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. Author manuscript; available in PMC: 2025 Apr 1.
Published in final edited form as: J Sleep Res. 2023 Oct 6;33(2):e14068. doi: 10.1111/jsr.14068

Associations of actigraphy-assessed sleep variables with adiposity and serum cardiometabolic outcomes in emerging adults

Jessica McNeil 1, Nathaniel T Berry 1,2, Jessica M Dollar 1,3, Lenka H Shriver 4, Susan P Keane 3, Lilly Shanahan 5,6, Laurie Wideman 1
PMCID: PMC10947974  NIHMSID: NIHMS1934929  PMID: 37803814

Abstract

This study assessed associations of actigraphy-assessed sleep with adiposity and serum cardiometabolic outcomes in emerging adults, and whether sex and race modified these associations. Data on 147 emerging adults (age=19.4±1.3 years; Body Mass Index (BMI)=26.4±7.0 kg/m2; 59% female; 65% White) from RIGHT Track Health were used. Actigraphy-based sleep measures included sleep duration, sleep efficiency, sleep timing midpoint, day-to-day sleep duration and sleep timing midpoint variability. Combined sleep duration and sleep timing behaviors were also derived (early-bed/late-rise, early-bed/early-rise, late-bed/late-rise, late-bed/early-rise). Outcomes included BMI and BodPod-assessed fat mass index (FMI); fasting serum leptin, C-Reactive Protein (CRP), and Homeostatic Model Assessment-Insulin Resistance (HOMA-IR). Sleep duration was 5.4 hours/night. We noted an inverse association between sleep duration and HOMA-IR. The early-bed/early-rise group had greater BMI, CRP and HOMA-IR compared to the early-bed/late-rise group (referent). Sex modified associations of sleep efficiency with CRP; stratified results revealed positive association between sleep efficiency and CRP in males, but not females. Race modified associations of sleep duration with BMI and leptin, and of sleep duration variability with CRP. Stratified analyses revealed inverse associations between sleep duration with BMI and leptin in Black, multiracial/other race individuals only. Positive association between sleep duration variability and CRP was noted in White individuals only. Shorter sleep duration, particularly when combined with earlier sleep timing, is associated with greater adiposity and serum cardiometabolic outcomes. Additional studies are needed to assess individual- and contextual-level factors that may contribute to sex and race differences in sleep health and cardiometabolic risk in emerging adults.

Keywords: Adiposity, Emerging Adulthood, Sleep, Cardiometabolic Health

INTRODUCTION

Emerging adulthood (approximately ages 18–28 years) represents a developmental period defined by “frequent change and exploration” (Arnett, 2000). Emerging adults often face competing demands (e.g., school, work, personal and social relationships) as they aim to establish their identities, independence, relationships, and career and life goals (Arnett, 2000). Adult health behaviors, including sleep, are often established during emerging adulthood, shaping the lifelong trajectory of cardiometabolic risk and wellbeing (Gooding et al., 2020). Furthermore, evidence suggests that sleep duration gradually declines during emerging adulthood (Maslowsky & Ozer, 2014). Concurrent with this decline in sleep duration, obesity rates double from adolescence through early emerging adulthood, and then double again by late emerging adulthood (Barbour-Tuck et al., 2018; Gordon-Larsen et al., 2004; Gordon-Larsen et al., 2010).

Short sleep duration (typically <7 hours of sleep/night in adults) and poor sleep quality (typically self-reported or based on actigraphy-measured sleep efficiency <85%) are consistently associated with higher body weight, fat mass (FM) and/or a greater obesity risk (Antza et al., 2021; Bacaro et al., 2020; Fatima et al., 2016). Recent reviews also reported that having a later sleep timing midpoint (i.e., wake-time – ½ sleep duration), as well as greater day-to-day sleep duration and sleep timing variability are associated with greater adiposity and adverse cardiometabolic outcomes (Chaput et al., 2020; Morales-Ghinaglia & Fernandez-Mendoza, 2023). Consistent evidence suggests that emerging adults and college students with shorter sleep duration and/or poorer sleep quality have greater adiposity (Bailey et al., 2014; Fernström et al., 2020; Kahlhöfer et al., 2016; Krističević et al., 2018; Meyer et al., 2012; Peltzer & Pengpid, 2017; Quick et al., 2014; Sa et al., 2020; Vargas et al., 2014; Yang et al., 2020), but much of this evidence is limited to self-reported sleep variables and/or using Body Mass Index (BMI) as a single indicator of adiposity/obesity.

Short sleep duration and poor sleep quality are also consistently associated with disruptions in serum metabolic and inflammatory outcomes (Irwin et al., 2016; Koren & Taveras, 2018; Leproult & Van Cauter, 2010; Singh et al., 2022), which increases cardiometabolic risk. In emerging adults, there is initial evidence to suggest that short sleep duration, poor sleep quality and sleep duration variability are associated with worse serum cardiometabolic outcomes, including greater insulin resistance (as assessed by Homeostatic Model Assessment of Insulin Resistance (HOMA-IR)) (Fernström et al., 2020) and elevated C-Reactive Protein (CRP) (Bakour et al., 2017; Okun et al., 2009; Park et al., 2020). Further evidence on associations of multiple actigraphy-assessed sleep variables and serum cardiometabolic outcomes is needed to compliment these findings in emerging adults.

The primary aim of this cross-sectional analysis was to assess associations between actigraphy-assessed sleep duration, sleep efficiency, sleep timing midpoint, as well as day-to-day sleep duration and sleep timing midpoint variability with adiposity (BMI and FM index (FMI)), and serum cardiometabolic (leptin, CRP, HOMA-IR) outcomes in emerging adults. Our secondary aim was to assess effect modification of these associations by sex and race since these demographic factors may influence associations between sleep and obesity risk (Koren & Taveras, 2018). We hypothesized that shorter sleep durations, lower sleep efficiencies, later sleep timing midpoints, and greater day-to-day sleep duration and sleep timing midpoint variability would be associated with BMI, FMI, as well as fasting serum leptin, HOMA-IR, and CRP. We also hypothesized that sex and race will modify these associations, indicating stronger associations in females, as well as in Black and multiracial/other race individuals.

METHODS

Study design and participants

Cross-sectional data were collected during emerging adulthood from the RIGHT Track Health study (Dollar et al., 2020; Wideman et al., 2016). Participants completed an in-laboratory visit following a 10-hour overnight fast from food; ad libitum water intake was allowed. Participants were also asked to refrain from smoking/vaping the morning of the visit, as well as alcohol intake and vigorous-intensity physical activity participation for at least 24 hours prior to this visit. The visit was re-scheduled if participants reported: 1) illness/injury in the past week or surgery in the past month, 2) immunizations within the past 2 weeks, and 3) use of antibiotics, corticosteroids, or other prescription anti-inflammatories within the past 10 days. During this visit, after completing a fasting blood draw, participants were offered a small snack. Then, they completed questionnaires, resting heart rate and blood pressure measures, body composition measures, resting orthostatic challenge, maximal exercise testing, and post-exercise orthostatic challenge. Following this visit, participants were given an accelerometer to wear for 7 consecutive days. The Institutional Review Board at the University of North Carolina at Greensboro approved all study procedures (IRB #11–0360). Written informed consent was obtained from all participants. A total of 319 participants completed at least one component of data collection during the emerging adulthood timepoint. Actigraphy data were available in 165 participants. Of these, 147 participants had at least 3 valid days of accelerometry wear time (≥13 hours/day of wear time) from which sleep variables could be derived and were thus included in the present analyses. All valid accelerometry days for each participant were used in the sleep calculations and analyses. Twelve participants (8%) had 3–4 valid accelerometry days, 44 participants (30%) had 5–6 valid accelerometry days and 91 participants (62%) had 7–10 valid accelerometry days. Details on the original participant recruitment, and the emerging adulthood health assessments are described elsewhere (Dollar et al., 2020; Wideman et al., 2016). A flow chart presenting the selection of participants for these analyses is presented in Figure 1. Measures relevant to these analyses are described in more details below.

Figure 1.

Figure 1.

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Study flow chart and selection of participants for analyses focused on associations between multiple actigraphy-assessed sleep variables with adiposity and serum cardiometabolic outcomes in emerging adults, The RIGHT TRACK Health study, North Carolina, USA, 2014–2018.CRP, C-Reactive Protein; HOMA-IR, Homeostatic Model Assessment-Insulin Resistance.

Actigraphy-derived sleep variables

Participants were asked to wear an Actigraph GT9X Link accelerometer (Actigraph LLC, Pensacola, FL) on their non-dominant wrist for 24 hours per day over 7 consecutive days. Some participants wore the device for more than 7 days prior to mailing it back to the research team. Participants were instructed to remove the device for water-based activities, and when required by sporting competition or occupation (Wideman et al., 2016). Periods of non-wear time were reported using a log sheet. Accelerometers collected data at a sampling rate of 30 Hz and aggregated to 60-second epoch files for analysis by the Actilife software (version 6.13.4). Sleep duration (hours/day) and sleep efficiency (sleep duration/time in bed; %) were derived from the Sadeh algorithm because it was originally validated on a sample of adolescents and emerging adults (Sadeh et al., 1994). Sleep duration was based on total sleep time (including multiple bouts of sleep in one night and daytime naps) accumulated during a 24-hour period. Following visual inspection of the daily sleep data for each participant after applying the Sadeh algorithm, some participants had multiple bouts of sleep in one night (e.g., two bouts of sleep separated by <3 hours). In these instances, the elapsed time between bedtime for the first sleep bout and waketime for the last sleep bout (when <3 hours separated each bout) was used as the denominator in the sleep efficiency calculation. This approach has been recommended by Reed & Sacco (2016) to account for periods of sleep discontinuation (i.e., time out of bed during nighttime awakenings). A nap was logged/identified if a prior sleep bout had occurred, and this nap was separated from that prior sleep bout by ≥3 hours. A total of only six naps were identified in six different participants, hence, we do not expect that the inclusion of the nap data into the total sleep duration calculation will meaningfully impact the results. The Sadeh algorithm was also used to derive bed- and wake-times, from which sleep timing midpoint (clock time) was calculated as wake-time – ½ sleep duration (Roenneberg et al., 2003). Sleep timing midpoint is meant to be an indicator of endogenous circadian phase (or when a person prefers to sleep) and has been referred to as the best phase anchor point for melatonin onset (Roenneberg et al., 2003). The sleep timing midpoint calculation was only applied to prolonged night- or daytime sleep behaviors (i.e., excluded naps) to have a single sleep timing midpoint value per day for each participant. Lastly, day-to-day sleep duration and sleep timing midpoint variability were calculated as the coefficient of variation in sleep duration and sleep timing midpoint (i.e., standard deviation (SD) of sleep duration or sleep timing midpoint across all valid accelerometry days divided by mean sleep duration or sleep timing midpoint*100), as previously described by Lemola et al. (2013).

Adiposity outcomes

Height was measured to the nearest 0.1 cm using a wall-mounted stadiometer (SECA, Chino, CA, USA); weight was measured to the nearest 0.1 kg with a balance-beam scale (Detecto-Medic, Brooklyn, NY, USA). BMI was calculated as body weight (kg)/height (m2). FM (kg) was assessed via air displacement plethysmography with a BOD POD (Cosmed, Concord, CA, USA). Participants entered the BOD POD chamber wearing minimal, skintight clothing (e.g., spandex shorts, sports bra), and a swim cap to cover their hair when possible. Standard manufacturer calibration and measurement procedures were followed. Participants’ thoracic lung volume was measured using the BOD POD breathing circuit system, and FM was calculated using age and race appropriate algorithms built into the BOD POD system. FMI was calculated as FM (kg)/height (m2). We chose to use BMI and FMI in these analyses to account for participants’ height. Previous research has found that FMI helps to classify obesity more accurately when compared to body fat percentage in men and women (Peltz et al., 2010).

Serum cardiometabolic outcomes

Fasting serum leptin (ng/mL), insulin (pg/ml), and CRP (mg/L) were analyzed at the University of North Carolina at Greensboro Exercise Physiology Laboratory, using multiplex ELISA kits (EMD Millipore Sigma, Burlington, MA) and the Luminex 200s (Luminex Corp, Austin, TX) plate reader. Fasting glucose (mg/dL) was measured using a colorimetric assay (Cayman Chemical, Ann Arbor, MI) and the EPOCH plate reader (Biotek, Santa Clara, CA). All samples were analyzed in duplicate with appropriate quality controls and all samples from a single participant were analyzed in the same ELISA plate to minimize inter-assay variability (Wideman et al., 2016). Fasting serum glucose and insulin levels were used to calculate HOMA-IR as follows: fasting insulin (μU/ml) * fasting glucose (mg/dL)/405. Insulin unit conversions followed standardized procedures, with a molecular weight of 5808 g/mol for insulin and the 1 μIU/ml equal to a 6 pmol/L conversion, as outlined by Knopp et al (Knopp et al., 2019).

Statistical Analyses

All analyses were performed using STATA software version 17 (StataCorp, College Station, Texas). Descriptive data are presented as mean ± SD. Differences in descriptive data between sex (male vs. female) and race (White vs. Black and multiracial/other race) were assessed using an independent sample t-test. Stem-and-leaf and Q-Q plots were visually inspected to assess normality of data distribution for all study outcomes (BMI, FMI, leptin, CRP, and HOMA-IR), which were not normally distributed, and log transformed. A log (x+1) transformation was also used for HOMA-IR and CRP because of the presence of negative log transformed values. Stem-and-leaf and Q-Q plots were repeated for the log transformed data to confirm that the normal distribution of these data was improved.

Linear regression models were used to assess associations between continuous sleep duration, sleep efficiency, sleep timing midpoint, day-to-day sleep duration variability and day-to-day sleep timing midpoint variability with adiposity and serum cardiometabolic outcomes. As previously described (Mikulovic et al., 2014; Zerón-Rugerio et al., 2020), combined sleep duration and sleep timing behaviors were defined using the following categories: 1-earlier sleep timing midpoint and longer sleep duration (early-bed/late-rise), 2-earlier sleep timing midpoint and shorter sleep duration (early-bed/early-rise), 3-later sleep timing midpoint and longer sleep duration (late-bed/late-rise), 4-later sleep timing midpoint and shorter sleep duration (late-bed/early-rise). To do so, a median split for sleep timing midpoint (4h39AM) was used to define the “earlier vs. later” sleep timing midpoint categories. Participants within each of these sleep timing groups were further subdivided into two categories (shorter vs. longer sleep durations) to have an equal number of participants across the four abovementioned groups. Linear regression models were also used to assess associations between these combined sleep duration and sleep timing midpoint categories with adiposity and serum cardiometabolic markers. The early-bed/late-rise category was used as the referent group. Covariates included age, sex, race, number of valid accelerometry/sleep days and actigraphy-assessed total activity time (total number of minutes per day spent ≥100 counts/minute) (Troiano et al., 2008). Continuous sleep duration, sleep efficiency, sleep timing midpoint, as well as sleep duration variability and sleep timing midpoint variability were considered as covariates when not the predictor of interest. For the combined sleep duration and sleep timing midpoint categories, only sleep efficiency, sleep duration variability and sleep timing variability were considered as sleep-related covariates. To determine the final model of best fit for each linear regression model, a stepwise backwards elimination linear regression model set at P <0.1 that included all covariates was first used to generate a sequence of covariates to be added one at-a-time to subsequent models and assess the Akaike information criterion (AIC) associated with each of these models. The AIC balances both the fit and simplicity of the model by considering “improvements in model fit” following the addition of each covariate one at-a-time. The model with the lowest AIC value is considered to have the best “goodness of fit” and was selected as the final model for each analysis (i.e., the AIC was used to identify the most appropriate covariates to include in each linear regression model). Variance inflation factor (VIF) assessed multicollinearity; all final models had a VIF <2 indicating no evidence of multicollinearity.

We tested for effect modification by sex (male vs. female) and race (White vs. Black and multiracial/other race) by adding interaction terms for each sleep variable with sex or race categories (one at-a-time) to each final model. Black and multiracial/other race categories were combined because only eight participants identified as multiracial/other racial descent, and interpretation of the effect modification results did not change when we removed these eight multiracial/other race participants (results not shown). Stratified analyses were conducted if the interaction term reached P <0.10. Statistical significance was set at P <0.05.

RESULTS

A total of 147 participants were included in the present analyses, which included 61 males (41.5%) and 86 females (58.5%). Racial breakdown of the sample included 96 White individuals (65.3%) and 51 Black, multiracial/other race individuals (34.7%). The proportion of males (n=39; 41%) and females (n=57; 59%) among White individuals was comparable to the entire sample. Similarly, the proportion of males (n=22; 43%) and females (n=29; 57%) among Black, multiracial/other race individuals was comparable to the entire sample. Descriptive data are presented in Table 1. Sleep duration was only 5.4 hours/night in this sample, which did not differ between sex and race categories. Despite these short sleep durations, sleep efficiency was good (87%), and was significantly higher in Black, multiracial/other race individuals compared to White individuals. Sleep duration variability was also significantly greater in Black, multiracial/other race individuals compared to White individuals. No significant differences in sleep timing midpoint and sleep timing midpoint variability were noted between sex and race categories. Regarding adiposity and serum cardiometabolic outcomes, significantly greater FMI and leptin were noted in females compared to males. Black, multiracial/other race individuals had significantly greater BMI, FMI, HOMA-IR, and CRP compared to White individuals.

Table 1.

Participant characteristics in all participants, and based on sex (male vs. female) and race (White vs. Black, multiracial/other) from the RIGHT Track Health Study, North Carolina, USA, 2014–2018

All participants Males only Females only Differences between sex White racial descent only Black and multiracial/other racial descent only Differences between racial groups
n Mean ± SD n Mean ± SD n Mean ± SD n Mean ± SD n Mean ± SD
Age (months) 147 233 ± 16 61 233 ± 16 86 232 ± 15 t (145)= 0.35, P= 0.73 96 233 ± 16 51 233 ± 15 t (145)= 0.002, P= 0.99
Age (years) 147 19.4 ± 1.3 61 19.4 ± 1.3 86 19.3 ± 1.3 96 19.4 ± 1.3 51 19.4 ± 1.3
BMI (kg/m 2 ) 147 26.4 ± 7.0 61 26.1 ± 6.9 86 26.6 ± 7.1 t (145)= −0.44, P= 0.66 96 24.8 ± 5.1 51 29.4 ± 8.9 t (145)= −4.00, P= 0.0001
FMI (kg/m 2 ) 147 7.8 ± 5.8 61 5.8 ± 5.6 86 9.3 ± 5.4 t (145)= −3.81, P= 0.0002 96 6.8 ± 4.4 51 9.8 ± 7.4 t (145)= −3.07, P= 0.003
Leptin (pg/ml) 137 14231 ± 17402 59 5378 ± 6973 78 20927 ± 19819 t (135)= −5.76, P< 0.0001 92 12198 ± 14037 45 18388 ± 22410 t (135)= −1.98, P= 0.05
HOMA-IR 136 7.3 ± 7.8 58 7.5 ± 6.3 78 7.2 ± 8.8 t (134)= 0.16, P= 0.88 91 6.1 ± 4.9 45 9.9 ± 11.4 t (134)= −2.73, P= 0.01
CRP (mg/L) 137 1.7 ± 3.7 59 1.3 ± 2.7 78 2.0 ± 4.3 t (135)= −1.13, P= 0.26 92 1.2 ± 1.9 45 2.7 ± 5.8 t (135)= −2.32, P= 0.02
Total activity time (minutes/day) 147 777 ± 93 61 790 ± 91 86 769 ± 94 t (145)= 1.36, P= 0.18 96 768 ± 92 51 794 ± 95 t (145)= −1.63, P= 0.11
Sleep duration (hours/day) 147 5.4 ± 1.3 61 5.2 ± 1.5 86 5.4 ± 1.1 t (145)= −0.83, P= 0.41 96 5.4 ± 1.1 51 5.3 ± 1.5 t (145)= 0.11, P= 0.91
Sleep efficiency (%) 147 87 ± 4 61 87 ± 4 86 87 ± 4 t (145)= −0.33, P= 0.74 96 86 ± 4 51 88 ± 4 t (145)= −2.99, P= 0.003
Sleep timing midpoint (clock time ± hours) 147 4h47AM ± 2.1 61 4h52AM ± 1.4 86 4h43AM ± 2.5 t (145)= 1.72, P= 0.09 96 4h43AM ± 1.4 51 4h53AM ± 3.1 t (145)= 1.17, P= 0.24
Sleep duration variability (%) 147 38 ± 15 61 38 ± 18 86 37 ± 13 t (145)= 0.27, P= 0.79 96 36 ± 13 51 42 ± 19 t (145)= −2.25, P= 0.03
Sleep timing midpoint variability (%) 147 45 ± 26 61 40 ± 24 86 49 ± 27 t (145)= −1.94, P= 0.05 96 42 ± 26 51 51 ± 26 t (145)= −1.86, P= 0.06
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Associations between sleep variables with adiposity and serum cardiometabolic outcomes

Results from adjusted linear regression models for associations between continuous sleep variables with adiposity and serum cardiometabolic outcomes are presented in Table 2. A significant inverse association was noted between sleep duration and HOMA-IR, suggesting that participants with lower sleep durations have greater HOMA-IR. No other significant associations were noted between continuous sleep variables with adiposity and serum cardiometabolic outcomes. Associations between combined sleep duration and sleep timing midpoint groups with adiposity and serum cardiometabolic outcomes are presented in Table 3. BMI, CRP, and HOMA-IR were significantly greater in the early-bed/early-rise group compared to the early-bed/late-rise group (referent). No significant differences in adiposity and serum cardiometabolic markers were noted between the late-bed/late-rise and late-bed/early-rise groups when compared to the early-bed/late-rise group (referent).

Table 2.

Associations between sleep duration, sleep efficiency, sleep timing midpoint, sleep duration variability and sleep timing midpoint variability with adiposity and serum cardiometabolic outcomes, the RIGHT Track Health study, North Carolina, USA, 2014–2018

Multivariable-adjusted linear regression model results, β (95% CI); P-value Included covariates
Sleep duration
Log-transformed BMI −0.03 (−0.05, 0.003); 0.08 Race
Log-transformed FMI −0.02 (−0.10, 0.06); 0.69 Number of sleep days, Sex, Race
Log-transformed leptin −0.09 (−0.25, 0.08); 0.30 Sleep timing midpoint, Sex, Race, Sleep timing midpoint variability
Log-transformed CRP −0.03 (−0.12, 0.06); 0.49 Race, Sex
Log-transformed HOMA-IR −0.10 (−0.19, −0.01); 0.02 Race
Sleep efficiency
Log-transformed BMI 0.01 (−0.004, 0.01); 0.25 Race, Sleep duration
Log-transformed FMI 0.01 (−0.01, 0.04); 0.25 Number of sleep days, Sex, Race
Log-transformed leptin 0.01 (−0.04, 0.06); 0.58 Sleep timing midpoint, Sex, Race
Log-transformed CRP 0.01 (−0.02, 0.04); 0.46 Sex, Race
Log-transformed HOMA-IR −0.01 (−0.03, 0.02); 0.63 Sleep duration, Race
Sleep timing midpoint
Log-transformed BMI −5.19−09 (−1.30−08, 2.63−09); 0.19 Race, Sleep duration, Sleep timing midpoint variability
Log-transformed FMI −1.33−08 (−3.54−08, 8.84−09); 0.24 Number of sleep days, Sex, Race, Sleep timing midpoint variability
Log-transformed leptin −3.71−08 (−7.52−08, 1.02−09); 0.06 Race, Sex
Log-transformed CRP −1.41−08 (−3.62−08, 7.97−09); 0.21 Sex, Race
Log-transformed HOMA-IR −8.05−09 (−2.89−08, 1.27−08); 0.45 Race, Sleep duration
Sleep duration variability
Log-transformed BMI −0.002 (−0.003, 0.002); 0.88 Race, Sleep duration
Log-transformed FMI 0.001 (−0.01, 0.01); 0.81 Number of sleep days, Sex, Race
Log-transformed leptin −0.01 (−0.02, 0.01); 0.34 Sleep timing midpoint, Sex, Race
Log-transformed CRP 0.003 (−0.005, 0.01); 0.49 Sex, Race
Log-transformed HOMA-IR −0.003 (−0.01, 0.004); 0.37 Sleep duration, Race
Sleep timing midpoint variability
Log-transformed BMI −0.001 (−0.002, 0.001); 0.33 Race, Sleep duration
Log-transformed FMI −0.003 (−0.01, 0.002); 0.22 Number of sleep days, Sex, Race
Log-transformed leptin −0.01 (−0.02, 0.004); 0.23 Sleep timing midpoint, Sex, Race
Log-transformed CRP 0.002 (−0.002, 0.01); 0.32 Sex, Race
Log-transformed HOMA-IR 0.001 (−0.003, 0.01); 0.65 Sleep duration, Race
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Table 3.

Associations between combined sleep duration and sleep timing midpoint categories with adiposity and serum cardiometabolic outcomes, the RIGHT Track Health study, North Carolina, USA, 2014–2018

Mean ± SD Multivariable-adjusted linear regression model results, β (95% CI); P-value Included covariates
Log-transformed BMI Race
Early-bed/late-rise (n= 37) 3.18 ± 0.18 Referent
Early-bed/early-rise (n= 37) 3.31 ± 0.25 0.13 (0.03, 0.23); 0.01
Late-bed/late-rise (n= 37) 3.24 ± 0.27 0.06 (−0.04, 0.16); 0.23
Late-bed/early-rise (n= 36) 3.24 ± 0.22 0.07 (−0.03, 0.18); 0.16
Log-transformed FMI Number of sleep days
Early-bed/late-rise (n= 37) 1.79 ± 0.64 Referent Sex
Early-bed/early-rise (n= 37) 1.90 ± 0.69 0.25 (−0.05, 0.55); 0.10 Race
Late-bed/late-rise (n= 37) 1.88 ± 0.82 0.14 (−0.15, 0.43); 0.35 Sleep efficiency
Late-bed/early-rise (n= 36) 1.68 ± 0.74 0.21 (−0.11, 0.53); 0.19
Log-transformed Leptin Race
Early-bed/late-rise (n= 34) 8.97 ± 1.40 Referent Sex
Early-bed/early-rise (n= 35) 8.89 ± 1.63 0.32 (−0.26, 0.90); 0.28
Late-bed/late-rise (n= 33) 8.94 ± 1.34 0.04 (−0.54, 0.63); 0.88
Late-bed/early-rise (n= 35) 8.18 ± 1.56 −0.11 (−0.71, 0.48); 0.70
Log-transformed CRP Race
Early-bed/late-rise (n= 34) 0.50 ± 0.50 Referent Sex
Early-bed/early-rise (n= 35) 0.81 ± 0.81 0.35 (0.02, 0.68); 0.04
Late-bed/late-rise (n= 33) 0.66 ± 0.71 0.17 (−0.15, 0.50); 0.29
Late-bed/early-rise (n= 35) 0.57 ± 0.70 0.17 (−0.16, 0.51); 0.31
Log-transformed HOMA-IR Race
Early-bed/late-rise (n= 33) 1.69 ± 0.55 Referent
Early-bed/early-rise (n= 35) 2.09 ± 0.88 0.38 (0.07, 0.69); 0.02
Late-bed/late-rise (n= 33) 1.85 ± 0.60 0.17 (−0.15, 0.48); 0.30
Late-bed/early-rise (n= 35) 1.84 ± 0.52 0.17 (−0.14, 0.48); 0.29
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Effect modification results

Sex modified associations between sleep efficiency with FMI (Pinteraction= 0.03), leptin (Pinteraction= 0.03) and CRP (Pinteraction= 0.02). Sex also modified associations between sleep duration variability with CRP (Pinteraction= 0.095). Results from stratified analyses are presented in Table 4 and revealed a significant positive association between sleep efficiency and CRP in males, but not females.

Table 4.

Associations between continuous sleep variables with adiposity and serum cardiometabolic outcomes, stratified by sex and race categories*, the RIGHT Track Health study, North Carolina, USA, 2014–2018

Multivariable-adjusted linear regression model results, β (95% CI); P-value Multivariable-adjusted linear regression model results, β (95% CI); P-value
Males Females White racial descent Black and multiracial/other racial descent
Sleep efficiency Sleep duration
FMI 0.04 (−0.005, 0.09); 0.08 −0.003 (−0.03, 0.02); 0.85 BMI 0.0004 (−0.03, 0.04); 0.98 −0.05 (−0.10, −0.003); 0.04
Leptin 0.07 (−0.02, 0.17); 0.12 −0.04 (−0.09, 0.02); 0.18 FMI 0.06 (−0.04, 0.16); 0.24 −0.07 (−0.22, −0.07); 0.30
CRP 0.05 (0.01, 0.08); 0.01 −0.02 (−0.06, 0.02); 0.43 Leptin 0.05 (−0.17, 0.28); 0.62 −0.29 (−0.53, −0.05); 0.02
Sleep duration variability Sleep duration variability
CRP −0.01 (−0.01, 0.004); 0.23 0.01 (−0.002, 0.02); 0.12 CRP 0.01 (0.0002, 0.02); 0.04 −0.01 (−0.02, −0.01); 0.42
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*

Stratified analyses were only conducted if the interaction term added to the final multivariable model reached P <0.10. Statistical significance for the stratified results was set at P <0.05.

Race modified associations between sleep duration with BMI (Pinteraction= 0.07), FMI (Pinteraction= 0.096) and leptin (Pinteraction= 0.06). Stratified analyses revealed significant inverse associations between sleep duration with BMI and leptin in Black, multiracial/other race individuals, but not White individuals (Table 4). Additionally, race modified the association between sleep duration variability with CRP (Pinteraction= 0.08), and stratified analyses revealed a significant positive association between sleep duration variability and CRP in White individuals, but not Black, multiracial and other race individuals (Table 4).

DISCUSSION

This study examined cross-sectional associations between multiple actigraphy-assessed sleep variables with adiposity and serum cardiometabolic outcomes in a sample of emerging adults with a very high prevalence of short sleep duration (~90% had <7 hours of sleep/night). We also explored potential effect modification of these associations by sex and race. While population-based data from the National Health Interview Survey (NHIS) in 2017 noted that ~67% of U.S. adults reported sleeping on average ≥7 hours/night (Sheehan et al., 2019), accumulating evidence in adolescents and emerging adults suggest that these individuals are more likely to have shorter sleep durations (Casper-Gallop;, 2022; Maslowsky & Ozer, 2014) and greater day-to-day variability in sleep patterns (Morales-Ghinaglia & Fernandez-Mendoza, 2023). Our findings add to this literature by suggesting that sleep habits in emerging adults fall short of minimum guidelines; making this an essential developmental period to examine and intervene on sleep behaviors to help reduce obesity and cardiometabolic disease risk.

A significant inverse association between sleep duration and HOMA-IR was noted in the entire sample, suggesting that those with shorter sleep durations have less advantageous HOMA-IR/greater insulin resistance. These results corroborate prior findings in emerging adults (Fernström et al., 2020) and provide further evidence of an association between short sleep duration and HOMA-IR as a risk factor for cardiometabolic disease in this population.

Additionally, BMI, HOMA-IR and CRP were significantly greater in the early-bed/early-rise group compared to the early-bed/late-rise group, whereas no significant differences were noted with the “late-bed” groups. Recent studies have also reported that having an early-bedtime/early-wake-time was associated with higher BMI and waist circumference in female emerging adults (Zerón-Rugerio et al., 2020), and that adolescents with earlier waketime and sleep timing midpoint had higher FM 1 year later (LeMay-Russell et al., 2021). Chronotype, which varies with age, may partially explain these associations in adolescents and emerging adults. Indeed, children have earlier chronotypes which gradually delay until reaching a peak of “lateness” during emerging adulthood and then gradually shift earlier with advancing age (McMahon et al., 2018; Roenneberg et al., 2007). While chronotype was not assessed in the present study, better aligning sleep timing with chronotype (which tends to be delayed during adolescence and emerging adulthood) in addition to promoting adequate sleep durations may be associated with lower obesity and cardiometabolic risk.

Our effect modification results revealed a significant positive association between sleep efficiency and CRP in males, but not females. The reason for these sex differences, or for the positive association between sleep efficiency and CRP are unclear. Richardson & Churilla (2017) did note that males who reported sleeping ≤6 hours/night had higher odds of elevated CRP when compared to males reporting 7–8 hours of sleep/night, whereas this association was non-significant in females. Although the association between sleep duration and CRP was non-significant, and sleep duration was considered as a covariate in the sleep efficiency-CRP model, it is possible that higher sleep efficiencies may be due to sleep deprivation or prolonged wakefulness prior to bed (i.e., individuals with shorter sleep durations may have greater sleep efficiencies because of prolonged wakefulness/tiredness prior to sleeping). Furthermore, the very high prevalence of short sleep durations in our sample likely undermined this underlying sleep duration-CRP association.

Our effect modification results also revealed that the inverse association between sleep duration with BMI and leptin was significant in Black, multiracial/other race individuals, but not White individuals. These results complement findings from the NHIS which reported higher odds of obesity among Black short duration sleepers compared to White short duration sleepers (Donat et al., 2013), as well as a recent study which noted that both short and long sleep durations were significantly associated with obesity in Black college students only (Sa et al., 2020). We also noted a significant positive association between sleep duration variability and CRP in White individuals, but not Black, multiracial/other race individuals. These results corroborate those reported by Park et al. (2020) where the positive association between sleep duration variability and CRP was present in European American youth, but not in Asian and Latino youth. The reasons for these racial differences in sleep-obesity/inflammation associations are not well understood and are likely multifactorial and systemic (Donat et al., 2013; Koren & Taveras, 2018). For instance, Park et al. (2020) suggested that additional sources of stress which are more common in minoritized populations (e.g., poverty, discrimination) may blunt the influence of sleep on inflammation. Additional studies are needed to focus on individual- and systemic-level “stress” factors that may contribute to these racial differences in sleep-obesity and cardiometabolic risk associations.

This study had several strengths and limitations. Strengths included a focus on multiple actigraphy-assessed sleep variables in a population-based sample of emerging adults with a very high prevalence of short sleep duration and the consideration of potential effect modification by sex and race. Limitations include the cross-sectional nature of the analyses, which limits our ability to identify sleep variables as a potential cause of obesity/weight gain and/or cardiometabolic risk, particularly during a transitive period of continued weight change such as emerging adulthood (Barbour-Tuck et al., 2018). The homogeneity of sleep duration patterns in this sample (i.e., ~90% of this sample had <7 hours of sleep/night) likely limited the strength of associations between sleep duration with adiposity and serum cardiometabolic outcomes. Information on self-reported bed- and wake-times were not collected with the accelerometer log, meaning that this information could not be used to verify bed- and wake-times detected with the Sadeh algorithm. To help mitigate this issue, all individual days with <13 hours of wear time were removed from the analyses. Furthermore, menstrual cycle phase was not reported nor accounted for during data collection in females, which may undermine some of the associations between sleep and serum cardiometabolic markers (leptin and CRP) that vary across the menstrual cycle. Lastly, we had to combine Black racial descent with multiracial/other race categories because only eight participants identified as multiracial/other racial descent.

CONCLUSION

Our results add to accumulating evidence that short sleep duration is prevalent in emerging adults (Casper-Gallop;, 2022; Maslowsky & Ozer, 2014), and indicate that shorter sleep duration, particularly when combined with earlier sleep timing midpoint (<4h39AM), is associated with greater adiposity and serum cardiometabolic outcomes. Our results also suggest that the association between shorter sleep duration and these outcomes may be stronger in Black, multiracial/other race individuals, whereas greater sleep duration variability and sleep efficiency are associated with higher CRP in White individuals and males, respectively. Additional longitudinal evidence is needed to corroborate these findings in emerging adults, with a particular need to focus on individual- and contextual-level factors that may influence both sleep health and cardiometabolic disease risk in emerging adults.

Acknowledgements:

The RIGHT Track Health study was supported by funding from NIH-NICHD grant 1R01HD078346 to the University of North Carolina at Greensboro and NIH-NIDDK grant P30DK056350 to the UNC Nutrition Obesity Research Center. The content is solely the responsibility of the authors and does not necessarily represent the official views of the NIH. The authors thank the participants who generously gave their time to contribute to this study.

Footnotes

CONFLICTS OF INTEREST

The authors declared no conflicts of interest.

DATA AVAILABILITY STATEMENT

Data described in the manuscript, code book, and analytic code can be made available upon request.

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Data Availability Statement

Data described in the manuscript, code book, and analytic code can be made available upon request.

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