DOI: 10.5061/dryad.3bk3j9kq4
title: Data from: Gut microbiota composition and functionality are associated with REM sleep duration and continuous glucose levels

description: 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&lt;0.001) and glucose variability (SD, β=-0.350; p&lt;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.
description: # REM sleep, glycemia and gut microbiota\_Supplemental material
 ## 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&lt;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&lt;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&lt;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&lt;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&lt;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&lt;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&lt;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&lt;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. ## Sharing/Access
 information n/a ## Code/Software n/a