Glucose Coefficient Of Variation Research Articles

Abstract PO2-02-02: BREAst Cancer Personalized NuTrition (BREACPNT): dietary intervention in HR+ breast cancer patients treated with endocrine therapy

Abstract BACKGROUND Breast cancer patients treated with adjuvant endocrine therapy commonly experience weight gain, which has been associated with low adherence to therapy and worse breast cancer prognosis. Dietary interventions are the first-line treatment for weight management in breast cancer patients and have beneficial results. Yet, the ability to maintain these outcomes requires further research. Here, we aimed to assess whether a personalized postprandial glucose targeting (PPT) diet is beneficial for weight management and metabolic health as compared with the recommended Mediterranean (MED) diet in this population. DESIGN AND METHODS BREACPNT is a phase-2 randomized trial in HR+ patients with breast cancer, treated with adjuvant endocrine therapy. We randomly assigned participants to follow a MED diet or a PPT diet for a 6-month dietary intervention and additional 6-month follow-up. The PPT diet is based on a machine-learning algorithm that integrates clinical and microbiome features to predict personal postprandial glucose responses (PPGRs). During the intervention, participants were monthly followed by a registered dietitian, they were additionally connected to a 2-week continuous glucose monitoring (CGM) on 3-timepoints, provided stool and blood samples and self-reported dietary intake using a smartphone application. RESULTS Among 127 randomized patients (mean ± SD age 53 ± 10 years, BMI 28.6 ± 5 kg/m2), 103 (81%) completed the 6-month intervention period. A total of 94 participants provided 12-month follow-up data. Both interventions led to a significant weight loss. The PPT-diet showed numerically greater weight loss with median change of -1.45kg (IQR -3.75 – 0) compared to the MED-diet with median change of -1kg (IQR -3.2 - 0.1) , however this was not significant (p=0.9). Notably, at the 12-month time point , participants in the PPT arm were more likely to maintain the weight loss (median -1.2kg, IQR -4 - 0.1) compared to participants in the MED-arm (median -0.15kg , IQR -4.25 - 1.5), p=0.14) and had significantly improved waist circumference (median, IQR; -5 cm, -8-(-2) in the PPT arm, compared to -1cm, -6.5-3 in the MED arm, p=0.01). In terms of glucose metabolism the PPT-diet led to improved glycemic control as measured by CGM, with significant lower average postprandial glucose responses (PPGRs) and glucose coefficient of variation (CV) compared to the MED-diet arm (mean 6-month change in PPGR was 2.1 ± 7.5 mg/dL × h for MED and −3.4 ± 5.6 mg/dL × h for PPT, p< 0.001, and the 6-month mean change for CV was 0.15 ± 2.76 for MED and −1.37 ± 3.2 for PPT, p< 0.01). Within the entire cohort, participants who experienced the greatest weight loss (upper quartile) were more likely to be postmenopausal compared to participants in the lower quartile. Additionally, participants who did not achieve weight loss (lower quartile) were significantly more likely to be treated with tamoxifen than AI (p=0.015). CONCLUSIONS In this dietary intervention in HR+ breast cancer patients, both PPT diet and MED diet led to weight loss and improved food quality, with a slight advantage to PPT diet. PPT diet additionally improved glycemic features as compared to MED diet, which are linked to lower risk of obesity and disease recurrence, suggesting an advantage in a glucose targeting approach. Premenopausal and Tamoxifen treated patients were less likely to lose weight through the intervention. These findings may have implications for dietary advice in clinical practice. Citation Format: Michal Rein, Maya Dadiani, Anastasia Godneva, Michal Bakalenik-Gavry, Dana Morzaev-Sulzbach, Maya Lotan-Pompan, Adina Weinberger, Eran Segal, Einav Gal-Yam. BREAst Cancer Personalized NuTrition (BREACPNT): dietary intervention in HR+ breast cancer patients treated with endocrine therapy [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO2-02-02.

Is It Time to Move Beyond TIR to TITR? Real-World Data from Over 20,000 Users of Continuous Glucose Monitoring in Patients with Type 1 and Type 2 Diabetes.

The growing use of continuous glucose monitoring (CGM) has been supported by expert consensus and clinical guidelines on glycemic management in diabetes with time in range (TIR 70-180 mg/dL) representing a key CGM-derived glucose metric. Time in tight range (TITR) has also been proposed for clinical use, spanning largely normal glucose levels of 70-140 mg/dL. However, keeping such narrow glucose ranges can be challenging, and understanding the factors modulating TITR can help achieve these tight glycemic targets. Our real-life study aimed to evaluate the relationship between average glucose (AG) and TIR/TITR in a large cohort (n = 22,006) of CGM users, divided into four groups: self-identified as having type 1 diabetes (T1D) treated with insulin using multiple daily injections (MDI) or pumps; type 2 diabetes (T2D) on MDI or insulin pumps; T2D on basal insulin only; and T2D not on insulin treatment. The T2D groups, regardless of treatment type, displayed the highest TIR and TITR values, associated with lowest glycemic variability measured as glucose coefficient of variation (CV; 23-30%). The T1D group showed the lowest TIR and TITR, associated with the highest CVs (36-38%). Overall, higher CV was associated with lower TIR and TITR for AG values below 180 and 140 mg/dL, respectively, with the reverse holding true for AG values above these thresholds. The discordance between AG and TIR/TITR was less pronounced in T2D compared with T1D, attributed to lower CV in the former group. It was also observed that TITR has advantages over TIR for assessing glycemia status and progress toward more stringent A1C, particularly when approaching normal glucose levels. The data detail how CV affects the AG relationship with TIR/TITR, which has implications for CGM interpretation. In many instances TITR, rather than TIR, may be preferable to employ once AG falls below 140 mg/dL and near-normal glucose levels are required clinically.

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

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

Modern methods for assessing compensation of diabetes mellitus. Effect of continuous glucose monitoring on glycemic control in patients with type 2 diabetes

Background. An integral part of diabetes mellitus (DM) management is its timely diagnosis. The reference method to study the quality of glycemic control is evaluating the level of glycated hemoglobin (HbA1c) as one of the most accessible and informative tools. However, the introduction of novel technologies, namely the use of continuous glucose monitoring (CGM), has given patients with DM, their relatives and healthcare professionals an access to new indicators of glycemic control such as time in range (TIR), time above range and time below range. These indicators are criteria for compensation of carbohydrate metabolism according to the 2023 American Diabetes Association guidelines. The purpose of the study was to compare the effectiveness of using glucometers and the FreeStyle Libre system for flash glucose monitoring in patients with DM. Materials and methods. An examination of 60 patients with type 2 DM who received insulin therapy was conducted. They were aged 45–65 years, with DM duration of 5 ± 2 years. All participants were divided into two equal groups of 30 people each. The first group consisted of patients with CGM devices, the second group used glucometers for daily glycemic control. Additional CGM parameters were used: glucose coefficient of variation, glucose management indicator, which shows the probable level of HbA1c. Results. When evaluating the data obtained from patients who used CGM, it is important to note their high adherence to the use of flash glucose monitoring. The use of CGM made it possible to achieve compensation of carbohydrate metabolism faster compared to patients who used glucometers to correct glycemia. Discontinuation of CGM resulted in poorer glycemic control. The obtained results prove that the compensation of patients depends primarily on their motivation, willingness to follow medical prescriptions, careful glycemic control, and compliance with medical recommendations. Conclusions. For better control of the DM course, patients should use CGM. In order to assess diabetes compensation, it is not enough to consider only TIR. Time below range indicating hypoglycemia, time above range indicating hyperglycemia, glucose management indicator should also be taken into account. Discontinuation of CGM resulted in a loss of approximately half of the initial TIR gain achieved while using CGM. CGM is more favored among patients than a 7-point self-monitoring of blood glucose.

Predicting the Risk of Developing Type 1 Diabetes Using a One-Week Continuous Glucose Monitoring Home Test With Classification Enhanced by Machine Learning: An Exploratory Study.

Detection of two or more autoantibodies (Ab) in the blood might describe those individuals at increased risk of developing type 1 diabetes (T1D) during the following years. The aim of this exploratory study is to propose a high versus low T1D risk classifier using machine learning technology based on continuous glucose monitoring (CGM) home data. Forty-two healthy relatives of people with T1D with mean ± SD age of 23.8 ± 10.5 years, HbA1c (glycated hemoglobin) of 5.3% ± 0.3%, and BMI (body mass index) of 23.2 ± 5.2 kg/m2 with zero (low risk; N = 21), and ≥2 (high risk; N = 21) Ab, were enrolled in an NIH (National Institutes of Health)-funded TrialNet ancillary study. Participants wore a CGM for a week and consumed three standardized liquid mixed meals (SLMM) instead of three breakfasts. Glycemic features were extracted from two-hour post-SLMM CGM traces, compared across groups, and used in four supervised machine learning Ab risk status classifiers. Recursive Feature Elimination (RFE) algorithm was used for feature selection; classifiers were evaluated through 10-fold cross-validation, using the receiver operating characteristic area under the curve (AUC-ROC) to select the best classification model. The percent time of glucose >180 mg/dL (T180), glucose range, and glucose CV (coefficient of variation) were the only significant differences between the glycemic features in the two groups with P values of .040, .035, and .028 respectively. The linear SVM (Support Vector Machine) model with RFE features achieved the best performance of classifying low-risk versus high-risk individuals with AUC-ROC = 0.88. A machine learning technology, combining a potentially self-administered one-week CGM home test, has the potential to reliably assess the T1D risk.

Interaction between haptoglobin genotype and glycemic variability on diabetic macroangiopathy: a population-based cross-sectional study.

Haptoglobin (Hp) is a hemoglobin-binding protein that functions as an antioxidant in human plasma. It is reported that glycemic variability (GV) plays a key role in diabetes-related complications associated with impaired glucose metabolism and oxidative stress. Here we aim to investigate whether the effect of GV on diabetic macroangiopathy depends on Hp genotype in type 2 diabetes. A number of 860 Chinese patients with type 2 diabetes was genotyped and assigned to two Hp subgroups (Hp 2-2 and Hp 1 carriers). Glycemic variability (GV) was assessed by using a retrospective continuous glucose monitoring system for three consecutive days, and it was measured using the glucose coefficient of variation (%CV), which is calculated as the ratio of glucose standard deviation to glucose mean. Clinical features, history of cardiac surgery, and vascular imaging tests were utilized to diagnose macroangiopathy. We evaluated the interaction between Hp genotypes and %CV on diabetic macroangiopathy. Furthermore, serum concentration of 8-hydroxy-2'-deoxyguanosine (8-OHdG) was measured using an enzyme-linked immunosorbent assay as a biomarker of oxidative stress. Serum 8-OHdG levels were positively correlated with %CV in Hp 1 carriers (r = 0.117; p = 0.021). Patients in the highest %CV tertile were associated with a higher prevalence of diabetic macroangiopathy than those in the lowest %CV tertile in Hp 1 carriers (OR = 2.461 [95% CI, 1.183-5.121], p = 0.016), but not in those with Hp 2-2 genotype (OR = 0.540 [95% CI, 0.245-1.191], p = 0.127). A significant interactive effect of Hp genotypes and %CV on diabetic macroangiopathy was found (p interaction = 0.008). Hp genotype modifies the effect of GV on diabetic macroangiopathy among Chinese patients with type 2 diabetes.

Glucose variability and mood in people with type 1 diabetes using ecological momentary assessment

ObjectiveMood fluctuations related to blood glucose excursions are a commonly reported source of diabetes-distress, but research is scarce. We aimed to assess the relationship between real-time glucose variability and mood in adults with type 1 diabetes (T1D) using ecological momentary assessments. MethodsIn this prospective observational study, participants wore a masked continuous glucose monitor and received prompts on their smartphone 6 times a day to answer questions about their current mood (Profile Of Mood States (POMS)-SF (dimensions: Anxiety, Depressive symptoms, Anger, Fatigue, Vigor)) for 14 days. Mixed model analyses examined associations over time between daily Coefficient of Variation (CV) of blood glucose and mean and variability (CV) of POMS scores. Further, within-person differences in sleep and nocturnal hypoglycemia were explored. Results18 people with T1D (10 female, mean age 44.3 years) participated. A total of 264 out of 367 days (70.2%) could be included in the analyses. No overall significant associations were found between CV of blood glucose and mean and CV of POMS scores, however, nocturnal hypoglycemia moderated the associations between CV of blood glucose and POMS scales (mean Fatigue Estimate 1.998, p < .006, mean Vigor Estimate −3.308, p < .001; CV Anger Estimate 0.731p = 0.02, CV Vigor Estimate −0.525, p = .006). ConclusionWe found no overall relationship between real-time glycemic variability and mood per day. Further research into within-person differences such as sleep and nocturnal hypoglycemia is warranted.

Continuous Glucose Monitoring vs. Capillary Blood Glucose in Hospitalized Type 2 Diabetes Patients.

The emergence of continuous glucose monitoring devices revolutionized the monitoring of diabetes, allowing real-time measurement of interstitial glucose levels. These devices are especially important for people with diabetes treated with insulin therapy and have been extensively studied in outpatient settings. In hospitalized patients, studies using continuous glucose monitoring have focused mainly on evaluating its accuracy and feasibility, but the results were unclear on whether continuous glucose monitoring was superior to capillary blood glucose in improving glycemic control and further research is needed to support the use of these devices in hospitalized patients with diabetes. The primary endpoint of this study was to assess the increase in time-in-range (glycemic readings between 100-180 mg/dL) in hospitalized patients with continuous glucose monitoring, compared to capillary blood glucose. The secondary endpoints included the assessment of reductions in hypoglycemia incidence, mean glucose levels, and glucose coefficient of variation. Additionally, we assessed the intervention's impact on reducing the length of hospital stay, mortality rates, and incidence of inpatient infections. This was a retrospective, cohort study of 60 hospitalized patients with type 2 diabetes, divided into two groups of 30 individuals each: an intervention group monitored through continuous glucose monitoring and a control group using capillary blood glucose. Both groups were comparable in terms of demographic and clinical characteristics. Continuous glucose monitoring users had a higher number of readings per day (six vs. four, p < 0.001), in-range readings (53.5% vs. 35%, p = 0.027), fewer above-range readings (25.5% vs. 56.5%, p = 0.003), particularly above 250 mg/dL (5% vs. 27.5%, p = 0.001), with no difference in the percentage of hypoglycemia occurence (1% vs. 0%, p = 0.107). Lower mean glucose (161.9 mg/dL vs. 206.5 mg/dL, p < 0.001) was also observed in this group. No difference was observed in mortality, length of stay, or in infection rate (p = 1.000, p = 0.455, and p = 0.606, respectively). This retrospective study supports the use of continuous glucose monitoring in optimizing glycemic control in hospitalized patients with type 2 diabetes on intensive insulin therapy. These findings suggest that continuous glucose monitoring can improve time-in-range and prevent hyperglycemia.

946-P: Impacts of MAGE and SD on the Relationship between TIR and Glucose Management Indicator in T2D Patients

Aims: Previous studies proved that glucose coefficient of variation (CV), an index of glycemic variability (GV), affected the relationship between TIR and glucose management indicator (GMI) in patients with diabetes. This study aimed to explore whether other GV indexes, including MAGE and SD of glucose, affect the relationship between TIR and GMI in T2D patients. Methods: Data were collected from T2D patients who received 48-h to 72-h CGM with iPro2 between January 2018 and October 2019. GV indexes, TIR, and GMI were derived from iPro2. A linear regression equation was generated with values of TIR and GMI. Patients were then divided into 3 subgroups according to the values of MAGE or SD. Predicted TIR in given GMI was calculated via the linear regression equations in the respective tertiles groups of MAGE or SD. Results: A total of 118 T2D patients who were 57.2±11.6 years old, with a median diabetes duration of 6.0 years, HbA1c of 6.9±1.2%, and TIR of 87.5±12.9% were included. There was a strong negative correlation between TIR and GMI (r=-0.762, P&amp;lt;0.001). The linear regression equation was TIR%=-21.2× GMI%+224.6. Multiple linear regression analysis showed that both MAGE and SD have impacts on the relationship between TIR and GMI (P&amp;lt;0.001). The predicted TIR decreased when MAGE or SD increased with different given GMI (Table 1). Conclusions: MAGE and SD influenced the relationship between TIR and GMI in T2D patients. Disclosure D.Chen: None. Z.Liu: None. B.Lin: None. D.Yang: None. J.Yan: None. Y.Yang: None. W.Xu: None.

814-P: Intensive Insulin Therapy within One Week Can Improve ß-Cell Function in T2D Patients

Objective: Short-term intensive insulin therapy (IIT), usually over 2 weeks, can improve β-cell function in T2D. However, whether shorter duration of IIT could also have impact on β-cell function remained unclear. This study was thus designed to explore this question. Methods: One hundred and ninety-two T2D patients who received continuous subcutaneous insulin infusion within 1 week and underwent comprehensive evaluation of β-cell function before and after IIT were included. β-cell function indices including HOMA-β, insulinogenic index, disposition index (DI), fasting and postprandial C-peptide index (CPI), AUC of insulin and C-peptide were adopted. In a subset patients underwent CGM, the correlation of glucose variability and β-cell function were analyzed. Results: The patients were 56.2±13.7 (mean±SD) years old, with diabetic duration of 6.0 (12.1) [median (interquartile)] years, HbA1c of 9.3±2.0%. After 3-7 days (median: 5 days) of IIT, insulinogenic index [3.6 (5.3) mU/mmol vs. 2.9 (4.4) mU/mmol, p=0.005], DI [358.3 (398.5) vs. 233.3 (275.5), p&amp;lt;0.001], fasting CPI [1.2 (0.8) nmol/mmol vs. 1.0 (0.8) nmol/mmol, p&amp;lt;0.001], postprandial CPI [1.8 (1.5) nmol/mmol vs. 1.4 (1.2) nmol/mmol, p&amp;lt;0.001] and AUC of C-peptide [113.0 (67.1) nmol∙min/L vs. 103.7 (55.9) nmol∙min/L, p=0.002] were significantly improved compared with baseline. Among these patients, 45 subjects underwent CGM. It was found that changes in DI from baseline (ΔDI) were negatively correlated with TAR (r=-0.417, P=0.004), standard deviation (r=-0.366, P=0.013), MAGE (r=-0.295, P=0.049), glucose coefficient of variation (r=-0.321, P=0.031) and positively correlated with TIR (r=0.359, P=0.004). Multiple linear regression revealed that duration of IIT (β=0.453, P=0.009) and TIR (β=0.527, P=0.006) were independent factors of ΔDI. Conclusions: IIT within 1 week can also improved β-cell function in T2D patients. Duration of IIT and TIR could be the independent factors responsible for the improvement of β-cell function. Disclosure W.Jiang: None. X.Chen: None. Z.Liu: None. B.Lin: None. W.Xu: None. L.Zeng: None.

Relationship Between Time in Range and Dusk Phenomenon in Outpatients with Type 2 Diabetes Mellitus.

The dusk phenomenon refers to a spontaneous and transient pre-dinner hyperglycemia that affects glucose fluctuation and glycemic control, and the increasing use of continuous glucose monitoring (CGM) has facilitated its diagnosis. We investigated the frequency of the dusk phenomenon and its relationship with the time in range (TIR) in patients with type 2 diabetes mellitus (T2DM). This study involved 102 patients with T2DM who underwent CGM for 14 days. CGM-derived metrics and clinical characteristics were evaluated. A consecutive dusk blood glucose difference (pre-dinner glucose minus 2-hour post-lunch glucose) of ≥ 0 or once-only dusk blood glucose difference of < 0 was diagnosed as the clinical dusk phenomenon (CLDP). We found that the percentage of CLDP was 11.76% (10.34% in men, 13.64% in women). Compared with the non-CLDP group, the CLDP group tended to be younger and have a lower percentage of TIR (%TIR3.9-10) and higher percentage of time above range (%TAR>10 and %TAR>13.9) (P ≤ 0.05). Adjusted for confounding factors, the binary logistic regression analysis showed a negative association of CLDP with %TIR (odds ratio < 1, P < 0.05). We repeated the correlation analysis based on 70%TIR and found significant differences in hemoglobin A1c, fasting blood glucose, mean blood glucose, standard deviation of the sensor glucose values, glucose coefficient of variation, largest amplitude of glycemic excursions, mean amplitude of glycemic excursions, glucose management indicator, and percentage of CLDP between the two subgroups of TIR ≤ 70% and TIR > 70% (P < 0.05). The negative association between TIR and CLDP still remained after adjustment by the binary logistic regression analysis. The CLDP was frequently present in patients with T2DM. The TIR was significantly correlated with the CLDP and could serve as an independent negative predictor.

COMPARISON OF EFFECTS OF BLOOD PRESSURE, BLOOD GLUCOSE AND LIPID VARIABILITY ON CARDIOVASCULAR PROGNOSIS IN DIABETIC PATIENTS

Objective: Metabolic abnormalities such as dyslipidemia, glucose and high blood pressure are common in diabetic patients. Variabilities in these measures have been reported as a potential residual cardiovascular risk factors. This study aimed to analyze the relationship between variabilities of blood pressure, blood glucose, total cholesterol and triglyceride levels (metabolic variability parameters) and their effects on cardiovascular prognosis in diabetic patients. Design and method: A total of 22,310 diabetic patients aged 40 years or older who had their blood pressure, blood glucose, total cholesterol, and triglyceride levels measured three or more times for three years after January 1, 2017 at three tertiary general hospitals were selected. They were divided into high/low groups based on their systolic blood pressure (SBP), blood glucose, total cholesterol (TC) and triglyceride coefficient of variation (CV) over a 3-year period. The incidence of major adverse cardiovascular events (MACE), a composite of cardiovascular death, myocardial infarction, and stroke, was compared between groups, and significant risk factors for MACE were estimated by multivariable Cox regression analysis. Results: The number of measurements for 3 years was 15.1 times for SBP, 7.8 times for TC, 6.9 times for triglyceride, and 8.6 times for glucose. The correlation between the CV of SBP and the CV of glucose was highest, and the correlation between the CV of SBP and the CV of triglycerides was the lowest. All groups with high CV had a higher incidence of MACE than those with low CV (2.5% vs 6.0% for SBP CV groups, 3.0% vs 5.5% for TC CV groups, 3.8% vs 4.7% for triglyceride CV groups, 2.7% vs 5.8% for glucose CV groups). Multivariable Cox regression analysis suggested male, creatinine, prior myocardial infarction, SCORE2, SBP CV, TC CV, mean TC, and mean triglyceride as independent risk predictors for MACE in diabetic patients (HR 1.087 (95% CI 1.068 – 1.107) for SBP CV, 1.023 (1.016 – 1.031) for TC CV). Conclusions: Metabolic variability parameters, especially SBP CV and TC CV are important residual risk factors for cardiovascular events in diabetic patients.

Relationship between key continuous glucose monitoring-derived metrics and specific cognitive domains in patients with type 2 diabetes mellitus

BackgroundContinuous glucose monitoring (CGM)-derived time in range (TIR) is closely associated with micro- and macrovascular complications in type 2 diabetes mellitus (T2DM). This study was performed to investigate the relationship between key CGM-derived metrics and specific cognitive domains in patients with T2DM.MethodsOutpatients with T2DM who were otherwise healthy were recruited for this study. A battery of neuropsychological tests was performed to evaluate cognitive function, including memory, executive functioning, visuospatial ability, attention, and language. Participants wore a blinded flash continuous glucose monitoring (FGM) system for 72 h. The key FGM-derived metrics were calculated, including TIR, time below range (TBR), time above range (TAR), glucose coefficient of variation (CV), and mean amplitude of glycemic excursions (MAGE). Furthermore, the glycemia risk index (GRI) was also calculated by the GRI formula. Binary logistic regression was used to assess risk factors for TBR, and we further analysed the associations between neuropsychological test results and key FGM-derived metrics with multiple linear regressions.ResultsA total of 96 outpatients with T2DM were recruited for this study, with 45.8% experiencing hypoglycemia (TBR< 3.9 mmol/L). Spearman analysis results revealed that a higher TBR< 3.9 mmol/L was correlated with worse performance on the Trail Making Test A (TMTA), Clock Drawing Test (CDT), and cued recall scores (P < 0.05). Logistic regression analysis results indicated that the TMTA (OR = 1.010, P = 0.036) and CDT (OR = 0.429, P = 0.016) scores were significant factors influencing the occurrence of TBR< 3.9 mmol/L. Multiple linear regressions further demonstrated that TBR< 3.9 mmol/L (β = -0.214, P = 0.033), TAR> 13.9 mmol/L (β = -0.216, P = 0.030) and TAR10.1–13.9 mmol/L (β = 0.206, P = 0.042) were significantly correlated with cued recall scores after adjusting for confounding factors. However, TIR, GRI, CV and MAGE showed no significant correlation with the results of neuropsychological tests (P > 0.05).ConclusionsA higher TBR< 3.9 mmol/L and TAR> 13.9 mmol/L were associated with worse cognitive functions (memory, visuospatial ability, and executive functioning). Conversely, a higher TAR of 10.1–13.9 mmol/L was associated with better memory performance in memory tasks.

Rebound Hypoglycemia and Hyperglycemia in Type 1 Diabetes.

The aim was to investigate rebound hypoglycemic and hyperglycemic events, and describe their relation to other glycemic metrics. Data from intermittently scanned continuous glucose monitoring were downloaded for 90 days for 159 persons with type 1 diabetes. A hypoglycemic event was defined as glucose <3.9 mmol/l for at least two 15-minute periods. Rebound hypoglycemia (Rhypo) was a hypoglycemic event preceded by glucose >10.0 mmol/l within 120 minutes and rebound hyperglycemia (Rhyper) was hypoglycemia followed by glucose >10.0 mmol/l within 120 minutes. A total of 10 977 hypoglycemic events were identified of which 3232 (29%) were Rhypo and 3653 (33%) were Rhyper, corresponding to a median frequency of 10.1, 2.5, and 3.0 events per person/14 days. For 1267 (12%) of the cases, Rhypo and Rhyper coexisted. The mean peak glucose was 13.0 ± 1.6 mmol/l before Rhypo; 12.8 ± 1.1 mmol/l in Rhyper. The frequency of Rhyper was significantly (P < .001) correlated with Rhypo (Spearman's rho 0.84), glucose coefficient of variation (0.78), and time below range (0.69) but not with time above range (0.12, P = .13). The strong correlation between Rhyper and Rhypo suggests an individual behavioral characteristic toward intensive correction of glucose excursions.

Is fear of hypoglycemia a major barrier to an active lifestyle in children and adolescents with type 1 diabetes? The Diactive-1 Study.

Studies on fear of hypoglycemia as a barrier to physical activity among youth with type 1 diabetes (T1D) have been limited and controversial, most of which used self-reported assessment. The aim of the study was to evaluate the relationship between fear of hypoglycemia and physical activity and glycemic metrics in children and adolescents with T1D. Seventy-four participants (6-18 years of age; 44.6% females) with T1D were included in the study. Physical activity was assessed through accelerometry on nine consecutive days, and blood glucose metrics were simultaneously tracked using continuous glucose monitoring (time-in-range and hypoglycemic events). A closed question was used to evaluate the avoidance of physical activity due to fear of hypoglycemia. Fifteen participants (20%) reported avoiding physical activity due to fear of hypoglycemia. The group reporting no fear of hypoglycemia showed lower total physical activity (-35.33 min/day, 95% confidence interval [CI] (-77.57 to -1.47)) and light physical activity (-29.81 min/day, 95% CI -64.01 to -2.75) and higher sedentary time (77.95 min/day, 95% CI 26.46-136.87) per day compared with those with fear of hypoglycemia. No difference was found between those patients with fear of hypoglycemia in terms of meeting the recommendations of glycated hemoglobin, glucose coefficient of variation, and time-in-range when compared to those with no fear of hypoglycemia. In conclusion, children and adolescents with fear of hypoglycemia were more active, less sedentary, and had similar glycemic metrics to those without fear. Our results therefore suggest that fear of hypoglycemia may be less of a barrier to an active lifestyle than previously believed.

Serum Metabolomics Reveals a Potential Benefit of Methionine in Type 1 Diabetes Patients with Poor Glycemic Control and High Glycemic Variability.

Glycemic variability (GV) in some patients with type 1 diabetes (T1D) remains heterogeneous despite comparable clinical indicators, and whether other factors are involved is yet unknown. Metabolites in the serum indicate a broad effect of GV on cellular metabolism and therefore are more likely to indicate metabolic dysregulation associated with T1D. To compare the metabolomic profiles between high GV (GV-H, coefficient of variation (CV) of glucose ≥ 36%) and low GV (GV-L, CV < 36%) groups and to identify potential GV biomarkers, metabolomics profiling was carried out on serum samples from 17 patients with high GV, 16 matched (for age, sex, body mass index (BMI), diabetes duration, insulin dose, glycated hemoglobin (HbA1c), fasting, and 2 h postprandial C-peptide) patients with low GV (exploratory set), and another 21 (GV-H/GV-L: 11/10) matched patients (validation set). Subsequently, 25 metabolites were significantly enriched in seven Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways between the GV-H and GV-L groups in the exploratory set. Only the differences in spermidine, L-methionine, and trehalose remained significant after validation. The area under the curve of these three metabolites combined in distinguishing GV-H from GV-L was 0.952 and 0.918 in the exploratory and validation sets, respectively. L-methionine was significantly inversely related to HbA1c and glucose CV, while spermidine was significantly positively associated with glucose CV. Differences in trehalose were not as reliable as those in spermidine and L-methionine because of the relatively low amounts of trehalose and the inconsistent fold change sizes in the exploratory and validation sets. Our findings suggest that metabolomic disturbances may impact the GV of T1D. Additional in vitro and in vivo mechanistic studies are required to elucidate the relationship between spermidine and L-methionine levels and GV in T1D patients with different geographical and nutritional backgrounds.

Hyperglycemia and Other Glycemic Measures Throughout Therapy for Pediatric Acute Lymphoblastic Leukemia and Lymphoma.

Transient hyperglycemia during induction chemotherapy is associated with increased morbidity and mortality in patients with acute lymphoblastic leukemia (ALL). Treatment with glucocorticoids, asparaginase, and stress are the proposed causal factors. Although these risks are not exclusive to induction, glycemic control throughout the remainder of ALL/lymphoma (ALL/ALLy) therapy has not been described. Furthermore, prior research has been limited to transient hyperglycemia. This study aimed to characterize glycemic control throughout ALL/ALLy and to evaluate risk factors and outcomes associated with increased mean glucose and glucose coefficient of variation (glucose CV) during induction chemotherapy. The records for 220 pediatric/young adult patients, age 1 to 26 years, who underwent treatment for ALL/ALLy from 2010 to 2014 at Children's Hospital Colorado were retrospectively reviewed. Measures of glycemic control were calculated for each cycle. For the cycle with the highest mean glucose, induction (n=208), multivariable models were performed to identify potential risk factors and consequences of increased glucose. Highest mean glucose by cycle were induction 116mg/dL, pretreatment 108mg/dL, delayed intensification 96mg/dL, and maintenance 93mg/dL; these cycles also had the most glycemic variability. During induction, patients with Down syndrome, or who were ≥12 years and overweight/obese, had higher mean glucoses; age and overweight/obese status were each associated with increased glucose CV. In multivariable analysis, neither induction mean glucose nor glucose CV were associated with increased hazard of infection, relapse, or death.

Glucose variability is a marker for COVID-19 severity and mortality.

We aimed to investigate the association between glucose coefficient of variation (CV) and mortality and disease severity in hospitalized patients with coronavirus disease-19 (COVID-19). Retrospective cohort study in a tertiary center of patients with COVID-19 admitted to designated departments between March 11th, 2020, and November 2nd, 2020. We divided patients based on quartiles of glucose CV after stratification to those with and without diabetes mellitus (DM). Main outcomes were length of stay and in-hospital mortality. The cohort included 565 patients with a mean age of 67.71 ± 15.45 years, and 62.3% were male. Of the entire cohort, 44.4% had DM. The median glucose CV was 32.8% and 20.5% in patients with and without DM, respectively. In patients with DM, higher glucose CV was associated with a longer hospitalization in the unadjusted model (OR = 2.7, 95% CI [1.3,5.6] for Q4), and when adjusted for age, sex, comorbidities, and laboratory markers, this association was no longer statistically significant (OR = 1.3, 95% CI [0.4,4.5] for Q4). In patients with and without DM, higher glucose CV was associated with higher rates of in-hospital mortality in the unadjusted model, but adjustment for comorbidities and laboratory markers eliminated the association (OR = 0.5, 95% CI [0.1,3.4] for Q4 in patients with DM). Higher glucose CV was associated with increased in-hospital mortality and length of stay, but this association disappeared when the adjustment included laboratory result data. Glucose CV can serve as a simple and cheap marker for mortality and severity of disease in patients with COVID-19.

Cross-sectional association of metrics derived from continuous glucose monitoring with cognitive performance in older adults with type 2 diabetes.

To examine the association between continuous glucose monitoring (CGM)-derived metrics and cognitive performance in older adults with type 2 diabetes (T2D). A total of 100 outpatients with T2D aged 70 years or older were analysed. Participants underwent CGM for 14 days. As CGM-derived metrics, mean sensor glucose (SG), glucose coefficient of variation (CV), time in range (TIR; 70-180 mg/dl), time above range (TAR; > 180 mg/dl) and time below range (TBR; < 70 mg/dl), were calculated. Participants underwent cognitive tests, including the Japanese version of the Montreal Cognitive Assessment (MoCA-J), a delayed word-recall test from the Alzheimer's Disease Assessment Scale-cognitive subscale, a digit symbol substitution test, a letter word fluency test, a trail-making test (TMT) and digit span test (DSP). In multiple regression analyses adjusted for confounders, a higher mean SG was associated with a lower performance in MoCA-J and TMT part B (TMT-B) (P < .05). A higher TAR was associated with a lower performance in TMT-B and DSP-backward (P < .05). By contrast, a higher TIR was associated with better function in TMT-B and DSP-backward (P < .05). Furthermore, CV and TBR were not associated with any cognitive function. Hyperglycaemia metrics and TIR derived from CGM are associated with cognitive functions, especially with executive function and working memory, in older adults with T2D.

Factors associated with hypoglycemia unawareness and severe hypoglycemia in type 1 diabetes mellitus patients.

Several factors are associated with hypoglycemia unawareness and severe hypoglycemia, but few large studies have analyzed Japanese patients with type 1 diabetes. The aim of this study was to analyze the risk factors for hypoglycemia unawareness and severe hypoglycemia in Japanese type 1 diabetes patients. A self-administered questionnaire investigated events, complications and treatments associated with hypoglycemia in patients with type 1 diabetes. Multiple logistic regression analysis of factors associated with hypoglycemia unawareness and severe hypoglycemia requiring medical treatment was carried out. The coefficient of variation (CV) of blood glucose levels was determined using blood samples collected at six outpatient visits. Of the 1,619 participants, 44.2% and 10.4% experienced hypoglycemia unawareness and severe hypoglycemia, respectively. Mean HbA1c levels in patients with hypoglycemia unawareness were lower than those in patients without hypoglycemia unawareness. The type 1 diabetes subtype, glycated hemoglobin (HbA1c) level, CV of blood glucose levels and history of severe hypoglycemia requiring medical treatment were significant independent variables predicting the presence of hypoglycemia unawareness. The glucose CV and a history of hypoglycemia unawareness were significant independent variables predicting severe hypoglycemia requiring medical treatment. In stratified analyses of patients divided into four groups according to glucose CV and HbA1c levels, the high-glucose-CV/low-HbA1c group had the highest odds ratios for hypoglycemia unawareness (2.60) and severe hypoglycemia requiring medical treatment (2.55). The ambulant glucose CV correlated with both hypoglycemia unawareness and severe hypoglycemia. Patients with high glucose CV and low HbA1c are at high risk of such adverse events, and their treatment strategies should be reviewed.