Normal reference range for mean tissue glucose and glycemic variability derived from continuous glucose monitoring for subjects without diabetes in different ethnic groups

Minimum Overnight Interstitial Glucose Concentration in Professional Cyclists During Two Consecutive Annual Training Camps: The Limited Impact of Daily Exercise Energy Expenditure

This observational study investigated the use of continuous glucose monitoring (CGM) in a team of professional cyclists without diabetes during two consecutive annual training camps. The goal of the study was twofold: to present the aggregated CGM metrics such as day/overnight CGM average (DAYAVG/OVNAVG) for this group of professional cyclists and to study the association between exercise energy expenditure (megajoules per day), carbohydrate intake (grams), and minimum overnight CGM values (millimoles per liter). Linear mixed models were employed in the analysis. Data were available for 26 cyclists (22 participated in both training camps). CGM levels reported (DAYAVG = 6.37 ± 0.54 mmol/L and OVNAVG = 5.30 ± 0.52 mmol/L), are not typically seen in healthy individuals not engaged in intensive exercise routines. Results showed that minimum overnight CGM values significantly fluctuated throughout the training camp, but a statistically significant association between exercise energy expenditure (p = .0839) or carbohydrate intake (p = .059) and minimum overnight CGM values could not be detected. This research contributes to the literature on CGM use in professional athletes and underscores the need for further studies to fully understand the benefits and limitations of CGM to guide sports performance.

A DiRECT approach to weight loss in a culturally diverse, low-income population: Pilot randomised controlled trial and meta-analysis of similar interventions

AIMS

To consider an intensive lifestyle intervention for weight loss in type 2 diabetes or prediabetes when delivered within a primary care service catering to indigenous (Māori), Pacific, refugee, and low-income clientele.

MATERIALS AND METHODS

Adults with obesity, type 2 diabetes or prediabetes, and a desire to lose weight were randomised to dietitian-supported usual care or the Diabetes Remission Clinical Trial (DiRECT)-type intervention (3 months of total diet replacement followed by 9 months of food reintroduction and supported weight loss maintenance). Both interventions included equal dietetic support delivered within primary care. Primary outcome was weight loss at 3 and 12 months. We performed random-effects meta-analysis of body weight of existing DiRECT-type interventions.

RESULTS

Forty participants were randomised to the dietitian-led DiRECT-type intervention or dietitian-supported usual care. At 3 months, weight loss among DiRECT-type intervention participants was -6.1 kg (95% CI -10.2, -2.0) greater than with dietetic support. At 12 months, this difference decreased to -3.8 kg (-7.6, -0.1) due to gradual weight loss with dietitian-supported usual care, not weight regain in DiRECT. Meta-analyses indicated -8.5 kg (-11.1, -5.9) and -6.0 kg (-8.4, -3.5) greater weight loss for DiRECT-type interventions than usual diabetes care (with or without dietary advice) at 3 and 12 months.

CONCLUSIONS

In this pilot effectiveness trial, the DiRECT-type intervention generated clinically relevant and greater weight loss than dietitian-supported usual care at 3 and 12 months. These results align with the effect sizes generated by meta-analyses of existing DiRECT-type interventions, demonstrating the potential use of DiRECT-type approaches across a much broader spectrum of the population than previously considered including those with both type 2 and prediabetes. ACTRN12622000151730.

Exploring glycemic variability and time in range: Emerging indicators of at-risk pregnancy in type 2 diabetes mellitus

OBJECTIVE

This study compares ambulatory glycemic profile and glycemic variability between pregnant women diagnosed with type 2 diabetes mellitus (T2DM) receiving pharmacotherapy and healthy pregnant women without diabetes and assesses their correlation with fetal outcome.

METHOD

This was a case-control study involving 60 pregnant women (40 with T2DM and 20 healthy controls) in the third trimester of pregnancy. A flash glucose monitor device was applied over the upper arm to obtain the ambulatory glucose profile. Various glycemic parameters were analyzed and correlated with fetal outcomes among the two groups.

RESULTS

A total of 720 days of glucose data, comprising 69 120 data points, were analyzed. This included 46 080 glucose values from the T2DM group and 23 040 from the healthy control group. Mean glucose levels in the T2DM group were 23.75% higher compared to the controls. All measures of glycemic variability were significantly elevated in the T2DM group, including mean amplitude of glycemic excursions (MAGE) (52.85 mg/dL vs. 35.72 mg/dL, P = 0.036) and standard deviation (22.84 mg/dL vs. 14.00 mg/dL, P = 0.029). A MAGE >55 mg/dL was associated with adverse fetal outcomes, such as large-for-gestational-age (LGA) infants, neonatal hypoglycemia, and stillbirth. In the T2DM group, poor outcomes were also linked to a higher time above range (TAR) (13.39% vs. 2.5%, P = 0.0001). The LGA subgroup exhibited an elevated TAR (15.2%) compared to those with normal outcomes.

CONCLUSION

Pregnant women with T2DM exhibit higher glycemic variability compared to healthy pregnant women. A MAGE >55 mg/dL was associated with adverse fetal outcomes, and elevated TAR significantly influenced these outcomes.

Improved detection of decreased glucose handling capacities via continuous glucose monitoring-derived indices

BACKGROUND

Efficiently assessing glucose handling capacity is a critical public health challenge. This study assessed the utility of relatively easy-to-measure continuous glucose monitoring (CGM)-derived indices in estimating glucose handling capacities calculated from resource-intensive clamp tests.

METHODS

We conducted a prospective study of 64 individuals without prior diabetes diagnosis. The study performed CGM, oral glucose tolerance tests (OGTT), and hyperglycemic and hyperinsulinemic-euglycemic clamp tests. We validated CGM-derived indices characteristics using an independent dataset from another country and mathematical models with simulated data.

RESULTS

A CGM-derived index reflecting the autocorrelation function of glucose levels (AC_Var) is significantly correlated with clamp-derived disposition index (DI), a well-established measure of glucose handling capacity and predictor of diabetes onset. Multivariate and machine learning models indicate AC_Var's contribution to predicting clamp-derived DI independent from other CGM-derived indices. The model using CGM-measured glucose standard deviation and AC_Var outperforms models using commonly used diabetes diagnostic indices, such as fasting blood glucose, HbA1c, and OGTT measures, in predicting clamp-derived DI. Mathematical simulations also demonstrate the association of AC_Var with DI.

CONCLUSIONS

CGM-derived indices, including AC_Var, serve as valuable tools for predicting glucose handling capacities in populations without prior diabetes diagnosis. We develop a web application that calculates these CGM-derived indices ( https://cgm-ac-mean-std.streamlit.app/ ).

Parameters of glycemic variability in continuous glucose monitoring as predictors of diabetes: a prospective evaluation in a non-diabetic general population

Objectives

To prospectively examine the ability of some glycemic variability metrics from continuous glucose monitoring (CGM) to predict the development of diabetes in a non-diabetic population.

Methods

A total of 497 non-diabetic patients from the AEGIS study were included. Participants used a CGM system (iPro2®) over a six-day period. The following parameters were analyzed: standard deviation (SD), coefficient of variation (CV) and mean amplitude of glucose excursion (MAGE). Six-years follow-up was performed. ROC curves were constructed to determine the predictive value of glycemic variability metrics. Sensitivity and specificity were calculated.

Results

Of the 497 participants, 16 women (4.9 %) and 9 men (5.2 %) developed diabetes. Initial HbA1c and fasting glucose levels were significantly higher in the participants who ultimately developed diabetes. Glycemic variability metrics were also significantly higher in these subjects (SD: 18 vs. 13 mg/dL; CV: 17 vs. 14 %; MAGE: 36 vs. 27 mg/dL; p<0.001 in all cases). SD showed the highest AUC (0.81), with a sensitivity of 80 % and a specificity of 72 % for a cut-off of 14.9 mg/dL. AUCs were higher in men for all metrics.

Conclusions

The metrics obtained by MCG, especially SD, are effective predictors of progression to type 2 diabetes in a non-diabetic population. These findings suggest that glycemic variability is useful for the early identification of subjects at a higher risk of developing diabetes.

Parámetros de variabilidad glucémica de la monitorización continua de glucosa como predictores de diabetes: evaluación prospectiva en una población general sin diabetes

Objetivos

Evaluar prospectivamente la capacidad de distintas métricas de variabilidad glucémica obtenidas mediante monitorización continua de glucosa (MCG) para la predicción del desarrollo de diabetes en una población sin diabetes.

Métodos

Se incluyeron 497 participantes sin diabetes del estudio AEGIS. Los participantes utilizaron un sistema de MCG (iPro2®) durante seis días. Se evaluaron las siguientes métricas: desviación estándar (SD), coeficiente de variación (CV) y amplitud media de las excursiones glucémicas (MAGE). Los sujetos fueron seguidos durante una media de 6 años. Se utilizaron curvas ROC para determinar la capacidad predictiva de las métricas de variabilidad glucémica y se calcularon la sensibilidad y especificidad.

Resultados

De los 497 participantes, 16 mujeres (4,9 %) y 9 hombres (5,2 %) desarrollaron diabetes. Las concentraciones iniciales de HbA1c y glucosa en ayunas fueron significativamente más altos en aquellos que progresaron a diabetes. Las métricas de variabilidad glucémica también fueron significativamente mayores en estos individuos (SD: 18 vs. 13 mg/dL; CV: 17 % vs. 14 %; MAGE: 36 vs. 27 mg/dL; p<0,001 en todos los casos). La SD mostró la mayor AUC (0,81), con una sensibilidad del 80 % y una especificidad del 72 % para un punto de corte de 14,9 mg/dL. Las AUC fueron mayores en hombres para todas las métricas estudiadas.

Conclusiones

Las métricas obtenidas por MCG, especialmente la SD, son predictores efectivos de la progresión a diabetes tipo 2 en una población sin diabetes. Estos hallazgos sugieren la utilidad de la variabilidad glucémica en la identificación temprana de individuos en riesgo de desarrollar diabetes.

Predictive models of post-prandial glucose response in persons with prediabetes and early onset type 2 diabetes: A pilot study

OBJECTIVE

Post-prandial glucose response (PPGR) is a risk factor for cardiovascular disease. Meal carbohydrate content is an important predictor of PPGR, but dietary interventions to mitigate PPGR are not always successful. A personalized approach, considering behaviour and habitual pattern of glucose excursions assessed by continuous glucose monitor (CGM), may be more effective.

RESEARCH DESIGN AND METHODS

Data were collected under free-living conditions, over 2 weeks, in older adults (age 60 ± 7, BMI 33.0 ± 6.6 kg/m2), with prediabetes (n = 35) or early onset type 2 diabetes (n = 3), together with sleep and physical activity by actigraphy. We assessed the predictive value of habitual CGM glucose excursions and fasting glucose on PPGR after a research meal (hereafter MEAL-PPGR) and during an oral glucose tolerance test (hereafter OGTT-PPGR).

RESULTS

Mean amplitude of glucose excursions (MAGE) and fasting glucose were highly predictive of all measures of OGTT-PPGR (AUC, peak, delta, mean glucose and glucose at 120 min; R2 between 0.616 and 0.786). Measures of insulin sensitivity and β-cell function (Matsuda index, HOMA-B and HOMA-IR) strengthened the prediction of fasting glucose and MAGE (R2 range 0.651 to 0.832). Similarly, MAGE and premeal glucose were also strong predictors of MEAL-PPGR (R2 range 0.546 to 0.722). Meal carbohydrates strengthened the prediction of 3 h AUC (R2 increase from 0.723 to 0.761). Neither anthropometrics, age nor habitual sleep and physical activity added to the prediction models significantly.

CONCLUSION

These data support a CGM-guided personalized nutrition and medicine approach to control PPGR in older individuals with prediabetes and diet and/or metformin-treated type 2 diabetes.

Effects of four-weeks porcine-collagen hydrolysate consumption on glucose concentrations, glycemic variability, and fasting/postprandial cardiometabolic risk markers in men and women with overweight or obesity: A randomized, controlled trial

BACKGROUND

Different collagen hydrolysate sources have reduced fasting glucose concentrations. Although porcine-derived collagen hydrolysate predicts in vitro the highest potency for improving glucose metabolism, these effects have not been studied in humans.

AIM

To evaluate the effects of porcine-derived collagen hydrolysate on continuously monitored glucose concentrations in real-life conditions in individuals with overweight/obesity. Additionally, postprandial responses following a mixed meal test were examined.

METHODS

Fifty-six men and women participated in this randomized placebo-controlled parallel trial. After a two-week run-in period, participants consumed daily for four weeks 10 g porcine-derived collagen hydrolysate or placebo (erythritol). The primary outcome parameter was the interstitial glucose area under the curve (AUC) during daytime (07:00 to 22:00) measured during three consecutive days. In addition, glycemic variability (GV) was quantified. For this, a continuous glucose monitor (Freestyle Libre ProiQ, Wiesbaden, Germany) was used at the end of the run-in and intervention periods. Postprandial glucose, insulin, and triacylglycerol concentrations were also evaluated after a mixed meal tolerance test. Furthermore, fasting glucose, insulin, hemoglobin A1c (HbA1c), homeostatic model assessment for insulin resistance (HOMA-IR), HOMA of β-cell function (HOMA-β), and triacylglycerol changes were analyzed. Physical activity profiles and dietary intakes were monitored to exclude confounding by these lifestyle factors.

RESULTS

Collagen hydrolysate consumption did not significantly affect daytime interstitial glucose AUC concentrations (95%CI for the effect size: -5.1, 30.0 mmol/(L∗h); p-value = 0.159), but increased several GV metrics: standard deviation (95%CI: 0.0, 0.2 mmol/L; p-value = 0.011), continuous overall net glycemic action (CONGA-4) (95%CI: 0.1, 0.4 mmol/L; p-value = 0.015), coefficient of variation (95%CI: 0.1, 3.0 %; p-value = 0.036), M-value (95%CI: 0.2, 1.8; p-value = 0.036), and mean amplitude of glycemic excursions (MAGE) (95%CI: 0.2, 1.8 mmol/L; p-value = 0.036). Furthermore, the postprandial glucose AUC after the mixed meal test significantly increased (95%CI: 0, 103 mmol/L∗4-h; p-value = 0.049), as well as fasting insulin concentrations (p-value = 0.005), HOMA-IR (p-value = 0.008), and HOMA-β (p-value = 0.009). Other parameters, anthropometrics, physical activity, and energy/nutrient intakes were not significantly changed.

CONCLUSION

Four-week collagen hydrolysate intake did not change free-living glucose concentrations, but increased GV, postprandial glucose AUC, fasting insulin, HOMA-IR, and HOMA-β. However, these changes were small with limited clinical relevance. Therefore, it can be concluded that this porcine-derived collagen hydrolysate does not improve glucose metabolism or other cardiometabolic risk markers.

CLINICAL TRIAL REGISTRATION

This clinical trial was registered in November 2021 as NCT05282641.

The efficacy of using continuous glucose monitoring as a behaviour change tool in populations with and without diabetes: a systematic review and meta-analysis of randomised controlled trials

BACKGROUND

Continuous glucose monitoring (CGM) holds potential as a precision public health intervention, offering personalised insights into how diet and physical activity affect glucose levels. Nevertheless, the efficacy of using CGM in populations with and without diabetes to support behaviour change and behaviour-driven outcomes remains unclear. This systematic review and meta-analysis examines whether using CGM-based feedback to support behaviour change affects glycaemic, anthropometric, and behavioural outcomes in adults with and without diabetes.

METHODS

Ovid MEDLINE, Cochrane Central Register of Controlled Trials, Elsevier Embase, EBSCOhost PsycINFO, and ProQuest Dissertations & Theses Global were searched through January 2024. Eligible studies were randomised controlled trials in adults that implemented CGM-based feedback in at least one study arm compared to a control without CGM feedback. Dual screening, data extraction, and bias assessment were conducted independently. Mean differences in outcomes between intervention and comparison groups were analysed using generic inverse variance models and random effects. Robustness of pooled estimates from random-effects models was considered with sensitivity and subgroup analyses.

RESULTS

Twenty-five clinical trials with 2996 participants were included. Most studies were conducted in adults with type 2 diabetes (n = 17/25; 68%), followed by type 1 diabetes (n = 3/25, 12%), gestational diabetes (n = 3/25, 12%), and obesity (n = 3/25, 12%). Eleven (44%) studies reported CGM-affiliated conflicts of interest. Interventions incorporating CGM-based feedback reduced HbA1c by 0.28% (95% CI 0.15, 0.42, p < 0.001; I2 = 88%), and increased time in range by 7.4% (95% CI 2.0, 12.8, p < 0.008; I2 = 80.5%) compared to arms without CGM, with non-significant effects on time above range, BMI, and weight. Sensitivity analyses showed consistent mean differences in HbA1c across different conditions, and differences between subgroups were non-significant. Only 4/25 studies evaluated the effect of CGM on dietary changes; 5/25 evaluated physical activity.

CONCLUSIONS

This evidence synthesis found favourable, though modest, effects of CGM-based feedback on glycaemic control in adults with and without diabetes. Further research is needed to establish the behaviours and behavioural mechanisms driving the observed effects across diverse populations.

TRIAL REGISTRATION

CRD42024514135.

Weight-neutral early time-restricted eating improves glycemic variation and time in range without changes in inflammatory markers

Early time-restricted eating (eTRE) is a dietary strategy that restricts caloric intake to the first 6-8 h of the day and can effect metabolic benefits independent of weight loss. However, the extent of these benefits is unknown. We conducted a randomized crossover feeding study to investigate the weight-independent effects of eTRE on glycemic variation, multiple time-in-range metrics, and levels of inflammatory markers. Ten adults with prediabetes were randomized to eTRE (8-h feeding window, 80% of calories consumed before 14:00 h) or usual feeding (50% of calories consumed after 16:00 h) for 1 week followed by crossover to the other schedule. Using continuous glucose monitoring, we showed that eTRE decreased glycemic variation (mean amplitude of glycemic excursion) and time in hyperglycemia greater than 140 mg/dL without affecting inflammatory markers (erythrocyte sedimentation rate and C-reactive protein). These data implicate eTRE as a candidate dietary intervention for the weight-independent management of dysglycemia in high-risk individuals.

Effects of insulin glargine U300 versus insulin degludec U100 on glycemic variability, hypoglycemia, and diet evaluated by continuous glucose monitoring in type 1 diabetes: a retrospective cross-sectional study

The impacts of insulin degludec U100 (Deg-100) and insulin glargine U300 (Gla-300) on glycemic variability (GV) in patients with type 1 diabetes, as well as the impact of major nutrient components on GV in these patients, remain unclear. This was an observational, cross-sectional, retrospective study. Type 1 diabetes mellitus patients treated with either Deg-100 or Gla-300 as basal insulin were enrolled. After the participants underwent continuous glucose monitoring, GV indices and major nutrient components were analyzed. Forty patients with type 1 diabetes were enrolled, and 20 participants used Deg-100, and 20 used Gla-300. There was no significant difference in major nutrient components between the two groups. Better GV indices of standard deviation, coefficient of variation, mean amplitude of glycemic excursion, AUCn, M-value, CONGA1, CONGA2, and CONGA4 were noted in the Gla-300 group versus Deg-100 group. Compared with patients who received once-daily injection in the morning (QD), Deg-100 administration once daily at bedtime (HS) yielded a higher low blood glucose index during both day and nocturnal periods, indicating a higher risk of hypoglycemic events. By contrast, there were significantly lower levels of CONGA1, CONGA2, and CONGA4 during insulin Gla-300 QD administration than during HS administration, indicating a lower GV of a short interval. In this real-world study involving type 1 diabetes patients, Gla-300 appears to offer more stable glucose variability than Deg-100. Administering once-daily injections could lower the risk of hypoglycemia in the Deg-100 group and minimize GV in the Gla-300 group compared to bedtime injections.

The clinical importance of measuring glycaemic variability: Utilising new metrics to optimise glycaemic control

With the widespread use of continuous glucose monitoring (CGM), glycaemic variability (GV) is a glucose metric that has been gaining increasing attention. However, unlike other glucose metrics that are easily defined and have clear targets, GV has a large number of different measures given the complexity involved in assessment. While variabilities in HbA1c, fasting and postprandial glucose have been incorporated under the GV banner, short-term variability in glucose, within day and between days, is more in keeping with the correct definition of GV. This review is focused on short-term GV, as assessed by CGM data, although studies calculating GV from capillary glucose testing are also mentioned as appropriate. The different measures of GV are addressed, and their potential role in microvascular and macrovascular complications, as well as patient-related outcomes, discussed. It should be noted that the independent role of GV in vascular pathology is not always clear, given the inconsistent findings in different populations and the close association between GV and hypoglycaemia, itself an established risk factor for adverse outcomes. Therefore, this review attempts, where possible, to disentangle the contribution of GV to diabetes complications from other glycaemic parameters, particularly hypoglycaemia. Evidence to date strongly suggests an independent role for GV in vascular pathology but future large-scale outcome studies are required to fully understand the exact contribution of this metric to vascular complications. This can be followed by setting appropriate GV measures and targets in different diabetes subgroups, in order to optimise glycaemic management and limit the risk of complications.

Early continuous glucose monitoring-derived glycemic patterns are associated with subsequent insulin resistance and gestational diabetes mellitus development during pregnancy

BACKGROUND

Gestational diabetes mellitus (GDM) and insulin resistance (IR) increase the risk of adverse pregnancy outcomes. We aimed to examine the relationship of interstitial glucose assessed by continuous glucose monitoring (CGM) at early gestation, and the subsequent development of IR and GDM, and to determine 24-h interstitial glucose centile distributions in women with normal (non-IR and non-GDM) and suboptimal glycemic status (IR and/or GDM).

METHODS

CGM measurements were taken for 3-10 days at 18-24 weeks' gestation, followed by fasting serum insulin and oral glucose tolerance testing at 24-28 weeks' gestation. IR and GDM were determined by the updated Homeostasis Model Assessment of IR score of ≥ 1.22 and 2013 World Health Organization criteria, respectively. Risks of IR and GDM were estimated using modified Poisson models, and hourly interstitial glucose centiles determined using Generalized Additive Models for Location, Scale and Shape.

RESULTS

This prospective cohort study involved 167 pregnant women in Singapore, with a mean age of 31.7 years, body mass index of 22.9 kg/m2, and gestation of 20.3 weeks. 25% of women exhibited IR and 18% developed GDM. After confounders adjustment, women with suboptimal glycemic control, indicated by higher mean daily glucose (risk ratio 1.42; 95% confidence interval 1.16, 1.73), glucose management indicator (1.08; 1.03, 1.12), and J-index (1.04; 1.02, 1.06), as well as those with greater glycemic variability, indicated by higher standard deviation (1.69; 1.37, 2.09), coefficient of variation (1.03; 1.00, 1.06), and mean amplitude of glycemic excursions (1.4; 1.14, 1.35) derived from CGM in early gestation were associated with higher risks of developing IR in later gestation. These associations were similarly observed for the development of GDM. Centile curves showed that, compared to those with normal glycemic status, women with suboptimal glycemic status had higher glucose levels, with greater fluctuations throughout 24 h.

CONCLUSIONS

In pregnant women who subsequently developed IR and GDM, interstitial glucose levels assessed by CGM were elevated and varied greatly. This supports the potential use of CGM to screen for glycemic changes early in pregnancy.

A comparative study of the relationship between time in range assessed by self-monitoring of blood glucose and continuous glucose monitoring with microalbuminuria outcome, HOMA-IR and HOMA-β test

AIMS

To compare the time in range (TIR) obtained from self-monitoring of blood glucose (SMBG) with that obtained from continuous glucose monitoring (CGM), and explore the relationship of TIR with microalbuminuria outcome, HOMA-IR and HOMA-β test.

METHODS

We recruited 400 patients with type 2 diabetes to carry out blood glucose monitoring by both SMBG and CGM for 3 consecutive days. TIR, TAR, TBR and other blood glucose variation indices were calculated respectively through the glucose data achieved from SMBG and CGM. The HOMA-IR and HOMA-β test was evaluated by an oral glucose tolerance test. Urinary microalbumin-to-creatinine ratio completed in the laboratory.

RESULTS

The median (25 %, 75 % quartile) of TIRCGM and TIRSMBG were 74.94(44.90, 88.04) and 70.83(46.88, 87.50) respectively, and there was no significant difference, p = 0.489; For every 1 % increase in TIRCGM, the risk of microalbuminuria decreased by 1.6 % (95%CI:0.973, 0.995, p = 0.006) and for every 1 % increase in TIRSMBG, the risk of microalbuminuria decreased by 1.3 % (95%CI:0.975, 0.999, p = 0.033). Stepwise multiple linear regression analysis showed an independent positive correlation between TIR (including TIRCGM and TIRSBMG) and LnDI30 and LnDI120 levels (p = 0.000).

CONCLUSIONS

The TIR calculated by SMBG was highly consistent with that reported by CGM and was significantly associated with the risk of microalbuminuria and the HOMA-β. Higher TIR quartiles were associated with lower incidence of microalbuminuria as well as higher lever of HOMA-β. For patients with limited CGM application, SMBG-derived TIR may be an alternative to CGM-derived TIR, to assess blood glucose control.

Evaluation of Glycemic Variability and Discharge Outcomes in Patients with Ischemic Stroke Following Thrombolysis

Background and Purpose

Hyperglycemia following acute ischemic stroke (AIS) is associated with adverse outcomes including, hemorrhagic conversion and increased length of stay; however, the impact of glycemic variability is largely unknown. This study aims to evaluate the effect of glycemic variability on discharge outcomes in patients treated with alteplase for AIS.

Methods

A retrospective review of ischemic stroke patients who presented within 4.5 hours from symptom onset and received alteplase was completed. Patients hospitalized for at least 48 hours were included. Glycemic variability was measured using J-index. Groups were defined by normal or abnormal J-indices. Logistic regression models were developed to determine odds ratios for select clinical characteristics, NIHSS score, mRS, and disposition at discharge.

Results

Of the 229 patients, 97 (42%) had an abnormal J-index. In the univariate analysis, abnormal J-index was associated with worse outcomes in terms of NIHSS score, mRS, and discharge disposition compared to a normal J-index. In the unadjusted multivariate analysis, abnormal J-index was associated with higher odds of unfavorable mRS (3-6) at discharge (OR 2.1; 95% CI 1.2 - 3.5, P = .009). In the adjusted multivariate analysis, patients with an abnormal J-index had higher odds of hemorrhagic transformation (OR 5.7; 95% CI 2.1 - 15.6, P < .0001). There was no difference in mortality.

Conclusion

Glycemic variability with abnormal J-index following AIS is associated with adverse functional outcomes at discharge and increased odds of hemorrhagic conversion in patients treated with alteplase. Additional studies validating glycemic variability indices post-ischemic stroke are needed to determine the full clinical impact.

Glucose Variability in People with Type 1 Diabetes: Associations with Body Weight, Body Composition, and Insulin Sensitivity

The prevalence of overweight and obesity increases in people with type 1 diabetes (T1D). However, the impact of fat accumulation on glucose dynamics in T1D is poorly understood. We assessed continuous glucose monitoring (CGM) parameters in patients with T1D depending on their body weight, body composition, and insulin sensitivity. In 547 patients, including 238 overweight/obese individuals, CGM-derived time in range (TIR) and glucose variability (GV) were estimated. Body composition was assessed by DXA. Estimated glucose disposal rate (eGDR) was used as an indicator of insulin sensitivity. Overweight/obese patients, when compared to normal-weight ones, have a lower time below range (TBR) (<3 mmol/L), GV, and experienced fewer episodes of low glucose. In men, lower TIR, higher time above range (TAR), and GV reduction were associated with central adiposity assessed by total, trunk, and android fat mass. In women, gynoid fat mass only was associated with a lower TIR and higher TAR. The eGDR was a positive predictor of TIR and a negative predictor of TAR, TBR, and GV in men and women. In conclusion, adiposity in people with T1D is associated with a lower risk of CGM-confirmed hypoglycemia, higher TAR, and reduced GV. These features of daily glucose dynamics may be mediated by insulin resistance.

Twenty-Four Hour Glucose Profiles and Glycemic Variability during Intermittent Religious Dry Fasting and Time-Restricted Eating in Subjects without Diabetes: A Preliminary Study

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

The effect of different times of day for exercise on blood glucose fluctuations

AIMS

This study aims to explore blood glucose variations before and after short-term intensive exercise in the morning or afternoon of a day and the trend of blood glucose fluctuations during exercise in patients with T2DM (type 2 diabetes, T2DM).

METHODS

Blood glucose variations of Fouty during morning exercise 8:00-12:00 hours and twenty during afternoon exercise 14:30-18:30 hours). Patients with T2DM discharged from the hospital were analyzed retrospectively, with the baseline data checked through the medical record system before intervention. We were asked to perform seven times of treadmill aerobic exercise, which lasted for 30 minutes with incremental intensity for each time, for two weeks under the supervision of the Continuous Glucose Monitor (CGM) and the heart rate armband. The exercise intensity has been adjusted by the clinicians and specialist nurses from the Department of Diabetes Mellitus according to the blood glucose levels and heart rate curves during exercise; data including the height, weight, body mass index (BMI), waist-to-hip ratio, fasting blood glucose, glycosylated hemoglobin, in-exercise CGM-measured blood glucose value/min, and after-exercise fingertip blood glucose value of patients with T2DM were collected after the intensive exercise (2 weeks). SPSS 22.0 and GraphPad Prism 7 were adopted for statistical analysis using the T-test and ANOVA.

RESULT

No difference was observed in the baseline data between the morning and afternoon exercise groups before intervention; compared to the morning exercise group, the fasting C-peptide value (2.15±0.97 vs. 1.53±0.46) in the afternoon exercise group was higher than that in the morning exercise group, with a superior (p=0.029) effect after two weeks of intervention, exhibiting a significant difference in the results. According to the results of repeated variance ANOVA analysis, the time for the appearance of significant improvement in blood glucose in the afternoon exercise group was 5 minutes earlier (11th minute vs 1 minute)than that in the morning exercise group (15th minute vs 1 min); significant differences were observed in both time (p=0.048 vs p<0.01) between the two groups on exercise days, as revealed by the results of bivariate ANOVA; in comparison to the morning exercise group (7.42±1.68), there was a significant difference (p=0.049)in the mean blood glucose between the two groups 25 min after patients with T2DM in the afternoon exercise group (6.25±1.53) started to exercise; in addition, a significant statistical difference (p=0.021) was revealed in the CGM-measured hourly the mean blood glucose on exercise days between the morning(8.18±1.88) and afternoon exercise (6.75±1.40)groups at 4:00 pm in week one and two w.

CONCLUSIONS

Glycaemic improvement in the short-term intensive afternoon exercise group may be superior to that of the morning exercise group, which may be related to greater fasting C-peptide secretion and longer effective exercise duration. The time to exercise is a factor affecting blood glucose variations during exercise. However, significant variations in the level of blood glucose during exercise must be further observed through exercise intervention over a more extended period.

Cross-sectional association of continuous glucose monitoring-derived metrics with cerebral small vessel disease in older adults with type 2 diabetes

AIM

To examine cross-sectional associations between continuous glucose monitoring (CGM)-derived metrics and cerebral small vessel disease (SVD) in older adults with type 2 diabetes.

MATERIALS AND METHODS

In total, 80 patients with type 2 diabetes aged ≥70 years were analysed. Participants underwent CGM for 14 days. From the CGM data, we derived mean sensor glucose, percentage glucose coefficient of variation, mean amplitude of glucose excursion, time in range (TIR, 70-180 mg/dl), time above range (TAR) and time below range metrics, glycaemia risk index and high/low blood glucose index. The presence of cerebral SVD, including lacunes, microbleeds, enlarged perivascular spaces and white matter hyperintensities, was assessed, and the total number of these findings comprised the total cerebral SVD score (0-4). Ordinal logistic regression analyses were performed to examine the association of CGM-derived metrics with the total SVD score.

RESULTS

The median SVD score was 1 (interquartile range 0-2). Higher hyperglycaemic metrics, including mean sensor glucose, TAR >180 mg/dl, TAR >250 mg/dl, and high blood glucose index and glycaemia risk index, were associated with a higher total SVD score. In contrast, a higher TIR (per 10% increase) was associated with a lower total SVD score (odds ratio 0.73, 95% confidence interval 0.56-0.95). Glycated haemoglobin, percentage glucose coefficient of variation, mean amplitude of glucose excursions, time below range and low blood glucose index were not associated with total cerebral SVD scores.

CONCLUSIONS

The hyperglycaemia metrics and TIR, derived from CGM, were associated with cerebral SVD in older adults with type 2 diabetes.

Effect of glycemic variability on infectious outcomes in critically Ill burn patients

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

Adherence to the Mediterranean diet and sleep quality are inter-correlated with flash glucose monitoring (FGM)-measured glycemia among children with type 1 diabetes

BACKGROUND

We examined the inter-correlation between diet quality, objectively measured sleep duration, and subjectively measured sleep quality with flash glucose monitoring (FGM)-measured glycemia among young patients with type 1 diabetes (T1D).

MATERIALS AND METHODS

Following cross-sectional design, Fitbit® accelerometers were used to objectively assess sleep duration, while the validated questionnaires Pittsburgh sleep quality index and Mediterranean diet (MD) adherence were used to subjectively assess sleep quality and diet quality, respectively. Glycated hemoglobin (HbA1c) and FGM-reported glycemia components among children with T1D were assessed as well.

RESULTS

Of the 47 participants surveyed (25 boys, 22 girls, 9.31 ± 2.88 years), the majority reported high HbA1c, good sleep quality, and high adherence to the MD. However, only one-third of the participants reported a healthy sleep duration. Only the sleep latency was significantly (P < 0.05) associated with the time above range level 2 and time below range level 2 (P = 0.048) components of the FGM. A positive correlation (r = 0.309, P = 0.035) was reported between adherence to MD and time in range of the FGM.

CONCLUSIONS

Diet quality and sleep quality are variably inter-correlated with FGM-measured glycemia among young patients with T1D and are suggested to be considered influential factors in FGM-monitored diabetes research on this age group.

Overexpression of miR-199b-5p in Colony Forming Unit-Hill's Colonies Positively Mediates the Inflammatory Response in Subclinical Cardiovascular Disease Model: Metformin Therapy Attenuates Its Expression

Well-controlled type 1 diabetes (T1DM) is characterized by inflammation and endothelial dysfunction, thus constituting a suitable model of subclinical cardiovascular disease (CVD). miR-199b-5p overexpression in murine CVD has shown proatherosclerotic effects. We hypothesized that miR-199b-5p would be overexpressed in subclinical CVD yet downregulated following metformin therapy. Inflammatory and vascular markers were measured in 29 individuals with T1DM and 20 matched healthy controls (HCs). miR-199b-5p expression in CFU-Hill's colonies was analyzed from each study group, and correlations with inflammatory/vascular health indices were evaluated. Significant upregulation of miR-199b-5p was observed in T1DM, which was significantly downregulated by metformin. miR-199b-5p correlated positively with vascular endothelial growth factor-D and c-reactive protein (CRP: nonsignificant). ROC analysis determined miR-199b-5p to define subclinical CVD by discriminating between HCs and T1DM individuals. ROC analyses of HbA1c and CRP showed that the upregulation of miR-199b-5p in T1DM individuals defined subclinical CVD at HbA1c > 44.25 mmol and CRP > 4.35 × 106 pg/mL. Ingenuity pathway analysis predicted miR-199b-5p to inhibit the target genes SIRT1, ETS1, and JAG1. Metformin was predicted to downregulate miR-199b-5p via NFATC2 and STAT3 and reverse its downstream effects. This study validated the antiangiogenic properties of miR-199b-5p and substantiated miR-199b-5p overexpression as a biomarker of subclinical CVD. The downregulation of miR-199b-5p by metformin confirmed its cardio-protective effect.

Diabetes Telemedicine Mediterranean Diet (DiaTeleMed) Study: study protocol for a fully remote randomized clinical trial evaluating personalized dietary management in individuals with type 2 diabetes

BACKGROUND

The Diabetes Telemedicine Mediterranean Diet (DiaTeleMed) Study is a fully remote randomized clinical trial evaluating personalized dietary management in individuals with type 2 diabetes (T2D). The study aims to test the efficacy of a personalized behavioral approach for dietary management of moderately controlled T2D, versus a standardized behavioral intervention that uses one-size-fits-all dietary recommendations, versus a usual care control (UCC). The primary outcome will compare the impact of each intervention on the mean amplitude of glycemic excursions (MAGE).

METHODS

Eligible participants are between 21 and 80 years of age diagnosed with moderately controlled T2D (HbA1c: 6.0 to 8.0%) and managed on lifestyle alone or lifestyle plus metformin. Participants must be willing and able to attend virtual counseling sessions and log meals into a dietary tracking smartphone application (DayTwo), and wear a continuous glucose monitor (CGM) for up to 12 days. Participants are randomized with equal allocation (n = 255, n = 85 per arm) to one of three arms: (1) Personalized, (2) Standardized, or (3) UCC. Measurements occur at 0 (baseline), 3, and 6 months. All participants receive isocaloric energy and macronutrient targets to meet Mediterranean diet guidelines, in addition to 14 intervention contacts over 6 months (4 weekly then 10 biweekly) to cover diabetes self-management education. The first 4 UCC intervention contacts are delivered via synchronous videoconferences followed by educational video links. Participants in Standardized receive the same educational content as those in the UCC arm, following the same schedule. However, all intervention contacts are conducted via synchronous videoconferences, paired with Social Cognitive Theory (SCT)-based behavioral counseling, plus dietary self-monitoring of planned meals using a mobile app that provides real-time feedback on calories and macronutrients. Participants in the Personalized arm receive all elements of the Standardized intervention, in addition to real-time feedback on predicted post-prandial glycemic response (PPGR) to meals and snacks logged into the mobile app.

DISCUSSION

The DiaTeleMed Study aims to address an important gap in the current landscape of precision nutrition by determining the contributions of behavioral counseling and personalized nutrition recommendations on glycemic control in individuals with T2D. The fully remote methodology of the study allows for scalability and innovative delivery of personalized dietary recommendations at a population level.

TRIAL REGISTRATION

ClinicalTrials.gov NCT05046886. Registered on September 16, 2021.

Development and Validation of a Machine Learning Model to Predict Weekly Risk of Hypoglycemia in Patients with Type 1 Diabetes Based on Continuous Glucose Monitoring

Aim: The aim of this study was to develop and validate a prediction model based on continuous glucose monitoring (CGM) data to identify a week-to-week risk profile of excessive hypoglycemia. Methods: We analyzed, trained, and internally tested two prediction models using CGM data from 205 type 1 diabetes patients with long-term CGM monitoring. A binary classification approach (XGBoost) combined with feature engineering deployed on the CGM signals was utilized to predict excessive hypoglycemia risk defined by two targets (time below range [TBR] >4% and the upper TBR 90th percentile limit) of TBR the following week. The models were validated in two independent cohorts with a total of 253 additional patients. Results: A total of 61,470 weeks of CGM data were included in the analysis. The XGBoost models had an area under the receiver operating characteristic curve (ROC-AUC) of 0.83-0.87 (95% confidence interval; 0.83-0.88) in the test dataset. The external validation showed ROC-AUCs of 0.81-0.90. The most discriminative features included the low blood glucose index, the glycemic risk assessment diabetes equation (GRADE), hypoglycemia, the TBR, waveform length, the coefficient of variation and mean glucose during the previous week. This highlights that the pattern of hypoglycemia combined with glucose variability during the past week contains information on the risk of future hypoglycemia. Conclusion: Prediction models based on real-world CGM data can be used to predict the risk of hypoglycemia in the forthcoming week. The models showed good performance in both the internal and external validation cohorts.

Diabetes Telemedicine Mediterranean Diet (DiaTeleMed) Study: study protocol for a fully remote randomized clinical trial evaluating personalized dietary management in individuals with type 2 diabetes

Background

The Diabetes Telemedicine Mediterranean Diet (DiaTeleMed) Study is a fully remote randomized clinical trial evaluating personalized dietary management in individuals with type 2 diabetes (T2D). The study aims to test the efficacy of a personalized behavioral approach for dietary management of moderately-controlled T2D, versus a standardized behavioral intervention that uses one-size-fits-all dietary recommendations, versus a usual care control (UCC). The primary outcome will compare the impact of each intervention on the mean amplitude of glycemic excursions (MAGE).

Methods

Eligible participants are between 21 to 80 years of age diagnosed with moderately-controlled T2D (HbA1c: 6.0-8.0%), and managed on lifestyle alone or lifestyle plus metformin. Participants must be willing and able to attend virtual counseling sessions and log meals into a dietary tracking smartphone application (DayTwo), and wear a continuous glucose monitor (CGM) for up to 12 days. Participants are randomized with equal allocation (n = 255, n = 85 per arm) to one of three arms: 1) Personalized, 2) Standardized, or 3) UCC. Measurements occur at 0 (baseline), 3, and 6 months. All participants receive isocaloric energy and macronutrients targets to meet Mediterranean diet guidelines plus 14 intervention contacts over 6 months (4 weekly then 10 biweekly) to cover diabetes self-management education. The first 4 UCC intervention contacts are delivered via synchronous videoconferences followed by educational video links. Participants in Standardized receive the same education content as UCC on the same schedule. However, all intervention contacts are conducted via synchronous videoconferences, paired with Social Cognitive Theory (SCT)-based behavioral counseling, plus dietary self-monitoring of planned meals using a mobile app that provides real-time feedback on calories and macronutrients. Participants in the Personalized arm receive all elements of the Standardized intervention, plus real-time feedback on predicted post-prandial glycemic response (PPGR) to meals and snacks logged into the mobile app.

Discussion

The DiaTeleMed study will address an important gap in the current landscape of precision nutrition by determining the contributions of behavioral counseling and personalized nutrition recommendations on glycemic control in individuals with T2D. The fully remote methodology of the study allows for scalability and innovative delivery of personalized dietary recommendations at a population level.

Trial registration

The DiaTeleMed Study is registered with ClinicalTrials.gov (Identifier: NCT05046886).

Plant-Based Meat Analogs and Their Effects on Cardiometabolic Health: An 8-Week Randomized Controlled Trial Comparing Plant-Based Meat Analogs With Their Corresponding Animal-Based Foods

BACKGROUND

With the growing popularity of plant-based meat analogs (PBMAs), an investigation of their effects on health is warranted in an Asian population.

OBJECTIVES

This research investigated the impact of consuming an omnivorous animal-based meat diet (ABMD) compared with a PBMAs diet (PBMD) on cardiometabolic health among adults with elevated risk of diabetes in Singapore.

METHODS

In an 8-wk parallel design randomized controlled trial, participants (n = 89) were instructed to substitute habitual protein-rich foods with fixed quantities of either PBMAs (n = 44) or their corresponding animal-based meats (n = 45; 2.5 servings/d), maintaining intake of other dietary components. Low-density lipoprotein (LDL) cholesterol served as primary outcome, whereas secondary outcomes included other cardiometabolic disease-related risk factors (e.g. glucose and fructosamine), dietary data, and within a subpopulation, ambulatory blood pressure measurements (n = 40) at baseline and postintervention, as well as a 14-d continuous glucose monitor (glucose homeostasis-related outcomes; n = 37).

RESULTS

Data from 82 participants (ABMD: 42 and PBMD: 40) were examined. Using linear mixed-effects model, there were significant interaction (time × treatment) effects for dietary trans-fat (increased in ABMD), dietary fiber, sodium, and potassium (all increased in PBMD; P-interaction <0.001). There were no significant effects on the lipid-lipoprotein profile, including LDL cholesterol. Diastolic blood pressure (DBP) was lower in the PBMD group (P-interaction=0.041), although the nocturnal DBP dip markedly increased in ABMD (+3.2% mean) and was reduced in PBMD (-2.6%; P-interaction=0.017). Fructosamine (P time=0.035) and homeostatic model assessment for β-cell function were improved at week 8 (P time=0.006) in both groups. Glycemic homeostasis was better regulated in the ABMD than PBMD groups as evidenced by interstitial glucose time in range (ABMD median: 94.1% (Q1:87.2%, Q3:96.7%); PBMD: 86.5% (81.7%, 89.4%); P = 0.041). The intervention had no significant effect on the other outcomes examined.

CONCLUSIONS

An 8-wk PBMA diet did not show widespread cardiometabolic health benefits compared with a corresponding meat based diet. Nutritional quality is a key factor to be considered for next generation PBMAs. This trial was registered at https://clinicaltrials.gov/as NCT05446753.

Diet and Meal Pattern Determinants of Glucose Levels and Variability in Adults with and without Prediabetes or Early-Onset Type 2 Diabetes: A Pilot Study

This observational pilot study examined the association between diet, meal pattern and glucose over a 2-week period under free-living conditions in 26 adults with dysglycemia (D-GLYC) and 14 with normoglycemia (N-GLYC). We hypothesized that a prolonged eating window and late eating occasions (EOs), along with a higher dietary carbohydrate intake, would result in higher glucose levels and glucose variability (GV). General linear models were run with meal timing with time-stamped photographs in real time, and diet composition by dietary recalls, and their variability (SD), as predictors and glucose variables (mean glucose, mean amplitude of glucose excursions [MAGE], largest amplitude of glucose excursions [LAGE] and GV) as dependent variables. After adjusting for calories and nutrients, a later eating midpoint predicted a lower GV (β = -2.3, SE = 1.0, p = 0.03) in D-GLYC, while a later last EO predicted a higher GV (β = 1.5, SE = 0.6, p = 0.04) in N-GLYC. A higher carbohydrate intake predicted a higher MAGE (β = 0.9, SE = 0.4, p = 0.02) and GV (β = 0.4, SE = 0.2, p = 0.04) in N-GLYC, but not D-GLYC. In summary, our data suggest that meal patterns interact with dietary composition and should be evaluated as potential modifiable determinants of glucose in adults with and without dysglycemia. Future research should evaluate causality with controlled diets.

A low-glucose eating pattern is associated with improvements in glycemic variability among women at risk for postmenopausal breast cancer: an exploratory analysis

Background

High glycemic variability (GV) is a biomarker of cancer risk, even in the absence of diabetes. The emerging concept of chrononutrition suggests that modifying meal timing can favorably impact metabolic risk factors linked to diet-related chronic disease, including breast cancer. Here, we examined the potential of eating when glucose levels are near personalized fasting thresholds (low-glucose eating, LGE), a novel form of timed-eating, to reduce GV in women without diabetes, who are at risk for postmenopausal breast cancer.

Methods

In this exploratory analysis of our 16-week weight loss randomized controlled trial, we included 17 non-Hispanic, white, postmenopausal women (average age = 60.7 ± 5.8 years, BMI = 34.5 ± 6.1 kg/m2, HbA1c = 5.7 ± 0.3%). Participants were those who, as part of the parent study, provided 3-7 days of blinded, continuous glucose monitoring data and image-assisted, timestamped food records at weeks 0 and 16. Pearson's correlation and multivariate regression were used to assess associations between LGE and GV, controlling for concurrent weight changes.

Results

Increases in LGE were associated with multiple unfavorable measures of GV including reductions in CGM glucose mean, CONGA, LI, J-Index, HBGI, ADDR, and time spent in a severe GV pattern (r = -0.81 to -0.49; ps < 0.044) and with increases in favorable measures of GV including M-value and LBGI (r = 0.59, 0.62; ps < 0.013). These associations remained significant after adjusting for weight changes.

Conclusion

Low-glucose eating is associated with improvements in glycemic variability, independent of concurrent weight reductions, suggesting it may be beneficial for GV-related disease prevention. Further research in a larger, more diverse sample with poor metabolic health is warranted.Clinical trial registration: ClinicalTrials.gov, NCT03546972.

Metabolic health tracking using Ultrahuman M1 continuous glucose monitoring platform in non- and pre-diabetic Indians: a multi-armed observational study

Continuous glucose monitoring (CGM) device adoption in non- and pre-diabetics for preventive healthcare has uncovered a paucity of benchmarking data on glycemic control and insulin resistance for the high-risk Indian/South Asian demographic. Furthermore, the correlational efficacy between digital applications-derived health scores and glycemic indices lacks clear supportive evidence. In this study, we acquired glycemic variability (GV) using the Ultrahuman (UH) M1 CGM, and activity metrics via the Fitbit wearable for Indians/South Asians with normal glucose control (non-diabetics) and those with pre-diabetes (N = 53 non-diabetics, 52 pre-diabetics) for 14 days. We examined whether CGM metrics could differentiate between the two groups, assessed the relationship of the UH metabolic score (MetSc) with clinical biomarkers of dysglycemia (OGTT, HbA1c) and insulin resistance (HOMA-IR); and tested which GV metrics maximally correlated with inflammation (Hs-CRP), stress (cortisol), sleep, step count and heart rate. We found significant inter-group differences for mean glucose levels, restricted time in range (70-110 mg/dL), and GV-by-SD, all of which improved across days. Inflammation was strongly linked with specific GV metrics in pre-diabetics, while sleep and activity correlated modestly in non-diabetics. Finally, MetSc displayed strong inverse relationships with insulin resistance and dysglycemia markers. These findings present initial guidance GV data of non- and pre-diabetic Indians and indicate that digitally-derived metabolic scores can positively influence glucose management.

[Features of glycemic variability in men with different types of obesity]

BACKGROUND

Obesity generally determines the metabolic basis for the development of type 2 diabetes. Therefore the analysis of glycemic variability in obese individuals, especially in its different phenotypes, acquires particular relevance.

AIM

To investigate the features of glycemic variability in men with different adipose tissue distribution topography within usual dietary conditions.

MATERIALS AND METHODS

The study enrolled 43 men aged 25-65 years. Group 1 (n=17) represented obese men with subcutaneous fat distribution (SFD) while group 2 (n=16) consisted of obese men with abdominal fat distribution (AFD) and group 3 (comparator) included 10 male subjects with normal body weight (NBW). A 2-day continuous glucose monitoring (CGM) under condition of usual diet, work and physical activity was performed in each study subject. A number of parameters, indices and ratios had been assessed describing glycemic variability (GV) for daytime (6.00-23.59) and night (0.00-5.59) hours.

RESULTS

Comparative analysis of key parameters and indices describing daytime and night GV in NBW and obese men without fat distribution adjustment did not reveal statistically significant differences. After fat distribution adjustment  significantly higher mean glucose levels, standard deviation of glycemic levels and coefficient of variation were found in AFD group; also statistically significant differences were revealed in CONGA index and J-index. An analysis of the LBGI and HBGI indices that are respectively reflecting the risks of hypo- and hyperglycemia showed that the LBGI index was higher in obese men with SFD while the НBGI index was higher in men with AFD. A comparative analysis of GV parameters showed that daytime indicators values were significantly higher relative to nighttime. However the ambiguous changes in the mean glucose levels was found between study groups. Specifically in NBW men daytime and nighttime glycemia didn't differ, whereas in AFD group there was a trend to decrease in night glucose levels (p = 0.08) while in men with SFD night decrease in glycemia became statistically significant (p=0.005).

CONCLUSION

Results of glycemic variability assessment in obese men suggest that abdominal and subcutaneous types of fat distribution are associated with specific features of carbohydrate metabolism and determine different risk levels for developing type 2 diabetes in patients with AFD and SFD.

Glycemic variability in diagnosis of gestational diabetes as predictor of pharmacological treatment

INTRODUCTION

To establish whether glycemic variability (GV) parameters used when gestational diabetes mellitus (GDM) has been diagnosed could help predict the probability that a patient will need pharmacological treatment, and to analyze the link of these parameters to the development of maternal-fetal complications.

MATERIALS AND METHODS

A prospective study of 87 women with GDM who underwent retrospective continuous glucose monitoring (CGM) for six days between weeks 26 and 32 of gestation, following diagnosis. The mean glycemia levels and GV variables were analyzed together with their link to maternal-fetal complications, and the need for pharmacological treatment. ROC (receiver operating characteristic) curves were developed to determine validity to detect the need for pharmacological treatment.

RESULTS

Patients with higher mean glycemia (p < 0.001) and continuous overlapping of net glycemic action in a period of n-hours (CONGAn) (p = 0.001) required pharmacological treatment. The ROC curves showed cut-off points of 98.81 mg/dL for mean glycemia, and 86.70 mg/dL for CONGAn, with 83.3% sensitivity and 67.8% specificity for both parameters. No relation between the GV parameters and development of maternal-fetal complications was observed.

CONCLUSIONS

The use of CGM, once GDM is diagnosed, enables us to identify those patients who would benefit from closer monitoring during gestation, and facilitate a speedier take-up of pharmacological treatment. However, prospective studies involving a higher number of patients are needed, as well as a cost assessment for recommending the use of CGM following GDM diagnosis.

Difference on Glucose Profile From Continuous Glucose Monitoring in People With Prediabetes vs. Normoglycemic Individuals: A Matched-Pair Analysis

INTRODUCTION

Comprehensive characteristics of the glycemic profile for prediabetes derived by continuous glucose monitoring (CGM) are unknown. We evaluate the difference of CGM profiles between individuals with prediabetes and normoglycemic individuals, including the response to oral glucose tolerance test (OGTT).

METHODS

Individuals with prediabetes matched for age, sex, and BMI with normoglycemic individuals were instructed to use professional CGM for 1 week. OGTT was performed on the second day. The primary outcomes were percentages of glucose readings time below range (TBR): <54 or <70 mg/dL, time in range (TIR): 70 to 180 mg/dL, and time above range (TAR): >180 or >250 mg/dL. Area under the curve (AUC) was calculated following the OGTT. Glucose variability was depicted by coefficient of variation (CV), SD, and mean amplitude of glucose excursion (MAGE). Wilcoxon sign-ranked test, McNemar mid P-test and linear regression models were employed.

RESULTS

In all, 36 participants (median age 51 years; median body mass index [BMI] = 26.4 kg/m2) formed 18 matched pairs. Statistically significant differences were observed for 24-hour time in range (TIR; median 98.5% vs. 99.9%, P = .013), time above range (TAR) >180 mg/dl (0.4% vs. 0%, P = .0062), and 24-hour mean interstitial glucose (113.8 vs. 108.8 mg/dL, P = .0038) between people with prediabetes compared to normoglycemic participants. Statistically significant differences favoring the normoglycemic group were found for glycemic variability indexes (median CV 15.2% vs. 11.9%, P = .0156; median MAGE 44.3 vs. 33.3 mg/dL, P = 0.0043). Following OGTT, the AUC was significantly lower in normoglycemic compared to the prediabetes group (median 18615.3 vs. 16370.0, P = .0347 for total and 4666.5 vs. 2792.7, P = .0429 for incremental 2-hour post OGTT).

CONCLUSION

Individuals with prediabetes have different glucose profiles compared to normoglycemic individuals. CGM might be helpful in individuals with borderline glucose values for a more accurate reclassification.

Neuromuscular electrical stimulation changes glucose, but not its variability in type 2 diabetes: a randomized clinical trial

Neuromuscular electrical stimulation (NMES) can be an alternative to conventional exercising. This randomized clinical trial evaluated the effect of NMES in type 2 diabetes patients. Twenty-eight individuals with type 2 diabetes were assigned to NMES (n=14) or NMES-placebo (n=14) applied to knee extensor muscles for 60 minutes. Glucose variability, microvascular function and endothelial function were evaluated through continuous glucose monitoring system, near infrared spectroscopy and flow-mediated dilatation, respectively. Glucose levels (mg/dl) decreased 2h (184 ± 11 vs 223 ±15), 3h (179 ± 12 vs 219 ±14) and 4h (177 ± 12 vs 212 ±12) after NMES, in comparison to NMES-placebo. No differences in glucose variability were found: coefficient of variation (%) at 0-6h (11.4±1.3 vs 11.4±1.2), 6-12h (9.8±1.0 vs 11.6±1.6), 12-18h (15.5±2.0 vs 11.4±2.1), 18-24h (12.8±2.3 vs 10.0±1.6); standard deviation (mg/dl) at 0-6h (21.6±2 vs 24.6±3.5), 6-12h (19.5±1.8 vs 20.3±2.8), 12-18h (29.9±3.5 vs 21.3±2.8),18-24h (22.8±4.1 vs 16.6±2.0) and mean amplitude of glycemic excursions (mg/dl) 54.9±25.0 vs 70.3±35.7. Endothelial and microvascular functions did not change. In conclusion, one acute NMES session was strong enough to trigger glucose reduction in individuals with type 2 DM, but it failed to induce any significant change in glucose variability, endothelial and microvascular functions.

Association of physical activity metrics with glucose variability in people with type 1 diabetes: A cross‐sectional study

This study aims to investigate the association of physical activity metrics with measures of glucose variability in people with type 1 diabetes. From August 2019 to January 2022, people with type 1 diabetes, attending clinics or participating in ongoing research at the Dasman Diabetes Institute in Kuwait, were invited to participate in the study. Physical activity was measured over a 7‐day period using a wrist‐worn accelerometer, and glucose variability data were measured by continuous glucose monitoring (CGM) of the same period. Three hundred and eleven participants were recruited (age 33 (10) years, BMI 27(5) kg/m2 and n  = 311 (169 female and 142 male)). Overall physical activity levels were not associated with any measure of glucose variability. The intensity gradient, which measures the distribution of physical activity intensity, was negatively associated with mean glucose (−1.01(−0.28, −1.74) and p  = 0.007), CONGA (−1.00(−0.28, −1.72) and p  = 0.007), J‐index (−11.7(−2.23, 21.2) and p  = 0.016), HBGI (−2.73(−0.44, −5.02) and p  = 0.020), GRADE (−2.27(−0.59, −3.95), p  = 0.009) and GRADE – euglycaemia (−4.26(−0.46, −8.06) and p  = 0–029) and the M‐value (−4.41 (−0.05, −8.77) and p  = 0.049). Overall physical activity remains important, but it may be worth recommending people with type 1 diabetes to spend proportionately more of their day doing moderate to higher intensity physical activity, although this remains to be confirmed in an appropriately designed trial.

Physical activity is recommended to people with type 1 diabetes due to its broad health benefits. The relationship between physical activity and glucose variability is unclear. The current study shows that overall physical activity levels are not associated with measures of glucose variability, but spending proportionately more of their day doing moderate to higher intensity physical activity was associated with better glucose variability.

Continuous glucose monitoring captures glycemic variability in obesity after sleeve gastrectomy: A prospective cohort study

Objective

HbA1c is an insensitive marker for assessing real-time dysglycemia in obesity. This study investigated whether 1-h plasma glucose level (1-h PG) ≥155 mg/dL (8.6 mmol/L) during an oral glucose tolerance test (OGTT) and continuous glucose monitoring (CGM) measurement of glucose variability (GV) better reflected dysglycemia than HbA1c after weight loss from metabolic and bariatric surgery.

Methods

This was a prospective cohort study of 10 participants with type 2 diabetes compared with 11 participants with non-diabetes undergoing sleeve gastrectomy (SG). At each research visit; before SG, and 6 weeks and 6 months post-SG, body weight, fasting lipid levels, and PG and insulin concentrations during an OGTT were analyzed. Mean amplitude of glycemic excursions (MAGE), a CGM-derived GV index, was analyzed.

Results

The 1-h PG correlated with insulin resistance markers, triglyceride/HDL ratio and triglyceride glucose index in both groups before surgery. At 6 months, SG caused 22% weight loss in both groups. Despite a reduction in HbA1c by 3.0 ± 1.3% in the diabetes group (p < 0.01), 1-h PG, and MAGE remained elevated, and the oral disposition index, which represents pancreatic β-cell function, remained reduced in the diabetes group when compared to the non-diabetes group.

Conclusions

Elevation of GV markers and reduced disposition index following SG-induced weight loss in the diabetes group underscores persistent β-cell dysfunction and the potential residual risk of diabetes complications.

Statistical Packages and Algorithms for the Analysis of Continuous Glucose Monitoring Data: A Systematic Review

BACKGROUND

Continuous glucose monitoring (CGM) measures glucose levels every 1 to 15 minutes and is widely used in clinical and research contexts. Statistical packages and algorithms reduce the time-consuming and error-prone process of manually calculating CGM metrics and contribute to standardizing CGM metrics defined by international consensus. The aim of this systematic review is to summarize existing data on (1) statistical packages for retrospective CGM data analysis and (2) statistical algorithms for retrospective CGM analysis not available in these statistical packages.

METHODS

A systematic literature search in PubMed and EMBASE was conducted on September 19, 2023. We also searched Google Scholar and Google Search until October 12, 2023 as sources of gray literature and performed reference checks of the included literature. Articles in English and Danish were included. This systematic review is registered with PROSPERO (CRD42022378163).

RESULTS

A total of 8731 references were screened and 46 references were included. We identified 23 statistical packages for the analysis of CGM data. The statistical packages could calculate many metrics of the 2022 CGM consensus and non-consensus CGM metrics, and 22/23 (96%) statistical packages were freely available. Also, 23 statistical algorithms were identified. The statistical algorithms could be divided into three groups based on content: (1) CGM data reduction (eg, clustering of CGM data), (2) composite CGM outcomes, and (3) other CGM metrics.

CONCLUSION

This systematic review provides detailed tabular and textual up-to-date descriptions of the contents of statistical packages and statistical algorithms for retrospective analysis of CGM data.

Glycemic variability and hypoglycemia before and after Roux-en-Y Gastric Bypass and Sleeve Gastrectomy - A cohort study of females without diabetes

BACKGROUND

Roux-en-Y gastric bypass (RYGB) and sleeve gastrectomy (SG) lead to lower fasting glucose concentrations, but might cause higher glycemic variability (GV) and increased risk of hypoglycemia. However, it has been sparsely studied in patients without preoperative diabetes under normal living conditions.

OBJECTIVES

To study 24-hour interstitial glucose (IG) concentrations, GV, the occurrence of hypoglycemia and dietary intake before and after laparoscopic RYGB and SG in females without diabetes.

SETTING

Outpatient bariatric units at a community and a university hospital.

METHODS

Continuous glucose monitoring and open-ended food recording over 4 days in 4 study periods: at baseline, during the preoperative low-energy diet (LED) regimen, and at 6 and 12 months postoperatively.

RESULTS

Of 47 patients included at baseline, 83%, 81%, and 79% completed the remaining 3 study periods. The mean 24-hour IG concentration was similar during the preoperative LED regimen and after surgery and significantly lower compared to baseline in both surgical groups. GV was significantly increased 6 and 12 months after surgery compared to baseline. The self-reported carbohydrate intake was positively associated with GV after surgery. IG concentrations below 3.9 mmol/L were observed in 14/25 (56%) of RYGB- and 9/12 (75%) of SG-treated patients 12 months after surgery. About 70% of patients with low IG concentrations also reported hypoglycemic symptoms.

CONCLUSIONS

The lower IG concentration in combination with the higher GV after surgery, might create a lower margin to hypoglycemia. This could help explain the increased occurrence of hypoglycemic episodes after RYGB and SG.

Postbariatric hypoglycemia, abdominal pain and gastrointestinal symptoms after Roux-en-Y gastric bypass explored by continuous glucose monitoring

BACKGROUND

Abdominal pain and postbariatric hypoglycemia (PBH) are common after bariatric surgery.

OBJECTIVES

This study aimed to explore the potential relationship between abdominal pain, gastrointestinal symptoms, and PBH more than a decade after Roux-en-Y gastric bypass (RYGB) and whether continuous glucose monitoring (CGM) with dietary intervention has an educational role in reducing symptoms.

SUBJECTS

At two public hospitals in Norway (one University Hospital) 22 of 46 invited patients who reported abdominal pain more than weekly took part. Recruited from a prospective follow-up study of 546 patients 14.5 years after RYGB.

METHODS

They used a CGM for two 14-day periods, with a dietary intervention between periods. The Gastrointestinal Symptom Rating Scale (GSRS) and the Dumping Severity Score (DSS) questionnaires were completed at the start and end of the study.

RESULTS

The 22 women had preoperative age 39.6 ± 7.7 years and body mass index (BMI) 42.0 ± 4.0 kg/m2, present age 54.6 ± 7.7 years and BMI 29.8 ± 4.8 kg/m2. The total GSRS score and DSS of early dumping decreased after the diet intervention. The number of events with Level 1 (<3.9 mmol/L) or Level 2 (<3.0 mmol/L) hypoglycemia did not change in the second period. Half of the patients had fewer, three had unchanged, and eight had more frequent events with Level 1 hypoglycemia after the intervention. Ten patients had Level 2 hypoglycemia.

CONCLUSION

Though inconclusive findings, a personalized dietary intervention reduces GSRS. This intervention was accompanied by lower mean absolute glucose in patients with recurrent abdominal pain after bariatric surgery. However, further studies are needed to explore the benefits of CGM in this setting.

Glycaemic variability, assessed with continuous glucose monitors, is associated with diet, lifestyle and health in people without diabetes

Background

Continuous glucose monitors (CGMs) provide high-frequency information regarding daily glucose variation and are recognised as effective for improving glycaemic control in individuals living with diabetes. Despite increased use in individuals with non-diabetic blood glucose concentrations (euglycemia), their utility as a health tool in this population remains unclear.

Objectives

To characterise variation in time in range (TIR) and glycaemic variability in large populations without diabetes or impaired glucose tolerance; describe associations between CGM-derived glycaemic metrics and metabolic and cardiometabolic health traits; identify key diet and lifestyle factors associated with TIR and glycaemic variability.

Design

Glycaemic variability (coefficient of variation) and time spent in both the ADA secondary target range (TIRADA; 3.9-7.8 mmol/L) and a more stringent range (TIR3.9-5.6; 3.9-5.6 mmol/L) were calculated during free-living in PREDICT 1, PREDICT 2, and PREDICT 3 euglycaemic community-based volunteer cohorts. Associations between CGM derived glycaemic metrics, markers of cardiometabolic health, diet (food frequency questionnaire and logged diet records), diet-habits, and lifestyle were explored.

Results

Data from N=4135 participants (Mean SD; Age: 47 12 y; Sex: 83% Female, BMI: 27 6 kg/m2). Median glycaemic variability was 14.8% (IQR 12.6-17.6%), median TIRADA was 95.8% (IQR 89.6-98.6%) and TIR3.9-5.6 was 75.0% (IQR 64.6-82.8%). Greater TIR3.9-5.6 was associated with lower HbA1c, ASCVD 10y risk and HOMA-IR (all p < 0.05). Lower glycaemic variability was associated with lower % energy derived from carbohydrate (rs: 0.17, p < 0.01), ultra-processed foods (NOVA 4, % EI; rs: 0.12, p = 0.01) and a longer overnight fasting duration (rs: -0.10, p = 0.01).

Conclusions

A stringent TIR target provides sensitivity to detect changes in HOMA-IR, ASCVD 10 y risk and HbA1c that were not detected using ADA secondary targets. Associations among TIR, glycaemic variability, dietary intake (e.g. carbohydrate and protein) and habits (e.g. nocturnal fasting duration) highlight potential strategic targets to improve glycaemic metrics derived from continuous glucose monitors.

Serum Cytokines and Growth Factors in Subjects with Type 1 Diabetes: Associations with Time in Ranges and Glucose Variability

The detrimental effect of hyperglycemia and glucose variability (GV) on target organs in diabetes can be implemented through a wide network of regulatory peptides. In this study, we assessed a broad panel of serum cytokines and growth factors in subjects with type 1 diabetes (T1D) and estimated associations between concentrations of these molecules with time in ranges (TIRs) and GV. One hundred and thirty subjects with T1D and twenty-seven individuals with normal glucose tolerance (control) were included. Serum levels of 44 cytokines and growth factors were measured using a multiplex bead array assay. TIRs and GV parameters were derived from continuous glucose monitoring. Subjects with T1D compared to control demonstrated an increase in concentrations of IL-1β, IL-1Ra, IL-2Rα, IL-3, IL-6, IL-7, IL-12 p40, IL-16, IL-17A, LIF, M-CSF, IFN-α2, IFN-γ, MCP-1, MCP-3, and TNF-α. Patients with TIR ≤ 70% had higher levels of IL-1α, IL-1β, IL-6, IL-12 p70, IL-16, LIF, M-CSF, MCP-1, MCP-3, RANTES, TNF-α, TNF-β, and b-NGF, and lower levels of IL-1α, IL-4, IL-10, GM-CSF, and MIF than those with TIR > 70%. Serum IL-1β, IL-10, IL-12 p70, MCP-1, MCP-3, RANTES, SCF, and TNF-α correlated with TIR and time above range. IL-1β, IL-8, IL-10, IL-12 p70, MCP-1, RANTES, MIF, and SDF-1α were related to at least one amplitude-dependent GV metric. In logistic regression models, IL-1β, IL-4, IL-10, IL-12 p70, GM-CSF, HGF, MCP-3, and TNF-α were associated with TIR ≤ 70%, and MIF and PDGF-BB demonstrated associations with coefficient of variation values ≥ 36%. These results provide further insight into the pathophysiological effects of hyperglycemia and GV in people with diabetes.

Effects of reducing free sugars on 24-hour glucose profiles and glycemic variability in subjects without diabetes

Background

The Western diet, especially beverages and high processed food products, is high in sugars which are associated with the development of obesity and diabetes. The reduction of refined carbohydrates including free and added sugars improves glycemic control in individuals with diabetes, but the data regarding effects in subjects without diabetes are limited.

Objective

This study aimed to evaluate the effects of reducing free sugar intake on 24-h glucose profiles and glycemic variability using continuous glucose monitoring (CGM).

Methods

In the randomized controlled study, 21 normal weight and overweight/obese subjects (BMI 18-40 kg/m2) without diabetes were assigned to a 4-week reduced-sugar (RS) diet or control diet after a 2-week baseline phase. During the baseline phase, all participants were advised not to change their habitual diet. During the intervention phase, RS participants were asked to avoid added sugar and white flour products, whereas participants of the control group were requested to proceed their habitual diet. Anthropometric parameters and HbA1c were assessed before and at the end of the intervention phase. Interstitial glucose was measured using continuous glucose monitoring (CGM), and the food intake was documented by dietary records for 14 consecutive days during the baseline phase and for the first 14 consecutive days during the intervention phase. Mean 24-h glucose as well as intra- and inter-day indices of glucose variability, i.e., standard deviation (SD) around the sensor glucose level, coefficient of variation in percent (CV), mean amplitude of glucose excursions (MAGE), continuous overlapping net glycemic action (CONGA), and mean absolute glucose (MAG), were calculated for the baseline and intervention phases.

Results

During the intervention, the RS group decreased the daily intake of sugar (i.e., -22.4 ± 20.2 g, -3.28 ± 3.61 EN %), total carbohydrates (-6.22 ± 6.92 EN %), and total energy intake (-216 ± 108 kcal) and increased the protein intake (+2.51 ± 1.56 EN %) compared to the baseline values, whereby this intervention-induced dietary changes differed from the control group. The RS group slightly reduced body weight (-1.58 ± 1.33 kg), BMI, total fat, and visceral fat content and increased muscle mass compared to the baseline phase, but these intervention-induced changes showed no differences in comparison with the control group. The RS diet affected neither the 24-h mean glucose levels nor intra- and inter-day indices of glucose variability, HbA1c, or diurnal glucose pattern in the within- and between-group comparisons.

Conclusion

The dietary reduction of free sugars decreases body weight and body fat which may be associated with reduced total energy intake but does not affect the daily mean glucose and glycemic variability in individuals without diabetes.

Clinical trial registration

German Clinical Trials Register (DRKS); identifier: DRKS00026699.

Longitudinal MRI Shows Progressive Decline in Pancreas Size and Altered Pancreas Shape in Type 1 Diabetes

CONTEXT

Individuals with type 1 diabetes (T1D) have a smaller pancreas, but longitudinal changes in pancreas size and shape are unclear.

OBJECTIVE

We monitored changes in pancreas size and shape after diagnosis with T1D.

DESIGN

We conducted a prospective cohort study at an academic medical center between 2014 and 2022.

PATIENTS AND HEALTHY CONTROLS

Individuals with T1D (n = 91) or controls (n = 90) underwent magnetic resonance imaging (MRI) of the pancreas, including longitudinal MRI in 53 individuals with new-onset T1D.

INTERVENTION

Interventions included MRI and continuous glucose monitoring (CGM).

MAIN OUTCOME MEASURES

Pancreas size and shape were measured from MRI. For participants who used CGM, measures of glycemic variability were calculated.

RESULTS

On longitudinal imaging, pancreas volume and pancreas volume index normalized for body weight declined during the first year after diagnosis. Pancreas volume index continued to decline through the fifth year after diagnosis. A cross-sectional study of individuals with diabetes duration up to 60 years demonstrated that pancreas size in adults negatively correlated with age and disease duration, whereas pancreas volume and pancreas volume index remained stable in controls. Pancreas volume index correlated inversely with low blood glucose index, a measure of risk for hypoglycemia. Pancreas shape was altered in individuals with T1D and further diverged from controls over the first 5 years after diagnosis. Pancreas size and shape are altered in nondiabetic individuals at genetic risk for T1D. Combined pancreas size and shape analysis better distinguished the pancreas of individuals with T1D from controls than size alone.

CONCLUSIONS

Pancreas size declines most rapidly near the clinical diagnosis of T1D and continues to decline throughout adulthood. Declines in pancreas size are accompanied by changes in pancreas shape.

Efficacy and safety of closed-loop control system for type one diabetes in adolescents a meta analysis

This meta-analysis compares the efficacy and safety of Closed-Loop Control (CLC) to Sensor-Augmented Insulin Pump (SAP) for adolescent patients with Type 1 Diabetes Mellitus (T1DM). Eleven randomized-controlled trials were included with a total of 570 patients, from a total of 869 articles found adhering to PRISMA guidelines. The efficacy of the therapies were evaluated from the day, night and during physical activities monitoring of the of the mean blood glucose (BG), Time In Range (TIR), and Standard Deviation (SD) of the glucose variability. The safety measure of the therapies, was assessed from the day and night recording of the hypoglycemic and hyperglycemic events occurred. Pooled results of comparison of mean BG values for day, night and physical activities, - 4.33 [- 6.70, - 1.96] (P = 0.0003), - 16.61 [- 31.68, - 1.54] (P = 0.03) and - 8.27 [- 19.52, 2.99] (P = 0.15). The monitoring for day, night and physical activities for TIR - 13.18 [- 19.18, - 7.17] (P < 0.0001), - 15.36 [- 26.81, - 3.92] (P = 0.009) and - 7.39 [- 17.65, 2.87] (P = 0.16). The day and night results of SD of glucose variability was - 0.40 [- 0.79, - 0.00] (P = 0.05) and - 0.86 [- 2.67, 0.95] (P = 0.35). These values shows the superiority of CLC system in terms of efficacy. The safety evaluation, of the day, night and physical activities observations of average blood glucose goal hypoglycemic events - 0.54 [- 1.86, 0.79] (P = 0.43), 0.04 [- 0.20, 0.27] (P = 0.77) and 0.00 [- 0.25, 0.25] (P = 1.00) and hyperglycemic events - 0.04 [- 0.20, 0.27] (P = 0.77), - 7.11 [- 12.77, - 1.45] (P = 0.01) and - 0.00 [- 0.10, 0.10] (P = 0.97), highlights the commendable safety factor of CLC. The CLC systems can be considered as an ideal preference in the management of adolescents with type 1 diabetes to be used during a 24 h basis.

Association of carotid atherosclerosis and peripheral artery disease in patients with type 2 diabetes: risk factors and biomarkers

BACKGROUND: Carotid atherosclerosis (CA) and lower extremity peripheral artery disease (PAD) is a common and potentially life-threatening comorbidity in diabetes.

AIM: to determine risk factors and biomarkers of the association of CA and PAD in patients with type 2 diabetes.

MATERIALS AND METHODS: A single-center cross-sectional comparative study was carried out. Three hundred ninety one patients with type 2 diabetes were included. Duplex ultrasound of carotid and low limb arteries, screening/monitoring of diabetic complications and associated diseases, and assessment of glycemic control, biochemical and coagulation parameters were performed. Factors involved in vascular wall remodeling, including calponin-1, relaxin, L-citrulline, matrix metalloproteinase-2 and -3, were measured in blood serum by ELISA.

RESULTS: The signs of CA and PAD were observed in 330 and 187 patients respectively. In 178 patients, both CA and PAD were revealed. The risk of combined involvement of carotid and lower extremity arteries was higher in patients with diabetic retinopathy (OR=2.57, p<0.001), chronic kidney disease (OR=4.48, p<0.001), history of myocardial infarction (OR=5.09, p<0.001), coronary revascularization (OR=4.31, p<0.001) or cerebrovascular accident (OR=3.07, p<0.001). In ROC-analysis, age ≥65.5 years (OR=3.43, p<0.001), waist-to-hip ratio ≥0.967 (OR=3.01, p=0.001), diabetes duration ≥12.5 years (OR=3.7, p<0.001), duration of insulin therapy ≥4.5 years (OR=3.05, p<0.001), duration of arterial hypertension ≥16.5 years (OR=1.98, p=0.002), serum L-citrulline ≥68 µmol/l (OR=3.82, p=0.003), and mean amplitude of glucose excursions ≥3.72 mmol/l (OR=1.79, p=0.006) were the risk factors for atherosclerosis of two vascular beds. In multivariate logistic regression analysis, age, diabetes duration and waist-to-hip ratio were independent risk factors for association of CA and PAD (p=0.005, p=0.0003, and p=0.004 respectively).

CONCLUSION: In subjects with type 2 diabetes, carotid and lower extremity atherosclerotic disease is associated with age, diabetes duration, abdominal obesity, microvascular and macrovascular complications, glucose variability, and high serum levels of L-citrulline.

Continuous Glucose Monitoring in Patients Following Simultaneous Pancreas-Kidney Transplantation: Time in Range and Glucose Variability

Simultaneous pancreas-kidney transplantation (SPKT) can improve long-term patient survival and restore endogenous insulin secretion in recipients with type 1 diabetes (T1D). There are currently few data on glucose fluctuations assessed by continuous glucose monitoring (CGM) after SPKT. Aim: to evaluate CGM-derived time in range (TIR) and glucose variability (GV) in patients with T1D and functioning pancreatic grafts after SPKT. Fifty-four CGM recordings from 43 patients, 15 men and 28 women, aged 34 (31; 39) years were analyzed. Time since SKPT was up to 1 year (group 1, n = 13), from 1 to 5 years (group 2, n = 15), and from 5 to 12 years (group 3, n = 26). TIR (3.9-10 mmol/L), Time Above Range (TAR), Time Below Range (TBR), and GV parameters were estimated. There were no differences in mean glucose (5.5 [5.1; 6.2], 5.9 [5.4; 6.2], and 5.9 [5.6; 6.7] mmol/L), TIR (97.6 [92.8-99.1], 97.2 [93.2; 99.1], and 97.5 [93.4; 99]%); TAR (0, 1.8 [1.3; 3.7], and 2.5 [2; 5]%), TBR (5 [3.3; 12.7], 4.1 [2.2; 10.1], and 3.5 [1.3; 6.5]%) and GV parameters between three groups (all p > 0.05). Thus, recipients with functioning pancreatic grafts demonstrate remarkably high TIR and low GV after SPKT.

Continuous glucose monitoring-based composite metrics: a review and assessment of performance in recent-onset and long-duration type 1 diabetes

This study examined correlations between continuous glucose monitoring (CGM)-based composite metrics and standard glucose metrics within CGM data sets from individuals with recent-onset and long-duration type 1 diabetes. First, a literature review and critique of published CGM-based composite metrics was undertaken. Second, composite metric results were calculated for the two CGM data sets and correlations with six standard glucose metrics were examined. Fourteen composite metrics met selection criteria; these metrics focused on overall glycemia (n = 8), glycemic variability (n = 4), and hypoglycemia (n = 2), respectively. Results for the two diabetes cohorts were similar. All eight metrics focusing on overall glycemia strongly correlated with glucose time in range; none strongly correlated with time below range. The eight overall glycemia-focused and two hypoglycemia-focused composite metrics were all sensitive to automated insulin delivery therapeutic intervention. Until a composite metric can adequately capture both achieved target glycemia and hypoglycemia burden, the current two-dimensional CGM assessment approach may offer greatest clinical utility.

Continuous Glucose Monitoring Reveals Perioperative Hypoglycemia in Most Patients With Diabetes Undergoing Major Surgery: A Prospective Cohort Study

OBJECTIVE

To investigate the frequency and duration of hypo- and hyperglycemia, assessed by continuous glucose monitoring (CGM) during and after major surgery, in departments with implemented diabetes care protocols.

SUMMARY BACKGROUND DATA

Inadequate glycemic control in the perioperative period is associated with serious adverse events, but monitoring currently relies on point blood glucose measurements, which may underreport glucose excursions.

METHODS

Adult patients without (A) or with diabetes [non-insulin-treated type 2 (B), insulin-treated type 2 (C) or type 1 (D)] undergoing major surgery were monitored using CGM (Dexcom G6), with an electrochemical sensor in the interstitial fluid, during surgery and for up to 10 days postoperatively. Patients and health care staff were blinded to CGM values, and glucose management adhered to the standard diabetes care protocol. Thirty-day postoperative serious adverse events were recorded. The primary outcome was duration of hypoglycemia (glucose <70 mg/dL). Clinicaltrials.gov: NCT04473001.

RESULTS

Seventy patients were included, with a median observation time of 4.0 days. CGM was recorded in median 96% of the observation time. The median daily duration of hypoglycemia was 2.5 minutes without significant difference between the 4 groups (A-D). Hypoglycemic events lasting ≥15 minutes occurred in 43% of all patients and 70% of patients with type 1 diabetes. Patients with type 1 diabetes spent a median of 40% of the monitoring time in the normoglycemic range 70 to 180 mg/dL and 27% in the hyperglycemic range >250 mg/dL. Duration of preceding hypo- and hyperglycemia tended to be longer in patients with serious adverse events, compared with patients without events, but these were exploratory analyses.

CONCLUSIONS

Significant duration of both hypo- and hyperglycemia was detected in high proportions of patients, particularly in patients with diabetes, despite protocolized perioperative diabetes management.

The impact of physical inactivity on glucose homeostasis when diet is adjusted to maintain energy balance in healthy, young males

BACKGROUND & AIMS

It is unclear if dietary adjustments to maintain energy balance during reduced physical activity can offset inactivity-induced reductions in insulin sensitivity and glucose disposal to produce normal daily glucose concentrations and meal responses. Therefore, the aim of the present study was to examine the impact of long-term physical inactivity (60 days of bed rest) on daily glycemia when in energy balance.

METHODS

Interstitial glucose concentrations were measured using Continuous Glucose Monitoring Systems (CGMS) for 5 days before and towards the end of bed rest in 20 healthy, young males (Age: 34 ± 8 years; BMI: 23.5 ± 1.8 kg/m2). Energy intake was reduced during bed rest to match energy expenditure, but the types of foods and timing of meals was maintained. Fasting venous glucose and insulin concentrations were determined, as well as the change in whole-body glucose disposal using a hyperinsulinemic-euglycemic clamp (HIEC).

RESULTS

Following long-term bed rest, fasting plasma insulin concentration increased 40% (p = 0.004) and glucose disposal during the HIEC decreased 24% (p < 0.001). Interstitial daily glucose total area under the curve (tAUC) from pre-to post-bed rest increased on average by 6% (p = 0.041), despite a 20 and 25% reduction in total caloric and carbohydrate intake, respectively. The nocturnal period (00:00-06:00) showed the greatest change to glycemia with glucose tAUC for this period increasing by 9% (p = 0.005). CGMS measures of daily glycemic variability (SD, J-Index, M-value and MAG) were not changed during bed rest.

CONCLUSIONS

Reduced physical activity (bed rest) increases glycemia even when daily energy intake is reduced to maintain energy balance. However, the disturbance to daily glucose homeostasis was much more modest than the reduced capacity to dispose of glucose, and glycemic variability was not negatively affected by bed rest, likely due to positive mitigating effects from the contemporaneous reduction in dietary energy and carbohydrate intake.

CLINICAL TRIALS RECORD

NCT03594799 (registered July 20, 2018) (https://clinicaltrials.gov/ct2/show/NCT03594799).

The Use of Continuous Glucose Monitors in Sport: Possible Applications and Considerations

This review discusses the potential value of tracking interstitial glucose with continuous glucose monitors (CGMs) in athletes, highlighting possible applications and important considerations in the collection and interpretation of interstitial glucose data. CGMs are sensors that provide real time, longitudinal tracking of interstitial glucose with a range of commercial monitors currently available. Recent advancements in CGM technology have led to the development of athlete-specific devices targeting glucose monitoring in sport. Although largely untested, the capacity of CGMs to capture the duration, magnitude, and frequency of interstitial glucose fluctuations every 1-15 min may present a unique opportunity to monitor fueling adequacy around competitive events and training sessions, with applications for applied research and sports nutrition practice. Indeed, manufacturers of athlete-specific devices market these products as a "fueling gauge," enabling athletes to "push their limits longer and get bigger gains." However, as glucose homeostasis is a complex phenomenon, extensive research is required to ascertain whether systemic glucose availability (estimated by CGM-derived interstitial glucose) has any meaning in relation to the intended purposes in sport. Whether CGMs will provide reliable and accurate information and enhance sports nutrition knowledge and practice is currently untested. Caveats around the use of CGMs include technical issues (dislodging of sensors during periods of surveillance, loss of data due to synchronization issues), practical issues (potential bans on their use in some sporting scenarios, expense), and challenges to the underpinning principles of data interpretation, which highlight the role of sports nutrition professionals to provide context and interpretation.

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.