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Respiratory Physiology & Neurobiology 183 (2012) 59– 66

Contents lists available at SciVerse ScienceDirect

Respiratory Physiology & Neurobiology

j our nal ho me p age: www.elsev ier .com/ locate / resphys io l

enioglossus fatigue in obstructive sleep apnea

avid McSharrya,1, Ciara O’Connorb, Triona McNicholasb, Simon Langranc, Michael O’Sullivanc,adeleine Loweryb,2, Walter T. McNicholasa,∗,2

Sleep Research Laboratory, St. Vincent’s University Hospital, Dublin, IrelandSchool of Electrical, Electronic & Communications Engineering, University College Dublin, IrelandDublin Dental University Hospital, Trinity College Dublin, Ireland

r t i c l e i n f o

rticle history:ccepted 29 May 2012

eywords:uscle fatigabilitypper airway physiologyathophysiology

a b s t r a c t

Obstructive sleep apnea (OSA) is a prevalent disorder that may cause cardiovascular disease and fataltraffic accidents but the pathophysiology remains incompletely understood. Increased fatigability of thegenioglossus (the principal upper airway dilator muscle) might be important in OSA pathophysiology butthe existing literature is uncertain. We hypothesized that the genioglossus in OSA subjects would fatiguemore than in controls. In 9 OSA subjects and 9 controls during wakefulness we measured maximumvoluntary tongue protrusion force (Tpmax). Using surface electromyography arrays we measured the

rate of decline in muscle fiber conduction velocity (MFCV) during an isometric fatiguing contractionat 30% Tpmax. The rate of decline in MFCV provides an objective means of quantifying localized musclefatigue. Linear regression analysis of individual subject data demonstrated a significantly greater decreasein MFCV in OSA subjects compared to control subjects (29.2 ± 20.8% [mean ± SD] versus 11.2 ± 20.8%;p = 0.04). These data support increased fatigability of the genioglossus muscle in OSA subjects which maybe important in the pathophysiology of OSA.

. Introduction

Obstructive sleep apnea syndrome (OSA) affects at least 2–4%f the adult population (Young et al., 1993). Despite having seriousdverse sequelae, including road traffic accidents and cardiovascu-ar disease (Marin et al., 2005; McNicholas and Bonsigore, 2007;

ulgrew et al., 2008; Yaggi et al., 2005), the pathophysiology ofSA remains incompletely understood. OSA is characterized by

ncreased collapsibility of the upper airway (UA) during sleep,hich results in markedly reduced (hypopnea) or absent (apnea)

irflow resulting in intermittent hypoxia. The current widely heldiew is that the UA collapses because dilator muscles are unableo sustain patency during portions of the respiratory cycle when

∗ Corresponding author at: Department of Respiratory Medicine, St. Vincent’s Uni-ersity Hospital, Elm Park, Dublin 4, Ireland. Tel.: +353 1 2773702;ax: +353 1 2697949.

E-mail addresses: dmcsharry@partners.org (D. McSharry),iara.oconnor@ucd.ie (C. O’Connor), trionamcnicholas@gmail.com (T. McNicholas),imon.langran@dental.tcd.ie (S. Langran), michael.osullivan@dental.tcd.ieM. O’Sullivan), madeleine.lowery@ucd.ie (M. Lowery), walter.mcnicholas@ucd.ieW.T. McNicholas).

1 Current address: Sleep Disorders Research Program, Brigham and Women’s Hos-ital and Harvard Medical School, 221 Longwood Avenue, Boston, MA 02446, Unitedtates.2 These authors contributed equally to the design, implementation and reporting

f this study.

569-9048/$ – see front matter © 2012 Elsevier B.V. All rights reserved.ttp://dx.doi.org/10.1016/j.resp.2012.05.024

© 2012 Elsevier B.V. All rights reserved.

the (usually anatomically susceptible) airway is vulnerable (Deeganand McNicholas, 1995; Remmers et al., 1978; White, 1995). Thegenioglossus (GG) is the major UA dilator muscle. The precise rea-son why these dilator muscles function inadequately is unclearbut one potential mechanism by which these UA dilating musclesfail is by fatigue. Fatigue may be defined as “a loss in the capac-ity for developing force and/or velocity of a muscle, resulting frommuscle activity under load and which is reversible by rest” (1990).Fatigue is an ongoing process, or series of processes, which beginat the onset of muscle contraction, well before task failure occurs(Bigland-Ritchie et al., 1986).

In OSA, the UA dilating muscles are subjected to repeated burstsof forceful contraction at the end of each obstructive apnea, whichmay occur several hundred times each night. In OSA patients, theGG muscles have been shown to be structurally and functionallyabnormal, with elevated levels of activation while awake (Fogelet al., 2005; Mezzanotte et al., 1992; Shepard et al., 1991). Intu-itively, one would expect that such activity could lead to musclefatigue. Also, the frequency and duration of obstructive apneas aregreater in the latter part of the night in OSA potentially implicatingGG fatigue as a contributing factor (Charbonneau et al., 1994; Herset al., 1997).

The possibility of GG fatigue, has not been studied extensivelyand the reported results have been conflicting (Blumen et al., 2004;Carrera et al., 2004; Eckert et al., 2011). Previous studies havetended to utilize indirect measures of muscle activity based on

6 siology & Neurobiology 183 (2012) 59– 66

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Table 1Demographics and baseline characteristics.

Characteristic OSA subjects Control subjects

Total number 9 9Age median (25%, 75%) 49.0 (40.5, 59.5) 31.0 (29, 36.5),

p = 0.002Gender (no. of males) 7 6Height (cm) 177.9 (10.1) 177.1(10.3),

p = 0.87Weight (kg) 99.9 (14.0) 84.5(22.8),

p = 0.10BMI 31.9 (7.0) 26.2(4.6),

p = 0.054Epworth sleepiness score 12.6 (5.2) 4.8(3.2),

p = 0.002Apnea hypopnea index median

(25%, 75%)43.7 (24.4, 79.2) 2.8 (1.4, 5.6)a,

p = 0.002Neck circumference (cm),

median (25%, 75%)40.0 (37.5, 43.0) 40.0 (33.0, 43.5),

p = 0.69Smoking (no.) 1 3Co-morbidities (no. of subjects) 5b 1b

Data presented as mean values with standard deviations following in brackets unlessotherwise stated.

a

0 D. McSharry et al. / Respiratory Phy

he muscle contraction endurance times. The decline in muscleber conduction velocity during sustained fatiguing contraction

s a classic myoelectric manifestation of fatigue (De Luca, 1984;erletti et al., 1990). It is widely used as an electrophysiologicaleasure of localized muscle fatigue and is considered to be a sen-

itive indicator of peripheral fatigue and integrity at the level of thearcolemma (Boerio et al., 2012; Linssen et al., 1993). To date, notudy has used the decline in GG muscle fiber conduction velocity,n extensively used index of fatigue due to changes within the mus-le, as the primary outcome variable to measure fatigue in patientsith OSA. In the present study, we use a novel intra oral surface

lectromyography (EMG) array capable of measuring muscle fiberonduction velocity, and previously validated in controls (O’Connort al., 2007), to quantify muscle fiber conduction velocity for therst time in OSA patients.

Our primary hypothesis is that the GG muscle fiber conductionelocity of an OSA patient will decline more rapidly than in a controlubject during an isometric contraction.

. Methods

.1. Subjects

18 subjects participated in the study, 9 with OSA (apnea-ypopnea index > 10 and having excessive daytime sleepiness) and

controls. Subjects were recruited from St. Vincent’s Universityleep clinic before their sleep study. Depending on their sleep studyhey were classified as either a control or a subject with OSA. Otherontrol subjects were recruited from University College Dublin.hese control subjects had either (a) no symptoms suggestive ofSA or (b) had a sleep study to exclude OSA if there were any sug-estive symptoms (e.g. snoring, excessive daytime sleepiness), orf they were overweight. The OSA patients were CPAP naive. Allubjects were free of known cardiorespiratory disease and sleepisorders other than OSA. No subject was taking medications whichould alter skeletal muscle strength and each subject abstainedrom caffeine and alcohol for 24 h prior to the experiment. The studyas carried out in accordance with The Code of Ethics of the Worldedical Association (Declaration of Helsinki). Ethical approval was

ranted by the Ethics Committee of St. Vincent’s University Hos-ital. Informed consent was obtained from each individual. Theaseline demographics of the subjects are presented in Table 1.hese data were compared between the 2 groups with t-tests orann–Whitney rank sum tests using Sigma Plot 11.0 (Systat Soft-are, Inc. San Jose, California).

.2. Materials

.2.1. Surface EMG arrayAn intra oral surface EMG array was manufactured for each

ubject. Firstly, a plaster cast of the mandibular teeth and floor ofhe mouth was made for each subject. Acrylic resin was then fittedo the model to form the appliance. An array of silver–silver chlo-ide electrodes was incorporated in the device. Each electrode waspherical in shape, with a diameter of 1.2 mm. The electrodes wereligned in columns along the longitudinal axis of the genioglossususcle, parallel to the muscle fiber direction, on the right side of

he mouth. The electrodes were arranged in a 3 × 6 grid (18 elec-rodes) or in a 3 × 4 grid (12 electrodes), with an inter-electrodeistance of 3 mm depending on the available area for measure-ent. 0.7 mm stainless steel ball-end clasps (Dentaurum) were

lso incorporated to provide stability and retention in the mouthuring recording, Fig. 1(a). A more detailed description of theonstruction of the intra-oral electrode can be found in O’Connort al. (2007). After manufacture, the custom-made devices were

6 controls had sleep studies, the other 3 were asymptomatic young females ofnormal weight.

b Please see Table 2 for comorbidities.

evaluated intra-orally for each subject – to ensure that the devicefitted comfortably and that good contact was made between theelectrodes and the floor of the mouth. In addition the clasps wereadjusted to ensure that the appliance was stable during testing.

The EMG signals were amplified using Grass amplifiers (GrassTechnologies, RI, USA) and visualized and analyzed with Spike 2software (CED, Cambridge, England). This allowed the EMG signalsto be observed as the action potentials propagated from posteriorto anterior to ensure that the velocity of the muscle fiber actionpotentials could be later calculated off-line.

2.2.2. Force transducerTongue protrusion force generated by the GG was measured

using a custom designed device, which was held between the sub-ject’s teeth. The subject protruded his/her tongue against a mobilebutton in this device which contained a force transducer (Futek,USA). The transducer signals were transmitted to an amplifier andthe force recorded.

2.3. Procedures

Subjects arrived at the Sleep Research Laboratory 2 h beforetheir usual bedtime. Each subject was instrumented with his/herown custom made intra oral surface EMG electrode array. Theground electrode for each subject was placed in the center of his/herforehead. The EMG signals from the GG were amplified (Grass Tech-nologies, RI, USA), analog to digital converted and analyzed on-lineusing Spike 2 software (CED, Cambridge, England). The EMG signalswere recorded in bipolar pairs from adjacent electrodes along eachcolumn of electrodes at a sampling frequency of 2500 Hz. The EMGdata were high-pass filtered at 10 Hz and low pass-filtered at 1 kHz.

The subject was seated comfortably in front of a computerscreen which presented the EMG signals to the patient. The sub-ject was asked to report any discomfort to ensure that the patientwas able to talk and swallow with the device in situ. The subject wasasked to protrude the tongue and EMG signals were then checkedby the investigator to ensure they were of the requisite quality and

that the characteristic delay between the bipolar signals betweenposterior electrodes and anterior electrodes was present, Fig. 2.

The subject was given the force transducer apparatus and placedit in the front of his/her mouth. It was held in place by placing

D. McSharry et al. / Respiratory Physiology & Neurobiology 183 (2012) 59– 66 61

Fig. 1. (a) Example of intra-oral appliance for recording GG surface EMG. The underside of the appliance is shown illustrating the 18-electrode array and location of theclasps that hold it in place. The EMG data recorded from the most medial electrode array (1–6) were chosen for analysis. (b) Schematic representation of the experimentalp back

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rotocol. The subject sits facing a computer monitor which provides real time feedn the floor of the mouth. The ground electrode is situated on the forehead. The tonutton. The force transducer is housed within a plastic device which is held in situ

heir teeth in the broad groove at the front of the device and hold-ng it also with either hand. Subjects were instructed not to biteown on the device as little force was needed to hold the device

n situ. The tongue contacted with a button which contacted withhe force transducer. The button was freely mobile and movedndependently of the other part of the apparatus which was heldn place between the teeth. The subject pressed against the but-on/force transducer with the tongue and the resultant force wasegistered live to the computer the subject was sitting in front of,roviding visual feedback. A schematic representation of the exper-

mental protocol is illustrated in Fig. 1(b). The person was providedith a few minutes to familiarize themselves with the system. Theaximum voluntary tongue protrusion force (Tpmax) was theneasured. Up to five consecutive attempts (of a few seconds dura-

ion) at maximum protrusion were recorded with a 1 min breaketween attempts. For each attempt the highest force within a 2 seriod was recorded. If the first three attempts produced resultsithin 10% of each other (i.e. not showing a significant improve-ent with subsequent attempts) the highest was taken as the

pmax. Otherwise additional attempts to a maximum of two (so aso not induce fatigue) were undertaken until the values stabilized

nd the highest was taken as the Tpmax.

The subject was then asked to sustain a tongue protrusion at0% of the Tpmax, thus undertaking an isometric fatiguing con-raction. Visual feedback was provided on the computer screen of

ig. 2. Raw GG sEMG data. (a) 250 ms epoch of data for one OSA subject during the fatigction potential, showing propagation in an anterior direction. (c) Cross-correlation betwhe contraction, dotted line. The location of the peak of the cross-correlation indicates the

of force. The surface electrode array makes smooth contact with the genioglossuschematically represented as a filled black arrow) pushes the force transducer via a

subject by gently biting down on it and by supporting it with their hand.

real-time force output and target force level. The intra-oral surfaceelectrodes concurrently measured the surface EMG signals. Whenthe subject could no longer sustain 30% Tpmax, (i.e. the force hadfallen by 10%) for more than 5 s or if the subject voluntarily endedthe contraction, the experiment ended. The endurance time wasdefined as the time at which the force fell by greater than 10% ofthe target value. The same investigator decided on the endurancelimit for all the experiments.

Additional data were collected during the recovery period start-ing 3 min after the fatiguing contraction. The subjects (again usingvisual feedback) undertook 5 isometric genioglossus contractionsto 30% MVC of 5 s duration every 3 min after the end of the originalfatiguing contraction.

2.4. Data analysis

In addition to the hardware filtering prior to sampling, therecorded EMG signals were processed off-line using MATLAB (TheMathWorks, Inc., USA). The signals were high-pass filtered at 20 Hz(6th order zero-phase Chebyshev filter) to remove motion artifact

and notch filtered at 50 Hz (6th order zero-phase Butterworth filter,48.8–51.2 Hz) to remove mains noise. High-order filtering in bothdirections was used to provide a zero phase response, and minimizesignal distortion which could adversely affect CV estimation.

uing contraction. (b) A close-up image (amplitude normalized) of the highlightedeen EMG signals from adjacent electrodes shown in (a), solid line, and at the end of

time delay between the EMG signal, �t.

62 D. McSharry et al. / Respiratory Physiology & Neurobiology 183 (2012) 59– 66

Fig. 3. Muscle fiber conduction velocity of the genioglossus muscle in OSA patients and control subjects during isometric fatiguing contraction. The mean and standarddeviation of all subjects is presented. (a) Genioglossus muscle fiber conduction velocity normalized with respect to its initial value during sustained contraction at 30% MVC.The rate of muscle fiber conduction velocity decrease was significantly greater in the OSA patients than in the control subjects. ANOVA revealed a significant effect of botht time

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ime (p < 0.01) and subject group (p = 0.026) and a significant interaction betweenontrols and OSA patients. The initial value of the conduction velocity was higher inetween groups.

The EMG data recorded from the most medial electrode arrayere chosen for analysis. To reduce bias due to non-propagating

omponents on the muscle fiber conduction velocity estimationBroman et al., 1985), the double differential EMG signals was con-tructed off-line from the single-differential data recorded alonghe array prior to estimation of the conduction velocity. The EMGata from the two most anteriorly positioned double differentialonfigurations were used (Fig. 1, electrodes 1–2–3 and 2–3–4). Inve subjects, stable propagation of the EMG signal towards the frontf the electrode array for the duration of the fatiguing contractionas not apparent on the most anteriorly positioned electrodes.

n these subjects, differential EMG data recorded from the nextost anteriorly positioned electrodes were used instead (Fig. 1,

lectrodes 2–3–4 and 3–4–5).The EMG data were analyzed in overlapping 500 ms epochs,

00 ms apart for the duration of each fatiguing contraction. Theross correlation between each pair of adjacent EMG signals wasalculated for each epoch, and the maximum cross correlation valueCCmax) and the time lag at which this occurred (�t) were obtainedFig. 2). To achieve sufficient resolution in the time domain forhe conduction velocity estimation, data were first interpolated byero padding the Fourier transform of the signal, to achieve a newampling rate of 250 kHz. An estimate of the global muscle fiberonduction velocity, MFCV (Naeije and Zorn, 1983) which repre-ents an average or aggregate measure of the conduction velocityithin each epoch of data, was then calculated as:

FCV = IED�t

ssuming CCmax > 0.5, where IED is the inter-electrode distance.The root mean square (RMS) amplitude of the EMG signals was

hen calculated for each 500 ms epoch along with the EMG powerpectrum median frequency, fmed, defined as the 50th percentilerequency (Merletti et al., 1990).

For each fatiguing contraction, the MFCV was normalized to itsnitial value at the onset of the fatiguing contraction which was

alculated as the mean value of the MFCV between 0 and 5% ofhe contraction duration. The data was also normalized in timeith respect to the endurance time for each individual to allow

omparison across subjects.

and subject group (p = 0.04). (b) Initial muscle fiber conduction velocity values inSA patients than in the control subjects (p = 0.04). * Indicates a significant difference

To compare the rate of change of the EMG variables between theOSA and control groups, a linear regression was applied to the RMS,median frequency and MFCV data (Linssen et al., 1993). The ratesof change of the surface EMG variables (% s−1) were defined as theslopes of these regression lines when normalized with respect totheir initial values expressed in percent per second (% s−1). The per-centage decrease in each variable was also estimated as the valueof the normalized fitted data at the end of the contraction.

Values of the MFCV, EMG RMS and fmed were calculated for eachof the five contractions conducted during the recovery period. Thevalues were calculated as the average value of each variable duringeach of the 5 s contractions and were normalized with respect totheir initial values at the onset of the fatiguing contraction.

2.5. Statistical analysis

The percentage changes in each variable during the fatiguingcontraction, and initial MFCV values, were compared between theOSA and control subjects using unpaired Student t-tests. An ANOVAwas also conducted to examine the effect of time and group (controlvs. OSA) on MFCV, EMG median frequency and RMS amplitude.

The percentage change in each of the 3 variables between each5 s contraction during the recovery period was similarly comparedusing Student t-tests. An ANOVA was conducted to examine theeffect of time and group (control vs. OSA) on each of the 3 variablesduring the recovery contractions.

3. Results

The baseline demographics, medical histories, co-morbiditiesand polysomnography details of group are listed in Tables 1 and 2.The maximum tongue protrusion force (Tpmax) was 15.5 ± 6.2 N inthe control subjects and 13.6 ± 7.6 in the OSA subjects, with no sig-nificant difference between the two groups (p = 0.58). Endurancetimes were similar in both the control (210.1 ± 163.8 s) and OSAgroups (208.2 ± 218.0 s).

Muscle fiber conduction velocity decreased progressively dur-

ing the sustained fatiguing contractions, as expected. The rate ofdecrease of the genioglossus muscle fiber conduction velocity inthe OSA patients and control subjects during the fatiguing contrac-tion is compared in Fig. 3(a). ANOVA confirmed a significant effect

D. McSharry et al. / Respiratory Physiology & Neurobiology 183 (2012) 59– 66 63

Table 2Comorbidities of the subjects.

Subject number Comorbidities

OSA subject 1 Tonsillectomy, adenoidectomyOSA subject 2 Hepatitis COSA subject 3 Hypertension, gout, type 2 diabetes mellitusOSA subject 4 Hypercholesterolemia

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Fig. 4. Normalized EMG RMS of the genioglossus muscle in OSA patients and controlsubjects during isometric fatiguing contraction. The mean and standard deviation of

tion consistent with fatigue, it was surprising that there were no

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OSA subject 5 TonsillectomyControl subject 1 Pneumonia 1996, dermatofibroma

f both time (p < 0.001) and condition (control vs. OSA) (p = 0.026)n muscle fiber conduction velocity, and a significant interactionetween time and condition (p < 0.001), with the conduction veloc-

ty of the OSA patients decreasing at faster rate and by a greatermount than that of the control subjects. The initial value of GGuscle fiber conduction velocity was also significantly higher inSA patients (p = 0.04), Fig. 3(b). When the individual subject dataere fitted with a linear regression a 29.2 ± 20.8% decrease in mus-

le fiber conduction velocity was observed in the OSA patients. Thisas significantly greater (p = 0.04) than the decrease of 11.2 ± 7.08%

bserved in the control subjects. The rate of decline of the MFCVas also greater in the OSA patients (0.21 ± 0.21% s−1) than in the

ontrols (0.12 ± 0.10% s−1), but this was not found to be statisticallyignificant (p = 0.25).

The RMS and median frequency results are presented inigs. 4 and 5 respectively. ANOVA revealed a significant effect ofime on the EMG RMS amplitude in both control and OSAS subjectsp < 0.01), with no significant difference between subject groupsp = 0.615). A significant effect of time (p < 0.01), on the EMG medianrequency was similarly observed. However, although the rate ofecrease of the median frequency was greater in the control sub-

ects than in the OSA subjects, this difference was not found to beignificant (p = 0.294).

During the recovery contractions, the MFCV recovered slightlyn the control subjects but remained below the initial conductionelocity in the OSA subjects 15 min after the end of the fatigu-ng contraction, Fig. 6(a). ANOVA confirmed as significant effectf group on MFCV during the recovery period (p = 0.0055), but

o effect of time (0.98). There was also a difference in EMG RMSmplitude (p = 0.024) and EMG median frequency between the tworoups during the recovery period (p = 0.013), Fig. 6(b) and (c).

ig. 5. Genioglossus EMG median frequency in OSA patients and control subjects during

resented. (A) Genioglossus EMG median frequency normalized with respect to its initiaffect of time (p < 0.01), however, although the rate of decrease of the median frequency wound to be significant (p = 0.294). (B) Initial median frequency values in controls and OSA

all subjects is presented. ANOVA revealed a significant effect of time on the EMG RMSamplitude in both control and OSA subjects (p < 0.01), with no significant differencebetween subject groups (p = 0.615).

4. Discussion

The results presented indicate a significantly greater decrease ingenioglossus muscle fiber conduction velocity in obstructive sleepapnea syndrome patients compared to controls during sustainedisometric tongue protrusion, indicating increased fatigability of thegenioglossus muscle fibers in the OSA group, Fig. 3(a). The initialvalue of GG muscle fiber conduction velocity was also significantlyhigher in OSA patients, Fig. 3(b), which suggests that there may bea higher proportion of type II fibers in the GG muscle of the OSApatients (Andreassen and Arendt-Nielsen, 1987; Kupa et al., 1995).

There were no significant differences in endurance times, initialvalues or rates of decrease of the EMG median frequency or in therate of increase of the EMG RMS value. Whilst the median frequencyof both controls and OSA subjects did reduce during the contrac-

significant differences between the groups as with the conductionvelocity decay, Fig. 5. The median frequency of the EMG signal isstrongly affected by muscle fiber conduction velocity and typically

isometric fatiguing contraction. The mean and standard deviation of all subjects isl value during sustained contraction at 30% MVC. ANOVA confirmed a significantas greater in the control subjects than in the OSA patients, this difference was not

patients. No significant difference was found between subject groups (p = 0.49).

64 D. McSharry et al. / Respiratory Physiolog

Fig. 6. (a) Normalized muscle fiber conduction velocity (MFCV), (b) normalizedEMG median frequency and (c) normalized EMG root mean square (RMS) values(d

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of hypoxia and intermittent hypoxia (as occurs in OSA) (Bradford

mean ± SD) during the brief (5 s, 3 min apart) isometric contractions at 30% Tpmaxuring the recovery period following the fatiguing contraction.

ecreases progressively during sustained fatiguing contractions inarallel with, and often reflecting, changes in muscle fiber conduc-ion velocity. However, whilst the median frequency is determinedo a large extent by the muscle fiber conduction velocity, other fac-ors also contribute in particular, the amount of synchronizationetween the firing times of the individual motor units, motor unitring rates and the distance of the surface EMG electrode from theuscle fibers and tendon insertions (Hermens et al., 1992; Lowery

t al., 2000). The decline of the muscle fiber conduction velocity

s, therefore, recognized as a more robust indicator of peripheral

uscle fatigue than indices based on EMG amplitude or spectralarameters alone (Broman et al., 1985).

y & Neurobiology 183 (2012) 59– 66

It is possible that the absence of a significant difference in thebehavior of the EMG median frequency in the control and OSAgroups may be partially due to the specific properties of the musclesof the tongue. Control properties, including motor unit synchro-nization, have been shown to differ between tongue muscles,including the genioglossus, and other respiratory muscles (Riceet al., 2011). Consistent with the results presented here, Blumenet al. (2004) observed no difference in endurance times betweenOSA and control subjects and also a lower rate of decay of theEMG median frequency in OSA patients, as observed in the presentstudy, Fig. 5 (Blumen et al., 2004). They also reported a slowerrate of recovery in tongue mechanical activity, rather than electri-cal activity as examined here, in OSA patients following fatiguingcontraction.

A progressive increase in EMG amplitude during the fatigu-ing contraction was observed in both groups, indicating increaseddrive to the muscle to maintain the force output as the musclefatigues (Gandevia, 2001). The observed significant differences inmuscle fiber conduction velocity between the two groups suggestsa greater level of peripheral fatigue in the patient population duringthe sustained isometric contraction, but a similar response in termsof central drive to the muscle as evidenced by the rate of increasein EMG amplitude to maintain the target force.

There is evidence that fatigue might be important in OSA(Carrera et al., 1999), and this possibility is supported by reportsthat treatment of OSA with nasal continuous airway pressure(CPAP) for the first half of the night results in a reduction in thefrequency and duration of apneas in the second half of the nightcompared to nights when no CPAP therapy is given (Hers et al.,1997). Since we have shown that nasal CPAP therapy is associ-ated with a marked reduction in tonic and phasic contraction ofthe GG muscle (Deegan et al., 1996), the delivery of CPAP in theearly part of the night to a patient with OSA would be associatedwith resting of the GG muscle, which could make it more resistantto fatigue later in the night when CPAP is withdrawn. Hitherto,there have been very few studies that have formally assessed therole of GG fatigue in the pathophysiology of OSA although Carreraand Agusti have shown using in vitro methods that non-obese OSApatients are more likely to exhibit GG fatigue (Carrera et al., 2004).Reduced genioglossus endurance time in untreated OSA patientsduring a repeated, short-duration isometric contraction fatigueprotocol was recently demonstrated suggesting that the upper air-way muscles in OSA may be vulnerable to fatigue (Eckert et al.,2011). Differences between the results presented by Eckert et al.,and the current study in which no difference in endurance timesbetween groups was observed, could be accounted for by differ-ences in the fatigue protocols employed, with subjects maintaininga constant force in this study.

Consistent with this UA fatigue hypothesis, systematic changesin the structure and contractile properties of the upper airway mus-cles, including the GG, have been observed. Specifically, an increasein fast-twitch (type II) muscle fibers in OSA patients (Carrera et al.,1999, 2004) and animal models of OSA (Petrof et al., 1994) has beennoted. Following treatment with CPAP, the distribution of GG type IIfibers in patients has been found to be similar to that in normal con-trols (Carrera et al., 1999). Fast-twitch muscle fibers fatigue morerapidly than slow-twitch (type I) fibers. An increase in type II fiberswould be expected to increase the fatigability of the upper airwaymuscles, leaving the airway susceptible to collapse and leading to acycle of increasingly severe episodes as the level of fatigue increasesfollowing repeated activation during the night. Other animal stud-ies on rats show increased upper airway (UA) fatigue during periods

et al., 2005; Fuller and Fregosi, 2000; Pae et al., 2005). Another rel-evant finding is the inflammatory cell infiltration and denervationchanges affecting the UA muscle of patients with OSA (Boyd et al.,

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D. McSharry et al. / Respiratory Phy

004). Thus, one could expect the GG to malfunction and poten-ially fatigue. Identification of fatigue as an underlying contributory

echanism is important as it has therapeutic implications. Thebserved efficacy of the didgeridoo as an effective treatment alter-ative in patients with moderate OSA possibly relates to muscleraining (Puhan et al., 2006). However, despite the persuasive liter-ture implicating GG fatigue in the pathophysiology of OSA, Blumennd Lofaso did not show excessive fatigability of the GG muscles inSA patients (Blumen et al., 2004). Thus, our study has shown for

he first time in vivo that the genioglossus fatigues more in OSAubjects than in controls.

Our study also shows that the muscle fiber conduction velocitiesemain below baseline in the OSA subjects (and are significantly lesshan the values seen in the control subjects) for the 15 min after theatiguing contraction. Our finding is different to van der Hoevent al. who demonstrated supernormal conduction velocities in therachioradialis 1 h after a fatiguing contraction (van der Hoevent al., 1993). van der Hoeven’s results might not be applicable tours because they pertained to a large limb muscle. Also, the targetorce level of the fatiguing contraction in van der Hoeven’s studyas 100% of the maximum voluntary contraction which might have

esulted in a greater amount of intracellular water and alterationsn membrane properties, hence higher velocities. The repeated 5 sontractions to 30% MVC during the recovery period in the presenttudy may also have prevented recovery of the muscle during thiseriod. Our data suggest that for at least 15 min after a fatiguingontraction the genioglossus remains functionally abnormal withubnormal conduction velocities. This may explain the worseningf apneas in the latter half of the night of OSA patients after hoursf irregular, repeated contractions of the genioglossus which maye fatiguing the muscle.

Potential weaknesses of this study include the discrepancies inge and body mass indices between the two groups. However, theseifferences are not likely to have affected the results for the follow-

ng reasons. Cross-sectional studies indicate that muscle properties,ncluding motor unit type and numbers, are well maintained untilhe 7th decade and begin to decline thereafter (Doherty, 2003). Aecent study of MFCV in a healthy population with a similar rangef ages (22–69 years; mean 40.8 years) to that considered in theresent study (25–64 years; mean 40.9 years), reported no statisti-al influence of age on conduction velocity (Vogt and Fritz, 2006). Its thus unlikely that there were significant age-related differencesn muscle structure or function in the present study. Moreover, age-elated changes in fiber type, and hence in muscle fiber conductionelocity, that would be expected as a result of aging would yieldhanges in the opposite direction than was observed here. Muscleber conduction velocity has been shown to decay less in elderlyhan in younger individuals during sustained fatiguing contractionsBazzucchi et al., 2004, 2005; Merletti et al., 2002; Yamada et al.,000), most likely due to an increased proportion of type I fibers inlder muscle. In contrast in our study, a greater decrease of MFCVas observed in the OSA group than in the control subjects.

The skeletal muscle structure of obese human individuals haseen characterized in the vasa lateralis where the proportion oflow type fibers increased with obesity (Wade et al., 1990). It isifficult to determine whether this finding is also relevant to theG. To our knowledge, the direct effects that obesity per se hasn human upper airway muscle structure and function has noteen characterized. Carrera and Agusti’s finding that in obese OSAatients, genioglossus endurance was indistinguishable from nor-al while, non-obese OSA patients showed increased genioglossus

atigability (Carrera et al., 2004) is relevant to our study. Also,

an Lunteren showed that genetic obesity in rats was not asso-iated with any significant alterations in the contractile propertiesncluding fatigability of the sternohyoid (upper airway dilator mus-le) (van Lunteren, 1996). It is difficult to comment further upon

y & Neurobiology 183 (2012) 59– 66 65

the effect obesity may have on muscle fiber conduction velocitybecause it has not been studied but the cited publications suggestthat it is not a major confounder. There were, however, no statisti-cally significant differences in BMI or neck circumference betweenthe 2 study groups.

5. Conclusions

In summary, we have shown that genioglossus muscle fiber con-duction velocity decreases significantly more in OSA subjects thanin controls during a sustained, isometric, fatiguing contraction, sug-gestive of an increased fatigability of the genioglossus in OSA. Thereduced muscle excitability also persists after the fatiguing con-traction to a greater degree in OSA patients than in controls. Wehave used a novel intra-oral surface electrode array for measur-ing muscle fiber conduction velocity for the first time in OSA. Thisstudy gives a potential rationale for the benefit of upper airwaymuscle training in the management of some OSA patients (Puhanet al., 2006). We recognize that further larger studies are requiredto fully establish the role of muscle fatigue in OSA.

Disclosures

The authors have no conflicts of interest.

Acknowledgements

The authors wish to thank the study subjects who generouslygave us their time. Also, we wish to thank Julian Saboisky of HarvardMedical School for helping with Fig. 2 and Eoin Duggan of UniversityCollege Dublin who worked on this project.

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