like peptide-1 receptor agonists considerably reduce atherosclerotic cardiovascular disease in patients with type 2 diabetes mellitus (T2DM). Oxidative stress, a surrogate marker of cardiovascular risk, is associated with glucose variability. However, to the best of our knowledge, no studies have compared the effects of injectable semaglutide and dulaglutide therapies on oxidative stress and glucose variability assessed via continuous glucose monitoring (CGM). This study aimed to analyze and compare the effects of semaglutide and dulaglutide therapies on oxidative stress and glucose variability as assessed through CGM. METHODS: This is an open-label, multicenter, randomized, prospective, parallel-group comparison study. Overall, 37 patients with T2DM treated with dulaglutide for at least 12 weeks were randomized into two groups: one receiving continuous dulaglutide therapy (n = 19) and one receiving injectable semaglutide therapy (n = 18) groups. The coprimary endpoints were changes in the results of the diacron-reactive oxygen metabolites test, an oxidative stress marker, and CGM-evaluated glucose variability after 24 weeks. The secondary endpoint was changes in the Diabetes Treatment Satisfaction Questionnaire (DTSQ) scores. RESULTS: Switching to semaglutide therapy was better than continuous dulaglutide therapy in reducing oxidative stress, glucose variability, and glycated hemoglobin levels. Conversely, continuous dulaglutide therapy was better than semaglutide therapy in terms of DTSQ scores for "Convenience" and "Recommend." CONCLUSION: Injectable semaglutide therapy may be more effective than dulaglutide therapy in ameliorating oxidative stress and regulating glucose metabolism, including glucose variability, in patients with T2DM, while dulaglutide therapy may be more effective in terms of treatment satisfaction. CLINICAL TRIAL REGISTRATION: UMIN-CRT ID: UMIN000042670 (registered 7 December 2020).

Correlation between blood glucose level and poor wound healing after posterior lumbar interbody fusion in patients with type 2 diabetes.

To investigate the correlation of blood glucose level with poor wound healing (PWH) after posterior lumbar interbody fusion (PLIF) in patients with type 2 diabetes (T2D). From January 2016 to January 2023, a case-control study was conducted to analyse the clinical data of 400 patients with T2D who were treated by PLIF and internal fixation at our hospital. The following data were recorded: gender; age; body mass index (BMI); surgical stage; average perioperative blood glucose level; perioperative blood glucose variance; perioperative blood glucose coefficient of variation; glycated haemoglobin level; preoperative levels of total protein, albumin and haemoglobin; postoperative levels of total protein, albumin and haemoglobin; surgical time; intraoperative bleeding volume; operator; postoperative drainage volume; and postoperative drainage tube removal time of each group. The indicators for monitoring blood glucose variability (GV) included the SD of blood glucose level (SDBG), coefficient of variation (CV) and maximum amplitude of variation (LAGE) before and after surgery. According to the diagnostic criteria for PWH, patients with postoperative PWH were determined and assigned to two groups: Group A (good wound healing group; n = 330 patients) and Group B (poor wound healing group; n = 70 patients). The preoperative and postoperative blood GV indicators, namely SDBG, CV and LAGE, were compared between these two groups. We also determined the relationship between perioperative blood GV parameters and PWH after PLIF surgery and its predictive value through correlation analysis and receiver-operating characteristic curve. Of the 400 enrolled patients, 70 patients had PWH. Univariate analysis revealed significant differences between the two groups in the course of diabetes, mean fasting blood glucose (MFBG), SDBG, CV, LAGE, preoperative hypoglycaemic program, surgical segment, postoperative drainage time, incision length and other factors (p < 0.05). However, no significant differences were noted in factors such as gender, age, body mass index, hypertension, coronary heart disease, admission fasting blood glucose, preoperative haemoglobin A1c, surgical time, intraoperative bleeding volume, intraoperative blood transfusion volume and postoperative drainage volume (p > 0.05). The area under the curve (AUC) values of preoperative SDBG, CV and LAGE were 0.6657, 0.6432 and 0.6584, respectively. The cut-off values were 1.13 mmol/L, 6.97% and 0.75 mmol/L, respectively. The AUC values for postoperative SDBG, CV and LAGE were 0.5885, 0.6255 and 0.6261, respectively. The cut-off values were 1.94 mmol/L, 24.32% and 2.75 mmol/L, respectively. The multivariate ridge regression analysis showed that preoperative MFBG, SDBG, CV and LAGE; postoperative SDBG, CV and LAGE; postoperative long drainage time; and multiple surgical segments were independent risk factors for T2D patients to develop surgical site infection after PLIF (p < 0.05). The perioperative blood GV in patients with T2D is closely related to the occurrence of PWH after PLIF. Reducing blood GV may help to reduce the occurrence of PWH after PLIF.

Continuous Glucose Monitoring Alarms in Adults with Type 1 Diabetes: User Characteristics and the Impact of Hypoglycemia and Hyperglycemia Alarm Thresholds on Glycemic Control.

Background: Few studies have evaluated the implications of the alarm thresholds of continuous glucose monitoring (CGM) systems for individuals with diabetes. The present study aimed to investigate the influence of hypoglycemia and hyperglycemia alarm thresholds on glycemic control in adults with type 1 diabetes (T1DM) and the characteristics of patients who use these alarms more frequently. Methods: This observational cross-sectional study included 873 users of the FreeStyle Libre 2 system (501 men, median age 48 years, range 18-90 years) with T1DM from a single center. We investigated the role of demographic and metabolic factors on the use of alarms and the impact of hypoglycemia and hyperglycemia alarms and their thresholds on glycemic control. Results: Alarm users were older than nonusers (median age 49 vs. 43 years, respectively; P < 0.001). The hypoglycemia alarms were set by 76.1% of women and by 69.1% of men (P = 0.022). The hypoglycemia alarms reduced hypoglycemia features and glucose variability, although at the expense of shorter time in range. The higher the hypoglycemia alarm threshold, the greater these effects. The hyperglycemia alarms were effective in reducing hyperglycemia and lowering the glucose management indicator, although at the expense of a greater tendency to hypoglycemia. The lower the hyperglycemia alarm threshold, the greater these effects. Conclusions: CGM alarms contribute to better glycemic control. However, hypoglycemia and hyperglycemia alarms have advantages and disadvantages. Adults with T1DM should explore, under medical supervision, which alarm thresholds will best help them achieve their individual glycemic goals.