| HOME | HELP | FEEDBACK | SUBSCRIPTIONS | ARCHIVE | SEARCH | TABLE OF CONTENTS |
|
|
|
|||||||
|
|||||||||
1School of Human Movement and Exercise Science, The University of Western Australia; and 2Department of Cardiology and 3Cardiac Transplant Unit, Royal Perth Hospital, Western Australia, West Australian Institute for Medical Research, Crawley, Western Australia 6009
Submitted 16 June 2003 ; accepted in final form 18 August 2003
 |
ABSTRACT |
|---|
 TOP ABSTRACT METHODSRESULTSDISCUSSIONDISCLOSURESREFERENCES |
|---|
acetylcholine; flow-mediated dilation; resistance vessel; conduit artery; cardiovascular
The mechanisms responsible for the beneficial effects of exercise training on endothelial function are controversial. Exercise training has been variably reported to improve several risk factors for cardiovascular disease, such as hypercholesterolemia, obesity, glycemic control, and hypertension, factors that are also associated with endothelial dysfunction. Some early studies of exercise training in humans suggested that the improvement in endothelial function observed was secondary to amelioration of these coincident risk factors (25). An alternate explanation is that repeated exposure of the vasculature to increased shear stress, a primary physiological stimulus to NO production, may explain upregulation of the NO-dilator system (39). In the present study, we aimed to examine the hypothesis that exercise-induced improvement in conduit and/or resistance vessel endothelial function is associated with improvement in risk factors for cardiovascular disease.
|
METHODS |
|---|
|
TOPABSTRACTMETHODSRESULTSDISCUSSIONDISCLOSURESREFERENCES |
|---|
A list of subject groups and their baseline characteristics are reported in Table 1. Inclusion criteria required untreated hypercholesterolemic subjects (UTHC; n = 11) to have an initial total cholesterol of >6.5 mmol/l and/or a low-density lipoprotein (LDL) of >4.0 mmol/l, and none were taking any medication. Treated hypercholesterolemic subjects (THC; n = 11) were taking a HMG-CoA reductase inhibitor in a stable dose for at least 3 mo (9 on atorvastatin, 1 simvastatin, and 1 cerivastatin) and had documentation that the total cholesterol was >6.5 mmol/l and/or LDL >4.0 mmol/l before treatment. Four THC subjects were also taking aspirin; 1 amlodipine (subject was normotensive for study duration) and 1 constant dose of estradiol. Coronary artery disease (CAD) subjects (n = 10) had CAD requiring surgical (coronary artery bypass grafting) or nonsurgical revascularization (percutaneous transluminal coronary angioplasty). All were taking aspirin, 9 were on HMG-CoA reductase inhibitor (statin) therapy, 7 were on 
-blocking therapy, 5 were on an angiotensin-converting enzyme (ACE) inhibitor, 2 were on a proton pump inhibitor, and 1 each was on a diuretic, a calcium channel blocking drug, and cholestyramine. Chronic heart failure (CHF) subjects (n = 12) were all classified between New York Heart Association class I and class III, possessed left ventricular ejection fraction of 26 ± 3%, and did not have overt evidence of congestive (right heart) failure at the time of study. Eleven CHF patients were taking ACE inhibitors; 8 were on aspirin; 7 were on warfarin; 6 took a diuretic; 4 took digoxin; 5 were on statin therapy; 3 were taking a nitrate; 3 were taking a potassium supplement; 2 were on carvedilol; and 2 were on an antiarrhythmic drug. All but 1 of the Type 2 diabetic subjects (T2D; n = 15) were taking oral hypoglycemic medication, 5 were taking an ACE inhibitor; 2 were on statin therapy, and 2 were taking aspirin. None had evidence of microor macrovascular disease. None of the healthy control subjects (CON; n = 16) were taking medication. For all subjects taking medication, treatment did not alter throughout the study period. All subjects were recruited from hospital clinics or via public advertisement.
|
Subjects were excluded if they were current smokers, hypertensive [resting blood pressure (BP)] > 160/90 mmHg); hypercholesterolemic (total cholesterol > 6.0 mmol/l or LDL > 4.0 mmol/l; except the UTHC subgroup); diabetic (except the T2D group); asthmatic; displayed evidence of coronary or valvular heart disease from history, examination, and exercise electrocardiography (except the CAD and CHF subgroups); performed >2 sessions of light-moderate exercise per week; or were unable to exercise due to physical limitations. No subject had undergone a surgical procedure within the 3 mo preceding the study. The Royal Perth Hospital Ethics Committee approved the study protocols, and all subjects gave written informed consent.
Study Design
Study designs and assessment techniques for each group are described in individual papers and were almost identical (30–32, 52, 53). After preliminary screening and baseline assessments, subjects were randomly assigned to remain sedentary or perform exercise training for 8-wk periods, followed by crossover. The exercise training protocol and assessment procedures are outlined below. Subjects were requested to make no changes to their diet, therapy, or other routines for the duration of the study. Interventions commenced within 7 days of the completion of baseline assessments, and all repeat assessments, including resistance and conduit vessel function, were performed with 7 days of the cessation of exercise training or control periods.
Assessment of Vascular Function
Vascular function assessments were conducted in a quiet, temperature-controlled environment at separate attendances if both conduit and resistance vessel function were assessed. Repeat investigations were performed at the same time of day for individual subjects. Subjects fasted for 8 h, abstained from alcohol and caffeine for 12 h, and did not perform any exercise for 24 h before assessments. Conduit vessel function was assessed by flow-mediated dilation (FMD) of the brachial artery in all UTHC, THC, CAD, and T2D subjects. Forearm resistance vessel function was examined by plethysmography in 10 UTHC, 10 THC, 8 CAD, and all T2D, CHF, and CON subjects.
Assessment of Conduit Vessel Function
As the subject rested in the supine position, the nondominant arm was extended and immobilized with foam supports and positioned at an angle of 
80° from the torso. Heart rate (HR) was continuously monitored with a three-lead ECG, and mean arterial pressure (MAP) was determined from an automated sphygmomanometer (Dinamap 8100, Critikon; FL) on the contralateral arm. Resting HR and BP measures were recorded after 30 min of supine rest in all subjects.
A rapid inflation/deflation pneumatic cuff was then positioned on the imaged arm immediately distal to the olecranon process to provide a stimulus to forearm ischemia (6). A 10-MHz multifrequency linear array probe attached to a high-resolution ultrasound machine (Aspen; Acuson, CA) was used to image the brachial artery in the distal third of the upper arm. When an optimal image was attained, the probe was held stable in a stereotactic clamp. Ultrasound parameters were set to optimize longitudinal, B-mode images of the lumen/arterial wall interface.
After a 20-min rest, baseline images were recorded on a S-VHS video cassette recorder (SVO-9500 MDP, Sony; Tokyo, Japan) over 2 min. The forearm cuff was then inflated to 200 mmHg for 5 min. Images were recorded 30 s before cuff deflation and for 2 min after deflation. After a 10-min rest, to allow arterial diameter to return to baseline, another 2-min baseline recording was made before a sublingual 400-µg spray dose of glyceryl trinitrate (GTN) with images recorded for a further 5 min.
Brachial artery diameters were analyzed using custom-designed edge detection and wall-tracking software, which minimizes investigator bias and has the power to detect an absolute change in FMD of 2% in a cross-over design study with only six subjects (54). Briefly, an edge-detection algorithm averages >300 diameter measurements per frame, with 20–30 frames assessed per second. Those average diameter measures that coincide with the ECG R wave (also autodetected), that is, occurring at end diastole, were subsequently analyzed using a third-order polynomial curve (54). FMD and GTN responses were then calculated from the peak value derived from this polynomial curve and related to the average of all R wave-gated diameters collected during the baseline period preceding either the FMD or GTN manipulations. The mean intraobserver coefficient of variation of repeated measures of FMD using this software is 6.7%, which is significantly lower than that for traditional manual methods (54).
Assessment of Resistance Vessel Function
While the subject was in the supine position, a 20-gauge cannula (Arrow) was inserted into the brachial artery of the nondominant arm, under local anesthesia with <2 ml of 1% lignocaine, to infuse vasoactive agents and sterile saline and for blood sampling and measurement of intra-arterial pressure. Subjects were then positioned with elbows at heart level and hands at a comfortable height to allow forearm venous drainage. Pneumatic cuffs (SC10 and SC5, D. E. Hokanson) and strain gauges (SG 24, Medasonics; Fremont, CA) were positioned for forearm blood flow (FBF) measurements. Wrist and upper arm cuffs were connected to rapid inflation devices (E-20 and AG 101, Hokanson); strain gauges were positioned 8–10 cm distal to the olecranon process of each arm. Strain-gauge placement and hand and elbow elevation were the same for repeat tests. An online microcomputer (SPG 16, Medasonics) sampled amplified output from the strain gauges at 75 Hz, which was displayed in real time. A software program controlled cuff inflation/deflation as well as data acquisition, storage, and display to ensure blood flow measurements were synchronized with upper arm cuff inflation.
Arterial pressure was monitored continuously with a Hewlett-Packard 78353A monitoring system. Acetylcholine (ACh, Miochol-Ciba Vision; New South Wales, Australia) was infused at 10, 20, and 40 µg/min, each for 3 min, and sodium nitroprusside (SNP, David Bull Laboratories; Victoria, Australia) at 2, 4, and 8 µg/min, each for 3 min using a constant-rate infusion pump (IVAC 770). NG-monomethyl-L-arginine (L-NMMA, Clinalfa; Laufelfingen, Switzerland) was infused at 2, 4, and 8 µmol/min, each for 4 min. All solutions were prepared aseptically immediately before infusion.
The study protocol was identical for each subject. Baseline measurements were made 20 min after cannulation. Blood flow measurements were made after inflation of the wrist cuffs to 200 mmHg to exclude the hands from the circulation and by rapidly inflating the upper arm cuffs to 45 mmHg to occlude venous flow for 10 s out of every 15 s during baseline and drug infusion periods. For each data collection period, the last five measurements of FBF were averaged to give a representative flow for that period. There was a minimum of a 10-min rest between ACh and SNP infusions and 15 min between SNP and L-NMMA infusions. The latter was infused last because of its more prolonged duration of action.
Assessment of Maximal Exercise Capacity
O2 peak, HR, BP, rate-pressure product, and exercise duration (in s) were determined from a graded maximal exercise test that was performed on an electronically braked bicycle ergometer (Orival 400, Lode). Initial resistance was set between 20 and 60 W and increased in 20- to 25-W increments, depending on subject ability, every 3 min until fatigue or termination, according to standard indications for stopping an exercise test (12). HR and rhythm were continuously recorded by 12-lead electrocardiogram.
Volumes of oxygen consumed (O2) and carbon dioxide produced (CO2) during exercise were calculated from minute ventilation and measured by using mass flow ventilometry and simultaneous mixing chamber analysis of expired gas fractions. Gas analyzers and flow probes were calibrated before each test. O2 and CO2 (expressed in l/min and ml·kg–1·min–1) were recorded during the final 40 s of each stage of the test. O2 peak was calculated as the average of the two highest consecutive 20-s periods of gas exchange data occurring in the last minute before volitional exhaustion, which was generally due to leg fatigue or breathlessness. Each subject performed a familiarization O2 peak test before their definitive O2 peak assessment at each phase of testing.
Anthropometric Assessment
Body weight and height were measured before each exercise test, and body mass index was calculated. Skinfolds were measured using spring-loaded calipers (Harpenden) at eight standard sites: triceps, biceps, subscapulare, supraspinale, iliocristale, midabdominal, anterior thigh, and medial calf. All sites were measured in triplicate, with the median score recorded. Muscle girths were similarly recorded at the following standard sites using an anthropometric steel tape (Lufkin): relaxed arm, flexed arm, waist, hip, thigh, and the waist-to-hip ratio (waist/hip) was calculated.
Assessment of Muscular Strength
Maximal isotonic voluntary contractile strength (MVC7) was assessed for seven distinct muscle groups using the one repetition maximum (1 RM) technique and custom-designed pin-loaded weight stack resistance equipment (Pulsestar; Cheshire, UK), with minimum 2.5-kg increments. These machines were also used during the exercise training program (see Exercise Training Protocol). The seven resistance exercises consisted of the following: dual-seated leg press, left and right hip extension, pectoral exercises, shoulder extension, seated abdominal flexion, and dual leg flexion. Subjects were instructed in correct lifting technique to avoid Valsalva maneuver and hand gripping. MVC7 was calculated as the sum of strength measures on each apparatus.
Exercise Training Protocol
Subjects performed three sessions of exercise per week composed of either three supervised combined aerobic and resistance circuit training sessions or two supervised circuit training sessions in addition to one home exercise training session per week, monitored for compliance (CAD, UTHC, and THC). Circuit training sessions were performed at the Cardiac Gymnasium, Royal Perth Hospital, with the focus on the large muscles of the lower limbs. Upper body exercises did not involve the forearm, and subjects were instructed to avoid hand gripping. They were also instructed on correct lifting techniques to avoid the Valsalva maneuver.
The 8-wk "circuit" training protocol involved a combination of resistance training, cycle ergometry, and treadmill walking. The resistance exercises (listed above) were alternated with cycle stations at a work-to-rest ratio of 45:15 s. Subjects performed one lift every 3 s, completing 15 lifts in the 45-s work period. At completion of the circuit, subjects performed an additional 5 min of treadmill walking. Training intensity and duration were progressively increased during the first 2 to 3 wk, as tolerated. Resistance intensity commenced at 55% of pretraining 1 RM and increased to 65% at week 4. Cycling and treadmill walking intensities were initially 70% of peak HR, determined from a prestudy graded maximal exercise test, and were increased up to 85% of peak HR at week 6.
Home training sessions, where performed, were individually prescribed and involved subjects performing continuous aerobic exercise at 70–85% maximal HR for up to 45–60 min. To ensure compliance, sessions were recorded in a diary, and HR were recorded using Polar heart rate monitors (Polar Electro Oy; Kempele, Finland).
Analysis of Data
In plethysmographic, resistance vessel function studies, FBF responses were initially calculated as a ratio of that in the infused arm to that in the noninfused arm, and changes in the ratio being expressed as percentage changes from the baseline immediately preceding the drug infusion period (2). FBF responses to each drug infusion were then expressed as the area under the curve (AUC) of percent changes in FBF ratio responses to the three doses of the drug. To compare trained and untrained data for all variables, including conduit and resistance vessel responses, Student's paired t-test was used. To examine relationships between variables at baseline (i.e., pretraining), we performed univariate analysis between all variables and baseline FMD, GTN, ACh-AUC, SNP-AUC, and L-NMMA-AUC, thereby providing correlation coefficients and associated significance levels. This approach was repeated for change in all variables (i.e., trained-untrained) and change in FMD, GTN, AUC ACh, AUC SNP, and AUC L-NMMA. Results of these univariate analyses were then used to select variables for stepwise multivariate linear regression analysis. Finally, to avoid the possibility of "regression to the mean" in the comparison of relationships between changes in variables with training, we created a multivariate model, as indicated above, which included both baseline values and posttraining data. Data are reported as means ± SE. Significance was set at P < 0.05. In the majority of cases for correlation between variables, a minimum of 45 matched pairs were available. Power analysis indicates that, assuming a two-tailed 5% test, this number is sufficient to detect a significant correlation of 0.40 with 80% power (26).
|
RESULTS |
|---|
|
TOPABSTRACTMETHODSRESULTSDISCUSSIONDISCLOSURESREFERENCES |
|---|
Relationship Between Variables Before Training
Correlation coefficients for the relationships between variables before training are presented in Table 2. Before training, FMD significantly correlated with O2 peak (n = 45; r = 0.368; P = 0.013) as did the FBF response to ACh, expressed as the area under the dose-response curve (ACh-AUC) (n = 69; r = 0.266; P = 0.027). The presence of a significant relationship between FMD and exercise capacity was further confirmed by correlation between exercise test duration and FMD (n = 47; r = 0.466; P < 0.001). No significant correlations were evident among the responses to SNP, GTN, or L-NMMA and either O2 peak or exercise test duration.
|
There was an inverse relationship between age and FMD (n = 47; r = –0.326; P = 0.025) and also age and AUC-ACh (n = 71; r = –0.276; P = 0.020). In addition, baseline FMD was inversely correlated with glycated hemoglobin concentration (n = 42; r = –0.376; P = 0.014), whereas ACh-AUC correlated directly with high-density lipoprotein (HDL) concentration (n = 65; r = 0.248; P = 0.046) and inversely with the waist/hip (n = 68; r = –0.422; P < 0.001). SNP-AUC also correlated directly with HDL concentration (n = 62; r = 0.295; P < 0.05) and inversely with measures of body composition (body mass: n = 67, r = –0.462; P < 0.001; body mass index: n = 67; r = –0.413; P < 0.001; waist/hip: n = 65; r = –0.301; P < 0.001).
No significance was found between baseline measures of conduit and resistance vessel endothelium-dependent (FMD vs. ACh: n = 43; r = 0.191; P = 0.220) or -independent (SNP vs. GTN: n = 41; r = –0.015; P = 0.925) function. In addition, no correlations were evident at baseline between basal and stimulated endothelium-dependent NO dilator function in either conduit (L-NMMA vs. FMD: n = 42; r = –0.243; P = 0.121) or resistance vessels (L-NMMA vs. ACh: n = 68; r = 0.148; P = 0.229).
From the above univariate results, age, O2 peak, exercise test duration, and glycated hemoglobin were entered as independent variables in a stepwise multiple regression analysis in an effort to predict pretraining FMD. This revealed peak duration was the single best predictor (R = 0.49, adjusted R2 = 0.217) and that no other variables contributed significantly to the prediction of FMD beyond this. Stepwise regression was also performed for the effect of age, O2 peak, waist/hip, and HDL cholesterol on ACh responses. This analysis revealed the waist/hip as the best predictor (R = 0.423, adjusted R2 = 0.165), with an additional significant contribution of age (R = 0.485, adjusted R2 = 0.209). Stepwise regression for the effect of body weight, body mass index, waist/hip ratio, and HDL cholesterol on SNP responses indicated weight as the single best predictor (R = 0.508, adjusted R2 = 0.249). Multivariate regression analyses were not performed on GTN and L-NMMA responses due to lack of significant univariate relationships.
Effects of Exercise Training
The effects of exercise training on physiological and vascular function responses are shown in Table 3. Training significantly increased FMD from 3.3 ± 0.4 to 5.9 ± 0.5% (P < 0.001), whereas the response to GTN was not altered (Table 3). The FBF-ACh AUC response significantly increased from 405 ± 43 to 637 ± 67% (P = 0.001) after training as did the FBF ratio to the highest dose of ACh (Ach-dose 3 359 ± 33 to 537 ± 58 P = 0.002). The responses to SNP-AUC, SNP-dose 3, L-NMMA-AUC, and L-NMMA-dose 3 did not change with training.
|
Training significantly improved O2 peak from 2.15 ± 0.06 to 2.37 ± 0.06 l/min (P < 0.001) and also O2 peak when expressed relative to body mass (P < 0.001). This evidence for improvement in exercise capacity was reinforced by a significant increase in exercise test duration (932 ± 33 to 1,096 ± 40 s; P < 0.0001) and decrease in resting HR (Table 4). Exercise training was also associated with a significant reduction in peripheral adiposity (161 ± 5 vs. 153 ± 7 mm; P = 0.001, Table 4), without a change in girths measured at corresponding landmarks, whereas muscular strength increased (435.9 ± 13.8 vs. 481.1 ± 13.7 mm; P = 0.0001, Table 3). These data infer a change in body composition with exercise training favoring an increase in lean body mass.
|
Plasma lipids, blood pressure, and resting blood glucose did not change with training, although there was a significant decrease in glycated hemoglobin concentration (6.41 ± 0.22 vs. 6.26 ± 0.20%; P < 0.05, Table 4). This improvement in glycated hemoglobin was largely due to an effect in the T2D subjects (33).
Relationship Between Variables After Exercise Training
Relationship between baseline data and changes with training. The change in FMD after exercise training was significantly negatively correlated with baseline FMD (n = 47; r = –0.533; P < 0.001) and, similarly, the change in ACh-AUC after exercise training was significantly negatively correlated with the baseline ACh-AUC (n = 71; r = –0.363; P < 0.01) and directly correlated with age (n = 71; r = 0.318; P = 0.007).
No correlation was observed among baseline lipid fractions, resting MAP, blood glucose, glycated hemoglobin, weight, body mass index, or sum of skinfolds and change in FMD or change in ACh-AUC.
Relationship between changes in variables with training. Correlations between training-induced changes in endothelium-dependent and -independent, conduit and resistance vessel function, and other variables are presented in Table 5. The change in FMD following training was not correlated with the change in O2 peak (n = 45; r = –0.084; P = 0.585) or change in exercise test duration (n = 47; r = –0.090; P = 0.549). Likewise, the change in ACh-AUC was not correlated with the change in O2 peak (n = 69; r = 0.113; P = 0.356) or change in exercise test duration (n = 71; r = 0.043; P = 0.723). No correlations were observed among changes in FMD or in ACh AUC and changes in lipid fractions, resting MAP, blood glucose, glycated hemoglobin, weight, waist/hip, body mass index, sum of skinfolds, or muscular strength (Table 5).
|
No significance was found between changes in conduit and resistance vessel endothelium-dependent (FMD vs. ACh: n = 43; r = –0.037; P = 0.816) or -independent (SNP vs. GTN: n = 39; r = –0.222; P = 0.175) function with training, or between changes in basal and stimulated endothelium-dependent NO dilator function in either conduit (L-NMMA vs. FMD: n = 40; r = –0.046; P = 0.777) or resistance vessels (L-NMMA vs. ACh: n = 66; r = –0.055; P = 0.660).
Because of the absence of relationships between changes in cardiovascular risk factor variables and changes in conduit and resistance vessel function on univariate analysis, multivariate regression analysis was not performed.
|
DISCUSSION |
|---|
|
TOPABSTRACTMETHODSRESULTSDISCUSSIONDISCLOSURESREFERENCES |
|---|
The above conclusion is at odds with some previous findings. For example, it has been suggested that exercise training-mediated improvements in vascular function may occur as a result of improvements in plasma lipid concentrations (25). Before exercise training in the present study, the response to ACh positively correlated with HDL, whereas glycated hemoglobin and waist/hip were inversely correlated with FMD. Furthermore, glycated hemoglobin and subcutaneous body fat decreased after training. However, there was no correlation between the changes in any of these variables and those in either FMD or ACh responses, indicating that improvements evident in vascular function were not associated with modulation of these conventional risk factors.
An alternate explanation for the effect of exercise training on vascular function is that exercise exerts a direct effect on the vasculature by generating a recurrent intermittent increase in shear stress, a known physiological stimulus to NO bioactivity (37). Animal studies have found that exercise training is associated with an increase in vascular NO production (45, 48) and upregulation of NO synthase expression (45), attributable to repeated episodes of vascular wall shear stress. Furthermore, we recently demonstrated that, during a discrete session of lower-limb exercise, an increase in NO-dilator bioactivity occurs in the resting upper-limb vessels, which, because of the very short half-life of NO, indicates that the NO has been produced in those vessels (14). The effect is probably largely due to the generalized impact of hemodynamic variables acting through vessel wall shear stress (39). Hence, recurrent augmentation of vessel wall shear stress, as a consequence of repeated exercise-induced hemodynamic changes, likely results in a generalized increase in NO bioactivity throughout the vasculature (24, 29–31, 52, 53), which would explain the improvements in resistance and conduit vessel function we observed. Interestingly though, we did not observe evidence of improved basal NO function in the present study, because no changes were evident in L-NMMA responses. Future studies will be required to determine the precise relationship between vascular shear stress and changes in endothelial function, and measures of oxidative stress will also be essential to determination of changes in bioavailability of NO (13).
An interesting secondary outcome of this study relates to the relationship between improvements in vascular function and those in measures of exercise capacity. Some recent animal and human studies have examined the hypothesis that decreased NO-mediated dilator capacity and consequent decreased blood flow and oxygen transport to active skeletal muscle during exercise, attenuates aerobic capacity (16, 35, 40). Limb blood flow and O2 peak were impaired in hypercholesterolemic mice with endothelial dysfunction and in wild-type mice administered a NO synthase inhibitor (35). The latter suggests that depressed vasodilator function can limit exercise capacity under some conditions. In a subsequent study, 4 wk of exercise training improved both NO bioactivity and O2 peak in normal mice with diet-induced and genetically induced hypercholesterolemia (40). Taken together, these studies are consistent with the proposal that impaired endothelial function inhibits exercise-induced redistribution of blood flow to skeletal muscle during exercise, that loss of endothelium-dependent vasodilator function can be rate limiting to oxygen delivery and exercise performance (35), and that training-induced correction of resistance vessel endothelial dysfunction and correspondingly preferential redistribution of blood flow to the working muscles during exercise might be related to enhanced O2 peak. In humans, a number of exercise training studies have reported significant correlations between improvements in reactive hyperemic responses in trained muscle vascular beds and O2 peak in healthy subjects (34) or O2 peak in CHF patients (8, 47). In a well-conducted recent trial involving a small number of CHF patients, Hambrecht et al. (16) reported a significant correlation between improvement in O2 peak following 6 mo of cycle ergometer training and an increase in ACh-mediated femoral artery blood flow. In the present study, we observed significant correlations between FMD and both O2 peak and exercise test duration across the groups before exercise training. In addition, the area under the ACh dose-response curve at baseline, dependent on resistance vessel vasodilation, also correlated with baseline O2 peak. At first glance it would be tempting to conclude from these data that exercise capacity is dependent to some extent on vasodilator function, yet if this was the case, then changes in vascular function with training should correlate with changes observed in O2 peak and exercise test duration. No such correlations were evident, indicating to us that improvement in functional capacity as a result of exercise training is not dependent on generalized improvement in conduit or resistance vessel vasodilator function. The disparity between the above studies and our results may relate to the subject groups studied, the magnitude of vascular dysfunction evident at entry, different training protocols used, or, quite likely, because we studied the systemic (forearm) vascular response to training rather than vascular function specifically in the trained (lower limb) muscles.
There are several potential limitations of the present study. A broad range of subjects were examined, with differing etiologies, medications, and disease severity. However, medications were not altered in any subject across the period of study, and the purpose of pooling the data from the various groups studied (30–32, 52, 53) was to provide adequate power to examine relationships between variables both before training and as a result of training. Furthermore, it might be argued that detection of relationships between variables is facilitated by a study of groups with a wide variation in baseline characteristics. Another possible statistical limitation relates to the possibility that we failed to observe relationships between changes in vascular function and changes in cardiovascular risk factors with training due to regression to the mean. We think this unlikely, however, because we repeated our univariate analysis for the prediction of change in vascular function measures with the inclusion of both pre- and posttraining data in the model as independent variables rather than delta scores for training baseline. Finally, we cannot exclude the possibility that a larger cohort of subjects or a more prolonged period of exercise training may have uncovered an effect of exercise training on some cardiovascular risk factors. However, we have demonstrated that in the relatively short time frame of the present study, there were minimal changes in cardiovascular risk factors despite significant improvements in vascular function, indicating that cardiovascular risk factors may not be the only factors involved in improvement in vascular function.
In summary, we did not observe a relationship between the changes in risk factors for cardiovascular disease and the improvements we observed in conduit and resistance vessel function, suggesting that the beneficial effects of exercise training on vascular function are not solely mediated through the effects of exercise on conventional cardiovascular risk factors. This raises the intriguing possibility that exercise training improves vascular function, and possibly decreases cardiovascular risk, through the direct effect of recurrent augmentation of vessel wall shear stress, as a consequence of repeated, exercise-induced hemodynamic changes. Our data therefore support the notion that exercise is indicated in primary/secondary prevention programs due to its possible direct beneficial effects on vascular function and associated cardiovascular risk, irrespective of effects on other risk factors in the short term.
|
DISCLOSURES |
|---|
|
TOPABSTRACTMETHODSRESULTSDISCUSSIONDISCLOSURESREFERENCES |
|---|
|
ACKNOWLEDGMENTS |
|---|
|
FOOTNOTES |
|---|
The costs of publication of this article were defrayed in part by the payment of page charges. The article must therefore be hereby marked "advertisement" in accordance with 18 U.S.C. Section 1734 solely to indicate this fact.
|
REFERENCES |
|---|
|
TOPABSTRACTMETHODSRESULTSDISCUSSIONDISCLOSURESREFERENCES |
|---|
This article has been cited by other articles:
|
|
 |
|
  M A Spruit, R-M A Eterman, V A C V Hellwig, P P Janssen, E F M Wouters, and N H M K Uszko-Lencer Effects of moderate-to-high intensity resistance training in patients with chronic heart failure Heart, September 1, 2009; 95(17): 1399 - 1408. [Abstract] [Full Text] [PDF]
|
|
|
|
 |
|
 K. Pahkala, O. J. Heinonen, H. Lagstrom, P. Hakala, O. Simell, J. S.A. Viikari, T. Ronnemaa, M. Hernelahti, L. Sillanmaki, and O. T. Raitakari Vascular Endothelial Function and Leisure-Time Physical Activity in Adolescents Circulation, December 2, 2008; 118(23): 2353 - 2359. [Abstract] [Full Text] [PDF]
|
|
|
|
 |
|
 D. J. Green, G. O'Driscoll, M. J. Joyner, and N. T. Cable Exercise and cardiovascular risk reduction: Time to update the rationale for exercise? J Appl Physiol, August 1, 2008; 105(2): 766 - 768. [Full Text] [PDF]
|
|
|
|
 |
|
 M. Kooijman, D. H. J. Thijssen, P. C. E. de Groot, M. W. P. Bleeker, H. J. M. van Kuppevelt, D. J. Green, G. A. Rongen, P. Smits, and M. T. E. Hopman Flow-mediated dilatation in the superficial femoral artery is nitric oxide mediated in humans J. Physiol., February 15, 2008; 586(4): 1137 - 1145. [Abstract] [Full Text] [PDF]
|
|
|
|
 |
|
 M. M Hartge, T. Unger, and U. Kintscher The endothelium and vascular inflammation in diabetes Diabetes and Vascular Disease Research, June 1, 2007; 4(2): 84 - 88. [Abstract] [PDF]
|
|
|
|
|
|
S. J Hamilton, G. T Chew, and G. F Watts Therapeutic regulation of endothelial dysfunction in type 2 diabetes mellitus Diabetes and Vascular Disease Research, June 1, 2007; 4(2): 89 - 102. [Abstract] [PDF]
|
|
|
|
 |
|
 V. G. Barauna, K. T. Rosa, M. C. Irigoyen, and E. M. de Oliveira Effects of Resistance Training on Ventricular Function and Hypertrophy in a Rat Model Clin. Med. Res., June 1, 2007; 5(2): 114 - 120. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
T. Loizidis, A. Sioga, L. Economou, A. Frosinis, A. Kyparos, A. Zotou, and M. Albani The role of ascorbic acid and exercise in chronic ischemia of skeletal muscle in rats J Appl Physiol, January 1, 2007; 102(1): 321 - 330. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
K W Lee, A D Blann, K Jolly, G Y H Lip, and on behalf of the BRUM Investigators Plasma haemostatic markers, endothelial function and ambulatory blood pressure changes with home versus hospital cardiac rehabilitation: the Birmingham Rehabilitation Uptake Maximisation Study Heart, December 1, 2006; 92(12): 1732 - 1738. [Abstract] [Full Text] [PDF]
|
|
|
|
 |
|
 T. O. Kiviniemi, A. Snapir, M. Saraste, J. O. Toikka, O. T. Raitakari, M. Ahotupa, J. J. Hartiala, M. Scheinin, and J. W. Koskenvuo Determinants of coronary flow velocity reserve in healthy young men Am J Physiol Heart Circ Physiol, August 1, 2006; 291(2): H564 - H569. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
L. H. Naylor, C. J. Weisbrod, G. O'Driscoll, and D. J. Green Measuring peripheral resistance and conduit arterial structure in humans using Doppler ultrasound J Appl Physiol, June 1, 2005; 98(6): 2311 - 2315. [Abstract] [Full Text] [PDF]
|
|
|
|
 |
|
 J. M.R. Gill, A. Al-Mamari, W. R. Ferrell, S. J. Cleland, C. J. Packard, N. Sattar, J. R. Petrie, and M. J. Caslake Effects of prior moderate exercise on postprandial metabolism and vascular function in lean and centrally obese men J. Am. Coll. Cardiol., December 21, 2004; 44(12): 2375 - 2382. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
D. J. Green, J. H. Walsh, A. Maiorana, V. Burke, R. R. Taylor, and J. G. O'Driscoll Comparison of resistance and conduit vessel nitric oxide-mediated vascular function in vivo: effects of exercise training J Appl Physiol, August 1, 2004; 97(2): 749 - 755. [Abstract] [Full Text] [PDF]
|
|
|
|
 |
|
 D. H. Endemann and E. L. Schiffrin Endothelial Dysfunction J. Am. Soc. Nephrol., August 1, 2004; 15(8): 1983 - 1992. [Abstract] [Full Text] [PDF]
|
|
| ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| HOME | HELP | FEEDBACK | SUBSCRIPTIONS | ARCHIVE | SEARCH | TABLE OF CONTENTS |
| Visit Other APS Journals Online |
