European Archives of Oto-Rhino-Laryngology

, Volume 271, Issue 4, pp 825–831 | Cite as

Obstructive sleep apnea syndrome is associated with metabolic syndrome and inflammation

  • Qi-Chang Lin
  • Li-Da Chen
  • Yao-Hua Yu
  • Kai-Xiong Liu
  • Shao-Yong Gao
Miscellaneous

Abstract

Obstructive sleep apnea (OSA) is an independent risk factor for cardiovascular morbidity and mortality. However, the underlying mechanism is unclear. In this cross-sectional study, we investigated the influence of OSA on metabolic syndrome (MetS) and inflammation, which were considered as cardiovascular risks. A total of 144 consecutive male patients who underwent standard polysomnography were enrolled. Fasting blood samples were obtained from all patients for glucose, high-sensitivity C-reactive protein (hs-CRP) and lipids measurement. A metabolic score was established as the total number of the positive diagnostic criteria of metabolic syndrome for each patient. Systolic blood pressure, diastolic blood pressure, fasting glucose, hs-CRP and metabolic score significantly increased with the aggravation of OSA severity. Metabolic score increased from 1.74 ± 1.20 to 2.89 ± 0.99 with OSA severity (p = 0.000). hs-CRP increased from 0.68 (0.43–1.10) to 1.44 (0.62–4.02) mg/L with OSA severity (p = 0.002). After adjustment for confounders, apnea–hypopnea index and body mass index (BMI) were the major contributing factors for metabolic score (β = 0.257, p = 0.003 and β = 0.344, p = 0.000, respectively), lowest O2 saturation and BMI were the independent predictors of hs-CRP (β = −0.255, p = 0.003 and β = 0.295, p = 0.001, respectively). OSA is independently associated with sum of metabolic components and hs-CRP.

Keywords

Obstructive sleep apnea Metabolic syndrome High-sensitivity C-reactive protein Cardiovascular risk 

Introduction

Obstructive sleep apnea (OSA) is a common disorder that is characterized by repeated recurrent episodes of partial or complete cessation of breathing during sleep, heavy snoring and daytime sleepiness. The prevalence of OSA among the adult population is estimated to be between 2 and 4 % in western counties [1]. A growing body of evidence from clinical research supports the relationships of OSA with increased cardiovascular and cerebrovascular morbidity and mortality [2, 3, 4]. The underlying mechanisms mediating these associations are not completely understood. The pathogenesis seems to be a multifactorial process including metabolic dysregulation and inflammation.

Metabolic syndrome (MetS) represents a combination of a clustering of metabolic abnormalities including central obesity, insulin resistance, dyslipidemia and increased blood pressure [5], which are all risk factors that promote atherosclerotic cardiovascular disease. Insulin resistance seems to play a central role in the pathogenesis of the MetS [6]. MetS has been shown to increase cardiovascular disease (CVD) [7, 8, 9]. However, other factors which are not included in the MetS definition may also contribute to the high cardiovascular burden.

Inflammatory biomarkers are increasingly recognized as independent risk indicators for CVD and have also attracted attention in the investigation of the relationship between cardiovascular risk and OSA. C-reactive protein (CRP) is an important serum biomarker of systemic inflammation which is associated with an increased risk of atherosclerosis and CVD [10, 11]. Inflammation plays a central role in all stages of atherosclerosis, from initiation of the fatty streak to plaque rupture and, ultimately, the clinical thrombotic complications [12, 13]. High-sensitivity assays to detect low concentrations of the protein have become available and high-sensitivity C-reactive protein (hs-CRP) is currently the most extensively used marker for cardiovascular risk evaluation [14].

The common coexistence of OSA and obesity complicates studies that try to determine the independent role of OSA on MetS, CRP and other pathophysiologic process. Studies on the associations of OSA with MetS and CRP showed conflicting results. Some investigations supported the direct associations [15, 16, 17, 18, 19], while others did not [20, 21, 22, 23]. The aim of the present study was to evaluate if the presence of OSA was independently associated with MetS as a complete entity by enrolling consecutive male patients with no previous diagnosis of OSA. We also wanted to assess the possible influence of OSA on other parameter associated with cardiovascular risk, but not included in the MetS definition.

Materials and methods

Subjects

Consecutive male patients from May 2011 to May 2012, who presented to our sleep laboratory because of symptoms of snoring, with or without witness episodes of sleep apnea and daytime sleepiness, were included to the study. None had been previously diagnosed with or treated for OSA. All patients completed an Epworth sleepiness scale (ESS) and a detailed questionnaire on sleep symptoms, current history of smoking and alcohol consumption, medical history and medications. All patients were free from cerebrovascular disease, congestive heart failure, symptomatic ischemic heart disease, asthma, chronic obstructive pulmonary disease (COPD), chronic renal failure, rheumatologic diseases and hypothyroidism according to self-reported medical histories. Patients with any kind of acute inflammatory disease such as recent infection were also excluded. Subjects provided written informed consent prior to study participation, and this study was approved by the local institutional ethics committee.

Anthropometric and biochemical measurements

All the participants’ weight and height were measured with the same equipments. Body weight and height were measured in bare feet and normal indoor clothing in the morning. Body mass index (BMI) was calculated by dividing body weight to height square (kg/m2). Waist circumference was measured midway between the lower costal margin and the iliac crest [24] and neck circumference at the level of the laryngeal prominence. Blood pressure was gauged by a standard mercury sphygmomanometer on the right arm with the participants in a sitting position after at least 5 min of rest and the average of two readings taken at a 1-min interval was documented. Fasting blood was drawn the morning after polysomnographical evaluation. Serum glucose, and lipid profile comprising total cholesterol, high density lipoprotein-cholesterol (HDL-C), low density lipoprotein-cholesterol (LDL-C) and triglycerides were analyzed using the H-7600 autoanalyzer (Hitachi). hs-CRP was measured with a BNII nephelometer (Dade Behring, Deerfield, IL, USA).

Polysomnographical evaluation

Overnight polysomnography (P Series Sleep System; Compumedics; Melbourne, Australia) was performed between 22:00 and 06:00, which recorded the following parameters: electroencephalography, electrooculography, electromyography, airflow by nasal and oral thermistors, respiratory effort by thoracic and abdominal impedance belts, arterial oxyhemoglobin saturation by pulse oximetry, snoring by tracheal microphone, and changing of the body position during sleep by sensor. Sleep staging was scored according to the criteria of American Academia of Sleep Medicine (AASM) published in 2007 [25]. Apnea was defined as cessation of airflow for ≥10 s. Hypopnea was defined as a ≥30 % reduction of airflow, lasting ≥10 s, and associated with a ≥4 % decrease in oxyhemoglobin saturation (or followed by an arousal when OSA was diagnosed overnight PSG). The number of apneas and hypopneas per hour of sleep were calculated to obtain the apnea–hypopnea index (AHI). The oxygen desaturation index (ODI) was defined as the number of dips in oxygen saturation (SaO2) ≥4 % per hour of study. Polysomnographical parameters including the percentage of sleep time with SaO2 <90 % (T90 %), lowest O2 saturation (LaSO2) and mean nocturnal oxygen saturation (average SaO2) were also recorded. The whole study subjects were divided into three groups according to their AHI in tertiles, as follows. Group 1, AHI < 13.2 events/h; group 2, 13.2 ≤ AHI < 34.4 events/h; group 3, AHI ≥ 34.4 events/h [26].

Metabolic score

The MetS was diagnosed according to the third report of the national cholesterol education program (NCEP) [5]. A diagnosis of MetS was made if the patient had at least three of the following five clinical features: (1) arterial pressure ≥130 or 85 mmHg for systolic and diastolic blood pressure, respectively; (2) triglycerides ≥1.7 mmol/L; (3) HDL-C <1.03 mmol/L in men and <1.29 mmol/L in women; (4) fasting glucose ≥5.6 mmol/L; and (5) waist circumference ≥90 cm in men and ≥80 cm in women. Patients who were already receiving therapy for hypertension, dyslipidemia, or diabetes were considered to have these features. The cut-off value for central obesity was recommended for Chinese populations [27]. A metabolic score was established as the total number of the positive diagnostic criteria identified for each patient.

Statistical analysis

All variables were tested for normal distribution prior to analysis. Data were expressed as mean ± SD, median (interquartile range), number (percentage) for normally distributed, skewed and categorical data, respectively. Normally distributed data were compared by using one-way ANOVA for multiple-group comparison. Skewed data were compared by using Kruskal–Wallis H(K) for multiple-group comparison. χ2 test or Fisher’s exact test was performed for categorical variables. All descriptive data not in normal distribution were log-transformed before analysis. Correlations between variables were explored using the Spearman rank test. Multiple linear regression analysis was performed to determine the independent predictors of metabolic score, hs-CRP. A stepwise selection method was used with the multivariate analysis. A value of p < 0.05 was considered statistically significant. SPSS v 17.0 for Windows (SPSS Inc., Chicago, IL) was used for statistical analysis.

Results

We initially enrolled 183 consecutive male patients; 39 subjects were excluded from the final analysis: previously treated OSA, 14; COPD, 4; asthma, 3; chronic renal failure, 2; cerebrovascular disease, 5; congestive heart failure, 4; other diseases which may influence hs-CRP, 7. The final sample comprised 144 subjects, with 35 in the group 1 (AHI < 13.2), 44 in the group 2 (13.2 ≤ AHI < 34.4) and 65 in the group 3 (AHI ≥ 34.4). The anthropometric and polysomnographic characteristics of the patients are summarized in Table 1. There were no significant differences among the three groups with respect to age, current smoking, alcohol consumption, medications, ESS score. A positive, statistically significant linear association was observed between OSA severity and the indices of BMI, neck circumference, waist circumference (all p = 0.000).
Table 1

Anthropometric and polysomnographic parameters of all patients and between-group comparisons of the tertile groups

 

AHI events/h

p values

<13.2

13.2–34.4

≥34.4

Subjects (n)

35

44

65

 

Age

42.77 ± 12.46

46.61 ± 10.99

44.86 ± 10.28

0.311

Current smoking, number (%)

16 (45.7)

16 (36.4)

23 (35.4)

0.572

Alcohol consumption, number (%)

5 (14.3)

5 (11.4)

7 (10.8)

0.868

Antilipemic agents, number (%)

1 (2.9)

0 (0.0)

1 (1.5)

0.722

Antidiabetic agents, number (%)

1 (2.9)

2 (4.5)

5 (7.7)

0.724

Antihypertensive agents, number (%)

5 (14.3)

9 (20.5)

15 (23.1)

0.578

Body mass index (kg/m2)

25.14 ± 2.52

26.15 ± 2.27

28.83 ± 3.23

0.000

Neck circumference (cm)

39.07 ± 2.50

40.27 ± 2.06

41.64 ± 2.86

0.000

Waist circumference (cm)

91.86 ± 7.29

96.76 ± 6.88

100.15 ± 10.04

0.000

AHI

5.20 (1.85–8.20)

21.60 (16.73–27.43)

57.10 (44.80–69.90)

0.000

ODI

2.70 (1.50–5.50)

18.80 (13.92–27.38)

50.20 (34.25–64.05)

0.000

T90 % (%)

0.05 (0.00–0.17)

2.21 (0.67–6.43)

15.33 (7.39–31.81)

0.000

LaSO2 (%)

88.00 (84.00–92.00)

82.00 (78.25–86.00)

73.00 (62.00–80.00)

0.000

Average SaO2 (%)

97.00 (96.00–97.00)

95.00 (95.00–96.00)

93.00 (89.50–95.00)

0.000

ESS score

9.06 ± 4.88

8.18 ± 4.83

10.49 ± 5.41

0.064

Normally distributed data were expressed as mean ± SD, skewed data (including AHI, ODI, T90 %, LaSO2, average SaO2) were presented as median (interquartile range). Categorical variables were expressed as number (percentage)

AHI apnea–hypopnea index, ODI oxygen desaturation index, T90 % the percentage of total sleep time spent with SaO2 <90 %, LaSO2 lowest O2 saturation, average SaO2 average O2 saturation, ESSscore Epworth sleepiness scale score

Table 2 shows the metabolic and biochemical parameters. No significant differences in TC, TG, HDL-C, and LDL-C were detected among the three groups. SBP, DBP, hs-CRP, fasting glucose and the mean sum of components (metabolic score) increased significantly with an increase in OSA severity. Metabolic score increased from 1.74 ± 1.20 to 2.89 ± 0.99 with OSA severity (p = 0.000). hs-CRP increased from 0.68 (0.43–1.10) to 1.44 (0.62–4.02) mg/L with OSA severity (p = 0.002). The prevalence of hypertriglyceridemia and low HDL-C were similar in the three groups, while a statistically significant increase in the rate of hypertension, central obesity, hyperglycemia and metabolic syndrome was observed from groups 1 to 3.
Table 2

Metabolic and biochemical characteristics of all patients and between-group comparisons of the tertile groups

 

AHI events/h

p values

<13.2

13.2–34.4

≥34.4

Subjects (n)

35

44

65

 

SBP (mmHg)

119.71 ± 12.67

125.52 ± 11.71

129.02 ± 11.18

0.001

DBP (mmHg)

76.11 ± 8.01

81.66 ± 10.83

85.72 ± 10.93

0.000

TC (mmol/L)

5.34 ± 0.97

5.22 ± 0.96

5.32 ± 1.10

0.834

TG (mmol/L)

1.75 (1.25–2.46)

1.48 (1.13–1.91)

1.82 (1.27–2.72)

0.186

HDL-C (mmol/L)

1.30 ± 0.30

1.37 ± 0.34

1.25 ± 0.24

0.107

LDL-C (mmol/L)

3.10 ± 0.74

3.04 ± 0.74

3.24 ± 0.87

0.431

Fasting glucose (mmol/L)

5.06 (4.83–5.36)

5.28 (5.06–5.81)

5.37 (4.98–5.88)

0.021

Hs-CRP (mg/L)

0.68 (0.43–1.10)

1.04 (0.46–1.95)

1.44 (0.62–4.02)

0.002

Hypertension, number (%)

14 (40.0)

26 (59.1)

54 (83.1)

0.000

Central obesity, number (%)

17 (48.6)

36 (81.8)

59 (90.8)

0.000

Hypertriglyceridemia, number (%)

19 (54.3)

15 (34.1)

34 (52.3)

0.110

Low HDL-C, number (%)

6 (17.1)

5 (11.4)

10 (15.4)

0.747

Hyperglycemia, number (%)

5 (14.3)

16 (36.4)

31 (47.7)

0.004

Metabolic syndrome, number (%)

10 (28.6)

18 (40.9)

40 (61.5)

0.004

Metabolic score

1.74 ± 1.20

2.23 ± 1.03

2.89 ± 0.99

0.000

Normally distributed data were expressed as mean ± SD, skewed data (including TG, hs-CRP, fasting glucose) were presented as median (interquartile range). Categorical variables were expressed as number (percentage)

SBP systolic blood pressure, DBP diastolic blood pressure, TC total cholesterol, TG triglycerides, HDL-C high density lipoprotein-cholesterol, LDL-C low density lipoprotein-cholesterol, hs-CRP high-sensitivity C-reactive protein

The associations between metabolic score, log hs-CRP and the other variables are summarized in Table 3. Metabolic score and log hs-CRP were significantly correlated with BMI, waist circumference, neck circumference and all indices of OSA (all p = 0.000). Multivariate analysis was conducted to evaluate the independent predictors of metabolic score and log hs-CRP. When using metabolic score as a dependent variable, log AHI and BMI significantly predicted the metabolic score after adjustment for confounders (β = 0.257, p = 0.003 and β = 0.344, p = 0.000, respectively) (Table 4), when using log hs-CRP as dependent variable, log LaSO2 and BMI were the independent predictors of log hs-CRP (β = −0.255, p = 0.003 and β = 0.295, p = 0.001, respectively) (Fig. 1).
Table 3

Spearman’s rank correlation coefficients between log hs-CRP, MS score and clinical and polysomnographic characteristics

 

MS score

Log hs-CRP

R

p values

R

p values

Age

0.079

0.349

0.049

0.558

Current smoking

0.116

0.166

−0.007

0.936

Alcohol consumption

0.002

0.977

−0.017

0.844

Neck circumference

0.439

0.000

0.312

0.000

Waist circumference

0.508

0.000

0.411

0.000

Body mass index

0.442

0.000

0.421

0.000

ESS score

0.129

0.123

0.212

0.011

Log AHI

0.429

0.000

0.366

0.000

Log ODI

0.430

0.000

0.399

0.000

Log T90 %

0.420

0.000

0.403

0.000

Log average SaO2

−0.448

0.000

−0.395

0.000

Log LaSO2

−0.330

0.000

−0.387

0.000

Log hs-CRP

0.370

0.000

MS score

0.370

0.000

AHI apnea–hypopnea index, ODI oxygen desaturation index, T90 % the percentage of total sleep time spent with SaO2 <90 %, average SaO2 average O2 saturation, LaSO2 lowest O2 saturation, hs-CRP high-sensitivity C-reactive protein, MS score metabolic score

Table 4

Multivariate analysis in study subjects

 

β coefficient

p

Constant

 

0.021

BMI

0.344

0.000

Log AHI

0.257

0.003

Correlation between log AHI and MS score (r = 0.429, p = 0.000); we entered age, current smoking, alcohol consumption, BMI, log AHI, log ODI, log T90 %, log average SaO2, log LaSO2 as independent variables, stepwise linear regression showed that BMI and log AHI were included in the final model. Log AHI was the independent predictor of MS score (β = 0.257 adjusted r2 = 0.267, p = 0.003)

AHI apnea–hypopnea index, ODI oxygen desaturation index, T90 % the percentage of total sleep time spent with SaO2 <90 %, LaSO2 lowest O2 saturation, average SaO2 average O2 saturation, BMI body mass index, MS score metabolic score

Fig. 1

Correlation between log LaSO2 and log hs-CRP (r = −0.387, p = 0.000); we entered age, current smoking, alcohol consumption, BMI, neck circumference, waist circumference, log AHI, log ODI, log T90 %, log LaSO2, log average SaO2 as independent variables, stepwise linear regression showed that BMI and log LaSO2 were included in the final model. Log LaSO2 was the independent predictor of log hs-CRP (β = −0.255, adjusted r2 = 0.213, p = 0.003). AHI apnea–hypopnea index, ODI oxygen desaturation index, T90 % the percentage of total sleep time spent with SaO2 <90 %, LaSO2 lowest O2 saturation, average SaO2 average O2 saturation, BMI body mass index, hs-CRP high-sensitivity C-reactive protein

Discussion

Our results showed that: (1) the sum of metabolic components was independently correlated with the severity of OSA, which was largely driven by central obesity, hypertension and hyperglycemia; (2) obesity was strongly associated with elevated level of hs-CRP, but nocturnal hypoxia also had an independent effect on it.

MetS

Rapidly accumulating data from both epidemiological and clinical studies suggested that OSA was independently associated with MetS [15, 16, 19]. In a cross-sectional analysis of 98 male consecutive patients suspected for OSA, central obesity, insulin resistance and MetS increased with increasing severity of OSA, and the number of components of metabolic syndrome increased with OSA severity regardless of BMI [19]. A study performed in Hong Kong community showed that subjects with OSA had 5-times more likelihood of developing the MetS [16]. This hypothesis has been strengthened by evidence from intervention trials. Oktay and colleagues [28] have shown that the prevalence of MetS decreased by 45 % after 1 year of CPAP treatment, and significant difference was mainly observed in waist circumference, HDL cholesterol and BMI after treatment. Another interventional study reported that after 6 months of follow-up on APAP treatment, prevalence of MetS decreased from 63.5 to 47.3 %, which contributed to blood pressure and serum triglycerides reduction [29]. In the present study, we showed that the prevalence of central obesity, hypertension, hyperglycemia, metabolic syndrome and mean metabolic score increased significantly with severity of OSA, and OSA was associated with metabolic score independently of BMI and age. But we failed to find a difference between three groups in the prevalence of dyslipidemia. Despite a large number of evidence supporting an independent relationship between OSA and MetS, the issue still remains controversial. A case–control study reported that OSA was not independently associated with insulin resistance, and dyslipidemia, and obesity was the major determinant of metabolic abnormalities [20]. A randomized controlled study in patients with moderate-to-severe OSA showed that glucose, lipids and insulin resistance were unaffected after 6 weeks of effective CPAP [30]. Taken together, there is clearly a need for future, large-scale, prospective longitudinal cohort studies and interventional trails to address the question of whether OSA is causally linked to MetS or its components.

CRP

Studies on the relationship between OSA and CRP have yielded conflicting results due to the confounding effects of obesity. Some investigations have found an independent correlation of OSA and CRP levels [17, 18, 31, 32, 33], while others have failed to show this relationship [21, 22, 23, 34, 35, 36]. A study including 111 healthy middle-aged male patients showed that CRP was higher in subjects with moderate-to-severe OSA when comparing control group which was similar in MRI visceral fat volume, and elevated CRP level is independently associated with OSA after adjusting for visceral obesity [18]. Another study identified that both T90 % and obesity were the best predicting variables for CRP levels [33]. Our study demonstrated that the association of OSA and hs-CRP levels remained significant after controlling obesity, which was consistent with previous report [33]. However, the Wisconsin sleep cohort study involving 907 adults found that there was no significant association between CRP levels and sleep disordered breathing (SDB) after adjustment for age, sex and BMI [22]. Su et al. [34] showed that hs-CRP was usually elevated in patients with SDB, but it was not independently associated with the severity of SDB. Factors such as sample size, statistical methodology and study design could be responsible for this disparity. Conflicting results were also observed in the effect of CPAP treatment on CRP levels [37, 38, 39]. Ishida et al. [38] found that CRP levels decreased significantly after 6 months of nasal CPAP treatment. In contrast, Kohler and colleagues [39] have failed to show this beneficial effect on hs-CRP in patients with moderate-severe OSA by 4 weeks of treatment with nasal CPAP. But the duration of follow-up of CPAP treatment was only 4 weeks in the above-mentioned negative study, which raised the question of insufficient CPAP use might be a potential confounding factor.

The present study had several limitations that warrant additional comment. It was a cross-sectional study, the findings of significant association was not sufficient to clarify a cause–effect relationship. Second, confounding factors including BMI and waist circumference were not matched for each group, and we used multiple linear regression to overcome the limitation. Third, we would like to emphasize that the sample size of the present study was relatively small. Fourth, female patients were excluded to avoid the gender effect, therefore, our results could not be extrapolated to female patients. Fifth, we did not measure fasting insulin for accessing the insulin resistance, which was considered to play an important role in the MetS. Finally, our studies were limited by selection bias, because we included subjects referred for sleep studies due to sleep-related complaints, and majority of patients were severe in OSA.

In summary, the present study reported that prevalence of central obesity, hypertension and hyperglycemia and metabolic syndrome increased with an increase in severity of OSA. OSA was independently associated with sum of metabolic components and hs-CRP. This may be the mechanism for the link between OSA and increased cardiovascular and cerebrovascular morbidity and mortality.

Notes

Acknowledgments

This work was supported by grant C07100008 for natural science foundation from Fujian province of China. We would like to thank Bin Yang for assistance with blood analysis. Xiao-Bin Zhang is thanked for his help with statistics.

Conflict of interest

The authors declare that they have no conflict of interest.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Qi-Chang Lin
    • 1
    • 2
    • 3
  • Li-Da Chen
    • 1
    • 2
    • 3
  • Yao-Hua Yu
    • 1
    • 2
    • 3
  • Kai-Xiong Liu
    • 1
    • 2
    • 3
  • Shao-Yong Gao
    • 1
    • 2
    • 3
  1. 1.Fujian Provincial Sleep-Disordered Breathing Clinic CenterFuzhouPeople’s Republic of China
  2. 2.Laboratory of Respiratory Disease of the Fujian Medical UniversityFuzhouPeople’s Republic of China
  3. 3.Department of Respiratory MedicineThe First Affiliated Hospital of Fujian Medical UniversityFuzhouPeople’s Republic of China

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