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Cardiorespiratory fitness and cardiovascular burden in chronic kidney disease
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Original research
Cardiorespiratory fitness and cardiovascular burden in chronic kidney disease Erin J. Howden a,∗ , Kassia Weston a , Rodel Leano b , James E. Sharman d , Thomas H. Marwick d , Nicole M. Isbel b,c , Jeff S. Coombes a a
School of Human Movement Studies, The University of Queensland, Australia School of Medicine, The University of Queensland, Australia c Department of Nephrology, Princess Alexandra Hospital, Brisbane, Australia d Menzies Research Institute, University of Tasmania, Hobart, Australia b
a r t i c l e
i n f o
Article history: Received 25 March 2014 Received in revised form 4 June 2014 Accepted 10 July 2014 Available online xxx Keywords: Exercise physiology Echocardiography Arterial stiffness Cardiac function Functional capacity Physical activity
a b s t r a c t Objectives: Reduced functional capacity is associated with poor prognosis. In patients with chronic kidney disease the factors that contribute to low cardiorespiratory fitness are unclear. The objective of this study was to evaluate the cardiorespiratory and cardiovascular response to exercise in chronic kidney disease patients, and secondly investigate the relationships between cardiorespiratory fitness and cardiovascular burden. Design: Cross-sectional analysis. Methods: Baseline demographic, anthropometric and biochemical data were examined in 136 patients with moderate chronic kidney disease (age 59.7 ± 9.6 yrs, eGFR 40 ± 9 ml/min/1.73 m2 , 55% male, 39% with a history of cardiovascular disease, 38% diabetic and 17% current smokers). Cardiorespiratory fitness was measured as peak VO2 , left ventricular morphology and function using echocardiography, central arterial stiffness by aortic pulse wave velocity and left ventricular afterload using augmentation index. Physical activity levels were assessed using the Active Australia questionnaire. Results: Peak VO2 (22.9 ± 6.5 ml/kg/min) and peak heart rate (148 ± 22 bpm) were 17% and 12% lower than the age-predicted values, respectively. The low fit group were significantly older, and were more likely to have type II diabetes, cardiovascular disease, a higher BMI and be less active than the high fit group (P < 0.05). The independent predictors of peak VO2 were age, type II diabetes, hemoglobin level, physical activity, aortic pulse wave velocity, augmentation index, and global longitudinal strain. Conclusion: In patients with chronic kidney disease, the peak VO2 and heart rate response is markedly impaired. Reduced cardiorespiratory fitness is independently associated with increased aortic stiffness, increased left ventricle afterload, poor left ventricle function and higher burden of cardiovascular risk. © 2014 Sports Medicine Australia. Published by Elsevier Ltd. All rights reserved.
Chronic kidney disease (CKD) is associated with a significant reduction in exercise capacity and a marked increase in the risk of atherosclerosis and cardiovascular disease (CVD),1 which is not fully explained by a high number of traditional risk factors. Like the general population, low cardiorespiratory fitness2 (CRF) and physical activity3 are associated with increased risk of morbidity and mortality in CKD. It has been irrefutably demonstrated over the past 50 years that higher levels of physical activity and CRF lowers the risk of CVD in the general population.4–6 Whether higher
∗ Corresponding author. E-mail addresses: [email protected], [email protected] (E.J. Howden).
levels of fitness and physical activity contribute beneficially to lower cardiovascular risk in patients with CKD is unknown. CKD patients have an increased burden of CVD, which includes ischemic heart disease, systolic and diastolic dysfunction and increased arterial stiffness.1,7 Moreover, a large proportion of patients are obese and inactive,8 which perpetuates deconditioning, muscle wasting and chronic low-grade inflammation,9 and may lower fitness. Surprisingly, the relationship between CRF and possible contributory factors has not been previously evaluated in this population. Due to the integrative nature and coordinated response of multiple systems, CRF is an excellent indicator of overall cardiovascular health. Therefore the aim of this study was to evaluate the cardiorespiratory and cardiovascular response to exercise in CKD patients, and secondly investigate the relationship between CRF and cardiovascular burden. We hypothesized that individuals
http://dx.doi.org/10.1016/j.jsams.2014.07.005 1440-2440/© 2014 Sports Medicine Australia. Published by Elsevier Ltd. All rights reserved.
Please cite this article in press as: Howden EJ, et al. Cardiorespiratory fitness and cardiovascular burden in chronic kidney disease. J Sci Med Sport (2014), http://dx.doi.org/10.1016/j.jsams.2014.07.005
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with CKD and higher CRF would demonstrate enhanced cardiovascular function as assessed by left ventricular (LV) and vascular function, and better traditional cardiovascular risk factor profiles than individuals with lower fitness.
1. Methods This study was a cross-sectional analysis of patients with stage 3 and 4 CKD enrolled in an interventional randomized controlled trial of cardiovascular risk modification. Details of the interventional study have been published.10 Briefly, patients were eligible for inclusion if they were aged 18–75 yrs, had moderate CKD (eGFR 25–60 ml/min/1.73 m2 ) and one or more uncontrolled cardiovascular risk factors. Exclusion criteria for the study were: Intervention for or symptomatic coronary artery disease (within 3 months), current heart failure (NYHA class III and IV) or significant valvular heart disease, pregnant or planning to become pregnant, life expectancy or anticipated time to dialysis or transplant <6 months as determined by their treating nephrologist. Participants provided written informed consent and the study complied with the Declaration of Helsinki. The study protocol was approved by the Princess Alexandra Human Research Ethics Committee (HREC 2007/190), and was registered at www.anzctr.org.au (Registration Number ANZCTR12608000337370). All patients were pre-screened by the study research nurse. Detailed medical histories were obtained including prior history of CVD, for example myocardial infarction, coronary angioplasty, coronary artery bypass surgery, stroke, transient ischemic attack and/or peripheral vascular disease. Patients were subsequently screened for the presence of inducible myocardial ischemia as indicated by a positive exercise stress echocardiogram. Peak VO2 was determined from a graded treadmill exercise test to exhaustion with 12-lead ECG monitoring (CASE V6.51, GE Medical Systems, Milwaukee, WI, USA). Participants completed a Duke Activity Status Index to assist in determining which exercise protocol to use. Based on their response, patients performed either a Bruce, Balke or Naughton exercise protocol. Breath-by-breath gas analysis (Vmax29c, SensorMedics, CA, USA) and ventilatory volumes were measured continuously, and VO2 peak was determined from the peak 20 s average during the final minute of exercise. Peak exercise blood pressure (BP) was measured in the last minute of exercise using a mercury sphygmomanometer. Achieved VO2 peak was compared to sex specific normative VO2 data for women [VO2 peak = (14.7 − (0.13 × age) × 3.5)]11 and men [VO2 peak = (18.4 − (0.16 × age) × 3.5)].12 Predicted maximal heart rate (HR) was determined by the following formula [208 − (0.7 × age)].13 Chronotropic incompetence was evaluated using heart reserve, where the difference between resting and peak heart rate was divided by the difference between resting heart rate and age predicted maximal heart rate. Failure to attain ≥80% of the heart reserve was used to identify chronotropic incompetence, as previously reported.14 To minimize diurnal influence on testing parameters all participants were tested between 9 am and 11 am. Imaging was performed by an experienced sonographer using a standard echocardiography machine (Vivid 7, General Electric Medical Systems, Milwaukee, WI) with a 3.5 MHz transducer. Images were acquired in the standard parasternal and apical views and were digitally recorded for offline analysis. Early (E) and late (A) diastolic mitral inflow velocities were measured from transmitral flow profile, recorded in apical four-chamber view with the sample volume placed at the level of the mitral valve leaflets in diastole. Pulsed wave tissue Doppler was used to measure the early diastolic relaxation velocity (e ) at the septal mitral annulus. The E/e ratio was calculated as an index of LV filling pressures.15 Left atrial (LA) volume was measured using the
area-length method and indexed to body surface area (LA volume index, LAVI). The LV end-systolic and end-diastolic volumes were assessed using the modified Simpson biplane method and indexed to body surface area. LV mass was assessed according to the method of Devereux and indexed to body surface area (LVMI). Global longitudinal strain and strain rate were measured with speckle tracking echocardiography off-line using specialized software (Echopac BT 2008, GE Medical Systems) and reported as the average of 6 basal segments from 3 standard apical views as previously reported.16 Central arterial stiffness was non-invasively determined by aortic pulse wave velocity (aPWV). The pulse wave distance was taken as the sternal notch-to-femoral distance minus the sternal notchto-carotid distance. Radial tonometry was used to estimate central BP using a validated generalized transfer function (SpygmoCor 8.1, AtCor Medical, Sydney, Australia). The radial artery waveform was calibrated with brachial BP acquired in duplicate at the time of the measurement. Augmentation index (AIx) a marker of LV afterload was calculated as the ratio of augmented pressure to central pulse pressure. As AIx is influenced by HR, we used the value normalized for HR of 75 bpm (AIx75).17 Self-reported physical activity was measured using items from the Active Australia questionnaire.18 Patients were classified as meeting physical activity guidelines if they reported performing ≥600 MET minutes per week.19 Exercise capacity was assessed by patients completing the six-minute walk test (6MWT) according to published recommendations.20 METs were derived by the treadmill from the time, speed and grade at test termination. Grip strength was measured using a hand-grip dynamometer (Jamar 5030 J1, Illinois, United States). The protocol was standardized and the peak value from three trials was recorded.21 For muscular power, patients completed the “get up and go” test.22 After an overnight fast, patients provided blood and urine samples for the measurement of serum/plasma concentrations of creatinine, hemoglobin, glucose and lipids (total cholesterol, LDL cholesterol, and HDL cholesterol) using standard automated techniques. Kidney function was determined as eGFR using the standard MDRD-175 formula. Results are reported as mean ± SD, median (interquartile range [IQR]), or frequencies (%) depending on the distribution of the data. Patients were grouped post hoc into tertiles based on their fitness (VO2 peak) as low, moderate and high fitness. Analysis of variance (ANOVA), the Kruskal Wallis test or the Pearson X2 test, were used as appropriate to assess group differences. Post hoc comparisons were performed using the Fishers Least Significant Difference test. Students paired t-test was used to compare achieved and predicted exercise variables (peak VO2 and predicted maximum HR). An analysis of covariance (ANCOVA) was performed to determine the influence of increasing age on VO2 peak in CKD patients. Univariate correlation (Pearson’s and Spearman Rho) were used to determine the relationship between peak VO2 and covariates (clinical variables – sex, age, history of CVD, type II diabetes, previous myocardial infarction, creatinine, eGFR, albumin, total cholesterol, HDL and LDL cholesterol, hemoglobin, BP, physical activity; vascular variables – AIx75, aPWV, peripheral pulse pressure, central pulse pressure; and cardiac variables – intraventricular relaxation time (IVRT), LAVI, e , s , E/e , ejection fraction, LVMI, EDVI, global longitudinal strain, average strain rate) and are reported in the Supplementary material. Multivariable regression models were developed to evaluate clinical and cardiovascular parameters that were predictors of peak VO2 using three nested models. Standard regression diagnostics were performed and co-linearity tested. Statistical significance was defined as ˛ < 0.05. Statistical analysis was performed using commercially available software (SPSS v 20, SPSS Inc, Chicago, IL).
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2. Results The baseline characteristics of the study participants are depicted in Table 1. Participants were stratified in to fitness tertiles based on their VO2 peak. Traditional cardiovascular risk factors blood pressure and lipid levels, were well controlled in all groups with pharmacotherapy as evidenced by the high use of statins and antihypertensive therapy (Table 1). There were significantly more obese individuals in the low fitness group when compared to moderate and high fit groups, which was supported by a higher proportion of inactive individuals (Table 1). Table 1 indicates the low fitness group were significantly older and more likely to have diabetes or ischemic heart disease than those in the highest group. Overall, only 30% (n = 41) of participants met current physical activity recommendations. The response to exercise is displayed in Table 2. The average VO2 peak was 22.9 ± 6.5 ml/kg/min, which was 17% lower than age predicted (28.1 ± 5.9 ml/kg/min). VO2 peak was significantly lower than predicted in both the low and moderate fitness group (P < 0.001), but not the high fitness group. To control for difference in age between groups, an ANCOVA was performed using age as a covariate. After controlling for differences in age, VO2 peak was still significantly reduced in the low fitness group (P < 0.001). The average peak HR achieved was 148 ± 22 bpm, or 92% of age predicted maximum HR (160 ± 10 bpm). Despite withholding rate limiting medication prior to testing (for >24 hrs), peak HR was below the age-predicted HR value for all groups (P < 0.001). In total 50% (n = 64) of patients failed to achieve 80% of their age predicted heart rate maximum, suggesting chronotropic incompetence. The majority of patients with chronotropic incompetence also fell in the lowest fitness tertile (22%, n = 28), while 21% (n = 21) were in the middle tertile and 12% (n = 15) were in the highest tertile. Systolic BP increased significantly with maximal exercise (137 ± 22 to 179 ± 23 mmHg, P < 0.001), while diastolic BP did not change (82 ± 13 to 83 ± 12 mmHg). There was an inverse correlation between VO2 and HR peak, and age (Supplemental material, Figs. I and II). The low fitness group performed poorly in the functional assessments – 6MWT, and the muscular strength and power tests (Table 2). There was a significantly higher rate of inducible ischemia on exercise stress echo in the low fit group (16%) compared to the high (2%). Systolic function (ejection fraction, s , global longitudinal strain) was well preserved (Table 2). Likewise, traditional measures of diastolic filling were not different, but E/e was significantly higher in the low fitness group. Vascular stiffness (aPWV and AIx75) was also higher in the low fit group when compared to the high fitness group (Table 2). The significant univariate correlates of VO2 peak are presented in the supplemental material (Table I, Figs. III and IV). To determine the differential effects of clinical, vascular and cardiac covariates of VO2 peak, we constructed three multivariate backward regression models (Table 3). Model 1 which, included age, diabetes status, history of CVD, hemoglobin and physical activity levels explained 32% of the variability in VO2 peak (Table 3). Model 2 included the variables from Model 1 and AIx75 and aPWV, which improved the model fit (Model 2: R2 = 0.39, n = 100, P < 0.001). The final model included the addition of cardiac variables (subclinical ischemia, E/e and GLS), which further improved the model fit (Model 3: R2 = 0.41, n = 100, P < 0.001). The incremental value of the vascular and cardiac variables over the clinical variables in predicting VO2 peak was compared. Using the enter method a multivariate model of significant clinical variables (Model 1) plus the addition of variables from Model 2, significantly improved the predictive power of the model by 6.6% (adjusted R2 = 0.40, P = 0.008 for change). The power of the model was improved further by addition of
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the cardiac variable by 5.9% (adjusted R2 = 0.46, P = 0.026 for change). 3. Discussion In this study, we examined the cardiorespiratory and cardiovascular response to peak exercise in CKD patients, and sought to evaluate the relationship between CRF and indicators of cardiovascular health. The main findings of this current study can be summarized as: (1) peak VO2 and HR were markedly reduced compared to age-predicted values in the majority of patients; (2) higher CRF was associated with lower aortic stiffness, AIx and better LV function; (3) the variance in CRF can be largely explained by cardiovascular risk factors (e.g. increasing age, type II diabetes and physical activity). Although we are unable to infer cause and effect, these findings suggest that CKD patients who maintain CRF also benefit from lower cardiovascular burden. Perhaps one of the most striking findings from this current study was the high rate of physical inactivity. Only 30 percent of participants met current physical activity recommendations.19 The number of CKD patients in Australia who meet the recommendations of the physical activity guidelines have dramatically decreased from 50 percent in 2009.23 The decline in physical activity levels is concerning, as it has occurred during a period where the Australian Government has increased public health campaigns to raise awareness of the benefits of being physically active. Given the association between higher levels of physical activity and improved survival in CKD patients,3 interventional studies are urgently required to determine the optimal way to increase physical activity levels and improve fitness in CKD patients, and ultimately determine if these interventions improve survival. Indeed, a 1 MET increase in fitness improves survival by 12 percent,12 while exercise tolerance of >10 METs in individuals with significant burden of subclinical atherosclerosis greatly improved cardiovascular prognosis.24 Interventions that incorporate a multidisciplinary approach will likely be the most effective in increasing physical activity levels due to the high cardiovascular burden and limited level of exercise counseling provided by nephrologists.25 In addition to low levels of physical activity, we found that peak CRF was also markedly impaired. In a small study of 32 patients with CKD (eGFR 29.9 ± 17.0), VO2 peak was found to be reduced compared to age-predicted norms.26 We extend these findings by showing that: (a) peak HR is also significantly reduced below agepredicted values, and (b) like the general population increasing age is associated with a decline in fitness albeit at a faster rate in patients with CKD. The reduction in VO2 peak may partially be explained by the blunted HR response to exercise and thus reduced central component to oxygen delivery. The HR response to dynamic exercise is complex, however a blunted HR response may reflect autonomic disturbances, and is associated with increased mortality and coronary heart disease incidence.27 Maximal HR clearly declines with age,13 however the rate of decline in patients with CKD is greater with the majority of our patients failing to achieve their age-predicted maximum response. Prior studies in ESKD have demonstrated that maximal HR is significantly reduced compared to healthy controls, but is increased following kidney transplantation28 suggesting impaired kidney function influences the central response to exercise. No prior studies have evaluated the relationship between fitness, LV function and arterial stiffness in CKD patients. In this study, patients with lower fitness had significantly higher central arterial stiffness and AIx when compared to the high fit group. Increased arterial stiffness is independent predictors of cardiovascular and all-cause mortality in kidney disease.29 The combination of poor CRF, low physical activity levels and increased arterial
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Table 1 Characteristics of patients grouped by cardiorespiratory fitness tertiles. All (n = 136)
Low fitness (n = 44)
Moderate fitness (n = 46)
High fitness (n = 46)
P ANOVA
Demographics Age (yr) Male sex (%) BMI (kg/m2 ) History CVD, n (%) Diabetes type II, n (%) Current smoker, n (%) Physically active, n (%) Positive ESE, n (%)
60 ± 10 75 (55) 33 ± 7 53 (39) 52 (38) 23 (17) 41 (30) 13 (10)
62 ± 8a 21 (48) 36 ± 6ac 23 (52)a 26 (59)a 8 (18) 9 (20)a 7 (16)a
62 ± 8b 25 (54) 32 ± 7 17 (37) 19 (41)b 7 (15) 10 (22)b 5 (12)
55 ± 11 29 (63) 31 ± 7 13 (28) 7 (15) 8 (17) 22 (48) 1 (2)
0.001 0.96 0.002 0.005 <0.001 0.36 0.02 0.05
Clinical eGFR (ml/min/1.73 m2 ) Hemoglobin (g/L) Glucose (mmol/L) Total cholesterol (mmol/L) HDL-cholesterol (mmol/L) LDL-cholesterol (mmol/L) Systolic BP (mmHg) Diastolic BP (mmHg) Waist circumference (cm) Hip circumference (cm)
40 ± 9 133 ± 15 6.7 ± 3.0 4.5 ± 1.1 1.1 ± 0.4 2.5 ± 0.9 139 ± 21 82 ± 13 110 ± 17 114 ± 13
38 ± 10 126 ± 13a 6.9 ± 2.6 4.4 ± 1.2 1.1 ± 0.5 2.2 ± 0.9a 136 ± 23 77 ± 13ac 118 ± 17ac 120 ± 14ac
41 ± 9 131 ± 14b 7.2 ± 3.7 4.2 ± 1.0b 1.1 ± 0.3 2.3 ± 0.8b 143 ± 17 83 ± 9 108 ± 14 113 ± 12
40 ± 9 140 ± 16 6.0 ± 2.4 4.8 ± 1.1 1.2 ± 0.5 2.8 ± 1.0 138 ± 23 87 ± 13 103 ± 17 108 ± 10
0.31 <0.001 0.27 0.013 0.27 0.003 0.31 <0.001 <0.001 <0.001
Medications Total anti-hypertensive Statin, n (%)
2.6 ± 1.4 93 (68)
3.2 ± 1.3a 33 (75)
2.5 ± 1.5 36 (78)
2.2 ± 1.2 24 (52)
0.003 0.99
Data are mean ± SD and n (%). eGFR, estimated glomerular filtration rate; ESE, exercise stress echocardiogram; HDL, high density lipoprotein; LDL, low density lipoprotein. a indicates significant difference (P < 0.05) between low and high fitness tertiles. b indicates significant difference (P < 0.05) between moderate and high fitness tertiles. c indicates significant difference (P < 0.05) between low and moderate fitness tertiles.
stiffness likely results in a pronounced increase in cardiovascular risk, presenting a patient population who require aggressive risk factor modification. Recent evidence suggests that lifestyle modification that includes exercise training and sodium restriction is clinically efficacious therapeutic interventions for preventing and even treating arterial stiffness.30 Thus, lifestyle interventions
which include exercise training, complemented with dietary interventions could be useful lifestyle strategy to prevent derangement in vascular function in CKD patients and warrant further investigation. In addition to derangement in arterial health, patients with CKD have abnormalities in LV function. We observed an inverse
Table 2 Cardiorespiratory fitness, functional capacity and cardiovascular function across fitness tertiles in CKD patients. All Peak VO2 (ml/kg/min) Peak VO2 (L/min) % of predicted peak VO2 RER Peak HR (bpm) % of HR peak SBP peak (mmHg) DBP peak (mmHg) METs Get up and Go (s) 6MWT (m) Grip strength (kg) E/A Intraventricular relaxation time (cm/s) Deceleration time (cm/s) Diastolic tissue velocity (cm/s) Systolic tissue velocity (cm/s) E/e Left atrial volume index (ml/m2 ) LV end diastolic volume index (ml/m2 ) Left ventricular mass index (g/m2 ) Ejection fraction (%) Global longitudinal strain (%) Strain rate (1/s) aPWV (m/s) AIx 75 Resting HR (bpm)
22.9 2.1 83 1.1 148 89 179 83 7.0 5.4 464 33 1.00 124 238 5.6 6.1 13.1 29 45 101 66 −18 −1.08 9.9 21.1 68
Low fitness ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ±
6.5 0.7 22 0.1 22 13 23 12 3.0 1.5 104 11 0.4 20 41 1.6 1.1 4.7 9 14 28 7 4 0.21 2.8 8.2 11
16.7 1.7 66 1.1 137 83 169 79 4.9 6.2 382 30 1.02 122 247 5.4 6.0 15.2 31 46 102 66 −17 −1.08 10.9 22.4 72
± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ±
1.7 0.4ac 14ac 1.1 21ac 13ac 24ac 10a 1.8ac 1.4ac 98ac 9a 0.5 19 50 1.8 1.3 5.7ac 10 16 29 8 4 0.24 3.3a 7.4a 11
Moderate fitness 21.8 2.0 83 1.1 148 90 180 80 6.6 5.2 475 33 1.00 125 233 5.7 6.2 12.5 28 44 96 65 −18 −1.06 10.2 23.1 67
± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ±
1.3 0.5b 14b 0.1 22b 13 19 17b 2.3b 1.3 76b 10 0.4 22 49 1.3 1.0 3.9 7 14 27 7 3 0.22 2.3b 8.3b 11
High fitness 30.0 2.7 100 1.1 157 93 188 84 9.3 4.7 528 38 0.99 126 236 5.8 6.1 11.7 30 44 106 67 −19 −1.10 8.6 18.0 68
± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ±
5.6 0.6 23 0.9 20 11 23 21 2.9 1.3 86 11 0.3 19 41 1.7 1.0 3.8 9 12 28 7 3 0.2 2.3 8.0 11
P ANOVA na <0.001 <0.001 0.30 <0.001 0.001 0.001 <0.001 <0.001 <0.001 <0.001 0.003 0.89 0.71 0.35 0.53 0.77 0.002 0.10 0.63 0.26 0.45 0.11 0.78 <0.001 0.006 0.094
Data are mean ± SD. aPWV, aortic pulse wave velocity; AIx 75, augmentation index corrected for heart rate; DBP, diastolic blood pressure; E/A, mitral inflow ratio; E/e , estimated LV filling pressures; HR, heart rate; METs, metabolic equivalents; RER, respiratory exchange ratio; SBP, systolic blood pressure; LV, left ventricle. a indicates significant difference (P < 0.05) between low and high fitness groups. b indicates significant difference (P < 0.05) between moderate and high fitness groups. c indicates significant difference (P < 0.05) between low and moderate fitness group.
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Table 3 Multivariable regression models for predictors of cardiorespiratory fitness. Model 1: Clinical (R2 = 0.32, n = 100, P < 0.0001)
Model 2: Clinical + vascular (R2 = 0.39, n = 100, P < 0.0001)
Coefficient
95% CI
P
Age
−0.21
−0.32, −0.09
0.001
0.11
Type II diabetes
−3.14
−5.57, −0.70
0.012
−2.82
Hemoglobin
0.09
0.01, 0.17
0.035
Physical activity
2.81
0.41, 5.21
0.022
Coefficient
Model 3: Clinical + vascular + cardiac (R2 = 0.41, n = 100, P < 0.0001)
95% CI
P
Coefficient
95% CI
P
−0.24, 0.02 −5.18, −0.47
0.08
−0.19
−0.31, −0.08
0.001
0.02
−2.62
−4.98, 0.26
0.09
0.03
0.01, 0.17
0.03
0.09
0.02, 0.17
0.024
2.49
0.31, 4.83
0.026
0.18, 4.80
0.04
2.57
AIx 75
−0.16
−0.30, −0.03
0.02
−0.19
aPWV
−0.49
−0.93, −0.05
0.03
−0.49
Global longitudinal strain
relationship between VO2 peak, estimated LV filling pressures (E/e ) and global longitudinal strain. Elevated LV filling pressure is suggestive of diastolic dysfunction,7 which typically manifests as symptoms of dyspnea on exertion and reduced exercise tolerance. Estimated LV filling pressures (E/e ) were elevated in the low fit group, but this measurement is highly preload dependent and may not accurately reflect LV filling pressures.31 Prior studies have demonstrated that diastolic dysfunction impairs CRF,32,33 thus the association likely contributed to reduced CRF in CKD patients. Moreover, global longitudinal strain, a sensitive indicator of LV systolic function, was also a significant independent predictor of VO2 peak. We speculate that the mild impairment in cardiac function at rest may impair the ability of the LV to optimally deliver blood to the working tissues during exercise, and thus supports the hypothesis of impaired central factors. Individuals with higher levels of fitness have enhanced LV function, through improved LV compliance.34 We have recently shown that 1-year of moderate intensity exercise training improves diastolic function in CKD patients.10 While others have demonstrated that high intensity interval training has greater effect on LV function when compared to moderate intensity exercise in similar patient groups.35 Future work should seek to evaluate the most optimal training intensity to enhance cardiac function in CKD patients. This study has several limitations; we are unable to infer causality between fitness and cardiovascular burden as the relationship could be bi-directional, with poor fitness being driven by the presence of vascular disease and vice versa. We used normative data to compare peak cardiorespiratory fitness and heart rate in CKD patients, which is a limitation of the study. Future studies should include age and gender matched controls, tested under the same conditions. We only included patients with an eGFR of 25–59 ml/min/1.73 m2 , which limits the ability to generalize these findings. The Active Australia questionnaire was used to measure physical activity and whilst the questionnaire is considered to be valid and reliable, and comparable with other commonly used questionnaires,36 the questionnaire measures activity performed in the previous week may not be indicative of usual activity. Assessment of physical activity by patient recall has limitations due to respondent’s inability to accurately recall average frequency and duration of activity performed. We attempted to minimize the influence of rate limiting medications by asking patients to withhold -blockers and non-dihydropyridine calcium channel blockers for 24 h prior to exercise testing; this washout period may be insufficient to normalize the chronotropic response to exercise. In addition, we did not measure blood volume or control diet prior to testing which may have influenced our assessment of cardiac and vascular function. Finally our participants did not perform a familiarization exercise test which may have improved their maximal exercise response.
–
−0.32, −0.06
0.005
–
–
−0.85, −0.13
0.008
4. Conclusion In patients with moderate CKD, the peak cardiorespiratory and HR response to exercise is markedly impaired compared to normative values. Lower CRF is associated with higher cardiovascular burden as evident by the independent relationship between peak VO2 , LV function, arterial stiffness and cardiovascular risk factors. Targeted programs to increase physical activity levels, and ultimately CRF are urgently required. Practical implications • Peak cardiorespiratory fitness and heart rate is markedly reduced in the majority of CKD patients. Consideration must be taken when prescribing exercise intensity based on age-predicted heart rate equations to this patient population. Based on this, we would recommend ratings of perceived exertion as a meaningful indicator of exercise intensity. • Older CKD patients have lower cardiorespiratory fitness and higher cardiovascular burden. These patients in particular would likely benefit significantly from targeted strategies to increase fitness, which would also improve functional capacity and ability to perform activities of daily living. • Overall physical activity levels in CKD patients are extremely low, targeted strategies to encourage physical activity in this patient population are urgently required. Financial disclosures This research was supported by the NHMRC-funded Centre for Clinical Research Excellence – Vascular and Metabolic Health (CCRE), University of Queensland (UQ) and the Department of Nephrology, Princess Alexandra Hospital. Acknowledgements This research was supported by the NHMRC-funded Centre for Clinical Research Excellence – Vascular and Metabolic Health (CCRE), University of Queensland (UQ) (2007000276). The authors would like to thank Karen Sonnenburg and Lisa Ditterich (Research Nurses) for their time and effort in recruiting, and assisting with participants in the trial. The authors also would like to gratefully acknowledge the study participants for volunteering for this research study. Appendix A. Supplementary data Supplementary data associated with this article can be found, in the online version, at doi:10.1016/j.jsams.2014.07.005.
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References 1. Go AS, Chertow GM, Fan D et al. Chronic kidney disease and the risks of death, cardiovascular events, and hospitalization. N Engl J Med 2004; 351(13):1296–1305. 2. Sietsema KE, Amato A, Adler SG et al. Exercise capacity as a predictor of survival among ambulatory patients with end-stage renal disease. Kidney Int 2004; 65(2):719–724. 3. Beddhu S, Baird BC, Zitterkoph J et al. Physical activity and mortality in chronic kidney disease (NHANES III). Clin J Am Soc Nephrol 2009; 4(12):1901–1906. 4. Morris JN, Heady JA, Raffle PA et al. Coronary heart-disease and physical activity of work. Lancet 1953; 265(6796):1111–1120 [concl.]. 5. Blair SN, Kampert JB, Kohl 3rd HW et al. Influences of cardiorespiratory fitness and other precursors on cardiovascular disease and all-cause mortality in men and women. JAMA 1996; 276(3):205–210. 6. Paffenbarger Jr RS, Hyde RT, Wing AL et al. The association of changes in physicalactivity level and other lifestyle characteristics with mortality among men. N Engl J Med 1993; 328(8):538–545. 7. Redfield MM, Jacobsen SJ, Burnett Jr JC et al. Burden of systolic and diastolic ventricular dysfunction in the community: appreciating the scope of the heart failure epidemic. JAMA 2003; 289(2):194–202. 8. Stengel B, Tarver-Carr ME, Powe NR et al. Lifestyle factors, obesity and the risk of chronic kidney disease. Epidemiology 2003; 14(4):479–487. 9. Dungey M, Hull KL, Smith AC et al. Inflammatory factors and exercise in chronic kidney disease. Int J Endocrinol 2013; 2013:569831. 10. Howden EJ, Leano R, Petchey W et al. Effects of exercise and lifestyle intervention on cardiovascular function in CKD. Clin J Am Soc Nephrol 2013; 8(9):1494– 1501. 11. Gulati M, Black HR, Shaw LJ et al. The prognostic value of a nomogram for exercise capacity in women. N Engl J Med 2005; 353(5):468–475. 12. Myers J, Prakash M, Froelicher V et al. Exercise capacity and mortality among men referred for exercise testing. N Engl J Med 2002; 346(11):793–801. 13. Tanaka H, Monahan KD, Seals DR. Age-predicted maximal heart rate revisited. J Am Coll Cardiol 2001; 37(1):153–156. 14. Brubaker PH, Kitzman DW. Chronotropic incompetence: causes, consequences, and management. Circulation 2011; 123(9):1010–1020. 15. Nagueh SF, Middleton KJ, Kopelen HA et al. Doppler tissue imaging: a noninvasive technique for evaluation of left ventricular relaxation and estimation of filling pressures. J Am Coll Cardiol 1997; 30(6):1527–1533. 16. Marwick TH. Measurement of strain and strain rate by echocardiography: ready for prime time? J Am Coll Cardiol 2006; 47(7):1313–1327. 17. Wilkinson IB, MacCallum H, Flint L et al. The influence of heart rate on augmentation index and central arterial pressure in humans. J Physiol 2000; 525(Pt 1):263–270. 18. Australian Institute of Health and Welfare.The Active Australia Survey: a guide and manual for implementation, analysis and reporting, In: AIHW, editor, Canberra, Australian Institute of Health and Welfare, 2003, p. 1–49.
19. Nelson ME, Rejeski WJ, Blair SN et al. Physical activity and public health in older adults: recommendation from the American College of Sports Medicine and the American Heart Association. Med Sci Sports Exerc 2007; 39(8):1435–1445. 20. Butland RJ, Pang J, Gross ER et al. Two-, six-, and 12-minute walking tests in respiratory disease. Brit Med J 1982; 284(6329):1607–1608. 21. Bechtol CO. Grip test; the use of a dynamometer with adjustable handle spacings. J Bone Joint Surg [American volume] 1954; 36-A(4):820–824 [passim]. 22. Mathias S, Nayak US, Isaacs B. Balance in elderly patients: the “get-up and go” test. Arch Phys Med Rehabil 1986; 67(6):387–389. 23. Fassett RG, Robertson IK, Geraghty DP et al. Physical activity levels in patients with chronic kidney disease entering the LORD trial. Med Sci Sports Exerc 2009; 41(5):985–991. 24. LaMonte MJ, Fitzgerald SJ, Levine BD et al. Coronary artery calcium, exercise tolerance, and CHD events in asymptomatic men. Atherosclerosis 2006; 189(1):157–162. 25. Johansen KL, Sakkas GK, Doyle J et al. Exercise counseling practices among nephrologists caring for patients on dialysis. Am J Kidney Dis 2003; 41(1):171–178. 26. Padilla J, Krasnoff J, Da Silva M et al. Physical functioning in patients with chronic kidney disease. J Nephrol 2008; 21(4):550–559. 27. Lauer MS, Okin PM, Larson MG et al. Impaired heart rate response to graded exercise. Prognostic implications of chronotropic incompetence in the Framingham Heart Study. Circulation 1996; 93(8):1520–1526. 28. Painter P, Krasnoff JB, Kuskowski M et al. Effects of modality change and transplant on peak oxygen uptake in patients with kidney failure. Am J Kidney Dis 2011; 57(1):113–122. 29. Blacher J, Guerin AP, Pannier B et al. Impact of aortic stiffness on survival in end-stage renal disease. Circulation 1999; 99(18):2434–2439. 30. Tanaka H, Safar ME. Influence of lifestyle modification on arterial stiffness and wave reflections. Am J Hypertens 2005; 18(1):137–144. 31. Prasad A, Popovic ZB, Arbab-Zadeh A et al. The effects of aging and physical activity on Doppler measures of diastolic function. Am J Cardiol 2007; 99(12):1629–1636. 32. Kitzman DW, Higginbotham MB, Cobb FR et al. Exercise intolerance in patients with heart failure and preserved left ventricular systolic function: failure of the Frank-Starling mechanism. J Am Coll Cardiol 1991; 17(5):1065–1072. 33. Vanoverschelde JJ, Essamri B, Vanbutsele R et al. Contribution of left ventricular diastolic function to exercise capacity in normal subjects. J Appl Physiol (1985) 1993; 74(5):2225–2233. 34. Arbab-Zadeh A, Dijk E, Prasad A et al. Effect of aging and physical activity on left ventricular compliance. Circulation 2004; 110(13):1799–1805. 35. Wisloff U, Stoylen A, Loennechen JP et al. Superior cardiovascular effect of aerobic interval training versus moderate continuous training in heart failure patients: a randomized study. Circulation 2007; 115(24):3086–3094. 36. Brown WJ, Burton NW, Marshall AL et al. Reliability and validity of a modified self-administered version of the Active Australia physical activity survey in a sample of mid-age women. Aust N Z J Public Health 2008; 32(6):535–541.
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Contents lists available at ScienceDirect
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Original research
Cardiorespiratory fitness and cardiovascular burden in chronic kidney disease Erin J. Howden a,∗ , Kassia Weston a , Rodel Leano b , James E. Sharman d , Thomas H. Marwick d , Nicole M. Isbel b,c , Jeff S. Coombes a a
School of Human Movement Studies, The University of Queensland, Australia School of Medicine, The University of Queensland, Australia c Department of Nephrology, Princess Alexandra Hospital, Brisbane, Australia d Menzies Research Institute, University of Tasmania, Hobart, Australia b
a r t i c l e
i n f o
Article history: Received 25 March 2014 Received in revised form 4 June 2014 Accepted 10 July 2014 Available online xxx Keywords: Exercise physiology Echocardiography Arterial stiffness Cardiac function Functional capacity Physical activity
a b s t r a c t Objectives: Reduced functional capacity is associated with poor prognosis. In patients with chronic kidney disease the factors that contribute to low cardiorespiratory fitness are unclear. The objective of this study was to evaluate the cardiorespiratory and cardiovascular response to exercise in chronic kidney disease patients, and secondly investigate the relationships between cardiorespiratory fitness and cardiovascular burden. Design: Cross-sectional analysis. Methods: Baseline demographic, anthropometric and biochemical data were examined in 136 patients with moderate chronic kidney disease (age 59.7 ± 9.6 yrs, eGFR 40 ± 9 ml/min/1.73 m2 , 55% male, 39% with a history of cardiovascular disease, 38% diabetic and 17% current smokers). Cardiorespiratory fitness was measured as peak VO2 , left ventricular morphology and function using echocardiography, central arterial stiffness by aortic pulse wave velocity and left ventricular afterload using augmentation index. Physical activity levels were assessed using the Active Australia questionnaire. Results: Peak VO2 (22.9 ± 6.5 ml/kg/min) and peak heart rate (148 ± 22 bpm) were 17% and 12% lower than the age-predicted values, respectively. The low fit group were significantly older, and were more likely to have type II diabetes, cardiovascular disease, a higher BMI and be less active than the high fit group (P < 0.05). The independent predictors of peak VO2 were age, type II diabetes, hemoglobin level, physical activity, aortic pulse wave velocity, augmentation index, and global longitudinal strain. Conclusion: In patients with chronic kidney disease, the peak VO2 and heart rate response is markedly impaired. Reduced cardiorespiratory fitness is independently associated with increased aortic stiffness, increased left ventricle afterload, poor left ventricle function and higher burden of cardiovascular risk. © 2014 Sports Medicine Australia. Published by Elsevier Ltd. All rights reserved.
Chronic kidney disease (CKD) is associated with a significant reduction in exercise capacity and a marked increase in the risk of atherosclerosis and cardiovascular disease (CVD),1 which is not fully explained by a high number of traditional risk factors. Like the general population, low cardiorespiratory fitness2 (CRF) and physical activity3 are associated with increased risk of morbidity and mortality in CKD. It has been irrefutably demonstrated over the past 50 years that higher levels of physical activity and CRF lowers the risk of CVD in the general population.4–6 Whether higher
∗ Corresponding author. E-mail addresses: [email protected], [email protected] (E.J. Howden).
levels of fitness and physical activity contribute beneficially to lower cardiovascular risk in patients with CKD is unknown. CKD patients have an increased burden of CVD, which includes ischemic heart disease, systolic and diastolic dysfunction and increased arterial stiffness.1,7 Moreover, a large proportion of patients are obese and inactive,8 which perpetuates deconditioning, muscle wasting and chronic low-grade inflammation,9 and may lower fitness. Surprisingly, the relationship between CRF and possible contributory factors has not been previously evaluated in this population. Due to the integrative nature and coordinated response of multiple systems, CRF is an excellent indicator of overall cardiovascular health. Therefore the aim of this study was to evaluate the cardiorespiratory and cardiovascular response to exercise in CKD patients, and secondly investigate the relationship between CRF and cardiovascular burden. We hypothesized that individuals
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with CKD and higher CRF would demonstrate enhanced cardiovascular function as assessed by left ventricular (LV) and vascular function, and better traditional cardiovascular risk factor profiles than individuals with lower fitness.
1. Methods This study was a cross-sectional analysis of patients with stage 3 and 4 CKD enrolled in an interventional randomized controlled trial of cardiovascular risk modification. Details of the interventional study have been published.10 Briefly, patients were eligible for inclusion if they were aged 18–75 yrs, had moderate CKD (eGFR 25–60 ml/min/1.73 m2 ) and one or more uncontrolled cardiovascular risk factors. Exclusion criteria for the study were: Intervention for or symptomatic coronary artery disease (within 3 months), current heart failure (NYHA class III and IV) or significant valvular heart disease, pregnant or planning to become pregnant, life expectancy or anticipated time to dialysis or transplant <6 months as determined by their treating nephrologist. Participants provided written informed consent and the study complied with the Declaration of Helsinki. The study protocol was approved by the Princess Alexandra Human Research Ethics Committee (HREC 2007/190), and was registered at www.anzctr.org.au (Registration Number ANZCTR12608000337370). All patients were pre-screened by the study research nurse. Detailed medical histories were obtained including prior history of CVD, for example myocardial infarction, coronary angioplasty, coronary artery bypass surgery, stroke, transient ischemic attack and/or peripheral vascular disease. Patients were subsequently screened for the presence of inducible myocardial ischemia as indicated by a positive exercise stress echocardiogram. Peak VO2 was determined from a graded treadmill exercise test to exhaustion with 12-lead ECG monitoring (CASE V6.51, GE Medical Systems, Milwaukee, WI, USA). Participants completed a Duke Activity Status Index to assist in determining which exercise protocol to use. Based on their response, patients performed either a Bruce, Balke or Naughton exercise protocol. Breath-by-breath gas analysis (Vmax29c, SensorMedics, CA, USA) and ventilatory volumes were measured continuously, and VO2 peak was determined from the peak 20 s average during the final minute of exercise. Peak exercise blood pressure (BP) was measured in the last minute of exercise using a mercury sphygmomanometer. Achieved VO2 peak was compared to sex specific normative VO2 data for women [VO2 peak = (14.7 − (0.13 × age) × 3.5)]11 and men [VO2 peak = (18.4 − (0.16 × age) × 3.5)].12 Predicted maximal heart rate (HR) was determined by the following formula [208 − (0.7 × age)].13 Chronotropic incompetence was evaluated using heart reserve, where the difference between resting and peak heart rate was divided by the difference between resting heart rate and age predicted maximal heart rate. Failure to attain ≥80% of the heart reserve was used to identify chronotropic incompetence, as previously reported.14 To minimize diurnal influence on testing parameters all participants were tested between 9 am and 11 am. Imaging was performed by an experienced sonographer using a standard echocardiography machine (Vivid 7, General Electric Medical Systems, Milwaukee, WI) with a 3.5 MHz transducer. Images were acquired in the standard parasternal and apical views and were digitally recorded for offline analysis. Early (E) and late (A) diastolic mitral inflow velocities were measured from transmitral flow profile, recorded in apical four-chamber view with the sample volume placed at the level of the mitral valve leaflets in diastole. Pulsed wave tissue Doppler was used to measure the early diastolic relaxation velocity (e ) at the septal mitral annulus. The E/e ratio was calculated as an index of LV filling pressures.15 Left atrial (LA) volume was measured using the
area-length method and indexed to body surface area (LA volume index, LAVI). The LV end-systolic and end-diastolic volumes were assessed using the modified Simpson biplane method and indexed to body surface area. LV mass was assessed according to the method of Devereux and indexed to body surface area (LVMI). Global longitudinal strain and strain rate were measured with speckle tracking echocardiography off-line using specialized software (Echopac BT 2008, GE Medical Systems) and reported as the average of 6 basal segments from 3 standard apical views as previously reported.16 Central arterial stiffness was non-invasively determined by aortic pulse wave velocity (aPWV). The pulse wave distance was taken as the sternal notch-to-femoral distance minus the sternal notchto-carotid distance. Radial tonometry was used to estimate central BP using a validated generalized transfer function (SpygmoCor 8.1, AtCor Medical, Sydney, Australia). The radial artery waveform was calibrated with brachial BP acquired in duplicate at the time of the measurement. Augmentation index (AIx) a marker of LV afterload was calculated as the ratio of augmented pressure to central pulse pressure. As AIx is influenced by HR, we used the value normalized for HR of 75 bpm (AIx75).17 Self-reported physical activity was measured using items from the Active Australia questionnaire.18 Patients were classified as meeting physical activity guidelines if they reported performing ≥600 MET minutes per week.19 Exercise capacity was assessed by patients completing the six-minute walk test (6MWT) according to published recommendations.20 METs were derived by the treadmill from the time, speed and grade at test termination. Grip strength was measured using a hand-grip dynamometer (Jamar 5030 J1, Illinois, United States). The protocol was standardized and the peak value from three trials was recorded.21 For muscular power, patients completed the “get up and go” test.22 After an overnight fast, patients provided blood and urine samples for the measurement of serum/plasma concentrations of creatinine, hemoglobin, glucose and lipids (total cholesterol, LDL cholesterol, and HDL cholesterol) using standard automated techniques. Kidney function was determined as eGFR using the standard MDRD-175 formula. Results are reported as mean ± SD, median (interquartile range [IQR]), or frequencies (%) depending on the distribution of the data. Patients were grouped post hoc into tertiles based on their fitness (VO2 peak) as low, moderate and high fitness. Analysis of variance (ANOVA), the Kruskal Wallis test or the Pearson X2 test, were used as appropriate to assess group differences. Post hoc comparisons were performed using the Fishers Least Significant Difference test. Students paired t-test was used to compare achieved and predicted exercise variables (peak VO2 and predicted maximum HR). An analysis of covariance (ANCOVA) was performed to determine the influence of increasing age on VO2 peak in CKD patients. Univariate correlation (Pearson’s and Spearman Rho) were used to determine the relationship between peak VO2 and covariates (clinical variables – sex, age, history of CVD, type II diabetes, previous myocardial infarction, creatinine, eGFR, albumin, total cholesterol, HDL and LDL cholesterol, hemoglobin, BP, physical activity; vascular variables – AIx75, aPWV, peripheral pulse pressure, central pulse pressure; and cardiac variables – intraventricular relaxation time (IVRT), LAVI, e , s , E/e , ejection fraction, LVMI, EDVI, global longitudinal strain, average strain rate) and are reported in the Supplementary material. Multivariable regression models were developed to evaluate clinical and cardiovascular parameters that were predictors of peak VO2 using three nested models. Standard regression diagnostics were performed and co-linearity tested. Statistical significance was defined as ˛ < 0.05. Statistical analysis was performed using commercially available software (SPSS v 20, SPSS Inc, Chicago, IL).
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2. Results The baseline characteristics of the study participants are depicted in Table 1. Participants were stratified in to fitness tertiles based on their VO2 peak. Traditional cardiovascular risk factors blood pressure and lipid levels, were well controlled in all groups with pharmacotherapy as evidenced by the high use of statins and antihypertensive therapy (Table 1). There were significantly more obese individuals in the low fitness group when compared to moderate and high fit groups, which was supported by a higher proportion of inactive individuals (Table 1). Table 1 indicates the low fitness group were significantly older and more likely to have diabetes or ischemic heart disease than those in the highest group. Overall, only 30% (n = 41) of participants met current physical activity recommendations. The response to exercise is displayed in Table 2. The average VO2 peak was 22.9 ± 6.5 ml/kg/min, which was 17% lower than age predicted (28.1 ± 5.9 ml/kg/min). VO2 peak was significantly lower than predicted in both the low and moderate fitness group (P < 0.001), but not the high fitness group. To control for difference in age between groups, an ANCOVA was performed using age as a covariate. After controlling for differences in age, VO2 peak was still significantly reduced in the low fitness group (P < 0.001). The average peak HR achieved was 148 ± 22 bpm, or 92% of age predicted maximum HR (160 ± 10 bpm). Despite withholding rate limiting medication prior to testing (for >24 hrs), peak HR was below the age-predicted HR value for all groups (P < 0.001). In total 50% (n = 64) of patients failed to achieve 80% of their age predicted heart rate maximum, suggesting chronotropic incompetence. The majority of patients with chronotropic incompetence also fell in the lowest fitness tertile (22%, n = 28), while 21% (n = 21) were in the middle tertile and 12% (n = 15) were in the highest tertile. Systolic BP increased significantly with maximal exercise (137 ± 22 to 179 ± 23 mmHg, P < 0.001), while diastolic BP did not change (82 ± 13 to 83 ± 12 mmHg). There was an inverse correlation between VO2 and HR peak, and age (Supplemental material, Figs. I and II). The low fitness group performed poorly in the functional assessments – 6MWT, and the muscular strength and power tests (Table 2). There was a significantly higher rate of inducible ischemia on exercise stress echo in the low fit group (16%) compared to the high (2%). Systolic function (ejection fraction, s , global longitudinal strain) was well preserved (Table 2). Likewise, traditional measures of diastolic filling were not different, but E/e was significantly higher in the low fitness group. Vascular stiffness (aPWV and AIx75) was also higher in the low fit group when compared to the high fitness group (Table 2). The significant univariate correlates of VO2 peak are presented in the supplemental material (Table I, Figs. III and IV). To determine the differential effects of clinical, vascular and cardiac covariates of VO2 peak, we constructed three multivariate backward regression models (Table 3). Model 1 which, included age, diabetes status, history of CVD, hemoglobin and physical activity levels explained 32% of the variability in VO2 peak (Table 3). Model 2 included the variables from Model 1 and AIx75 and aPWV, which improved the model fit (Model 2: R2 = 0.39, n = 100, P < 0.001). The final model included the addition of cardiac variables (subclinical ischemia, E/e and GLS), which further improved the model fit (Model 3: R2 = 0.41, n = 100, P < 0.001). The incremental value of the vascular and cardiac variables over the clinical variables in predicting VO2 peak was compared. Using the enter method a multivariate model of significant clinical variables (Model 1) plus the addition of variables from Model 2, significantly improved the predictive power of the model by 6.6% (adjusted R2 = 0.40, P = 0.008 for change). The power of the model was improved further by addition of
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the cardiac variable by 5.9% (adjusted R2 = 0.46, P = 0.026 for change). 3. Discussion In this study, we examined the cardiorespiratory and cardiovascular response to peak exercise in CKD patients, and sought to evaluate the relationship between CRF and indicators of cardiovascular health. The main findings of this current study can be summarized as: (1) peak VO2 and HR were markedly reduced compared to age-predicted values in the majority of patients; (2) higher CRF was associated with lower aortic stiffness, AIx and better LV function; (3) the variance in CRF can be largely explained by cardiovascular risk factors (e.g. increasing age, type II diabetes and physical activity). Although we are unable to infer cause and effect, these findings suggest that CKD patients who maintain CRF also benefit from lower cardiovascular burden. Perhaps one of the most striking findings from this current study was the high rate of physical inactivity. Only 30 percent of participants met current physical activity recommendations.19 The number of CKD patients in Australia who meet the recommendations of the physical activity guidelines have dramatically decreased from 50 percent in 2009.23 The decline in physical activity levels is concerning, as it has occurred during a period where the Australian Government has increased public health campaigns to raise awareness of the benefits of being physically active. Given the association between higher levels of physical activity and improved survival in CKD patients,3 interventional studies are urgently required to determine the optimal way to increase physical activity levels and improve fitness in CKD patients, and ultimately determine if these interventions improve survival. Indeed, a 1 MET increase in fitness improves survival by 12 percent,12 while exercise tolerance of >10 METs in individuals with significant burden of subclinical atherosclerosis greatly improved cardiovascular prognosis.24 Interventions that incorporate a multidisciplinary approach will likely be the most effective in increasing physical activity levels due to the high cardiovascular burden and limited level of exercise counseling provided by nephrologists.25 In addition to low levels of physical activity, we found that peak CRF was also markedly impaired. In a small study of 32 patients with CKD (eGFR 29.9 ± 17.0), VO2 peak was found to be reduced compared to age-predicted norms.26 We extend these findings by showing that: (a) peak HR is also significantly reduced below agepredicted values, and (b) like the general population increasing age is associated with a decline in fitness albeit at a faster rate in patients with CKD. The reduction in VO2 peak may partially be explained by the blunted HR response to exercise and thus reduced central component to oxygen delivery. The HR response to dynamic exercise is complex, however a blunted HR response may reflect autonomic disturbances, and is associated with increased mortality and coronary heart disease incidence.27 Maximal HR clearly declines with age,13 however the rate of decline in patients with CKD is greater with the majority of our patients failing to achieve their age-predicted maximum response. Prior studies in ESKD have demonstrated that maximal HR is significantly reduced compared to healthy controls, but is increased following kidney transplantation28 suggesting impaired kidney function influences the central response to exercise. No prior studies have evaluated the relationship between fitness, LV function and arterial stiffness in CKD patients. In this study, patients with lower fitness had significantly higher central arterial stiffness and AIx when compared to the high fit group. Increased arterial stiffness is independent predictors of cardiovascular and all-cause mortality in kidney disease.29 The combination of poor CRF, low physical activity levels and increased arterial
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Table 1 Characteristics of patients grouped by cardiorespiratory fitness tertiles. All (n = 136)
Low fitness (n = 44)
Moderate fitness (n = 46)
High fitness (n = 46)
P ANOVA
Demographics Age (yr) Male sex (%) BMI (kg/m2 ) History CVD, n (%) Diabetes type II, n (%) Current smoker, n (%) Physically active, n (%) Positive ESE, n (%)
60 ± 10 75 (55) 33 ± 7 53 (39) 52 (38) 23 (17) 41 (30) 13 (10)
62 ± 8a 21 (48) 36 ± 6ac 23 (52)a 26 (59)a 8 (18) 9 (20)a 7 (16)a
62 ± 8b 25 (54) 32 ± 7 17 (37) 19 (41)b 7 (15) 10 (22)b 5 (12)
55 ± 11 29 (63) 31 ± 7 13 (28) 7 (15) 8 (17) 22 (48) 1 (2)
0.001 0.96 0.002 0.005 <0.001 0.36 0.02 0.05
Clinical eGFR (ml/min/1.73 m2 ) Hemoglobin (g/L) Glucose (mmol/L) Total cholesterol (mmol/L) HDL-cholesterol (mmol/L) LDL-cholesterol (mmol/L) Systolic BP (mmHg) Diastolic BP (mmHg) Waist circumference (cm) Hip circumference (cm)
40 ± 9 133 ± 15 6.7 ± 3.0 4.5 ± 1.1 1.1 ± 0.4 2.5 ± 0.9 139 ± 21 82 ± 13 110 ± 17 114 ± 13
38 ± 10 126 ± 13a 6.9 ± 2.6 4.4 ± 1.2 1.1 ± 0.5 2.2 ± 0.9a 136 ± 23 77 ± 13ac 118 ± 17ac 120 ± 14ac
41 ± 9 131 ± 14b 7.2 ± 3.7 4.2 ± 1.0b 1.1 ± 0.3 2.3 ± 0.8b 143 ± 17 83 ± 9 108 ± 14 113 ± 12
40 ± 9 140 ± 16 6.0 ± 2.4 4.8 ± 1.1 1.2 ± 0.5 2.8 ± 1.0 138 ± 23 87 ± 13 103 ± 17 108 ± 10
0.31 <0.001 0.27 0.013 0.27 0.003 0.31 <0.001 <0.001 <0.001
Medications Total anti-hypertensive Statin, n (%)
2.6 ± 1.4 93 (68)
3.2 ± 1.3a 33 (75)
2.5 ± 1.5 36 (78)
2.2 ± 1.2 24 (52)
0.003 0.99
Data are mean ± SD and n (%). eGFR, estimated glomerular filtration rate; ESE, exercise stress echocardiogram; HDL, high density lipoprotein; LDL, low density lipoprotein. a indicates significant difference (P < 0.05) between low and high fitness tertiles. b indicates significant difference (P < 0.05) between moderate and high fitness tertiles. c indicates significant difference (P < 0.05) between low and moderate fitness tertiles.
stiffness likely results in a pronounced increase in cardiovascular risk, presenting a patient population who require aggressive risk factor modification. Recent evidence suggests that lifestyle modification that includes exercise training and sodium restriction is clinically efficacious therapeutic interventions for preventing and even treating arterial stiffness.30 Thus, lifestyle interventions
which include exercise training, complemented with dietary interventions could be useful lifestyle strategy to prevent derangement in vascular function in CKD patients and warrant further investigation. In addition to derangement in arterial health, patients with CKD have abnormalities in LV function. We observed an inverse
Table 2 Cardiorespiratory fitness, functional capacity and cardiovascular function across fitness tertiles in CKD patients. All Peak VO2 (ml/kg/min) Peak VO2 (L/min) % of predicted peak VO2 RER Peak HR (bpm) % of HR peak SBP peak (mmHg) DBP peak (mmHg) METs Get up and Go (s) 6MWT (m) Grip strength (kg) E/A Intraventricular relaxation time (cm/s) Deceleration time (cm/s) Diastolic tissue velocity (cm/s) Systolic tissue velocity (cm/s) E/e Left atrial volume index (ml/m2 ) LV end diastolic volume index (ml/m2 ) Left ventricular mass index (g/m2 ) Ejection fraction (%) Global longitudinal strain (%) Strain rate (1/s) aPWV (m/s) AIx 75 Resting HR (bpm)
22.9 2.1 83 1.1 148 89 179 83 7.0 5.4 464 33 1.00 124 238 5.6 6.1 13.1 29 45 101 66 −18 −1.08 9.9 21.1 68
Low fitness ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ±
6.5 0.7 22 0.1 22 13 23 12 3.0 1.5 104 11 0.4 20 41 1.6 1.1 4.7 9 14 28 7 4 0.21 2.8 8.2 11
16.7 1.7 66 1.1 137 83 169 79 4.9 6.2 382 30 1.02 122 247 5.4 6.0 15.2 31 46 102 66 −17 −1.08 10.9 22.4 72
± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ±
1.7 0.4ac 14ac 1.1 21ac 13ac 24ac 10a 1.8ac 1.4ac 98ac 9a 0.5 19 50 1.8 1.3 5.7ac 10 16 29 8 4 0.24 3.3a 7.4a 11
Moderate fitness 21.8 2.0 83 1.1 148 90 180 80 6.6 5.2 475 33 1.00 125 233 5.7 6.2 12.5 28 44 96 65 −18 −1.06 10.2 23.1 67
± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ±
1.3 0.5b 14b 0.1 22b 13 19 17b 2.3b 1.3 76b 10 0.4 22 49 1.3 1.0 3.9 7 14 27 7 3 0.22 2.3b 8.3b 11
High fitness 30.0 2.7 100 1.1 157 93 188 84 9.3 4.7 528 38 0.99 126 236 5.8 6.1 11.7 30 44 106 67 −19 −1.10 8.6 18.0 68
± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ±
5.6 0.6 23 0.9 20 11 23 21 2.9 1.3 86 11 0.3 19 41 1.7 1.0 3.8 9 12 28 7 3 0.2 2.3 8.0 11
P ANOVA na <0.001 <0.001 0.30 <0.001 0.001 0.001 <0.001 <0.001 <0.001 <0.001 0.003 0.89 0.71 0.35 0.53 0.77 0.002 0.10 0.63 0.26 0.45 0.11 0.78 <0.001 0.006 0.094
Data are mean ± SD. aPWV, aortic pulse wave velocity; AIx 75, augmentation index corrected for heart rate; DBP, diastolic blood pressure; E/A, mitral inflow ratio; E/e , estimated LV filling pressures; HR, heart rate; METs, metabolic equivalents; RER, respiratory exchange ratio; SBP, systolic blood pressure; LV, left ventricle. a indicates significant difference (P < 0.05) between low and high fitness groups. b indicates significant difference (P < 0.05) between moderate and high fitness groups. c indicates significant difference (P < 0.05) between low and moderate fitness group.
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Table 3 Multivariable regression models for predictors of cardiorespiratory fitness. Model 1: Clinical (R2 = 0.32, n = 100, P < 0.0001)
Model 2: Clinical + vascular (R2 = 0.39, n = 100, P < 0.0001)
Coefficient
95% CI
P
Age
−0.21
−0.32, −0.09
0.001
0.11
Type II diabetes
−3.14
−5.57, −0.70
0.012
−2.82
Hemoglobin
0.09
0.01, 0.17
0.035
Physical activity
2.81
0.41, 5.21
0.022
Coefficient
Model 3: Clinical + vascular + cardiac (R2 = 0.41, n = 100, P < 0.0001)
95% CI
P
Coefficient
95% CI
P
−0.24, 0.02 −5.18, −0.47
0.08
−0.19
−0.31, −0.08
0.001
0.02
−2.62
−4.98, 0.26
0.09
0.03
0.01, 0.17
0.03
0.09
0.02, 0.17
0.024
2.49
0.31, 4.83
0.026
0.18, 4.80
0.04
2.57
AIx 75
−0.16
−0.30, −0.03
0.02
−0.19
aPWV
−0.49
−0.93, −0.05
0.03
−0.49
Global longitudinal strain
relationship between VO2 peak, estimated LV filling pressures (E/e ) and global longitudinal strain. Elevated LV filling pressure is suggestive of diastolic dysfunction,7 which typically manifests as symptoms of dyspnea on exertion and reduced exercise tolerance. Estimated LV filling pressures (E/e ) were elevated in the low fit group, but this measurement is highly preload dependent and may not accurately reflect LV filling pressures.31 Prior studies have demonstrated that diastolic dysfunction impairs CRF,32,33 thus the association likely contributed to reduced CRF in CKD patients. Moreover, global longitudinal strain, a sensitive indicator of LV systolic function, was also a significant independent predictor of VO2 peak. We speculate that the mild impairment in cardiac function at rest may impair the ability of the LV to optimally deliver blood to the working tissues during exercise, and thus supports the hypothesis of impaired central factors. Individuals with higher levels of fitness have enhanced LV function, through improved LV compliance.34 We have recently shown that 1-year of moderate intensity exercise training improves diastolic function in CKD patients.10 While others have demonstrated that high intensity interval training has greater effect on LV function when compared to moderate intensity exercise in similar patient groups.35 Future work should seek to evaluate the most optimal training intensity to enhance cardiac function in CKD patients. This study has several limitations; we are unable to infer causality between fitness and cardiovascular burden as the relationship could be bi-directional, with poor fitness being driven by the presence of vascular disease and vice versa. We used normative data to compare peak cardiorespiratory fitness and heart rate in CKD patients, which is a limitation of the study. Future studies should include age and gender matched controls, tested under the same conditions. We only included patients with an eGFR of 25–59 ml/min/1.73 m2 , which limits the ability to generalize these findings. The Active Australia questionnaire was used to measure physical activity and whilst the questionnaire is considered to be valid and reliable, and comparable with other commonly used questionnaires,36 the questionnaire measures activity performed in the previous week may not be indicative of usual activity. Assessment of physical activity by patient recall has limitations due to respondent’s inability to accurately recall average frequency and duration of activity performed. We attempted to minimize the influence of rate limiting medications by asking patients to withhold -blockers and non-dihydropyridine calcium channel blockers for 24 h prior to exercise testing; this washout period may be insufficient to normalize the chronotropic response to exercise. In addition, we did not measure blood volume or control diet prior to testing which may have influenced our assessment of cardiac and vascular function. Finally our participants did not perform a familiarization exercise test which may have improved their maximal exercise response.
–
−0.32, −0.06
0.005
–
–
−0.85, −0.13
0.008
4. Conclusion In patients with moderate CKD, the peak cardiorespiratory and HR response to exercise is markedly impaired compared to normative values. Lower CRF is associated with higher cardiovascular burden as evident by the independent relationship between peak VO2 , LV function, arterial stiffness and cardiovascular risk factors. Targeted programs to increase physical activity levels, and ultimately CRF are urgently required. Practical implications • Peak cardiorespiratory fitness and heart rate is markedly reduced in the majority of CKD patients. Consideration must be taken when prescribing exercise intensity based on age-predicted heart rate equations to this patient population. Based on this, we would recommend ratings of perceived exertion as a meaningful indicator of exercise intensity. • Older CKD patients have lower cardiorespiratory fitness and higher cardiovascular burden. These patients in particular would likely benefit significantly from targeted strategies to increase fitness, which would also improve functional capacity and ability to perform activities of daily living. • Overall physical activity levels in CKD patients are extremely low, targeted strategies to encourage physical activity in this patient population are urgently required. Financial disclosures This research was supported by the NHMRC-funded Centre for Clinical Research Excellence – Vascular and Metabolic Health (CCRE), University of Queensland (UQ) and the Department of Nephrology, Princess Alexandra Hospital. Acknowledgements This research was supported by the NHMRC-funded Centre for Clinical Research Excellence – Vascular and Metabolic Health (CCRE), University of Queensland (UQ) (2007000276). The authors would like to thank Karen Sonnenburg and Lisa Ditterich (Research Nurses) for their time and effort in recruiting, and assisting with participants in the trial. The authors also would like to gratefully acknowledge the study participants for volunteering for this research study. Appendix A. Supplementary data Supplementary data associated with this article can be found, in the online version, at doi:10.1016/j.jsams.2014.07.005.
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Please cite this article in press as: Howden EJ, et al. Cardiorespiratory fitness and cardiovascular burden in chronic kidney disease. J Sci Med Sport (2014), http://dx.doi.org/10.1016/j.jsams.2014.07.005