Dysglycemia in adults at risk for or living with non-insulin treated type 2 diabetes: Insights from continuous glucose monitoring

Barua, Souptik; Sabharwal, Ashutosh; Glantz, Namino M.; Conneely, Casey; Larez, Arianna; Bevier, Wendy C.; Kerr, David

doi:10.1016/j.eclinm.2021.100853

Cited by 24 publications

(17 citation statements)

References 45 publications

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“…Despite seeing no difference in mean glucose levels between, afternoon, and evening, our study shows that CV steadily declines during the day reaching lowest values ‘overnight’ and reports that morning CV was significantly higher compared to other times-of-day. This agrees with trends observed in non-diabetic men and women (n=60) that reported significantly higher Daytime CV (06:00-21:59) compared to Overnight CV (22:00-05:59) ( 38 ) but disagrees with evidence from adolescent boys and girls (n=107; 13.1 ± 2.6 years) that suggests CV increases from early morning (06:00) and peaks from midday to late-night (12:00-23:00) ( 39 ). However, the significance in temporal CV patterns was not formally assessed for adolescents, so its importance is uncertain.…”

Section: Discussionsupporting

confidence: 91%

Observational assessments of the relationship of dietary and pharmacological treatment on continuous measures of dysglycemia over 24 hours in women with gestational diabetes

Dingena

Holmes²,

Campbell³

et al. 2023

Front. Endocrinol.

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ObjectivesStudies that use continuous glucose monitoring (CGM) to monitor women with gestational diabetes (GDM), highlight the importance of managing dysglycemia over a 24-hour period. However, the effect of current treatment methods on dysglycemia over 24-hrs are currently unknown. This study aimed to characterise CGM metrics over 24-hrs in women with GDM and the moderating effect of treatment strategy.MethodsRetrospective analysis of CGM data from 128 women with GDM in antenatal diabetes clinics. CGM was measured for 7-days between 30-32 weeks gestation. Non-parametric tests were used to evaluate differences of CGM between periods of day (morning, afternoon, evening, and overnight) and between treatment methods (i.e., diet alone or diet+metformin). Exploratory analysis in a subgroup of 34 of participants was performed to investigate the association between self-reported macronutrient intake and glycaemic control.ResultsGlucose levels significantly differed during the day (i.e., morning to evening; P<0.001) and were significantly higher (i.e., mean blood glucose and area under the curve [AUC]) and more variable (i.e., SD and CV) than overnight glucose levels. Morning showed the highest amount of variability (CV; 8.4% vs 6.5%, P<0.001 and SD; 0.49 mmol/L vs 0.38 mmol/L, P<0.001). When comparing treatment methods, mean glucose (6.09 vs 5.65 mmol/L; P<0.001) and AUC (8760.8 vs 8115.1 mmol/L.hr; P<0.001) were significantly higher in diet+metformin compared to diet alone. Finally, the exploratory analysis revealed a favourable association between higher protein intake (+1SD or +92 kcal/day) and lower mean glucose (-0.91 mmol/L p, P=0.02) and total AUC (1209.6 mmol/L.h, P=0.021).ConclusionsGlycemia varies considerably across a day, with morning glycemia demonstrating greatest variability. Additionally, our work supports that individuals assigned to diet+metformin have greater difficulty managing glycemia and results suggest that increased dietary protein may assist with management of dysglycemia. Future work is needed to investigate the benefit of increased protein intake on management of dysglycemia.

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Section: Discussionsupporting

confidence: 91%

Observational assessments of the relationship of dietary and pharmacological treatment on continuous measures of dysglycemia over 24 hours in women with gestational diabetes

Dingena

Holmes²,

Campbell³

et al. 2023

Front. Endocrinol.

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“…8 In contrast to the above reports, TIR 70-140 mg/dL or TBR <70 mg/dL did not differ between individuals with prediabetes compared to normoglycemic Latino individuals. 9 Regarding the glycemic variability metrics such as SD of glucose and MAGE, it was reported to be higher in individuals with prediabetes compared to normoglycemic subjects. 5,9,10 Morbidly obese individuals, either normoglycemic or with prediabetes, have higher glycemic variability compared with normal weight, individuals without diabetes.…”

Section: Discussionmentioning

confidence: 99%

“…9 Regarding the glycemic variability metrics such as SD of glucose and MAGE, it was reported to be higher in individuals with prediabetes compared to normoglycemic subjects. 5,9,10 Morbidly obese individuals, either normoglycemic or with prediabetes, have higher glycemic variability compared with normal weight, individuals without diabetes. However, glycemic variability metrics did not seem to differ between individuals with prediabetes and normoglycemic morbidly obese subjects.…”

Section: Discussionmentioning

confidence: 99%

“…Instead, heterogeneity prevails in the definition of time in range (TIR) on individuals with prediabetes or normoglycemia, as previous studies reported on various thresholds particularly for the upper limit of TIR, which ranges from 125 to 180 mg/dL of glucose values. [3][4][5][6][7][8][9][10][11][12][13][14] Among various CGM devices, professional CGMs provide retrospective (blinded or unblinded) data for analysis and are mainly used to identify patterns of hypo-and hyperglycemia. 2 As such, they can be helpful for describing the glycemic profile of prediabetes or normoglycemia subsets since targeted behavioral modification is not warranted contrary to real-time CGM devices in patients with diabetes, where the actual (real-time) glucose value can be seen by the individual.…”

Section: Introductionmentioning

confidence: 99%

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

Rizos

Kanellopoulou

Filis

et al. 2022

J Diabetes Sci Technol

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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.

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“…GV is defined by the metrics that characterize the glucose fluctuations or glucose homoeostasis within a given time interval (1). We derived a set of short-term GV metrics listed below that best represents the intra-day glucose dynamics according to clinical literature (16,(24)(25)(26)(27)(28)(29)(30)(31), with necessary adaptation to the objective of this study. Other GV metrics that characterize day-to-day glucose dynamics were discarded as they were not relevant to this study.…”

Section: Glycemic Variability Metricsmentioning

confidence: 99%

Mining associations between glycemic variability in awake-time and in-sleep among non-diabetic adults

Liang

2022

Front. Med. Technol.

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It is often assumed that healthy people have the genuine ability to maintain tight blood glucose regulation. However, a few recent studies revealed that glucose dysregulation such as hyperglycemia may occur even in people who are considered normoglycemic by standard measures and were more prevalent than initially thought, suggesting that more investigations are needed to fully understand the within-day glucose dynamics of healthy people. In this paper, we conducted an analysis on a multi-modal dataset to examine the relationships between glycemic variability when people were awake and that when they were sleeping. The interstitial glucose levels were measured with a wearable continuous glucose monitoring (CGM) technology FreeStyle Libre 2 at every 15 min interval. In contrast to the traditional single-time-point measurements, the CGM data allow the investigation into the temporal patterns of glucose dynamics at high granularity. Sleep onset and offset timestamps were recorded daily with a Fitbit Charge 3 wristband. Our analysis leveraged the sleep data to split the glucose readings into segments of awake-time and in-sleep, instead of using fixed cut-off time points as has been done in existing literature. We combined repeated measure correlation analysis and quantitative association rules mining, together with an original post-filtering method, to identify significant and most relevant associations. Our results showed that low overall glucose in awake time was strongly correlated to low glucose in subsequent sleep, which in turn correlated to overall low glucose in the next day. Moreover, both analysis techniques identified significant associations between the minimal glucose reading in sleep and the low blood glucose index the next day. In addition, the association rules discovered in this study achieved high confidence (0.75–0.88) and lift (4.1–11.5), which implies that the proposed post-filtering method was effective in selecting quality rules.

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Dysglycemia in adults at risk for or living with non-insulin treated type 2 diabetes: Insights from continuous glucose monitoring

Cited by 24 publications

References 45 publications

Observational assessments of the relationship of dietary and pharmacological treatment on continuous measures of dysglycemia over 24 hours in women with gestational diabetes

Observational assessments of the relationship of dietary and pharmacological treatment on continuous measures of dysglycemia over 24 hours in women with gestational diabetes

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

Mining associations between glycemic variability in awake-time and in-sleep among non-diabetic adults

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