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Sleep quality and continuous glucose monitoring - derived glycemic traits in individuals with and without diabetes

BACKGROUND: Diabetes mellitus (DM) affects millions of people within the United States, and type 2 DM is the most common form of DM. Lifestyle factors such as physical inactivity and poor diet have been associated with type 2 DM, but the relationship between sleep quality and type 2 DM is less clear. The overall aim of the study was to evaluate the association between the mean hours of self-reported and Fitbit-measured total sleep time and sleep quality with glycemic traits. Furthermore, we aimed to assess whether adjusting for diet quality attenuates such associations between sleep and glycemic control.
METHODS: In this study, we evaluated data from the Framingham Heart Study Third Generation, New Offspring Spouse, and Omni 2 participants in attendance for the first 8 months of exam 4 (778 participants). We excluded participants who did not wear a continuous glucose monitor (CGM), complete a sleep log and diet assessment, and wear a Fitbit physical activity and sleep monitoring device (n=384). We used multivariable linear regression to examine our primary objective, to determine the association of the mean hours of total sleep time (measured by self-report and Fitbit) and sleep quality (sleep latency and sleep efficiency, measured using a Fitbit) with glycemic traits (mean glucose, CV, CONGA-1, MODD, and MAGE measured using CGM). We examined these associations separately among participants with normoglycemia (n=197), prediabetes mellitus (preDM) (n=151) and DM (n=36). DM was defined by self-reported or history of DM, taking glucose-lowering medication, fasting blood glucose ≥126mg/dL, or hemoglobin (Hb)A1c ≥6.5%. Participants with prediabetes were defined by having fasting glucose 100-126mg/dL or HbA1c 5.7-6.5%.
RESULTS: Compared to individuals without DM (normoglycemia and prediabetes), individuals with DM spent less time asleep and in bed (7.1 hours and 7.6 hours respectively). Effect sizes of our primary analyses were similar between the sample with normoglycemia and preDM, so we reported results for analysis in which we combined these samples. In individuals with DM, we observed an association between more sleep and less glycemic variability in the CGM outcomes, CV, CONGA-1, MODD, and MAGE, after adjusting for physical activity and dietary factors. Self-reported and Fitbit-measured total sleep time were negatively associated with CGM-measured CV (a metric of glycemic variability) when adjusting for age, sex, lot number, BMI, average steps, total energy intake, and refined grains in individuals without DM. We observed no association between venous glucose and any sleep parameter across all DM status groups. In our secondary analysis, we did not observe any associations between sleep quality and the CGM outcomes, despite the varying adjustment models.
CONCLUSIONS: We concluded that total sleep time is a predictor of glycemic control in individuals with and without DM. Further research is necessary to determine the causality of this relationship, the role of meal timing and dietary patterns, and whether changes in sleep time have an effect on glycemic control.

Identiferoai:union.ndltd.org:bu.edu/oai:open.bu.edu:2144/47974
Date29 January 2024
CreatorsGatanti, Abigail
ContributorsSpartano, Nicole, Walker, Maura
Source SetsBoston University
Languageen_US
Detected LanguageEnglish
TypeThesis/Dissertation

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