Contribution of cardiorespiratory fitness, relative to traditional cardiovascular disease risk factors, to common carotid intima–media thickness

Authors

  • J. Scholl,

    1. Dr. Scholl Prevention First GmbH, Private Practice for Preventive Medicine, Ruedesheim am Rhein, Germany
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  • M. L. Bots,

    1. Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht, The Netherlands
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  • S. A. E. Peters

    Corresponding author
    1. Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht, The Netherlands
    • Correspondence: Sanne A. E. Peters, Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Stratenum 6.131, Heidelberglaan 100, 3584 CX, Utrecht, The Netherlands.

      (fax: +31-(0)-88-75-68099; e-mail: s.a.e.peters@umcutrecht.nl).

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Abstract

Background

Studies have suggested that being slightly overweight but fit is better for cardiovascular health than being somewhat leaner but unfit. Here, we sought to determine the contribution of cardiorespiratory fitness (CRF), relative to the presence of risk factors, to common carotid intima–media thickness (CIMT), a measurement of atherosclerosis and cardiovascular disease risk.

Methods

Data were analysed from a cohort of 7300 German employed individuals aged 46 (±7) years who participated in a preventive health check-up at a specialized prevention centre. In addition to traditional cardiovascular disease risk factor assessment, participants performed an exercise test with spirometry to exhaustion, and common CIMT was measured. Individuals were defined as being fit or unfit based on the median age- and sex-specific relative maximum oxygen consumption.

Results

In a multivariable analysis, there was a strong inverse association between CRF and common CIMT. Individuals who were considered fit and did not have any cardiovascular disease risk factors had the lowest common CIMT values (reference group). Those who were unfit and had an increased risk factor level always had the highest common CIMT values. Good CRF partly compensated for the increased common CIMT due to a risk factor. However, unfit individuals without increased risk factor levels had a common CIMT that was not significantly different from that of the reference group, whereas fit individuals who smoked, had a high body mass index, a low HDL cholesterol concentration or a high systolic blood pressure had an increase in common CIMT.

Conclusion

Cardiorespiratory fitness is a major determinant of common CIMT. Improved CRF does slightly, but not completely, abolish the adverse consequences of cardiovascular disease risk factors on common CIMT.

Introduction

There is considerable evidence to show that physical inactivity and poor cardiorespiratory fitness linearly relate to increased risk of cardiovascular disease [1-3]. Furthermore, improved CRF has been associated with reduced risk of cardiovascular events [4]. It has been reported that physical activity and CRF mainly lead to cardiovascular benefit through an influence on established cardiovascular disease risk factors and partly through an effect on the atherosclerosis process [4, 5].

The assessment of carotid plaques and carotid intima–media thickness (CIMT) using ultrasound is an established and valid method to study the atherosclerosis process and to evaluate the cardiovascular disease risk in large cohorts [6]. Several cross-sectional studies, mostly conducted in middle-aged and older individuals, have demonstrated that physical inactivity or reduced CRF is associated with increased CIMT and the presence of carotid plaque [5, 7, 8]. Furthermore, in a study of middle-aged men, good CRF was found to be associated with a slower 4-year progression of early atherosclerosis [9]. The contribution of the presence of traditional cardiovascular disease risk factors to increased atherosclerosis, relative to the contribution of good CRF, has not been widely studied. However, the findings of some studies have suggested that good CRF might eliminate the detrimental effects of other risk factors in such a way that, for example, it might be better in terms of cardiovascular disease risk to be slightly overweight but fit than lean but unfit [10].

The aim of this study was to determine the contribution of CRF, relative to the presence of various cardiovascular disease risk factors, to common CIMT using data from a large cohort of 7300, healthy, middle-aged and employed subjects.

Methods

Study population

The study population consisted of 4688 male and 2612 female employees of German companies who voluntarily participated in a comprehensive occupational health screen between 2002 and 2012. Overall, 40–60% of those eligible to participate did so, depending on the promotion of the programmes within the company. In general, companies set a minimum age of 35 or 40 years for participants; the mean age of the present cohort was 46 years (range 35–65 years). The examinations were performed at one of three Prevention First Centers: Ruedesheim/Rhein (2002–2012), Frankfurt/Main (2006–2012) or Munich (2008–2012). All health screens including fitness assessment and CIMT measurement were performed by physicians specializing in preventive medicine.

Risk factor assessment

All participants were asked to complete a questionnaire concerning family history, personal history, current complaints, smoking status, vaccination status, diet (food frequency questionnaire) and exercise (Harvard Alumni Health Study questionnaire). A blood sample was collected after an overnight fast. Serum levels of total cholesterol, HDL cholesterol and triglycerides were measured by photometry. LDL cholesterol was calculated using the Friedewald formula, unless the concentration of triglycerides was >400 mg dL−1. Weight, height, waist circumference, body fat content and blood pressure (after resting for 5 min) were measured and a resting ECG and a lung function test were performed.

Cardiorespiratory fitness

Relative maximum oxygen consumption (VO2max; in mL kg−1 min−1), defined as VO2max (i.e., at the time of exhaustion) divided by body weight, was used as a measure of CRF. All participants performed a spirometry exercise test with the aim of reaching exhaustion within 10–15 min and thus measure VO2max. Participants were advised not to exercise for 2 days before the test. Maximum exhaustion was defined as a respiratory exchange ratio of >1.1 and/or a lactate concentration of >8 mmol L−1; according to this definition, exhaustion was reached in >95% of exercise tests. Depending on the prearranged health screens with individual companies, individuals did or did not get a lactate measurement. When only an exercise test was performed, a ramp protocol was used to reach exhaustion. This ramp protocol comprised 3 min of low-level pedalling and a low workload initially of 10–25 W with an increment of 10–25 W min−1. In the case of an exercise test with lactate measurement, we used an exercise protocol between four and six steps each of 3-min duration to reach exhaustion. In both the exercise only and the exercise plus lactate measurement protocols, the increase in workload was individualized according to the participant′s expected fitness level, and the protocol type did not influence exhaustion or VO2max measurement.

Carotid ultrasound measurements

Participants were placed in the supine position for manual measurements of the maximum CIMT on the posterior wall of the right and the left common carotid artery (CCA), 1 centimetre before the beginning of the carotid bulb. From 2009 onwards, common CIMT was specifically measured in diastole. The mean of the left and right maximum CIMT was recorded as an individual's ‘MeanMax’ common CIMT. Carotid plaques (defined as CIMT ≥ 1.4 mm) were, if present, not included in the measurement of common CIMT. Intra-observer variability of common CIMT measurements was 0.03 mm, and interobserver variability was 0.03–0.05 mm.

Statistical analyses

Baseline characteristics of the study population were determined overall, and in quartiles of relative VO2max. MeanMax common CIMT was determined in age- and sex-specific quartiles of relative VO2max. Linear regression analyses were used to examine the relationships between common CIMT and relative VO2max. Two levels of adjustment for potential confounders were used. The first model assessed age- and sex-adjusted associations between CIMT and relative VO2max, and in the second model, adjustments were made for age, sex, systolic blood pressure (SBP), smoking, LDL cholesterol, HDL cholesterol, total cholesterol, body mass index (BMI) and diabetes. We performed stratified analyses to evaluate whether the relation between MeanMax common CIMT and relative VO2max differed by age, sex, BMI, smoking status, SBP or cholesterol levels. The joint contribution of age- and sex-specific VO2max and traditional cardiovascular disease risk factors to maximum common CIMT was examined in four mutually exclusive groups: (i) a VO2max of or below the age- and sex-specific median and low-risk factor levels, (ii) a VO2max of or below the age- and sex-specific median and high-risk factor levels, (iii) a VO2max above the age- and sex-specific median and low-risk factor levels and (iv) a VO2max above the age- and sex-specific median and high-risk factor levels. High-risk factor levels were defined as BMI > 25 kg m−2, current smoking, SBP > 140 mmHg, HDL cholesterol < 40 mg dL−1 in women and <50 mg dL−1 in men, LDL cholesterol > 130 mg dL−1 and total cholesterol > 200 mg dL−1. Statistical analyses were performed using r statistical software (R Foundation for Statistical Computing, Vienna, Austria), and a two-sided P-value of 0.05 was considered statistically significant.

Results

Table 1 shows the baseline characteristics of the 7300 individuals (36% women) included in this study by sex and by quartile of relative VO2max. Mean (SD) relative VO2max was 35 (8) mL kg−1 min−1 in men and 28 (7) mL kg−1 min−1 in women, and mean (SD) MeanMax common CIMT was 0.72 (0.14) and 0.66 (0.12) mm, respectively. Age- and sex-specific quartiles of relative VO2max and the corresponding MeanMax common CIMT values are presented in Table S2 and Fig. 1.

Table 1. Baseline characteristics of the studied population by sex
 Male (= 4688)Female (= 2612)
  1. BMI, body mass index; VO2max, maximum oxygen consumption; MeanMax CIMT, mean of the left and right maximum carotid intima–media thickness.

  2. Data are presented as mean (SD), unless otherwise stated.

Age, years46 (7)46 (7)
BMI, kg m−226 (4)25 (5)
LDL cholesterol, mg dL−1136 (34)125 (34)
HDL cholesterol, mg dL−154 (13)68 (16)
Total cholesterol, mg dL−1217 (39)213 (39)
Triglycerides, mg dL−1136 (80)101 (51)
Systolic blood pressure, mmHg128 (16)121 (17)
Diastolic blood pressure, mmHg83 (9)79 (9)
Current smoker,%1416
Diabetes,%21
VO2max, L min−13.0 (0.6)1.9 (0.4)
Relative VO2max, mL kg−1 min−135 (7.7)28 (6.7)
MeanMax CIMT, mm0.72 (0.14)0.66 (0.12)
Carotid plaques,%189
Figure 1.

Mean of the left and right maximum CIMT (MeanMax CIMT) in age- and sex-specific quartiles of relative maximum oxygen consumption (VO2max). Data are presented as mean (SD), unless otherwise stated. VO2max, maximum oxygen consumption; MeanMax CIMT, mean of the left and right maximum carotid intima–media thickness; Q, quartile.

Relative VO2max and common CIMT

Table 2 shows the relation between relative VO2max and common CIMT overall and in groups stratified by risk factor levels. In the age- and sex-adjusted analyses, there was a statistically significant association between common CIMT and relative VO2max: a higher relative VO2max (indicating a greater CRF) was related to a reduced common CIMT (−0.026 mm per unit increase [95% confidence interval (CI) −0.032; −0.020]). This inverse relationship was stronger in individuals with a high BMI, with a low HDL cholesterol concentration and in smokers compared with those with a low BMI, with a high HDL cholesterol concentration and in nonsmokers, respectively.

Table 2. Relation between CIMT and relative VO2max: overall and in subgroups
 Adjusted for age and sexMultivariable adjustment
Beta (95% CI)P-value for interactionBeta (95% CI)P-value for interaction
  1. BMI, body mass index; VO2max, maximum oxygen consumption; MeanMax CIMT, mean of the left and right maximum carotid intima–media thickness; CI, confidence interval.

  2. Multivariable-adjusted model includes adjustments for age, sex, SBP, systolic blood pressure, smoking, LDL cholesterol, HDL cholesterol, total cholesterol, BMI and diabetes. Beta (95% CI) reflects the change in MeanMax common CIMT with one unit increase in cardiorespiratory fitness.

Overall−0.026 (−0.030; −0.023)−0.007 (−0.012; −0.003)
BMI
≤25−0.008 (−0.013; −0.002)<0.001−0.002 (−0.007; 0.003)0.002
>25−0.030 (−0.036; −0.023) −0.013 (−0.020; −0.006) 
Current smoker
No−0.022 (−0.026; −0.018)0.003−0.005 (−0.010; 0.000)0.01
Yes−0.042 (−0.053; −0.030) −0.022 (−0.036; −0.008) 
Systolic blood pressure
≤140−0.022 (−0.026; −0.018)0.06−0.006 (−0.011; −0.002)0.50
>140−0.036 (−0.050; −0.022) −0.011 (−0.028; 0.007) 
LDL cholesterol
≤130−0.024 (−0.029; −0.020)0.06−0.005 (−0.010; 0.001)0.12
>130−0.028 (−0.034; −0.022) −0.010 (−0.017; −0.003) 
HDL cholesterol

>40 (women)

>50 (men)

0.020 (−0.024; −0.016)<0.0010.005 (−0.010; −0.001)0.02

≤40 (women)

≤50 (men)

−0.034 (−0.043; −0.026) −0.010 (−0.021; 0.001) 
Total cholesterol
≤200−0.029 (−0.034; −0.023)0.99−0.009 (−0.016; −0.003)0.90
>200−0.025 (−0.030; −0.019) −0.006 (−0.012; 0.000) 

The inverse association between relative VO2max and common CIMT was attenuated, but remained statistically significant, in the multivariable model [−0.007 mm per unit increase in CRF (95% CI −0.012; −0.003)]. Moreover, the differences in the association between relative VO2max and common CIMT were still present for those with a low versus a high BMI (P for interaction = 0.002), for nonsmokers versus smokers (P for interaction = 0.01) and for those with a high versus a low HDL cholesterol concentration (P for interaction = 0.02).

Quartiles of relative VO2max and common CIMT

Table 3 shows the incremental relation between relative VO2max and common CIMT in quartiles of VO2max. When compared with the lowest quartile of relative VO2max (i.e., representing the lowest CRF), common CIMT was significantly reduced in those in the second, third and fourth age- and sex-adjusted quartiles. Multivariable adjustment attenuated these associations; however, CIMT was reduced in individuals in the second, third and fourth quartiles by 0.090 mm (95% CI −0.169; −0.011), 0.130 mm (95% CI −0.216; −0.043) and 0.147 mm (95% CI −0.242; −0.051) compared with those in the first quartile, respectively.

Table 3. Relationship between common CIMT and quartiles of relative VO2max
 Adjusted for age and sexMultivariable adjustment
Beta (95% CI)Beta (95% CI)
  1. VO2max, maximum oxygen consumption; common CIMT, mean of the left and right maximum carotid intima–media thickness; CI, confidence interval; Q, quartile.

  2. Multivariable-adjusted model includes adjustments for age, sex, systolic blood pressure, smoking, LDL cholesterol, HDL cholesterol, total cholesterol, body mass index and diabetes. Beta (95% CI) reflects the difference in MeanMax CIMT (in mm) between the quartile-specific value and the value in the first quartile.

Q1ReferenceReference
Q2−0.284 (−0.360; −0.208)−0.090 (−0.169; −0.011)
Q3−0.420 (−0.499; −0.341)−0.130 (−0.216; −0.043)
Q4−0.535 (−0.619; −0.451)−0.147 (−0.242; −0.051)

Combined effect of VO2max and cardiovascular disease risk factors on common CIMT

The mean differences in MeanMax common CIMT between groups with the combination of either high or low levels of relative VO2max and cardiovascular disease risk factors relative to fit subjects with a low-risk factor level are shown in Table 4. In comparison with those who were fit and had a low-risk factor level, those who were unfit and had a high-risk factor level had a considerably greater common CIMT in the age- and sex-adjusted models for all risk factors examined. These associations were attenuated after adjustment for other cardiovascular disease risk factors, but remained statistically significant in individuals who were unfit (i.e., had a low VO2max) and had a high BMI, an increased SBP, a low HDL cholesterol level or who smoked.

Table 4. Mean differences in common CIMT between groups by fitness and risk factor level
 Adjusted for age and sexMultivariable adjustment
Beta (95% CI)Beta (95% CI)
  1. BMI, body mass index; common CIMT, mean of the left and right maximum common carotid intima–media thickness; systolic blood pressure; CI, confidence interval; TC, total cholesterol.

  2. The values represent the difference in common CIMT (in mm) between groups, with those who are fit and have a low-risk factor level as the reference group. Multivariable-adjusted model includes adjustments for age, sex, systolic blood pressure, smoking, LDL cholesterol, HDL cholesterol, total cholesterol, BMI and diabetes.

Unfit – low BMI0.100 (0.017; 0.182)0.039 (−0.044; 0.122)
Fit – high BMI0.248 (0.168; 0.328)0.145 (0.063; 0.226)
Unfit – high BMI0.511 (0.445; 0.577)0.310 (0.238; 0.382)
Unfit – nonsmoker0.255 (0.196; 0.314)0.035 (−0.028; 0.099)
Fit – smoker0.239 (0.120; 0.358)0.239 (0.121; 0.357)
Unfit – smoker0.632 (0.536; 0.727)0.416 (0.317; 0.514)
Unfit – low SBP0.261 (0.203; 0.318)0.053 (−0.009; 0.115)
Fit – high SBP0.375 (0.238; 0.512)0.302 (0.165; 0.438)
Unfit – high SBP0.715 (0.618; 0.812)0.377 (0.271; 0.483)
Unfit – high HDL0.241 (0.177; 0.304)0.023 (−0.044; 0.090)
Fit – low HDL0.194 (0.098; 0.289)0.105 (0.009; 0.201)
Unfit – low HDL0.526 (0.445; 0.606)0.240 (0.151; 0.328)
Unfit – low LDL0.261 (0.186; 0.337)0.004 (−0.075; 0.083)
Fit – high LDL0.123 (0.046; 0.199)−0.011 (−0.106; 0.084)
Unfit – high LDL0.438 (0.364; 0.512)0.092 (−0.006; 0.191)
Unfit – low TC0.315 (0.226; 0.404)0.058 (−0.034; 0.149)
Fit – high TC0.115 (0.039; 0.191)0.001 (−0.091; 0.093)
Unfit – high TC0.399 (0.323; 0.476)0.056 (−0.040; 0.152)

Compared with the reference group, the magnitude of the increase in common CIMT was smaller in those who were unfit but had no cardiovascular disease risk factors than in the group of fit individuals with one or more risk factors. Common CIMT was not significantly increased in unfit individuals with low-risk factor levels, whereas it was significantly increased in fit individuals with a high BMI, high SBP or low HDL cholesterol level or in smokers. In individuals who were unfit but had a low BMI, common CIMT was not statistically different from that of the reference group [0.039 mm (95% CI −0.044; 0.122)], whereas common CIMT was significantly increased in those who were fit but had a high BMI [0.145 mm (95% CI 0.063; 0.382)]. Similarly, in unfit individuals with a high HDL cholesterol level, common CIMT was not significantly greater than in the reference group [0.023 mm (95% CI −0.044; 0.090)], but was increased by 0.105 mm (95% CI 0.009; 0.201) in fit individuals with a low HDL cholesterol level. These differences were even more pronounced for smoking and high SBP levels; those who were unfit but either did not smoke or had a low SBP did not have a significantly increased common CIMT, whereas fit individuals who either smoked or had a high SBP had common CIMT values that were higher by 0.239 mm (95% CI 0.121; 0.357) and 0.302 mm (95% CI 0.165; 0.438), respectively, than fit individuals without these risk factors.

Discussion

We evaluated the contribution of CRF, relative to traditional risk factors, to common CIMT in a population of healthy middle-aged individuals. Individuals who were considered to be fit and did not have a cardiovascular disease risk factor had the lowest common CIMT values, whereas those who were unfit and had an increased cardiovascular risk factor had the highest values of common CIMT. An improved CRF did slightly, but not completely, compensate for the increased CIMT due to a risk factor, especially amongst individuals who were fit but smoked, had an increased SBP or low HDL cholesterol concentration or were overweight.

Our results are consistent with and confirm the abundant evidence suggesting incremental and strong relationships between CRF, CIMT and risk of cardiovascular events, independent of established risk factors [2-5, 8, 11-14]. In addition, we provide further evidence by showing that the magnitude of the relationships is higher in individuals with compared to those without risk factors. A similar finding has been reported by Kokkinos and co-workers in a study including 4631 individuals with hypertension [15]. The authors demonstrated that the magnitude of the reduction in mortality related to increased exercise was more pronounced amongst those with risk factors for cardiovascular disease as compared to those with no risk factors. Although an increase in CRF has previously been shown to have health benefits [4], our findings further support recommendations to increase physical activity especially in those at high cardiovascular disease risk [16].

The findings of several studies have provided support for the notion that an increase in CRF may outweigh the adverse effect of cardiovascular disease risk factors [10, 17, 18]. Of note, fitness has consistently been shown to attenuate mortality risk regardless of body weight or any other risk factor. Faselis and co-workers showed much lower mortality rates in those who were overweight but fit compared with unfit individuals of normal weight [10]. Furthermore, results from the Nurses' Health Study showed that the mortality risk associated with obesity was attenuated, but not totally eliminated, by higher levels of physical activity. Similarly, being lean did not completely counteract the increased risk associated with being physically inactive [17, 18]. Our findings are in agreement with the results from the Nurses' Health Study, as we showed that increased CRF attenuated but did not abolish the adverse effect of risk factors.

Practical impact of the present results

The present results in conjunction with those reported by others support the most recent 2013 American Heart Association/American College of Cardiology guidelines on lifestyle management to reduce cardiovascular disease risk [19]. The guidelines recommend that individuals should be encouraged to practise ‘heart healthy’ lifestyle behaviours, primarily through eating a healthy diet, engaging in moderate to vigorous physical activity and aiming to achieve and maintain a healthy weight. More specifically, with regard to physical activity, it is recommended that individuals have at least 2.5 h per week of moderate intensity or 1.25 h per week of vigorous intensity aerobic physical activity, or an equivalent combination, preferably in episodes of at least 10 min, throughout the week. Our findings further support the initiative to prescribe increased physical activity as a measure to reduce cardiovascular disease risk in society. Yet, to include prescription of physical activity within routine clinical practice, combined coordinated efforts are needed from patients, physicians, society and governments to address the global health problem of physical inactivity [16, 20, 21].

Strengths and limitations

The major strength of this study is that it was conducted in a comparatively young and healthy group of individuals and is amongst the largest to date to examine the relationship between CRF and common CIMT, thus allowing for detailed subgroup evaluations. However, some limitations should be considered. First, this study was cross-sectional in nature and no data on clinical events were available. Secondly, individuals were classified as being either fit or unfit based on the age- and sex-specific median values of relative VO2max of the study cohort. This classification is arbitrary and may not fully acknowledge that CRF is a continuum, nor may it be directly applicable to other populations. These limitations, however, do not invalidate our findings. As CRF provides a valid estimate of recent patterns of physical activity, it may be that some participants had recently started to improve their CRF; therefore, the potential effect of CRF on CIMT may be underestimated. Similarly, individuals who were temporarily less active than previously might have performed less well in the exercise test resulting in a lower CRF and a weaker relation between CRF and CIMT. In addition, underestimation of the magnitude of the relations may also result from a difference in measurement of common CIMT during the study; from 2009 onwards, common CIMT was specifically measured during diastole, which was not the case for earlier measurements [6]. This is important because there is a difference in absolute value of common CIMT between systolic and diastolic measurements [22]. However, the measurement protocol was systematically changed in all individuals, irrespective of CRF, thus leading to random misclassification.

Conclusion

Cardiorespiratory fitness is an important and independent determinant of common CIMT. Improved CRF does not appear to completely abolish the adverse effects of cardiovascular disease risk factors on common CIMT.

Conflict of interest statement

The authors have no conflict of interest to declare.

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