The association between obstructive sleep apnea and metabolic markers and lipid profiles
Wei-Te Wu a, Su-Shan Tsai b, Tung-Sheng Shih c, d, Ming-Hsiu Lin c, Tzu-Chieh Chou d, Hua Ting e, f, Trong-Neng Wu g, Saou-Hsing Liou a, d, h*
a Division of Environmental Health and Occupational Medicine, National Health Research Institutes, Miaoli, Taiwan.
b Graduate Institute of Life Sciences, National Defense Medical Center, Taipei, Taiwan
c Institute of Occupational Safety and Health, Council of Labor Affairs, Taipei, Taiwan
d Institute of Environmental Health, College of Public Health, China Medical University and Hospital, Taichung, Taiwan
e Department of Physical Medicine and Rehabilitation, Chung-Shan Medical University, Taichung, Taiwan
f Center of Sleep Medicine, Chung-Shan Medical University Hospital, Taichung, Taiwan
g Graduate Institute of Biostatistics, China Medical University, Taichung, Taiwan h Department of Public Health, National Defense Medical Center, Taipei, Taiwan
Running head: Obstructive sleep apnea and metabolic syndrome Correspondence:
Saou-Hsing Liou,
Division of Environmental Health & Occupational Medicine, National Health Research Institutes, 35 Keyan Road, Zhunan Town, Miaoli County, 35053, Taiwan, ROC.
Tel: 886-37-246166, ext. 36500 Fax: 886-37-584-075
ABSTRACT
Background: Obstructive Sleep Apnea (OSA) is a common sleep disorder
characterized by repeated episodes of obstruction of the upper airway. Despite the tight link between Metabolic Syndrom (MetS) and OSA, it is not clear why there is a significant proportion of OSA among MetS patients, especially in Asians. The purpose of this study was to investigate (1) the association between apnea-hypopnea index (AHI) and metabolic markers, and (2) whether the elevated risk of OSA is induced by other cardiovascular risk factors other than MetS.
Methods: This cross-sectional study recruited 245 male bus drivers from one
transportation company in Taiwan. Each participant was evaluated by a
polysomnography (PSG) test and by blood lipids examination (total cholesterol, triglyceride, high-density lipoprotein cholesterol, and fasting blood glucose). Severity of OSA was categorized according to the apnea-hypopnea index (AHI): no OSA (AHI <5), mild-moderate (AHI: 5-30), and severe (AHI> 30).
Results: The results showed that subjects were categorized as severe OSA group
(n=44; 17.9%), moderate and mild OSA group (n=117; 47.8%), and no OSA group (n=84; 34.3%). A 67.7 % prevalence of MetS in OSA (AHI > 5) and a 86.4% prevalence of MetS in severe OSA (AHI>30) were found. After adjusting for
with Body-Mass Index (BMI) and two non-MetS cardiovascular risk factors, total cholesterol/HDL-C ratio and TG/HDL-C ratio.
Conclusions: The findings showed a high prevalence of MetS in OSA, especially in
the severe group catagory. BMI was the major contributing factor to OSA. However, the present study did not find a sensitive clinical marker of a detrimental metabolic profile in OSA patients.
1. Introduction
Obstructive Sleep Apnea (OSA) is a common sleep disorder that is characterized by intermittent, complete, and partial airway collapse, resulting in frequent episodes of apnea and hypopnea . The reduction of airflow often leads to acute derangements in gas exchange and recurrent arousals from sleep . Approximately three to seven percent for adult men and two to five percent for adult women in the general population have OSA . OSA is a complex condition and is not limited to a single symptom or feature of the disease . Observational and experimental evidence show that OSA contributes to the development of systemic hypertension, cardiovascular disease, and abnormalities in glucose metabolism . Despite the clinical and scientific advancements in the management of OSA in the last two decades, a great majority (70–80%) of those affected remain undiagnosed .
The current literature clearly shows that OSA is an emerging risk factor for modulating the cardiometabolic consequences of obesity . In the adult population, the prevalence of OSA is estimated to be as high as 45% in obese subjects . Metabolic Syndrome (MetS) is a cluster of metabolic abnormalities and is considered as a multi-morbid condition in which the fundamental components are obesity, insulin
resistance, hypertension, hypertriglyceridaemia and low high-density lipoprotein cholesterol . Moreover, OSA has been associated with heightened metabolic and
inflammatory dysregulation, as shown by increases in cytokines . The correlates of OSA, including excess body weight and hypertension, overlap with those of diabetes mellitus. OSA was reported to be associated with factors related to MetS .
Despite the tight link between MetS and OSA, it is not clear why a significant proportion of OSA patients also have MetS. Previous studies were limited by having a small sample size, which made them unable to adequately assess whether the overlap between OSA and MetS is simply a result of underlying obesity or if OSA represents an additional burden that exacerbates metabolic dysfunction in subjects with MetS. Additionally, patterns of body fat distribution also vary with ethnicity . Previous studies show that Asian populations are more prone to developing central or
abdominal fat, which predisposes them to OSA . However, the contribution of OSA to clinical features related to MetS in Asian populations has been seldom investigated.
To assess the contribution of OSA to metabolic markers, the purpose of this study was to investigate (1) the association between apnea-hypopnea index (AHI) and metabolic markers after adjusting for several confounding variables, and (2) to determine whether the elevated risk of OSA was induced by cardiovascular risk factors other than MetS.
2. Methods
2.1 Study subjects and data collection
Between April and November 2007, 985 male, professional long-haul bus drivers were recruited from one transportation company in Taiwan. All subjects completed the interview questionnaire and the biochemistry test. Among them, 247 subjects agreed to complete a polysomnography (PSG) test at a sleep medicine center. The researchers excluded 2 female subjects. A total of 245 male bus drivers were included in the subsequent analysis. The study procedures are presented in Figure1. The comparison with demographic characteristics between completed PSG group (n=247) and uncompleted PSG group (n=738) are shown in Supplement 1. These two groups are similar in term of gender, marital status, smoking and drinking habits, job types and weekly driving hours. The completed PSG group had older ages, higher duration of employment, and a higher BMI and education level in comparison with the
uncompleted PSG group.
This study was approved by the institutional review board of the National Health Research Institutes, Tri-Service General Hospital, and Chung-Shan Medical
University, Taiwan. Informed consent was obtained from each of the subjects after a detailed explanation of the nature and possible consequences of the study were explained by the interviewer on the day of the personal interview. After the written
informed consent was obtained from individual participants, the subjects were interviewed in person using a structured questionnaire that included demographics (age, ethnicity, marital and education status), work conditions (work hours and schedule of rotating shifts), lifestyle habits (smoking and drinking), and disease histories. For each participant biochemistry indices such as blood lipids (total cholesterol, triglyceride (TG), high-density lipoprotein cholesterol (HDL-C), and fasting blood glucose (FG)) and blood pressure (systolic blood pressure and diastolic blood pressure) were obtained during a health examination.
2.2 Polysomnography (PSG)
Participants underwent one night of PSG monitoring in the sleep laboratory. This included a full electroencephalography (EEG) montage in order to rule out epilepsy. The following channels were recorded: EEG, electrooculogram, electrocardiography (ECG), chin electromyogram, pulse oximetry, chest and abdominal excursion by inductance plethysmography, airflow by thermal sensor, and body position. Apnea was identified by the complete or near-complete cessation in airflow that lasted for at least 10 seconds. Hypopnea was identified by a clearly discernible decrease in airflow or by having a chest or abdominal plethysmograph amplitude that lasted for at least 10 seconds. Both apneas and hypopneas required an associated 4% or greater
oxyhemoglobin desaturation to be found significant. The AHI was defined as the average number of apneas plus hypopneas per hour of sleep. Severity of OSA was categorized according to the American Academy of Sleep Medicine (AASM) guidelines: severe (AHI>30), moderate (AHI: 16-30), mild (AHI: 5-15), and no (AHI<5) . In addition, the Arousal Index (AI) was calculated as the total number of arousals per hour of sleep. SaO2 monitoring with a high-resolution pulse oximeter wristwatch (PULSOX-300i, Konica Minolta Sensing, Inc., Osaka, Japan) was performed. Each oxygen probe of the oximeter and PSG were attached to different fingers of the nondominant hand. The sampling frequency of the oximeter PULSOX-300i is 1 Hz on memory interval and an averaging time of 3 seconds. Oxygen
Desaturation Index (ODI) is the hourly average number of desaturation episodes, which are defined as at least 4% decrease in saturation from the average saturation in the preceding 120 seconds, and lasting > 10 seconds.
2.3 Metabolic Syndrome (MetS)
Participants with MetS were defined by using the criteria proposed by the Taiwan Health Promotion Administration, Ministry of Health and Welfare in 2007 . MetS was defined as the presence of three or more of the following five criteria: (1) waist circumference ≥ 90 cm in men, ≥ 80 cm in women (replaced by BMI >27 Kgs/m2);
(2) blood pressure ≥130/ 85 mm-Hg or self-reported hypertension; (3) fasting glucose (FG) ≥ 100 mg/dl or self-reported diabetes mellitus; (4) triglyceride (TG) ≥ 150 mg/dl; and (5) high-density lipoprotein cholesterol (HDL-C) < 40 mg/dl in men or < 50 mg/dl in women.
2.4 Statistical Analysis
Each subject’s characteristics were compared among three groups that were stratified by the AASM guidelines: severe (AHI>30), mild-moderate (AHI: 5-30), and no (AHI<5). The level of indices exhibited skewed distributions. Therefore, the original data was transformed by using a natural logarithm to approximate normal distribution. The means and standard deviations were used to describe the
distributions of continuous variables. The percentages were used to describe the distributions of categorical variables. Analysis of Variance (ANOVA) and a trend test were used to test the differences among the three groups. Multiple linear regression models were used to test the association between AHI levels and components of MetS after adjusting for age, sex, smoking, drinking, and BMI. Multivariate logistic
regression analyses were conducted to evaluate the impact of metabolic markers on OSA. Following those analyses, the hierarchical logistic regression was used to determine the impact of predictors on OSA by using the explained variance
(Nagelkerke R Square) from an optimal model. The statistical analyses were
performed using SPSS, version 19.0, for Windows. All statistical tests were two-sided with p<0.05 set as the level of statistical significance.
3. Results
3.1 Characteristics of Participants
The descriptive statistics of these study participants in terms of demographic characteristics, lifestyle behavior, and job types are presented in Table 1. For the severity of OSA subjects were categorized into three groups according to the AASM guidelines: severe OSA group (n=44; 17.9%), moderate and mild OSA group (n=117; 47.8%), and no OSA group (n=84; 34.3%). There was no significant difference in the distribution of marriage status, education status, smoking and drinking habits, and weekly driving hours among the three groups. A trend of an older age, higher duration of employment, and higher BMI was shown to be in agreement with the severity of OSA. Therefore, age and BMI were adjusted in the subsequent analyses.
A high percentage of subjects having a history of heart disease (n=11, 25%), hypertension (n=11, 25%) and MetS (n=38, 86.4%) was found in the severe OSA group in comparison to the others. No significant differences in the distribution of diabetes and hyperlipidemia were found among these three groups.
3.2 Biochemistry indices in relation to the severity of OSA
The researchers performed ANOVA and a trend analysis in order to examine the association of the dose-response gradients for blood pressure, blood lipid, PSG, and
with severe OSA had a significantly increased trend in metabolic markers including systolic blood pressure, total cholesterol, TG, HDL-C, total cholesterol/HDL-C, and TG/HDL-C and PSG parameters including AI, HI, AHI, and ODI.
3.3 Associations between AHI and metabolic markers
The multivariate linear regression analyses were conducted to investigate the association between AHI levels and metabolic markers (Table 3). As shown in model 1, the presence of MetS was highly associated with AHI levels. A significant
association between AHI and BMI was also found after adjusting for confounders, including age, smoking, and drinking.
After adjustment for such covariables, it was found that ODI-4% and ODI-3% were independently associated with BMI (β=2.113, p<0.001; β=2.161, p<0.001). However, ODI (ODI-4% and ODI-3%) and sleep duration (total sleep time (min) and sleep period time (SPT) (min)) had no effect on the other metabolic parameters (data not shown).
3.3 OSA risks and metabolic markers
The multivariate logistic regression analyses were conducted to investigate the effect of metabolic markers on OSA (Table 4). The presence of MetS was a
absence of MetS subjects, even after adjusting for age, smoking, and drinking.
No matter what, BMI was evaluated as a continuous or categorical variable. BMI levels significantly increased OSA risks in all categories of OSA subjects when compared to no OSA group. However, the presence of OSA was not associated with other criteria for MetS (triglycerides and glucose) after adjustments.
Moreover, there was a strong trend for an independent association between the presence of OSA and total cholesterol/HDL ratio, even after adjusting for age,
smoking, drinking, and BMI. The presence of OSA was also independently associated with the abnormally elevated triglycerides/HDL ratio.
The hierarchical regression model used categorical variables including the BMI, SBP, DBP, HDL, FG, TG, and cofounder factors including age, smoking, and
drinking. The Nagelkerke R2 of BMI was 6.3% among moderate and mild OSA subjects when compared with the no-OSA group. The second contributing factor was HDL-C (Nagelkerke R2, 3.2%). Moreover, the Nagelkerke R2 of BMI was 11.9% among the severe OSA when compared with the no-OSA group.
4. Discussions
This study focused on determining whether OSA had MetS prevalence and whether OSA was independently associated with biomarkers of metabolic
dysfunction. The major finding of this study was that there was a 67.7 % prevalence of MetS in OSA (AHI > 5) and a 86.4% prevalence of MetS in those with severe OSA (AHI>30). MetS was twelve times more likely to be present in subjects with severe OSA. The elevated risk of OSA was associated with BMI and with two non-MetS cardiovascular risk factors, total cholesterol/HDL-C ratio and TG/HDL-C ratio.
4.1 Prevalence of the Metabolic Syndrome
The study design allowed for the researchers to systematically examine the prevalence of unrecognized MetS in subjects with OSA. A 67.7 % prevalence of MetS in OSA (AHI > 5) and a 86.4% prevalence of Mets in those with severe OSA (AHI>30) was found. In a Spanish Population study, the prevalence of MetS was higher in OSA patients (43% in the mild-moderate group and 81% in the severe group) than in non-OSA subjects (32%) . Ambrosetti et al. studied 89 consecutive OSA patients (AHI ≥ 15) following The US National Cholesterol Education
Programme Adult Treatment Panel III (NCEP ATP III) recommendations and found a prevalence of MetS in 53% . In a large series of Mediterranean OSA patients (AHI>5)
the prevalence of MetS was 51.2% . Moreover, the prevalence rates were reported by clinical studies in Western countries ranging between 30% and 87% . Results showed that the prevalence rate of MetS was higher in severe OSA and mild-moderate OSA subjects than in the above mentioned studies. This is probably due to gender
proportions in the study populations or ethnic differences.
4.2 Components of the Metabolic Syndrome and OSA
There has been no consistent explanation of the number of components of MetS increases with OSA severity in the previous studies . In this study, researchers found an independent correlation between OSA severity and BMI with two non-MetS cardiovascular risk factors- total cholesterol/HDL-C ratio and TG/HDL-C ratio.In animal models, intermittent hypoxia, a key feature of OSA, is involved in the pathogenesis of an abnormal lipid profile, modifying the expression of lipoprotein lipase. It is key in the HDL-C synthesis and increases the liver content of triglycerides in mice . OSA patients have been shown to have increased triglycerides, increased total cholesterol/HDL-C ratio, and lower HDL values . They may also have reduced HDL-mediated inhibition of low density lipoprotein oxidation . In many subjects from the Sleep Heart Health study there was a positive association between OSA severity and an increased amount of serum total cholesterol and triglycerides, as well as a
decreased amount of serum HDL in subjects under the age of 65 . In hospital populations, severe OSA is associated with low HDL-C levels independently of confusing factors such as obesity . Although an independent association between OSA and low levels of HDL-C or total cholesterol/HDL-C ratio seem to be observed in the above mentioned studies and in the present study, the ultimate relationship between OSA and the alterations in lipid metabolism remains to be defined.
4.3 Relationships between Metabolic Syndrome, OSA and Obesity
One of the most important associations between MetS and OSA is that obesity is tightly connected. Central, or visceral, obesity is associated with the greatest risk for OSA because fat deposits in the upper airway affect distensibility . The increased volume of abdominal fat could predispose one to hypoventilation during sleep and/or reduce the oxygen reserve, leading to oxygen desaturation during sleep .These results are consistent with findings from the researchers other study, in which a correlation between AHI and BMI was found.
In contrast,nocturnal awakenings and sleep disruption in OSA lead to sleep debt that, in turn, is translated to less activity during the diurnal hours and promotes obesity . The disrupted sleep patterns characteristic of OSA are correlatd with metabolic effects and weight gain . These results also imply that obesity is the
4.4 Strengths and Limitations
Previous studies seldom address the relationship between MetS and sleep disordered breathing in Asian subjects. An interesting and unexpected observation that has emerged is that the patterns of body fat distribution also vary with ethnicity . Although Asians are less obese than Whites, Asians have greater disease severity than Whites adjusted for age, sex, and BMI . Differences in craniofacial features between Asians and Whites have been demonstrated and are considered as etiologic factors for the increased risk and greater severity of OSA in Asians despite lesser degrees of obesity .
There were some limitations to this study. First, it is a cross-sectional study. Therefore, results may not be interpreted as having a causal association due to a lack of temporality. Secondly, the study used BMI of 27 kg/m2 instead of waist
circumference to assess the central obesity in the metabolic profile. According to Asian-Pacific guidelines for central obesity, optimal waist circumference in Asians is men >90 cm and women >80 cm) . Based on the receiver operating characteristic curve analysis from the National Nutrition and Health Survey in Taiwan, the BMI of 27 kg/m2 can be used to predict waist circumference for about 90 cm for men and 80 cm for women abdominal visceral obesity . Thirdly, the results do not exclude the
possibility that in certain cases increased parapharyngeal fat may be predisposed one to upper airway narrowing and OSA.
4.5 Conclusion
In summary, the present study showed that OSA was strongly associated with MetS, and the inter-correlation may be involved in the pathogenesis of lipid abnormalities. The study also showed that BMI was the major contributing factor between OSA and MetS. However, the present study did not find a sensitive clinical marker of a detrimental metabolic profile in OSA patients.
Acknowledgments
The authors thank the administrators and drivers from the bus company for their participation and cooperation. This study was partly supported by the National Health Research Institutes of Taiwan (98-EO-PP01, 99-EO-PP01, and 00-EO-PP01) and the Institute of Occupational Safety and Health (IOSH96-M102 and IOSH97-M102), Taiwan. The funder had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
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Legend of Figure:
Figure 1 Flow diagram summarized the enrollment of subjects who had undergone polysomnography (PSG)
