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Age and Ageing Advance Access originally published online on December 6, 2006
Age and Ageing 2007 36(1):57-62; doi:10.1093/ageing/afl127
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Copyright © The Author 2007. Published by Oxford University Press on behalf of the British Geriatrics Society.

Longitudinal Change in Coronary Heart Disease Risk Factors in Older Runners

E. Todd Schroeder1, Steven A. Hawkins1,2, Dan Hyslop1, Alberto F. Vallejo1, Nicole E. Jensky1 and Robert A. Wiswell1

1 Department of Biokinesiology and Physical Therapy, University of Southern California, Los Angeles, CA 90033, USA
2 School of Kinesiology and Nutritional Science, California State University Los Angeles, Los Angeles, CA 90032, USA

Address correspondence to: E. Todd Schroeder. Tel: (323) 442-2498 Fax: (323) 442-1515. Email: eschroed{at}usc.edu


   Abstract
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 Abstract
Introduction
 Methods
 Results
 Discussion
 Key points
 Conflict of interest
 References
 
Background: it is currently not clear how coronary heart disease (CHD) risk factors change over time in chronic exercisers. Therefore, the purpose of this study is to describe the longitudinal change in CHD risk factors in chronically endurance-trained men and women, and to determine the exercise and nutritional factors associated with those respective changes.

Methods and results: ninety-one middle-aged runners (56 male, 35 female) were tested on two occasions approximately 10 years apart (aged 50.8 ± 8.0 versus 60.0 ± 7.9 years at respective visits). Body composition, VO2max, blood pressure (BP) and blood chemistries were measured, and the subjects' self-reported training and nutritional history. Data were analysed by factorial analysis of variance (ANOVA) and multivariate step-wise regression. Among the entire sample, training volume decreased (61.1 ± 28.2 versus 44.7 ± 24.6 km/week, P<0.05) but nutritional variables did not change. Body fat (16.9 ± 5.3% for men versus 21.1 ± 5.3% for women, P<0.05), blood lipids, blood glucose and systolic and diastolic BP all changed negatively over the study duration. These changes occurred similarly in both genders and irrespective of menstrual and hormone replacement status among the women. Lastly, the changes in CHD risk factors were not predicted by change in exercise or nutritional patterns.

Conclusions: despite the maintenance of significant volumes of exercise and the absence of changes in diet, most CHD risk factors demonstrated unfavourable changes over 10 years in chronic men and women runners. However, the absolute values for most CHD risk factors remained better than those reported for sedentary peers of comparable age.

Keywords: endurance exercise, blood lipids, blood pressure, elderly


   Introduction
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 Abstract
 Introduction
Methods
 Results
 Discussion
 Key points
 Conflict of interest
 References
 
The study of older athletes had focussed on describing primary age-related changes in physical fitness and sports performance [1–6], with less attention focussed on health-related changes such as those that reflect coronary heart disease (CHD). While exercise clearly protects against CHD and premature mortality [7, 8], risk is not absent in chronic exercisers, and there is a need to understand the factors that may change over time and contribute to that risk.

The few studies that have described longitudinal changes in CHD risk factors in older athletes [6, 9, 10], reflecting small, men-only samples, have demonstrated little or no change in various CHD risk factors over 7–33 years. It is plausible that CHD risk factors may not change over time in chronic exercisers, as health-related variables are responsive to relatively low volumes and intensities of physical activity [11, 12]. However, cross-sectional research has clearly demonstrated a dose–response relationship between CHD risk factors, exercise volume [13, 14] and exercise intensity [15] in several thousand men and women. Therefore, declines in training volume and intensity, possibly a requisite with advancing age [16], may lead to negative changes in CHD risk factors. Moreover, several studies have suggested that changes in CHD risk factors may reflect primary ageing [17, 18]. It is also possible that CHD risk factors may change differently over time in men and women runners considering the influence of menopause [19].

Therefore, the purpose of this study is to describe the longitudinal change in CHD risk factors in chronically endurance-trained men and women, and to determine the exercise and nutritional factors associated with those respective changes. We hypothesised that CHD risk factors would change negatively over time in these chronic runners, and that these CHD risk factor changes would be related to changes in training, fitness and nutrition.


   Methods
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 Abstract
 Introduction
 Methods
Results
 Discussion
 Key points
 Conflict of interest
 References
 
Subjects
Ninety-one runners (56 male, 35 female) were selected from 232 older athletes (147 male, 85 female) participating in a longitudinal study previously described in Wiswell et al. (2001) [20]. The subjects were tested biannually for various parameters of physical fitness and health. The subjects' first and last visits were selected for comparison in this study. Subjects from the parent study were excluded if they had been tested on only one occasion or had less than five years between their first and last test, had a known cardiovascular disease, including hypertension, at the first visit, or were on medication at either visit that would influence any of the CHD risk factors. All subjects gave written informed consent according to guidelines established by the Institutional Review Board of the University of Southern California.

Anthropometric measures
Subjects reported to the laboratory following a 12-h overnight fast. On arrival in the laboratory, their height was measured with a stadiometer, and weight determined on a calibrated Homs beam-scale. Residual lung volume was assessed using the nitrogen dilution technique [21], and body composition determined by hydrodensitometry [20].

Pre-exercise examination
Resting ECG was recorded on a Hewlett Packard 1500 Electrocardiograph (Palo Alto, CA). Blood pressure (BP) was measured in a supine position using an auscultatory technique. The study physician examined subjects, and an in-dwelling angiocatheter was inserted into a forearm vein. Resting blood samples were drawn into pre-chilled ethylenediaminetetraacetic acid (EDTA) Vacutainer tubes. The samples were immediately centrifuged, pipetted into storage vials and stored at –80°C until analysis.

Maximal aerobic capacity
VO2max was determined using a continuous, incremental protocol on a motorised treadmill. The initial speed and grade were 2.5 mph and 0%, respectively, with increases of 0.5 mph and 2%, respectively, every 2 min of exercise. The test was terminated at the point of subjective exhaustion. The volume of expired air (VE), volume of oxygen consumption (VO2) and volume of carbon dioxide production (VCO2) were determined on an Ametek metabolic system (Pittsburgh, PA).

Blood chemistry
Fasting glucose, triglyceride, total cholesterol and high-density lipoprotein (HDL) cholesterol concentrations were determined from the resting blood sample. Samples were analysed using an Ortho-Clinical Diagnostics Ectachem DT60 (Johnson & Johnson, Rochester, NY). Coefficients of variation for the various assays at 5 mM were 1% for glucose, 1.5% for triglycerides and 2.5% for the total cholesterol and HDL cholesterol assays.

Cardiovascular risk
Estimated risk of CHD was determined using the Framingham Risk equation [22]. This method uses age, HDL cholesterol, total cholesterol, systolic BP, smoking history, diabetes and ECG-related left ventricular hypertrophy (LVH) to predict CHD risk. The risk estimates are reported according to the 10-year probabilities per 100 of developing CHD. The 10-year risk estimate was then compared with the ‘average’ 10-year risk for each subject's age and gender group [22].

Performance
The subjects' endurance training and performance data were self-reported via a questionnaire. The parameters included years of training, distance run per week, days run per week, personal best for 5 km, 10 km and marathon distances and best performance within the preceding year for the same distances. The subjects also recorded cross training in swimming, cycling and/or resistance exercise, as well as any periods of inactivity due to injury/illness since their previous test. The subject responses were confirmed by oral interview on the day of testing.

Dietary history
The subjects were asked to keep a 3-day dietary history prior to testing. Subject responses were confirmed by oral interview on the day of testing. These diet histories were analysed using the Food Processor Plus program (ESHA v. 2, Salem Oregon) for total caloric consumption in kilocalories and nutrient intake in grams, including fat, protein (Pro), carbohydrate (CHO), saturated fat and cholesterol.

Menstrual history
Female subjects completed a menstrual history questionnaire, including age at menarche, current menstrual history, age at menopause (if relevant) and hormone replacement therapy (HRT) use. Self-reported values were confirmed by oral interview.

Statistics
Data were analysed using the Statistical Package for Social Sciences (SPSS) software v. 11.0. Factorial analysis of variance (ANOVA) was used to determine significant main and interaction effects within a 2 (Time) x 2 (Gender) design. Predicted CHD risk was compared to population risk estimates using independent sample t-tests. Differences among women groups based upon hormonal status were determined by one-way ANOVA. Multivariate step-wise regression was used to investigate variables that predicted change in CHD risk factors. Data are reported as mean ± SD, and significance was pre-determined at P<0.05.


   Results
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 Abstract
 Introduction
 Methods
 Results
Discussion
 Key points
 Conflict of interest
 References
 
As no interactions were noted, we report differences between two time points and between genders. The mean age at baseline was 50.8 ± 8.0 years and at post-testing 60.0 ± 7.9 years. Table 1 shows the subject characteristics for the entire cohort. Body mass index (BMI) and body fat changed significantly over time (P<0.05, Table 1), whereas lean body mass (LBM) did not. By gender, men had greater body mass (71.4 ± 7.2 versus 55.4 ± 5.3 kg), height (177.3 ± 6.1 versus 164.1 ± 6.1 cm), LBM (61.0 ± 5.5 versus 43.7 ± 4.0 kg), and lower body fat (14.5 ± 4.1 versus 20.8 ± 4.7%) compared to women (P<0.05).


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  		Table 1. Age, anthropometric and body composition variables at baseline and post-testing (mean ± SD)

 
Exercise training and nutritional variables for the entire sample are presented in Table 2. Training volume, training frequency and fitness parameters all declined significantly (P<0.05), whereas none of the nutritional variables changed. By gender, men had greater oxygen consumption (4.0 ± 0.7 versus 2.6 ± 0.4 l/min, P<0.05) and fitness (56.2 ± 9.3 versus 47.3 ± 7.2 ml/kg/min, P<0.05), while training volume and frequency did not differ. Men also had a greater total caloric (2277 ± 698 versus 1921 ± 564 kcal, P<0.05) and CHO intake (328 ± 122 versus 264 ± 88 g, P<0.05) compared to women. No other dietary variables differed by gender.


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  		Table 2. Exercise training and nutritional variables at baseline and post-testing (mean ± SD)

 
Table 3 demonstrates the CHD risk factor variables for the entire cohort. Total cholesterol, triglycerides, glucose, systolic BP and diastolic BP increased, and HDL cholesterol decreased (P<0.05). Men had significantly lower HDL cholesterol (67 ± 17 versus 82 ± 17 mg/dl, P<0.05), and significantly higher blood glucose (96 ± 12 versus 88 ± 9 mg/dl, P<0.05), systolic BP (128 ± 11 versus 115 ± 10 mmHg, P<0.05), and diastolic BP (78 ± 8 versus 72 ± 8 mmHg, P<0.05) than women. Among men, there were two smokers, two diabetics and six with LVH. No women had diabetes or LVH, nor were there any smokers. Since LVH in chronic runners is considered a physiological and not a pathological adaptation [23], we did not include those with diagnosed LVH at baseline as having evidence of CHD.


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  		Table 3. Coronary heart disease risk factor variables at baseline and post-testing (mean ± SD)

 
Among the women subjects, 11 (42.6 ± 2.3 years at visit 1) were pre-menopausal at both visits and 24 (51.3 ± 7.0 years at visit 1) were post-menopausal at one or both visits. Of the latter, 19 women (52.0 ± 7.6 years) were on HRT at the post visit and 5 (50.8 ± 6.5 years) were not. While differences existed between the pre- and post-menopausal women for several variables (age, weight, LBM, total cholesterol, predicted CHD risk) there were no differences between the post-menopausal groups based on HRT status for any of the reported CHD risk factor variables or CHD risk estimate. Therefore, age appears to be a greater influence then menstrual status in this cohort.

The 10-year Framingham Risk of CHD increased (P<0.001) in the entire cohort from 4.2 ± 3.8% to 9.2 ± 7.2%. Men had significantly greater 10-year predicted CHD risk than women (5.7 ± 4.0% versus 1.7 ± 1.4%, P<0.05). However, the estimated 10-year risk of CHD was significantly lower than the average 10-year risk for age- and gender-matched population norms developed by the original authors [22]. In brief, the authors developed prediction equations for CHD endpoints, which were based on measurements of several known risk factors using 5573 original and offspring subjects from the Framingham Heart Study. For comparison, the calculated population norms for the respective time points were 10.5 ± 6.2% and 16.4 ± 6.9%.

In several multiple step-wise regression analyses, training (fitness, km/week and days/week) and nutritional (total calories, CHO, Pro and Fat intake) and body composition (LBM, % fat) variables were entered as predictors, with the change in CHD risk factors as the dependent variable. For these analyses, gender data were combined and age was normalised using statistical modelling. These analyses revealed that training, nutritional and body composition variables did not predict any of the noted changes in CHD risk factor variables.


   Discussion
Top
 Abstract
 Introduction
 Methods
 Results
 Discussion
Key points
 Conflict of interest
 References
 
This study demonstrates significant changes in CHD risk factors over 10 years in men and women chronic runners. These changes occurred despite their participation in chronic endurance training (≥ three 20-min physical activity sessions per week). While this result contrasts with the previous longitudinal studies of CHD risk factors in runners [6, 10], the observed changes in this study were expected for several reasons. First, although aerobic exercise was continued throughout, both training volume and aerobic fitness declined significantly during the 10-year follow-up. Second, diet was better assessed in this current study compared with previous studies [6, 10]. Lastly, the larger and more diverse sample reported here would be less likely to include genetic predisposition as an explanation of the results [10], and may represent primary age-related changes in CHD risk factors.

CHD risk factors and running appear to exhibit a dose–response relationship up to 80 km/week [13–15]. HDL was directly related to running volume, whereas BP, triglycerides, total cholesterol and adiposity were inversely related to running volume 13, 14. Therefore, we assumed that the 27% reduction in training volume in these subjects would lead to negative changes in CHD risk factors. While the latter did occur, we did not see any association between the respective changes in training volume and CHD risk factors, making the influence of reduced exercise training on the changes in BP and blood chemistries unclear. Furthermore, menopause and the replacement of oestrogen did not appear to influence the change in CHD risk factors in the women runners in this study. This is similar to the reports by Williams [14] and Dreyer et al. [18], suggesting improved lipid profiles in post-menopausal chronic runners regardless of oestrogen replacement status.

The interaction of exercise and nutrition has been shown to have a strong influence on CHD risk factors compared to exercise alone [24]. Therefore, changes in nutritional patterns could confound the interpretation of how exercise influences blood chemistries and body composition with age. In fact, Mengelkoch et al. suggest that favourable changes in dietary habits may be an explanation for the improved total cholesterol profile noted in their athletes [10]. In this current study, dietary habits of the athletes compare favourably with established healthy eating patterns [25] and did not change over the course of the study. Moreover, there were no associations between change in dietary habits and change in CHD risk factors. Therefore, as with exercise training, it is unclear what impact nutritional changes have had on the results from this study.

It could be argued that the increases in CHD risk factors in these older runners reflect primary age-related changes not influenced by lifestyle patterns such as exercise and nutrition [17]. This would be consistent with reported patterns for age-related loss of fitness and performance variables [1–6]. However, exercise clearly establishes healthier profiles that ultimately increase functional capacity at most ages and reduces the lifetime risk of disease and disability. These chronic exercisers demonstrated better HDL cholesterol, triglyceride and BP profiles than reported in the literature for sedentary adults of similar age [8, 26]. This is consistent with previous reports comparing chronic exercisers to sedentary adults [27, 28].

Predicted CHD risk increased over time in this group but remained lower than sedentary normative values. Indeed, at baseline ({approx} 50 years of age) and post-testing ({approx} 60 years of age), the predicted CHD risk of this group was approximately half that of age- and gender-matched population norms. We anticipated this outcome, since exercise is well known to protect against CHD and premature mortality [7, 8]. The reduced risk reflects the high fitness level of this cohort (VO2max{approx} 45 ml/kg/·min at 60 years of age) compared to non-athletes of similar age (VO2max{approx} 30 ml/kg/·min), as well as favourable CHD risk factor profiles. Of interest, at pre-testing it was predicted that 4.5% of the athletes would develop CHD within 10 years and no athletes in our study were diagnosed or demonstrated evidence of CHD during follow-up.

We excluded approximately 90 subjects from our analyses that were part of the cohort from the parent study, a large number of whom had ceased exercise training. However, most of them refused to continue in the study after stopping exercise, and we did not have a large enough sample to include these individuals in the analyses, which limits the conclusion that changes may reflect primary ageing. This conclusion is also limited by lack of a sedentary comparison group [20]. Unfortunately, time and financial constraints limited our ability to add sedentary subjects during the course of the study. While we have data from sedentary older subjects collected for other studies during the same time period, these data do not constitute an acceptable comparison group for the large number of master athletes.

In conclusion, despite the maintenance of significant volumes of exercise and the absence of changes in diet, most CHD risk factors demonstrated unfavourable changes over 10 years in chronic men and women runners. However, the absolute values for most CHD risk factors remained higher than those reported for sedentary peers of comparable age.


   Key points
Top
 Abstract
 Introduction
 Methods
 Results
 Discussion
 Key points
Conflict of interest
 References
 

  • Changes in CHD risk occur with ageing despite chronic exercise.
  • CHD risk factors remain better in chronic exercisers compared to their peers.


   Acknowledgements
 
The authors wish to thank all the students and athletes who have participated over the past 18 years in this study at the University of Southern California. Funding provided by the Pickford Foundation, Malibu, CA, and the R.M. Wadt Memorial Research Fund, Los Angeles, CA.


   Conflict of interest
Top
 Abstract
 Introduction
 Methods
 Results
 Discussion
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 Conflict of interest
References
 
All contributing authors have no conflicts of interest with the information provided in this manuscript, nor do they have any financial interest with competing companies.


   References
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 Abstract
 Introduction
 Methods
 Results
 Discussion
 Key points
 Conflict of interest
 References
 

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Received 1 November 2005; accepted in revised form 1 September 2006.


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