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Modeling continuous glucose monitoring (CGM) data during sleep.

ABSTRACT: We introduce a multilevel functional Beta model to quantify the blood glucose levels measured by continuous glucose monitors for multiple days in study participants with type 2 diabetes mellitus. The model estimates the subject-specific marginal quantiles, quantifies the within- and between-subject variability, and produces interpretable parameters of blood glucose dynamics as a function of time from the actigraphy-estimated sleep onset. Results are validated via simulations and by studying the association between the estimated model parameters and hemoglobin A1c, the gold standard for assessing glucose control in diabetes.

SUBMITTER: Gaynanova I

PROVIDER: S-EPMC8942115 | biostudies-literature | 2022 Jan

REPOSITORIES: biostudies-literature

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Modeling continuous glucose monitoring (CGM) data during sleep.

Gaynanova Irina I Punjabi Naresh N Crainiceanu Ciprian C

Biostatistics (Oxford, England) 20220101 1

We introduce a multilevel functional Beta model to quantify the blood glucose levels measured by continuous glucose monitors for multiple days in study participants with type 2 diabetes mellitus. The model estimates the subject-specific marginal quantiles, quantifies the within- and between-subject variability, and produces interpretable parameters of blood glucose dynamics as a function of time from the actigraphy-estimated sleep onset. Results are validated via simulations and by studying the ...[more]

PMID: 32443145

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Project description:The objective of this pilot study was to compare glucose concentrations in capillary blood (CB) samples analysed in a laboratory by a validated method and glucose concentrations measured in the interstitial fluid (ISF) by continuous glucose monitoring (CGM) under different physical activity levels in a postprandial state in healthy athletes without diabetes. As a physiological shift occurs between glucose concentration from the CB into the ISF, the applicability of CGM in sports, especially during exercise, as well as the comparability of CB and ISF data necessitate an in-depth assessment. Ten subjects (26 ± 4 years, 67 ± 11 kg bodyweight (BW), 11 ± 3 h) were included in the study. Within 14 days, they underwent six tests consisting of (a) two tests resting fasted (HC_Rest/Fast and LC_Rest/Fast), (b) two tests resting with intake of 1 g glucose/kg BW (HC_Rest/Glc and LC_Rest/Glc), (c) running for 60 min at moderate (ModExerc/Glc), and (d) high intensity after intake of 1 g glucose/kg BW (IntExerc/Glc). Data were collected in the morning, following a standardised dinner before test day. Sensor-based glucose concentrations were compared to those determined from capillary blood samples collected at the time of sensor-based analyses and subjected to laboratory glucose measurements. Pearson's r correlation coefficient was highest for Rest/Glc (0.92, p < 0.001) compared to Rest/Fast (0.45, p < 0.001), ModExerc/Glc (0.60, p < 0.001) and IntExerc/Glc (0.70, p < 0.001). Mean absolute relative deviation (MARD) and standard deviation (SD) was smallest for resting fasted and similar between all other conditions (Rest/Fast: 8 ± 6%, Rest/Glc: 17 ± 12%, ModExerc/Glc: 22 ± 24%, IntExerc/Glc: 18 ± 17%). However, Bland-Altman plot analysis showed a higher range between lower and upper limits of agreement (95% confidence interval) of paired data under exercising compared to resting conditions. Under resting fasted conditions, both methods produce similar outcomes. Under resting postprandial and exercising conditions, respectively, there are differences between both methods. Based on the results of this study, the application of CGM in healthy athletes is not recommended without concomitant nutritional or medical advice.

Project description:ImportanceUnderstanding the interplay between trajectories of sleep duration, sleep onset timing, and glycemic dynamics is crucial for improving preventive strategies against diabetes and related metabolic diseases.ObjectiveTo examine the associations of sleep duration and onset timing trajectories with continuous glucose monitoring (CGM)-derived glycemic metrics in adults.Design, setting, and participantsThis cohort study analyzed data collected from January 2014 to December 2023 in the Guangzhou Nutrition and Health Study, a prospective cohort in Guangdong province, China, among participants aged 46 to 83. Participants who had repeated sleep assessments at several study visits and were equipped with CGM devices at the last visit were included. Data analyses were conducted between January and June 2024.ExposuresThe trajectories of sleep duration and onset timing were constructed using self-report sleep duration and sleep onset timing, recorded at multiple study visit points.Main outcomes and measuresMeasurements of glycemic variability and glycemic control were collected using a masked CGM device worn by patients for 14 consecutive days. Huber robust regression models were used to assess the associations between sleep trajectories and CGM-derived metrics.ResultsIn this study of 1156 participants (mean [SD] age, 63.0 [5.1] years, 816 [70.6%] women), we identified 4 distinct sleep duration trajectory groups: severe inadequate, moderate inadequate, mild inadequate, and adequate. Severe sleep inadequacy was associated with an increment of glycemic variability indicators: 2.87% (95% CI, 1.23%-4.50%) for coefficient of variation and 0.06 (95% CI, 0.02-0.09) mmol/L for mean of daily differences. We found 2 trajectories of sleep onset timing: persistent early and persistent late groups. Late sleep onset was associated with larger coefficient of variation (β = 1.18%; 95% CI, 0.36%-2.01%) and mean of daily differences (β = 0.02 mmol/L; 95% CI, 0.01-0.04 mmol/L). Inappropriate sleep duration and timing trajectories in combination were associated with greater glycemic variability.Conclusions and relevanceIn this cohort study of middle-aged and older participants, persistent inadequate sleep duration and late sleep onset, whether alone or in combination, were associated with greater glycemic variability. These findings emphasize the importance of considering both sleep duration and timing for optimizing glycemic control in the general population.

Dataset Information

Modeling continuous glucose monitoring (CGM) data during sleep.

Publications

Modeling continuous glucose monitoring (CGM) data during sleep.

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