Search

Objective: We aimed to compare glycemic variability (GV) parameters using both a flash glucose monitoring (FGM) system and cardiometabolic risk parameters in hospitalized patients with type 2 diabetes mellitus (T2DM) between cohorts receiving short-term intensive insulin infusion (STII) plus empagliflozin (EMPA) combination therapy vs. STII therapy alone. Methods: In a 2-week, open-label, randomized, parallel-group clinical trial, newly diagnosed patients with T2DM [fasting plasma glucose (FPG) > 11.1 mmol/L or hemoglobin A1c (HbA1c) > 9.0%] or patients with poor glycemic control (HbA1c > 7.0%) on oral antidiabetic drugs (OAD) received either STII+EMPA therapy (n = 30) or STII therapy alone (n = 30). FGM was carried over 14 days, and the data were used to calculate time in range (TIR [3.9–10 mmol/L]) and compare GV parameters. 1,5-Anhydroglucitol (1,5-AG) and cardiometabolic indicators of oxidative stress, inflammation, and vascular endothelial function were also compared. Results: After treatment, the TIR percentage was significantly higher (p < 0.05), and the time below range (TBR; < 3.9 mmol/L) was significantly lower (p < 0.05) in the STII+EMPA group than that in the STII group. The various measured glycemic parameters were significantly lower, and the average daily dose of insulin was also significantly lower in patients with STII+EMPA treatment (all p < 0.05). Plasma 1,5-AG levels were significantly higher (p < 0.05) in the STII+EMPA group than that in the control group. Conclusions: Newly diagnosed patients with T2DM or with poor glycemic control on OAD attained greater benefit and lower GV from STII+EMPA treatment than that for STII treatment alone. The 1,5-AG marker is a good indicator of the effects of short-term glycemic control. [ABSTRACT FROM AUTHOR]

Background: Continuous glucose monitoring (CGM) devices provide detailed information on daily glucose control and glycemic variability. Yet limited population-based studies have explored the association between CGM metrics and fatty liver. We aimed to investigate the associations of CGM metrics with the degree of hepatic steatosis. Methods: This cross-sectional study included 1180 participants from the Guangzhou Nutrition and Health Study. CGM metrics, covering mean glucose level, glycemic variability, and in-range measures, were separately processed for all-day, nighttime, and daytime periods. Hepatic steatosis degree (healthy: n = 698; mild steatosis: n = 242; moderate/severe steatosis: n = 240) was determined by magnetic resonance imaging proton density fat fraction. Multivariate ordinal logistic regression models were conducted to estimate the associations between CGM metrics and steatosis degree. Machine learning models were employed to evaluate the predictive performance of CGM metrics for steatosis degree. Results: Mean blood glucose, coefficient of variation (CV) of glucose, mean amplitude of glucose excursions (MAGE), and mean of daily differences (MODD) were positively associated with steatosis degree, with corresponding odds ratios (ORs) and 95% confidence intervals (CIs) of 1.35 (1.17, 1.56), 1.21 (1.06, 1.39), 1.37 (1.19, 1.57), and 1.35 (1.17, 1.56) during all-day period. Notably, lower daytime time in range (TIR) and higher nighttime TIR were associated with higher steatosis degree, with ORs (95% CIs) of 0.83 (0.73, 0.95) and 1.16 (1.00, 1.33), respectively. For moderate/severe steatosis (vs. healthy) prediction, the average area under the receiver operating characteristic curves were higher for the nighttime (0.69) and daytime (0.66) metrics than that of all-day metrics (0.63, P < 0.001 for all comparisons). The model combining both nighttime and daytime metrics achieved the highest predictive capacity (0.73), with nighttime MODD emerging as the most important predictor. Conclusions: Higher CGM-derived mean glucose and glycemic variability were linked with higher steatosis degree. CGM-derived metrics during nighttime and daytime provided distinct and complementary insights into hepatic steatosis. [ABSTRACT FROM AUTHOR]

Background/Objectives: Elevated glycemic variability (GV) has been associated with postoperative morbidity. Traditional preoperative fasting guidelines may contribute to high GV by driving the body into catabolism. Enhanced recovery after surgery (ERAS) protocols that include a preoperative carbohydrate load (PCL) reduce hospital length of stay and healthcare costs; however, it remains unclear whether PCL improves GV in surgical patients. The aim of this retrospective study was to determine the effect of a PCL on postoperative GV in diabetic and non-diabetic patients having gynecological surgery. Methods: Retrospective data were collected on patients who had gynecological surgery before and after the rollout of an institutional ERAS protocol that included PCL ingestion. The intervention group included patients who underwent surgery in 2019 and were enrolled in the ERAS protocol and, therefore, received a PCL. The control group included patients who underwent surgery in 2016 and, thus, were not enrolled in the protocol. The primary endpoint was GV, calculated by the coefficient of variance (CV) and glycemic lability index (GLI). Results: A total of 63 patients in the intervention group and 45 in the control were analyzed. GV was not statistically significant between the groups for CV (19.3% vs. 18.6%, p = 0.65) or GLI (0.58 vs. 0.54, p = 0.86). Postoperative pain scores (4.5 vs. 5.2 p = 0.23) and incentive spirometry measurements (1262 vs. 1245 p = 0.87) were not significantly different. A subgroup analysis of patients with and without type 2 diabetes mellitus revealed no significant differences in GV for any of the subgroups. Conclusions: This retrospective review highlights the need for additional GV research, including consensus agreement on a gold standard GV measurement. Large-scale prospective studies are needed to test the effectiveness of the PCL in reducing GV. [ABSTRACT FROM AUTHOR]

Intermittent religious fasting increases the risk of hypo- and hyperglycemia in individuals with diabetes, but its impact on those without diabetes has been poorly investigated. The aim of this preliminary study was to examine the effects of religious Bahá'í fasting (BF) on glycemic control and variability and compare these effects with time-restricted eating (TRE). In a three-arm randomized controlled trial, 16 subjects without diabetes were assigned to a BF, TRE, or control group. Continuous glucose monitoring and food intake documentation were conducted before and during the 19 days of the intervention, and the 24 h mean glucose and glycemic variability indices were assessed. The BF and TRE groups, but not the control group, markedly reduced the daily eating window while maintaining macronutrient composition. Only the BF group decreased caloric intake (−677.8 ± 357.6 kcal, p = 0.013), body weight (−1.92 ± 0.95 kg, p = 0.011), and BMI (−0.65 ± 0.28 kg, p = 0.006). Higher maximum glucose values were observed during BF in the within-group (+1.41 ± 1.04, p = 0.039) and between-group comparisons (BF vs. control: p = 0.010; TRE vs. BF: p = 0.022). However, there were no alterations of the 24 h mean glucose, intra- and inter-day glycemic variability indices in any group. The proportions of time above and below the range (70–180 mg/dL) remained unchanged. BF and TRE do not exhibit negative effects on glycemic control and variability in subjects without diabetes. [ABSTRACT FROM AUTHOR]

Background and Aim: In patients with Diabetes Mellitus (DM), Enteral Nutrition (EN) is associated with less hyperglycemia and lower insulin requirements compared to Parenteral Nutrition (PN). The primary aim of this study was to assess changes in glycemic control (GC) in DM patients on EN therapy. The secondary objectives included evaluating the impact of the specialized formula on various clinical parameters and the tolerability of the nutritional formula by monitoring potential gastrointestinal side effects. Methods: We report a case series on the effects of a Diabetes-Specific Formula (DSF) on GC, lipid profile (LP), and renal and hepatic function in a DM cohort receiving EN support. Results: Twenty-two DM subjects with total dysphagia (thirteen men, nine women) on continuous EN were observed. The use of a DSF in EN was associated with an improvement in glycemic indices across all patients studied, leading to a reduction in average insulin demand. No hospitalizations were reported during the study period. Conclusion: The study demonstrated that the use of DSFs in a multi-dimensional home care management setting can improve glycemic control, reduce glycemic variability and insulin need, and positively impact the lipid profile of the DM cohort. The metabolic improvements were supported by the clinical outcomes observed. [ABSTRACT FROM AUTHOR]

Aims/Introduction: Although several studies have shown the association between continuous glucose monitoring (CGM)‐derived glycemic variability (GV) and diabetic peripheral neuropathy, no studies have focused on outpatients or used NC‐stat®/DPNCheck™, a new point‐of‐care device for nerve conduction study (NCS). We investigated the association between CGM‐derived GV and NCS using DPNCheck™ in outpatients with type 2 diabetes, and further analyzed the difference in results between patients with and without well‐controlled HbA1c levels. Materials and Methods: All outpatients with type 2 diabetes using the CGM device (FreeStyle Libre Pro®) between 2017 and 2022 were investigated. Sural nerve conduction was evaluated by sensory nerve action potential (SNAP) amplitude and sensory conduction velocity (SCV) using DPNCheck™. Associations of CGM‐derived GV metrics with SNAP amplitude and SCV were investigated. Results: In total, 304 outpatients with type 2 diabetes were included. In a linear regression model, most CGM‐derived GV metrics except for the mean amplitude of glucose excursion and low blood glucose index were significantly associated with SCV, but not with SNAP amplitude. The significant associations of most CGM‐derived GV metrics with SCV remained after adjustment for possible confounding factors, but not after adjustment for glycated hemoglobin (HbA1c). Most CGM‐derived GV metrics were significantly associated with SCV after adjustment for HbA1c in patients with a HbA1c ≤ 6.9%, but not in those with a HbA1c ≥ 7.0%. Conclusions: In outpatients with type 2 diabetes, multiple CGM‐derived GV metrics were significantly associated with SCV obtained by DPNCheck™. GV may have independent impacts on peripheral nerve function, particularly in patients with well‐controlled HbA1c levels. [ABSTRACT FROM AUTHOR]

After acute burn injury, patients experience a hypermetabolic state often complicated by a stress-induced hyperglycemia. Recent research points towards glycemic variability as a contributing factor in adverse outcomes in critically ill patients. In burn patients, greater glycemic variability has been associated with increased rates of mortality and sepsis. However, no studies to date have examined the impact of glycemic variability on rates of infection in this population or determined which measure may be most useful. Infection, and subsequent sepsis, remains the leading contributor to morbidity and mortality after burn injury. The primary objective of this study is to evaluate the relationship between different measures of glycemic variability and infectious complications in burn patients. This retrospective study included patients admitted to a single American Burn Association-verified burn center between January 1, 2020 and December 31, 2020 with burn or inhalation injury. The primary outcome was a composite of autograft loss, mortality, and proven infection. Secondary outcomes included hospital length of stay and a further analysis of the proven infection component of the composite primary outcome. In addition to mean glucose, several different measures of glycemic variability were used for comparison, including standard deviation, coefficient of variation, mean amplitude of glycemic excursions, and J-index. Outcomes were analyzed using multiple logistic regression analysis while controlling for revised Baux score. A quantile analysis was performed to do determine the optimal mean threshold. Three hundred and ninety-two patients were admitted and screened for inclusion during the study period. Most patients were excluded due to a LOS less than 72 h. 112 patients were included in the study. Of the 112 patients, 22.3% experienced an infectious complication (25 patients with 28 complications). Mean glucose (OR 1.024; 95% CI 1.004–1.045) and J-index (OR 1.044; 95% CI 1.003–1.087) were associated with occurrence of infectious complications. Regarding target mean glucose threshold, a daily mean glucose above 150 mg/dL showed the strongest association with infectious complications (OR 3.634; 95% CI 1.008–13.101). Mean glucose, standard of deviation, and J-index were all independently associated with proven infection. • Mean glucose and J-index were associated with greater rates of infectious complications in burn patients. • Supporting previous literature, a mean glucose of > 150 mg/dL was predictive of infection, graft loss, and death. • Studies examining the effect of glycemic variability on infectious outcomes in higher acuity burn patients is needed. [ABSTRACT FROM AUTHOR]

Continuous glucose monitoring (CGM) is now advocated for the clinical management of individuals with type 1 diabetes (T1D). However, this glucose monitoring strategy is not routinely used in type 2 diabetes (T2D), given the large population, significant cost implications and relatively limited supporting evidence. T2D is a more heterogenous condition compared with T1D with various glucose lowering therapies that do not necessarily require CGM to ensure within target glucose levels. While all individuals with T2D may benefit from CGM at certain time points, the whole T2D population does not necessarily require this technology continuously, which should be prioritized based on patient benefit and cost effectiveness. In this pragmatic opinion piece, we describe the rationale and evidence for CGM use in different subgroups of individuals with T2d, divided according to the stage of the condition, glycemic therapies, presence of diabetes complications, or associated co-morbidities. We discuss a total of 16 T2D subgroups and provide a clinical view on CGM use in each, based on current evidence while also highlighting areas of knowledge gaps. This work provides health care professionals with a simple guide to CGM use in different T2D groups and gives suggestion for future studies to justify expansion of this technology. [ABSTRACT FROM AUTHOR]

Purpose: To evaluate glycemic variability (GV) using continuous glucose monitoring (CGM) in individuals with and without type 2 diabetes mellitus (T2DM) undergoing Roux-en-Y gastric bypass (RYGB). Methods: This prospective cohort study compared the CGM data of fourteen patients with T2DM (n = 7) and without T2DM (n = 7) undergoing RYGB. After 6 months, these patients were compared to a non-operative control group (n = 7) matched by BMI, sex, and age to the T2DM group. Results: Fourteen patients underwent RYGB, with a mean BMI of 46.9 ± 5.3 kg/m2 and an average age of 47.9 ± 8.9 years; 85% were female. After 6 months post-surgery, the total weight loss (TWL) was 27.1 ± 6.3%, with no significant differences between the groups. Patients without diabetes had lower mean interstitial glucose levels (81 vs. 94 and 98 mg/dl, p < 0.01) and lower glucose management indicator (GMI) (5.2 vs. 5.6 and 5.65%, p = 0.01) compared to the control and T2DM groups, respectively. The coefficient of variation (CV) significantly increased only in patients with diabetes (17% vs. 26.7%, p < 0.01). Both groups with (0% vs. 2%, p = 0.03) and without (3% vs. 22%, p = 0.03) T2DM experienced an increased time below range with low glucose (54–69 mg/dL). However, patients without T2DM had significantly less time in rage (70–180 mg/dL) (97% vs. 78%, p = 0.04). Conclusion: Significant differences in CGM metrics among RYGB patients suggest an increase in glycemic variability after surgery, with a longer duration of hypoglycemia, especially in patients without T2DM. [ABSTRACT FROM AUTHOR]

Aim: To understand the association between macronutrient composition of a diet with Time in Range (TIR), Time above Range (TAR) and Time below Range (TBR) derived using a Continuous Glucose Monitoring (CGM) device for 14 days. Methodology: An exploratory analysis on the baseline data of 50 Type 2 Diabetes Mellitus participants with age 25–55 years, HbA1c upto 8% and on Metformin only) enrolled for an interventional clinical trial was performed. Results: Participants consuming adequate carbohydrates (CHO) of 55 to 60% of total calories had better Average Blood Glucose of 142.0 ± 24.0 mg/dL with a significance of p = 0.03 and Glucose Management Indicator (GMI) of 6.6 ± 0.7% significant at p = 0.01, than those with high CHO intake >60% of the total calories, with Average Blood Glucose - 155.0 ± 13.4 mg/dL and GMI - 7.06 ± 0.4%. Similarly, TIR - 68.2 ± 5.1% and TAR - 23.0 ± 10.8% was significantly better (p = 0.00) among those consuming adequate protein (12–15%) as compared to low protein (≤ 10%) with TIR- 61.0 ± 5.1% & TAR- 32.9 ± 10.3%. A correlation (r = −0.482 & p = 0.00) and simple linear regression analysis (R² = 0.33, F = 7.72, p = 0.000) revealed that when CHO intake increases the TIR decreases whereas TAR increases (r = 0.380 & p = 0.006). We did not find any significant relation between fat intake and TIR, TAR or TBR. Conclusion: Our results suggest that lowering CHO, while increasing protein in the diet may help improve TIR. Further in-depth studies are needed to confirm these findings. [ABSTRACT FROM AUTHOR]

Diabetic retinopathy (DR), a well-known microvascular complication of diabetes mellitus, remains the main cause of vision loss in working-age adults worldwide. Up to now, there is a shortage of information in the study regarding the contributing factors of DR in diabetes. Accumulating evidence has identified glycemic variability (GV), referred to fluctuations of blood glucose levels, as a risk factor for diabetes-related complications. Recent reports demonstrate that GV plays an important role in accounting for the susceptibility to DR development. However, its exact role in the pathogenesis of DR is still not fully understood. In this review, we highlight the current landscape and relevant mechanisms of GV in DR, as well as address the mechanism-based therapeutic strategies, aiming at better improving the quality of DR management in clinical practice. [ABSTRACT FROM AUTHOR]

Background: The Diabetes Telemedicine Mediterranean Diet (DiaTeleMed) Study is a fully remote randomized clinical trial evaluating personalized dietary management in individuals with type 2 diabetes (T2D). The study aims to test the efficacy of a personalized behavioral approach for dietary management of moderately controlled T2D, versus a standardized behavioral intervention that uses one-size-fits-all dietary recommendations, versus a usual care control (UCC). The primary outcome will compare the impact of each intervention on the mean amplitude of glycemic excursions (MAGE). Methods: Eligible participants are between 21 and 80 years of age diagnosed with moderately controlled T2D (HbA1c: 6.0 to 8.0%) and managed on lifestyle alone or lifestyle plus metformin. Participants must be willing and able to attend virtual counseling sessions and log meals into a dietary tracking smartphone application (DayTwo), and wear a continuous glucose monitor (CGM) for up to 12 days. Participants are randomized with equal allocation (n = 255, n = 85 per arm) to one of three arms: (1) Personalized, (2) Standardized, or (3) UCC. Measurements occur at 0 (baseline), 3, and 6 months. All participants receive isocaloric energy and macronutrient targets to meet Mediterranean diet guidelines, in addition to 14 intervention contacts over 6 months (4 weekly then 10 biweekly) to cover diabetes self-management education. The first 4 UCC intervention contacts are delivered via synchronous videoconferences followed by educational video links. Participants in Standardized receive the same educational content as those in the UCC arm, following the same schedule. However, all intervention contacts are conducted via synchronous videoconferences, paired with Social Cognitive Theory (SCT)-based behavioral counseling, plus dietary self-monitoring of planned meals using a mobile app that provides real-time feedback on calories and macronutrients. Participants in the Personalized arm receive all elements of the Standardized intervention, in addition to real-time feedback on predicted post-prandial glycemic response (PPGR) to meals and snacks logged into the mobile app. Discussion: The DiaTeleMed Study aims to address an important gap in the current landscape of precision nutrition by determining the contributions of behavioral counseling and personalized nutrition recommendations on glycemic control in individuals with T2D. The fully remote methodology of the study allows for scalability and innovative delivery of personalized dietary recommendations at a population level. Trial registration: ClinicalTrials.gov NCT05046886. Registered on September 16, 2021. [ABSTRACT FROM AUTHOR]

Objective: To study the 24-h glucose profile of patients with mild GDM using the commercially available Abbot Libre continuous glucose monitoring system (CGMS) and compare them with pregnant women with normoglycemia (gestational age comparable). Methods: A case control study conducted between 2019-2020 followed eligible pregnant women diagnosed with GDM according to Diabetes in Pregnancy Study Group India criteria, after the placement of a CGMS. Results: Twenty-one GDM patients whose mean age was 27.1 ± 3.3 years with gestational age 28 weeks (24–32) and thirty pregnant women with normoglycemia whose mean age was 25.7 ± 4.2 years and gestational age 26 weeks (23–34) were enrolled in the study. Fasting, pre-breakfast, 2 h post lunch, day time and lowest nocturnal glucose were significantly higher in the GDM group than in controls. Glycemic variability indices like standard deviation of blood glucose, J index, and mean amplitude of glycemic excursions were also significantly higher in GDM patients. GDM patients spent more time above >140 mg/dl than controls. Conclusion: GDM patients, who have mild hyperglycemia but not overt diabetes, also have an abnormal 24 h glucose profile as compared to normal pregnancy. [ABSTRACT FROM AUTHOR]

Introduction: The use of continuous glucose monitoring (CGM) for in-hospital glucose control has been widely studied. However, its application in intensive care unit (ICU) settings is debatable owing to its perceived limitations. In ICU patients with diabetes, glycemic excursions, including frequent hypoglycemic episodes, are more prevalent. This retrospective study was undertaken to evaluate the utility of CGM in the management of diabetes patients admitted to the ICU. Methods: We collected data of patients with diabetes mellitus who were admitted to the ICU at our institution and were advised CGM by the intensivist. Aretrospective analysis was conducted, examining demographic, clinical, and laboratory data, along with glucose meter readings and CGM data. The results are presented as numbers, percentages, or median values, as appropriate. Results: Out of 65 patients in the ICU, the median values of percentage time in range, time below range, and time above range were 59%, 2%, and 36%, respectively. The median HbA1 C and capillary glucose on admission were 8.8% and 246 mg/dl, respectively. During the ICU stay, the CGM derived average glucose level was 167 mg/dl (percent coefficient of variation [%CV] =25.96) and the glucose management indicator was 7%. In total, 65 episodes of hypoglycemia were detected, all confirmed by capillary glucose tests. A total of 27 patients experienced hypoglycemia, of which 12 had nocturnal hypoglycemia. Conclusion: The use of CGM in the ICU improved glycemic control and helped to detect hypoglycemia, particularly nocturnal with acceptably low glycemic variability as measured by percentage CV. Further studies are required to corroborate and confirm these findings. [ABSTRACT FROM AUTHOR]

Introduction: From the introduction of continuous glucose monitoring (CGM) in treatments of type 1 diabetes, particularly its integration with insulin pumps, there has been a quest for new parameters that describe optimal glycemic control. As of the consensus reached in 2019, the ambulatory glucose profile (AGP) has become the standard, with time in range (TIR) emerging as a fundamental parameter for metabolic control assessment. However, with technological advancements, new parameters, such as the glycemia risk index (GRI), have been introduced and clinically utilized. Therefore, exploring the relationships between traditional and novel parameters to understand metabolic control comprehensively is imperative. Materials and methods: This study was conducted at the Pediatric Clinic of the University Hospital of the Republic of Srpska Banja Luka between January and July 2023. The participants were randomly selected, with the inclusion criteria specifying an age greater than eight years and a diabetes type 1 duration exceeding two years. All participants were required to use a sensoraugmented insulin pump for the next three months (90 days), irrespective of prior use, with the suspend-before-low option activated. Results: Of the 35 participants, 30 completed the study, 14 (46.7%) of whom were male. The mean age of the subjects was 14.90 ± 2.88 years, and the mean duration of diabetes was 7.83 ± 4.76 years. Over the 90-day period, HbA1c increased to an average of 7.31%. The analysis revealed significant effects of TIR (b=-0.771) and GRI (b=0.651) on HbA1c. Furthermore, GRI and TIR strongly correlated (b=-0.953). Discussion and conclusion: New parameters generated from the ambulatory glucose profile (AGP) can help clinicians create a complete picture of a patient's metabolic control in relation to HbA1c levels. Additionally, the GRI is a mathematically tailored parameter that incorporates all components of the ambulatory glucose profile and demonstrates strong correlations with laboratory-measured HbA1c and TIR. The GRI potentially can become a valuable statistical parameter for evaluating and managing patients in routine clinical practice. [ABSTRACT FROM AUTHOR]

Aim of the study was to quantify postprandial glucose levels in response to isocaloric protein load at main meals in men with different types of fat distribution. Material and methods. The study enrolled men aged 25 to 65 years. Group 1 (n = 17) consisted of obese men with subcutaneous fat distribution (SFD) type while group 2 (n = 16) was represented by obese men with abdominal type of fat distribution (AFD). Group 3 (comparators) consisted of 10 men with normal body weight (NBW). Glycemic response to standard isocaloric protein load was assessed by the results of glucose levels within 3 hours starting 5 minutes after end of food consumption on different days and mealtime. Standard protein lunch was introduced during the second day while standard protein dinner and breakfast were performed at the third and fourth days respectively. Results. It was found that protein intake leads to neither pronounced postprandial glycemic fluctuations nor decrease in glucose levels by the end of the 3rd hour of the test. Accordingly, it also doesn’t provoke hunger, unlike carbohydrate intake does. In men with NBW a more pronounced increase in glucose level after protein meal was found, both relative to the baseline levels and comparing with men from SFD and AFD groups. In NBW group at lunchtime and especially in the evening a double-humped glycemic curve was noted. Glycemic variability in men with different types of fat distribution was characterized by the fact that glycemic increment was more pronounced in men with AFD than in men with SFD whose glycemic curve was almost flat after all meals. Conclusions. Protein intake in men with NBW and different types of fat distribution does not lead to significant changes in postprandial glucose levels. Glycemic fluctuations don’t exceed 1 mmol/L within a 3-hour period after consuming of isocaloric breakfast, lunch or dinner. Protein intake results in greater postprandial glucose levels in men with AFD than in SFD men.

Abstract Background Continuous glucose monitoring (CGM) devices provide detailed information on daily glucose control and glycemic variability. Yet limited population-based studies have explored the association between CGM metrics and fatty liver. We aimed to investigate the associations of CGM metrics with the degree of hepatic steatosis. Methods This cross-sectional study included 1180 participants from the Guangzhou Nutrition and Health Study. CGM metrics, covering mean glucose level, glycemic variability, and in-range measures, were separately processed for all-day, nighttime, and daytime periods. Hepatic steatosis degree (healthy: n = 698; mild steatosis: n = 242; moderate/severe steatosis: n = 240) was determined by magnetic resonance imaging proton density fat fraction. Multivariate ordinal logistic regression models were conducted to estimate the associations between CGM metrics and steatosis degree. Machine learning models were employed to evaluate the predictive performance of CGM metrics for steatosis degree. Results Mean blood glucose, coefficient of variation (CV) of glucose, mean amplitude of glucose excursions (MAGE), and mean of daily differences (MODD) were positively associated with steatosis degree, with corresponding odds ratios (ORs) and 95% confidence intervals (CIs) of 1.35 (1.17, 1.56), 1.21 (1.06, 1.39), 1.37 (1.19, 1.57), and 1.35 (1.17, 1.56) during all-day period. Notably, lower daytime time in range (TIR) and higher nighttime TIR were associated with higher steatosis degree, with ORs (95% CIs) of 0.83 (0.73, 0.95) and 1.16 (1.00, 1.33), respectively. For moderate/severe steatosis (vs. healthy) prediction, the average area under the receiver operating characteristic curves were higher for the nighttime (0.69) and daytime (0.66) metrics than that of all-day metrics (0.63, P

ABSTRACT Aims/Introduction Although several studies have shown the association between continuous glucose monitoring (CGM)‐derived glycemic variability (GV) and diabetic peripheral neuropathy, no studies have focused on outpatients or used NC‐stat®/DPNCheck™, a new point‐of‐care device for nerve conduction study (NCS). We investigated the association between CGM‐derived GV and NCS using DPNCheck™ in outpatients with type 2 diabetes, and further analyzed the difference in results between patients with and without well‐controlled HbA1c levels. Materials and Methods All outpatients with type 2 diabetes using the CGM device (FreeStyle Libre Pro®) between 2017 and 2022 were investigated. Sural nerve conduction was evaluated by sensory nerve action potential (SNAP) amplitude and sensory conduction velocity (SCV) using DPNCheck™. Associations of CGM‐derived GV metrics with SNAP amplitude and SCV were investigated. Results In total, 304 outpatients with type 2 diabetes were included. In a linear regression model, most CGM‐derived GV metrics except for the mean amplitude of glucose excursion and low blood glucose index were significantly associated with SCV, but not with SNAP amplitude. The significant associations of most CGM‐derived GV metrics with SCV remained after adjustment for possible confounding factors, but not after adjustment for glycated hemoglobin (HbA1c). Most CGM‐derived GV metrics were significantly associated with SCV after adjustment for HbA1c in patients with a HbA1c ≤ 6.9%, but not in those with a HbA1c ≥ 7.0%. Conclusions In outpatients with type 2 diabetes, multiple CGM‐derived GV metrics were significantly associated with SCV obtained by DPNCheck™. GV may have independent impacts on peripheral nerve function, particularly in patients with well‐controlled HbA1c levels.

Abstract Diabetic retinopathy (DR), a well-known microvascular complication of diabetes mellitus, remains the main cause of vision loss in working-age adults worldwide. Up to now, there is a shortage of information in the study regarding the contributing factors of DR in diabetes. Accumulating evidence has identified glycemic variability (GV), referred to fluctuations of blood glucose levels, as a risk factor for diabetes-related complications. Recent reports demonstrate that GV plays an important role in accounting for the susceptibility to DR development. However, its exact role in the pathogenesis of DR is still not fully understood. In this review, we highlight the current landscape and relevant mechanisms of GV in DR, as well as address the mechanism-based therapeutic strategies, aiming at better improving the quality of DR management in clinical practice.

Abstract Background The Diabetes Telemedicine Mediterranean Diet (DiaTeleMed) Study is a fully remote randomized clinical trial evaluating personalized dietary management in individuals with type 2 diabetes (T2D). The study aims to test the efficacy of a personalized behavioral approach for dietary management of moderately controlled T2D, versus a standardized behavioral intervention that uses one-size-fits-all dietary recommendations, versus a usual care control (UCC). The primary outcome will compare the impact of each intervention on the mean amplitude of glycemic excursions (MAGE). Methods Eligible participants are between 21 and 80 years of age diagnosed with moderately controlled T2D (HbA1c: 6.0 to 8.0%) and managed on lifestyle alone or lifestyle plus metformin. Participants must be willing and able to attend virtual counseling sessions and log meals into a dietary tracking smartphone application (DayTwo), and wear a continuous glucose monitor (CGM) for up to 12 days. Participants are randomized with equal allocation (n = 255, n = 85 per arm) to one of three arms: (1) Personalized, (2) Standardized, or (3) UCC. Measurements occur at 0 (baseline), 3, and 6 months. All participants receive isocaloric energy and macronutrient targets to meet Mediterranean diet guidelines, in addition to 14 intervention contacts over 6 months (4 weekly then 10 biweekly) to cover diabetes self-management education. The first 4 UCC intervention contacts are delivered via synchronous videoconferences followed by educational video links. Participants in Standardized receive the same educational content as those in the UCC arm, following the same schedule. However, all intervention contacts are conducted via synchronous videoconferences, paired with Social Cognitive Theory (SCT)-based behavioral counseling, plus dietary self-monitoring of planned meals using a mobile app that provides real-time feedback on calories and macronutrients. Participants in the Personalized arm receive all elements of the Standardized intervention, in addition to real-time feedback on predicted post-prandial glycemic response (PPGR) to meals and snacks logged into the mobile app. Discussion The DiaTeleMed Study aims to address an important gap in the current landscape of precision nutrition by determining the contributions of behavioral counseling and personalized nutrition recommendations on glycemic control in individuals with T2D. The fully remote methodology of the study allows for scalability and innovative delivery of personalized dietary recommendations at a population level. Trial registration ClinicalTrials.gov NCT05046886. Registered on September 16, 2021.

Abstract Background Given the increasing attention to glycemic variability (GV) and its potential implications for cardiovascular outcomes. This study aimed to explore the impact of acute GV on short-term outcomes in Chinese patients with ST-segment elevation myocardial infarction (STEMI). Methods This study enrolled 7510 consecutive patients diagnosed with acute STEMI from 274 centers in China. GV was assessed using the coefficient of variation of blood glucose levels. Patients were categorized into three groups according to GV tertiles (GV1, GV2, and GV3). The primary outcome was 30-day all-cause death, and the secondary outcome was major adverse cardiovascular events (MACEs). Cox regression analyses were conducted to determine the independent correlation between GV and the outcomes. Results A total of 7136 patients with STEMI were included. During 30-days follow-up, there was a significant increase in the incidence of all-cause death and MACEs with higher GV tertiles. The 30-days mortality rates were 7.4% for GV1, 8.7% for GV2 and 9.4% for GV3 (p = 0.004), while the MACEs incidence rates was 11.3%, 13.8% and 15.8% for the GV1, GV2 and GV3 groups respectively (p

Lining Huang,1 Zhaoxiang Wang,1 Ying Pan,1 Kaixin Zhou,2 Shao Zhong1 1Gusu School, Nanjing Medical University, the First People’s Hospital of Kunshan, Kunshan, 215300, People’s Republic of China; 2Guangzhou Laboratory, Guangzhou, 510005, People’s Republic of ChinaCorrespondence: Shao Zhong, Department of Endocrinology, Gusu School, Nanjing Medical University, the First People’s Hospital of Kunshan, Kunshan, 215300, People’s Republic of China, Tel +86 13328056828, Email drzhong@163.comObjective: Type 2 diabetes mellitus (T2DM) is a metabolic disease characterized by insulin resistance and progressively impaired insulin secretion resulting in dynamic fluctuations in glucose levels.High blood urea nitrogen (BUN) levels have been linked to decreased insulin sensitivity, suppressed insulin synthesis and increased risk of incident diabetes mellitus in humans as well as insulin use in patients with T2DM.This study characterize the association between BUN levels and short-term and long-term glycemic variability(GV) in the elderly patients with T2DM who were hospitalized.Methods: A total of 927 elderly patients with T2DM were included in the study. The short-term GV was quantified using parameters such as standard deviation (SD), coefficient of variation (CV), time in range (TIR), and mean amplitude of glycemic excursions (MAGE), based on multi-point fingertip blood glucose monitoring. The long-term GV was quantified using parameters such as SD, CV, variation independent of the mean (VIM), and average successive variability (ARV), based on fasting blood glucose(FPG). The relationship between BUN levels and short-term and long-term GV in elderly T2DM who were hospitalized was explored using methods such as Spearman correlation coefficient, linear regression analysis, logistic regression analysis, and interaction tests.Results: In elderly patients with T2DM were hospitalized, there is a significant correlation between BUN levels and both short-term and long-term GV. BUN is negatively correlated with the GV parameter TIR (r=− 0.12, P=0.000), and positively correlated with SD (r=0.12, P=0.000), CV (r=0.07, P=0.026), MAGE (r=0.11, P=0.001), FPG-SD (r=0.08, P=0.013), and FPG-CV (r=0.08, P=0.014).Furthermore, the association remains consistent across different age, gender, BMI, and haemoglobin A1c (HbA1c) subgroups (P interaction > 0.05).Conclusion: In elderly patients with T2DM were hospitalized, BUN levels were positively associated with GV.Therefore, monitoring BUN levels were beneficial in assessing the degree of GV.Keywords: diabetes mellitus, glycemic variability, vascular lesions of diabetes mellitus, glycemic variability parameters

Abstract Background Type 2 Diabetes Mellitus (T2DM) presents a significant healthcare challenge, with considerable economic ramifications. While blood glucose management and long-term metabolic target setting for home care and outpatient treatment follow established procedures, the approach for short-term targets during hospitalization varies due to a lack of clinical consensus. Our study aims to elucidate the impact of pre-hospitalization and intra-hospitalization glycemic indexes on in-hospital survival rates in individuals with T2DM, addressing this notable gap in the current literature. Methods In this pilot study involving 120 hospitalized diabetic patients, we used advanced machine learning and classical statistical methods to identify variables for predicting hospitalization outcomes. We first developed a 30-day mortality risk classifier leveraging AdaBoost-FAS, a state-of-the-art ensemble machine learning method for tabular data. We then analyzed the feature relevance to identify the key predictive variables among the glycemic and routine clinical variables the model bases its predictions on. Next, we conducted detailed statistical analyses to shed light on the relationship between such variables and mortality risk. Finally, based on such analyses, we introduced a novel index, the ratio of intra-hospital glycemic variability to pre-hospitalization glycemic mean, to better characterize and stratify the diabetic population. Results Our findings underscore the importance of personalized approaches to glycemic management during hospitalization. The introduced index, alongside advanced predictive modeling, provides valuable insights for optimizing patient care. In particular, together with in-hospital glycemic variability, it is able to discriminate between patients with higher and lower mortality rates, highlighting the importance of tightly controlling not only pre-hospital but also in-hospital glycemic levels. Conclusions Despite the pilot nature and modest sample size, this study marks the beginning of exploration into personalized glycemic control for hospitalized patients with T2DM. Pre-hospital blood glucose levels and related variables derived from it can serve as biomarkers for all-cause mortality during hospitalization.

Cardiac autonomic neuropathy (CAN) is closely associated with an approximately five-fold increase in the risk of cardiovascular mortality in patients with diabetes mellitus (DM). Impaired autonomic function of the cardiovascular system in DM, which leads to the development of CAN, can be accompanied by coronary artery ischemia, heart rhythm disturbances, “silent” myocardial infarction, severe orthostatic hypotension, and sudden cardiac death syndrome. The article provides an analysis of literature data on the impact of glycemic variability (GV) on diabetic CAN development. This review analyzed the possible relationships between GV in people with diabetic CAN. In particular, the issues related to glycemic control and CAN, the link between GV and CAN in diabetes were analyzed. Unsatisfactory glycemic control and uncontrolled glycemic status are considered the main risk factors for chronic complications of DM, in particular CAN. An increase of GV is associated with a higher risk of chronic complications of DM, cardiovascular risk, all-cause mortality and morbidity. The clinical trial results demonstrated that time in range might be a promising metric for assessing glycemic control and prognosis of diabetic complications. This review is based on a search in PubMed and MEDLINE, Scopus, BIOSIS, EMBASE, Google Scholar and Springer Online Archives Collection. The following keywords were used: glycemic variability, cardiac autonomic neuropathy and diabetes mellitus. Research findings missed by the web search have been identified through a manual search of the bibliography of publications. CAN is one of the frequent long-term complications of DM, and reasonable control of GV may be necessary for its prevention. Determination of GV may have advantages for predicting future complications of DM in clinical trials and practice. The association of autonomic dysfunction and glucose levels, insulin resistance, and HbA1c variability suggest further research to reduce chronic complications development. Further investigation is needed to study the mechanisms of GV and evaluate them as therapeutic targets in the treatment of patients with T2DM. [ABSTRACT FROM AUTHOR]

Background: There is limited evidence evaluating the impact of insulin treatment strategies on glucose variability in critically ill patients without preexisting diabetes. Objective: Compare basal plus insulin (BPI) and sliding scale insulin (SSI) impact on glycemic control outcomes in critically ill patients without preexisting diabetes experiencing hyperglycemia. Methods: This multicenter, retrospective review analyzed critically ill patients with hyperglycemia who received either BPI or SSI. Patients with a hemoglobin A1C >6.5% during the admission of interest or in the previous 3 months, or a diagnosis of diabetes at the time of discharge were excluded. The primary outcome was glucose variability during the intensive care unit (ICU) admission. Secondary outcomes included hypoglycemia frequency, frequency of goal glucose levels, mortality, and length of stay. Results: The analysis included 228 patients (39 in BPI, 189 in SSI). Average glucose variability was higher in the BPI group compared with the SSI group (85.8 mg/dL ± 33.1 vs 70.2 mg/dL ± 30.7; P = 0.009), which remained when controlling for baseline confounding (−12.1 [5.6], 95% CI −23.2 to −0.99; P = 0.033). Hypoglycemia incidence was similar between groups. BPI patients had a lower incidence of glucose values within goal range than SSI patients (P = 0.046). There was no difference in length of stay or hospital mortality. Conclusions and Relevance: The use of SSI compared with a BPI regimen may result in reduced glycemic variability in critically ill patients without preexisting diabetes. Future prospective studies, with a larger sample size, are warranted to confirm our exploratory findings and characterize clinically significant benefits. [ABSTRACT FROM AUTHOR]

Background and objective: Psychological insulin resistance (PIR), which refers to the reluctance of diabetic patients to use insulin, is a frequently encountered clinical issue. Needle-free injection (NFI) offers advantages in terms of expediting insulin absorption and mitigating adverse reactions related to injection. To evaluate the effects of subcutaneous injection of insulin aspart 30 with NFI on PIR and insulin dosage in patients with type 2 diabetes mellitus (T2DM). Methods: Sixty-four patients with T2DM participated in this randomized, prospective, open, crossover study. Insulin aspart 30 was administered subcutaneously to each subject via QS-P NFI and Novo Pen 5 (NP) successively. The effects of NFI on PIR were analyzed. Differences in insulin dosage, glycemic variability, and injection safety were compared at similar levels of glycemic control. Results: After the administration of NFI, the insulin treatment attitude scale score decreased (53.7 ± 7.3 vs. 58.9 ± 10.7, p<0.001), the insulin treatment adherence questionnaire score increased (46.3 ± 4.9 vs. 43.8 ± 7.1, p<0.001), and the insulin treatment satisfaction questionnaire score increased (66.6 ± 10.5 vs. 62.4 ± 16.5, p<0.001). At the same blood glucose level, NFI required a smaller dosage of insulin aspart 30 compared with that of NP (30.42 ± 8.70 vs. 33.66 ± 9.13 U/d, p<0.001). There were no differences in glycemic variability indices (standard deviation, mean amplitude of glycemic excursion or coefficient of variation) between the two injection methods. Compared with NP, NFI did not increase the incidence of hypoglycemia (17.2% vs. 14.1%, p=0.774), and it decreased the incidence of induration (4.7% vs. 23.4%, p=0.002) and leakage (6.3% vs. 20.3%, p=0.022) while decreasing the pain visual analog scale score (2.30 ± 1.58 vs. 3.11 ± 1.40, p<0.001). Conclusion: NFI can improve PIR in patients with T2DMand be used with a smaller dose of insulin aspart 30 while maintaining the same hypoglycemic effect. [ABSTRACT FROM AUTHOR]

Background: Type 2 Diabetes Mellitus (T2DM) presents a significant healthcare challenge, with considerable economic ramifications. While blood glucose management and long-term metabolic target setting for home care and outpatient treatment follow established procedures, the approach for short-term targets during hospitalization varies due to a lack of clinical consensus. Our study aims to elucidate the impact of pre-hospitalization and intra-hospitalization glycemic indexes on in-hospital survival rates in individuals with T2DM, addressing this notable gap in the current literature. Methods: In this pilot study involving 120 hospitalized diabetic patients, we used advanced machine learning and classical statistical methods to identify variables for predicting hospitalization outcomes. We first developed a 30-day mortality risk classifier leveraging AdaBoost-FAS, a state-of-the-art ensemble machine learning method for tabular data. We then analyzed the feature relevance to identify the key predictive variables among the glycemic and routine clinical variables the model bases its predictions on. Next, we conducted detailed statistical analyses to shed light on the relationship between such variables and mortality risk. Finally, based on such analyses, we introduced a novel index, the ratio of intra-hospital glycemic variability to pre-hospitalization glycemic mean, to better characterize and stratify the diabetic population. Results: Our findings underscore the importance of personalized approaches to glycemic management during hospitalization. The introduced index, alongside advanced predictive modeling, provides valuable insights for optimizing patient care. In particular, together with in-hospital glycemic variability, it is able to discriminate between patients with higher and lower mortality rates, highlighting the importance of tightly controlling not only pre-hospital but also in-hospital glycemic levels. Conclusions: Despite the pilot nature and modest sample size, this study marks the beginning of exploration into personalized glycemic control for hospitalized patients with T2DM. Pre-hospital blood glucose levels and related variables derived from it can serve as biomarkers for all-cause mortality during hospitalization. [ABSTRACT FROM AUTHOR]

Glycemic variability (GV) has been related to complications in patients with diabetes. The aim of the systematic review and meta-analysis was to investigate whether GV is also associated with the incidence of diabetic peripheral neuropathy (DPN). A systematic search of Medline, Web of Science, Embase, and Cochrane Library database was conducted to identify relevant observational studies with longitudinal follow-up. The Newcastle-Ottawa Scale was used for study quality evaluation. A random-effects model was utilized to pool the results, accounting for heterogeneity. Ten observational studies including 72 565 patients with diabetes were included. The quality score was 8–9, indicating generally good quality of the included studies. With a mean follow-up duration of 7.1 years, 11 532 patients (15.9%) were diagnosed as DPN. Compared to patients with low GV, patients with high GV were associated with an increased risk incidence of DPN (risk ratio: 1.51, 95% confidence interval: 1.23 to 1.85, p<0.001; I2=78%). In addition, subgroup analysis showed consistent results in patients with type 1 and type 2 diabetes, and in studies evaluating the short-term and long-term GV (p for subgroup difference=0.82 and 0.53). Finally, results of subgroup analysis also suggested that the association between GV and risk of DPN were not significantly affected by study design, follow-up durations, diagnostic methods for DPN, adjustment of mean glycated hemoglobin A1c, or study quality scores (p for subgroup difference all>0.05). A high GV may be associated with an increased incidence of DPN. [ABSTRACT FROM AUTHOR]

Monitoring glycemic control status is the cornerstone of diabetes management. This study aimed to reveal whether moderate-carbohydrate (CHO) diets increase the risk of free fatty acid (FFA) levels, and it presents the short-term effects of four different diet models on blood sugar, glycemic variability (GV), and FFA levels. This crossover study included 17 patients with type 1 diabetes mellitus to identify the effects of four diets with different CHO contents and glycemic index (GI) on GV and plasma FFA levels. Diet 1 (D1) contained 40% CHO with a low GI, diet 2 (D2) contained 40% CHO with a high GI, diet 3 (D3) contained 60% CHO with a low GI, and diet 4 (D4) contained 60% CHO with a high GI. Interventions were performed with sensor monitoring in four-day periods and completed in four weeks. No statistical difference was observed among the groups in terms of blood glucose area under the curve (p = 0.78), mean blood glucose levels (p = 0.28), GV (p = 0.59), and time in range (p = 0.567). FFA and total triglyceride levels were higher in the D1 group (p < 0.014 and p = 0.002, respectively). Different diets may increase the risk of cardiovascular diseases by affecting GI, FFA, and blood glucose levels. [ABSTRACT FROM AUTHOR]

Aim and background: Hyperglycemia is considered an adaptive metabolic manifestation of stress and is associated with poor outcomes. Herein, we analyzed the association between glycemic variability (GV) and hospital mortality in patients with coronavirus disease 2019 (COVID-19) admitted to the intensive care unit (ICU), and the association between GV and mechanical ventilation (MV), ICU stay, length of hospital stays, renal replacement therapy (RRT), hypoglycemia, nosocomial infections, insulin use, and corticosteroid class. Materials and methods: In this retrospective observational study, we collected information on blood glucose levels during the first 10 days of hospitalization in a cohort of ICU patients with COVID-19 and its association with outcomes. Results: In 239 patients, an association was observed between GV and hospital mortality between the first and last quartiles among patients without diabetes [odds ratio (OR), 3.78; confidence interval, 1.24-11.5]. A higher GV was associated with a greater need for RRT (p = 0.002), regular insulin (p < 0.001), and episodes of hypoglycemia (p < 0.001). Nosocomial infections were associated with intermediate GV quartiles (p = 0.02). The corticosteroid class had no association with GV (p = 0.21). Conclusion: Glycemic variability was associated with high mortality in patients with COVID-19 and observed in the subgroup of patients without diabetes. Clinical significance: Glycemic control in critically ill patients remains controversial and hyperglycemia is associated with worse outcomes. Diabetes mellitus (DM) is one of the most prevalent comorbidities in patients with COVID-19. In addition, they require corticosteroids due to pulmonary involvement, representing a challenge and an opportunity to better understand how glycemic changes can influence the outcome of these patients. [ABSTRACT FROM AUTHOR]

Aims: Management of blood glucose fluctuation is essential for diabetes. Exercise is a key therapeutic strategy for diabetes patients, although little is known about determinants of glycemic response to exercise training. We aimed to investigate the effect of combined aerobic and resistance exercise training on blood glucose fluctuation in type 2 diabetes patients and explore the predictors of exercise‐induced glycemic response. Materials and Methods: Fifty sedentary diabetes patients were randomly assigned to control or exercise group. Participants in the control group maintained sedentary lifestyle for 2 weeks, and those in the exercise group specifically performed combined exercise training for 1 week. All participants received dietary guidance based on a recommended diet chart. Glycemic fluctuation was measured by flash continuous glucose monitoring. Baseline fat and muscle distribution were accurately quantified through magnetic resonance imaging (MRI). Results: Combined exercise training decreased SD of sensor glucose (SDSG, exercise‐pre vs exercise‐post, mean 1.35 vs 1.10 mmol/L, p =.006) and coefficient of variation (CV, mean 20.25 vs 17.20%, p =.027). No significant change was observed in the control group. Stepwise multiple linear regression showed that baseline MRI‐quantified fat and muscle distribution, including visceral fat area (β = −0.761, p =.001) and mid‐thigh muscle area (β = 0.450, p =.027), were significantly independent predictors of SDSG change in the exercise group, as well as CV change. Conclusions: Combined exercise training improved blood glucose fluctuation in diabetes patients. Baseline fat and muscle distribution were significant factors that influence glycemic response to exercise, providing new insights into personalized exercise intervention for diabetes. [ABSTRACT FROM AUTHOR]

Continuous glucose monitoring (CGM) provides data on short-term glycemic variability (GV). GV is associated with adverse outcomes in individuals with diabetes. Whether GV is associated with cardiometabolic risk in individuals without diabetes is unclear. We systematically reviewed the literature to assess whether GV is associated with cardiometabolic risk markers or outcomes in individuals without diabetes. Searches were performed in PubMed/Medline, Embase and Cochrane from inception through April 2022. Two researchers were involved in study selection, data extraction and quality assessment. Studies evaluating GV using CGM for ≥24 h were included. Studies in populations with acute and/or critical illness were excluded. Both narrative synthesis and meta-analyzes were performed, depending on outcome. Seventy-one studies were included; the majority were cross-sectional. Multiple measures of GV are higher in individuals with compared to without prediabetes and GV appears to be inversely associated with beta cell function. In contrast, GV is not clearly associated with insulin sensitivity, fatty liver disease, adiposity, blood lipids, blood pressure or oxidative stress. However, GV may be positively associated with the degree of atherosclerosis and cardiovascular events in individuals with coronary disease. GV is elevated in prediabetes, potentially related to beta cell dysfunction, but less clearly associated with obesity or traditional risk factors. GV is associated with coronary atherosclerosis development and may predict cardiovascular events and type 2 diabetes. Prospective studies are warranted, investigating the predictive power of GV in relation to incident disease. GV may be an important risk measure also in individuals without diabetes. • GV is elevated in prediabetes, primarily related to beta cell dysfunction. • GV is not clearly associated with obesity or traditional risk factors. • GV is associated with measures of coronary atherosclerosis development. • GV may predict cardiovascular events and type 2 diabetes. • Prospective studies in individuals without diabetes are warranted. [ABSTRACT FROM AUTHOR]

Glucocorticoids, which are administered with chemotherapy, cause hyperglycemia. Glycemic variability among breast cancer patients without diabetes is not well known. A retrospective cohort study was conducted involving early-stage breast cancer patients without diabetes who received dexamethasone prior to neoadjuvant or adjuvant taxane chemotherapy between August 2017-December 2019. Random blood glucose levels were analyzed, and steroid-induced hyperglycemia (SIH) was defined as a random glucose level of >140 mg/dL. A multivariate proportional hazards model was used to identify the risk factors of SIH. Out of 100 patients, the median age was 53 years (IQR: 45-63.5). A total of 45% of patients were non-Hispanic White, 28% Hispanic, 19% Asian, and 5% African American. The incidence of SIH was 67%, and glycemic fluctuations were highest in those with glucose levels of >200 mg/dL. Non-Hispanic White patients represented a significant predictor for time to SIH, with a hazard ratio of 2.5 (95% CI: 1.04, 5.95, p = 0.039). SIH was transient in over 90% of the patients, and only seven patients remained hyperglycemic after glucocorticoid and chemotherapy completion. Pretaxane dexamethasone-induced hyperglycemia was observed in 67% of the patients, with the greatest glycemic lability in those patients with blood glucose levels of >200 mg/dL. The non-Hispanic White patients had a higher risk of developing SIH.

Abstract Background Stress hyperglycemia and glycemic variability (GV) can reflect dramatic increases and acute fluctuations in blood glucose, which are associated with adverse cardiovascular events. This study aimed to explore whether the combined assessment of the stress hyperglycemia ratio (SHR) and GV provides additional information for prognostic prediction in patients with coronary artery disease (CAD) hospitalized in the intensive care unit (ICU). Methods Patients diagnosed with CAD from the Medical Information Mart for Intensive Care-IV database (version 2.2) between 2008 and 2019 were retrospectively included in the analysis. The primary endpoint was 1-year mortality, and the secondary endpoint was in-hospital mortality. Levels of SHR and GV were stratified into tertiles, with the highest tertile classified as high and the lower two tertiles classified as low. The associations of SHR, GV, and their combination with mortality were determined by logistic and Cox regression analyses. Results A total of 2789 patients were included, with a mean age of 69.6 years, and 30.1% were female. Overall, 138 (4.9%) patients died in the hospital, and 404 (14.5%) patients died at 1 year. The combination of SHR and GV was superior to SHR (in-hospital mortality: 0.710 vs. 0.689, p = 0.012; 1-year mortality: 0.644 vs. 0.615, p = 0.007) and GV (in-hospital mortality: 0.710 vs. 0.632, p = 0.004; 1-year mortality: 0.644 vs. 0.603, p

Abstract Background Time in range (TIR), as an important glycemic variability (GV) index, is clearly associated with disease complications in type 1 diabetes (T1D). Metabolic dysregulation is also involved in the risks of T1D complications. However, the relationship between metabolites and TIR remains poorly understood. We used metabolomics to investigate metabolic profile changes in T1D patients with different TIR. Methods This study included 85 T1D patients and 81 healthy controls. GV indices, including TIR, were collected from continuous glucose monitoring system. The patients were compared within two subgroups: TIR-L (TIR 70%, n = 14). To screen for differentially abundant metabolites and metabolic pathways, serum and urine samples were obtained for untargeted metabolomics by ultra-performance liquid chromatography‒mass spectrometry. Correlation analysis was conducted with GV metrics and screened biomarkers. Results Metabolites were significantly altered in T1D and subgroups. Compared with healthy controls, T1D patients had higher serum levels of 5-hydroxy-L-tryptophan, 5-methoxyindoleacetate, 4-(2-aminophenyl)-2,4-dioxobutanoate, and 4-pyridoxic acid and higher urine levels of thromboxane B3 but lower urine levels of hypoxanthine. Compared with TIR-H group, The TIR-L subgroup had lower serum levels of 5-hydroxy-L-tryptophan and mevalonolactone and lower urine levels of thromboxane B3 and phenylbutyrylglutamine. Dysregulation of pathways, such as tryptophan, vitamin B6 and purine metabolism, may be involved in the mechanism of diabetic complications related to glycemic homeostasis. Mevalonolactone, hypoxanthine and phenylbutyrylglutamine showed close correlation with TIR. Conclusions We identified altered metabolic profiles in T1D individuals with different TIR. These findings provide new insights and merit further exploration of the underlying molecular pathways relating to diabetic complications.

Abstract Over the past two decades, there has been continuous advancement in the accuracy and complexity of continuous glucose monitoring devices. Continuous glucose monitoring provides valuable insights into blood glucose dynamics, and can record glucose fluctuations accurately and completely. Glycemic variability (GV) is a straightforward measure of the extent to which a patient's blood glucose levels fluctuate between high peaks and low nadirs. Many studies have investigated the relationship between GV and complications, primarily in the context of type 2 diabetes. Nevertheless, the exact contribution of GV to the development of diabetes complications remains unclear. In this literature review, we aimed to summarize the existing evidence regarding the measurement, target level, pathophysiological mechanisms relating GV and tissue damage, and population‐based studies of GV and diabetes complications. Additionally, we introduce novel methods for measuring GV, and discuss several unresolved issues of GV. In the future, more longitudinal studies and trials are required to confirm the exact role of GV in the development of diabetes complications.

Background and Objectives: Degludec (Deg) and glargine U300 (Gla-300) are insulin analogs with longer and smoother pharmacodynamic action than glargine U100 (Gla-100), a long-acting insulin that has been widely used for many years in type 1 and type 2 diabetes. Both improve glycemic variability (GV) and the frequency of hypoglycemia, unlike Gla-100. However, it is unclear which insulin analog affects GV and hypoglycemia better in patients with insulin-dependent type 1 diabetes. We evaluated the effects of switching from Deg to Gla-300 on the day-to-day GV and the frequency of hypoglycemia in patients with insulin-dependent type 1 diabetes treated with Deg-containing basal-bolus insulin therapy (BBT). Materials and Methods: We conducted a retrospective study on 24 patients with insulin-dependent type 1 diabetes whose treatment was switched from Deg-containing BBT to Gla-300-containing BBT. We evaluated the day-to-day GV measured as the standard deviation of fasting blood glucose levels (SD-FBG) calculated by the self-monitoring of blood glucose records, the frequency of hypoglycemia (total, severe, and nocturnal), and blood glucose levels measured as fasting plasma glucose (FPG) levels and hemoglobin A1c (HbA1c). Results: The characteristics of the patients included in the analysis with high SD-FBG had frequent hypoglycemic events, despite the use of Deg-containing BBT. For this population, SD-FBG and the frequency of nocturnal hypoglycemia decreased after the switch from Deg to Gla-300. Despite the decrease in the frequency of nocturnal hypoglycemia, the FPG and HbA1c did not worsen by the switch. The change in the SD-FBG had a negative correlation with the SD-FBG at baseline and a positive correlation with serum albumin levels. Conclusions: Switching from Deg to Gla-300 improved the SD-FBG and decreased the frequency of nocturnal hypoglycemia in insulin-dependent type 1 diabetes treated with Deg-containing BBT, especially in cases with low serum albumin levels and a high GV. [ABSTRACT FROM AUTHOR]

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 patientswith 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. [ABSTRACT FROM AUTHOR]

Background: Stress hyperglycemia and glycemic variability (GV) can reflect dramatic increases and acute fluctuations in blood glucose, which are associated with adverse cardiovascular events. This study aimed to explore whether the combined assessment of the stress hyperglycemia ratio (SHR) and GV provides additional information for prognostic prediction in patients with coronary artery disease (CAD) hospitalized in the intensive care unit (ICU). Methods: Patients diagnosed with CAD from the Medical Information Mart for Intensive Care-IV database (version 2.2) between 2008 and 2019 were retrospectively included in the analysis. The primary endpoint was 1-year mortality, and the secondary endpoint was in-hospital mortality. Levels of SHR and GV were stratified into tertiles, with the highest tertile classified as high and the lower two tertiles classified as low. The associations of SHR, GV, and their combination with mortality were determined by logistic and Cox regression analyses. Results: A total of 2789 patients were included, with a mean age of 69.6 years, and 30.1% were female. Overall, 138 (4.9%) patients died in the hospital, and 404 (14.5%) patients died at 1 year. The combination of SHR and GV was superior to SHR (in-hospital mortality: 0.710 vs. 0.689, p = 0.012; 1-year mortality: 0.644 vs. 0.615, p = 0.007) and GV (in-hospital mortality: 0.710 vs. 0.632, p = 0.004; 1-year mortality: 0.644 vs. 0.603, p < 0.001) alone for predicting mortality in the receiver operating characteristic analysis. In addition, nondiabetic patients with high SHR levels and high GV were associated with the greatest risk of both in-hospital mortality (odds ratio [OR] = 10.831, 95% confidence interval [CI] 4.494–26.105) and 1-year mortality (hazard ratio [HR] = 5.830, 95% CI 3.175–10.702). However, in the diabetic population, the highest risk of in-hospital mortality (OR = 4.221, 95% CI 1.542–11.558) and 1-year mortality (HR = 2.013, 95% CI 1.224–3.311) was observed in patients with high SHR levels but low GV. Conclusions: The simultaneous evaluation of SHR and GV provides more information for risk stratification and prognostic prediction than SHR and GV alone, contributing to developing individualized strategies for glucose management in patients with CAD admitted to the ICU. [ABSTRACT FROM AUTHOR]

Background: Dysbiosis of gut microbiota is causally linked to impaired host glucose metabolism. We aimed to study effects of the new method of fecal microbiota transplantation, washed microbiota transplantation (WMT), on reducing glycemic variability (GV) in unstable diabetes. Methods: Fourteen eligible patients received three allogenic WMTs and were followed up at 1 week, 1 month, and 3 months. Primary outcomes were daily insulin dose, glucose excursions during meal tests, and GV indices calculated from continuous monitoring or self‐monitoring glucose values. Secondary outcomes were multiomics data, including 16S rRNA gene sequencing, metagenomics, and metabolomics to explore underlying mechanisms. Results: Daily insulin dose and glucose excursions markedly dropped, whereas GV indices significantly improved up to 1 month. WMT increased gut microbial alpha diversity, beta diversity, and network complexity. Taxonomic changes featured lower abundance of genera Bacteroides and Escherichia‐Shigella, and higher abundance of genus Prevotella. Metagenomics functional annotations revealed enrichment of distinct microbial metabolic pathways, including methane biosynthesis, citrate cycle, amino acid degradation, and butyrate production. Derived metabolites correlated significantly with improved GV indices. WMT did not change circulating inflammatory cytokines, enteroendocrine hormones, or C‐peptide. Conclusions: WMT showed strong ameliorating effect on GV, raising the possibility of targeting gut microbiota as an effective regimen to reduce GV in diabetes. [ABSTRACT FROM AUTHOR]

Objective: HbA1c is an insensitive marker for assessing real‐time dysglycemia in obesity. This study investigated whether 1‐h plasma glucose level (1‐h PG) ≥155 mg/dL (8.6 mmol/L) during an oral glucose tolerance test (OGTT) and continuous glucose monitoring (CGM) measurement of glucose variability (GV) better reflected dysglycemia than HbA1c after weight loss from metabolic and bariatric surgery. Methods: This was a prospective cohort study of 10 participants with type 2 diabetes compared with 11 participants with non‐diabetes undergoing sleeve gastrectomy (SG). At each research visit; before SG, and 6 weeks and 6 months post‐SG, body weight, fasting lipid levels, and PG and insulin concentrations during an OGTT were analyzed. Mean amplitude of glycemic excursions (MAGE), a CGM‐derived GV index, was analyzed. Results: The 1‐h PG correlated with insulin resistance markers, triglyceride/HDL ratio and triglyceride glucose index in both groups before surgery. At 6 months, SG caused 22% weight loss in both groups. Despite a reduction in HbA1c by 3.0 ± 1.3% in the diabetes group (p < 0.01), 1‐h PG, and MAGE remained elevated, and the oral disposition index, which represents pancreatic β‐cell function, remained reduced in the diabetes group when compared to the non‐diabetes group. Conclusions: Elevation of GV markers and reduced disposition index following SG‐induced weight loss in the diabetes group underscores persistent β‐cell dysfunction and the potential residual risk of diabetes complications. [ABSTRACT FROM AUTHOR]

The introduction of continuous glucose monitoring (CGM) systems in clinical practice has allowed a more detailed picture of the intra- and interdaily glycemic fluctuations of individuals with type 1 diabetes (T1D). However, CGM-measured glucose control indicators may be occasionally inaccurate. This study aims to assess the discrepancy between the glucose management indicator (GMI) and glycated hemoglobin (HbA1c) (ΔGMI-HbA1c) within a cohort of children and adolescents with T1D, exploring its correlation with other CGM metrics and blood count parameters. In this single-center, cross-sectional study, we gathered demographic and clinical data, including blood count parameters, HbA1c values, and CGM metrics, from 128 pediatric subjects with T1D (43% female; mean age, 13.4 ± 3.6 years). Our findings revealed higher levels of the coefficient of variation (CV) (p < 0.001) and time above range > 250 mg/dL (p = 0.033) among subjects with ΔGMI-HbA1c > 0.3%. No association was observed between blood count parameters and ΔGMI-HbA1c. In conclusion, despite the advancements and the widespread adoption of CGM systems, HbA1c remains an essential parameter for the assessment of glycemic control, especially in individuals with suboptimal metabolic control and extreme glycemic variability. [ABSTRACT FROM AUTHOR]

Background: Time in range (TIR), as an important glycemic variability (GV) index, is clearly associated with disease complications in type 1 diabetes (T1D). Metabolic dysregulation is also involved in the risks of T1D complications. However, the relationship between metabolites and TIR remains poorly understood. We used metabolomics to investigate metabolic profile changes in T1D patients with different TIR. Methods: This study included 85 T1D patients and 81 healthy controls. GV indices, including TIR, were collected from continuous glucose monitoring system. The patients were compared within two subgroups: TIR-L (TIR < 50%, n = 21) and TIR-H (TIR > 70%, n = 14). To screen for differentially abundant metabolites and metabolic pathways, serum and urine samples were obtained for untargeted metabolomics by ultra-performance liquid chromatography‒mass spectrometry. Correlation analysis was conducted with GV metrics and screened biomarkers. Results: Metabolites were significantly altered in T1D and subgroups. Compared with healthy controls, T1D patients had higher serum levels of 5-hydroxy-L-tryptophan, 5-methoxyindoleacetate, 4-(2-aminophenyl)-2,4-dioxobutanoate, and 4-pyridoxic acid and higher urine levels of thromboxane B3 but lower urine levels of hypoxanthine. Compared with TIR-H group, The TIR-L subgroup had lower serum levels of 5-hydroxy-L-tryptophan and mevalonolactone and lower urine levels of thromboxane B3 and phenylbutyrylglutamine. Dysregulation of pathways, such as tryptophan, vitamin B6 and purine metabolism, may be involved in the mechanism of diabetic complications related to glycemic homeostasis. Mevalonolactone, hypoxanthine and phenylbutyrylglutamine showed close correlation with TIR. Conclusions: We identified altered metabolic profiles in T1D individuals with different TIR. These findings provide new insights and merit further exploration of the underlying molecular pathways relating to diabetic complications. [ABSTRACT FROM AUTHOR]

DPP-4 inhibitors are frequently used as first-line agents for the treatment of type 2 diabetes in Japan. This study aimed to examine the effects of vildagliptin on glucose metabolism and arterial stiffness. Twenty treatment-naïve patients with type 2 diabetes (8 males and 12 females) received vildagliptin 50 mg twice daily for 6 months. Self-monitored blood glucose measurements and a 75 g OGTT were performed. Arterial stiffness was assessed using the CAVI. After the vildagliptin treatment, a significant decrease in the median HbA1c (from 8.3 to 6.4%) and fasting HOMA-β (from 26.1 to 34.5%), and a marginally significant decrease in the CAVI (from 8.9 to 8.4, p = 0.087) were observed. The glycemic variability parameters also improved, whereas the insulin sensitivity and oxidative stress remained unchanged. Participants with a lower glycemic variability on the 75 g OGTT after vildagliptin treatment showed a significant decrease in their CAVI. The baseline BMI was significantly higher for the participants with a decreased CAVI than in those with no change in their CAVI (24.5 vs. 20.8 kg/m2). After vildagliptin treatment, a decrease in the CAVI was observed, especially in the individuals with improved glycemic variability on the 75 g OGTT. Vildagliptin may be suitable for vascular protection in individuals with high glycemic variability and/or an elevated BMI. [ABSTRACT FROM AUTHOR]

Multiple studies have analyzed the possible correlations between diabetes and Alzheimer's disease. Less is known about the context of cognitive deterioration among patients with atypical Parkinsonian syndromes and glucose metabolism impairment. The aim of this study was to evaluate the association between the impaired glucose metabolism and cognitive decline among patients with progressive supranuclear palsy (PSP) and corticobasal syndrome (CBS). The study included 22 patients with PSP and CBS with disease durations varying from 3 to 6 years. The levels of glycated hemoglobin (HbA1C), fasting blood glucose, fasting C-peptide and the presence of microalbuminuria were evaluated, and oral glucose tolerance tests (OGTT) were performed. Based on the OGTT results, the glycemic variability, mean glycemia, glycemia standard deviation (SD) and coefficient of variation (%CV) were calculated. All patients underwent a three-Tesla brain magnetic resonance (MRI) examination and neuropsychological cognitive assessment with the use of standardized scales: Montreal Cognitive Assessment (MoCA), Mini-Mental State Examination (MMSE) and Frontal Assessment Battery (FAB). A statistical analysis revealed that poor control of glycemia with high glycemic variability and increased atrophy of the medial temporal lobe among patients with PSP and CBS correlated with worse cognitive performance independent of age or sex, even among patients who did not fulfill the criteria for diabetes. The study results indicate the importance of glucose metabolism control and optimal treatment in the context of cognition maintenance among patients with PSP and CBS. Due to the relatively small number of analyzed patients, the issue requires further assessment. To the best of our knowledge, this is the first study discussing the role of glycemic variability in atypical Parkinsonian syndromes. [ABSTRACT FROM AUTHOR]

Background: Glycemic variability (GV) is a novel parameter used in evaluating the quality of diabetes management. Current guidelines recommend the use of GV indexes alongside the traditional parameter to evaluate glycemic control: hemoglobin A1c (HbA1c). This study aims to evaluate the extent to which HbA1c explains the GV phenomena in patients with Type 1 diabetes (T1DM). Methods: In 147 patients with T1DM, associations between HbA1c and several GV indexes were analyzed. Results: Patients with an HbA1c < 7% had a lower median standard deviation of glycemia (60 vs. 48; p < 0.001), a lower coefficient of variation (34.1 vs. 38.0; p < 0.001), and a significantly increased median time in range (78 vs. 58; p < 0.001). HbA1c was positively correlated with the coefficient of variation (r = 0.349; p < 0.001) and the standard deviation (r = 0.656; p < 0.001) but reversely correlated with a lower time in range (r = −0.637; p < 0.001). Conclusions: HbA1c only partially explains the GV phenomena in patients with T1DM. The HbA1c value is associated more strongly with the time in range and standard deviation than with the coefficient of variation. [ABSTRACT FROM AUTHOR]