association between obstructive sleep apnea severity and endothelial dysfunction in an increased...

Association between obstructive sleep apnea severity andendothelial dysfunction in an increased background ofcardiovascular burden

FAD I SE I F 1 , S AN JAY R . PATE L 2 , 3 , H ARNEET WAL I A 1 , M I CHAELRUESCHMAN 2 , DEEPAK L . BHATT 2 , 4 , D AN I E L J . GOTT L I EB 2 , 4 ,E LDR I N F . L EW I S 2 , SUSHEEL P . PA T I L 5 , NARESH M . PUN JAB I 5 ,D EN I SE C . BAB I NEAU 6 , SUSAN REDL I NE 2 , 3 and REENA MEHRA 1 , 7

1Department of Medicine, Case School of Medicine, Cleveland, OH, USA, 2Brigham and Women’s Hospital, Harvard Medical School, Boston,MA, USA, 3Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA, 4VA Boston Healthcare System, HarvardMedical School, Boston, MA, USA, 5Johns Hopkins University, Baltimore, MD, USA, 6Center for Clinical Investigation, Case Western ReserveUniversity, Cleveland, OH, USA and 7Case Center for Transdisciplinary Research on Energetics and Cancer, Case Comprehensive CancerCenter, Case School of Medicine, Cleveland, OH, USA

Keywordscardiovascular disease, endothelial dysfunction,sleep apnea

CorrespondenceReena Mehra, MD, MS, Case Western ReserveUniversity, 11100 Euclid Avenue, Cleveland,OH 44106-6003, USA.Tel.: 216-844-5218;fax: 216-844-8708;e-mail: [email protected]

Accepted in revised form 25 November 2012;received 19 August 2012

DOI: 10.1111/jsr.12026

SUMMARYThe objective of this study is to examine whether increasing obstructivesleep apnea (OSA) severity is associated with worsening endothelialfunction. The design is a cross-sectional examination of the baselineassessment of a multi-centre randomized controlled clinical trial exam-ining the effects of oxygen, continuous positive airway pressure (CPAP)therapy or lifestyle modifications on cardiovascular biomarkers. Partic-ipants were recruited from cardiology clinics at four sites. Participantswith an apnea–hypopnea index (AHI) of 15–50 and known cardio/cerebrovascular disease (CVD) or CVD risk factors were included. OSAseverity indices [oxygen desaturation index (ODI), AHI and percentage ofsleep time below 90% oxygen saturation (total sleep time <90)] and ameasure of endothelium-mediated vasodilatation [Framingham reactivehyperaemia index (F-RHI) derived from peripheral arterial tonometry(PAT)] were assessed. The sample included 267 individuals with a meanAHI of 25.0 � 8.5 SD and mean F-RHI 0.44 � 0.38. In adjusted models,the slope of the relationship between ODI and F-RHI differed above andbelow an ODI of 24.6 (P = 0.04), such that above an ODI of 24.6 therewas a marginally significant decline in the geometric mean of the PATratio by 3% [95% confidence interval (CI): 0%, 5%; P = 0.05], whilebelow this point, there was a marginally significant incline in thegeometric mean of the PAT ratio by 13% (95% CI: 0%, 27%; P = 0.05)per 5-unit increase in ODI. A similar pattern was observed between AHIand F-RHI. No relation was noted with total sleep time <90 and F-RHI.There was evidence of a graded decline in endothelial function inassociation with higher levels of intermittent hypoxaemia.

INTRODUCTION

Obstructive sleep apnea (OSA) is a prevalent disordercharacterized by repetitive complete or partial upper airwaycollapse leading to adverse physiological consequences.Epidemiological data provide strong evidence implicatingOSA as an independent risk factor for cardiovascular

morbidity and mortality (Peker et al., 2002; Peppard et al.,2000). Several physiological effects of OSA have beenproposed to explain the pathogenesis of cardiovascularmorbidity. Endothelial dysfunction is one mechanism thatmay result from OSA-related intermittent hypoxaemia, oxi-dative stress, enhanced sympathetic nervous system acti-vation and increased blood pressure (Dean and Wilcox,

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J Sleep Res. (2013) 22, 443–451 Sleep apnea and endothelial dysfunction

1993). Endothelial dysfunction is characterized by alterationof normal endothelial physiology consisting of a reduction inthe bioavailability of vasodilators such as nitric oxide leadingto impaired endothelium-dependent vasodilation. Endothelialdysfunction is considered to represent the integrated func-tional expression of cardiovascular risk factor burden and areflection of atherogenic vascular milieu (Corretti et al.,2002). The clinical relevance of endothelial dysfunction issupported by the consistency of its associations withincident cardiovascular disease events (Brunner et al.,2005). In fact, the persistent impairment of endothelialfunction in individuals with established coronary arterydisease despite optimal medical therapy has been observedto be a strong independent predictor of adverse cardiovas-cular events (Corretti et al., 2002; Poredos and Jezovnik,2012).Clinic-based studies of individuals mostly without overt

cardio/cerebrovascular disease (CVD) have suggested thatOSA is associated with impaired brachial artery flow-mediateddilation (FMD), a surrogate of endothelial dysfunction (Katoet al., 2000). The association of OSA with the primarily nitricoxide-dependent (Nohria et al., 2006) endothelial-mediatedvasodilation impairment, as assessed by brachial ultrasoundFMD or finger plethysmography, has been demonstratedin several studies (Ip et al., 2004; Itzhaki et al., 2005).However, other studies have failed to demonstrate theserelationships (Chami et al., 2009; Nieto et al., 2004). The twolargest epidemiological studies (Chami et al., 2009; Nietoet al., 2004) showed no association between OSA defined bythe apnea–hypopnea index (AHI) and endothelial dysfunctionmeasured by FMD after adjusting for obesity. Also, disparatefindings have been noted with sleep-related hypoxia andendothelial dysfunction (Chami et al., 2009; Nieto et al.,2004). Although endothelial function has been reported toimprove after OSA treatment, these studies were non-randomized (Bayram et al., 2009) or involved a small samplesize (Ip et al., 2000). Furthermore, many studies have beenlimited by single-centre geographic distributions (Bayramet al., 2009; Itzhaki et al., 2005), and involved evaluation ofendothelial dysfunction with techniques such as brachialartery ultrasound that may be prone to intra- and interoperatorvariability (Ip et al., 2004; Itzhaki et al., 2005).In this analysis, we examined the relationship of several

common metrics of OSA severity with reactive hyperaemiaperipheral arterial tonometry (PAT), a measure shown toassess endothelial dysfunction accurately and associatedwith fewer technical difficulties and operator dependencythan arterial ultrasound (Corretti et al., 2002) in a sample ofindividuals with moderate to severe OSA and CVD riskfactors participating in the baseline examination of a multi-centre trial. We carefully explored the thresholds and ‘dose–response’ relationships among OSA metrics and reactivehyperaemia. We postulate that increasing severity of OSAdefined by AHI, and alternatively by measures of hypoxa-emia, will be associated linearly with impaired endothelial

function, even after adjustment for confounders such asobesity and standard cardiovascular risk factors.

METHODS

Study samples

Patients with moderate to severe OSA were recruited fromoutpatient cardiology clinics at four sites (Brigham andWomen’s Hospital, Case Medical Center, Johns HopkinsUniversity and Veteran’s Affairs Boston Healthcare System)as part of a randomized controlled trial (Heart BiomarkerEvaluation in Apnea Treatment—HeartBEAT) aimed at com-paring conservative medical therapy, supplemental nocturnaloxygen therapy and positive airway pressure therapy oncardiovascular biomarkers in OSA (www.clinicaltrials.govTrial Registration number: NCT01086800).

Study protocol

Screening sleep questionnaires were administered eitherthrough mailings to targeted participants receiving care atcollaborating clinics, or by direct administration at the time ofroutine clinic appointments to determine potential eligibility.These screening questionnaires included the Epworth Sleep-iness Scale (ESS;) (Johns, 1991), which quantifies thelikelihood of falling asleep in a number of common situations,and the Berlin Questionnaire (Netzer et al., 1999), a simple10-item questionnaire that categorizes OSA risk in threedomains: snoring/nocturnal breathing disruption, sleepiness/fatigue and obesity or hypertension. Those who scored � 16on the ESS or had drowsy driving were excluded fromparticipation. Subjects who had a positive score (greater thantwo of three domains) on the Berlin questionnaire indicating ahigh likelihood of OSA underwent more detailed eligibilityassessment. Inclusion criteria were: age 45–75 years andpatients at high risk for cardiovascular disorders defined as:(i) established stable coronary artery disease (documentedprior myocardial infarction or coronary revascularization>3 months prior to entry or angiographically documented� 50% stenosis in a major coronary artery); or (ii) � 3cardiovascular risk factors characterized by: (a) hypertension(HTN), defined by physician-reported hypertension or anti-hypertensive medication use [including angiotensin-convertingenzyme (ACE) inhibitor, angiotensin receptor blocker, betaadrenergic blocker, alpha adrenergic blocker, diuretic andcalcium channel blocker usage] or systolic blood pressure>140 mmHg or diastolic blood pressure >90 mmHg; (b)diabetes mellitus treated by a physician; (c) body mass index(BMI) � 30 kg m2; or (d) dyslipidaemia defined by a totalcholesterol >240 mg dl�1, low-density lipoprotein (LDL)cholesterol >160 mg dl�1 or high-density lipoprotein (HDL)cholesterol <45 mg dl�1; or physician-diagnosed dyslipida-emia treated by medication). Exclusion criteria were: centralsleep apnea (central apnea index >5); nocturnal oxygensaturation <85% for >10% of the record; heart failure [ejection

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fraction <35% or New York Heart Association (NYHA) � 2];poorly controlled hypertension (HTN; >170 mmHg/>110 mmHg); prior stroke with functional impairment; severeuncontrolled medical problems; severe chronic insomnia orcircadian rhythm disorder with <4 h of sleep per night; restingwake oxygen saturation <90%; smoking in the location ofsleep; current use of either supplemental oxygen or positiveairway pressure; and <3 months since myocardial infarction,stroke or any revascularization procedure. Subjects who metthese eligibility criteria then underwent unattended type 3sleep studies; those with an AHI between 15 and 50events h�1 were considered eligible and scheduled for aresearch visit. Institutional Review Board approval wasobtained from all sites. Full written informed consent wasobtained.

Data collection

Sleep apnea assessment

At the screening visit, subjects were instructed in the use ofthe sleep monitor (Embletta; Embla, Broomfield, CO, USA).The studies were scored by a trained, registered polysom-nologist following the American Academy of Sleep Medicineguidelines for alternative hypopnea definitions with modifica-tion, such that arousal was not considered in the identificationof hypopneas (Ruehland et al., 2009). An apnea was definedas a complete cessation of airflow, measured using nasalpressure, for � 10 s. Hypopnea was defined as 50%reduction in breathing amplitude lasting � 10 s associatedwith � 3% oxygen desaturation. The following parameterswere obtained: oxygen desaturation index (ODI) defined asthe number of oxygen desaturations � 3% per hour ofanalysed recording time, AHI defined as the number ofapneas and hypopneas per hour of analysed recording timeand total sleep time (TST) below 90% oxygen saturation(TST <90). In the determination of the total recording time oranalysed time, sleep onset and offset were marked by thescorer by taking into consideration data self-reported in thesleep log.

Measure of endothelial function

Peripheral arterial tonometry (PAT) was measured using theEndo-PAT2000 device (Itamar Medical Ltd, Caesarea,Israel). The test was approximately 20 min in duration andperformed in the morning in a quiet environment with theparticipants in a supine position after a 12-h fasting periodthat included refraining from smoking and drinking caffeinat-ed beverages. A blood pressure cuff was placed on the non-dominant arm; the other arm was used as a control andmeasurements made according to published guidelines(Brunner et al., 2005). The study consisted of three phases:(i) a 5-min period of baseline recording; (ii) a 5-min period ofocclusion of the brachial artery where the blood pressure cuffis inflated to 60 mmHg above systolic blood pressure (and to

at least 200 mmHg); and (iii) a release period where the cuffis deflated rapidly (Corretti et al., 2002; Poredos and Jezov-nik, 2012). Endothelium-mediated vasodilatation wasassessed by measuring pulse wave amplitude in the fingerbefore and after 5 min of brachial artery occlusion. TheFramingham reactive hyperaemia index (F-RHI), which hasbeen associated with multiple CVD risk factors (Hamburget al., 2008) and identified to have superior reproducibilityrelative to the standard reactive hyperaemia index (RHI;Selamet Tierney et al., 2009), was calculated as the naturallogarithm of the PAT ratio, given by the ratio of the averagepulse amplitude in the post-hyperaemic phase (during the 90–120-s post-deflation interval) divided by the average base-line amplitude (Hamburg et al., 2008), normalized by the ratioof pulse amplitudes obtained from corresponding measure-ments in the non-occluded arm. A lower F-RHI is consistentwith poorer endothelial function, as it is reflective of a smallerincrease in the post-hyperaemic pulse amplitude relative tobaseline. All individual PAT raw data tracings were reviewedmanually (F.S.) to assess signal quality and to assign qualitygrades. Those observations with poor-quality tracings wereexcluded from analysis (n = 41 of 318 baseline examina-tions, 12.9%). The reliability of this quality grade assignmentwas ascertained by a random rescoring of data qualitygrades (n = 50 studies) and was consistent with excellentintra-observer reliability with an overall intraclass correlationcoefficient (ICC) using Kendall’s coefficient of concordance of0.88 and an ICC using Cohen’s kappa in distinguishing poor-quality studies (grade 4) from those that were included in thecurrent study (good- and adequate-quality studies, grades1–3) of 0.86. Details of the quality grading protocol areprovided in an online supplement.

Statistical methods

Standard descriptive statistics were used to describe thestudy sample. Continuous data were presented asmean � SD and categorical data in percentages. The OSAexposure metrics evaluated included: ODI, AHI and TST <90.To identify possible inflection points in the associationbetween OSA metrics and endothelial dysfunction, the linearmodel as well as three piecewise linear models (definedusing one knot at the first, second and third quartile for eachOSA metric) were compared using a 0.632 bias-correctedmean squared error (MSE) that was obtained from a leave-one-out bootstrap cross-validation procedure based on 5000bootstrap samples. The final model was selected using themodel that minimized the MSE. Cross-validation proceduresof this type are used commonly to select the best-fittingmodel among competing models.To examine the effect of potential confounders on these

associations, three pre-specified multivariable models wereconsidered: model 1 (adjusted for the OSA exposure and siteonly), model 2 (adjusted for model 1 covariates as well asage, sex, race and BMI) and model 3 (adjusted for model 2covariates as well as HTN, diabetes (diagnosed or taking oral


Obstructive sleep apnea severity 445

hypoglycaemic medications or insulin), dyslipidaemia (diag-nosed or on statin medication), smoking (pack years) andestablished CVD. Established CVD was defined by thepresence of coronary artery disease (CAD; per above,outlined under inclusion criteria) or >3 months since strokeoccurred without functional impairment. We also explored ifeither CVD or diabetes mellitus was an effect modifier of theassociation between each exposure and F-RHI by includingan interaction term between either CVD or diabetes mellitusand each exposure in the model. In order to facilitateinterpretation of the linear and piecewise linear models, theparameter estimates were exponentiated and these trans-formed estimates were interpreted as the geometric meanratio of the PAT ratio. All tests were performed assuming asignificance level of 0.05 and using SAS version 9.2 (SASInstitute Inc., Cary, NC, USA) for analyses and R version2.9.2 for graphs.

RESULTS

Subject characteristics

Of the 318 participants with baseline examinations, fivehad missing PAT data, 41 had poor-quality pulse waveamplitude tracings and five had missing covariate data,resulting in 267 participants in the analytical sample. Therewere no statistically significant differences in subjectcharacteristics between the analytical sample and thosewho were excluded (age 62.9 � 7.2 versus 63.3 �7.9 years, male gender 71.5% versus 84.3% and BMI34.4 � 6.6 versus 34.1 � 6.2 kg m2, respectively). Asexpected in a cohort at high CVD risk with OSA, themajority of the analytical sample included older, obeseparticipants who were predominantly men. By design, thesample was not excessively sleepy (ESS = 8.9 � 3.7).More than half the cohort had documented coronary arterydisease and there was a high prevalence of hypertension(Table 1).

ODI and F-RHI

The piecewise linear regression model that resulted in theminimum MSE for ODI was based on an inflection point at thefirst quartile of ODI (24.6; MSE = 0.1234 versus 0.1236–0.1249 for all other models). This model was then used toestimate the geometric mean ratio in the PAT ratio per 5-unitincrease in ODI above and below an ODI of 24.6. As shownin Table 2 and Fig. 1, we found that the relationship betweenODI and F-RHI differed above and below an ODI of 24.6(P = 0.035). Furthermore, there was a marginally significant13% increase in the geometric mean of the PAT ratio whenODI was less than 24.6 [95% confidence interval (CI): 0%,27%; P-value = 0.05] and a marginally significant 3%decrease in the geometric mean of the PAT ratio after thisinflection point (95% CI: 0%, 5%; P = 0.05) for every 5-unitincrease in ODI.

AHI and F-RHI

The piecewise linear regression model that resulted in theminimum MSE for AHI was based on an inflection point at thefirst quartile of AHI (18.4; MSE = 0.1244 versus 0.1251–0.1259 for all other models). As seen in Table 3 and Fig. 2,there was evidence that the fully adjusted associationbetween AHI and F-RHI differed when AHI <18.4 versusAHI � 18.4 (P = 0.04). Additionally, while there was astatistically significant 26% increase in the geometric meanof the PAT ratio per 5-unit increase in AHI when AHI was lessthan 18.4 (95% CI: 11%, 58%; P-value = 0.04), there was nostatistically significant association between AHI and F-RHIabove this inflection point.

Time at oxygen saturation <90% and F-RHI

The linear regression model for TST <90 resulted in theminimum MSE (MSE = 0.1251 versus 0.1251–0.1259 forpiecewise linear regression models). However, there was nostatistically significant association between TST <90 andF-RHI; i.e. for every 5-unit increase in TST <90, the adjustedgeometric mean of the PAT ratio decreased by 1% (95% CI:0%, 3%; P-value = 0.12).Finally, to explore whether the association between any of

the three OSA metrics and F-RHI was modified by CVD ordiabetes mellitus, separate interaction terms were included inall models. There was no evidence that either CVD ordiabetes mellitus was an effect modifier.

DISCUSSION

Although numerous studies have demonstrated that OSA isassociated with CVD as well as with abnormalities inintermediate endpoints, such as endothelial function, bloodpressure and insulin resistance, there is uncertainty regard-ing which levels of OSA severity confer greatest risk forCVD. In this secondary analysis of a sample of patients withan AHI range of 15–50, a series of rigorous statisticalanalyses found some evidence of declining endothelialfunction in individuals with a moderate to severely elevatedODI (� 24.6). In contrast, there was a positive, albeitmarginally significant, trend between increasing ODI andendothelial function at levels of ODI between 13.9 and 24.6.A similar pattern was observed when modelling the asso-ciation between AHI and F-RHI; i.e. a positive slope wasobserved below an AHI threshold of 18.4 while a negativeslope was seen after this threshold. The finding thatimpaired endothelial function is most evident at higherlevels of hypoxaemia is consistent with prior work from theSleep Heart Health Study, which has demonstrated thatrates of mortality (Punjabi et al., 2009), stroke (Redlineet al., 2010) and coronary artery disease and heart failure(Gottlieb et al., 2010) increase most at moderately elevatedAHI levels (20–30). In contrast, increased prothromboticpotential as measured by plasminogen activator inhibitor-1


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and fibrinogen has been described at low levels of AHIexposure with a plateau effect at moderate levels of OSA(Mehra et al., 2010). Further research is needed to address

differences in the sensitivity of various intermediate mea-sures of cardiovascular disease to different degrees ofOSA-related stress.

Table 1 Baseline subject characteristics

Baseline characteristicsAnalytical sample (n = 267)Mean (SD)/frequency (percentage)

Age (years) 62.9 (7.2)Male 191 (71.5%)Caucasian 212 (79.4%)Body mass index (kg m2) 34.4 (6.6)Dyslipidaemia 257 (96.3%)History of smoking 166 (62.2%)Smoking (pack years) 20.1 (27.8)Coronary artery disease 138 (51.7%)Stroke 13 (4.9%)Cardio/cerebrovascular disease 140 (52.4%)Diabetes mellitus 123 (46.1%)Hypertension + blood pressure medication use 236 (88.4%)Anti-hypertensive medications 257 (96.3%)ACE inhibitor or ARB 189 (70.8%)Beta adrenergic blocker 177 (66.3%)Alpha adrenergic blocker 6 (2.2%)Calcium channel blocker 79 (29.6%)Diuretics 102 (38.2%)Epworth Sleepiness Score 8.9 (3.7)Site CMC: 85 (31.8%)

BWH: 44 (16.5%)JHU: 73 (27.3%)BVA: 65 (24.3%)

Apnea–hypopnea index (AHI) 25.0 (8.5)Oxygen desaturation index (ODI) 32.3 (10.1)Minimum oxygen saturation 79.3 (5.6)Percentage sleep time less than 90% oxygen saturation 9.8 (13.9)F-RHI 0.44 (0.38)PAT ratio 1.55 (1.46)1

Continuous data were presented as mean � SD and categorical data as frequencies and percentages.ACE, angiotensin-converting enzyme; ARB, angiotensin receptor blocker; CMC, Case Medical Center; BWH, Brigham and Women’sHospital; JHU, Johns Hopkins University; BVA, Boston Veteran’s Administration Hospital; F-RHI, Framingham reactive hyperaemia index;PAT, peripheral arterial tonometry.1The geometric mean and geometric SD.

Table 2 Geometric mean ratio of pulse arterial tonometry ratio (PAT) for every 5-unit increase in oxygen desaturation index

Model ODI Geometric mean ratio of PAT ratio (95% CI); P-value*Test if geometric mean ratio of PAT ratio differsbefore and after 24.6**

Model 1 <24.6 (n = 66) 1.14 (1.01, 1.29); P = 0.04 0.02� 24.6 (n = 201) 0.97 (0.95, 1.00); P = 0.04

Model 2 <24.6 (n = 66) 1.14 (1.01, 1.28); P = 0.03 0.03� 24.6 (n = 201) 0.98 (0.95, 1.00); P = 0.07

Model 3 <24.6 (n = 66) 1.13 (1.00, 1.27); P = 0.05 0.04� 24.6 (n = 201) 0.97 (0.95, 1.00); P = 0.05

Model 1: adjusted for site.Model 2: adjusted for site, age, gender, race and body mass index.Model 3: adjusted for site, age, gender, race, body mass index, hypertension + high blood pressure medication use, diabetes, dyslipidaemia,smoking pack per years and cardiovascular disease.*Geometric mean ratio of the PAT ratio for every 5-unit increase in the oxygen desaturation index.**Test if geometric mean ratio of the PAT ratio for every 5-unit increase in the oxygen desaturation index differs before and after 24.6.CI, confidence interval; ODI, oxygen desaturation index.



The results of this study suggest that endothelial dysfunc-tion may manifest at a certain critical level of OSA severity,where protective mechanisms are lost. The finding suggest-ing a trend for improved endothelial function at an ODI of 13.9–24.6 in our study is consistent with a potential protectiveinfluence of mild to moderate levels of intermittent hypoxia(Pohlman and Harlan, 2000). This inference is limited by therestricted AHI range represented in this study sample andlack of data on individuals with no or milder levels of OSA.However, the mean F-RHI in those with an ODI <24.6 in thisstudy is similar to that noted in a non-OSA sample

(0.44 � 0.38 versus a normal range of 0.5–0.6; SelametTierney et al., 2009).Although ODI and AHI were correlated strongly (r = 0.85)

and the relationship of each with F-RHI was similar, asso-ciations appeared to be somewhat stronger for ODI. The ODIcaptures oxygen desaturation events not associated withscoreable reductions in flow, and incorporates a runningaverage to identify baseline levels probably accounting forthe higher ODI versus AHI values. This may reflect lessmeasurement error in an index that is derived automaticallycompared to one that requires manual annotation, orbecause ODI may measure more directly the relevantexposure (intermittent hypoxia). Abundant research suggeststhat chronic intermittent hypoxia may affect endothelialfunction adversely through a myriad of pathways, includingup-regulation of nuclear factor (NF) kappa B (Jurado-Gamezet al., 2012), increased reactive oxygen species (Lavie,2003) and reduced availability of endothelial nitric oxide(Ip et al., 2000).Endothelial function was measured using PAT, which is a

non-invasive technique that enables plethysmographicrecording of pulse wave amplitude, a measure of changesin the arterial pulsatile volume of the distal phalanx of thefinger, before and during reactive hyperaemia (Corretti et al.,2002; Poredos and Jezovnik, 2012). PAT measures flowresponse hyperaemia, which is related to the endothelialfunction of small arteries and to the endothelial function of themicrocirculation, therefore providing information on the func-tional capability of the microcirculation. The baseline pulsewave analysis is ascertained by plethysmographic fingercuffs placed simultaneously on the index fingers of bothhands for 5 min. An inflatable cuff is used to inducehyperaemia by occluding blood flow through the brachialartery for 5 min. The reactive hyperaemia index is thencalculated as the ratio of the average pulse wave amplitudebetween the post- and pre-occlusion values (Poredos andJezovnik, 2012).

ODI

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13.9 20 30 40 50 60 65.6

Figure 1. Plot of oxygen desaturation index versus pulse arterialtonometry (PAT) ratio. Each point represents the oxygendesaturation index (ODI) and corresponding PAT ratios for eachgiven subject. The axis opposite to the PAT ratio provides ahistogram for the PAT ratio, while the axis opposite to ODIprovides a histogram for ODI. The solid line indicates the modelestimates for the adjusted geometric mean of the PAT ratio acrossthe range of ODI for a hypothetical 63-year-old Caucasian malestudied at Case Medical Center with a body mass index (BMI) of33.3, 6.5 smoking pack years, hypertension, dyslipidaemia andcoronary vascular disease, but without diabetes. Dotted lines indicatethe associated 95% confidence interval.

Table 3 Geometric mean ratio of the pulse arterial tonometry ratio (PAT) for every 5-unit increase in apnea–hypopnea index (AHI)

Model AHI Geometric mean ratio of PAT ratio (95% CI); P-value*Test if geometric mean ratio of PAT ratiodiffers before and after a threshold of 18.4**

Model 1 <18.4 (n = 66) 1.25 (0.99, 1.56); P = 0.06 0.05�18.4 (n = 201) 0.98 (0.95, 1.01); P = 0.16

Model 2 <18.4 (n = 66) 1.26 (1.01, 1.58); P = 0.04 0.04�18.4 (n = 201) 0.99 (0.96, 1.01); P = 0.32

Model 3 <18.4 (n = 66) 1.26 (1.01, 1.58); P = 0.04 0.04�18.4 (n = 201) 0.98 (0.95, 1.01); P = 0.26

Model 1: adjusted for site.Model 2: adjusted for site, age, gender, race and body mass index.Model 3: adjusted for site, age, gender, race, body mass index, hypertension + high blood pressure medication use, diabetes, dyslipidaemia,smoking pack per years and cardiovascular disease.*Geometric mean ratio of PAT ratio for every 5-unit increase in the apnea–hypopnea index.**Test if geometric mean ratio of PAT ratio for every 5-unit increase in the apnea–hypopnea index differs before and after 18.4.


448 F. Seif et al.

Due to the superior widespread applicability, lesser degreeof operator dependence, lower technical difficulty, ability toprovide information regarding the control of arterial tone atrest and comparable accuracy of PAT compared to brachialartery ultrasound, PAT was used as a measure of endothelialfunction in the current study (Corretti et al., 2002). Weanalysed the F-RHI, which is considered to represent themost clinically relevant portion of the hyperaemia responseand has a stronger relationship to known cardiovascular riskfactors than does the traditional RHI (Hamburg et al., 2008).Digital vascular function measured by PAT and conduitvascular function measured by brachial artery ultrasoundmay correlate with different risk factors in subjects with lowcardiovascular disease burden (Hamburg et al., 2011).However, in samples with increased cardiovascular diseaseprevalence (~50%) similar to our study sample, digitalvascular function measured by PAT is related significantlyto vasodilator function in conduit vessels (Dhindsa et al.,2008).The relationship between hypoxia and impaired endothelial

function has been studied in epidemiological (Nieto et al.,2004) as well as clinic-based studies (Bayram et al., 2009;Kraiczi et al., 2001). Similar to our findings, data from theFramingham Heart Study site of the Sleep Heart HealthStudy did not report a significant association between AHIand hypoxia (TST <90) with endothelial dysfunction mea-sured by brachial artery percentage FMD (Chami et al.,2009). In a different Sleep Heart Health Study subgrouprepresenting older participants from the CardiovascularHealth Study, a significant association was noted with

increased baseline brachial artery diameter and overallhypoxia (TST <90; Nieto et al., 2004). However, FMD andbaseline diameter were not associated statistically with AHIafter adjusting for BMI. In contrast, in a clinic-based sample,FMD was correlated significantly and inversely with TST <90;however, the effect of confounders such as obesity was notassessed (Kraiczi et al., 2001). Similar to our findings, clinic-based studies have identified associations between ODI withFMD and reactive hyperaemia (Jurado-Gamez et al., 2012),but not TST <90, after consideration of obesity (Chung et al.,2007). Unlike the current investigation, the vast majority ofthese studies used brachial artery ultrasound to defineendothelial dysfunction, which may be prone to intra- andinterobserver measurement variability. None of these studiesevaluated inflection points carefully in the OSA–endothelialfunction relationship.The strengths of the current study include the use of a

technique to evaluate endothelial function that is reliable,reproducible, not operator-dependent and able to characterizeendothelial function accurately (Corretti et al., 2002) in amulti-centre sample with a high background of cardiovascularrisk. We employed a quality grading system involving carefulreview of the endothelial arterial tonometry raw data with highintrascorer reliability. Standardized methods were used forthe collection of sleep and vascular measures, which werescored by certified technicians using centralized reading andsubject to quality control procedures. We also accountedcarefully for various confounding factors. To identify thepossibility of threshold effects when modelling the associa-tion between OSA severity metrics and endothelial dysfunc-tion, we used a commonly used statistical method based oncross-validation to select the final model.The main limitation in our study is the restricted range of

AHI, which precludes our ability to make inferences on levelsof AHI below 15 or above 50. However, despite this limitedrange of OSA severity, we found evidence of a non-linearassociation with endothelial function across this OSA severityrate, with evidence of an inflection point near the first quartileof ODI (24.6) and the first quartile of AHI (18.4). Also, ourstudy is limited by its cross-sectional nature precluding theinference of temporal relationships. Given the increasedparticipant burden, we did not investigate vascular reactivityafter administration of nitroglycerin, an endothelium-indepen-dent donor of nitric oxide.In summary, the findings of this study provide evidence

that moderate to severe intermittent hypoxia (defined by ODI)is associated with decrements in endothelial function amongindividuals with high cardiovascular risk or with establishedcardiovascular disease. Future studies should considerpotential non-linear effects of intermittent hypoxia.

ACKNOWLEDGEMENTS

This study was supported by NIH National Heart Lung BloodInstitute RC2 HL101417 and K23 HL079114, NIH M01RR00080, American Heart Association National Scientist

AHI

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1.28

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4.62

14.6 20 30 40 49.3

Figure 2. Plot of the apnea–hypopnea index (AHI) versus pulsearterial tonometry ratio. Each point represents the AHI andcorresponding pulse arterial tonometry (PAT) ratio for each givensubject. The axis opposite to the PAT ratio provides a histogram forthe PAT ratio, while the axis opposite to AHI provides a histogram forAHI. The solid line indicates the model estimate for the adjustedgeometric mean of the PAT ratio across the range of AHI for ahypothetical 63-year-old Caucasian male studied at Case MedicalCenter with a body mass index of 33.3, 6.5 smoking pack years,hypertension, dyslipidaemia and coronary vascular disease butwithout diabetes. Dotted lines indicate the associated 95%confidence interval.



Development Award 0530188N, Central Society of ClinicalResearch, NHLBI K08 HL081385 and NCI 1U54CA116867.The project described was also supported by UL1 RR024989from the National Center for Research Resources (NCRR), acomponent of the National Institutes of Health (NIH) and itscontents are solely the responsibility of the authors and donot necessarily represent the official view of NCRR or NIH.

DISCLOSURE STATEMENT

Dr Sanjay Patel has served as a consultant for Sleep HealthCenters and Apnex and has received research support fromPhilips Respironics. Dr Deepak L. Bhatt discloses thefollowing relationships: Advisory Board, Medscape Cardiol-ogy; Board of Directors, Boston VA Research Institute,Society of Chest Pain Centers; Chair, American HeartAssociation Get With The Guidelines Science Subcommittee;honoraria from American College of Cardiology (Editor,Clinical Trials, Cardiosource), Duke Clinical Research Insti-tute (clinical trial steering committees), Slack Publications(Chief Medical Editor, Cardiology Today Intervention), Web-MD (CME steering committees); research grants from Ama-rin, AstraZeneca, Bristol-Myers Squibb, Eisai, Ethicon,Medtronic, Sanofi Aventis, The Medicines Company; andunfunded Research: FlowCo, PLxPharma and Takeda.Dr Eldrin F. Lewis discloses the following: ResMed, researchgrant support (minor); Novartis, Inc., research grant support(major); Amgen, Inc., research grant support and consulting(major); Theracos, research grant support (minor); Sunovian,research grant support (minor); and Sanofi Aventis, researchgrant support (minor). Dr Naresh M. Punjabi receivedresearch grant support paid to Johns Hopkins University fora multi-centre study on CPAP therapy in patients withobstructive sleep apnea. Dr Susan Redline discloses thefollowing: receipt of a grant paid to Brigham and Women’sHospital from ResMed Foundation, and Brigham andWomen’s Hospital has received equipment for use in NIHstudies from ResMed Inc and Philips-Respironics. Dr ReenaMehra serves on the Medical Advisory board for CareCoreand has given presentations for the American Academy ofSleep Medicine. University Hospitals Case Medical Centerhas received positive airway pressure machines and equip-ment from Respironics for research for which Dr Mehra is thePrinciple Investigator. Dr Fadi Seif, Dr Harneet Walia,Michael Rueschman, Dr Daniel J. Gottlieb, Dr Susheel Patiland Dr Denise Babineau have no disclosures.

REFERENCES

Bayram, N. A., Ciftci, B., Keles, T. et al. Endothelial function innormotensive men with obstructive sleep apnea before and6 months after CPAP treatment. Sleep, 2009, 32: 1257–1263.

Brunner, H., Cockcroft, J. R., Deanfield, J. et al. Endothelial functionand dysfunction. Part II. Association with cardiovascular riskfactors and diseases. A statement by the Working Group onEndothelins and Endothelial Factors of the European Society ofHypertension. J. Hypertens., 2005, 23: 233–246.

Chami, H. A., Keyes, M. J., Vita, J. A. et al. Brachial artery diameter,blood flow and flow-mediated dilation in sleep-disordered breath-ing. Vasc. Med., 2009, 14: 351–360.

Chung, S., Yoon, I.Y., Shin, Y.K. et al. Endothelial dysfunction andC-reactive protein in relation with the severity of obstructive sleepapnea syndrome. Sleep, 2007, 30: 997–1001.

Corretti, M. C., Anderson, T. J., Benjamin, E. J. et al. Guidelines forthe ultrasound assessment of endothelial-dependent flow-medi-ated vasodilation of the brachial artery: a report of the InternationalBrachial Artery Reactivity Task Force. J. Am. Coll. Cardiol., 2002,39: 257–265.

Dean, R.T. and Wilcox, I. Possible atherogenic effects of hypoxiaduring obstructive sleep apnea. Sleep, 1993, 8(Suppl): S15–21;discussion S2.

Dhindsa, M., Sommerlad, S. M., DeVan, A. E. et al. Interrelationshipsamong noninvasive measures of postischemic macro- and micro-vascular reactivity. J. Appl. Physiol., 2008, 105: 427–432.

Gottlieb, D. J., Yenokyan, G., Newman, A. B. et al. Prospective studyof obstructive sleep apnea and incident coronary heart diseaseand heart failure: the Sleep Heart Health Study. Circulation, 2010,122: 352–360.

Hamburg, N. M., Keyes, M. J., Larson, M. G. et al. Cross-sectionalrelations of digital vascular function to cardiovascular risk factors inthe Framingham Heart Study. Circulation, 2008, 117: 2467–2474.

Hamburg, N. M., Palmisano, J., Larson, M. G. et al. Relation ofbrachial and digital measures of vascular function in the community:the Framingham Heart Study. Hypertension, 2011, 57: 390–396.

Ip, M.S., Lam, B., Chan, L.Y. et al. Circulating nitric oxide issuppressed in obstructive sleep apnea and is reversed by nasalcontinuous positive airway pressure. Am J Respir Crit Care Med.,2000, 162: 2166–2171.

Ip, M. S., Tse, H. F., Lam, B., Tsang, K. W. and Lam, W. K.Endothelial function in obstructive sleep apnea and response totreatment. Am. J. Respir. Crit. Care Med., 2004, 169: 348–353.

Itzhaki, S., Lavie, L., Pillar, G., Tal, G. and Lavie, P. Endothelialdysfunction in obstructive sleep apnea measured by peripheralarterial tone response in the finger to reactive hyperemia. Sleep,2005, 28: 594–600.

Johns, M. W. A new method for measuring daytime sleepiness: theEpworth Sleepiness Scale. Sleep, 1991, 14: 540–545.

Jurado-Gamez, B., Bujalance Cabrera, C., Caballero Ballesteros, L.et al. Association of cellular adhesion molecules and oxidativestress with endothelial function in obstructive sleep apnea. Intern.Med., 2012, 51: 363–368.

Kato, M., Roberts-Thomson, P., Phillips, B.G. et al. Impairment ofendothelium-dependent vasodilation of resistance vessels inpatients with obstructive sleep apnea. Circulation, 2000, 102:2607–2610.

Kraiczi, H., Caidahl, K., Samuelsson, A., Peker, Y. and Hedner, J.Impairment of vascular endothelial function and left ventricularfilling: association with the severity of apnea-induced hypoxemiaduring sleep. Chest, 2001, 119: 1085–1091.

Lavie, L. Obstructive sleep apnea syndrome—an oxidative stressdisorder. Sleep Med. Rev., 2003, 7: 35–51.

Mehra, R., Xu, F., Babineau, D. C. et al. Sleep-disordered breathingand prothrombotic biomarkers: cross-sectional results of theCleveland Family Study. Am. J. Respir. Crit. Care Med., 2010,182: 826–833.

Netzer, N. C., Stoohs, R. A., Netzer, C. M., Clark, K. and Strohl, K. P.Using the Berlin Questionnaire to identify patients at risk for thesleep apnea syndrome. Ann. Intern. Med., 1999, 131: 485–491.

Nieto, F. J., Herrington, D. M., Redline, S., Benjamin, E. J. andRobbins, J. A. Sleep apnea and markers of vascular endothelialfunction in a large community sample of older adults. Am. J.Respir. Crit. Care Med., 2004, 169: 354–360.

Nohria, A., Gerhard-Herman, M., Creager, M. A., Hurley, S., Mitra, D.and Ganz, P. Role of nitric oxide in the regulation of digital pulse


450 F. Seif et al.

volume amplitude in humans. J. Appl. Physiol., 2006, 101:545–548.

Peker, Y., Hedner, J., Norum, J., Kraiczi, H. and Carlson, J.Increased incidence of cardiovascular disease in middle-agedmen with obstructive sleep apnea: a 7-year follow-up. Am. J.Respir. Crit. Care Med., 2002, 166: 159–165.

Peppard, P. E., Young, T., Palta, M. and Skatrud, J. Prospectivestudy of the association between sleep-disordered breathing andhypertension. N. Engl. J. Med., 2000, 342: 1378–1384.

Pohlman, T. H. and Harlan, J. M. Adaptive responses of theendothelium to stress. J. Surg. Res., 2000, 89: 85–119.

Poredos, P. and Jezovnik, M. K. Testing endothelial function andits clinical relevance. J. Atheroscler. Thromb., 2012 Sep 10. [Epubahead of print].

Punjabi, N. M., Caffo, B. S., Goodwin, J. L. et al. Sleep-disorderedbreathing and mortality: a prospective cohort study. PLOS Med.,2009, 6: e1000132.

Redline, S., Yenokyan, G., Gottlieb, D. J. et al. Obstructive sleepapnea–hypopnea and incident stroke: the Sleep Heart HealthStudy. Am. J. Respir. Crit. Care Med., 2010, 182: 269–277.

Ruehland, W. R., Rochford, P. D., O’Donoghue, F. J., Pierce, R. J.,Singh, P. and Thornton, A. T. The new AASM criteria for scoringhypopneas: impact on the apnea hypopnea index. Sleep, 2009, 32:150–157.

Selamet Tierney, E. S., Newburger, J. W., Gauvreau, K. et al.Endothelial pulse amplitude testing: feasibility and reproducibility inadolescents. J. Pediatr., 2009, 154: 901–905.

SUPPORTING INFORMATION

Additional Supporting Information may be found in the onlineversion of this article:

Data S1. The quality grading protocol.



association between obstructive sleep apnea severity and endothelial dysfunction in an increased...

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