Current landscape and comprehensive management of glycemic variability in diabetic retinopathy.

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.

Pre-hospital glycemia as a biomarker for in-hospital all-cause mortality in diabetic patients - a pilot study.

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.

Simultaneous assessment of stress hyperglycemia ratio and glycemic variability to predict mortality in patients with coronary artery disease: a retrospective cohort study from the MIMIC-IV database.

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.

Two-week continuous glucose monitoring-derived metrics and degree of hepatic steatosis: a cross-sectional study among Chinese middle-aged and elderly participants.

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.

Impact of acute glycemic variability on short-term outcomes in patients with ST-segment elevation myocardial infarction: a multicenter population-based study.

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 < 0.001). High GV levels during hospitalization were significantly associated with an increased risk of 30-day all-cause mortality and MACEs. When analyzed as a continuous variable, GV was independently associated with a higher risk of all-cause mortality (hazard ratio [HR] 1.679, 95% confidence Interval [CI] 1.005-2.804) and MACEs (HR 2.064, 95% CI 1.386-3.074). Additionally, when analyzed as categorical variables, the GV3 group was found to predict an increased risk of MACEs, irrespective of the presence of diabetes mellitus (DM). CONCLUSION: Our study findings indicate that a high GV during hospitalization was significantly associated with an increased risk of 30-day all-cause mortality and MACE in Chinese patients with STEMI. Moreover, acute GV emerged as an independent predictor of increased MACEs risk, regardless of DM status.

Factors associated with nocturnal and diurnal glycemic variability in patients with type 2 diabetes: a cross-sectional study.

PURPOSE: There is little information on factors that influence the glycemic variability (GV) during the nocturnal and diurnal periods. We aimed to examine the relationship between clinical factors and GV during these two periods. METHODS: This cross-sectional study included 134 patients with type 2 diabetes. 24-h changes in blood glucose were recorded by a continuous glucose monitoring system. Nocturnal and diurnal GV were assessed by standard deviation of blood glucose (SDBG), coefficient of variation (CV), and mean amplitude of glycemic excursions (MAGE), respectively. Robust regression analyses were performed to identify the factors associated with GV. Restricted cubic splines were used to determine dose-response relationship. RESULTS: During the nocturnal period, age and glycemic level at 12:00 A.M. were positively associated with GV, whereas alanine aminotransferase was negatively associated with GV. During the diurnal period, homeostatic model assessment 2-insulin sensitivity (HOMA2-S) was positively associated with GV, whereas insulin secretion-sensitivity index-2 (ISSI2) was negatively associated with GV. Additionally, we found a J-shape association between the glycemic level at 12:00 A.M. and MAGE, with 9.0 mmol/L blood glucose level as a cutoff point. Similar nonlinear associations were found between ISSI2 and SDBG, and between ISSI2 and MAGE, with ISSI2 value of 175 as a cutoff point. CONCLUSION: Factors associated with GV were different between nocturnal and diurnal periods. The cutoff points we found in this study may provide the therapeutic targets for beta-cell function and pre-sleep glycemic level in clinical practice.

Perioperative Glycemic Management in Cardiac Surgery: A Narrative Review.

Diabetes and hyperglycemic events in cardiac surgical patients are associated with postoperative morbidity and mortality. The causes of dysglycemia, the abnormal fluctuations in blood glucose concentrations, in the perioperative period include surgical stress, surgical techniques, medications administered perioperatively, and patient factors. Both hyperglycemia and hypoglycemia lead to poor outcomes after cardiac surgery. While trying to control blood glucose concentration tightly for better postoperative outcomes, hypoglycemia is the main adverse event. Currently, there is no definite consensus on the optimum perioperative blood glucose concentration to be maintained in cardiac surgical patients. This review provides an overview of perioperative glucose homeostasis, the pathophysiology of dysglycemia, factors that affect glycemic control in cardiac surgery, and current practices for glycemic control in cardiac surgery.

Commonly Used Indicators of Low Energy Availability Risk Fail to Identify Female Amateur Soccer Players With Measured Low Energy Availability During Preseason.

This study aimed to determine energy availability (EA) and within-day energy balance (WDEB) in female soccer players during preseason and also explored eating disorder risk and athlete food choice. We hypothesized commonly used indicators of low energy availability (LEA) risk would correlate with measured EA and WDEB variables, and that food choice determinants would differ according to EA. Eleven National Premier League female soccer players participated in this observational cross-sectional study over 3 weeks. Assessment of resting metabolic rate and physique traits, including bone mineral density, was conducted during Weeks 1 or 3. During Week 2, dietary intake, energy expenditure, and continuous monitor-derived glucose were measured for 5 days. EA was calculated daily and WDEB calculated hourly with deficits/surpluses carried continuously. Questionnaires were administered throughout the 3 weeks, including the Athlete Food Choice Questionnaire, the Eating Disorders Screen for Athletes, and the Low Energy Availability in Females Questionnaire. Resting metabolic rate ratio, bone mineral density, Low Energy Availability in Females Questionnaire, and Eating Disorders Screen for Athletes scores were used as indicators of LEA risk. EA averaged 30.7 ± 7.5 kcals·kg fat-free mass-1·day-1. Approximately one-third (36%) of athletes were at risk of an eating disorder, while approximately half (45%) were identified at risk of the female athlete triad via Low Energy Availability in Females Questionnaire, compared with approximately one-third (36%) of athletes identified with EA < 30 kcal·kg fat-free mass-1·day-1. No athlete achieved EA >45 kcal·kg fat-free mass-1·day-1, and no indicator of LEA risk was associated with calculated EA or WDEB. However, overnight glycemic variability was positively correlated with measured EA (r = .722, p = .012).

Acute Effect of Imeglimin Add-on Therapy on 24-h Glucose Profile and Glycemic Variability in Patients with Type 2 Diabetes Receiving Metformin.

INTRODUCTION: Imeglimin is a novel antidiabetic drug with insulinotropic and insulin-sensitizing effects that targets mitochondrial bioenergetics. We investigated acute effects of add-on therapy with imeglimin to preceding metformin on the 24-h glucose profile and glycemic variability assessed by continuous glucose monitoring (CGM) in patients with type 2 diabetes. METHODS: We studied 30 outpatients with type 2 diabetes inadequately controlled with metformin. CGM was used for 14 days straight during the research period. Imeglimin 2,000 mg/day was started on day 7 after initiating CGM. Several CGM parameters were compared between days 4-6 (prior to imeglimin treatment) and 11-13 (following the initiation of imeglimin treatment). RESULTS: After treatment with imeglimin, 24-h mean glucose was acutely decreased from 161.6 ± 48.0 mg/dL to 138.9 ± 32.2 mg/dL (p < 0.0001), while time in range (i.e., at a glucose level of 70-180 mg/dL) was significantly increased from 69.9 ± 23.9% to 80.6 ± 21.0% (p < 0.0001). Addition of imeglimin to metformin significantly decreased the standard deviation (SD) of 24-h glucose and mean amplitude of glycemic excursions, 2 indexes of glycemic variability. Baseline serum high-density lipoprotein (HDL) cholesterol was negatively correlated with changes in mean 24-h glucose (r = -0.3859, p = 0.0352) and those in SD (r = -0.4015, p = 0.0309). CONCLUSIONS: Imeglimin add-on therapy to metformin acutely lowered 24-h glucose levels and improved glycemic variability in patients with type 2 diabetes on metformin. A higher serum HDL cholesterol at baseline was associated with a better response to acute effects of imeglimin on 24-h glucose levels and glycemic variability.

Effects of Switching from Degludec to Glargine U300 in Patients with Insulin-Dependent Type 1 Diabetes: A Retrospective Study.

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.

Effects of Glycemic Variability in Critically Ill Patients with Coronavirus Disease 2019: A Retrospective Observational Study.

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. How to cite this article: Boschi E, Friedman G, Moraes RB. Effects of Glycemic Variability in Critically Ill Patients with Coronavirus Disease 2019: A Retrospective Observational Study. Indian J Crit Care Med 2024;28(4):381-386.

Impact of early postoperative blood glucose variability on serum endocan level in cardiac surgery patients: a sub study of the ENDOLUNG observational study.

BACKGROUND: Early postoperative glycemic variability is associated with worse outcome after cardiac surgery, but the underlying mechanisms remain unknown. This study aimed to describe the relationship between postoperative glycemic variability and endothelial function, as assessed by serum endocan level in cardiac surgery patients. METHODS: We performed a post hoc analysis of patients included in the single-center observational ENDOLUNG study. Adult patients who underwent planned isolated coronary artery bypass graft surgery were eligible. Postoperative glycemic variability was assessed by calculating the coefficient of variability (CV) of blood glucose measured within 24 (CV24) and 48 (CV48) hours after surgery. Serum endocan level was measured at 24 (Endocan24) and 48 (Endocan48) hours after surgery. Pearson's correlation coefficient with 95% confidence interval (95% CI) was calculated between CV24 and Endocan24, and between CV48 and Endocan48. RESULTS: Data from 177 patients were analyzed. Median CV24 and CV48 were 18% (range 7 to 39%) and 20% (range 7 to 35%) respectively. Neither CV48 nor CV24 were significantly correlated to Endocan48 and Endocan24 respectively (r (95% CI) = 0.150 (0.001 to 0.290; and r (95% CI) = 0.080 (-0.070 to 0.220), respectively). CONCLUSIONS: Early postoperative glycemic variability within 48 h after planned cardiac surgery does not appear to be correlated with postoperative serum endocan level. CLINICAL TRIAL REGISTRATION NUMBER: NCT02542423.

Glycemic variability and in-hospital death of critically ill patients and the role of ventricular arrhythmias.

BACKGROUND: Abnormal glycemic variability is common in the intensive care unit (ICU) and is associated with increased in-hospital mortality and major adverse cardiovascular events, but little is known about whether adverse outcomes are partly mediated by ventricular arrhythmias (VA). We aimed to explore the association between glycemic variability and VA in the ICU and whether VA related to glycemic variability mediate the increased risk of in-hospital death. METHODS: We extracted all measurements of blood glucose during the ICU stay from The Medical Information Mart for Intensive Care IV (MIMIC-IV) database version 2.0. Glycemic variability was expressed by the coefficient of variation (CV), which was calculated by the ratio of standard deviation (SD) and average blood glucose values. The outcomes included the incidence of VA and in-hospital death. The KHB (Karlson, KB & Holm, A) is a method to analyze the mediation effect for nonlinear models, which was used to decompose the total effect of glycemic variability on in-hospital death into a direct and VA-mediated indirect effect. RESULTS: Finally, 17,756 ICU patients with a median age of 64 years were enrolled; 47.2% of them were male, 64.0% were white, and 17.8% were admitted to the cardiac ICU. The total incidence of VA and in-hospital death were 10.6% and 12.8%, respectively. In the adjusted logistic model, each unit increase in log-transformed CV was associated with a 21% increased risk of VA (OR 1.21, 95% CI: 1.11-1.31) and a 30% increased risk (OR 1.30, 95% CI: 1.20-1.41) of in-hospital death. A total of 3.85% of the effect of glycemic variability on in-hospital death was related to the increased risk of VA. CONCLUSION: High glycemic variability was an independent risk factor for in-hospital death in ICU patients, and the effect was caused in part by an increased risk of VA.

Prognostic importance of glycemic variability on left ventricular reverse remodeling after the first episode of ST-segment elevation myocardial infarction.

BACKGROUND: This study aimed to investigate the effect of glycemic variability (GV), determined using a continuous glucose monitoring system (CGMS), on left ventricular reverse remodeling (LVRR) after ST-segment elevation myocardial infarction (STEMI). METHODS: A total of 201 consecutive patients with STEMI who underwent reperfusion therapy within 12 h of onset were enrolled. GV was measured using a CGMS and determined as the mean amplitude of glycemic excursion (MAGE). Left ventricular volumetric parameters were measured using cardiac magnetic resonance imaging (CMRI). LVRR was defined as an absolute decrease in the LV end-systolic volume index of > 10% from 1 week to 7 months after admission. Associations were also examined between GV and LVRR and between LVRR and the incidence of major adverse cardiovascular events (MACE; cardiovascular death, acute coronary syndrome recurrence, non-fatal stroke, and heart failure hospitalization). RESULTS: The prevalence of LVRR was 28% (n = 57). The MAGE was independent predictor of LVRR (odds ratio [OR] 0.98, p = 0.002). Twenty patients experienced MACE during the follow-up period (median, 65 months). The incidence of MACE was lower in patients with LVRR than in those without (2% vs. 13%, p = 0.016). CONCLUSION: Low GV, determined using a CGMS, was significantly associated with LVRR, which might lead to a good prognosis. Further studies are needed to validate the importance of GV in LVRR in patients with STEMI.

Association of glycemic variability with death and severe consciousness disturbance among critically ill patients with cerebrovascular disease: analysis of the MIMIC-IV database.

BACKGROUND: The association of glycemic variability with severe consciousness disturbance and in-hospital all-cause mortality in critically ill patients with cerebrovascular disease (CVD) remains unclear, This study aimed to investigate the association of glycemic variability with cognitive impairment and in-hospital death. METHOD: We extracted all blood glucose measurements of patients diagnosed with CVD from the Medical Information Mart for Intensive Care IV (MIMIC-IV). Glycemic variability was defined as the coefficient of variation (CV), which was determined using the ratio of standard deviation and the mean blood glucose levels. Cox hazard regression models were applied to analyze the link between glycemic variability and outcomes. We also analyzed non-linear relationship between outcome indicators and glycemic variability using restricted cubic spline curves. RESULTS: The present study included 2967 patients diagnosed with cerebral infarction and 1842 patients diagnosed with non-traumatic cerebral hemorrhage. Log-transformed CV was significantly related to cognitive impairment and in-hospital mortality, as determined by Cox regression. Increasing log-transformed CV was approximately linearly with the risk of cognitive impairment and in-hospital mortality. CONCLUSION: High glycemic variability was found to be an independent risk factor for severe cognitive decline and in-hospital mortality in critically ill patients with CVD. Our study indicated that enhancing stability of glycemic variability may reduced adverse outcomes in patients with severe CVD.

Leveraging the future of diagnosis and management of diabetes: From old indexes to new technologies.

BACKGROUND: Diabetes is a heterogeneous and multifactorial disease. However, glycemia and glycated hemoglobin have been the focus of diabetes diagnosis and management for the last decades. As diabetes management goes far beyond glucose control, it has become clear that assessment of other biochemical parameters gives a much wider view of the metabolic state of each individual, enabling a precision medicine approach. METHODS: In this review, we summarize and discuss indexes that have been used in epidemiological studies and in the clinical practice. RESULTS: Indexes of insulin secretion, sensitivity/resistance and metabolism have been developed and validated over the years to account also with insulin, C-peptide, triglycerides or even anthropometric measures. Nevertheless, each one has their own objective and consequently, advantages and disadvantages for specific cases. Thus, we discuss how new technologies, namely new sensors but also new softwares/applications, can improve the diagnosis and management of diabetes, both for healthcare professionals but also for caretakers and, importantly, to promote the empowerment of people living with diabetes. CONCLUSIONS: In long-term, the solution for a better diabetes management would be a platform that allows to integrate all sorts of relevant information for the person with diabetes and for the healthcare practitioners, namely glucose, insulin and C-peptide or, in case of need, other parameters/indexes at home, sometimes more than once a day. This solution would allow a better and simpler disease management, more adequate therapeutics thereby improving patients' quality of life and reducing associated costs.

The Relationship Between Dietary Patterns and Glycemic Variability in People with Impaired Glucose Tolerance.

BACKGROUND: Diurnal glucose fluctuations are increased in prediabetes and might be affected by specific dietary patterns. OBJECTIVES: The present study assessed the relationship between glycemic variability (GV) and dietary regimen in people with normal glucose tolerance (NGT) and impaired glucose tolerance (IGT). METHODS: Forty-one NGT (mean age: 45.0 ± 9.0 y, mean BMI: 32.0 ± 7.0 kg/m2) and 53 IGT (mean age: 48.4 ± 11.2 y, mean BMI: 31.3 ± 5.9 kg/m2) subjects were enrolled in this cross-sectional study. The FreeStyleLibre Pro sensor was used for 14 d, and several parameters of GV were calculated. The participants were provided with a diet diary to record all meals. ANOVA analysis, Pearson correlation, and stepwise forward regression were performed. RESULTS: Despite no difference in diet patterns between the 2 groups, GV parameters were higher in IGT than in NGT. GV worsened with an increase in overall daily carbohydrate and refined grain consumption and improved with the increase in whole grain intake in IGT. GV parameters were positively related [r = 0.14-0.53; all P < 0.02 for SD, continuous overall net glycemic action 1 (CONGA1), J-index, lability index (LI), glycemic risk assessment diabetes equation, M-value, and mean absolute glucose (MAG)], and low blood glucose index (LBGI) inversely (r = -0.37, P = 0.006) related to the total percentage of carbohydrate, but not to the distribution of carbohydrate between the main meals in the IGT group. A negative relationship existed between total protein consumption and GV indices (r = -0.27 to -0.52; P < 0.05 for SD, CONGA1, J-index, LI, M-value, and MAG). The total EI was related to GV parameters (r = 0.27-0.32; P < 0.05 for CONGA1, J-index, LI, and M-value; and r = -0.30, P = 0.028 for LBGI). CONCLUSIONS: The primary outcome results showed that insulin sensitivity, calories, and carbohydrate content are predictors of GV in individuals with IGT. Overall, the secondary analyses suggested that carbohydrate and daily consumption of refined grains might be associated with higher GV, whereas whole grains and daily protein intake were related to lower GV in people with IGT.

Continuous glucose monitoring metrics (Mean Glucose, time above range and time in range) are superior to glycated haemoglobin for assessment of therapeutic efficacy.

AIM: To evaluate continuous glucose monitoring (CGM) metrics for use as alternatives to glycated haemoglobin (HbA1c) to evaluate therapeutic efficacy. METHODS: We re-analysed correlations among CGM metrics from studies involving 545 people with type 1 diabetes (T1D), 5910 people with type 2 diabetes (T2D) and 98 people with T1D during pregnancy and the postpartum period. RESULTS: Three CGM metrics, interstitial fluid Mean Glucose level, proportion of time above range (%TAR) and proportion of time in range (%TIR), were correlated with HbA1c and provided metrics that can be used to evaluate therapeutic efficacy. Mean Glucose showed the highest correlation with %TAR (r = 0.98 in T1D, 0.97 in T2D) but weaker correlations with %TIR (r = -0.92 in T1D, -0.83 in T2D) or with HbA1c (r = 0.78 in T1D). %TAR and %TIR were highly correlated (r = -0.96 in T1D, -0.91 in T2D). After 6 months of use of real-time CGM by people with T1D, changes in Mean Glucose level were more highly correlated with changes in %TAR (r = 0.95) than with changes in %TIR (r = -0.85) or with changes in HbA1c level (r = 0.52). These metrics can be combined with metrics of hypoglycaemia and/or glycaemic variability to provide a more comprehensive assessment of overall quality of glycaemic control. CONCLUSION: The CGM metrics %TAR and %TIR show much higher correlations with Mean Glucose than with HbA1c and provide sensitive indicators of efficacy. Mean glucose may be the best metric and shows consistently higher correlations with %TAR than with %TIR.

Trimethylamine N-oxide predicts stroke severity in diabetic patients with acute ischaemic stroke and is related to glycemic variability.

BACKGROUND AND PURPOSE: The present study analyzed the relationship between circulating trimethylamine N-oxide (TMAO) levels and stroke severity in diabetic patients with acute ischaemic stroke. A further aim was to investigate whether higher TMAO levels were associated with platelet aggregation and glycemic variability. METHODS: This was a cross-sectional analysis of 108 patients with type 2 diabetes mellitus (DM) undergoing acute ischaemic stroke and 60 healthy controls. Fasting plasma TMAO was measured using high-performance liquid chromatography with online electrospray ionization tandem mass spectrometry. RESULTS: Plasma TMAO levels of patients with acute ischaemic stroke were significantly higher than those of healthy controls. Amongst stroke patients, 50 were defined as undergoing mild stroke, and their plasma TMAO levels were lower compared to those with moderate to severe stroke. Platelet aggregation and mean amplitude of glycemic excursions were both correlated with plasma TMAO levels and these relationships remained significant in multiple linear regression analyses. Moreover, in streptozotocin-induced diabetic rats fed a diet enriched with choline to increase TMAO synthesis, platelet aggregation was significantly increased in the DM + choline and fluctuating DM (FDM) + choline groups compared to the control group. This increase was abolished in rats receiving oral antibiotics, which markedly reduced plasma TMAO levels. Importantly, compared with the DM + choline group, the FDM + choline group displayed significantly elevated TMAO levels and higher platelet aggregation. CONCLUSIONS: Our results demonstrated that higher plasma TMAO levels were associated with stroke severity and suggested a novel link between plasma TMAO levels and glycemic variability in diabetic patients with acute ischaemic stroke.

Evaluation of Basal Plus Versus Sliding Scale Insulin Therapy on Glucose Variability in Critically Ill Patients Without Preexisting Diabetes.

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.