Comparison of the Continuous Glucose Monitoring Profiles of Four Glucose-Lowering Medications in the GRADE Randomized Trial.

BackgroundCohort data on continuous glucose monitoring (CGM) metrics are scarce for liver glycogen storage diseases (GSDs) and idiopathic ketotic hypoglycemia (IKH). The aim of this study was to retrospectively describe CGM metrics for people with liver GSDs and IKH. Patients and methodsCGM metrics (descriptive, glycemic variation and glycemic control parameters) were calculated for 47 liver GSD and 14 IKH patients, categorized in cohorts by disease subtype, age and treatment status, and compared to published age-matched CGM metrics from healthy individuals. Glycemic control was assessed as time-in-range (TIR; ≥3.9 - ≤7.8 and ≥3.9 - ≤10.0 mmol/L), time-below-range (TBR; <3.0 mmol/L and ≥3.0 - ≤3.9 mmol/L), and time-above-range (TAR; >7.8 and >10.0 mmol/L). ResultsDespite all patients receiving dietary treatment, GSD cohorts displayed significantly different CGM metrics compared to healthy individuals. Decreased TIR together with increased TAR were noted in GSD I, GSD III, and GSD XI (Fanconi-Bickel syndrome) cohorts (all p < 0.05). In addition, all GSD I cohorts showed increased TBR (all p < 0.05). In GSD IV an increased TBR (p < 0.05) and decreased TAR were noted (p < 0.05). In GSD IX only increased TAR was observed (p < 0.05). IKH patient cohorts, both with and without treatment, presented CGM metrics similar to healthy individuals. ConclusionDespite dietary treatment, most liver GSD cohorts do not achieve CGM metrics comparable to healthy individuals. International recommendations on the use of CGM and clinical targets for CGM metrics in liver GSD patients are warranted, both for patient care and clinical trials.

IntroductionThe COVID-19 pandemic forced the Italian government to issue extremely restrictive measures on daily activities since 11 March 2020 (‘lockdown’), which may have influenced the metabolic control of type 1...

Lifestyle interventions and low glycemic diets have potential in diabetes prevention. However, dietary monitoring relies on self-report, which is prone to under-reporting. This observational study investigated the correlation between continuous glucose monitoring (CGM) metrics and glycemic load (GL) or daily macronutrients consumption. Based on one week of CGM data, actigraphy measurements, and food diaries, we investigated correlations between GL per meal, and 19 CGM metrics, selected based on 20 studies identified via a systematic literature review. Furthermore, we generated linear mixed models to predict GL and macronutrients intake using moderately correlated CGM metrics. Forty-eight healthy participants (27 women, average age of 28.2 years, average body mass index (BMI) of 23.4 kg/m2) were included. We found significant positive moderate correlations (P < .0004) between GL and area under the curve (ρ = 0.40, two-hour window), relative amplitude (ρ = 0.40, three hours and ρ = 0.42, four hours), standard deviation (SD) (ρ = 0.41, four hours), and variance (ρ = 0.43, four hours). Significant positive moderate correlations (P < .0004) were observed between carbohydrate and SD (ρ = 0.45), variance (ρ = 0.44), and mean amplitude of glycemic excursions (MAGE) (ρ = 0.40) over 24 hours. We obtained one valid mixed linear model for predicting GL from CGM metrics obtained two hours after food intake. A second model predicted energy intake using moderately correlated CGM metrics, body composition, sleep duration, and physical activity. We demonstrated moderate correlations between GL and CGM metrics in healthy populations. These CGM metrics were successfully used to predict GL or energy intake.

Rebound hyperglycemia (RHyper), rebound hypoglycemia (RHypo), extended hyperglycemia (EHyper), and extended hypoglycemia (EHypo) are newly defined continuous glucose monitoring (CGM) metrics. Here, we investigated the characteristics of these new metrics and the relationship between new CGM metrics and standard metrics. In this retrospective cohort study, 30,000 CGM users with at least 90 days of CGM data were randomly selected from Dexcom Clarity database. Standard and new CGM metrics were calculated for each user. Four different cutoffs were used to define RHyper and RHypo, and two cutoffs were used to define EHyper and EHypo events. The number of RHyper, RHypo, EHyper, and EHypo events per week, mean duration of events, and mean area under the curve of events were calculated. For rebound events, the rate of change (ROC) was calculated. Pearson correlation and simple linear regression were used to analyze the data. Mean time in 70-180 mg/dL was 61.8 ± 20.7%, mean glucose was 173 ± 37.1 mg/dL, and coefficient of variation (CV) was 32.1 ± 7.2%. RHyper, RHypo, and EHyper were more frequent during daytime and increased throughout the day. EHypo mostly occurred during nighttime. CV correlated strongly with RHyper (70-180 mg/dL) events/week (r = 0.67) and RHypo (180 to 70 mg/dL) events/week (r = 0.64). Time in range had the strongest correlation with EHyper events/week (r = -0.88) among new metrics. RHyper events and RHypo events were strongly correlated with each other (r = 0.92). RHyper and RHypo ROC have a stronger correlation with CV than the correlation between CV and time below range (TBR) metrics. For rebound and extended metrics, the most important metric was the number of events/week. RHyper and RHypo had a stronger correlation with CV and hypoglycemia metrics (TBR) than the correlation between CV and TBR. Thus, rebound events have the potential to detect hypoglycemia events caused by glycemic variability.

The optimal method of monitoring glycemia in pregnant women with type 1 diabetes remains controversial. This study aimed to assess the predictive performance of HbA1c, continuous glucose monitoring (CGM) metrics, and alternative biochemical markers of glycemia to predict obstetric and neonatal outcomes. One hundred fifty-seven women from the Continuous Glucose Monitoring in Women With Type 1 Diabetes in Pregnancy Trial (CONCEPTT) were included in this prespecified secondary analysis. HbA1c, CGM data, and alternative biochemical markers (glycated CD59, 1,5-anhydroglucitol, fructosamine, glycated albumin) were compared at ∼12, 24, and 34 weeks' gestation using logistic regression and receiver operating characteristic (ROC) curves to predict pregnancy complications (preeclampsia, preterm delivery, large for gestational age, neonatal hypoglycemia, admission to neonatal intensive care unit). HbA1c, CGM metrics, and alternative laboratory markers were all significantly associated with obstetric and neonatal outcomes at 24 weeks' gestation. More outcomes were associated with CGM metrics during the first trimester and with laboratory markers (area under the ROC curve generally <0.7) during the third trimester. Time in range (TIR) (63-140 mg/dL [3.5-7.8 mmol/L]) and time above range (TAR) (>140 mg/dL [>7.8 mmol/L]) were the most consistently predictive CGM metrics. HbA1c was also a consistent predictor of suboptimal pregnancy outcomes. Some alternative laboratory markers showed promise, but overall, they had lower predictive ability than HbA1c. HbA1c is still an important biomarker for obstetric and neonatal outcomes in type 1 diabetes pregnancy. Alternative biochemical markers of glycemia and other CGM metrics did not substantially increase the prediction of pregnancy outcomes compared with widely available HbA1c and increasingly available CGM metrics (TIR and TAR).

Aims: Standardized continuous glucose monitoring (CGM) metrics for clinical care were announced in 2019. There have been no reports, however, on the relationship between standardized CGM metrics and oxidative stress. We therefore decided to investigate the relationships between standardized CGM metrics, classical glycemic variability, and oxidative stress. Methods: This study was a cross-sectional analysis of 117 patients with type 2 diabetes mellitus (T2DM). Oxidative stress was estimated using the diacron-reactive oxygen metabolites (d-ROMs) test. The following parameters were used as CGM metrics: mean glucose level (MGL), percentage coefficient of variation for glucose (%CV), time above range (TAR), time in range (TIR), time below range (TBR), standard deviation (SD), and mean amplitude of glycemic excursions (MAGE), a classic index. Results: A total of 117 patients (mean age of 64.1 ± 12.6 years, mean disease duration of 13.1 ± 11.5 years, and HbA1c of 8.3 ± 1.5%) who met the study inclusion criteria were finally analyzed. The univariate analysis showed that age, triglyceride, HbA1c, MGL, %CV, SD, MAGE, and TAR were significantly correlated with d-ROMs. Further, a stepwise multiple regression analysis identified SD, MAGE, and sex as independent contributors to d-ROMs. Conclusions: Oxidative stress was associated with the SD and MAGE, two parameters affected by the mean glucose level, as CGM metrics in patients with T2DM. Disclosure Y. Kohata: None. M. Ohara: None. T. Fujikawa: None. H. Nagaike: None. H. Kushima: None. M. Hiromura: None. Y. Mori: Research Support; Self; Taisho Pharmaceutical Co., Ltd. T. Fukui: None. T. Hirano: None. S. Yamagishi: None.

Aims: Prediabetes is defined by HbA1c, fasting (FPG) and 2-hour plasma glucose (2h-PG) during 75 gram oral glucose test (OGTT) although these values often show poor concordance. Most diabetes prevention trials involved people with impaired glucose tolerance (IGT) diagnosed as 2h-PG 7.8-11.0 mmol/L. Continuous glucose monitoring (CGM) may detect early dysglycemia especially postprandial glucose excursions. We examined correlations amongst HbA1c, FPG, 1-h and 2h-PG during OGTT and CGM metrics in individuals with IGT. Methods: 85 Chinese with IGT (mean±SD age 57±8 years, 51 (60%) female, BMI 26.6±4.0 kg/m2) participating in a lifestyle modification study had measurement of HbA1c, PG during 75g OGTT and 14-day CGM metrics (Freestyle Libre, Abbott) during a 3-week period at baseline. We examined their correlations using Spearman coefficients. Results: The mean HbA1c was 5.8±0.3 %, FPG 5.3±0.4 mmol/L, 1h-PG 11.0±1.6 mmol/L and 2h-PG 8.5±1.3 mmol/L. For CGM, the mean glucose management index (GMI) was 5.9±0.2 % and time in tight glycemic range (TITR 3.9-7.8 mmol/L) was 86.7±6.6 %. There was a tendency for HbA1c (r=0.587, p=0.097) to correlate with GMI and negatively with TITR (r=-0.644, p=0.061). The respective correlates between FPG and GMI was r=0.623 (p=0.073) and that between FPG and TITR was r=-0.559 (p=0.118). There was weak correlation between 1h-PG and CGM time above range &amp;gt;7.8 mmol/L (r=0.301, p=0.43). There was no correlation amongst 1h-PG, 2h-PG with HbA1c and other conventional CGM metrics. Conclusions: In individuals with IGT, HbA1c and FPG showed better correlations with CGM metrics of average glycemia than PG values during OGTT. Future studies are needed to define the use of CGM and appropriate metrics in detection of IGT. Disclosure E.Chow: Research Support; Medtronic, Merck KGaA, Speaker's Bureau; Novartis, Bayer Inc., Sanofi. J.He: None. N.Chu: None. E.W.M.Poon: None. J.C.Chan: Board Member; Asia Diabetes Foundation, Consultant; Bayer Inc., Celltrion, Boehringer Ingelheim and Eli Lilly Alliance, Sanofi, Research Support; AstraZeneca, Servier Laboratories, Viatris Inc., Hua Medicine, Merck KGaA, Applied Therapeutics Inc., Lee Powder, Pfizer Inc., Speaker's Bureau; Novartis, Stock/Shareholder; GemVCare Ltd. Funding Health and Medical Research Fund (17180431)

Relationships between HbA1c and continuous glucose monitoring metrics of glycaemic control and glucose variability in a large cohort of children and adolescents with type 1 diabetes

Introduction and Objective: The Automated Insulin Delivery as an Adaptive NETwork FCL system may improve glycemic outcomes while reducing burden by eliminating meal announcement. Continuous glucose monitoring (CGM) metrics were compared in users with T1D using the FCL system who had high or low baseline A1c. Methods: Youth and adults with T1D were enrolled in a randomized crossover study with a supervised hotel stay followed by 7 days of home use in FCL compared to usual care (UC). Half were selected with baseline A1c &amp;lt;8% (low A1c) and half with baseline A1c 8-12% (high A1c). CGM metrics were analyzed by A1c subgroups. Results: Thirty-four participants (25.4±12.6 years, 62% female) completed the study. Those with high A1c showed non-inferiority of FCL vs UC on all CGM metrics and lower mean glucose with FCL (Table). Time in 70-180 mg/dL, in 70-140 mg/dL, &amp;gt;180 mg/dL, and &amp;gt;250 mg/dL showed significant improvement with FCL in the high A1c group. Those with low A1c had statistically equivalent mean glucose and time &amp;lt;54 mg/dL with FCL vs UC; other CGM metrics were inconclusive. Conclusion: Use of a FCL system results in significant improvement in CGM metrics in those with less optimal glycemic management, while not deteriorating control in those with more optimal A1c. FCL systems have the potential to make the most impact in those with challenges in meeting glycemic goals. Disclosure J.C. Wong: Research Support; Abbott, Dexcom, Inc., Tandem Diabetes Care, Inc. M. Moscoso-Vasquez: Other Relationship; Dexcom, Inc. Research Support; Tandem Diabetes Care, Inc, National Institute of Diabetes and Digestive and Kidney Diseases. L. Ekhlaspour: Other Relationship; Medtronic. Advisory Panel; Abbott, Medtronic. Consultant; Jaeb Center for Health Research. Research Support; MannKind Corporation. Speaker's Bureau; Insulet Corporation. Advisory Panel; Sequel Med Tech. Other Relationship; Tandem Diabetes Care, Inc. Research Support; Abbott. Other Relationship; Sanofi. S.A. Brown: Research Support; Dexcom, Inc., Insulet Corporation, Tandem Diabetes Care, Inc, Tolerion, Roche Diabetes Care. Other Relationship; MannKind Corporation. M.D. Breton: Speaker's Bureau; Sinocare Inc, Tandem Diabetes Care, Inc. Consultant; Roche Diabetes Care, Boydsense. G.P. Forlenza: Advisory Panel; Medtronic. Research Support; Medtronic, Dexcom, Inc. Consultant; Dexcom, Inc. Research Support; Insulet Corporation. Consultant; Insulet Corporation. Research Support; Tandem Diabetes Care, Inc. Advisory Panel; Tandem Diabetes Care, Inc. Research Support; Abbott. Advisory Panel; Sequel Med Tech. Funding Breakthrough T1D

&lt;p dir="ltr"&gt;OBJECTIVE Evidence for using continuous glucose monitoring (CGM) as an alternative to oral glucose tolerance tests (OGTT) in presymptomatic type 1 diabetes is primarily cross-sectional. We used longitudinal data to compare the diagnostic performance of repeated CGM, HbA1c and OGTT metrics to predict progression to stage 3 type 1 diabetes. RESEARCH DESIGN AND METHOD Thirty-four multiple autoantibody-positive first-degree relatives (FDRs) (BMI-SDS&lt;2) were followed in a multicenter study with semi-annual 5-day CGM recordings, HbA1c, and OGTT for a median (IQR) of 3.5 (2.0-7.5) years. Longitudinal patterns were compared based on progression status. Prediction of rapid (&lt;3 years) and overall progression to stage 3 was assessed using receiver operating characteristic (ROC) AUCs, Kaplan-Meier, baseline Cox models (concordance [C]) and extended Cox with time-varying covariates in multiple record data (n=197 OGTTs and concomitant CGM recordings), adjusted for intraindividual correlations (corrected Akaike information criterion [AICc]). RESULTS After a median (IQR) of 40 (20‑91) months, 17/34 FDRs (baseline median age: 16.6 years) developed stage 3. CGM metrics increased close to onset, paralleling changes in OGTT, both with substantial intra- and interindividual variability. Cross-sectionally, the best OGTT and CGM metrics similarly predicted rapid (ROC-AUC=0.86-0.92) and overall progression (C=0.73-0.78). In longitudinal models, OGTT-derived AUC glucose (AICc=71) outperformed the best CGM metric (AICc=75) and HbA1c (AICc=80) (all P&lt;0.001). HbA1c complemented repeated CGM metrics (AICc=68), though OGTT-based multivariable models remained superior (AICc=59). CONCLUSIONS In longitudinal models, repeated CGM and HbA1c were nearly as effective as OGTT in predicting stage 3 and may be more convenient for long-term clinical monitoring.&lt;/p&gt;

Standard continuous glucose monitoring (CGM) metrics: mean glucose, standard deviation, coefficient of variation, and time in range, fail to capture the shape of variability in the CGM time series. This information could facilitate improved diabetes management. We analyzed CGM data from 141 adults with type 2 diabetes in the Hyperglycemic Profiles in Obstructive Sleep Apnea (HYPNOS) trial. Participants in HYPNOS wore CGM sensors for up to two weeks at two time points, three months apart. We calculated the log-periodogram for each time period, summarizing using disjoint linear models. These summaries were combined into a single value, termed the Glucose Color Index (GCI), using canonical correlation analysis. We compared the between-wear correlation of GCI with those of standard CGM metrics and assessed associations between GCI and diabetes comorbidities in 398 older adults with type 2 diabetes from the Atherosclerosis Risk in Communities (ARIC) study. The GCI achieved a test-retest correlation of R = .75. Adjusting for standard CGM metrics, the GCI test-retest correlation was R = .55. Glucose Color Index was significantly associated (p < .05) with impaired physical functioning, frailty/pre-frailty, cardiovascular disease, chronic kidney disease, and dementia/mild cognitive impairment after adjustment for confounders. We developed and validated the GCI, a novel CGM metric that captures the shape of glucose variability using the periodogram signal decomposition. Glucose Color Index was reliable within participants over a three-month period and associated with diabetes comorbidities. The GCI suggests a promising avenue toward the development of CGM metrics which more fully incorporate time series information.

&lt;p dir="ltr"&gt;OBJECTIVE Evidence for using continuous glucose monitoring (CGM) as an alternative to oral glucose tolerance tests (OGTT) in presymptomatic type 1 diabetes is primarily cross-sectional. We used longitudinal data to compare the diagnostic performance of repeated CGM, HbA1c and OGTT metrics to predict progression to stage 3 type 1 diabetes. RESEARCH DESIGN AND METHOD Thirty-four multiple autoantibody-positive first-degree relatives (FDRs) (BMI-SDS&lt;2) were followed in a multicenter study with semi-annual 5-day CGM recordings, HbA1c, and OGTT for a median (IQR) of 3.5 (2.0-7.5) years. Longitudinal patterns were compared based on progression status. Prediction of rapid (&lt;3 years) and overall progression to stage 3 was assessed using receiver operating characteristic (ROC) AUCs, Kaplan-Meier, baseline Cox models (concordance [C]) and extended Cox with time-varying covariates in multiple record data (n=197 OGTTs and concomitant CGM recordings), adjusted for intraindividual correlations (corrected Akaike information criterion [AICc]). RESULTS After a median (IQR) of 40 (20‑91) months, 17/34 FDRs (baseline median age: 16.6 years) developed stage 3. CGM metrics increased close to onset, paralleling changes in OGTT, both with substantial intra- and interindividual variability. Cross-sectionally, the best OGTT and CGM metrics similarly predicted rapid (ROC-AUC=0.86-0.92) and overall progression (C=0.73-0.78). In longitudinal models, OGTT-derived AUC glucose (AICc=71) outperformed the best CGM metric (AICc=75) and HbA1c (AICc=80) (all P&lt;0.001). HbA1c complemented repeated CGM metrics (AICc=68), though OGTT-based multivariable models remained superior (AICc=59). CONCLUSIONS In longitudinal models, repeated CGM and HbA1c were nearly as effective as OGTT in predicting stage 3 and may be more convenient for long-term clinical monitoring.&lt;/p&gt;

The aim of this work was to define a unique remission status using glycaemia risk index (GRI) and other continuous glucose monitoring (CGM) metrics in individuals with type 1 diabetes for improved phenotyping. A group of 140 individuals with type 1 diabetes were recruited for a cross-sectional study. The participants were categorised into four groups based on their remission status, which was defined as insulin-dose-adjusted A1c (IDAA1c) <9 or C-peptide ≥300 pmol/l: new-onset (n=24); mid-remission (n=44); post-remission (n=44); and non-remission (individuals who did not experience remission, n=28). Participants in the remission phase were referred to as 'remitters', while those who were not in the remission phase were referred to as 'non-remitters', the latter group including new-onset, post-remission and non-remission participants. Clinical variables such as HbA1c, C-peptide and insulin daily dose, as well as IDAA1C and CGM data, were collected. The patterns of CGM metrics were analysed for each group using generalised estimating equations to investigate the glycaemic variability patterns associated with remission status. Then, unsupervised hierarchical clustering was used to place the participants into subgroups based on GRI and other CGM core metrics. The glycaemic variability patterns associated with remission status were found to be distinct based on the circadian CGM metrics. Remitters showed improved control of blood glucose levels over 14 days within the range of 3.9-10 mmol/l, and lower GRI compared with non-remitters (p<0.001). Moreover, GRI strongly correlated with IDAA1C (r=0.62; p<0.001) and was sufficient to distinguish remitters from non-remitters. Further, four subgroups demonstrating distinct patterns of glycaemic variability associated with different remission status were identified by clustering on CGM metrics: remitters with low risk of dysglycaemia; non-remitters with high risk of hypoglycaemia; non-remitters with high risk of hyperglycaemia; and non-remitters with moderate risk of dysglycaemia. GRI, an integrative index, together with other traditional CGM metrics, helps to identify different glycaemic variability patterns; this might provide specifically tailored monitoring and management strategies for individuals in the various subclusters.

OBJECTIVEEvidence for using continuous glucose monitoring (CGM) as an alternative to oral glucose tolerance tests (OGTTs) in presymptomatic type 1 diabetes is primarily cross-sectional. We used longitudinal data to compare the diagnostic performance of repeated CGM, HbA1c, and OGTT metrics to predict progression to stage 3 type 1 diabetes.RESEARCH DESIGN AND METHODSThirty-four multiple autoantibody-positive first-degree relatives (FDRs) (BMI SD score [SDS] <2) were followed in a multicenter study with semiannual 5-day CGM recordings, HbA1c, and OGTT for a median of 3.5 (interquartile range [IQR] 2.0–7.5) years. Longitudinal patterns were compared based on progression status. Prediction of rapid (<3 years) and overall progression to stage 3 was assessed using receiver operating characteristic (ROC) areas under the curve (AUCs), Kaplan-Meier method, baseline Cox proportional hazards models (concordance), and extended Cox proportional hazards models with time-varying covariates in multiple record data (n = 197 OGTTs and concomitant CGM recordings), adjusted for intraindividual correlations (corrected Akaike information criterion [AICc]).RESULTSAfter a median of 40 (IQR 20–91) months, 17 of 34 FDRs (baseline median age 16.6 years) developed stage 3 type 1 diabetes. CGM metrics increased close to onset, paralleling changes in OGTT, both with substantial intra- and interindividual variability. Cross-sectionally, the best OGTT and CGM metrics similarly predicted rapid (ROC AUC = 0.86–0.92) and overall progression (concordance = 0.73–0.78). In longitudinal models, OGTT-derived AUC glucose (AICc = 71) outperformed the best CGM metric (AICc = 75) and HbA1c (AICc = 80) (all P < 0.001). HbA1c complemented repeated CGM metrics (AICc = 68), though OGTT-based multivariable models remained superior (AICc = 59).CONCLUSIONSIn longitudinal models, repeated CGM and HbA1c were nearly as effective as OGTT in predicting stage 3 type 1 diabetes and may be more convenient for long-term clinical monitoring.

There was limited evidence to evaluate the association between lifestyle habits and continuous glucose monitoring (CGM) metrics. Thus, we aimed to depict the behavioral and metabolic determinants of CGM metrics in insulin-treated patients with type 2 diabetes mellitus (T2DM). This is a prospective observational study. We analyzed data from 122 insulin-treated patients with T2DM. Participants wore Dexcom G6 and Fitbit, and diet information was identified for 10 days. Multivariate-adjusted logistic regression analysis was performed for the simultaneous achievement of CGM-based targets, defined by the percentage of time in terms of hyper, hypoglycemia and glycemic variability (GV). Intake of macronutrients and fiber, step counts, sleep, postprandial C-peptide-to-glucose ratio (PCGR), information about glucose lowering medications and metabolic factors were added to the analyses. Additionally, we evaluated the impact of the distribution of energy and macronutrient during a day, and snack consumption on CGM metrics. Logistic regression analysis revealed that female, participants with high PCGR, low glycosylated hemoglobin (HbA1c) and daytime step count had a higher probability of achieving all targets based on CGM (odds ratios [95% confidence intervals] which were 0.24 [0.09 to 0.65], 1.34 [1.03 to 1.25], 0.95 [0.9 to 0.99], and 1.15 [1.03 to 1.29], respectively). And participants who ate snacks showed a shorter period of hyperglycemia and less GV compared to those without. We confirmed that residual insulin secretion, daytime step count, HbA1c, and women were the most relevant determinants of adequate glycemic control in insulin-treated patients with T2DM. In addition, individuals with snack consumption were exposed to lower times of hyperglycemia and GV.