Visit-to-visit HbA1c and glucose variability and the risks of macrovascular and microvascular events in the general population

Prognostic value of longitudinal HbA1c variability in predicting the development of diabetic sensorimotor polyneuropathy among patients with type 2 diabetes mellitus: A prospective cohort observational study

AIMS/INTRODUCTION

This prospective cohort study aims to identify the optimal measure of glycated hemoglobin (HbA1c) variability and to explore its relationship with the development of new diabetic sensorimotor polyneuropathy (DSPN) in individuals with type 2 diabetes mellitus, building upon previous cross-sectional studies that highlighted a significant association between HbA1c visit-to-visit variability and DSPN.

MATERIALS AND METHODS

In a prospective study, 321 participants diagnosed with type 2 diabetes mellitus underwent comprehensive clinical assessments, neurophysiologic studies, and laboratory evaluations at enrollment and follow-up. Various indices, including HbA1c standard deviation (HbA1c SD), coefficient of variation (HbA1c CV), HbA1c change score (HbA1c HVS), and average real variability (HbA1c ARV), were employed to calculate the visit-to-visit variability HbA1c based on 3 month intervals. The investigation focused on examining the associations between these indices and the development of new DSPN.

RESULTS

The average follow-up duration was 16.9 ± 6.9 months. The Cox proportional hazards model identified age (P = 0.001), diabetes duration (P = 0.024), and HbA1C ARV (P = 0.031) as the sole factors associated with the development of new DSPN. Furthermore, the cumulative risk of developing DSPN over 1 year demonstrated a significant association with HbA1C ARV (P = 0.03, log-rank test).

CONCLUSIONS

Apart from age and diabetes duration, HbA1c variability emerged as a robust predictor for the occurrence of new DSPN. Among the various measures of HbA1c variability evaluated, HbA1c ARV demonstrated the highest potential as a reliable indicator for anticipating the onset of new DSPN.

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

Background

Although studies have shown that glycemic variability is positively associated with an increased risk of cardiovascular disease, few studies have compared hemoglobin A1c (HbA1c) and fasting plasma glucose (FPG) variability with adverse cardiovascular events in patients with type 2 diabetes mellitus (T2DM).

Methods

This was a post hoc analysis of the Action to Control Cardiovascular Risk in Diabetes (ACCORD) study. Cox proportional hazards models were used to explore the relationship between HbA1c or FPG variability and the incidence of major adverse cardiovascular events (MACEs).

Results

In total, 9,547 patients with T2DM were enrolled in this study. During the median 4.6 ± 1.5 years follow-up period, 907 patients developed MACEs. The risk of MACEs increased in the HbA1c variability group in each higher quartile of HbA1c variability (P < 0.01). Compared with those in the first quartile of HbA1c variability, patients in the fourth quartile had a hazard ratio of 1.37 (Model 2, 95% confidence interval: 1.13-1.67) for MACEs. Higher FPG variability was not associated with a higher risk of MACEs in patients with T2DM (P for trend=0.28). A U-shaped relationship was observed between HbA1c and FPG variability, and MACEs. Glucose control therapy modified the relationship between HbA1c and MACEs; participants with higher HbA1c variability receiving intensive glucose control were more likely to develop MACEs (P for interaction <0.01).

Conclusion

In adults with T2DM, the relationship between glycemic variability evaluated using HbA1c and FPG was U-shaped, and an increase in HbA1c variability rather than FPG variability was significantly associated with MACEs. The relationship between HbA1c variability and MACEs was affected by the glucose control strategy, and a higher HbA1c variability was more strongly associated with MACEs in patients receiving an intensive glucose control strategy.

How to Measure Glycemic Variability? A Literature Review

Optimal glycemic control without the presence of diabetes-related complications is the primary goal for adequate diabetes management. Recent studies have shown that hemoglobin A1c level cannot fully evaluate diabetes management as glycemic fluctuations are demonstrated to have a major impact on the occurrence of diabetes-related micro- and macroangiopathic comorbidities. The use of continuous glycemic monitoring systems allowed the quantification of glycemic fluctuations, providing valuable information about the patients' glycemic control through various indicators that evaluate the magnitude of glycemic fluctuations in different time intervals. This review highlights the significance of glycemic variability by describing and providing a better understanding of common and alternative indicators available for use in clinical practice.

Visit-to-Visit Glucose Variability, Cognition, and Global Cognitive Decline: The Multi-Ethnic Study of Atherosclerosis

CONTEXT

Higher visit-to-visit glucose variability (GV) is associated with dysglycemia and type 2 diabetes (T2D), key risk factors for cognitive decline.

OBJECTIVE

Evaluate the association of GV with cognitive performance and decline in racially/ethnically diverse older populations with and without T2D.

METHODS

We calculated the standard deviation of glucose (SDG), average real variability (ARV), and variability independent of the mean (VIM) among 4367 Multi-Ethnic Study of Atherosclerosis participants over 6 clinical examinations. Participants completed a cognitive assessment at the fifth examination, and a subset completed a second assessment 6 years later. We used multivariable linear regression to estimate the association of intraindividual GV with cognitive test scores after adjustments for cardiovascular risk factors and mean glucose level over the study period.

RESULTS

Two-fold increments in the VIM and SDG were associated with worse Cognitive Abilities Screening Instrument (CASI) performance, while two-fold increments in VIM and ARV were associated with worse Digit Symbol Coding test score. GV measures were not associated with change in CASI performance among 1834 participants with repeat CASI data 6 years later. However, among 229 participants with incident T2D, the SDG and VIM were associated with decline in CASI (-1.7 [95% CI: -3.1, -0.3] and -2.1 [-3.7, -0.5] points, respectively). In contrast, single-timepoint glucose and HbA1c were not associated with CASI decline among participants with or without incident T2D.

CONCLUSION

Higher visit-to-visit GV over 16 to 18 years is associated with worse cognitive performance in the general population, and with modest global cognitive decline in participants with T2D.

Long-term glycemic variability predicts compromised development of heart failure with improved ejection fraction: a cohort study

Background

A substantial portion of heart failure (HF) patients adherent to guideline-directed medical therapies have experienced improved ejection fraction (EF), termed HFimpEF. Glycemic variability (GV) has emerged as a critical cardiometabolic factor. However, the relation between long-term GV and the incidence of HFimpEF is still unclear.

Methods

A total of 591 hospitalized HF patients with reduced EF (HFrEF, EF≤ 40%) admitted from January 2013 to December 2020 were consecutively enrolled. Repeat echocardiograms were performed at baseline and after around 12 months. The incidence of HFimpEF, defined as (1) an absolute EF improvement ≥10% and (2) a second EF > 40% and its association with long-term fasting plasma glucose (FPG) variability were analyzed.

Results

During a mean follow-up of 12.2 ± 0.6 months, 218 (42.0%) patients developed HFimpEF. Multivariate analysis showed FPG variability was independently associated with the incidence of HFimpEF after adjustment for baseline HbA1c, mean FPG during follow-up and other traditional risk factors (odds ratio [OR] for highest vs. lowest quartile of CV of FPG: 0.487 [95% CI 0.257~0.910]). Evaluation of GV by alternative measures yielded similar results. Subgroup analysis revealed that long-term GV was associated with HFimpEF irrespective of glycemic levels and diabetic conditions.

Conclusions

This study reveals that greater FPG variability is associated with compromised development of HFimpEF. A more stable control of glycemic levels might provide favorable effects on myocardial functional recovery in HF patients even without diabetes.

Gene-diet interaction analysis using novel weighted food scores discovers the adipocytokine signaling pathway associated with the development of type 2 diabetes

Introduction

The influence of dietary patterns measured using Recommended Food Score (RFS) with foods with high amounts of antioxidant nutrients for Type 2 diabetes (T2D) was analyzed. Our analysis aims to find associations between dietary patterns and T2D and conduct a gene-diet interaction analysis related to T2D.

Methods

Data analyzed in the current study were obtained from the Korean Genome and Epidemiology Study Cohort. The dietary patterns of 46 food items were assessed using a validated food frequency questionnaire. To maximize the predictive power of the RFS, we propose two weighted food scores, namely HisCoM-RFS calculated using the novel Hierarchical Structural Component model (HisCoM) and PLSDA-RFS calculated using Partial Least Squares-Discriminant Analysis (PLS-DA) method.

Results

Both RFS (OR: 1.11; 95% CI: 1.03- 1.20; P = 0.009) and PLSDA-RFS (OR: 1.10; 95% CI: 1.02-1.19, P = 0.011) were positively associated with T2D. Mapping of SNPs (P < 0.05) from the interaction analysis between SNPs and the food scores to genes and pathways yielded some 12 genes (CACNA2D3, RELN, DOCK2, SLIT3, CTNNA2, etc.) and pathways associated with T2D. The strongest association was observed with the adipocytokine signalling pathway, highlighting 32 genes (STAT3, MAPK10, MAPK8, IRS1, AKT1-3, ADIPOR2, etc.) most likely associated with T2D. Finally, the group of the subjects in low, intermediate and high using both the food scores and a polygenic risk score found an association between diet quality groups with issues at high genetic risk of T2D.

Conclusion

A dietary pattern of poor amounts of antioxidant nutrients is associated with the risk of T2D, and diet affects pathway mechanisms involved in developing T2D.

Structured time-dependent inverse regression (STIR)

We propose and study structured time-dependent inverse regression (STIR), a novel sufficient dimension reduction model, to analyze longitudinally measured, correlated biomarkers in relation to an outcome. The time structure is accommodated in an inverse regression model for the markers that can be applied both to equally and unequally spaced time points for each sample. The inverse regression structure also naturally accommodates retrospectively sampled markers, that is, markers measured in case-control studies. We estimate the corresponding linear combinations of the markers, the reduction, using least squares. We show that under additional distributional assumptions the reduction contains sufficient information about the outcome. In extensive simulations the STIR linear combinations perform well in predictive models based on samples of realistic size. A Wald-type test for association of a particular marker with outcome at any time point based on the STIR reduction has better power overall than assessing associations based on logistic or linear regression models that include all longitudinally measured markers as independent predictors. As illustrations we estimate the STIR reductions for a cohort study of diabetes and hyperlipidemia and a case-control study of brain cancer with multiple longitudinally measured biomarkers. We assess the STIR reductions' predictive performance and identify outcome-associated biomarkers.

The association between fasting plasma glucose variability and incident eGFR decline: evidence from two cohort studies

BACKGROUND

Glycemic variability (GV) is developing as a marker of glycemic control, which can be utilized as a promising predictor of complications. To determine whether long-term GV is associated with incident eGFR decline in two cohorts of Tehran Lipid and Glucose Study (TLGS) and Multi-Ethnic Study of Atherosclerosis (MESA) during a median follow-up of 12.2 years.

METHODS

Study participants included 4422 Iranian adults (including 528 patients with T2D) aged ≥ 20 years from TLGS and 4290 American adults (including 521 patients with T2D) aged ≥ 45 years from MESA. The Multivariate Cox proportional hazard models were used to assess the risk of incident eGFR decline for each of the fasting plasma glucose (FPG) variability measures including standard deviation (SD), coefficient of variation (CV), average real variability (ARV), and variability independent of the mean (VIM) both as continuous and categorical variables. The time of start for eGFR decline and FPG variability assessment was the same, but the event cases were excluded during the exposure period.

RESULTS

In TLGS participants without T2D, for each unit change in FPG variability measures, the hazards (HRs) and 95% confidence intervals (CI) for eGFR decline ≥ 40% of SD, CV, and VIM were 1.07(1.01-1.13), 1.06(1.01-1.11), and 1.07(1.01-1.13), respectively. Moreover, the third tertile of FPG-SD and FPG-VIM parameters was significantly associated with a 60 and 69% higher risk for eGFR decline ≥ 40%, respectively. In MESA participants with T2D, each unit change in FPG variability measures was significantly associated with a higher risk for eGFR decline ≥ 40%.Regarding eGFR decline ≥ 30% as the outcome, in the TLGS, regardless of diabetes status, no association was shown between FPG variability measures and risk of eGFR decline in any of the models; however, in the MESA the results were in line with those of GFR decline ≥ 40%.Using pooled data from the two cohorts we found that generally FPG variability were associated with higher risk of eGFR decline ≥ 40% only among non-T2D individuals.

CONCLUSIONS

Higher FPG variability was associated with an increased risk of eGFR decline in the diabetic American population; however, this unfavorable impact was found only among the non-diabetic Iranian population.

Glycemic profile variability: An independent risk factor for diabetic neuropathy in patients with type 2 diabetes

BACKGROUND

Impaired glycemic control is a potential predictor for macro- and microvascular complications of diabetes, which could be recognized by glycemic variability. The aim of this 10-year prospective cohort study presented here is to gain a better understanding of the correlation between GV and diabetic peripheral neuropathy (DPN) as one of the most common complications of T2DM.

METHODS

Since February 2010, 1152 adult patients with T2DM have been followed-up. Baseline features, anthropometric measurements, and laboratory findings were collected and documented during ten years. The association between DPN incidence and glycemic profile variability was evaluated using cox regression analysis. The coefficient of variation of glycemic indices within subjects was calculated and compared using an independent sample t-test.

RESULTS

Individuals who developed neuropathy had significantly higher mean levels of glycemic indices (HbA1c, FBS, and 2hpp), urinary albumin excretion, mean creatinine levels, and a longer duration of diabetes. A significant positive correlation between incidence of DPN and glycemic profile variability (cv-FBS10 %, cv-FBS20 %, cv-2hpp20 %, cv-HbA1c5 % and cv-HbA1c10 %) was revealed. Results also showed that higher variability of FBS was associated with the higher risk of neuropathy incidence (HR: 12.29, p-value: 0.045), which indicates that glycemic profile variability is an independent risk factor for DPN in patients with T2DM.

CONCLUSION

Variability of glycemic profiles from a visit to visit, regardless of sustained hyperglycemia, was indeed a significant risk factor for DPN in diabetic type 2 patients. CV-FBS was the most critical glycemic variability indices for DPN development.

Relationship between intra-individual variability in nutrition-related lifestyle behaviors and blood glucose outcomes under free-living conditions in adults without type 2 diabetes

AIMS

This study determined the relationship between intra-individual variability in day-to-day nutrition-related lifestyle behaviors (meal timing, eating window, food intake, movement behaviors, sleep conditions, and body weight) and glycemic outcomes under free-living conditions in adults without type 2 diabetes.

METHODS

We analyzed 104 adults without type 2 diabetes. During the 7-day measurement period, dietary intake, movement behaviors, sleep conditions, and glucose outcomes were assessed. Daily food intake was assessed using a mobile-based health application. Movement behaviors and sleep conditions were assessed using a tri-axial accelerometer. Meal timing was assessed from the participant's daily life record. Blood glucose levels were measured continuously using a glucose monitor. Statistical analyses were conducted using a linear mixed-effects model, with mealtime, food intake, body weight, movement behaviors, and sleep conditions as fixed effects and participants as a random effect.

RESULTS

Dinner time and eating window were positively significantly correlated with mean (dinner time, p = 0.003; eating window, p = 0.001), standard deviation (SD; both at p < 0.001), and maximum (both at p < 0.001) blood glucose levels. Breakfast time was negatively associated with glucose outcomes (p < 0.01). Sedentary time was positively significantly associated with blood glucose SD (p = 0.040). Total sleep time was negatively significantly correlated with SD (p = 0.035) and maximum (p = 0.032) blood glucose levels. Total daily energy intake (p = 0.001), carbohydrate intake (p < 0.001), and body weight (p < 0.05) were positively associated with mean blood glucose levels.

CONCLUSION

Intra-individual variations in nutrition-related lifestyle behaviors, especially morning and evening body weight, and food intake, were associated with mean blood glucose levels, and a long sedentary time and total sleep time were associated with glucose variability. Earlier dinner times and shorter eating windows per day resulted in better glucose control.

Longitudinal HbA1c trajectory modelling reveals the association of HbA1c and risk of hospitalization for heart failure for patients with type 2 diabetes mellitus

BACKGROUND

Inconsistent conclusions in past studies on the association between poor glycaemic control and the risk of hospitalization for heart failure (HHF) have been reported largely due to the analysis of non-trajectory-based HbA1c values. Trajectory analysis can incorporate the effects of HbA1c variability across time, which may better elucidate its association with macrovascular complications. Furthermore, studies analysing the relationship between HbA1c trajectories from diabetes diagnosis and the occurrence of HHF are scarce.

METHODS

This is a prospective cohort study of the SingHealth Diabetes Registry (SDR). 17,389 patients diagnosed with type 2 diabetes mellitus (T2DM) from 2013 to 2016 with clinical records extending to the end of 2019 were included in the latent class growth analysis to extract longitudinal HbA1c trajectories. Association between HbA1c trajectories and risk of first known HHF is quantified with the Cox Proportional Hazards (PH) model.

RESULTS

5 distinct HbA1c trajectories were identified as 1. low stable (36.1%), 2. elevated stable (40.4%), 3. high decreasing (3.5%), 4. high with a sharp decline (10.8%), and 5. moderate decreasing (9.2%) over the study period of 7 years. Poorly controlled HbA1c trajectories (Classes 3, 4, and 5) are associated with a higher risk of HHF. Using the diabetes diagnosis time instead of a commonly used pre-defined study start time or time from recruitment has an impact on HbA1c clustering results.

CONCLUSIONS

Findings suggest that tracking the evolution of HbA1c with time has its importance in assessing the HHF risk of T2DM patients, and T2DM diagnosis time as a baseline is strongly recommended in HbA1c trajectory modelling. To the authors' knowledge, this is the first study to identify an association between HbA1c trajectories and HHF occurrence from diabetes diagnosis time.

Glycaemic variability and progression of chronic kidney disease in people with diabetes and comorbid kidney disease: Retrospective cohort study

AIM

To investigate the association between glycaemic variability and the development of End-Stage-Kidney-Disease (ESKD) among individuals with diabetes and chronic kidney disease.

METHODS

A cohort study using UK electronic primary care health records from the Clinical Practice Research Datalink. Glycaemic variability was assessed using a variability score and intra-individual coefficient of variation (CV) of HbA1c. We calculated sub-distribution hazard ratios (sHR) for developing ESKD using competing risk regression analysis.

RESULTS

There were 37,222 eligible participants (45.5 % male), with a mean age of 76.4 years (SD ± 9.2), and a mean baseline eGFR 40.7 (±10.7) ml/min/1.73 m². There were 5,086 incidents of ESKD in the follow-up period. The adjusted sHR (95 %CI) for each variability score group, were as follows: 21-40, 1.38 (1.27-1.50); 41-60, 1.54 (1.41-1.68); 61-80, 1.61 (1.45-1.79); and 81-100, 1.42 (1.19-1.68), compared with the group (score 0-20) with least variability. The adjusted sHR for CV were as follows: 6.7-9.9, 1.29 (1.15-1.45); 10.0-13.9, 1.55 (1.39-1.74); 14.0-20.1, 1.79 (1.60-2.01) and ≥20.2, 2.10 (1.88-2.34) compared to reference group 0-6.6.

CONCLUSIONS

Glycaemic variability was strongly associated with the development of ESKD in people with diabetes and CKD.

Heterogeneous treatment effects of intensive glycemic control on major adverse cardiovascular events in the ACCORD and VADT trials: a machine-learning analysis

BACKGROUND

Evidence to guide type 2 diabetes treatment individualization is limited. We evaluated heterogeneous treatment effects (HTE) of intensive glycemic control in type 2 diabetes patients on major adverse cardiovascular events (MACE) in the Action to Control Cardiovascular Risk in Diabetes Study (ACCORD) and the Veterans Affairs Diabetes Trial (VADT).

METHODS

Causal forests machine learning analysis was performed using pooled individual data from two randomized trials (n = 12,042) to identify HTE of intensive versus standard glycemic control on MACE in patients with type 2 diabetes. We used variable prioritization from causal forests to build a summary decision tree and examined the risk difference of MACE between treatment arms in the resulting subgroups.

RESULTS

A summary decision tree used five variables (hemoglobin glycation index, estimated glomerular filtration rate, fasting glucose, age, and body mass index) to define eight subgroups in which risk differences of MACE ranged from - 5.1% (95% CI - 8.7, - 1.5) to 3.1% (95% CI 0.2, 6.0) (negative values represent lower MACE associated with intensive glycemic control). Intensive glycemic control was associated with lower MACE in pooled study data in subgroups with low (- 4.2% [95% CI - 8.1, - 1.0]), intermediate (- 5.1% [95% CI - 8.7, - 1.5]), and high (- 4.3% [95% CI - 7.7, - 1.0]) MACE rates with consistent directions of effect in ACCORD and VADT alone.

CONCLUSIONS

This data-driven analysis provides evidence supporting the diabetes treatment guideline recommendation of intensive glucose lowering in diabetes patients with low cardiovascular risk and additionally suggests potential benefits of intensive glycemic control in some individuals at higher cardiovascular risk.

Glycemic control and atrial fibrillation: an intricate relationship, yet under investigation

Atrial fibrillation (AF) and diabetes mellitus (DM) constitute two major closely inter-related chronic cardiovascular disorders whose concurrent prevalence rates are steadily increasing. Although, the pathogenic mechanisms behind the AF and DM comorbidity are still vague, it is now clear that DM precipitates AF occurrence. DM also affects the clinical course of established AF; it is associated with significant increase in the incidence of stroke, AF recurrence, and cardiovascular mortality. The impact of DM on AF management and prognosis has been adequately investigated. However, evidence on the relative impact of glycemic control using glycated hemoglobin levels is scarce. This review assesses up-to-date literature on the association between DM and AF. It also highlights the usefulness of glycated hemoglobin measurement for the prediction of AF and AF-related adverse events. Additionally, this review evaluates current anti-hyperglycemic treatment in the context of AF, and discusses AF-related decision-making in comorbid DM. Finally, it quotes significant remaining questions and sets some future strategies with the potential to effectively deal with this prevalent comorbidity.

Associations Between Device-Measured Physical Activity and Glycemic Control and Variability Indices Under Free-Living Conditions

Background: Disturbances of glycemic control and large glycemic variability have been associated with increased risk of type 2 diabetes in the general population as well as complications in people with diabetes. Long-term health benefits of physical activity are well documented but less is known about the timing of potential short-term effects on glycemic control and variability in free-living conditions. Materials and Methods: We analyzed data from 85 participants without diabetes from the Food & You digital cohort. During a 2-week follow-up, device-based daily step count was studied in relationship to glycemic control and variability indices using generalized estimating equations. Glycemic indices, evaluated using flash glucose monitoring devices (FreeStyle Libre), included minimum, maximum, mean, standard deviation, and coefficient of variation of daily glucose values, the glucose management indicator, and the approximate area under the sensor glucose curve. Results: We observed that every 1000 steps/day increase in daily step count was associated with a 0.3588 mg/dL (95% confidence interval [CI]: -0.6931 to -0.0245), a 0.0917 mg/dL (95% CI: -0.1793 to -0.0042), and a 0.0022% (95% CI: -0.0043 to -0.0001) decrease in the maximum glucose values, mean glucose, and in the glucose management indicator of the following day, respectively. We did not find any association between daily step count and glycemic indices from the same day. Conclusions: Increasing physical activity level was linked to blunted glycemic excursions during the next day. Because health-related benefits of physical activity can be long to observe, such short-term physiological benefits could serve as personalized feedback to motivate individuals to engage in healthy behaviors.

Long-Term Glycemic Variability and Risk of Cardiovascular Events in Type 2 Diabetes: A Meta-Analysis

Long-term glycemic fluctuation has been associated with cardiovascular risk in patients with type 2 diabetes mellitus (T2DM). However, the findings are inconsistent. We performed a meta-analysis to summarize the association between parameters of long-term glycemic variability and risk of cardiovascular events in T2DM patients. Medline, Embase, and Web of Science databases were searched for longitudinal follow-up studies comparing the incidence of cardiovascular events in T2DM patients with higher or lower long-term glycemic variability. A random-effect model incorporating the potential heterogeneity among the included studies was used to pool the results. Twelve follow-up studies with 146 653 T2DM patients were included. The mean follow-up duration was 4.9 years. Pooled results showed that compared to those with the lowest glycemic variability, patients with the highest glycemic variability had significantly increased risk of cardiovascular events, as evidenced by the standard deviation of glycated hemoglobin [HbA1c-SD: relative risk (RR)=1.44, 95% confidence interval (CI): 1.23 to 1.69, p<0.001; I²=70%], HbA1c coefficient of variation (HbA1c-CV: RR=1.46, 95% CI: 1.19 to 1.79. p<0.001; I²=83%), standard deviation of fasting plasma glucose (FPG-SD: RR=1.33, 95% CI: 1.07 to 1.65, p=0.009; I²=0%), and FPG coefficient of variation (FPG-CV: RR=1.29, 95% CI: 1.01 to 1.64, p=0.04; I²=47%). In conclusion, increased long-term glycemic variability may be an independent risk factor for cardiovascular events in T2DM patients.

Preoperative circulating peroxiredoxin 1 levels as a predictor of non-alcoholic fatty liver disease remission after laparoscopic bariatric surgery

BACKGROUND

Non-alcoholic fatty liver disease (NAFLD) is associated with obesity and insulin resistance and can be improved after bariatric surgery. Circulating Peroxiredoxin 1 (Prdx1) protein was reported to regulate energy metabolism and inflammation. This study aimed to investigate the roles of serum prdx1 in NAFLD patients with obesity undergoing LSG and to develop a prognostic model to predict the remission of severe NAFLD.

METHODS

The data of 93 participants from a tertiary hospital were assessed. Before laparoscopic sleeve gastrectomy (LSG) and three months after LSG, anthropometric parameters, laboratory biochemical data, and abdominal B-ultrasound results were collected, and their hepatic steatosis index (HSI) and triglyceride-glucose index (TyG) were calculated. A NAFLD improvement (NAFLD-I) nomogram prediction model was constructed using the least absolute shrinkage and selection operator (LASSO) regression and multiple regression, and its predictive ability was verified in a validation cohort.

RESULTS

The baseline Prdx1 (OR: 0.887, 95% CI: 0.816-0.963, p=0.004), preoperative TyG (OR: 8.207, 95% CI: 1.903-35.394, p=0.005) and HSI (OR: 0.861, 95% CI: 0.765-0.969, p=0.013) levels were independently associated with NAFLD-I at three months after LSG in NAFLD patients with obesity. In the primary and validation cohorts, the area under the receiver operating characteristic (AUC) of the developed nomogram model was 0.891 and 0.878, respectively. The preoperative circulating Prdx1 levels of NAFLD patients with obesity were significantly reduced after LSG (25.32 [18.99-30.88] vs. 23.34 [15.86-26.42], p=0.001). Prdx1 was related to obesity and hepatic steatosis based on correlation analysis.

CONCLUSION

The nomogram based on preoperative serum prdx1, HSI and TyG could be an effective tool for predicting remission of severe NAFLD after LSG.

Association between visit-to-visit variability of glycemic indices and lipid profile and the incidence of coronary heart disease in adults with type 2 diabetes

Coronary heart disease (CHD) is one of the major causes of mortality and morbidity in patients with type 2 diabetes mellitus. In this study, we aimed to assess the association between visit-to-visit variability of fasting blood sugar (FBS), HbA1c, blood sugar 2 h post-prandial (BS2hpp), lipid indices, creatinine, systolic and diastolic blood pressure (SBP, DBP) and incident CHD in patients with type 2 diabetes during a median follow-up of ten years. The current case-cohort study consisted of 1500 individuals with type 2 diabetes, followed up for the occurrence of CHD from 2002 to 2019. The patients had at least four annual follow-ups during which glycemic and lipid profile were measured. Co-efficient of variance (CV) for each parameter was calculated by 10-21 measurements. Cox regression analysis was performed to assess the association between CV of glycemic indices, lipid profile, blood pressure, creatinine, weight and incident CHD during the follow-up period. Hazard ratios (HR) were adjusted for the confounding variables. Glycemic indices variability (i.e., CV-HbA1c, CV-FBS, and CV-BS2hpp), were significantly higher in the group with incident CHD (P=0.034, P=0.042, and P=0.044, respectively). Hazard ratios were 1.42 (95 % CI=1.13-2.09) for CV-HbA1c, 1.37 (95 % CI=1.02-2.10) for CV-FBS, and 1.16 (95 % CI=1.01-1.63) for CV-BS2hpp (P=0.012, P=0.046, P=0.038, respectively). Creatinine was significantly higher in the group with incident CHD (P=0.036) and it was significantly associated with higher incidence of CHD (HR=1.14, 95 % CI=1.02-2.17, P=0.048). Visit to visit variability of glycemic indices of the patients with type 2 diabetes is associated with incident CHD independent of their baseline and mean values.

Continuous glucose monitoring in persons with type 2 diabetes not using insulin

INTRODUCTION

CGM is an evidence-based intervention to improve glycemic control in persons with T1D and T2D using insulin. Use of CGM in persons with T2D not using insulin is not well studied.

AREAS COVERED

Existing clinical evidence for the use of CGM in persons with T2D is reviewed with a focus on persons with T2D not using insulin. Additional perspective and consideration are provided on the role and rationale for using CGM in persons with T2D not using insulin.

EXPERT OPINION

On the basis of available evidence, persons with T2D not using insulin benefit clinically through reduction in HbA1c, and improvement in time in range. Additional benefits include improvement in behavior modification, satisfaction, quality of life, empowerment, and diabetes distress. Drivers of these benefits are independent of insulin use in persons with T2D and may include an improved understanding of how diet, lifestyle, and exercise impact diabetes through CGM use. Clinical benefits from CGM independent of medication use include ability to modify health behavior and subsequently improve self-management.

Fasting Blood Glucose Variability and Unfavorable Trajectory Patterns Are Associated with the Risk of Colorectal Cancer

Background/Aims

The relationship between fasting blood glucose (FBG) variability and colorectal cancer (CRC) remains ill-defined. This study aimed to evaluate the association of FBG variability with CRC risk in the healthy population without overt diabetes.

Methods

In the data from the Korean National Health Insurance Service-Health Screening Cohort, we included individuals examined by FBG testing at least 3 times between 2002 and 2007. FBG variability was calculated using standard deviation (SD) and coefficient of variation (CV).

Results

Regarding FBG variability, an increase in the quintile of SD or CV was independently associated with CRC risk (all p for trend <0.01). When the change in FBG was classified into six trajectory patterns, unfavorable trajectory patterns (high stable and upward) were significantly associated with increased CRC risk (hazard ratio [HR] 2.30, p=0.003; HR 1.19, p=0.007, respectively). In subgroup analyses according to the sex, a significant association between FBG variability (SD or CV) and CRC risk was observed in men but not in women. The high stable and upward pattern were also associated with CRC risk in men (HR 2.47, p=0.002; HR 1.21, p=0.012) but not in women.

Conclusions

This study identified that FBG variability and unfavorable trajectory patterns were significantly associated with increased CRC risk in the healthy population without overt diabetes. Our findings suggest that FBG variability as well as FBG itself may be a predictive factor for the development of CRC.

Association of glycemic variability assessed by continuous glucose monitoring with subclinical diabetic polyneuropathy in type 2 diabetes patients

Aims/Introduction

Diabetic peripheral neuropathy is a common diabetes‐related microvascular complication. The relationship between peripheral nerve function and glucose variability is unclear. We investigated the association of glucose variability with subclinical diabetic polyneuropathy in a large‐scale sample of patients with type 2 diabetes.

Materials and Methods

We enrolled 509 individuals with type 2 diabetes who were screened for diabetic peripheral neuropathy and monitored using a continuous glucose monitoring system. Multiple glycemic variability parameters, including the mean amplitude of glycemic excursions, glucose standard deviation (SD_gluc) and glucose coefficient of variation were calculated from 3‐day glucose profiles obtained from continuous glucose monitoring. All participants underwent nerve conduction studies, and the composite Z‐scores for nerve conduction parameters were calculated.

Results

Multivariate logistic regression analyses showed that SD_gluc and the conventional risk factor hemoglobin A1c (HbA1c) were independently associated with abnormal nerve function, and the corresponding odds ratios (95% confidence interval) were 1.198 (1.027–1.397, SD_gluc) and 1.182 (1.061–1.316, HbA1c), respectively. The composite Z‐score of nerve conduction velocity and response amplitude obviously decreased with greater SD_gluc, and the composite Z‐score of distal latency significantly increased with increasing tertiles of SD_gluc (all P trend <0.05). After adjusting for age, sex, body mass index, diabetes duration and HbA1c, SD_gluc was independently associated with nerve conduction velocity (β = −0.124, P = 0.021).

Conclusions

The SD_gluc is a significant independent contributor to subclinical diabetic polyneuropathy, in addition to conventional risk factors including diabetes duration and HbA1c.

Higher long-term visit-to-visit glycemic variability predicts new-onset atrial fibrillation in patients with diabetes mellitus

Background

Atrial fibrillation (AF) is prevalent in patients with type 2 diabetes mellitus (T2DM). Glycemic variability (GV) is associated with risk of micro- and macrovascular diseases. However, whether the GV can increase the risk of AF remains unknown.

Methods

The cohort study used a database from National Taiwan University Hospital, a tertiary medical center in Taiwan. Between 2014 and 2019, a total of 27,246 adult patients with T2DM were enrolled for analysis. Each individual was assessed to determine the coefficients of variability of fasting glucose (FGCV) and HbA1c variability score (HVS). The GV parameters were categorized into quartiles. Multivariate Cox regression models were employed to estimate the relationship between the GV parameters and the risk of AF, transient ischemic accident (TIA)/ischemic stroke and mortality in patients with T2DM.

Results

The incidence rates of AF and TIA/ischemic stroke were 21.31 and 13.71 per 1000 person-year respectively. The medium follow-up period was 70.7 months. In Cox regression model with full adjustment, the highest quartile of FGCV was not associated with increased risk of AF [Hazard ratio (HR): 1.12, 95% confidence interval (CI) 0.96–1.29, p = 0.148] or TIA/ischemic stroke (HR: 1.04, 95% CI 0.83–1.31, p = 0.736), but was associated with increased risk of total mortality (HR: 1.33, 95% CI 1.12–1.58, p < 0.001) and non-cardiac mortality (HR: 1.41, 95% CI 1.15–1.71, p < 0.001). The highest HVS was significantly associated with increased risk of AF (HR: 1.29, 95% CI 1.12–1.50, p < 0.001), total mortality (HR: 2.43, 95% CI 2.03–2.90, p < 0.001), cardiac mortality (HR: 1.50, 95% CI 1.06–2.14, p = 0.024) and non-cardiac mortality (HR: 2.80, 95% CI 2.28–3.44, p < 0.001) but was not associated with TIA/ischemic stroke (HR: 0.98, 95% CI 0.78–1.23, p = 0.846). The Kaplan–Meier analysis showed significantly higher risk of AF, cardiac and non-cardiac mortality according to the magnitude of GV (log-rank test, p < 0.001).

Conclusions

Our data demonstrate that high GV is independently associated with the development of new-onset AF in patients with T2DM. The benefit of maintaining stable glycemic levels to improve clinical outcomes warrants further studies.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12933-021-01341-3.

Habitual Miso (Fermented Soybean Paste) Consumption Is Associated with Glycemic Variability in Patients with Type 2 Diabetes: A Cross-Sectional Study

Glycemic control, including glycemic variability, is important for the prevention of diabetic vascular complications in patients with type 2 diabetes mellitus (T2DM). There was an association between miso soup intake and insulin resistance. However, the relationship between habitual miso consumption and glycemic control, including glycemic variability, in patients with T2DM remains unknown. We defined people without habitual miso consumption if they did not consume miso soup at all in a day. The average, standard deviation (SD), and coefficient of variation (CV), calculated as CV = (SD/average HbA1c) × 100 (%), of hemoglobin (Hb) A1c levels were evaluated. The proportions of habitual miso consumption of male and female were 88.1% and 82.3%, respectively. The average (7.0 [6.4-7.5] vs. 7.3 [6.8-8.4] %, p = 0.009), SD (0.21 [0.12-0.32] vs. 0.37 [0.20-0.72], p = 0.004), and CV (0.03 [0.02-0.04] vs. 0.05 [0.03-0.09], p = 0.005) of HbA1c levels in female with habitual miso consumption were lower than those of female without. Moreover, habitual miso consumption correlated with average (β = -0.251, p = 0.009), SD (β = -0.175, p = 0.016), and CV (β = -0.185, p = 0.022) of HbA1c levels after adjusting for covariates. However, no association between habitual miso consumption and any glycemic parameters was shown among male. This study clarified the association between habitual miso consumption and good glycemic control, including glycemic variability, in female, but not in male.

Associations of Physical Activity Level and Variability With 6-Month Weight Change Among 26,935 Users of Connected Devices: Observational Real-Life Study

Background

Physical activity (PA) is a modifiable lifestyle factor that can be targeted to increase energy expenditure and promote weight loss. However, the amount of PA required for weight loss remains inconsistent. Wearable activity trackers constitute a valuable opportunity to obtain objective measurements of PA and study large populations in real-life settings.

Objective

We aim to study the associations of initial device-assessed PA characteristics (average step counts and step count variability) and their evolution with 6-month weight change.

Methods

We analyzed data from 26,935 Withings-connected device users (wearable activity trackers and digital scales). To assess the initial PA characteristics and their 6-month changes, we used data recorded during the first and sixth 30-day periods of activity tracker use. For each of these periods, we used the monthly mean of daily step values as a proxy for PA level and derived the monthly coefficient of variation (CV) of daily step values to estimate PA level variability. Associations between initial PA characteristics and 6-month weight change were assessed using multivariable linear regression analyses controlled for age, sex, blood pressure, heart rate, and the predominant season. Restricted cubic spline regression was performed to better characterize the continuous shape of the associations between PA characteristics and weight change. Secondary analyses were performed by analyzing the 6-month evolution of PA characteristics in relation to weight change.

Results

Our results revealed that both a greater PA level and lower PA level variability were associated with weight loss. Compared with individuals who were initially in the sedentary category (<5000 steps/day), individuals who were low active (5000-7499 steps/day), somewhat active (7500-9999 steps/day), and active (≥10,000 steps/day) had a 0.21-kg, a 0.52-kg, and a 1.17-kg greater decrease in weight, respectively (95% CI −0.36 to −0.06, −0.70 to −0.33, and −1.42 to −0.93, respectively). Compared with users whose PA level CV was >63%, users whose PA level CV ranged from 51% to 63%, 40% to 51%, and was ≤40%, had a 0.19-kg, a 0.23-kg, and a 0.33-kg greater decrease in weight, respectively (95% CI −0.38 to −0.01, −0.41 to −0.04, and −0.53 to −0.13, respectively). We also observed that each 1000 steps/day increase in PA level over the 6-month follow-up was associated with a 0.26-kg (95% CI −0.29 to −0.23) decrease in weight. No association was found between the 6-month changes in PA level variability and weight change.

Conclusions

Our results add to the current body of knowledge that health benefits can be observed below the 10,000 steps/day threshold and suggest that not only increased mean PA level but also greater regularity of the PA level may play important roles in short-term weight loss.

Flash Continuous Glucose Monitoring: A Practical Guide and Call to Action for Pharmacists

Despite advances in diabetes technology, the proportion of patients with type 2 diabetes achieving recommended glycemic goals remains suboptimal. There is a growing interest in flash continuous glucose monitoring (CGM) among patients, pharmacists and providers. Pharmacists are well positioned to collaborate with patients and providers in ambulatory care or community-based settings to allow a greater number of patients with diabetes to harness the benefits of flash CGM. The purpose of this narrative review is to provide pharmacists with a background on flash CGM technology, review the data supporting pharmacist-driven flash CGM services, and address common questions that arise in pharmacy practice surrounding flash CGM.

Variability in Annual Fasting Glucose and the Risk of Peripheral Artery Disease in Patients with Diabetes Mellitus

PURPOSE

High glucose concentrations and swings are associated with endothelial dysfunction. We examined the effects of variability in fasting plasma glucose on peripheral artery disease (PAD) in patients with diabetes mellitus (DM).

PATIENTS AND METHODS

In this screening study for the risk factors of PAD, we retrospectively collected data on the ankle-brachial index (ABI) and the percentage of mean arterial pressure (%MAP) at the ankle between August 01, 2016 and July 31, 2017. We defined low ABI ≤0.90, high %MAP ≥45%, or both as high-risk PAD and others as low-risk PAD. We compared the standard deviation (SD) of the first fasting plasma glucose data available each year after January 01, 2007.

RESULTS

In 2577 patients, a higher SD of annual fasting glucose was observed in those with an ABI ≤0.90 than in patients with an ABI >0.90 (2.6 ± 2.1 vs 2.2 ± 2.3, P = 0.009), and in patients with %MAP ≥45% than in those with %MAP <45% (2.4 ± 2.1 vs 2.2 ± 2.3, P = 0.034). A high-risk PAD was significantly associated with the SD (P = 0.032) but not with the mean (P = 0.338) of annual fasting glucose. The former was an independent risk factor for high-risk PAD (odds ratio = 1.424; 95% CI = 1.118‒1.814; P = 0.004).

CONCLUSION

Variability but not mean of annual fasting plasma glucose was significantly associated with a high risk of PAD in patients with DM.

Prognostic Significance of Long-term HbA1c Variability for All-Cause Mortality in the ACCORD Trial

OBJECTIVE

The association between high glycemic variability and all-cause mortality has been widely investigated in epidemiological studies but rarely validated in glucose-lowering clinical trials. We aimed to identify the prognostic significance of visit-to-visit HbA_1c variability in treated patients in the Action to Control Cardiovascular Risk in Diabetes (ACCORD) trial population.

RESEARCH DESIGN AND METHODS

We studied the risk of all-cause mortality in relation to long-term visit-to-visit HbA_1c variability, expressed as coefficient of variation (CV), variability independent of the mean (VIM), and average real variability (ARV), from the 8th month to the transition from intensive to standard glycemic therapy. Multivariable Cox proportional hazards models were used to estimate adjusted hazard ratio (HR) and 95% CI.

RESULTS

Compared with the standard therapy group (n = 4,728), the intensive therapy group (n = 4,755) had significantly lower mean HbA_1c (6.6% [49 mmol/mol] vs. 7.7% [61 mmol/mol], P < 0.0001) and lower CV, VIM, and ARV (P < 0.0001). In multivariate adjusted analysis, all three HbA_1c variability indices were significantly associated with total mortality in all patients as well as in the standard- and intensive-therapy groups analyzed separately. The hazard ratios for a 1-SD increase in HbA_1c variability indices for all-cause mortality were 1.19 and 1.23 in intensive and standard therapy, respectively. Cross-tabulation analysis showed the third tertile of HbA_1c mean and VIM had significantly higher all-cause mortality (HR 2.05; 95% CI 1.17-3.61; P < 0.01) only in the intensive-therapy group.

CONCLUSIONS

Long-term visit-to-visit HbA_1c variability was a strong predictor of all-cause mortality. HbA_1c VIM combined with HbA_1c mean conferred an increased risk for all-cause mortality in the intensive-therapy group.

Key Relevance of Epigenetic Programming of Adiponectin Gene in Pathogenesis of Metabolic Disorders

Background & Objective::

Significant health and social burdens have been created by the growth of metabolic disorders like type 2 diabetes mellitus (T2DM), atherosclerosis, and non-alcoholic steatohepatitis, worldwide. The number of the affected population is as yet rising, and it is assessed that until 2030, 4−5 million individuals will acquire diabetes. A blend of environmental, genetic, epigenetic, and other factors, such as diet, are accountable for the initiation and progression of metabolic disorders. Several researches have shown strong relevance of adiponectin gene and metabolic disorders. In this review, the potential influence of epigenetic mechanisms of adiponectin gene “ADIPOQ” on increasing the risk of developing metabolic disorders and their potential in treating this major disorder are discussed.

Results & Conclusion::

Various studies have postulated that a series of factors such as maternal High fat diet (HFD), oxidative stress, pro-inflammatory mediators, sleep fragmentation throughout lifetime, from gestation to old age, could accumulate epigenetic marks, including histone remodeling, DNA methylation, and microRNAs (miRNAs) that, in turn, alter the expression of ADIPOQ gene and result in hypoadiponectinemia which precipitates insulin resistance (IR) that in turn might induce or accelerate the onset and development of metabolic disorder. A better understanding of global patterns of epigenetic modifications and further their alterations in metabolic disorders will bestow better treatment strategies design.

Evaluation of the relationship between cardiac autonomic function and glucose variability and HOMA-IR in prediabetes

AIMS

The present study aims to investigate the relationship between cardiac autonomic function (CAF) and glucose variability (GV) and HOMA-IR in subjects with prediabetes and normal glucose tolerance (NGT).

MATERIAL AND METHODS

Ninety-two subjects (59 with prediabetes and 33 with NGT), of mean age 50.3 ± 11.5 years, mean BMI 30.4 ± 6.0 kg/m², were included in this cross-sectional study. Glucose tolerance was assessed by OGTT according to WHO 2006 criteria. Glucose, HbA1c, insulin, oxLDL, and 3-Nitrotyrosine were measured. CGM was performed with a blinded sensor (FreeStyle Libre Pro). CAF was assessed by ANX-3.0 technology.

RESULTS

GV indices were increased in prediabetes. CAF was suppressed in subjects with any stage of dysglycemia. The prevalence of cardiac autonomic dysfunction was higher in prediabetes -20.3% as compared to NGT -3.0%, p = 0.028. HOMA-IR [OR 1.5 (95% CI: 1.1-2.1), p = 0.010] and time in target range [OR 0.8 (95% CI: 0.67-0.97), p = 0.021] were found to be predictive variables for impaired CAF. Sympathetic and parasympathetic activity negatively correlated with mean glycemia and GV indices and were independently related to JINDEX in prediabetes (F[1, 47] = 5.76, p = 0.021 and F[1, 47] = 5.94, p = 0.019, respectively); and to time above target range in NGT (F[1, 18] = 4.48, p = 0.049 and F[1, 18] = 4.65, p = 0.046, respectively).

CONCLUSION

CAF is declined in prediabetes and seems to be related to GV and HOMA-IR at early stages of dysglycemia.

Impact of HbA1c variability on subclinical left ventricular remodeling and dysfunction in patients with type 2 diabetes mellitus

BACKGROUND

Glycemic instability confers a risk of poor prognosis in patients with type 2 diabetes mellitus (T2DM). This study aimed to investigate whether HbA1c variability provided additional value over mean HbA1c for predicting subclinical left ventricular remodeling and dysfunction in T2DM patients.

METHODS

A total of 466 T2DM patients with normal cardiac structure and function were recruited and prospectively followed up for a median of 4.7 y. HbA1c was measured quarterly. The intrapersonal mean and standard deviation (SD) of HbA1c measurements were calculated, and SD-HbA1c was considered as a measure of HbA1c variability. All participants underwent transthoracic echocardiography at baseline and after follow-up.

RESULTS

In multivariable regression analyses, SD-HbA1c was independently associated with annualized changes in left ventricular end diastolic diameter, interventricular septum, left ventricular posterior wall, left ventricular mass index, left ventricular ejection fraction, E/e' ratio, and E/A ratio (P < 0.001). Subgroup analysis based on mean HbA1c levels (<7.0%, 7.0-7.5%, and ≥7.5%) further confirmed that SD-HbA1c was associated with most of the above parameters regardless of mean HbA1c levels.

CONCLUSION

This study indicates that HbA1c variability adds to the mean value in predicting subclinical left ventricular remodeling and dysfunction in T2DM patients.