The authors have declared that no competing interests exist.
Conceived and designed the experiments: G. Cizza HK G. Csako KIR. Performed the experiments: MSM HK G. Cizza MW. Analyzed the data: PP LdJ EL. Wrote the paper: PP LdJ AP FS G. Cizza G. Csako KIR EL.
Sleep abnormalities, including obstructive sleep apnea (OSA), have been associated with insulin resistance.
To determine the relationship between sleep, including OSA, and glucose parameters in a prospectively assembled cohort of chronically sleep-deprived obese subjects.
Cross-sectional evaluation of a prospective cohort study.
Tertiary Referral Research Clinical Center.
Sleep duration and quality assessed by actigraphy, sleep diaries and questionnaires, OSA determined by a portable device; glucose metabolism assessed by oral glucose tolerance test (oGTT), and HbA1c concentrations in 96 obese individuals reporting sleeping less than 6.5 h on a regular basis.
Sixty % of subjects had an abnormal respiratory disturbance index (RDI≥5) and 44% of these subjects had abnormal oGTT results. Severity of OSA as assessed by RDI score was associated with fasting glucose (R = 0.325,
OSA is associated with impaired glucose metabolism in obese, sleep deprived individuals. Since sleep apnea is common and frequently undiagnosed, health care providers should be aware of its occurrence and associated risks.
This study was conducted under the NIDDK protocol 06-DK-0036 and is listed in ClinicalTrials.gov
Several epidemiological studies have shown that people who report sleeping less than 6.5 h are at greater risk of gaining weight over time
Seminal studies conducted by Van Cauter et al. demonstrated that acute sleep deprivation can induce insulin resistance in lean volunteers
This analysis pertains to the Sleep Extension Study, a randomized, prospective, intervention trial of obese (BMI 30–55 kg/m2) men and premenopausal women 18 to 50 years old, who reported sleeping less than 6.5 h per night on average
The study was conducted at the NIH Clinical Center in Bethesda, MD, USA after obtaining approval from the NIDDK Institutional Review Board (ClinicalTrials.gov identifier: NCT00261898). Each subject signed an approved written informed consent.
Height was measured to the nearest centimeter using a wall-mounted stadiometer (SECA 242, SECA North America East, Hanover, MD, USA) and weight was measured using a stand-on-scale in a hospital gown to the nearest 1/10th of a kg (SR555 SR Scales, SR Instruments, INC, Tonawanda, NY, USA). Waist circumference was measured at the midpoint between the inferior tip of the ribcage and the superior aspect of the iliac crest. Neck circumference was measured at the minimal circumference with the subjects’ head in the Frankfort Horizontal Plane.
Dual-energy X-ray absorptiometry (DXA) for body composition assessment was performed with a Hologic DXA QDR 4500 (Hologic Inc., Bedford, MA, USA). Abdominal fat content and distribution was measured at the level of both L2–3 and L4–5, using a HiSpeed Advantage CT/I scanner (GE Medical Systems, Milwaukee, WI, USA) and analyzed on a SUN workstation using the MEDx image analysis software package (Sensor System, Sterling, VA, USA). Conventional (non-helical) 10 mm thick X-ray abdominal computed tomography images limited to the L2-3 and L4-5 levels were obtained at 120 kVp, with mAs adjusted according to patient size. Fully automatic processing of these images according to the method of Yao
Sleep was assessed by a combination of different methods. Subjects were instructed to wear a wrist activity monitor continuously for two weeks (Actiwatch-64, Mini Mitter/Respironics/Philips, Bend, OR, USA). Additional information on these instruments and data analysis was reported previously
The presence of sleep disordered breathing was evaluated over one night using a portable screening device (Apnea Risk Evaluation System, Advanced Brain Monitoring Inc., Carlsbad, CA, USA). This device provides an estimate of the respiratory disturbance index (RDI), which is the number of apneas and hypopneas per hour of sleep. An episode of apnea was defined as the complete cessation of airflow for at least 10 seconds. Hypopnea events were defined as at least 10 seconds with the airflow decreasing by more than 50% and with more than 3.5% oxygen desaturation, or more than 1% desaturation accompanied by at least one surrogate arousal indicator (head movement, changes in snoring, or changes in pulse rate)
Fasting serum glucose and insulin were measured after a 10-h overnight fast. Each subject underwent a 75g oral glucose tolerance test (oGTT) during which plasma glucose and serum insulin levels were determined at 0, 30, 60, 90 and 120 min. Glucose levels ≥100 mg/dL at baseline and ≥140 mg/dL at 120 min of the OGTT were defined as abnormal, and the diagnosis of diabetes was made if glucose levels were ≥126 mg/dL at baseline and ≥200 mg/dL at 120 min. Insulin resistance was determined using the homeostasis model assessment for insulin resistance (HOMA): (fasting insulin (mU/L) * fasting plasma glucose (mg/dL))/405. The insulinogenic index was calculated with the following equation: (30 min insulin –0 min insulin)/(30 min glucose –0 min glucose). The AUC for glucose and insulin was calculated using the trapezoidal rule: 15 * (0 min plasma levels) +2 * (30 min, 60 min and 90 min plasma levels) +120 min plasma levels.
Plasma glucose was determined with an enzymatic method. Plasma adrenocorticotropic hormone (ACTH), (total) serum cortisol, insulin, and growth hormone (GH) levels were measured with chemiluminescence immunoassays (Immulite 2000 and/or 2500 analyzers, Siemens). Urinary free cortisol (UFC) and catecholamines were collected in 24 h urine collection and measured using liquid chromatography-tandem mass spectrometry (LC-MS/MS) and high-performance liquid chromatography (HPLC), respectively.
Sixteen cytokines/chemokines were measured with an ELISA that uses the Quansys multiplex system (Quansys Biosciences, Logan, Utah, USA). All samples were run in duplicate. Values are reported in pg per mL after normalization to 1 µg total protein per mL of sample, to account for variations in the total protein content of the samples. CRP concentrations were measured in 87 subjects with a high sensitivity chemiluminescent immunometric assay with a detection limit of 0.1 mg/L (Immulite 2000, Siemens/DPC, Los Angeles, California, USA).
Descriptive statistics for each variable were calculated based on the presence of OSA according to a RDI cutoff value of 5 and on glucose status (i.e. normal and abnormal oGTT results). Statistical tests included Student’s
Of the 125 randomized into the study, six subjects were excluded due to the use of oral hypoglycemic agents and 96 subjects had measurements of OSA available at the Randomization Visit. Demographic, anthropometric and life-style characteristics of these 96 subjects are shown in
No sleep apnea(RDI <5)(N = 38) | Sleep apnea(RDI ≥5)(N = 58) | p-value | |
Age (years) | 39.9±6.6 | 42.5±5.9 | |
Female | 89.5% | 69.5% | |
0.646 | |||
Black | 60.5% | 50.8% | |
White | 34.2% | 42.4% | |
Other | 5.3% | 6.8% | |
Years of education | 16.5±2.4 | 15.8±2.5 | 0.162 |
Weight (kg) | 99.7±16.6 | 110.4±19.8 | |
Current or past smoking status | |||
Smoking history | 13.1% | 20.7% | 0.496 |
Currently smoker | 2.6% | 10.3% | 0.308 |
Psychotropics (Prozac, Zoloft) | 18.4% | 5.2% | 0.084 |
Hormonal contraceptives | 18.4% | 3.4% | |
Antihypertensives | 10.5% | 13.8% | 0.871 |
Statins | 5.3% | 6.9% | 0.909 |
Anti-asthma/allergy (Advair, Allegra) | 5.3% | 12.1% | 0.448 |
Synthroid | 2.6% | 5.2% | 0.919 |
BMI (kg/m2) | 36.5±5.8 | 38.9±6.2 | 0.058 |
Body fat (%) | 42.4±5.6 | 40.5±7.9 | 0.213 |
Body lean (%) | 55.2±5.5 | 57.1±7.7 | 0.191 |
Waist circumference (cm) | 108.7±12.3 | 116.4±12.4 | |
Neck circumference (cm) | 36.9±2.7 | 40.2±3.8 | |
Visceral fat by CT (cm3) | 250.8±122.9 | 416.2±168.7 | |
Subcutaneous fat by CT (cm3) | 921.6±294.8 | 888.3±296.1 | 0.606 |
Abdominal fat by CT (cm3) | 1172.4±319.8 | 1304.5±317.7 | 0.060 |
Values in each cell are reported as mean ± SD or as percentage.
Subjects with OSA had also significantly higher fasting glucose, fasting insulin, HOMA index and HbA1C (
No sleep apnea(RDI <5)(N = 38) | Sleep apnea(RDI ≥5)(N = 58) | p-value | |
Fasting glucose (mg/dL) | 84.9±6.7 | 91.3±11.5 | |
% subjects with abnormal fasting glucose (≥100 mg/dL) | 2.6% | 13.6% | 0.144 |
Fasting insulin (mU/L) | 8.8±6.4 | 11.7±6.7 | |
% subjects with abnormal oGTT | 21.1% | 44.1% | |
HOMA index |
1.4 (0.8–2.4) | 2.4 (1.5–3.8) | |
% subjects with abnormal HOMA (≥2.5) | 23.7% | 42.4% | 0.097 |
HbA1c (%) | 5.5±0.5 | 5.8±0.6 | |
% Subjects with abnormal HbA1c (>6.4%) | 2.6% | 6.8% | 0.661 |
Unless otherwise stated, values in each cell are reported as mean ± SD or as percentage.
Values are reported as median with interquartile range due to skewed distribution.
Glucose (Panel A) and insulin (Panel B) concentrations during 120-min OGTT in patients with a sleep apnea diagnosis (RDI ≥5, white circles, N = 58) and without a sleep apnea diagnosis (RDI<5, black circles, N = 38). The time-integrated area under the curve (AUC) for glucose and insulin are shown in panel C and D, respectively.
Because of the inclusion criteria, subjects in both groups had short sleep duration, approximately 6.5h by sleep diary and 6h by actigraphy (
No sleep apnea(RDI <5)(N = 38) | Sleep apnea(RDI ≥5)(N = 58) | p-value | |
Self-reported sleep duration (min/night) | 385±54 | 387±42 | 0.868 |
Actigraphy sleep duration (min/night) | 365±48 | 350±49 | 0.171 |
Actigraphy sleep efficiency (%) | 80.8±4.9 | 80.0±6.8 | 0.559 |
PSQI global score | 7.8±2.3 | 7.9±2.7 | 0.836 |
PSQI abnormal (>5) score | 81.1% | 84.5% | 0.876 |
ESS score | 8.7±4.7 | 8.1±4.4 | 0.504 |
ESS abnormal (>10) score | 34.3% | 26.3% | 0.541 |
RDI (events/h) |
2 (1–4) | 13 (8–20) | |
Normal (RDI <5) | 100% | 0% | |
Mild sleep apnea (RDI: 5–15) | 0% | 66.1% | |
Moderate sleep apnea (RDI: 16–30) | 0% | 22.0% | |
Severe sleep apnea (RDI >30) | 0% | 11.9% | |
Saturation of peripheral oxygen (%) | 96.8±0.9 | 95.6±2.3 |
Unless otherwise stated, values in each cell are reported as mean ± SD or as percentage.
RDI values are reported as median with interquartile range due to its skewed distribution.
Fasting glucose concentrations rose progressively from subjects without sleep apnea (84.9±6.7 mg/dL; mean±SD) to those with mild (90.4±12.9 mg/dL), moderate (92.5±8.9 mg/dL), and severe (93.9±17.9 mg/dL) sleep apnea (test for trend:
RDI is reported on a
Subjects with OSA had higher levels of IL-2, IL-4, IL-5, IL-6, IL-8, IL-13, and IFN-gamma and tended to have higher levels of IL-15 and TNF-beta (
No sleep apnea(RDI <5)(N = 38) | Sleep apnea(RDI ≥5)(N = 58) | p-value | |
IL-1a (pg/mL) | 14.1 (9.5–18.9) | 13.6 (0.3–20.7) | 0.803 |
IL-1b (pg/mL) | 30.1 (22.5–39.7) | 32.4 (24.6–47.3) | 0.418 |
IL-2 (pg/mL) | 7.4 (4.5–12.3) | 10.1 (7.5–15.7) | |
IL-4 (pg/mL) | 3.2 (2.3–4.6) | 4.1 (3.1–6.4) | |
IL-5 (pg/mL) | 6.9 (4.2–9.0) | 8.2 (5.3–10.7) | |
IL-6 (pg/mL) | 4.5 (2.6–9.0) | 5.8 (4.4–9.3) | |
IL-8 (pg/mL) | 10.8 (4.7–15.4) | 12.2 (7.7–21.8) | |
IL-10 (pg/mL) | 7.8 (4.6–12.0) | 8.0 (5.8–11.3) | 0.615 |
IL-12 (pg/mL) | 10.6 (5.0–12.8) | 11.5 (7.8–14.1) | 0.391 |
IL-13 (pg/mL) | 13.2 (10.2–15.0) | 14.3 (11.8–17.5) | |
IL-15 (pg/mL) | 12.4 (9.9–14.7) | 13.3 (11.3–15.5) | 0.102 |
IL-17 (pg/mL) | 13.9 (10.6–17.8) | 14.3 (12.2–17.5) | 0.358 |
IL-23 (pg/mL) | 134.7 (95.2–204.2) | 145.7 (123.9–187.2) | 0.268 |
IFN-gamma (pg/mL) | 27.0 (23.4–33.1) | 34.0 (27.6–40.4) | |
TNF-alfa (pg/mL) | 16.6 (13.1–22.0) | 19.9 (14.5–24.8) | 0.135 |
TNF-beta (pg/mL) | 14.1 (11.2–16.7) | 15.5 (12.8–18.6) | 0.095 |
C-Reactive Protein |
3.81 (1.09–8.65)0.53±0.54 | 5.27 (2.06–8.46)0.61±0.45 | 0.5840.448 |
<3.00 mg/L | 1.20 (0.70–1.75) | 1.77 (0.76–2.27) | 0.339 |
3.00–9.99 mg/L | 6.01 (4.59–7.49) | 6.40 (5.30–8.44) | 0.582 |
≥10.00 mg/L | 16.40 (13.45−22.35) | 14.90 (11.40−16.10) | 0.114 |
Values are reported as median with interquartile range due to skewed distribution.
N = 87
Subjects with OSA had approximately 36% higher plasma ACTH levels in the setting of similar serum cortisol and UFC (
No sleepapnea(RDI <5)(N = 38) | Sleep apnea(RDI ≥5)(N = 58) | p-value | |
Plasma morning ACTH (pg/mL) |
15.2 (11.7−21.5) | 20.0 (14.5−27.4) | |
Serum morning cortisol (µg/dL) | 9.1±4.3 | 10.0±4.6 | 0.361 |
UFC (µg/24 h) | 18.0±10.6 | 20.7±14.6 | 0.322 |
Urinary norepinephrine (µg/24 h) | 39.1±18.0 | 45.6±17.9 | 0.093 |
Urinary epinephrine (µg/24 h) | 4.4±2.9 | 4.2±3.0 | 0.807 |
Urinary dopamine (µg/24 h) | 249.0±87.8 | 259.3±98.4 | 0.614 |
Serum GH (ng/mL) |
0.70 (0.20−2.30) | 0.20 (0.10−0.45) | |
IGF-1 (ng/mL) | 141.2±52.6 | 135.0±43.6 | 0.530 |
Values are reported as median with interquartile range due to skewed distribution.
To convert gravimetric units for hormones to SI units, use the following conversion factors: cortisol, µg/dl * 27.59 = nmol/l; epinephrine, µg/24 h * 5.46 = nmol/24 h; NE, µg/24 h * 5.91 = nmol/24 h; corticotropin (ACTH), pg/ml * 0.22 = pmol/l; and dopamine, µg/24 h *6.58 = nmol/24 h; IGF-1, ng/mL*0.131 = nmol/L.
In a simple regression model, approximately 11% of the variability in fasting glucose was explained by RDI (
Dependent variable:Fasting Glucose (mg/dL) | Step 0unadjusted | Step 1adjusted for visceral fat | Step 2adjusted for visceralfat and age | Step 3adjusted for visceral fat, age, gender and sleep duration |
82.4(78.1 to 86.7) | 80.2(74.9 to 85.4) | 78.4(64.1 to 92.6) | 77.1(52.8 to 101.3) | |
RDI(logarithmic values) | ||||
Visceral fat by CT (cm3) | 0.012(−0.002 to 0.026) | 0.012(−0.002 to 0.026) | 0.016(−0.001 to 0.032) | |
Age(yrs) | 0.047(−0.301 to 0.395) | 0.084(−0.277 to 0.446) | ||
Gender(Female = 0, Male = 1) | −2.7(−8.7 to 3.2) | |||
Actigraphy sleep duration (min/night) | 0.004(−0.044 to 0.052) |
Beta coefficients in each cell were calculated after adjustment for the other independent variables in the multivariate model, and reported as mean values with 95% CI.
=
In this cohort of middle aged, obese men and premenopausal women who reported sleeping less than 6.5h on a regular basis, approximately 60% had OSA and 40% had abnormal glucose metabolisms. Prior to enrollment, the majority of subjects were unaware of having SA. SA is associated with worse glycemia and insulin resistance, known predictors of diabetes and cardiovascular disease.
The mechanisms of the relationship between abnormal glucose metabolism and OSA are incompletely understood. OSA is associated with chronic intermittent hypoxia and sleep loss due to sleep fragmentation, both of which contribute to insulin resistance
Consistent with the above-proposed mechanisms, we found, using a multiplex assay with high sensitivity, that a wide range of functionally related cytokines and chemokines were elevated in patients with OSA. Specifically, subjects with OSA had higher circulating concentrations of IL-2, IL-4, IL-5, IL-6, IL-8, IFN-gamma, and TNF-alpha and tended to have higher concentrations of TNF-beta, clearly indicating an activation of the Th-1 and Th-2 immune response. Increased cytokine levels have previously been observed in association with sleep deprivation, OSA, obesity, and insulin resistance in smaller samples using a limited number of cytokines
Subjects with OSA had 60% more visceral fat by CT. Visceral fat is a major source of inflammatory cytokines. Inflammatory cytokines are produced by adipocytes, as well as by a heterogeneous aggregate of immune cells made up mostly of macrophages, but also by T and NK cells embedded among adipocytes. Hypoxia represents a potent stimulus for cytokine production from the adipose tissue
There were some hormonal differences between subjects with and without OSA. Morning plasma ACTH and urinary norepinephrine were higher in subjects with OSA, suggesting an activation of the stress system. Single time-point serum GH levels were significantly lower in subjects with SA, which is compatible with the decreased activity of the GH axis in obese subjects
At variance with previous reports, sleep duration and glucose parameters were not related in our sample
Several studies have reported on the relationship between OSA and glucose metabolism
Polysomnography is the gold standard for diagnosing OSA. This method requires an overnight stay in the hospital, is associated with considerable cost and inconvenience and may interfere with sleep. Therefore, in the current study we used for practical purposes portable devices designed for home use. Given the already high and increasing prevalence of OSA, we recommend using these devices more routinely to make large-scale screening practical, while polysomnography should be reserved for more complicated cases in which a formal sleep study may be required. By analogy, while for research purposes insulin resistance is determined by insulin clamp, for clinical purposes determination of fasting glucose and insulin are used. As recently stated by the Centers for Disease Control and Prevention (CDC), universal screening for type 2 diabetes in middle aged African-American, a segment of the population very similar to our sample, is considered very cost-effective
Study limitations include the fact that we did not characterize sleep architecture, that this cross-sectional evaluation was not designed to assess causality, and that the composition of the sample did not allow analyses of gender or ethnic differences. In addition, measurements of OSA were available in 96 of 125 subjects randomized, whereas CRP measurements were available in 87 subjects. As study merits, we would like to note the relatively large and well characterized sample in a real life setting, and the determination of a large variety of cytokines measured simultaneously with a sensitive assay. Finally, this cohort was exclusively composed of obese subjects.
In summary, OSA was more prevalent in our chronically sleep-deprived obese population than previously reported, its presence and severity were closely linked to abnormalities of glucose metabolism and insulin resistance to a greater extent than abdominal fat. OSA should be suspected, diagnosed and treated early in this population. The general public and healthcare providers should be made more aware of the health consequences of OSA, including abnormal glucose metabolism. Ongoing and future studies determining optimal interventions for OSA to improve glycemia and lower cardiometabolic risk are of upmost importance.
We would like to thank in alphabetical order the following colleagues for their scientific advice and critical suggestions in the development and conduct of the study protocol: Karim Calis, Janet Gershengorn, Gregor Hasler, Emmanuel Mignot, Susan Redline, Nancy Sebring, Terry Phillips, Duncan Wallace, Robert Wesley, Elizabeth Wright, Xiong-ce Zhao. We would also like to thank the members of the study team: Peter Bailey, Laide Bello, Meredith Coyle, Paula Marincola, Patrick Michaels, Svetlana Primma, Angela Ramer, Rebecca Romero, Megan Sabo, Tanner Slayden, Sara Torvik, Elizabeth Widen, Lyda Williams and Sam Zuber. We would like to thank Dr. Alex Ling (NIH CC) for analysis of the computer tomography measurements. The bioinformatics support of Frank Pierce (Esprit Health) is gratefully acknowledged. Finally we are grateful to all of our enthusiastic study subjects.