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. Author manuscript; available in PMC: 2019 Dec 1.
Published in final edited form as: Psychoneuroendocrinology. 2018 Jul 25;98:46–51. doi: 10.1016/j.psyneuen.2018.07.024

Sleep Quality is Differentially Related to Adiposity in Adults

S Katherine Sweatt 1, Barbara A Gower 2, Angela Y Chieh 1, Yang Liu 1, Li Li 1,*
PMCID: PMC6204309  NIHMSID: NIHMS1503279  PMID: 30098512

Abstract

Objectives.

Sleep duration is associated with adiposity in adults. Abdominal adiposity specifically is strongly correlated with metabolic alterations, however, the relationships between abdominal adiposity and sleep quality are incompletely understood. The purpose of this study is to test the hypothesis that abdominal adiposity is related to poor sleep quality while total adiposity is not; and to explore whether pathways, including immune system and hypothalamic-pituitary-adrenal axis, link abdominal adiposity to poor sleep quality.

Methods.

Subjects were 101 men and women aged 38.88±11.96 years with body mass index between 29.35±6.93kg/m2. Subjective sleep quality was determined by the Pittsburgh Sleep Questionnaire Index (PSQI). Body composition was determined by dual energy x-ray absorptiometry. Saliva and blood samples were collected for assessment of cortisol and markers of inflammation. In a cross-sectional study design, correlation analysis was conducted to determine the relationships between poor sleep quality and adiposity. Participants were stratified based on PSQI score to evaluate differences in main outcomes between subjects with normal (NSQ; PSQI ≤5) vs poor sleep quality (PSQ; PSQI>5).

Results.

Poor sleep quality was related to greater visceral fat (r=0.26; p<0.05). but not total fat. The PSQ group had greater visceral fat compared to the NSQ group (1.11±0.83kg vs 0.79±0.62kg; p<0.05), however, there was no difference in total fat mass (33.18±14.21kg vs 29.39±13.03kg; p=0.24). The PSQ group had significantly greater leptin (1.37±0.07ng/ml vs 1.08±0.08ng/ml; p<0.05), but hypothalamic-pituitary-adrenal axis activity did not differ between the PSQ and NSQ groups.

Conclusions.

Poor sleep quality is associated with greater visceral adiposity and leptin secretion. Further research is needed to probe potential cause and effect relationships among visceral adipose tissue, leptin, and sleep quality.

Precis:

The relationship between sleep quality and adiposity was investigated in adults. Poor sleep quality was related to increased abdominal fat. Increased leptin may be involved in this relationship.

1. Introduction

The prevalence of obesity has steadily risen worldwide in the last few decades and has become a major public health issue. More than one-third of adults (34.9%) in the United States are obese [1]. Obesity, particularly in the visceral region, is related to increased risk of diseases such as diabetes, cardiovascular disease (CVD), and some cancers [2]. Measures such as body mass index (BMI) and weight give limited information about disease risk because they lack the ability to determine body fat distribution. Body fat itself is not necessarily the causal factor in the occurrence of CVD, diabetes, and other obesity-related metabolic disorders, but the metabolic consequences of body fat location and dysfunction are important. Visceral adipose tissue (VAT), specifically, is body fat in the intra-abdominal cavity surrounding the organs. The relationship between VAT and metabolic disorders, such as insulin resistance, CVD, and diabetes, is well established [3]. VAT has been shown to be a predominant source of chronic systemic inflammation which is a major contributor to the development of obesity-related diseases [4, 5]. Thus, it is critical to identify factors that are related to obesity, and specifically, to VAT.

One of the factors that may increase risk for obesity is insufficient sleep. Short sleep duration is commonly due to waking up too early, inability to fall asleep, and waking for long periods of time during the night. This can result in daytime fatigue, irritability, and reduced concentration [6]. Recent studies have reported short sleep duration is a predictor of obesity in adults [7]. Previous studies have shown that short sleep duration is related to increased abdominal adiposity in both adults and children [8-10]. In addition, a meta-analysis revealed that each reduction of 1 hour of sleep per day was associated with an increase in BMI by 0.35 per year [7]. Sleep duration is just one component of sleep quality, however. In addition to total sleep duration, sleep quality includes sleep latency, number of arousals, and subjective “depth” or “restfulness” of sleep [11]. Sleep modulates neuroendocrine function and glucose metabolism, and inadequate sleep is related to metabolic abnormalities such as decreased glucose tolerance and altered appetite regulating hormone [12], which in turn increases the risk of weight gain and obesity. However, limited research has been conducted to examine the relationship between sleep quality, and total and regional adiposity, such as VAT.

Adipose tissue is a secretory organ, and excess accumulation leads to increased release of pro-inflammatory cytokines, such as tumor necrosis factor- alpha (TNF-α), leptin, and interleukin-6 (IL-6), leading to low grade systemic inflammation [13]. Inflammation is associated with numerous diseases, including diabetes, CVD, and some cancers. In addition, several studies report plasma levels of cytokines are related to the sleep-wake cycle in humans [14]. IL-6, specifically, is inversely associated with short sleep duration and has been shown to be elevated after sleep restriction [15,16]. Furthermore, leptin, an adipocyte-derived hormone, is known to play a key role in the regulation of appetite and body weight [17]. Findings from the Spiegel group have proposed leptin as a possible link between the risk of obesity and abnormal sleep duration in healthy individuals [18,19]. Thus, chronic inflammation may be a link between adiposity and poor sleep quality.

It is well established that sleep is intricately linked with the hypothalamic-pituitary-adrenal (HPA) axis. Activation of the HPA axis is characterized by cortisol secretion in humans which leads to arousal [20]. Studies have shown that glucocorticoid administration causes arousal and reduced sleep time [20]. Poor sleep quality was shown to be associated with increased plasma cortisol levels [21]. Additionally, it has been shown that cortisol awakening response is higher the morning following short sleep duration [22]. Another study evaluated the diurnal cortisol secretion across the day and found that cortisol was higher in subjects with low sleep efficiency [23]. In addition, there is a strong relationship between HPA axis and energy homeostasis. Greater salivary cortisol has been shown to be related to increased BMI over time, suggesting elevations in cortisol are related to weight gain [24]. Furthermore, glucocorticoid receptor has been shown to be more abundant in VAT, indicating that glucocorticoid secretion may be linked to VAT mass [25]. Thus, it is plausible that there is a relationship between adiposity, specifically VAT, and poor sleep quality via HPA axis dysfunction.

While research supports the notion that poor sleep quality is related to obesity, specific fat depots have not been explored in relation to poor sleep quality. Therefore, the objective of this study was to test the hypothesis that abdominal adiposity is related to poor sleep quality while total adiposity is not. A secondary aim was to explore the hypothesis that chronic inflammation and/or HPA axis activity were involved in the relationship between obesity and sleep quality using correlation and regression analyses.

2. Material and methods

2.1. Subjects

This was a cross-sectional study design. All participants were recruited through the community, Birmingham, AL, or the inpatient/outpatient Psychiatric settings at the University of Alabama at Birmingham (UAB). The project was approved by the UAB Institutional Review Board and was conducted in accordance with the Helsinki Declaration of 1975. All participants provided written informed consent prior to participating in any research procedures. Of 120 men and women enrolled in the study, 101 participants completed all study procedures for data analysis. Of the 19 subjects that were excluded, 4 subjects did not complete the dual energy X-ray absorptiometry scan, 14 were excluded for sleep disorders, substance use, mania or diabetes, and 1 refused the blood draw. Participants included males and females between 19 and 55 years of age (Table 1). Participants were excluded if they: (1) had a known history of diabetes; (2) were taking medications known to affect body weight; (3) were pregnant or lactating; or (4) had a history of psychosis, bipolar disorder, or drug or alcohol use disorder within 1 year prior to enrollment. People with a diagnosis of sleep disorders, including sleep apnea, were also excluded from the study. Demographic data, medical history, and medications were recorded by self-report. Depressive symptoms or severity in all participants was measured using the Quick Inventory of Depressive Symptoms (QIDS) questionnaire [26]. The QIDS was designed to assess the severity of depressive symptoms by self-report. The scoring system of the QIDS comprises 9 domains of depressive symptoms, including 1) sad mood; 2) concentration; 3) self-criticism; 4) suicidal ideation; 5) interest; 6) energy/fatigue; 7) sleep disturbance (initial, middle, and late insomnia or hypersomnia); 8) decrease or increase in appetite or weight; and 9) psychomotor agitation or retardation. Each domain weights 0-3 and the total score ranges from 0 to 27.

Table 1.

Descriptive Statistics, n=101

 
Variable  Mean±S.D. or n 
Age (years) 38.88±11.96
Race (Caucasian/African American) 56/45
Sex (male/female) 43/58
Body mass index (kg/m2) 29.35±6.93
Waist/hip 0.87±0.08
Pittsburgh Sleep Quality Index total
score		 7.61±5.75
Education
   <10 years 2
   10-12 years 14
   >12 years 85
Employed 39
Smoking 17
Use of sleeping medication 13
QIDS score 8.10±6.58
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Abbreviations: kg/m2, kilograms per meter squared; QIDS, Quick Inventory of Depressive Symptoms.

2.2. Self-reported sleep quality

The Pittsburgh Sleep Quality Index (PSQI) is a 19-item self-rated questionnaire that measures sleep quality along seven dimensions: subjective sleep quality, sleep latency, sleep duration, habitual sleep efficiency, sleep disturbance, use of sleeping medications, and daytime dysfunction over the last month [27]. Each dimension is rated on a four-point Likert scale. Scores from these dimensions are added together to generate a global score ranging from 0 to 21. A score greater than 5 indicates poor sleep quality [11,27].

2.3. Determination of body composition

BMI was calculated using the Quetelet index (kg/m2). Waist-to-hip ratio was calculated from waist and hip circumferences. Body composition was determined by dual energy X-ray absorptiometry (Lunar iDXA, GE-Healthcare Madison, WI). Participants wore light clothing and removed metal objects from their body. CoreScan software was used to estimate the visceral fat mass based on the measurement of abdominal and subcutaneous adipose tissues [28].

2.4. Saliva samples collection and measurement of cortisol levels

All participants were given Salivette saliva collection devices (Sarstedt, NC), and received verbal and written instructions on how to collect saliva samples at home. They were instructed to collect saliva samples at awakening, and at 15-, 30-, and 60-mins post awakening. Participants kept the samples in their home freezer before they brought samples using coolers to the laboratory at the UAB, where samples were stored at −20°C until analysis. On the day of the assay, salivettes were centrifuged at 3000g at 4°C for 10 mins. All samples were assayed in duplicate using a high sensitivity salivary cortisol enzyme-linked immunosorbent assay kit (Salimetrics, PA). The cortisol awakening response (CAR) for each participant was determined by measuring the cortisol values at awakening and 15-, 30-, or 60-mins post awakening. Incremental area under the curve (AUC) for cortisol levels throughout the CAR was calculated using the trapezoidal rule. Cortisol AUC is typically used in this manner as an indicator of HPA axis activity [29].

2.5. Blood collection and serum measurement

Ten milliliters of blood were drawn from each participant and centrifuged at 3000g for 10 mins. Participants were not requested to fast before blood collection. Sera were immediately divided into aliquots, and frozen at −80°C until analysis. The analysis for inflammatory factors, including IL-6, C-reactive protein, and TNFα, was performed using a Meso Scale Discovery multiplex assay and analyzed with MPSQ Discovery Workbench software (Gaithersburg, MD). Concentrations for cytokines were expressed in pg/ml. Serum concentrations of leptin and adiponectin (expressed in ng/ml and μg/ml, respectively) were assayed in duplicate using commercially available radioimmunoassay kits according to the procedures supplied by the manufacturer (Millipore Corp, MA).

2.6. Statistical analysis

All analyses were completed with SPSS version 22 and p value was set at <0.05 as significant. All variables were tested for normality of distribution by means of Kolmogorov-Smirnoff tests, and would be log-transformed for distribution normality. Data are presented as means ± standard deviation. Differences between the two groups in variables of interest were compared using Chi-square test for categorical data, including sex, race, education and use of sleeping medication, and using independent T-test for continuous data, including age, BMI, waist-to-hip ratio and QIDS scores. Partial Pearson correlation analysis was used to determine the relationship between sleep quality and total and regional adiposity, HPA axis activity, and inflammation. Since age, race, sex, use of sleeping medication, and depressive symptoms may modify the relationship between sleep quality, adiposity, HPA axis activity, and inflammation, they were treated as covariates. Stepwise multiple linear regression analysis was used to identify the independent variables that best predicted total fat mass, trunk fat, android fat, and VAT in both total sample (n=101) and PSQ subjects (n=54). Variables that may contribute to adiposity and sleep quality were entered into the model, including age, race, sex, use of sleeping medication, PSQI scores, depressive severity, IL-6, leptin, adiponectin, and CAR.

To evaluate differences in total and regional fat distribution, HPA axis activity, and inflammation in subjects with normal sleep quality (NSQ) vs poor sleep quality (PSQ), subjects were placed into the PSQ (score >5) or NSQ (score ≤5) group based on PSQI results. Analysis of covariance (ANCOVA) adjusted for age, race, sex, depressive severity, and use of sleeping medication was used to determine differences in outcome variables between groups.

3. Results

3.1. Sample Characteristics

A summary of the 101 participants is reported in Table 1. Mean age was 38.88±11.96 and mean BMI was 29.35±6.93kg/m2. The mean total score on the PSQI was 7.61±5.75 with higher scores indicating worse sleep quality.

3.2. Relationships between sleep quality, adiposity, HPA axis activity, and inflammation

Partial Pearson correlation analysis showed a significant relationship between total sleep quality and VAT (r= 0.26; p<0.05), indicating reduced sleep quality is related to greater VAT (Fig. 1). There was no significant relationship between sleep quality and either HPA axis activation or markers of inflammation.

Fig. 1.

Fig. 1

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Relationship between VAT and PSQI score controlling for age, race, gender, use of sleeping medication, and depressive symptoms. r= 0.26, p<0.05.

Abbreviations: PSQI, Pittsburgh sleep quality index; VAT, visceral adipose tissue

3.3. Normal sleep quality vs. poor sleep quality

When participants were stratified into PSQ (n=54) and NSQ (n=47) groups, there were no significant differences in sex, race, and education level, however, there were significant differences in age, use of sleep medication, sleep quality and depressive severity (Table 2).

Table 2.

Descriptive statistics of normal vs poor sleep quality

 
NSQ (n=47) PSQ (n=54)
Mean±S.D. or % Mean±S.D.or
%		 p
Sex (male/female) 27/20 31/23 0.99
Race (White/Black) 27/20 29/25 0.71
Age (years) 34.26±10.36 42.91±11.89 0.00*
Body mass index (kg/m2) 27.90±1.00 30.62±0.93 0.06
Waist/hip 0.86±0.10 0.87±0.01 0.57
Education
   <10 years 0% 1.9% 0.20
   10-12 years 8.5% 18.9% 0.15
   >12 years 91.5% 79.2% 0.20
Use of sleeping medication 7.7% 22.2% 0.00*
QIDS score 2.81±2.50 12.30±5.71 0.00*
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Abbreviations: kg/m2, kilograms per meter squared; QIDS, Quick Inventory of Depressive Symptoms.

*

p<0.05.

Differences in total and regional adiposity between groups are reported in Figure 2. ANCOVA adjusting for age, race, sex, depressive severity, and sleep medication indicated that the PSQ group had significantly greater VAT compared to the NSQ group (1.11±0.83kg vs 0.79±0.62kg; p=0.046). However, there was no difference in total fat between groups. In addition, serum leptin was significantly greater in the PSQ group than in the NSQ group (1.37±0.07ng/ml vs 1.11±0.07ng/ml; p<0.05). There was no difference in CAR between groups.

Fig 2.

Fig 2.

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Total and regional adiposity between PSQ and NSQ groups. *indicates p<0.05

Abbreviations: PSQ, poor sleep quality; NSQ, normal sleep quality

Stepwise multiple linear regression analysis among both the total sample (n=101) and PSQ subjects only (n=54) indicated that VAT was best predicted by age, sex, leptin, and sleep quality score. Results are reported in Table 3.

Table 3.

Stepwise linear regression analysis predicting body composition

 
Total sample (n=101) PSQ (n=54)
Dependent
variable		 Significant predictors b R2 Significant
predictors		 b R2
Total Fat Leptin, IL-6 6884 0.54 Leptin 2959 0.40
Trunk fat Leptin, age −4052 0.46 Leptin, IL-6 7768 0.04
Trunk/total fat ratio Age, adiponectin, race,
sleep quality		 0.49 0.54 Sleep quality,
adiponectin		 0.89 0.29
Android fat Age, leptin, gender,
depressive symptoms		 −1970 0.52 Leptin, IL-6 1313 0.34
VAT Age, gender, leptin, PSQI
score		 −1402 0.60 Age, gender, leptin,
PSQI score		 −1963 0.53
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Notes: Independent variables included age (years), race (0=Caucasian, 1=African American), sex (1=female, 2=male), use of sleeping medication (0=no medication, 1=use of sleeping medication), depressive symptoms, PSQI scores, IL-6, leptin, adiponectin, and cortisol awakening response.

Abbreviations: IL-6, interleukin-6; VAT, visceral adipose tissue; PSQI, Pittsburgh Sleep Quality Index.

4. Discussion

The objective of this study was to test the hypothesis that abdominal adiposity is related to poor sleep quality and explore pathways that may contribute to the relationship. The main finding is that poor sleep quality is related to increased VAT. In addition, poor sleep quality indicated by the PSQI scores is a significant predictor of VAT. When participants were stratified by the PSQI scores, subjects with poor sleep quality had greater VAT compared to subjects with normal sleep quality, however, there was no difference in total body fat. To our knowledge, this is the first study to establish a link between poor sleep quality and adiposity, specifically in the abdominal region. In addition, we also observed that leptin levels were significantly greater in participants with poor sleep quality than in normal sleep quality people.

Previous studies have shown a relationship between sleep duration and body weight, BMI, and/or waist-to-hip ratio in both children and adults [30,31]. Different from prior studies, our current study focuses on sleep quality and specific adipose tissue regions because obesity is not a homogenous condition, and the regional distribution of adipose tissue is more important to understanding obesity-related conditions [32]. It is well documented that abdominal obesity is more commonly associated with increased mortality and risk of obesity-related diseases such as diabetes and CVD compared to peripheral adiposity [33]. Increased VAT, specifically, has now been established as being part of a complex phenotype characterizing adipose tissue storage dysfunction [34]. Our findings indicate that VAT, but not total body fat, is related to poor sleep quality and is also increased in subjects with poor compared to normal sleep quality. Furthermore, poor sleep quality was a significant predictor of VAT among participants. Taken together, these findings suggest a link between poor sleep quality and adiposity in the abdominal region.

Our second main finding was that leptin was significantly greater in the PSQ group compared to the NSQ group. Leptin is a hormone secreted from white adipose tissue and is secreted in proportion to adipose tissue mass [17]. Therefore, leptin levels inform about energy reserves of the body and play a role in energy homeostasis. Leptin secretion increases after a meal and is associated with decreased appetite [35]. In the arcuate nucleus of the hypothalamus, leptin activates neurons to reduce hunger [36]. Leptin is elevated in individuals with obesity and leptin resistance occurs where the functions of leptin are impaired, such as the inability of leptin to decrease food intake and body weight [37]. This is thought to contribute to the development and maintenance of obesity. Previous studies have shown varied effects of sleep restriction on leptin levels. One study found an 18% decrease in daytime leptin following partial sleep deprivation compared to rested state [38]. Another study observed lower morning leptin levels in subjects with shorter habitual sleep [39], however others have reported increased leptin levels after sleep restriction [40,41]. Our findings indicate that leptin secretion is elevated in subjects with poor sleep quality compared to those with normal sleep quality, despite similar total body fat mass. Further investigation is needed to determine directionality of this relationship and mechanisms linking sleep quality and leptin secretion.

Different from our hypothesis, there was no relationship between HPA axis activity measured using CAR and sleep quality. In addition, there was no difference in HPA axis activity indicated by CAR between PSQ and NSQ groups. These findings suggest that the HPA axis in our subjects may be intact. Previous reports of the relationship between HPA axis activity and sleep quality are inconsistent. Some studies have shown poor sleep quality is related to lower CAR [42] while others report no relationship [43]. Future studies may need to consider other methods to measure the HPA axis activity, such as Dexamethasone stimulation test, for a better understanding of the HPA axis role in poor sleep quality.

Findings from this study have important clinical implications. Our results suggest a relationship between sleep quality and VAT, therefore, poor sleep quality may be a risk factor for VAT accumulation. In our prediction model, poor sleep quality was a significant predictor of VAT with age, sex, leptin, and sleep quality predicting 53% of the variation in VAT among PSQ subjects after controlling for variables in table 3. Thus, sleep quality may be an important component in VAT accumulation. On the other hand, increased VAT and the metabolic abnormalities that accompany, may contribute to poor sleep quality. For instance, many studies have reported an association between altered glucose metabolism, VAT, and poor sleep quality [44,45]. Indeed, both laboratory studies and epidemiological studies have clearly linked short sleep duration and poor sleep quality to obesity risk, however the underlying mechanism remains unclear [46]. Further, both leptin secretion and VAT were increased in subjects with poor sleep quality, despite no difference in total body fat. This suggests an association between poor sleep quality, leptin resistance and VAT, however further longitudinal studies are needed to provide insight into cause and effect. Interventions to improve sleep quality may specifically reduce the burden of obesity-related diseases.

Our study has several strengths and limitations. Total and regional adiposity were measured using dual-energy x-ray absorptiometry. Among these, trunk fat and VAT were increased in PSQ subjects compared to NSQ subjects. Thus, our findings add valuable information to current understanding that poor sleep quality is related to regional adiposity, but not total fat, although the mechanisms are not clear. A limitation is the reliance on self-report for sleep quality, which could be affected by forgetting, non-disclosure, and reporting biases. However, validation studies indicate that the Pittsburgh Sleep Quality Index is a useful tool to differentiate between control and poor sleep quality subjects [11,27]. In future studies, controlled laboratory sleep studies would provide an objective measure of sleep quality. Another limitation is that this is a cross-sectional study, therefore directionality of the relationship between sleep quality and regional adiposity cannot be assumed. Saliva sampling for cortisol measurement was conducted by the subjects at their homes, therefore may be prone to measurement error due to a lack of compliance in collecting the sample at the specified time. Controlled laboratory environment might be useful to conduct future studies to minimize the confounding factors that influence cortisol levels. Lastly, a small proportion of participants were enrolled from psychiatric service settings when participants met the inclusionary and exclusionary criteria. Although these participants did not differ from the rest of the sample except depressive severity, it might be better not to add study stress to clinical patients.

Conclusion

In conclusion, this study revealed that poor sleep quality is related to greater trunk fat and VAT. Greater leptin levels among subjects with poor sleep quality suggests that elevated leptin is related to poor sleep quality. Further investigation into a cause and effect relationship between poor sleep quality and obesity-related conditions, such as insulin resistance, will lead to a better understanding of the impact of poor sleep quality on obesity and its related diseases

Highlights.

  • Poor sleep quality is related to increased visceral adipose tissue.

  • Visceral adipose tissue was greater, however total adiposity did not differ between subjects with poor vs those with normal sleep quality.

  • Leptin was greater in subjects with poor sleep quality compared to those with normal sleep quality.

Acknowledgments

This research was supported by awards, P30DK056336 and P30DK079626, from the National Institute of Diabetes And Digestive And Kidney Diseases to Nutrition Obesity Research Center and Diabetes Research Center, respectively, at UAB, and NIH award K-23DK107911to L.L.

Footnotes

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Author contributions

S.K.S. analyzed the date, drafted, edited and approved the final manuscript. B.A.G. designed the study, and edited and approved the final manuscript. Both A.C. and Y. L. performed the study, collected the data and approved the final manuscript. L.L. designed and performed the study, analyzed the data, and edited and approved the final manuscript.

Conflict of Interest

The authors report no conflicts of interest in this work.

Disclosure. The authors report no conflicts of interest in this work.

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