Data from: Gut microbiota composition and functionality are associated with REM sleep duration and continuous glucose levels
Data files
Feb 09, 2024 version files 1.55 MB
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README.md
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Supplemental_Material.xlsx
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Abstract
Context: Sleep disruption is associated with poorer glucose metabolic control and altered gut microbiota in animal models.
Objective: We aimed to evaluate the possible links among REM sleep duration, continuous glucose levels, and gut microbiota composition.
Design: This is an observational, prospective, real-life, cross-sectional case-control study.
Setting: Tertiary Hospital recruiting healthy volunteers.
Patients or Other Participants: One-hundred and eighteen (60 with obesity), middle-aged (39.1-54.8) subjects.
Intervention(s): Glucose variability and REM sleep duration were assessed by 10-day continuous glucose monitoring (CGM) (Dexcom G6®) and wrist-actigraphy (Fitbit Charge 3®), respectively
Main Outcome Measure(s):. Glucose variability was evaluated through standard deviation (SD), coefficient of variation (CV), and interquartile range (IQR). The percentage of time in range (% TIR), 126-139 mg/dL (TIR2), and 140-199 mg/dL (TIR3) were calculated. Shotgun metagenomics sequencing was applied to study gut microbiota taxonomy and functionality.
Results: Increased glycemic variability (SD, CV, and IQR) was observed among subjects with obesity in parallel to increased % TIR2 and % TIR3. REM sleep duration was independently associated with the % TIR3 (β=-0.339; p<0.001) and glucose variability (SD, β=-0.350; p<0.001). Microbial taxa from Christensenellaceae family (Firmicutes phylum) were positively associated with REM sleep and negatively with glucose continuous monitoring levels, while bacteria from Enterobacteriacea family and bacterial functions involved in iron metabolism showed opposite associations.
Conclusions: Decreased REM sleep duration was independently associated with a worse glucose profile. The associations of species from Christensenellaceae and Enterobacteriaceae families with REM sleep duration and continuous glucose values suggest an integrated picture of metabolic health.
https://doi.org/10.5061/dryad.3bk3j9kq4
Description of the data and file structure
Files:
Supplemental Material.xls
Contains the following spreadsheets:
- Supplemental Table 1 (ST1). Bivariate analyses of REM sleep duration and clinical and glycemic outcomes.
- Supplemental Table 2 (ST2). Glycemic outcomes and sleep monitoring over a 10-day period excluding subjects with OSAS.
- Supplemental Table 3 (ST3). Bivariate analyses of REM sleep and clinical and glycemic outcomes excluding subjects with OSAS.
- Supplemental Table 4 (ST4). Multivariate regression analysis to predict the % TIR3 and glucose variability (SD) excluding subjects with OSAS (n=101).
- Supplemental Table 5a (ST5a). Differential bacterial abundance (pFDR<0.1) associated with REM sleep duration as calculated by ANCOM-BC from shotgun metagenomic sequencing adjusting for age, BMI and sex.
- Supplemental Table 5b (ST5b). Differential bacterial abundance (pFDR<0.1) associated with mean insterstitial glucose as calculated by ANCOM-BC from shotgun metagenomic sequencing adjusting for age, BMI and sex.
- Supplemental Table 5c (ST5c). Differential bacterial abundance (pFDR<0.1) associated with mean amplitude of glycemic excursions (MAGE) as calculated by ANCOM-BC from shotgun metagenomic sequencing adjusting for age, BMI and sex.
- Supplemental Table 6a (ST6a). Differential bacterial abundance (pFDR<0.1) associated with REM sleep duration as calculated by ANCOM-BC from shotgun metagenomic sequencing adjusting for age, BMI, sex, hour of fecal collection, proton-pump inhibitors and fiber intake.
- Supplemental Table 6b (ST6b). Differential bacterial abundance (pFDR<0.1) associated with mean intestitial glucose as calculated by ANCOM-BC from shotgun metagenomic sequencing adjusting for age, BMI, sex, hour of fecal collection, proton-pump inhibitors and fiber intake.
- Supplemental Table 6c (ST6c). Differential bacterial abundance (pFDR<0.1) associated with mean amplitude of glycemic excursions (MAGE) as calculated by ANCOM-BC from shotgun metagenomic sequencing adjusting for age, BMI, sex, hour of fecal collection, proton-pump inhibitors and fiber intake.
- Supplemental Table 7a (ST7a). Differential bacterial abundance (pFDR<0.1) associated with REM sleep duration in subjects with obesity as calculated by ANCOM-BC from shotgun metagenomic sequencing adjusting for age, sex and hour of fecal collection.
- Supplemental Table 7b (ST7b). Differential bacterial abundance (pFDR<0.1) associated with REM sleep duration in subjects without obesity as calculated by ANCOM-BC from shotgun metagenomic sequencing adjusting for age, sex and hour of fecal collection.
- Supplemental Figure 1 (SF1). Linear correlation analyses of REM sleep duration with the % time in range 3 (%TIR3) (140-199 mg/dL) (a-c) and glucose variability (standard deviation, SD) (d-f) in all subjects (a,d), in subjects with obesity (b,e) and in subjects without obesity (c,f).
- Supplemental Figure 2 (SF2). Linear correlation analyses of REM sleep duration with transferrin saturation in men and women.
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