OSA AND OBESITY ASSOCIATE WITH REDUCED GPR 120 PLASMA LEVELS IN CHILDREN http://dx.doi.org/10.5665/sleep.3664

Obstructive Sleep Apnea and Obesity are Associated With Reduced GPR 120 Plasma Levels in Children David Gozal, MD; Leila Kheirandish-Gozal, MD, MSc; Alba Carreras, PhD; Abdelnaby Khalyfa, PhD; Eduard Peris, MSc

Section of Sleep Medicine, Department of Pediatrics, Pritzker School of Medicine, Biological Sciences Division, The University of Chicago, Chicago, IL

Background: Obstructive sleep apnea (OSA) is a common health problem, particularly in obese children, in whom a vicious cycle of obesity and OSA interdependencies promotes increased food intake. G protein-coupled receptor 120 (GPR 120) is a long-chain free fatty acid (FFA) receptor that plays an important role in energy homeostasis, and protects against insulin resistance and systemic inflammation. We hypothesized that GPR 120 levels would be reduced in children with OSA, particularly among obese children. Study Design: Cross-sectional prospectively recruited cohort. Setting: Academic pediatric sleep program. Methods: Two hundred twenty-six children (mean age: 7.0 ± 2.1 y) underwent overnight polysomnographic evaluation and a fasting blood draw the morning after the sleep study. In addition to lipid profile, homeostasis model assessment of insulin resistance (HOMA-IR) and high-sensitivity C-reactive protein (hsCRP) assays, monocyte GPR 120 expression, and plasma GPR 120 levels were assessed using quantitative polymerase chain reaction and enzyme-linked immunosorbent assay kits. Results: Obese children and those with OSA had significantly lower GPR 120 monocyte expression and plasma GPR 120 levels. Furthermore, when both obesity and OSA were present, GPR 120 levels were lowest. Linear associations emerged between GPR 120 plasma levels and body mass index (BMI) z score, as well as with apnea-hypopnea index (AHI), saturation of peripheral oxygen (SpO2) nadir, and respiratory arousal index (RAI), with RAI remaining statistically significant when controlling for age, ethnicity, sex, and BMI z score (P < 0.001). Similarly, HOMA-IR was significantly associated with GPR 120 levels, but neither low density lipoprotein nor high density lipoprotein cholesterol or hsCRP levels exhibited significant correlations. Conclusions: G protein-coupled receptor 120 (GPR 120) levels are reduced in pediatric OSA and obesity (particularly when both are present) and may play a role in modulating the degree of insulin resistance. The short- and long-term significance of reduced GPR 120 relative to food intake and glycemic deregulation remains undefined. Keywords: inflammation, insulin resistance, lipid profile, obesity, obstructive sleep apnea Citation: Gozal D, Kheirandish-Gozal L, Carreras A, Khalyfa A, Peris E. Obstructive sleep apnea and obesity are associated with reduced GPR 120 plasma levels in children. SLEEP 2014;37(5):935-941.

INTRODUCTION Increased obesity rates in children have now been identified all over the world, and represent a major threat to shortand long-term overall health.1,2 The adverse consequences of obesity involve multiple organ systems and represent the sustained burden of systemic inflammatory processes and oxidative stress.3,4 However, at any level of BMI z score in the overweight-obese range, there is a highly variable degree of morbidity, prompting the need for identification of potential biomarkers that can predict the presence of specific risks, particularly for metabolic and cardiovascular consequences.5–7 Obstructive sleep apnea (OSA) is also a common health problem in children, with an estimated prevalence of up to 3-4%.8 Children with OSA experience repetitive episodes of increased upper airway resistance culminating in partial or complete intermittent obstruction of the upper airway during sleep, leading to major changes in intrathoracic pressures and

recurrent arousals as well as episodic oxygen desaturations and hypercapnia.9 These events are associated with daytime sleepiness,10,11 the latter leading to reduced physical activity along with increased appetite particularly for high-fat, highcarbohydrate foods.12 The cumulative evidence derived from a large number of studies has further confirmed the significant associations between the spectrum of sleep disturbance in the context of sleep disordered breathing and the presence of insulin resistance and altered lipid homeostasis.13 However, the mechanisms leading to the vicious cycle of OSA/sleep disruption/obesity-promoting behaviors/obesity/increased severity of OSA remain thus far undefined. G protein-coupled receptor 120 (GPR 120) is a recently identified orphan receptor for which no endogenous ligands were initially recognized. In 2005, Hirasawa and colleagues reported that GPR 120 is abundantly expressed in both human and mouse intestine, where it operates as a long-chain free fatty acid (FFA) receptor that regulates a variety of gut-related hormone secretions such as glucagon-like peptide-1,14 and stimulates cholecystokinin secretion.15 Indeed, circulating FFAs not only provide substrate for energy production but can also act as lipid sensors and mediate the expression of genes and proteins to regulate lipid and energy homeostasis in a diverse range of physiological and pathophysiological conditions.16,17 GPR 120 recently was shown to exert important modulatory roles for adipocyte and macrophage function. For example, GPR 120 activation in adipocytes increased glucose uptake and adipogenesis, whereas GPR 120 signaling in macrophages

Submitted for publication September, 2013 Submitted in final revised form November, 2013 Accepted for publication December, 2013 Address correspondence to: Leila Kheirandish-Gozal, MD, MSc, or David Gozal, MD, Section of Pediatric Sleep Medicine, Department of Pediatrics, Pritzker School of Medicine, The University of Chicago, 5841 S. Maryland Avenue/MC2117, Chicago, IL 60637-1470; Tel: (773) 834-3815; Fax: (773) 834-1444; E-mail: [email protected] or [email protected] uchicago.edu SLEEP, Vol. 37, No. 5, 2014

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was associated with substantial anti-inflammatory actions.18–20 Interestingly, Oh and colleagues reported that ω-3 fatty acids such as docosahexaenoic acid (DHA), eicosapentaenoic acid, and palmitoleate are agonists of GPR 120,20 and furthermore, that activation of GPR 120 by DHA antagonizes the proinflammatory effects of tumor necrosis factor-alpha and lipopolysaccharide in a macrophage cell line, by not only blocking nuclear factor kappa-B and c-Jun-N-terminal kinase (JNK) pathways, but by also preventing expression of cytokines. Thus, GPR 120 emerges as a critical modulator of systemic inflammation and metabolic function. We hypothesized that OSA in children would be associated with reduced gene expression of GPR 120 in monocytes, and with reduced plasma levels of GPR 120. Furthermore, we postulated that significant associations between GPR 120 and serum lipids and insulin sensitivity would emerge.

The obstructive apnea-hypopnea index (AHI) was calculated as the number of apneas and hypopneas per hour of TST. Arousals were classified as either spontaneous or respiratory, and corresponding indices (SAI and RAI, respectively), were computed. The diagnosis of OSA was defined by the presence of an AHI ≥ 1/h of TST. Control children were nonsnoring children with AHI < 1/h of TST. Gene Expression Assays Fasting blood samples were drawn by venipuncture the morning after the sleep study within 1 h from awakening between 07:00-08:00. Peripheral white blood cells were isolated from the PAXgene Blood RNA tubes (Becton Dickinson, UK). Total RNA was isolated using a PAXgene Blood RNA Kit and treated with DNase I (QIAGEN, Valencia, CA), according to the manufacturer’s protocol. The RNA quantity and integrity were determined using a Nanodrop Spectrophotometer and Agilent 2100 Bioanalyzer Nano 6000 LabChip assay (Agilent Technologies, Wood Dale, IL). Probe and primers were designed using primer express software. Forward and reverse real-time transcription primers were CGATTTGCACACTGATTTGGC and TGCACAGTGTCATGTTGTAGA, respectively. Quantitative real-time polymerase chain reaction (QRT-PCR) was performed using ABI 7500 (Applied Biosystems, Foster City, CA). Complementary DNA (cDNA) was synthesized using a high-capacity cDNA Archive Kit (Applied Biosystems, Foster City, CA). Five hundred nanograms of total RNA from nonobstructive sleep apnea and OSA samples were used to generate cDNA templates for RT-PCR with primer specific for the EDN1 gene. The TaqMan® Master Mix Reagent Kit (Applied Biosystems) was in 25-mL reactions. Various negative controls were included in the PCR reaction to ensure specific amplification. Triplicate PCRs were performed in 96-well plates for each gene in parallel with the 18S ribosomal RNA (rRNA). The steps involved in the reaction program included the initial step of 2 min at 50°C; denaturation at 95°C for 10 min, followed by 45 thermal cycles of denaturation (15 sec at 95°C) and elongation (1 min at 60°C). The expression values were obtained from the cycle number (Ct value) using the Biosystems analysis software. The threshold cycle (CT) values were averaged from each reaction, and each gene was normalized to the 18S rRNA level. These Ct values were averaged and the difference between the 18S Ct (Avg) and the gene of interest Ct (Avg) was calculated (Ct-diff). The relative expression of the gene of interest was analyzed using the 2-ΔΔCt method.

MATERIALS AND METHODS The research protocol was approved by the University of Chicago (protocol 09-115-B) human research ethics committee. Informed consent was obtained from the parents, and ageappropriate assent was also obtained from the children. Children (4-11 y of age) were recruited from the sleep and the ear, nose, and throat clinics at Comer Children’s Hospital at the University of Chicago, as well as by advertisement in the community. Those children who had genetic or craniofacial syndromes and chronic diseases such as cardiac disease, diabetes, cerebral palsy, and chronic lung disease of prematurity were excluded. Overnight Polysomnographic Studies All children underwent standard nocturnal polysomnography (NPSG) evaluation as previously described,21 with assessment of eight standard electroencephalography channels, bilateral electrooculography, electromyography, two-lead electrocardiography (ECG), oronasal airflow measurement using thermistor, nasal pressure transducer, and end tidal carbon dioxide, chest and abdominal movement by respiratory inductance plethysmography, and pulse oximetry including pulse waveform using a commercially available data acquisition system (Polysmith; Nihon Kohden America Inc, Foothill Ranch, CA, USA). The NPSG studies were scored as per the 2007 American Association of Sleep Medicine guidelines for the scoring of sleep and associated events.22 The proportion of time spent in each stage of sleep was calculated as a percentage of total sleep time (TST). A respiratory event was scored as an obstructive apnea if it was associated with a > 90% decrease in signal amplitude for > 90% of the entire event compared with the baseline amplitude, the event lasted for at least two breaths, and there was continued or increased respiratory effort throughout the period of the event. A mixed apnea was scored if there was absent inspiratory effort in the initial part of the event, followed by resumption of inspiratory effort before the end of the event. A central apnea was scored if respiratory effort was absent throughout the duration of the event, the event lasted for at least two missed breaths, and was associated with an arousal/awakening or a ≥ 3% desaturation. A hypopnea was scored if the event was associated with a ≥ 50% fall in amplitude of the nasal pressure transducer, lasted for at least two breaths, and was associated with an arousal/awakening or ≥ 3% desaturation. SLEEP, Vol. 37, No. 5, 2014

Plasma Assays Blood samples were drawn into either ethylenediaminetetraacetic acid containing tubes (purple top) or tubes without any additive. Samples were centrifuged within 30 min at 3,000 g for 10 min and plasma or serum were separated and either analyzed immediately or kept at -80°C. High-sensitivity CRP (hsCRP) was measured within 2 to 3 h after collection using the Flex reagent cartridge (Dade Behring, Newark, DE), which is based on a particle-enhanced turbidimetric immunoassay technique. Serum levels of lipids, including total cholesterol, high-density lipoprotein (HDL) cholesterol, calculated low-density lipoprotein (LDL) cholesterol, and triglycerides, were also assessed with a Flex reagent cartridge. Plasma insulin levels were measured 936

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using a commercially available radioimmunoassay kit (CoatA-Count Insulin, Cambridge Diagnostic Products, Inc, Fort Lauderdale, FL). Plasma glucose levels were measured using a commercial kit based on the hexokinase-glucose-6-phosphate dehydrogenase method (Flex reagent cartridge). Insulin resistance was then assessed using the homeostasis model assessment of insulin resistance (HOMA-IR) equation (fasting insulin × fasting glucose ÷ 405).23 In addition, plasma samples were frozen at -80°C until assay.

respiratory arousal index. We also calculated the attributable GPR 120 change fraction, which corresponds to the proportion of GPR 120 change that could be explained assuming causality of the associations and elimination of the various confounding factors by using the aflogit command in STATA24 on the logistic regression framework, because such an approach enables potential confounders to be taken into account. All P values reported are two-tailed, with statistical significance set at < 0.05. RESULTS A total of 226 children fulfilling entry criteria completed the overnight polysomnographic evaluation and provided a fasting blood sample after the sleep study, whereas 28 children refused to participate in the study (five parents declined to participate altogether and 23 parents were not willing to participate in the blood draw portion of the study). The demographic and polysomnographic characteristics of these 28 children were similar to those of the cohort, and are shown in Table 1. In general, there were no significant differences in age, sex, or ethnicity across the four subgroups. However, obese children exhibited higher BMI z scores, as well as higher HOMA-IR, serum lipids, and hs-CRP, and reduced HDL cholesterol levels (Table 2). Similarly, children with OSA had significantly higher HOMA-IR, LDL cholesterol, and hs-CRP concentrations, and lower HDL cholesterol levels (Table 2). Primary sleep disturbance measures clinically used to characterize the severity of OSA were not significantly different in obese and nonobese children with OSA. Similarly, there were no differences in sleep measures in obese and nonobese children without OSA (Table 1). In a preliminary phase, quantitative PCR assays for expression of GPR 120 were conducted in 30 children with OSA (15 obese and 15 nonobese), and in 30 children without OSA (15 obese and 15 nonobese) who were also matched for age, sex, and ethnicity. These initial assays revealed significantly reduced GPR 120 expression levels in peripheral white blood cells of children with OSA when compared to controls (OSA versus no OSA: P < 0.0001). Indeed, using nonobese controls as the reference denominator, GPR 120 messenger RNA levels were 0.65 ± 0.13, 0.96 ± 0.04; and 0.58 ± 0.14 in nonobese children with OSA, obese children without OSA, and obese children with OSA, respectively. We therefore proceeded to analyze plasma levels of GPR 120 in the whole cohort. Obese children without OSA had lower GPR 120 levels than nonobese children without OSA (P < 0.01; Table 2). Similarly, nonobese children with OSA also exhibited lower GPR 120 levels compared with nonobese controls (P < 0.01; Table 2), with nonobese controls displaying remarkably similar plasma concentrations of GPR 120 when compared with the initial control set (P value, not significant). However, obese children with OSA demonstrated the lowest GPR 120 levels (P < 0.01; Table 2). In order to estimate potential associations between GPR 120 plasma levels, polysomnographic measures, and metabolic indices, we initially performed Pearson correlation analyses (Figure 1). Significant linear correlations emerged between GPR 120 and BMI (Figure 1A; r = -0.374, P < 0.001), AHI (Figure 1B, r = -0.469, P < 0.001), nadir saturation of peripheral oxygen, SpO2 (Figure 1C, r = 0.368, P < 0.001), and respiratory

GPR 120 Assay GPR 120 plasma levels were assessed in duplicate using a commercially available kit (Antibodies-Online Inc., Atlanta, GA; cat # ABIN839463). The assay exhibited a low- level detection threshold of 0.3 mg/dL, linearity up to 250 mg/dL, and interassay and intra-assay coefficients of variability of 6.7% and 5.2%, respectively. Because no normative data regarding GPR 120 plasma concentrations in children are available, we initially assessed GPR 120 concentrations in a cohort of 22 healthy children who were nonsnorers and whose overnight sleep study results were within normal limits. This cohort had a age range of 5-11 y, 50% were boys, 25% were African American, and all children had a body mass index (BMI) z score below 1.34 (i.e., cutoff for overweight). The mean morning GPR 120 levels for this initial group of children were 68.1 ± 18.9 μg/dL, and their findings served as reference values and a guide to the concentration ranges anticipated in the study cohort. Statistical Analysis All analyses were conducted using either SPSS software (version 19.0; SPPS Inc., Chicago, IL) or STATA (StataCorp LP, College Station, TX), and data are presented as mean ± standard deviation. Children were subdivided into four groups, based on the presence or absence of obesity (i.e., BMI z score > 1.65) and OSA. A priori assumptions on the presence of differences in GPR 120 levels between children with and without OSA were formulated such as to allow for 80% power and a twosided confidence level at 0.01, and indicated the need for 154 subjects in the cohort. Significant differences between groups were analyzed using two-way analysis of variance. If the data were not normally distributed, they were logarithmically transformed (i.e., hsCRP, respiratory arousal index). Pearson correlation analyses and linear regression analyses were conducted to examine potential associations between BMI, sleep variables, lipid profiles, log hsCRP, and HOMA, and plasma concentrations of GPR 120. To explore potential causal pathways in our data, we developed three logistic regression models with incremental complexity. First, we generated a simple model that was adjusted only for age, race, and sex. Then, a second model was adjusted for BMI z score, and the third model was adjusted BMI z score and sleep variables. Finally, a model was constructed by adjusting for demographic, anthropometric, and sleep measures and plasma levels of metabolic and inflammatory markers simultaneously, i.e., a fully adjusted model. All variables associated with GPR 120 (P < 0.05) in one model were included in the next modeling steps, except when the information contained in two or more variables was so similar (colinear) that only one could be taken into the next modeling step. For example, these circumstances became applicable in the case of AHI and SLEEP, Vol. 37, No. 5, 2014

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Table 1—Demographic and polysomographic data of obese and nonobese children with and without obstructive sleep apnea

Age (y) Sex (male, %) Ethnicity (Caucasian, %) BMI-z score Total sleep duration (min) Stage 1 (%) Stage 2 (%) Stage 3 (%) REM sleep (%) Sleep latency (min) REM latency (min) Total arousal index (events/h TST) Respiratory arousal index (events/h TST) Obstructive Apnea Hypopnea Index (events/h TST) SpO2 nadir (%)

Nonobese with OSA (n = 77) 6.8 ± 2.1 51.9 53.2 0.31 ± 0.87 a 477.7 ± 56.7 7.7 ± 3.7 c 37.9 ± 8.7 38.6 ± 14.1 c 19.5 ± 7.4 c 22.6 ± 16.2 a,c 128.9 ± 51.0 a,c 21.3 ± 12.1 c 6.3 ± 2.2 a,c 17.8 ± 7.1 c 80.2 ± 7.3 c

Nonobese without OSA (n = 50) 7.2 ± 2.0 54.0 56.0 0.32 ± 0.88 b 469.4 ± 48.9 4.4 ± 3.2 c 36.2 ± 11.5 46.4 ± 12.6 c 24.5 ± 5.5 c 32.2 ± 14.9 b,c 147.9 ± 65.1 b,c 9.6 ± 6.8 c 0.1 ± 0.2 b,c 0.3 ± 0.1 c 94.9 ± 0.5 c

Obese with OSA (n = 61) 6.9 ± 1.9 55.7 47.5 2.36 ± 0.38 a 488.1 ± 55.1 8.6 ± 5.4 d 41.9 ± 9.7 38.9 ± 14.8 16.1 ± 6.6 d 12.7 ± 11.3 a,d 108.4 ± 55.2 a,d 24.1 ± 1 4.7 d 8.3 ± 2.7 a,d 19.4 ± 7.6 d 78.4 ± 8.4 d

Obese without OSA (n = 38) 7.3 ± 2.1 52.6 50.0 2.35 ± 0.50 b 473.7 ± 54.0 5.4 ± 4.1 d 36.2 ± 11.5 43.4 ± 12.6 22.5 ± 7.5 d 26.2 ± 14.8 b,d 137.9 ± 65.1 b,d 11.7 ± 7.2 d 0.7 ± 0.4 b,d 0.6 ± 0.2 d 92.4 ± 1.5 d

All data are expressed as mean ± standard deviation. a Nonobese OSA versus obese OSA, P < 0.01. b Nonobese, non-OSA versus obese non-OSA, P < 0.01. c OSA nonobese versus nonobese non-OSA, P < 0.01. d OSA obese versus obese non-OSA, P < 0.01.

Table 2—Lipid profile, homeostatic model assessment of insulin resistance, high-sensitivity C-reactive protein, and G protein-coupled receptor 120 plasma levels in obese and nonobese children with and without obstructive sleep apnea

Total cholesterol (mg/dL) HDL cholesterol (mg/dL) LDL cholesterol (mg/dL) Triglycerides (mg/dL) HOMA-IR Log hsCRP [mean actual levels] GPR 120 (μg/dl)

Nonobese with OSA (n = 77) 166.6 ± 26.1 a,c 48.3 ± 11.4 a,c 118.5 ± 26.3 a,c 74.4 ± 60.6 a 1.9 ± 1.8 a,c 0.24 ± 0.26 a,c [2.42 ± 1.95 mg/dL] 35.6 ± 19.3 a,c

Nonobese without OSA (n = 50) 142.3 ± 25.3 b,c 59.5 ± 10.2 b,c 101.3 ± 23.8 b,c 77.8 ± 48.5 a 0.6 ± 0.5 b,c -0.06 ± 0.22 b,c [1.20 ± 1.69 mg/dL] 63.6 ± 16.9 b,c

Obese with OSA (n = 61) 192.0 ± 29.6 a,d 39.6 ± 11.6 a,d 156.3 ± 28.3 a,d 101.7 ± 54.1 b 4.8 ± 1.8 a,d 0.39 ± 0.32 a,d [3.96 ± 2.27 mg/dL] 14.4 ± 11.8 a,d

Obese without OSA (n = 38) 170.7 ± 29.2 b,d 49.6 ± 10.8 b,d 126.8 ± 26.8 b,d 97.7 ± 49.1 b 2.7 ± 1.6 b,d 0.12 ± 0.27 b,d [1.96 ± 1.47 mg/dL] 44.3 ± 19.4 b,d

Nonobese OSA versus obese OSA, P < 0.01. b Nonobese non-OSA versus obese non-OSA, P < 0.01. c OSA non-obese versus nonobese non-OSA, P < 0.01. OSA obese versus obese non-OSA, P < 0.01. GPR 120, G protein-coupled receptor 120; HDL, high-density lipoprotein; LDL, low-density lipoprotein; hsCRP, high-sensitivity C-reactive protein. a

d

arousal index (Figure 1D, r2 = -0.565, P < 0.001), with respiratory arousal index showing significantly improved fit when using exponential fitting procedures (r2: 0.31 for linear fit and r2: 0.57 for exponential fit; P < 0.00001). Furthermore, a very strong exponential association emerged between GPR 120 and HOMA-IR levels (Figure 1E; r2 = 0.631, P < 0.00001). However, log hsCRP and serum lipids, including HDL and LDL cholesterol levels or triglyceride concentrations, did not show any significant associations with GPR 120. To further explore independent predictors of GPR 120 levels, we performed stepwise multiple regression analyses with age, sex, ethnicity, and BMI z score included as potential confounders. In model 1 (adjusted only for age, race, and sex), AHI, SpO2 nadir, and respiratory arousal indices were independently associated with GPR 120 levels (Table 3, standardized coefficients ranging between 0.131-0.477, P < 0.001). In model SLEEP, Vol. 37, No. 5, 2014

2 (adjusted for age, race, sex, and BMI z score) AHI, SpO2 nadir, and respiratory arousal indices accounted for 22.1%, 12.7%, and 43.1% of the variance in GPR 120, respectively. In addition, in the context of iterative variations on model 2, BMI z score contributed approximately 12% of the variance in GPR 120 levels after adjusting for age, sex, race, and sleep measures (Table 3). However, when HOMA-IR was included in the model, the association between GPR 120 and AHI was markedly weakened, albeit remaining statistically significant (standardized coefficient: 0.187; P < 0.04). Although some reduction in the magnitude of the linear association between GPR 120 and respiratory arousal index occurred upon inclusion of HOMA-IR in the model, the standardized coefficient remained highly significant (0.287; P < 0.001). Based on the aforementioned various iterative models, we used the residuals from these models in the comprehensive model 938

GPR 120 in Pediatric OSA—Gozal et al.

Figure 1—Scatterplots of G protein-coupled receptor 120 (GPR 120) plasma levels versus body mass index (BMI) z score, obstructive apnea-hypopnea index (AHI), nadir saturation of peripheral oxygen (SpO2), respiratory arousal index, and homeostatic model assessment of insulin resistance (HOMAIR) expression in children with and without obesity or OSA. (A) r = -0.374, P < 0.001; (B) r = -0.469, P < 0.001; (C) r = 0.368, P < 0.001; (D) r2 = -0.565, P < 0.001*; (E) r2 = 0.631, P < 0.00001. TST, total sleep time. * Actual individual respiratory arousal indices rather than log-transformed values were used in the exponential fitting function shown.

aiming to establish the adjusted association between GPR 120 plasma levels and OSA polysomnographic severity measures, while adjusting for all other potential confounders and while accounting for the interdependencies between BMI z score and other plasma metabolic and inflammatory biomarker levels. In this comprehensive and adjusted model (model 3), and using a statistical approach that allows multiple risk factors to be taken into account,24 OSA severity accounted for 37% of the variance in GPR 120 levels (standardized coefficient: 0.376; P < 0.001), with BMI z score accounting for 16% of the variance (standardized coefficient: 0.127; P < 0.01). There were no apparent interactions in the fully adjusted model between OSA and BMI z score severity.

Table 3—Multivariate stepwise regression analyses between sleep measures, homeostatic model assessment of insulin resistance, and G protein-coupled receptor 120 levels GPR 120 Plasma Levels Variables Age Sex Race BMI z score

Standardized coefficients 0.021 0.018 0.092 0.137 0.127# a 0.278 Obstructive AHI 0.131 SpO2 nadir 0.477 Respiratory arousal index a HOMA-IR 0.587 LDL cholesterol 0.034 HDL cholesterol 0.079 0.027 hsCRPa

DISCUSSION This study shows that both obese children and children with OSA exhibit significantly lower GPR 120 plasma levels when compared with healthy controls. Furthermore, if both obesity and OSA are concurrently present, GPR 120 levels are further reduced. Linear associations emerged between GPR 120 plasma levels and BMI z score, as well as with the three major polysomnographic measures traditionally used to characterize OSA (AHI, SpO2 nadir, and respiratory arousal index), and the latter association in particular was not significantly altered when controlling for potential confounders such as age, ethnicity, sex, and BMI z score. Similarly, HOMA-IR was significantly associated with GPR 120 levels, but neither LDL SLEEP, Vol. 37, No. 5, 2014

P value 0.804 0.975 0.372 < 0.01 < 0.01 < 0.001 < 0.01 < 0.0001 < 0.00001 0.923 0.897 0.945

Data were log-transformed; data for age, gender and race are not adjusted. Data for BMI z score are shown after adjusting for age, race, and sex only, # and after adjusting for all sleep measures. All other data are shown after controlling for age, sex, race, and BMI z score. AHI, apneahypopnea index; BMI, body mass index; GPR 120, G protein-coupled receptor 120; HDL, high-density lipoprotein; HOMA-IR, homeostatic model assessment of insulin resistance; hsCRP: high-sensitivity C-reactive protein; LDL, low-density lipoprotein; SpO2, saturation of peripheral oxygen. a

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nor HDL cholesterol or hsCRP levels exhibited any significant correlation. These findings suggest that assessment of GPR 120 plasma levels may provide a reliable biomarker for children at risk for obesogenic behaviors and metabolic dysfunction, such as those conferred by the presence of obesity and OSA. Before we address the potential significance of our findings, several methodological considerations deserve comment. First, the prospective recruitment protocol was careful to standardize the blood collection procedures and coincide with a time window immediately after the overnight sleep study, such that fasting conditions could be strictly supervised and sampling times could be accurately standardized. Thus, any theoretical circadian influences that may be operational in GPR 120 plasma levels would not be expected to contribute to the differences in plasma levels of this analyte observed herein. Furthermore, overall, sleep duration in the night preceding the blood sample was also documented in the polysomnogram, and was not significantly different among the four subgroups. Second, all GPR 120 assays were conducted using same-batch commercial assays and were performed concomitantly, thereby reducing additional sources of assay variability. Finally, control children were recruited on two separate occasions from the community rather than from referral populations, thereby enabling the current results to serve as initial and reliable normative values for GPR 120 levels, because the norms for referral populations are not currently available. It remains thus far unknown whether sleep restriction, either acute or chronic, will affect GPR 120 levels. However, based on the current findings, whereby sleep fragmentation as indicated with the respiratory arousal index accounted for a significant component of the variance in GPR 120 levels, it would be worthwhile to explore whether sleep curtailment and experimental sleep disruption affect GPR 120 expression and plasma levels, and whether such alterations, if present, alter food intake and glycemic homeostasis.25–30 There is no doubt that future studies using experimental sleep manipulations will have to incorporate careful delineation of food intake and appetite measures in addition to more extensive and in-depth biochemical measures of glycemic control. In the context of the recently uncovered functional roles of GPR 120, it was expected that the declines in GPR 120 levels in both obese children and those with OSA would be associated with not only correlates of insulin resistance (i.e., HOMA-IR), but also with previously described alterations in serum lipid profiles and hs-CRP, with alterations in hs-CRP serving as a reporter of low-grade systemic inflammation.31–34 However, we found that only HOMA-IR was strongly and independently associated with GPR 120 levels, suggesting the intriguing possibility that reduced biological availability and activity of GPR 120 in clinical settings such as obesity or OSA may induce deficits in gut incretins, i.e., glucagon-like peptide-1, and thus promote the emergence of insulin resistance.14 Conversely, the recent development of GPR 120 agonists indicates potential availability of future therapeutic options.35 The potentiation of the effect of obesity and OSA on GPR 120 plasma concentrations was anticipated and confirmed in the current study. Although it remains unclear whether obesity and OSA exert their effects via similar and overlapping pathways,36 it is possible that upstream oxidative stress and inflammatory processes initiated by these SLEEP, Vol. 37, No. 5, 2014

two conditions may serve as the initial triggers leading to the activation of downstream signaling pathways that ultimately result in reduced expression of GPR 120 and insulin resistance. In addition, our current findings suggest that obesity and OSA may alter perception of food taste, particularly fat-containing foods, and also modify satiety-related pathways via disruption of orphan receptors such as GPR 120.37 As a corollary to such possibility, we have previously reported that obese children with OSA are more likely to consume fat-rich and energy- dense food items,12 a finding that has been subsequently corroborated by another investigative group.38 In summary, we have shown that the presence of obesity and the presence of OSA in children lead to reduced plasma levels of the long fatty acid receptor GPR 120, and that GPR 120 plasma levels are strongly associated with measures of insulin resistance, but not with dyslipidemia or hsCRP elevations. Improved understanding of the causal pathways underlying these associations may offer not only clinical diagnostic opportunities, but also enable delineation of therapeutic interventions; for example, with the goal to reduce disruptions in glycemic control and to prevent the unduly accelerated increases in body weight after treatment of OSA that are associated with OSA recurrence.39 ACKNOWLEDGMENTS The authors are thankful for the assistance of the Statistical Core Unit of the Center for Translational Medicine at the University of Chicago. Dr. Gozal provided the conceptual design of the project, analyzed data, drafted the manuscript, and is responsible for the financial support of the project and the manuscript content. Dr. Kheirandish-Gozal provided conceptual initiative and design for the project, recruited subjects, analyzed sleep data and metabolic data, drafted components of the manuscript, and analyzed data. Dr. Carreras conducted experiments and analyzed data. Dr. Khalyfa performed experiments, reviewed data, and provided critical input to data analysis. Dr. Peris performed experiments and analyzed data. All authors have reviewed and approved the final version of the manuscript. Dr. Gozal and Dr. Kheirandish-Gozal are the guarantors of this work, had full access to all the data, and take full responsibility for the integrity of data and the accuracy of data analysis. DISCLOSURE STATEMENT This was not an industry supported study. Dr. Gozal is supported by National Institutes of Health grants HL-65270, HL-086662, and HL-107160. He has also consulted for Galleon Pharmaceuticals. The other authors have indicated no financial conflicts of interest. Dr. Gozal and Dr. Kheirandish-Gozal were equal contributors to this article. REFERENCES

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Obstructive sleep apnea and obesity are associated with reduced GPR 120 plasma levels in children.

Obstructive sleep apnea (OSA) is a common health problem, particularly in obese children, in whom a vicious cycle of obesity and OSA interdependencies...
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