Glycemic Variability and Its Association With Demographics and Lifestyles in a General Adult Population

Relationship Between Glycosylated Hemoglobin Variability and the Severity of Coronary Artery Disease in Patients With Type 2 Diabetes Mellitus

Background: Glycosylated hemoglobin (HbA1c) variability is a risk factor for cardiovascular complications in patients with Type 2 diabetes mellitus (T2DM), but its relationship with the severity of coronary artery disease (CAD) is unclear. Methods: Patients with T2DM who underwent coronary angiography due to angina were enrolled. HbA1c variability was expressed as coefficient of variation (CV), standard deviation (SD), variability independent of mean (VIM), and time in range (TIR). The severity of CAD was expressed by the number of involved vessels and Gensini score. Multivariate regression models were constructed to test the relationship between HbA1c variability, number of involved vessels, and the Gensini score, followed by linear regression analysis. Results: A total of 147 patients were included. In multivariate analysis, VIM-HbA1c (OR = 2.604; IQR: 1.15, 5.90; r = 0.026) and HbA1cTIR (OR = 0.13; IQR: 0.04, 0.41; r < 0.001) were independent risk factors for the number of involved vessels. After adjustment, HbA1cTIR (OR = 0.01; IQR: 0.002, 0.04; r < 0.001), SD-HbA1c (OR = 4.12, IQR: 1.64, 10.35; r = 0.001), CV-HbA1c (OR = 1.41, IQR: 1.04, 1.92; r = 0.007), and VIM-HbA1c (OR = 3.26; IQR: 1.43, 7.47; r = 0.003) were independent risk factors for the Gensini score. In the linear analysis, the Gensini score was negatively correlated with HbA1cTIR (β = -0.629; r < 0.001) and positively correlated with SD-HbA1c (β = 0.271; r = 0.001) and CV-HbA1c (β = 0.176; r = 0.033). After subgroup analysis, HbA1cTIR was a risk factor for the number of involved vessels. The Gensini score was negatively correlated with HbA1cTIR and positively correlated with SD-HbA1c at subgroups of subjects with a mean HbA1c ≤ 7%. Conclusions: Our analysis indicates that HbA1c variability, especially HbA1cTIR, plays a role for the severity of CAD in patients with T2DM. HbA1c variability may provide additional information and require management even at the glycemic target. Translational Aspects: Studies have shown that HbA1c variability is related to cardiovascular complications. Further, we explore the correlation between HbA1c variability and the severity of CAD. HbA1c variability is a risk factor for coronary stenosis in T2DM. It may be a potential indicator reflecting glycemic control for the prevention and treatment of cardiovascular complications.

Multilevel functional data analysis modeling of human glucose response to meal intake

Glucose meal response information collected via Continuous Glucose Monitoring (CGM) is relevant to the assessment of individual metabolic status and the support of personalized diet prescriptions. However, the complexity of the data produced by CGM monitors pushes the limits of existing analytic methods. CGM data often exhibits substantial within-person variability and has a natural multilevel structure. This research is motivated by the analysis of CGM data from individuals without diabetes in the AEGIS study. The dataset includes detailed information on meal timing and nutrition for each individual over different days. The primary focus of this study is to examine CGM glucose responses following patients' meals and explore the time-dependent associations with dietary and patient characteristics. Motivated by this problem, we propose a new analytical framework based on multilevel functional models, including a new functional mixed R-square coefficient. The use of these models illustrates 3 key points: (i) The importance of analyzing glucose responses across the entire functional domain when making diet recommendations; (ii) The differential metabolic responses between normoglycemic and prediabetic patients, particularly with regards to lipid intake; (iii) The importance of including random, person-level effects when modelling this scientific problem.

Glycemic variability assessed using continuous glucose monitoring in individuals without diabetes and associations with cardiometabolic risk markers: A systematic review and meta-analysis

BACKGROUND & AIMS

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.

METHODS

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.

RESULTS

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.

CONCLUSION

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.

Multivariate reference and tolerance regions based on conditional transformation models: Application to glycemic markers

The reference interval is the most widely used medical decision-making, constituting a central tool in determining whether an individual is healthy or not. When the results of several continuous diagnostic tests are available for the same patient, their clinical interpretation is more reliable if a multivariate reference region (MVR) is available rather than multiple univariate reference intervals. MVRs, defined as regions containing 95% of the results of healthy subjects, extend the concept of the reference interval to the multivariate setting. However, they are rarely used in clinical practice owing to difficulties associated with their interpretability and the restrictions inherent to the assumption of a Gaussian distribution. Further statistical research is thus needed to make MVRs more applicable and easier for physicians to interpret. Since the joint distribution of diagnostic test results may well change with patient characteristics independent of disease status, MVRs adjusted for covariates are desirable. The present work introduces a novel formulation for MVRs based on multivariate conditional transformation models (MCTMs). Additionally, we take into account the estimation uncertainty of such MVRs by means of tolerance regions. These conditional MVRs imply no parametric restriction on the response, and potentially nonlinear continuous covariate effects can be estimated. MCTMs allow the estimation of the effects of covariates on the joint distribution of multivariate response variables and on these variables' marginal distributions, via the use of most likely transformation estimation. Our contributions proved reliable when tested with simulated data and for a real data application with two glycemic markers.

Reproducibility of continuous glucose monitoring results under real-life conditions in an adult population: a functional data analysis

Continuous glucose monitoring systems (CGM) are a very useful tool to understand the behaviour of glucose in different situations and populations. Despite the widespread use of CGM systems in both clinical practice and research, our understanding of the reproducibility of CGM data remains limited. The present work examines the reproducibility of the results provided by a CGM system in a random sample of a free-living adult population, from a functional data analysis approach. Functional intraclass correlation coefficients (ICCs) and their 95% confidence intervals (CI) were calculated to assess the reproducibility of CGM results in 581 individuals. 62% were females 581 participants (62% women) mean age 48 years (range 18-87) were included, 12% had previously been diagnosed with diabetes. The inter-day reproducibility of the CGM results was greater for subjects with diabetes (ICC 0.46 [CI 0.39-0.55]) than for normoglycaemic subjects (ICC 0.30 [CI 0.27-0.33]); the value for prediabetic subjects was intermediate (ICC 0.37 [CI 0.31-0.42]). For normoglycaemic subjects, inter-day reproducibility was poorer among the younger (ICC 0.26 [CI 0.21-0.30]) than the older subjects (ICC 0.39 [CI 0.32-0.45]). Inter-day reproducibility was poorest among normoglycaemic subjects, especially younger normoglycaemic subjects, suggesting the need to monitor some patient groups more often than others.

Time above range for predicting the development of type 2 diabetes

Aim

To investigate the prognostic value of time range metrics, as measured by continuous glucose monitoring, with respect to the development of type 2 diabetes (T2D).

Research design and methods

A total of 499 persons without diabetes from the general population were followed-up for 5 years. Time range metrics were measured at the start and medical records were checked over the period study.

Results

Twenty-two subjects (8.3 per 1,000 person-years) developed T2D. After adjusting for age, gender, family history of diabetes, body mass index and glycated hemoglobin concentration, multivariate analysis revealed 'time above range' (TAR, i.e., with a plasma glucose concentration of >140 mg/dL) to be significantly associated with a greater risk (OR = 1.06, CI 1.01-1.11) of developing diabetes (AUC = 0.94, Brier = 0.035).

Conclusions

Time above range provides additional information to that offered by glycated hemoglobin to identify patients at a higher risk of developing type 2 diabetes in a population-based study.

Regimen comprising GLP-1 receptor agonist and basal insulin can decrease the effect of food on glycemic variability compared to a pre-mixed insulin regimen

BACKGROUND

Increasing evidence suggests that glucagon-like peptide 1 (GLP-1) receptor agonists (RA) can stabilize glycemic variability (GV) and interfere with eating behavior. This study compared the impact of insulin, GLP-1 RA, and dietary components on GV using professional continuous glucose monitoring (CGM).

METHODS

Patients with type 2 diabetes underwent CGM before and after switching from a twice-daily pre-mixed insulin treatment regimen to a GLP-1 RA (liraglutide) plus basal insulin regimen. The dietary components were recorded and analyzed by a certified dietitian. The interactions between the medical regimen, GV indices, and nutrient components were analyzed.

RESULTS

Sixteen patients with type 2 diabetes were enrolled in this study. No significant differences in the diet components and total calorie intake between the two regimens were found. Under the pre-mixed insulin regimen, for increase in carbohydrate intake ratio, mean amplitude of glucose excursion (MAGE) and standard deviation (SD) increased; in contrast, under the new regimen, for increase in fat intake ratio, MAGE and SD decreased, while when the protein intake ratio increased, the coefficient of variation (CV) decreased. The impact of the food intake ratio on GV indices disappeared under the GLP-1 RA regimen. After switching to the GLP-1 RA regimen, the median MAGE, SD, and CV values decreased significantly. However, the significant difference in GV between the two regimens decreased during the daytime.

CONCLUSION

A GLP-1 RA plus basal insulin regimen can stabilize GV better than a regimen of twice-daily pre-mixed insulin, especially in the daytime, and can diminish the effect of food components on GV.

Association between visit-to-visit fasting glycemic variability and depression: a retrospective cohort study in a representative Korean population without diabetes

Glycemic variability (GV) is a risk factor for depression in patients with diabetes. However, whether it is also a predictor of incident depression in people without diabetes remains unclear. We aimed to investigate the association between visit-to-visit variability in fasting serum glucose (FSG) levels and the incidence of depression among Koreans without diabetes. This retrospective cohort study included data of people without diabetes who did not have depression at baseline and had at least three FSG measurements (n = 264,480) extracted from the 2002-2007 Korean National Health Insurance Service-National Health Screening Cohort. GV was calculated as the average successive variability of FSG. Among 264,480 participants, 198,267 were observed during 2008-2013 and their hazard ratios (HR) of incident depression were calculated. Participants with the highest GV showed a higher risk of depression in fully adjusted models than those with the lowest GV (HR, 1.09; 95% CI, 1.02-1.16). The risk of incident depression heightened with increasing GV (p for trend < 0.001). Greater visit-to-visit GV may be associated with the risk of developing depression in people without diabetes. Conversely, maintaining steady FSG levels may reduce the risk of incident depression in people without diabetes.

Aging of glucose profiles in an adult population without diabetes

AIMS

This study aimed to determine the effect of aging on glucose profiles in a population without diabetes.

METHODS

We investigated the evolution of glucose profiles in an adult population without diabetes using continuous glucose monitoring (CGM) in two periods separated by 5 years. Anthropometrics, laboratory tests (HbA1c, fasting blood glucose) and CGM data (mean glycemia level, coefficient of variation, time in range) were measured in both periods to study the change in values over time.

RESULTS

125 participants (68% women) mean age 43.1 ± 12.4 years and classified as normoglycemic at baseline were included. Of the total population 15.2% had worsened glycemic status after 5 years, age and baseline glucose values (HbA1c and percentage of values above 175 mg/dL) were the variables related with this change. Related to CGM, we found that after 5 years there was a decrease in the percentage of values between 70 and 99 mg/dl (45.0% to 38.7%, p = 0.002) and an increase in the 100-139 mg/dL range (52.9% to 57.5% p = 0.016).

CONCLUSIONS

Our results indicate that in an adult population without diabetes there are changes in glucose profiles with aging highlighting the reduction of blood glucose values below 100 mg/dL.

Kernel machine learning methods to handle missing responses with complex predictors. Application in modelling five-year glucose changes using distributional representations

BACKGROUND AND OBJECTIVES

Missing data is a ubiquitous problem in longitudinal studies due to the number of patients lost to follow-up. Kernel methods have enriched the machine learning field by successfully managing non-vectorial predictors, such as graphs, strings, and probability distributions, and have emerged as a promising tool for the analysis of complex data stemming from modern healthcare. This paper proposes a new set of kernel methods to handle missing data in the response variables. These methods will be applied to predict long-term changes in glycated haemoglobin (A1c), the primary biomarker used to diagnose and monitor the progression of diabetes mellitus, making emphasis on exploring the predictive potential of continuous glucose monitoring (CGM).

METHODS

We propose a new framework of non-linear kernel methods for testing statistical independence, selecting relevant predictors, and quantifying the uncertainty of the resultant predictive models. As a novelty in the clinical analysis, we used a distributional representation of CGM as a predictor and compared its performance with that of traditional diabetes biomarkers.

RESULTS

The results show that, after the incorporation of CGM information, predictive ability increases from R²=0.61 to R²=0.71. In addition, uncertainty analysis is useful for characterising some subpopulations where predictivity is worsened, and a more personalised clinical follow-up is advisable according to expected patient uncertainty in glucose values.

CONCLUSIONS

The proposed methods have proven to deal effectively with missing data. They also have the potential to improve the results of predictive tasks by including new complex objects as explanatory variables and modelling arbitrary dependence relations. The application of these methods to a longitudinal study of diabetes showed that the inclusion of a distributional representation of CGM data provides greater sensitivity in predicting five-year A1c changes than classical diabetes biomarkers and traditional CGM metrics.

Inhibition of SGLT1 Alleviates the Glycemic Variability-Induced Cardiac Fibrosis via Inhibition of Activation of Macrophage and Cardiac Fibroblasts

Glycemic variability has been considered one of the predictors of diabetes complications in patients with diabetes mellitus (DM). In this work, we evaluated whether glycemic variability induces cardiac fibrosis through regulating cardiac fibroblast activation and macrophage polarization. Moreover, we determined whether glucose transporter sodium-glucose cotransporter 1 (SGLT1) plays an important role in this process. Glycemic variability-induced mice were established using DM mice (GVDM mice), and intermittent high-glucose (IHG) treatment was used to simulate glycemic variability in RAW264.7 macrophages and cardiac fibroblasts. The short hairpin RNA for SGLT1 was used to knock down SGLT1. The results showed that glycemic variability aggravated the cardiac fibrosis in GVDM mice. Additionally, glycemic variability promoted the expression of fibrogenic cytokine and the extracellular matrix proteins in left ventricular tissues and cardiac fibroblasts. GVDM mice showed a higher incidence of macrophage infiltration and M1 polarization in left ventricular tissues. Moreover, IHG-promoted RAW264.7 macrophages tended to differentiate to M1 phenotype. SGLT1 knockdown alleviated cardiac fibrosis in GVDM mice and inhibited activations of cardiac fibroblast and macrophage M1 polarization. Our results indicated that glycemic variability aggravates cardiac fibrosis through activating cardiac fibroblast and macrophage M1 polarization, which could be partially inhibited by SGLT1 knockdown.

Cross-Sectional and Individual Relationships between Physical Activity and Glycemic Variability

Introduction/Purpose

Overweight or obese adults spend more time sedentary and less time performing physical activity (PA) and are at an increased risk for developing impaired glycemic health. Free-living environments may provide insight into glycemic health in addition to clinical assessments. The purpose of this study was to examine the relationship between PA and glycemic health assessed by continuous glucose monitoring (CGM).

Methods

Twenty-eight overweight or obese adults each wore an accelerometer and CGM over the same 7 consecutive days. Average daily time (minutes and metabolic-equivalent minutes (MET-minutes)) and associated energy expenditure performing light (LPA), moderate-to-vigorous (MVPA), total PA, and standard deviation (SD) across days were calculated. Average daily 24-h and waking glycemia, mean glucose concentration, glycemic variability measured as the continuous overlapping net glycemic action, mean amplitude of glycemic excursions, and mean of daily difference were assessed.

Results

LPA MET-minutes per day was positively associated with 24-h and waking glycemia time-in-range and negatively associated with 24-h and waking time in hyperglycemia. Total PA time and the SD of MVPA and total PA time were negatively associated with 24-h mean glucose concentration. Individual-level analysis identified that most participants (50%-71%) expressed negative associations between LPA and MVPA time with 24-h mean glucose concentration, mean amplitude of glycemic excursion, and 4-h continuous overlapping net glycemic action.

Conclusions

Expectedly, greater total PA time and intensity-specific PA time were associated with lower 24-h and waking mean glucose concentration, greater glycemia time-in-range, and less time in hyperglycemia. The relationship between glucose concentrations and PA time SD was unexpected, whereas most participants expressed hypothesized relationships, which necessitates further exploration.

Effects of Teneligliptin on HbA1c levels, Continuous Glucose Monitoring-Derived Time in Range and Glycemic Variability in Elderly Patients with T2DM (TEDDY Study)

BACKGROUND

To evaluate the effects of teneligliptin on glycosylated hemoglobin (HbA1c) levels, continuous glucose monitoring (CGM)-derived time in range, and glycemic variability in elderly type 2 diabetes mellitus patients.

METHODS

This randomized, double-blinded, placebo-controlled study was conducted in eight centers in Korea (clinical trial registration number: NCT03508323). Sixty-five participants aged ≥65 years, who were treatment-naïve or had been treated with stable doses of metformin, were randomized at a 1:1 ratio to receive 20 mg of teneligliptin (n=35) or placebo (n=30) for 12 weeks. The main endpoints were the changes in HbA1c levels from baseline to week 12, CGM metrics-derived time in range, and glycemic variability.

RESULTS

After 12 weeks, a significant reduction (by 0.84%) in HbA1c levels was observed in the teneligliptin group compared to that in the placebo group (by 0.08%), with a between-group least squares mean difference of -0.76% (95% confidence interval [CI], -1.08 to -0.44). The coefficient of variation, standard deviation, and mean amplitude of glycemic excursion significantly decreased in participants treated with teneligliptin as compared to those in the placebo group. Teneligliptin treatment significantly decreased the time spent above 180 or 250 mg/dL, respectively, without increasing the time spent below 70 mg/dL. The mean percentage of time for which glucose levels remained in the 70 to 180 mg/dL time in range (TIR70-180) at week 12 was 82.0%±16.0% in the teneligliptin group, and placebo-adjusted change in TIR70-180 from baseline was 13.3% (95% CI, 6.0 to 20.6).

CONCLUSION

Teneligliptin effectively reduced HbA1c levels, time spent above the target range, and glycemic variability, without increasing hypoglycemia in our study population.

Glycemic Variability Within 1 Year Following Surgery for Stage II-III Colon Cancer

OBJECTIVE

To examine glycemic variability within 1 month and 1 year following surgery among adult patients, with and without Type 2 Diabetes (T2D), treated for stage II-III colon cancer.

METHOD

A retrospective analysis of electronic health record data was conducted. Glycemic variability (i.e., standard deviation [SD] and coefficient of variation [CV] of > 2 blood glucose measures) was assessed within 1 month and within 1 year following colon surgery. Chi-square (χ²), Fisher's exact, and Mann-Whitney U tests were used for the analyses.

RESULTS

Among the sample of 165 patients with stage II-III colon cancer, those with T2D had higher glycemic variability compared to patients without T2D (p < .001), with values within 1 month following surgery (SD = 44.69 mg/dL, CV = 27.4%) vs (SD = 20.55 mg/dL, CV = 17.53%); and within 1 year following surgery (SD = 45.04 mg/dL, CV = 29.04%) vs (SD = 21.36 mg/dL, CV = 18.6%). Associations were found between lower body mass index and higher glycemic variability (i.e., SD [r = -.413, p < .05] and CV [r = -.481, p < .01]) within 1 month following surgery in patients with T2D. Higher preoperative glucose was associated with higher glycemic variability (i.e., SD r = .448, p < .01) within 1 year in patients with T2D. Demographic and clinical characteristics were weakly associated with glycemic variability in patients without T2D.

CONCLUSIONS

Patients with stage II-III colon cancer with T2D experienced higher glycemic variability within 1 month and within 1 year following surgery compared to those without T2D. Associations between glycemic variability and demographic and clinical characteristics differed by T2D status. Further research in prospective studies is warranted.

Tattoo Inks for Optical Biosensing in Interstitial Fluid

The persistence of traditional tattoo inks presents an advantage for continuous and long-term health monitoring in point of care devices. The replacement of tattoo pigments with optical biosensors aims a promising alternative for monitoring blood biomarkers. Tattoo inks functionalization enables the control of interstitial biomarkers with correlated concentrations in plasma, to diagnose diseases, evaluate progression, and prevent complications associated with physio pathological disorders or medication mismatches. The specific biomarkers in interstitial fluid provide a new source of information, especially for skin diseases. The study of tattoo inks displays insufficient regulation in their composition, a lack of reports of the related complications, and a need for further studies on their degradation kinetics. This review focuses on tattoo optical biosensors for monitoring dermal interstitial biomarkers and discusses the clinical advantages and main challenges for in vivo implantation. Tattoo functionalization provides a minimally invasive, reversible, biocompatible, real-time sensing with long-term permanence and multiplexing capabilities for the control, diagnosis, and prevention of illness; it enables self-controlling management by the patient, but also the possibility of sending the records to the doctor.

Modeling conditional reference regions: Application to glycemic markers

Many clinical decisions are taken based on the results of continuous diagnostic tests. Usually, only the results of one single test is taken into consideration, the interpretation of which requires a reference range for the healthy population. However, the use of two different tests, can be necessary in the diagnosis of certain diseases. This obliges a bivariate reference region be available for their interpretation. It should also be remembered that reference regions may depend on patient variables (eg, age and sex) independent of the suspected disease. However, few proposals have been made regarding the statistical modeling of such reference regions, and those put forward have always assumed a Gaussian distribution, which can be rather restrictive. The present work describes a new statistical method that allows such reference regions to be estimated with no insistence on the results being normally distributed. The proposed method is based on a bivariate location-scale model that provides probabilistic regions covering a specific percentage of the bivariate data, dependent on certain covariates. The reference region is estimated nonparametrically and the nonlinear effects of continuous covariates via polynomial kernel smoothers in additive models. The bivariate model is estimated using a backfitting algorithm, and the optimal smoothing parameters of the kernel smoothers selected by cross-validation. The model performed satisfactorily in simulation studies under the assumption of non-Gaussian conditions. Finally, the proposed methodology was found to be useful in estimating a reference region for two continuous diagnostic tests for diabetes (fasting plasma glucose and glycated hemoglobin), taking into account the age of the patient.

Glucodensities: A new representation of glucose profiles using distributional data analysis

Biosensor data have the potential to improve disease control and detection. However, the analysis of these data under free-living conditions is not feasible with current statistical techniques. To address this challenge, we introduce a new functional representation of biosensor data, termed the glucodensity, together with a data analysis framework based on distances between them. The new data analysis procedure is illustrated through an application in diabetes with continuous-time glucose monitoring (CGM) data. In this domain, we show marked improvement with respect to state-of-the-art analysis methods. In particular, our findings demonstrate that (i) the glucodensity possesses an extraordinary clinical sensitivity to capture the typical biomarkers used in the standard clinical practice in diabetes; (ii) previous biomarkers cannot accurately predict glucodensity, so that the latter is a richer source of information and; (iii) the glucodensity is a natural generalization of the time in range metric, this being the gold standard in the handling of CGM data. Furthermore, the new method overcomes many of the drawbacks of time in range metrics and provides more in-depth insight into assessing glucose metabolism.

Impact of mindfulness and self-compassion on anxiety and depression: The mediating role of resilience

Background/Objective 'Third-wave' psychotherapies have shown effectiveness for treating psychopathological symptoms such as anxiety and depression. There is burgeoning interest in examining how these therapies' core constructs produce their therapeutic benefits. This study explores the hypothetical mediating effect of resilience in the impact of mindfulness and self-compassion on anxiety and depressive symptoms. Method: Cross-sectional study design. The sample consisted of 860 Spanish general population participants. The measures included the Mindful Attention Awareness Scale (MAAS), the Self-Compassion Scale (SCS-12), the Connor-Davidson Resilience Scale (CD-RISC) and the Goldberg Anxiety and Depression Scale (GADS). Bivariate correlations were calculated, and path analysis models were performed. Results: Significant correlations were found between the study variables, always in the expected direction (all p values <.001). The path analysis models showed significant direct effects of mindfulness and self-compassion on anxiety and depression symptoms, but the only significant indirect effects through resilience were found on depression (MAAS: β = -.05, 95% CI = -.11 to -.02; SCS-12: β = -.06, 95% CI = -.33 to -.07). Conclusions: Resilience might partially mediate the effect of mindfulness and self-compassion on depression, but not on anxiety.

Glucose Variability Assessed with Continuous Glucose Monitoring: Reliability, Reference Values, and Correlations with Established Glycemic Indices-The Maastricht Study

Background: Glucose variability (GV) measured by continuous glucose monitoring (CGM) has become an accepted marker of glycemic control. Nevertheless, several methodological aspects of GV assessment require further study. We, therefore, investigated the minimum number of days needed to reliably measure GV, assessed GV reference values, and studied the correlation of GV with established glycemic indices (i.e., HbA_1c, seven-point oral glucose tolerance test [OGTT]-derived indices). Methods: We used cross-sectional data from The Maastricht Study, an observational population-based cohort enriched with type 2 diabetes. Participants with more than 48 h of CGM (iPro2; Medtronic) were included for analysis (n = 851; age: 60 ± 9years; 49% women; 23% type 2 diabetes). We used mean sensor glucose (MSG), standard deviation (SD), and coefficient of variation (CV) as CGM-derived indices (the latter two for GV quantification). We calculated reliability using the Spearman-Brown prophecy formula, established reference values by calculating 2.5th-97.5th percentiles, and studied correlations using Spearman's rho. Results: Sufficient reliability (R > 0.80) was achieved with two (MSG and SD), or three monitoring days (CV). The reference ranges, assessed in individuals with normal glucose metabolism (n = 470), were 90.5-120.6 mg/dL (MSG), 7.9-24.8 mg/dL (SD), and 7.74%-22.45% (CV). For MSG, the strongest correlation was found with fasting plasma glucose (rho = 0.65 [0.61; 0.69]); for SD, with the 1-h OGTT value (rho = 0.61 [0.56; 0.65]); and for CV, with both the incremental glucose peak (IGP) during the OGTT (rho = 0.50 [0.45; 0.55]) and the 1-h OGTT value (rho = 0.50 [0.45; 0.55]). Conclusions: The reliability findings and reference values are relevant for studies that aim to investigate CGM-measured GV. One-hour OGTT and IGP values can be used as GV indices when CGM is unavailable.

Glycemic variability indices can be used to diagnose islet transplantation success in type 1 diabetic patients

AIMS

High glycemic variability (GV) is the major indication for islet transplantation (IT) in patients with type 1 diabetes (T1D). The actual criteria used to assess graft function do not consider GV improvement. Our study aimed to describe GV indices' evolution in T1D patients who benefited from IT during the TRIMECO trial and to evaluate if thresholds might be defined to diagnose IT success.

METHODS

We collected data from 29 patients of the TRIMECO trial, a clinical trial (NCT01148680) comparing the metabolic efficacy of IT with intensive insulin therapy. Based on CGM data, we analyzed mean glucose level and four GV indices (standard deviation, coefficient of variation, MAGE and GVP) before (M0) and 6 months (M6) after IT.

RESULTS

Each GV index decreased significantly between M0 and M6: SD 53.9 mg/dL [44.6-61.5] versus 20.1 mg/dL [13.5-24.3]; CV 35.2% [30.6-37.7] versus 17.3% [12.0-20.5]; MAGE 134.9 mg/dl [111.2-155.8] versus 51.9 mg/dL [32.4-62.4]; GVP 35.3% [24.9-47.2] versus 12.2% [6.2-18.8] (p ≤ 0.0001). Thresholds diagnosing IT success at 6 months post-transplant were an SD at 22.76 mg/dL (sensibility 88.89%, specificity 80.00%), a CV at 17.47% (sensibility 88.89%, specificity 70.00%), a MAGE at 54.81 mg/dL (sensibility 88.89%, specificity 80.00%) and a GVP at 12.27% (sensibility 88.89%, specificity 70.00%).

CONCLUSIONS

This study confirms a positive impact of IT on GV. The proposed thresholds allow an easy evaluation of IT success using only CGM data and may be a clinical tool for the follow-up of transplanted patients.

Relation of the Psychological Constructs of Resilience, Mindfulness, and Self-Compassion on the Perception of Physical and Mental Health

Purpose

Health factors that enhance an individual’s ability to perceive and maintain health and well-being are referred to as “health assets”. Of these assets, resilience, mindfulness and self-compassion are considered to be of special importance. The objective of this study was to analyze the association between these psychological constructs on the perception of physical and mental health in a general population.

Patients and methods

A descriptive and analytical cross-sectional study was conducted with 845 participating subjects. The outcome variable was the individual´s perception of physical and mental health, measured using the SF-36 questionnaire. The independent variables were: sociodemographic variables, medical information, physical activity performance (using the International Physical Activity Questionnaire), tobacco and alcohol consumption, anxiety and/or depression (using the Goldberg Anxiety and Depression Scale), resilience (using the Connor-Davidson Resilience Scale), mindfulness (with the Five Facets of Mindfulness Questionnaire Short Form) and self-compassion (using the Self-compassion scale-short form). A correlation analysis, simple linear regression and multiple linear regression were carried out, controlling for the influence of the distinct independent variables.

Results

The constructs of resilience, mindfulness and self-compassion are significant, in the perception of both physical and mental health. Other factors appearing in the multiple regression are gender, age, educational level, physical activity and tobacco consumption, in a positive or negative sense.

Conclusion

The study of these associations is fundamental for the understanding of underlying regulation processes of healthy lifestyles in the general population.

Prediabetes defined by HbA1c and by fasting glucose: differences in risk factors and prevalence

AIMS

To investigate, in a sample of nondiabetic adults from a Spanish community, the differences between prediabetes as defined by HbA_1c ("H-prediabetes") and by fasting plasma glucose (FPG) ("F-prediabetes") in regard to prevalence and the influence of potential risk factors, adjusting the latter for confounders.

METHODS

A total of 1328 nondiabetic participants aged ≥ 18 years were classified as normoglycemic, H-prediabetic [HbA_1c 5.7-6.4% (39-47 mmol/mol)] or F-prediabetic (FPG 5.6-6.9 mmol/L). Multivariable analyses were used to compare the impacts of risk factors on the prevalence of H-prediabetes, F-prediabetes and their conjunctive and disjunctive combinations ("HaF-prediabetes" and "HoF-prediabetes," respectively).

RESULTS

Some 29.9% of participants were HoF-prediabetic, 21.7% H-prediabetic, 16.3% F-prediabetic and only 8.1% HaF-prediabetic. Whatever the definition of prediabetes, increasing age, fasting insulin and LDL cholesterol were each a risk factor after adjustment for all other variables. Increasing BMI and decreasing mean corpuscular hemoglobin (MCH) were additional risk factors for H-prediabetes; male sex and increasing uric acid for F-prediabetes and increasing BMI for HaF-prediabetes. The participants satisfying the compound condition "hypertension or hyperlipidemia or obesity or hyperuricemia" (59.9% of the whole study group) included 83.1% of all subjects with HoF-prediabetes.

CONCLUSIONS

In this population, the most sensitive risk factor for detection of prediabetes was age, followed by fasting insulin, LDL cholesterol, BMI, MCH, male sex and uric acid, with differences depending on the definition of prediabetes. MCH, an indirect measure of erythrocyte survival, significantly influences the prevalence of HbA_1c-defined prediabetes. This study suggests that screening of individuals with selected risk factors may identify a high proportion of prediabetic persons.

Postprandial glycemic response in a non-diabetic adult population: the effect of nutrients is different between men and women

Background

There is a growing interest in the pathopysiological consequences of postprandial hyperglycemia. It is well known that in diabetic patients 2 h plasma glucose is a better risk predictor for coronary heart disease than fasting plasma glucose. Data on the glycemic response in healthy people are scarce.

Objective

To evaluate the effect of macronutrients (carbohydrates, fats, and proteins) and fiber on postprandial glycemic response in an observational study of a non-diabetic adult population.

Design

Cross-sectional study. 150 non-diabetic adults performed continuous glucose monitoring for 6 days. During this period they recorded food and beverage intake. The participants were instructed not to make changes in their usual diet and physical exercise.

Variables analyzed included clinical parameters (age, sex, body weight, height, body mass index, blood pressure, and waist measurement), meal composition (calories, carbohydrates, fats, proteins, and fiber) and glycemic postprandial responses separated by sexes.

The study period was defined from the start of dinner to 6 h later.

Results

A total of 148 (51% women) subjects completed all study procedures. Dinner intake was higher in males than in females (824 vs 531 kcal). Macronutrient distribution was similar in both sexes. No significant differences were found in fiber intake between men and women (5.5 g vs 4.5 g).

In both sexes, the higher intake of carbohydrates corresponded to a significantly higher glycemic response (p = 0.0001 in women, p = 0.022 in men). Moreover, in women, as fat intake was higher, a flattening of the postprandial glycemic curve was observed (p = 0.003). With respect to fiber, a significantly lower glycemic response was observed in the group of women whose fiber intake at dinner was higher (p = 0.034).

Conclusions

Continuous glucose monitoring provides important information about glucose levels after meals. In this study, the postprandial glycemic response in women was different from that of men, and carbohydrates were the main determinant of elevated postprandial glucose levels.

Effects of long-term glycemic variability on incident cardiovascular disease and mortality in subjects without diabetes: A nationwide population-based study

Increased glycemic variability (GV) is an independent risk factor for cardiovascular complications in patients with diabetes. We evaluated the risk of future development of cardiovascular disease (CVD) and death according to GV in a general population without diabetes.We used the National Health Insurance Service, providing a population-based, nationwide database of Koreans. We included individuals without diabetes who underwent glucose measurement at least 3 times during 2002 to 2006. GV was calculated as standard deviation (SD) of fasting plasma glucose (FPG) levels. We observed development of CVD or all-cause death from 2007 to 2015, and also evaluated the mortality within 1 year after CVD.Among 3,211,319 people, we found 23,374 incident cases of myocardial infarction (MI), 27,705 cases of stroke, and 63,275 deaths during 8.3 years of follow-up. After multivariate adjustment, GV was found to be a significant predictor of MI, stroke and all-cause death for their highest quartile, with corresponding hazard ratios (HR) of 1.08 (95% confidence interval, CI 1.04-1.11), 1.09 (95% CI 1.06-1.13), and 1.12 (95% CI 1.10-1.15), respectively. The risk of death increased more in those who had both impaired fasting glucose and the highest quartile of GV (HR 1.24 [95% CI 1.21-1.28]). Moreover, early death rate after 1 year of CVD was higher in the highest quartile of GV compared to the lowest quartile (HR 1.21 [95% CI 1.03-1.41]).Long-term FPG variation was independently associated with CVD and mortality in a general population without diabetes.

Effect of exercise on glucose variability in healthy subjects: randomized crossover trial

The aim of this study was to evaluate the acute effect of aerobic (AER) and eccentric (ECC) exercise on glucose variability, correlating it with circulating markers of inflammation and oxidative stress in healthy subjects. Sixteen healthy subjects (32 ± 12 years old) wore a continuous glucose monitoring system for three days. Participants randomly performed single AER and ECC exercise sessions. Glucose variability was evaluated by glucose variance (VAR), glucose coefficient of variation (CV%) and glucose standard deviation (SD). Blood samples were collected to evaluate inflammatory and oxidative stress markers. When compared with the pre-exercise period of 0-6 h, all the indices of glucose variability presented comparable reductions 12-18 h after both exercises (∆AER: VAR= 151.5, ∆CV% = 0.55 and ∆SD = 3.1 and ECC: ∆VAR = 221.2 , ∆CV% = 3.7 and ∆SD = 6.5). Increased interleukin-6 (IL-6) levels after AER (68.5%) and ECC (30.8%) (P<0.001) were observed, with no differences between sessions (P = 0.459). Uric acid levels were increased after exercise sessions (3% in AER and 4% in ECC, P = 0.001). In conclusion, both AER and ECC exercise sessions reduced glucose variability in healthy individuals. Inflammatory cytokines, such as IL-6, and stress oxidative markers might play a role in underlying mechanisms modulating the glucose variability responses to exercise (clinicalTrials.gov NCT02262208).

Direct-acting antiviral hepatitis C virus treatment perturbation of the metabolic milieu

OBJECTIVE

Hepatitis C virus (HCV), cirrhosis, and HCV medications including direct-acting antivirals (DAAs) ±ribavirin may all influence the metabolic milieu. While interferon-based regimens improve glucose tolerance, evidence is limited on DAAs. Cases of elevated lactate have recently been reported in patients treated with DAAs, and lactic acidosis is a known complication of antivirals used to treat hepatitis B virus and HIV.

PATIENTS AND METHODS

Measures were evaluated at baseline, week 4, end of treatment, and 12-24 weeks after treatment. Mixed-effects modeling was used to determine factors influencing glucose and lactate over time.

RESULTS

In total, 442 patients were treated (mean age 56, 65% male, 72% genotype 1, 48% cirrhotic). Glucose did not change on or after DAA treatment from baseline (P=0.51) aside from those with untreated diabetes, which declined (P=0.02). Overall, there was a decline in lactate following HCV treatment (mean 2.4-2.1 mmol/l; P<0.001). Lactate initially increased on treatment and then decreased after treatment completion in male patients treated with ribavirin. This pattern was not observed in other groups. There was no evidence of lactic acidosis with HCV nucleotide use.

CONCLUSION

Distinct glucose and lactate trajectories were identified without evidence of DAA metabolic toxicity. HCV treatment does not improve random glucose levels aside from perhaps in untreated diabetic patients.

Reduced Glucose Variability With Glucose-Dependent Versus Glucose-Independent Therapies Despite Similar Glucose Control and Hypoglycemia Rates in a Randomized, Controlled Study of Older Patients With Type 2 Diabetes Mellitus

BACKGROUND

Few studies have evaluated continuous glucose monitoring (CGM) in older patients with type 2 diabetes mellitus (T2DM) not using injectable therapy. CGM is useful for investigating hypoglycemia and glycemic variability, which is associated with complications in T2DM.

METHODS

A CGM substudy of Individualized treatMent aPproach for oldER patIents in a randomized trial in type 2 diabetes Mellitus (IMPERIUM)) was conducted. Patients were vulnerable (moderately ill and/or frail) older (≥65 years) individuals with suboptimally controlled T2DM. Strategy A comprised glucose-dependent therapies (n = 26) with a nonsulfonylurea oral antihyperglycemic medication (OAM) and a glucagon-like peptide-1 receptor agonist as the first injectable. Strategy B comprised non-glucose-dependent therapies (n = 21) with sulfonylurea as the preferred OAM and insulin glargine as the first injectable. Primary endpoints were duration and percentage of time spent with blood glucose (BG) ≤70 mg/dL over 24 hours at week 24.

RESULTS

Duration and percentage of time spent with hypoglycemia at ≤70 mg/dL were similar for Strategy A and Strategy B; glycemic control improved similarly in both arms (LSM change in HbA1c at week 24; A = -1.2%, B = -1.4%). Duration and percentage time spent with euglycemia and hyperglycemia were also similar in both arms. However, Strategy A was associated with lower within-day (21.1 ± 1.2 vs 25.1 ± 1.4, P = .046) and between-day (5.4 ± 1.0 vs 9.1 ± 1.3, P = .038) BG variability (coefficient of variance [LSM ± SE]) at week 24.

CONCLUSIONS

This CGM substudy in older patients with T2DM showed lower within- and between-day BG variability with glucose-dependent therapies but similar HbA1c reductions and hypoglycemia duration with glucose-independent strategies.

Glycemic variability in the development of cardiovascular complications in diabetes

Diabetes mellitus represents a major risk factor for the development of coronary artery disease and other vascular complications. Glycated haemoglobin, fructosamine, and fasting blood glucose levels are partial parameters to exhaustively describe patient dysglycemic status. Thus, recently the new concept of glycemic variability has emerged, including information about two major aspects: the magnitude of blood glucose excursions (from nadir to peak, thus lower and higher spikes) and the time intervals in which these fluctuations occur. Despite the lack of consensus regarding the most appropriate definition and tools for its assessment, glycemic variability seems to have more deleterious effects than sustained hyperglycemia in the pathogenesis of diabetic cardiovascular complications. This manuscript aimed to review the most recent evidence on glycemic variability and its potential use in everyday clinical practice to identify diabetic patients at higher risk of cardiovascular complications and thus needing stricter monitoring and treatment.

Distinct impacts of sleep-disordered breathing on glycemic variability in patients with and without diabetes mellitus

BACKGROUND

Sleep-disordered breathing (SDB) is highly prevalent in patients with diabetes mellitus (DM) and heart failure (HF) and contributes to poor cardiovascular outcomes. Enlarged glycemic variability (GV) is a risk factor of cardiac events independently of average blood glucose level, but the influence of SDB on GV is uncertain. In this study, we examined whether the impact of SDB on GV is modified by the presence of DM with or without HF.

METHODS AND RESULTS

Two hundred three patients (67.5±14.1 [SD] years old, 132 males) who were admitted to our institute for examination or treatment of DM and/or HF underwent continuous glucose monitoring and polysomnography. Both HbA1c (8.0±2.0 vs. 5.7±0.4%) and mean amplitude of glycemic excursion (MAGE, median: 95.5 vs. 63.5 mg/dl) were significantly higher in a DM group (n = 100) than in a non-DM group (n = 103), but apnea-hypopnea index (AHI: 29.0±22.7 vs. 29.3±21.5) was similar in the two groups. AHI was correlated with log MAGE in the non-DM group but not in the DM group, and multivariate regression analysis revealed that AHI was an independent variable for log MAGE in the non-DM group but not in the DM group. We then divided the non-DM patients into two subgroups according to BNP level (100 pg/ml). AHI was positively correlated with log MAGE (r = 0.74, p<0.001) in the non-DM low-BNP subgroup, but such a correlation was not found in the non-DM high-BNP subgroup. Continuous positive airway pressure (CPAP) reduced MAGE from 75.3 to 53.0 mg/dl in the non-DM group but did not reduce MAGE in the DM group.

CONCLUSION

Severity of SDB was associated with higher GV, but DM as well as HF diminished the contribution of SDB to GV. Treatment with CPAP was effective for reduction of GV only in patients without DM.