Journal List > J Korean Soc Hypertens > v.20(2) > 1089822

J Korean Soc Hypertens. 2014 Jun;20(2):42-50. English.
Published online June 30, 2014.  https://doi.org/10.5646/jksh.2014.20.2.42
Copyright © 2014. The Korean Society of Hypertension
Association of Obstructive Sleep Apnea with Peripheral Endothelial Function Assessed by Reactive Hyperemia Index
Jaewon Oh, MD, Sungha Park, MD, Jong-Chan Youn, MD, Geu-Ru Hong, MD, Sang-Hak Lee, MD, Seok-Min Kang, MD and Donghoon Choi, MD
Division of Cardiology, Cardiovascular Research Institute, Severance Cardiovascular Hospital, Yonsei University College of Medicine, Seoul, Korea.

Correspondence to: Sungha Park, MD. Division of Cardiology, Cardiovascular Research Institute, Severance Cardiovascular Hospital, Yonsei University College of Medicine, 50 Yonsei-ro, Seodaemun-gu, Seoul 120-749, Korea. Tel: +82-2-2228-8460, Fax: +82-2-393-2041, Email: shpark0530@yuhs.ac
Received February 20, 2014; Revised June 11, 2014; Accepted June 13, 2014.

Abstract

Background

Obstructive sleep apnea (OSA) has been shown to be an important risk factor for metabolic syndrome and cardiovascular disease. Endothelial dysfunction plays a pivotal role in the pathophysiology of these diseases. However, little is known about the relationship between sleep apnea and microvascular endothelial dysfunction, as assessed by digital reactive hyperemia.

Methods

The study population consisted of 80 patients (mean age, 48 ± 12 years-old; 65 men; 59 hypertensive). We measured apnea-hypopnea index (AHI) and mild OSA was defined as 5 < AHI <15 and moderate to severe OSA as AHI ≥ 15. Reactive hyperemia index (RHI) derived from peripheral arterial tonometry (PAT) as measurement of endothelium-mediated vasodilatation.

Results

There were 61 OSA patients in the study population (AHI, 21.5 ± 16.7 vs. 2.7 ± 1.6 in non-OSA; p < 0.001). There were no significant difference in RHI and peripheral augmentation index (pAIx) between OSA and non-OSA group (RHI, 2.04 ± 0.48 vs. 2.06 ± 0.42; p = 0.894; pAIx, 21.7% ± 24.0% vs. 21.7% ± 30.0%; p = 1.000, respectively). Also, there was no significant difference in RHI and pAIx between mild (n = 31) and moderate to severe (n = 30) OSA group (RHI, 2.10 ± 0.47 vs. 1.98 ± 0.49; p = 0.333; pAIx, 24.2% ± 20.7% vs. 19.0% ± 27.2%; p = 0.407, respectively), either. Overall, no significant correlation between AHI and RHI was observed (r = -0.023, p = 0.837). The other OSA severity indices such as oxygen desaturation index, mean and minimum oxygen saturation were not correlated with RHI or pAIx. In the subgroup analysis for the OSA group, we could not find any significant relationships between AHI and PAT parameters, either.

Conclusions

OSA was not observed to be associated with reactive hyperemia measured by PAT.

Keywords: Obstructive sleep apnea; Vasodilation

Introduction

Obstructive sleep apnea (OSA), characterized by recurrent upper airway collapse, is a common condition that affects at least 10% of the general population, primarily overweight or obese person.1) Recently, many reports suggest that OSA is associated with cardiovascular disease (CVD) such as hypertension, coronary artery disease, cerebrovascular disease, and arrhythmia.2, 3, 4, 5) The mechanisms underlying this association are not fully elucidated, but endothelial dysfunction, an initiating pathophysiology for atherosclerosis, may represent a link between OSA and CVD.3, 6, 7) There have been diverging reports on the effect of OSA on endothelial function, with some studies showing impaired endothelial function,7, 8, 9, 10) while other large sample-sized study found no association between OSA and endothelial dysfunction as measured by brachial artery flow-mediated dilation (FMD).11)

Recently, a new measurement of peripheral vasodilator response as a measure for endothelial dysfunction using fingertip pulse amplitude tonometry (peripheral arterial tonometry, PAT) may emerge as a useful, non-invasive assessment of microvascular health.12, 13) Reactive hyperemia (RH) response (with PAT) as detected by the RH index (RHI) has recently been shown to be related to multiple traditional and metabolic risk factors and cardiovascular events.14, 15, 16) However, there have not been enough studies about the relationship between sleep apnea and endothelial dysfunction, especially digital RH by PAT. Therefore, we investigated the relationships between OSA and peripheral endothelial function assessed by RHI.

Subjects and methods

1. Study subjects

The study group was comprised of 80 consecutive subjects who underwent digital RH by PAT derived from the sleep apnea registry of Severance Cardiovascular Hospital. The medical history such as diabetes mellitus (DM), hypertension (HTN), and dyslipidemia were diagnosed based on medical and medication history. Blood sampling was performed from the forearm via the antecubital vein after a minimum of 12-hour fasting and collected into both EDTA-treated and plain tubes. Written, informed consent was obtained from all subjects and the protocol was approved by the institutional review board of Yonsei University College of Medicine (IRB no. 4-2011-0299).

2. Sleep apnea assessment

The apnea-hypopnea index (AHI) was calculated as the sum of the total events of the apnea index and hypopnea index. Apnea was defined as the absence of airflow for 10 seconds or longer. Hypopnea was defined as either 1) reduced airflow of at least 50% for 10 seconds or longer with the presence of either oxygen desaturation ≥ 3% of the normal level or an arousal or 2) reduced airflow of at least 30% for 10 seconds or longer with oxygen desaturation ≥ 4%.17, 18) Ambulatory Polysomnography was performed with Embletta X100 (Embla, Broomfield, CO, USA). OSA was defined as AHI > 5 and mild OSA as 5 < AHI < 15, moderated to severe OSA as AHI ≥ 15.

3. Digital measurements of vascular function

Pulse amplitude at rest was measured in the fingertips by positioning a PAT device (Endo-PAT2000; Itamar Medical, Caesarea, Israel). Two flexible probes were placed on the index fingers of the right (ischemic) and left (control) hands. Reactive hyperemia was provoked by a 5-minute forearm cuff occlusion. The recorded pulse amplitude was analyzed by a computerized, semi-automated algorithm (Itamar Medical). Endothelial dysfunction was assessed using RHI as described previously.12, 13) The RHI was calculated as the ratio of the average PAT amplitude over 60 seconds after 90 seconds of cuff deflation to the average PAT amplitude over 2.5 minutes prior to cuff inflation in the occluded hand divided by the same values in the control hand and then multiplied by a baseline correction factor.

4. Statistical analysis

Results are expressed as the mean ± standard deviation in continuous variables and a percentage of the total group in categorical variables. In this study, a comparison of discrete variables was made using chi-square test, while Student t-test was used for continuous variables. If the distribution was skewed, a non-parametric test was used. Correlations of RHI, AHI with other variables were examined using Pearson correlation analysis. A multivariable linear regression analysis was done in a model using variables related to RHI and peripheral augmentation index (pAIx). A two-tailed p-value < 0.05 was considered statistically significant. All statistical analyses were performed with IBM SPSS ver. 21.0 (IBM Co., Armonk, NY, USA).

Results

Table 1 showed the clinical and laboratory characteristics of the study population. Our study group consisted of 80 patients (65 males, 81.3%) with a mean age of 56 ± 11 years old. There were 59 HTN (73.8%), 7 DM (8.8%) and 8 dyslipidemic (10.0%) patients. Mean systolic blood pressure, total cholesterol (TC) and fasting blood glucose level were 134.8 ± 17.5 mm Hg, 189.5 ± 35.6 mg/dL and 103.1 ± 17.0 mg/dL, respectively. Thirty-nine patients were on angiotensin receptor blocker (48.8%) and 25 patients were on aspirin administrations (31.3%). Mean RHI and pAIx were 2.05 ± 0.46, 21.7 ± 25.4, respectively. When we compared the baseline characteristics between non-OSA and OSA group, TC, low density lipoprotein cholesterol, AHI and oxygen desaturation index (ODI) were significantly higher and mean and minimum oxygen saturation lower in OSA than non-OSA group. However, there was no significant difference in RHI and pAIx between OSA and non-OSA group (Table 1). Then, we further compared RHI and pAIx after separating OSA group into mild (n = 31) and moderate to severe (n = 30) OSA group. However, we could not find any significant differences in RHI and pAIx among non-OSA, mild and moderate to severe OSA group (RHI, 2.08 ± 0.44 vs. 2.10 ± 0.47 vs. 1.98 ± 0.49; p = 0.600; pAIx ,17.5% ± 23.6% vs. 24.2% ± 20.7% vs. 19.0% ± 27.2%; p = 0.735, respectively), either (Fig. 1A, B).


Fig. 1
Box diagrams showing (A) RHI and (B) pAIx in patients with no (AHI < 5), mild (5 ≤ AHI < 15) and moderate to severe (AHI ≥ 15) obstructive sleep apnea. In these plots, the upper and lower bars outside the boxes represent the 90% confidence interval. RHI, reactive hyperemia index; pAIx, peripheral augmentation index; AHI, apnea-hyponea index.
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Table 1
Baseline characteristics of study patients
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We analyzed the association among RHI, pAIx and clinical, laboratory variables by correlation analysis in overall study group (Table 2). No significant correlation between AHI and endothelial function was observed (r = -0.023, p = 0.837 for RHI; r = -0.094, p = 0.408 for pAIx) (Fig. 2A, B). The other OSA severity indices such as ODI, mean and minimum oxygen saturation were not correlated with RHI or pAIx. No clinical and laboratory parameter was significantly correlated with RHI whereas age, body mass index (BMI), heart rate (HR), and triglyceride (TG) were significantly correlated with pAIx. The correlation analysis for subjects with OSA did not show any significant association among OSA parameters and endothelial function parameters, either. Age, BMI, HR, TG were significantly correlated with pAIx in the OSA group. And BMI (r = 0.818, p = 0.026), ODI (r = 0.961, p < 0.001), mean and minimum oxygen saturation (r = -0.399, p < 0.001; r = -0.663, p < 0.001, respectively) were significantly correlated with AHI in overall study group. The same patterns of correlations with AHI were found in only OSA group (Table 3).


Fig. 2
Scatter diagrams showing the correlations between AHI and (A) RHI and (B) pAIx. AHI, apnea-hyponea index; RHI, reactive hyperemia index; pAIx, peripheral augmentation index.
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Table 2
Correlation analysis with RHI and peripheral AIx in overall groups
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Table 3
Correlation analysis with RHI and peripheral AIx in OSA groups
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To estimate the independent contribution of various parameters to peripheral endothelial function (RHI and pAIx), we carried out multivariable linear regression analysis. No variable was significantly associated with RHI in neither overall nor OSA group (Table 4). On the other hand, age, HR, AHI, and ODI tended to be associated with pAIx in both overall (r2 = 0.624) and OSA (r2 = 0.800) group without statistical significance (Table 5).


Table 4
Multiple regression analysis for the association between reactive hyperemia index and AHI
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Table 5
Multiple regression analysis for the association between peripheral augmentation index and AHI
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Discussion

The main findings of our study were that the parameters of sleep apnea derived from polysomnography were not associated with endothelial function assessed by reactive hyperemia.

Recently, OSA has been demonstrated to being a risk factor for metabolic syndrome and CVD.19) One of the mechanisms underlying this association between OSA and CVD is endothelial dysfunction, an initiating pathophysiology for atherosclerosis.3, 6) There have been some studies on the effect of OSA on endothelial function but their results have not been conclusive. Firstly, Chami et al.11) reported that they could find a moderate association between OSA and only brachial artery diameter but not find any link between OSA and endothelial dysfunction as measured by brachial artery FMD in over 600-sized observation study. However, recent two studies reported against this finding. Namtvedt et al.7) showed that OSA was associated with endothelial dysfunction independently of obesity and conventional risk factors. In this study, they also used FMD for assessing endothelial function. And Seif et al.20) showe that there was a decline of endothelial function, measured by RHI only in high AHI group. In our study, we could not find any significant correlation between RHI and AHI/ODI. There are several factors that may have resulted in the negative findings. Firstly, in contrast to previous studies that measured endothelial function of the brachial arteries, the measurement of microvascular endothelial function was done in our study. The discrepancy in measurement of endothelial function (FMD/RHI) may be the reason why there was no association between OSA and RHI parameters. Further study assessing endothelial function by both FMD and RHI simultaneously is warranted to answer this question. Secondly, as this study was based upon a tertiary hospital registry, the cardiovascular risk of the non OSA group tended to be more severe. This is demonstrated by the fact that there were more HTN patients in non-OSA group (47.4%) compared to OSA group (37.7%) with no significant difference in baseline characteristics between OSA and non-OSA group. Because endothelial dysfunction can be found in hypertension patients, high prevalence of HTN in non-OSA group could also explain the negative finding of our study.

By using fingertip PAT, a new measurement of peripheral vasodilator response as a measure for endothelial dysfunction, we were able to analyze data regarding pAIx as well. Heffernan et al.21) showed that RHI and pAIx provided distinct insight into systemic vascular aging and target organ damage. According to their study, pAIx derived from PAT was correlated with age-associated changes in vascular function and target organ damage (not coronary atherosclerotic burden) but RHI is associated with coronary atherosclerotic burden (not target organ damage or other measures of vascular aging).21) Recently, a large-sized study reported that pAIx was more closely related to age rather than RHI.22) However, this study could not demonstrate any significant association between pAIx and parameters of OSA. This is supported by a study by Butt et al.,23) which demonstrated that there were no significant differences between subjects with OSA, hypertension and healthy control in terms of augmentation index and PWV. The results from this study begs the question of whether the endothelial dysfunction found in OSA patients in previous studies is the result of the cardiovascular risk factors that are more frequently associated with OSA patients or due to the sleep apnea itself. Further study assessing the change in RHI and pAIx after CPAP may be required to answer this question.

Our study can't go without limitations. Firstly, our study also has inherent limitations with cross-sectional study design. We could not explain the cause and effect of the associations so more prospective and follow-up studies need to be followed. Secondly, the small sample-sized study could not generalize our finding. In power calculation analysis, the sample size of our study (n = 80) was not enough power (7.5%) to detect correlations between AHI and RHI (R = -0.023). So our study was not conclusive in this field of research. Thirdly, we do not have any data about FMD and other endothelial laboratory marker such as endothelial adhesion molecules, angiogenenic factors in our study. Comparing our results with other endothelial markers, it could help to understand the usefulness of PAT in assessing endothelial function. Finally, our negative results could be affected by HTN medication and meals of the patients although there were not any significant differences in HTN medications between OSA and non-OSA group.

Notes

No potential conflict of interest relevant to this article was as reported.

Acknowledgements

This study was supported by 2005 research grant from the Korean Society of Hypertension.

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