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      Impact of change in bedtime variability on body composition and inflammation: Secondary findings from the Go Red for Women Strategically Focused Research Network

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          Abstract

          Variability in daily sleep patterns is an emerging factor linked to metabolic syndrome. However, whether reducing bedtime variability improves markers of disease risk has not been tested. Here, we assessed whether body composition and inflammation were impacted by changes in bedtime variability over a 6-wk period, during which, women were instructed to maintain healthy, habitual sleep patterns (one arm of a randomized trial). Data were available for 37 women (age 34.9±12.4 y, BMI 24.7±2.9 kg/m 2, sleep duration 7.58±0.49 h/night). Body composition and leukocyte platelet aggregates (LPA) were measured at baseline and endpoint using magnetic resonance imaging and flow cytometry, respectively. Sleep data were collected weekly using wrist actigraphy. Change in bedtime variability was calculated as the difference in the standard deviation of bedtimes measured during the 2-wk screening period and the 6-wk intervention period. Results showed that women who reduced their bedtime variability (n=29) during the intervention had reductions in total (P<0.001) and subcutaneous adipose tissue (P<0.001) relative to women who increased/maintained (n=8) bedtime variability. Similar effects were observed for LPA levels between women who reduced vs increased/maintained bedtime variability (P=0.011). Thus, reducing bedtime variability, without changing sleep duration, could improve cardiometabolic health by reducing adiposity and inflammation.

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          Visceral adipose tissue: relations between single-slice areas and total volume.

          Visceral adipose tissue (VAT), which is linked with the metabolic consequences of obesity, is usually characterized by measuring VAT area at the L4-L5 vertebral interspace. However, the location of the slice with the strongest relation to VAT volume is not established. We sought to investigate the relations between cross-sectional VAT areas at different anatomic locations and VAT volume in a large, diverse sample of healthy subjects. VAT volume was derived from slice areas taken at 5-cm intervals from magnetic resonance images in 121 healthy men [x +/- SD age: 41.9 +/- 15.8 y; body mass index (BMI; in kg/m(2)): 26.0 +/- 3.2; VAT: 2.7 +/- 1.8 L] and 198 healthy women (age: 48.1 +/- 18.7 y; BMI: 27.0 +/- 5.4; VAT: 1.7 +/- 1.2 L). Regression models were developed to identify the best single slice for estimating VAT volume. The VAT area 10 cm above L4-L5 (A(+10)) in men (R(2) = 0.932, P < 0.001) and 5 cm above L4-L5 (A(+5)) in women (R(2) = 0.945, P < 0.001) had the highest correlation with abdominal VAT. R(2) increased by only 3.8% in men and 0.5% in women with adjustment for age, race, scanning position, BMI, and waist circumference. Studies using A(+10) in men and A(+5) in women will require 14% and 9% fewer subjects, respectively, than those using slices at L4-L5 and will have equivalent power. Measurement of slice areas at A(+10) in men and A(+5) in women provides greater power for the detection of VAT volume differences than does measurement at L4-L5.
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            Is Open Access

            Actigraphy-based sleep estimation in adolescents and adults: a comparison with polysomnography using two scoring algorithms

            Objectives Actigraphy is widely used to estimate sleep–wake time, despite limited information regarding the comparability of different devices and algorithms. We compared estimates of sleep–wake times determined by two wrist actigraphs (GT3X+ versus Actiwatch Spectrum [AWS]) to in-home polysomnography (PSG), using two algorithms (Sadeh and Cole–Kripke) for the GT3X+ recordings. Subjects and methods Participants included a sample of 35 healthy volunteers (13 school children and 22 adults, 46% male) from Boston, MA, USA. Twenty-two adults wore the GT3X+ and AWS simultaneously for at least five consecutive days and nights. In addition, actigraphy and PSG were concurrently measured in 12 of these adults and another 13 children over a single night. We used intraclass correlation coefficients (ICCs), epoch-by-epoch comparisons, paired t-tests, and Bland–Altman plots to determine the level of agreement between actigraphy and PSG, and differences between devices and algorithms. Results Each actigraph showed comparable accuracy (0.81–0.86) for sleep–wake estimation compared to PSG. When analyzing data from the GT3X+, the Cole–Kripke algorithm was more sensitive (0.88–0.96) to detect sleep, but less specific (0.35–0.64) to detect wake than the Sadeh algorithm (sensitivity: 0.82–0.91, specificity: 0.47–0.68). Total sleep time measured using the GT3X+ with both algorithms was similar to that obtained by PSG (ICC=0.64–0.88). In contrast, agreement between the GT3X+ and PSG wake after sleep onset was poor (ICC=0.00–0.10). In adults, the GT3X+ using the Cole–Kripke algorithm provided data comparable to the AWS (mean bias=3.7±19.7 minutes for total sleep time and 8.0±14.2 minutes for wake after sleep onset). Conclusion The two actigraphs provided comparable and accurate data compared to PSG, although both poorly identified wake episodes (i.e., had low specificity). Use of actigraphy scoring algorithm influenced the mean bias and level of agreement in sleep–wake times estimates. The GT3X+, when analyzed by the Cole–Kripke, but not the Sadeh algorithm, provided comparable data to the AWS.
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              Cross-sectional and Prospective Associations of Actigraphy-Assessed Sleep Regularity With Metabolic Abnormalities: The Multi-Ethnic Study of Atherosclerosis

              To cross-sectionally and prospectively investigate the association between irregular sleep patterns, a potential marker for circadian disruption, and metabolic abnormalities. In the Multi-Ethnic Study of Atherosclerosis, participants completed 7-day actigraphy at exam 5 (2010–2013) and were prospectively followed throughout exam 6 (2016 to 2017). Sleep regularity was quantified by the 7-day SD of actigraphy-assessed sleep duration and sleep onset timing. Metabolic abnormalities were defined by 1 ) the National Cholesterol Education Program Adult Treatment Panel III criteria and 2 ) a data-driven clustering of metabolic factors. In the exam 5 cross-sectional analysis adjusted for sociodemographic and lifestyle factors ( n = 2,003), every 1-h increase in the sleep duration SD was associated with 27% (95% CI 1.10, 1.47) higher odds of metabolic syndrome, and every 1-h increase in the sleep timing SD was associated with 23% (95% CI 1.06, 1.42) higher odds. The associations remained significant with additional adjustment for sleep-related factors including sleep duration. In the prospective analysis ( n = 970), the corresponding fully adjusted odds ratio (OR) was 1.27 (95% CI 0.97, 1.65) for sleep duration and 1.36 (1.03, 1.80) for sleep timing. Compared with the cluster of few metabolic changes, every 1-h increase in sleep variability was associated with almost doubled odds for the cluster characterized by incidence of multiple metabolic abnormalities (OR 1.97 [95% CI 1.18, 3.30] for sleep duration and OR 2.10 [95% CI 1.25, 3.53] for sleep timing). Increased variability in sleep duration and timing was associated with higher prevalence and incidence of metabolic abnormalities even after consideration of sleep duration and other lifestyle factors.
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                Author and article information

                Journal
                101256108
                32579
                Int J Obes (Lond)
                Int J Obes (Lond)
                International journal of obesity (2005)
                0307-0565
                1476-5497
                18 February 2020
                05 March 2020
                August 2020
                05 September 2020
                : 44
                : 8
                : 1803-1806
                Affiliations
                [1 ]Sleep center of excellence, Department of Medicine, Columbia University Irving Medical Center, New York, NY 10032
                [2 ]Department of Medicine, Columbia University Irving Medical Center, New York, NY 10032
                [3 ]Department of Biostatistics, Mailman School of Public Health, Columbia University Irving Medical Center, New York, NY 10032
                [4 ]Center for the Prevention of Cardiovascular Disease; Department of Medicine; New York University Langone Health, New York, NY 10010
                Author notes
                Corresponding author: Marie-Pierre St-Onge, PhD, CCSH, FAHA, 21 Audubon Avenue, SB01-132, New York, NY 10032, Phone 212-342-5607, ms2554@ 123456cumc.columbia.edu
                Article
                NIHMS1560830
                10.1038/s41366-020-0555-1
                7387143
                32132641
                ff6d3ea9-bb9c-4eb4-b72d-5c5c669dcb8d

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                Nutrition & Dietetics
                Nutrition & Dietetics

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