Regulation of middle cerebral artery blood velocity during recovery from dynamic exercise in humans

Regulation of Middle Cerebral Artery Blood Velocity during Dynamic

Exercise in Humans: Influence of Aging

James P. Fisher1,3, Shigehiko Ogoh4, Colin N. Young1,3, Peter B. Raven4,

& Paul J. Fadel1,2,3

1Department of Medical Pharmacology & Physiology, 2Dalton Cardiovascular Research

Center, University of Missouri, Columbia, MO; 3Harry S. Truman Memorial Veterans

Hospital, Department of Veterans Affairs Medical Center, Columbia, MO; 4Department

of Integrative Physiology, University of North Texas Health Science Center, Fort Worth,

TX.

Running Title: Age and cerebral blood velocity during exercise

Corresponding Author:

Paul J. Fadel, Ph.D.

Department of Medical Physiology and Pharmacology,

One Hospital Drive, MA415 Medical Sciences Building,

University of Missouri,

Columbia, MO, 65212, USA.

Tel: (573) 814-6000 ext. 53660

Fax: (573) 884-4276

Email: [email protected]

of 32 Articles in PresS. J Appl Physiol (May 8, 2008). doi:10.1152/japplphysiol.00118.2008

Copyright © 2008 by the American Physiological Society.

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ABSTRACT

Although cerebral autoregulation (CA) appears well-maintained during mild to moderate

intensity dynamic exercise in young subjects, it is presently unclear how aging influences

the regulation of cerebral blood flow during physical activity. Therefore, to address this

question, middle cerebral artery blood velocity (MCAV), mean arterial pressure (MAP),

and the partial pressure of arterial carbon dioxide (PaCO2) were assessed, at rest and

during steady-state cycling at 30% and 50% heart rate reserve (HRR) in nine young

(24±3 yrs; mean ± SD) and ten older middle-aged (57±7 yrs) subjects. Transfer function

analysis between changes in MAP and mean MCAV (MCAVmean) in the low-frequency

(LF) range were used to assess dynamic CA. No age-group differences were found in

PaCO2 at rest or during cycling. Exercise-induced increases in MAP were greater in older

subjects, while changes in MCAVmean were similar between groups. The cerebral vascular

conductance index (MCAVmean/MAP) was not different at rest (0.66±0.04 young vs.

0.67±0.03 older cm/s/mmHg; mean ± SE) or during 30%-HRR cycling between groups,

but was reduced in older subjects during 50%-HRR cycling (0.67±0.03 young vs.

0.56±0.02 older cm/s/mmHg; P<0.05). LF transfer function gain and phase between

MAP and MCAVmean was not different between groups at rest (LF gain: 0.95±0.05 young

vs. 0.88±0.06 older cm/s/mmHg; P>0.05) or during exercise (LF gain: 0.80±0.05 young

vs. 0.72±0.07 older cm/s/mmHg at 50%-HRR; P>0.05). We conclude that despite greater

increase in MAP the regulation of MCAVmean is well-maintained during dynamic exercise

in healthy older middle-aged subjects.

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INTRODUCTION

Cerebral autoregulation (CA) refers to the ability of the cerebral vasculature to

maintain blood flow relatively constant over a wide range of perfusion pressures via

changes in cerebrovascular resistance (44). Exercise presents a potential challenge to CA,

not only due to rapid and robust fluctuations in arterial blood pressure but also due to

increases in sympathetic nerve activity, cardiac output and cerebral metabolism (26, 47,

50). Although CA appears well-maintained during mild to moderate intensity dynamic

exercise in young individuals (4, 38, 40), it is presently unclear how aging influences the

regulation of cerebral blood flow during physical activity. This is an important question

considering age-related alterations in peripheral circulatory control have been reported at

rest (12, 33, 35) and during exercise (12, 45, 48). Indeed, exaggerated sympathetic

vasoconstrictor tone and blunted vasodilator responsiveness have been demonstrated in

dynamically exercising skeletal muscle of older individuals (12, 13, 31, 45). Whether

these peripheral vascular changes with age are manifest in the cerebral circulation and

alter CA is currently unknown. In addition, the resultant exaggerated pressor response to

exercise may present an additional challenge to the regulation of cerebral blood flow

during physical activity in older individuals (9, 14, 15).

Although age-related alterations in the control of cerebral blood flow have not

always been found under resting conditions (6, 7, 34, 54), several studies have observed

impairments in cerebral vascular function at rest in older compared to younger subjects

(17, 24). Moreover, Heckmann and colleagues (21) recently suggested that cerebral

autoregulatory mechanisms demonstrated a delayed responsiveness to exercise in older

subjects. These authors noted that cerebrovascular resistance increased more slowly in

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older compared to younger subjects in response to increases in cerebral blood flow at the

onset of a short bout (3 min) of supine leg cycling. Although this is suggestive of an age-

related impairment in cerebral vascular control, a potential caveat is that both young and

older subjects performed exercise at similar absolute workloads. Considering that peak

work rate and aerobic capacity typically decrease with age (15), it is plausible that the

older subjects were exercising at a greater relative intensity. Because cerebral blood flow

responses to exercise have been shown to be proportional to exercise intensity (40), this

may explain the greater increase in cerebral blood flow at exercise onset in the older

subjects. In addition, since exercise was performed for only 3 min it is probable that these

results were not representative of steady-state exercise conditions.

Given this background, the present study was designed to investigate middle

cerebral artery blood velocity (MCAV), mean arterial pressure (MAP) and the cerebral

vascular conductance index at rest, and during low and moderate intensity steady-state

cycling in healthy young and older middle-aged subjects. In addition, dynamic CA was

assessed using transfer function analysis between the changes in MAP and mean MCAV

(MCAVmean) in the low-frequency range (4, 38-40). We tested the hypothesis that

dynamic CA would be impaired in older subjects during dynamic exercise in association

with greater increases in blood pressure compared with younger subjects.

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METHODS

Nine young and ten older middle-aged subjects were recruited from the

University of Missouri community and the surrounding area (Table 1). Both young and

older subjects were recreationally active but importantly none were competitive athletes.

All experimental procedures and protocols conformed to the Declaration of Helsinki and

were approved by the University of Missouri-Columbia Health Sciences Institutional

Review Board and the Research and Development Committee of the Harry S. Truman

Memorial Veterans Hospital. Each subject gave written informed consent. Prior to

participation each subject completed a medical health history questionnaire and a blood

chemistry screening was performed after a 12 h overnight fast. No subjects had a history

or symptoms of cardiovascular, pulmonary, metabolic, or neurological disease and none

were using prescribed or over the counter medications. Although hormonal status of the

women participants was not directly assessed, all older women were postmenopausal and

not taking any hormone replacement and young women performed the main steady-state

exercise protocol (described below) around the early follicular phase of the menstrual

cycle in which plasma estrogen and progesterone concentrations are generally low (20).

Subjects were requested to abstain from caffeinated beverages for 12 h and strenuous

physical activity and alcohol for at least 24 h prior to experimental sessions. On

experimental days, the subjects arrived at the laboratory a minimum of 2 h following a

light meal. All subjects were familiarized with the equipment and procedures prior to any

experimental sessions.

Experimental Measurements

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Heart rate (HR) was continuously monitored using a lead II electrocardiogram

(ECG; Q710, Quinton Instruments, Bothell, WA). Beat-to-beat blood pressure was

measured using finger photoplethysmography (Finometer, Finapres Medical Systems,

Amsterdam, Netherlands) obtained from the middle-finger of the left hand, which was

supported at the level of the right atrium on an adjustable padded bedside table. In

addition, brachial artery blood pressure was measured at heart level in the right arm every

minute using an automated sphygmomanometer equipped with a microphone for the

detection of Korotkoff sounds (SunTech, Medical Instruments Raleigh, NC, USA).

Before recordings were started diastolic blood pressure (DBP) of the Finometer was

matched with DBP measurements obtained from the brachial artery. These methods of

blood pressure measurement have been previously validated for use during dynamic

exercise (2, 5, 43). The partial pressure of end-tidal carbon dioxide was obtained on a

breath-by-breath basis using a metabolic measurement system (TrueOne 2400,

ParvoMedics, Salt Lake City, UT) which was calibrated prior to each experimental

session using known standard gases. An estimate of arterial carbon dioxide tension

(PaCO2) was subsequently calculated using the equations of Jones et al., (28). Blood

velocity in the right middle cerebral artery was measured using transcranial Doppler

ultrasound (DWL, Sipplingen, Germany) with a 2-MHz probe placed over the temporal

window and fixed with an adjustable headband and adhesive ultrasonic gel (Tensive,

Parker Laboratories, Orange, NJ). The cerebral vascular conductance index (CVCi) was

calculated as MCAVmean/MAP. Ratings of perceived exertion were obtained using the

standard 6-20 Borg scale (3). All cardiovascular variables were sampled at 1000Hz and

stored for off-line analysis (Powerlab, AD Instruments, Bella Vista, NSW, Australia).

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Experimental Procedures

Incremental maximal exercise test

To exclude the possibility of any exercise-induced arrhythmias or blood pressure

abnormalities, and to ascertain peak HR for the determination of steady-state workloads,

all subjects performed a continuous incremental maximal exercise test. Subjects were

seated in a semi-recumbent position on a medical exam table equipped with an

electrically braked cycle ergometer with toe clips (Angio V2, Lode, Groningen,

Netherlands). Subjects were instrumented with a 12 lead ECG and blood pressure was

measured with the aforementioned automated sphygmomanometer. Following a 3 minute

warm-up period of cycling at 60 revolutions per minute (rpm), the workload was

increased by 25 Watts every minute. Peak responses were determined at the power output

where the subject could no longer maintain a pedal frequency of 60 rpm despite strong

verbal encouragement. All subjects gave a maximal rating of perceived exertion (i.e. 19-

20) at exhaustion.

Cerebral Vascular Responses to Steady-State Exercise

After ~3-7 days from the incremental maximal exercise test, subjects returned to

the laboratory to perform two bouts of cycling at steady-state heart rates corresponding to

30 and 50% of HR reserve (HRR) representing low and moderate intensity exercise (1).

Following instrumentation for the measurement of HR, arterial blood pressure and

cerebral blood flow velocity subjects rested quietly for 15 min. For the assessment of

PaCO2, subjects then respired through a low resistance mouthpiece (model 2700, Hans

Rudolph, Kansas City, MO) attached to the metabolic measurement system for 3 minutes

whilst a nose clip was worn to prevent nasal breathing. For each exercise bout, subjects

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maintained a pedal frequency of 60 rpm and the workload was gradually increased until

the target HR was achieved (~3-5 min), after which 15 minutes of steady-state cycling

was performed. During the last 2 min of exercise breath-by-breath samples were taken for

the determination of PaCO2. Prior to the cessation of exercise, subjects were asked to

provide a rating of perceived exertion. Each exercise bout was separated by a minimum

of 30 min to allow full recovery and the re-establishment of baseline HR and MAP. The

order of the low and moderate intensity trials was randomized. Throughout the test

subjects were reminded to keep their upper limbs relaxed to aid blood pressure

measurements, and their head facing forward to prevent movement artifacts in the

transcranial Doppler signal.

Transfer Function Analysis for Dynamic Cerebral Autoregulation

Five minute steady-state data segments at rest and during low and moderate

intensity cycling were used for transfer function analysis to identify indices of dynamic

CA. Beat-to-beat values of MAP and MCAVmean were obtained by integrating analogue

signals within each cardiac cycle, then linearly interpolated and resampled at 2 Hz for

spectral analysis (38, 40, 41, 55, 56). For an estimate of dynamic CA using the transfer

function, the cross-spectrum between changes in MAP and MCAVmean were calculated

and divided by the autospectrum of MAP. Transfer function gain measurements reflect

the relative amplitude relationship between changes in MAP and MCAVmean over a

particular frequency range and were used to quantify the ability of the cerebral vascular

bed to buffer changes in cerebral blood velocity induced by transient alterations in blood

pressure (55, 56). Transfer function phase measurements were used to determine the

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temporal relationship between changes in MAP and MCAVmean over a particular

frequency range (55, 56).

From the temporal sequences, the frequency-domain transforms were computed

with a fast Fourier transformation algorithm. The transfer function H(f) between the two

signals was calculated as H(f) = Sxy(f) / Sxx(f), where Sxx(f) is the autospectrum of the

input signal and Sxy(f) is the cross-spectrum between the two signals. The transfer

function magnitude H(f) and phase spectrum Ф(f) were obtained from the real HR(f)

and imaginary components HI(f) of the complex transfer function:

H(f) = [HR(f)2+HI(f)2] 1/2

Ф(f) = tan-1[HI(f) / HR(f)]

Additionally, the transfer function H(f) was normalized to the mean values of input (x)

and output (y) variables as H'(f) = Sxy(f)x/Sxx(f)y, and the normalized gain was calculated

as 20 log|H'(f)| to express values in decibels.

The squared coherence function (MSC(f)) was estimated as:

MSC(f) = |Sxy(f)|2/[Sxx(f) Syy(f)]

where Syy(f) is the autospectrum of MCAVmean. The squared coherence function reflects

the fraction of output power that can be linearly related to the input power at each

frequency. Similarly to a correlation coefficient, it varies between 0 and 1, with a

coherence of >0.5 taken as suggestive of a stable relationship between two oscillations

reflecting the statistical reliability of transfer function analysis between input and output.

Spectral power of MAP and MCAVmean, transfer function gain, phase and

coherence were calculated in the very low (VLF; 0.02-0.07 Hz), low (LF; 0.07–0.20 Hz)

and high (HF; 0.20–0.30 Hz) frequency ranges. The arterial blood pressure fluctuations in

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the HF range, such as those induced by respiration, are transferred to MCAVmean, whereas

arterial blood pressure fluctuations in the VLF and LF ranges are independent of the

respiratory frequency and are dampened by autoregulatory mechanisms (11). Thus, the

dynamic buffering capacity of the cerebral vasculature is dependent on the frequency of

the fluctuations in perfusion pressure. As such, we used the VLF and LF ranges of each

variable to identify dynamic CA at rest and during exercise (27, 40, 41, 56).

Statistical Analysis

Statistical comparisons of physiological variables were made utilizing a repeated-

measures two-way analysis of variance (ANOVA). A Student-Newman-Keuls test was

employed post hoc to investigate significant main effects and interactions of group

(young vs. older) and condition (rest vs. low exercise vs. moderate exercise). Statistical

significance was set at P<0.05. Analyses were conducted using SigmaStat (Jandel

Scientific Software, SPSS Inc., Chicago, IL, USA) for Windows.

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RESULTS

Subject Characteristics

The mean age difference between young and older subjects was 33 years. There

were no significant age-group differences in body weight, body mass index, triglycerides,

high density lipoprotein (HDL), blood urea nitrogen or electrolytes (Table 1). Total

cholesterol, low density lipoprotein (LDL) and glucose tended to be higher in the older

subjects; however, values were not substantially greater than the upper limit for healthy

individuals. All subjects had normal resting and maximal exercise ECGs and as expected,

the peak heart rate response to the incremental exercise test was significantly higher in

younger subjects (185±3 young vs. 160±4 bpm, older; mean ± SE; P<0.05).

Steady-State Cardiovascular Measures

All measurements were performed in the semi-recumbent position. At rest there

were no significant age-group differences in HR, systolic blood pressure (SBP), MAP,

MCAVmean, CVCi or PaCO2 (P>0.05), however, resting DBP was significantly higher in

the older compared to younger subjects (P<0.01; Table 2). During both low and moderate

intensity steady-state leg cycling, the SBP and MAP responses were greater in older

subjects (P<0.01; Table 2 and Figure 1A). Likewise, DBP was significantly greater in

older individuals during exercise (P<0.01). Overall, DBP remained the same or slightly

increased in the older group during low and moderate intensity cycling whereas it

progressively decreased from rest in the younger group. Indeed, during moderate

intensity cycling DBP was significantly lower than rest in the younger subjects (P<0.05;

Table 2). Pulse pressure was significantly increased during low and moderate intensity

exercise and no age-group differences were found (Table 2 and Figure 1B). For the

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group, MCAVmean increased slightly but significantly during low and moderate intensity

cycling (P<0.05), but no differences were observed between older and younger subjects

(P>0.05; Table 2 and Figure 1C). CVCi was not altered from rest in the young

individuals during either low or moderate intensity cycling (P>0.05). Similarly, no

changes in CVCi were observed in the older individuals during low intensity exercise.

However, a significant reduction in CVCi was observed during moderate intensity

exercise in the older subjects (P<0.01) leading to a significant age-group difference

(Figure 1D). No differences in PaCO2 were found between the young and older subjects,

however, PaCO2 was slightly but significantly reduced from rest during moderate

intensity exercise (P<0.05; Table 2). The RPE values obtained during low (9±1 young vs.

9±1 older; mean ± SE; P>0.05) and moderate intensity (13±1 young vs. 13±1 older;

P>0.05) cycling were similar in young and older subjects (P>0.05).

Spectral Analysis and Transfer Function Analysis

No significant age-group differences were found in the VLF and LF MCAVmean

power spectral density (PSD) at rest or during exercise (P>0.05; Table 3). Similarly, VLF

and LF MAP PSD were not different between young and older subjects. However, the

VLF MAP PSD exhibited a condition effect with a significant difference between the low

and moderate exercise bouts (Table 3). The VLF and LF transfer function gain between

MAP and MCAVmean were not significantly different between the young and older

subjects (P>0.05; Figure 2). The LF transfer function gain decreased from rest during

both low and moderate intensity cycling (P<0.05), whereas the VLF transfer function

gain was only different from rest during moderate exercise (Figure 2). Overall, similar

age-group and condition effects were found for the normalized VLF and LF transfer

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function gains (Table 3). An age-group difference was found in the VLF coherence

between MAP and MCAVmean with younger subjects exhibiting lower values under all

conditions (Figure 2). In contrast, the LF coherence was not different between young and

older subjects at any time point studied (P>0.05), but progressively decreased from rest to

low (P<0.01), and from low to moderate intensity exercise (P<0.05; Figure 2). However,

LF coherence values remained above 0.5 under all conditions. The VLF and LF phase

between MAP and MCAVmean was not significantly different between the young and

older subjects (P>0.05) or between rest and exercise (P>0.05; Figure 2).

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DISCUSSION

The present study is the first to examine the regulation of cerebral blood flow

during steady-state dynamic exercise in healthy older middle-aged subjects. First, we

found that despite greater increases in blood pressure during exercise in older individuals,

no differences in MCAVmean were observed between young and older subjects. Second,

no significant age-group differences were found in the VLF or LF transfer function gain

or phase between MAP and MCAVmean during low or moderate intensity cycling

indicating that the ability of the cerebral vasculature to respond to spontaneous

fluctuations in MAP (i.e., dynamic CA) was preserved with age during exercise.

Collectively, these data suggest that the regulation of MCAVmean is well-maintained

during dynamic exercise in healthy older middle-aged subjects.

Cerebral blood flow is influenced by neurogenic, neurohumoral, endothelial as

well as metabolic factors (26, 47, 50). As such, exercise presents a potential challenge to

CA, not only due to the rapid and robust fluctuations in arterial blood pressure but also

due to increases in sympathetic nerve activity, cardiac output and cerebral metabolism

(26, 47, 50). In the present study, we hypothesized that this may be particularly true in

older individuals considering previous reports of age-related alterations in peripheral

circulatory control during exercise (12, 45). Indeed, aging-induced impairments in

metabolic vasodilatation and exaggerated sympathetic vasoconstriction have been

reported in the vasculature of dynamically exercising skeletal muscle and are believed to

contribute to age-related reductions in exercising muscle blood flow (12, 13, 31, 45, 48).

In addition, exaggerated pressor responses to exercise may present an additional

challenge to the regulation of cerebral blood flow during physical activity in older

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individuals (9, 12, 14, 15). However, despite greater exercise-induced increases in blood

pressure in the older subjects, our results indicate that during low and moderate intensity

steady-state cycling MCAVmean responses were similar in young and older individuals. In

this regard, the slight but significant increases in MCAVmean observed during steady-state

cycling are in agreement with previous studies in young subjects and likely are requisite

to meet the demands of exercise-induced increases in cerebral metabolism (10, 23, 30,

40, 41, 50, 53). Thus, in contrast to age-related impairments in peripheral blood flow, the

regulation of cerebral blood flow appears to be well-maintained during dynamic exercise

with age.

Of note, we found that the cerebral vascular conductance index was significantly

reduced in older subjects during the moderate intensity cycling bout suggesting that

cerebral vasoconstriction occurred. Although the cause for this decrease in cerebral

vascular conductance is unclear, we suggest that this response may be normal and

necessary to offset the greater blood pressure response to exercise (∆ MAP from rest 9±3

young vs. 23±2 older mmHg; P<0.05). In this regard, a comparison of the moderate

intensity cycling bout in the young to the low intensity cycling bout in the older subjects,

in which MAP responses to exercise were closely matched (∆ MAP from rest 9±3 young

vs. 10±2 older mmHg; P<0.05), indicates very similar cerebral vascular conductance

responses (see Table 2). Thus, we interpret the greater decrease in conductance in the

older subjects during moderate exercise as a normal CA response. However, other factors

cannot be completely discounted including an enhanced activation of sympathetic

outflow directed to the cerebral vasculature (8). At present, sympathetic neural control of

the cerebral circulation is controversial, however it is known that cerebral arteries are

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richly innervated with sympathetic nerve fibers (36, 37), and a direct effect of

sympathetic activation on cerebral blood flow has been reported in several disease states

(25, 29). Considering previous reports have suggested an exaggerated exercise induced-

sympathoexcitation in older individuals (46, 52), it is plausible that an exaggerated

sympathetically-mediated cerebral vasoconstriction occurs in older individuals at

moderate to high exercise intensities (8). In addition, although no age-group effect was

found, the lower PaCO2 during moderate intensity exercise in older subjects may have

contributed to the significant decrease in cerebral vascular conductance from rest. Further

studies in this area are warranted.

Along with consideration of static CA, we also assessed dynamic measures of CA

at rest and during exercise. Indeed, recent studies have emphasized the importance of

evaluating dynamic CA using frequency-domain analysis in order to more fully examine

the ability of the cerebral vasculature to rapidly respond to changes in perfusion pressure

(18, 42, 55, 56). However, we found no significant age-group differences in either the

VLF or LF transfer function gain between MAP and MCAVmean during low or moderate

intensity cycling. These data indicate that the ability of the cerebral vasculature to

respond to spontaneous fluctuations in MAP was preserved in healthy older subjects

during exercise. Thus, similar to the static measurements made no age-related alterations

in dynamic CA was found. These findings are in agreement with previous studies

reporting that dynamic CA was maintained in healthy older subjects at rest and during

various laboratory stressors (6, 7, 34, 54). In contrast, the only other paper to examine

aging effects on cerebral vascular responses during exercise indicated that cerebral

autoregulatory mechanisms demonstrated a delayed responsiveness in older subjects (21).

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These authors found a delayed increase in cerebrovascular resistance in older individuals

in response to increases in cerebral blood flow at the onset of a short bout (3 min) of

supine leg cycling. The reason for the conflicting findings between these previous data

and the current results is unclear however, differences in exercise workloads between

young and older subjects (absolute vs. relative) and time points studied (onset vs. steady-

state) likely contributed. In this regard, the findings of the present study indicate that

when compared to younger subjects exercising at the same relative intensities, the

regulation of cerebral blood flow is well-maintained during steady-state dynamic exercise

in healthy older middle-aged subjects.

In the present study, in addition to the LF range between MAP and MCAVmean we

also utilized the VLF range to more completely assess dynamic CA. Although the

majority of studies have focused on the LF range (4, 27, 34, 40, 41), recent studies

suggest that CA may be more active in the VLF range than that in the LF range (32, 56).

Thus, we considered the possibility that changes in the VLF range may be important

when moving from rest to exercise and possibly contribute to age-related changes in

cerebral blood flow control. However, similar to the LF range, no age-group differences

were found in the VLF gain or phase between MAP and MCAVmean. Interestingly, the

VLF and LF transfer function gains decreased from rest to exercise in both the young and

older subjects. These findings are indicative of improved dynamic CA and suggest that

during low to moderate intensity dynamic exercise for any given change in blood

pressure smaller oscillations in MCAVmean occur, an effect that appears unaltered with

age. Although it remains to be determined if CA is more active in the VLF or in the LF

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range in humans, the relatively consistent responses between the VLF and LF ranges at

rest and during exercise clearly indicate a preserved dynamic CA with aging.

It should be noted that the coherence values for the VLF range tended to be lower

than those for the LF range, particularly in the younger subjects. Although these lower

values are consistent with previous studies (27, 41, 54-56), the reason for this finding is

unclear. Overall, the coherence analysis and interpretation should be considered.

Coherence functions have primarily been used to indicate the strength of the linear

relationship between MAP and MCAVmean and validate the measurements of transfer

function being made (4, 27, 34, 38, 40, 41, 54-56). Thus, when coherence is high (>0.5),

MAP and MCAVmean vary closely together and the transfer function gain can be used to

evaluate the effectiveness of CA. Indeed, in the present study, LF coherence values

remained above 0.5 under all conditions in both young and older subjects. However,

other ideas have recently evolved in regards to the coherence function, particularly in the

VLF range. It has been suggested that at these very low frequencies (<0.07 Hz) the

fluctuations in MCAVmean are more independent of changes in arterial pressure (low

coherence) because the cerebral vasculature can effectively buffer such slow changes in

blood pressure, and thus fluctuations in MCAVmean can occur independently of blood

pressure (27, 56). In fact, some researchers have proposed that a low coherence value is

suggestive of an effective CA (27). However, the usage of the coherence in this way

requires further validation and therefore, as in previous studies (4, 27, 34, 38, 40, 41) we

have relied on the LF transfer function gain as our primary index of dynamic CA.

Several potential limitations in the design and interpretation of the present

investigation should be considered. First, it is realized that changes in MCAVmean are only

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proportional to changes in cerebral blood flow if middle cerebral artery diameter remains

unchanged. Although we cannot completely rule out that changes in vessel diameter

influenced the MCAVmean measurements, previous studies in humans directly measuring

middle cerebral artery diameter have demonstrated that the diameter remains relatively

constant under a variety of experimental conditions and to various stimuli (19, 49).

Furthermore, during dynamic exercise it has been demonstrated that MCAVmean increases

in parallel with the inflow of the ipsilateral internal carotid artery (22) and with cerebral

blood flow determined using the "initial slope index" of the 133Xenon clearance method,

which is considered to represent the average cerebral blood flow (30). Indeed, a recent

review of the literature reported that the diameter of large cerebral arteries does not

change significantly during exercise, and that the regulation of cerebral blood flow takes

place in the smaller arteries (50). Thus, we would contend that the changes in MCAVmean

reported in the current investigation likely reflect changes in cerebral blood flow (50).

Another potential limitation of the present study is the small sample size and the

possibility that sex differences and in turn hormone status may have influenced our

results as both men and women were included. Although the number of subjects is

similar to previous studies that employed a within-between subject design to examine the

influence of aging, we cannot completely discount the potential that differences may have

been detected with a larger number of subjects (i.e., type II error). In addition, since all

older women were postmenopausal and not taking any hormone replacement and young

women completed the steady-state exercise protocol around the early follicular phase of

the menstrual cycle in which plasma estrogen and progesterone concentrations are

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generally low (20), further studies are needed to examine the influence of sex hormones

on cerebral autoregulation in women.

The mean age of the older subjects in the present study (57±7 years) was more

indicative of a middle-aged group. Although we did not observe any differences in CA,

this age range is similar to that of previous investigations reporting age-related alterations

in peripheral blood flow control as well as changes in cardiovascular responses to

exercise (13-15, 17, 35, 51, 54). Thus, we believe the results of the present study provide

novel insight into the regulation of cerebral blood flow during dynamic exercise as one

advances in age. However, we would caution readers in extrapolating our findings to

subjects over 70 years of age. In addition, the results of the present study are specific to

steady-state dynamic exercise at low and moderate relative intensities and thus, the

potential for age-related differences at higher exercise intensities or when comparing at

absolute workloads cannot be dismissed. Nonetheless, the intensities studied were chosen

because they are an integral part of the recommended exercise prescription for healthy

older adults (16).

In summary, we found that despite greater increases in blood pressure during

exercise in older individuals, no differences in MCAVmean were observed between young

and older subjects. Furthermore, we observed no significant age-group differences in

dynamic CA during low or moderate intensity steady-state dynamic exercise performed

at an equivalent relative intensity in young and older individuals. Thus, the ability of the

cerebral vasculature to respond to spontaneous fluctuations in MAP was preserved with

age during exercise. Collectively, these data suggest that the regulation of MCAVmean is

well-maintained during dynamic exercise in healthy older middle-aged subjects.

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Figure Legends.

Figure 1. Summary data showing the changes from rest in mean arterial pressure (MAP;

panel A), pulse pressure (panel B), middle cerebral artery mean blood velocity

(MCAVmean; panel C) and the cerebral vascular conductance index (CVCi; panel D)

during low (Low Ex) and moderate (Moderate Ex) intensity leg cycling in young and

older subjects. * Significantly different from young (P<0.05). † Significantly different

from rest (P<0.05). ‡ Significantly different from Low Ex (P<0.05). Values are means ±

SE.

Figure 2. Summary data for the transfer function gain, phase and coherence between

mean arterial pressure and middle cerebral artery mean blood velocity in the low

frequency (LF; panel A) and very low frequency (VLF; panel B) ranges at rest and during

low (Low Ex) and moderate (Moderate Ex) intensity leg cycling in young and older

subjects. † Significantly different from rest (P<0.05). Values are means ± SE.

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Table 1. Subject characteristics

Young Older

Men / Women 6/3 6/4

Age (years) 24 ± 3 57 ± 7*

Weight (Kg) 73 ± 10 75 ± 15

Height (cm) 174 ± 6 172 ± 8

BMI (Kg/m2) 24 ± 3 25 ± 4

Cholesterol (mg/dL-1) 146 ± 35 195 ± 25*

Triglycerides (mg/dL-1) 71 ± 23 88 ± 29

LDL (mg/dL-1) 87 ± 32 124 ± 19*

HDL (mg/dL-1) 46 ± 9 53 ± 17

Glucose (mg/dL-1) 84 ± 6 102 ± 12*

BUN (mg/dL-1) 13 ± 3 16 ± 4

Na+ (mEq/L-1) 139 ± 2 140 ± 2

K+ (mEq/L-1) 4.0 ± 0.3 4.2 ± 0.4

Values are means ± SD. BMI, body mass index; LDL, low density lipoprotein; HDL, high density lipoprotein; BUN, blood urea nitrogen; Na+, sodium; K+, potassium. * Significantly different from young (P < 0.05).

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Table 2. Steady-state physiological measurements at rest and during low and

moderate intensity leg cycling in young and older subjects

SBP DBP MAP Pulse Pressure Heart Rate MCAV mean CVCi PaCO2

(mmHg) (mmHg) (mmHg) (mmHg) (bpm) (cm/s) (cm/s/mmHg) (mmHg)

Rest

Young 120 ± 3 74 ± 1 90 ± 1 45 ± 3 65 ± 3 60 ± 4 0.66 ± 0.04 44 ± 1

Older 123 ± 3 81 ± 2* 95 ± 2 46 ± 6 61 ± 3 64 ± 2 0.67 ± 0.03 43 ± 1

Low Ex

Young 137 ± 5 70 ± 1 92 ± 2 67 ± 5 96 ± 3† 64 ± 3 0.70 ± 0.04 44 ± 2

Older 151 ± 4*† 82 ± 2* 105 ± 2*† 69 ± 4 90 ± 3† 68 ± 2 0.65 ± 0.02 43 ± 1

Moderate Ex

Young 160 ± 4†‡ 68 ± 3† 99 ± 2†‡ 92 ± 5 123 ± 3†‡ 66 ± 2 0.67 ± 0.03 43 ± 1

Older 182 ± 4*†‡ 86 ± 3* 118 ± 2*†‡ 96 ± 4 112 ± 3*†‡ 65 ± 2 0.56 ± 0.02*†‡ 41 ± 1

P value

Group 0.012 <0.001 <0.001 0.870 0.097 0.478 0.180 0.355

Condition <0.001 0.664 <0.001 <0.001 <0.001 0.021 0.001 0.003 Interaction 0.002 0.006 <0.001 0.290 0.005 0.244 0.001 0.942

Values are means ± SE. SBP, systolic blood pressure; DBP, diastolic blood pressure;

MAP, mean arterial pressure; MCAVmean, middle cerebral artery mean blood velocity;

CVCi, cerebral vascular conductance index; PaCO2, estimated arterial carbon dioxide

tension, Ex, exercise. * Significantly different from young (P<0.05). † Significantly

different from rest (P<0.05). ‡ Significantly different from Low Ex (P<0.05).

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Table 3. Spectral power, and normalized transfer function gain in the low (LF; 0.07-

0.2 Hz) and very low (VLF; 0.02-0.07 Hz) frequency ranges at rest and during low

and moderate intensity leg cycling in young and older subjects

LF MAP PSD LF MCAV mean PSD LF nGain VLF MAP PSD VLF MCAV mean PSD VLF nGain

(mmHg2) (cm2/s2) (db) (mmHg2) (cm2/s2) (db)

Rest

Young 3.12 ± 0.56 3.02 ± 0.71 2.75 ± 0.46 4.48 ± 0.63 3.18 ± 0.70 -2.64 ± 1.23

Older 3.39 ± 0.89 2.23 ± 0.52 1.80 ± 0.51 8.80 ± 1.21 4.74 ± 1.32 -2.82 ± 1.21

Low Ex

Young 3.34 ± 0.72 2.27 ± 0.41 0.52 ± 0.58 4.05 ± 0.65 2.96 ± 0.67 -3.54 ± 1.65

Older 3.29 ± 0.90 1.85 ± 0.40 0.72 ± 0.52 5.43 ± 1.15 2.33 ± 0.42 -3.54 ± 0.91

Moderate Ex

Young 3.26 ± 0.47 2.31 ± 0.19 0.72 ± 0.68 7.00 ± 1.24 3.69 ± 0.85 -3.95 ± 1.34

Older 3.48 ± 0.87 1.85 ± 0.29 1.51 ± 0.75 8.55 ± 2.10 2.52 ± 0.57 -4.36 ± 1.06

P value

Group 0.845 0.167 0.986 0.096 0.928 0.943

Condition 0.987 0.339 0.005 0.024 0.138 <0.001

Interaction 0.969 0.987 0.208 0.309 0.102 0.994

Values are means ± SE. MAP, mean arterial pressure; MCAVmean, middle cerebral artery

mean blood velocity; PSD, power spectral density; Ex, exercise; n, normalized.

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Figure 1.

Low Ex

Moderate Ex

∆ C

VC

i (c

m/s

/mm

Hg)

-0.25

0.00

0.25

∆ M

AP

(mm

Hg)

0

10

20

30 Young Older

∆ P

ulse

Pre

ssur

e(m

mH

g)

0

20

40

60

Low Ex

Moderate Ex

∆ M

CA

Vm

ean

(cm

/s)

0

5

10

*†‡

A B

C

*†

D

†‡

*†‡

†

†

†

†‡

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Figure 2.

VLF

Gai

n(c

m/s

/mm

Hg)

0.0

0.5

1.0

LF G

ain

(cm

/s/m

mH

g)

0.0

0.5

1.0

LF P

hase

(rad

ian)

0.0

0.5

1.0

1.5

Young Older

RestLow Ex

Moderate Ex

LF C

oher

ence

(cm

/s-1

)

0.0

0.5

1.0

†

†

A†

VLF

Pha

se(r

adia

n)

0.0

0.5

1.0

1.5

RestLow Ex

Moderate Ex

VLF

Coh

eren

ce

(cm

/s-1

)

0.0

0.5

1.0

B

†

†

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GRANTS

This research is the result of work supported with resources and the use of facilities at the

Harry S. Truman Memorial Veterans Hospital in Columbia, MO and by NIH Grant #

DK-076636 to PJF, by an American College of Sports Medicine Visiting Scholar Award

to SO and by an American Heart Association Postdoctoral Fellowship Award to JPF.

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ACKNOWLEDGEMENTS

The authors appreciate the time and effort expended by all the volunteer subjects.

Change of address:

The current address for J.P. Fisher is: School of Sport and Exercise Sciences, University

of Birmingham, Edgbaston, Birmingham, B15 2TT, UK.

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