Cardiorespiratory Fitness in Young Adulthood and the Development of Cardiovascular Disease Risk Factors FREE

Numerous clinical investigations have established a strong association between low cardiorespiratory fitness and mortality.^1- 7 Cardiovascular diseases (CVDs) account for a large proportion of mortality in adults older than 45 years.⁸ Numerous risk factors for CVD, including hypertension, diabetes, and hypercholesterolemia, are suspected to be influenced by fitness,^9- 13 and these factors may mediate the association between low fitness and mortality. However, this proposed association is complex because fitness modifies body mass, which is also implicated in the development of CVD risk factors. While previous research acknowledges the contribution of these risk factors on mortality, relatively few studies have investigated the role of objectively determined fitness on the development of CVD risk factors in healthy adults.

We investigated whether low cardiorespiratory fitness in young adults (hereafter referred to as "fitness"), estimated by shorter duration on an exercise treadmill test, was associated with the development of CVD risk factors (ie, hypertension, diabetes, the metabolic syndrome, hypercholesterolemia) independently or in association with obesity. Among participants who repeated the exercise test 7 years after baseline, we examined whether improving fitness was associated with a risk reduction.

The Coronary Artery Risk Development in Young Adults (CARDIA) study is a multicenter longitudinal cohort study designed to investigate the development of coronary heart disease risk factors in young adults. Black and white men and women aged 18 to 30 years from 4 geographic areas (Birmingham, Ala; Chicago, Ill; Minneapolis, Minn; and Oakland, Calif) were recruited and examined in 1985-1986 (n = 5115).¹⁴ Participants were reexamined at years 2 (n = 3800 [84.7%]), 5 (n = 4177 [93.1%]), 7 (n = 3922 [87.4%]), 10 (n = 3804 [84.8%]), and 15 (n = 3550 [79.1%]). The majority of participants returned to all 5 follow-up examinations (n = 2728 [60.8%]), while 935 (20.8%), 445 (9.9%), 159 (3.5%), and 220 (4.9%) returned to 4, 3, 2, and 1 examination, respectively. At year 15, there were 132 verified deaths in this cohort. The top 3 causes of mortality were AIDS (32%), homicide (17%), and unintentional injury (11%), with only 3% due to coronary heart disease (3 definite coronary heart disease and 1 definite stroke).

We excluded participants who did not complete the exercise treadmill test at baseline (n = 214),¹⁵ women who were pregnant at baseline (n = 3), participants who used β-blockers at baseline (n = 2), and participants who did not return to follow-up examinations after year 2 (n = 409). In this cohort of 4487, participants with a given prevalent condition under study were excluded from incident analyses of that risk factor. Analyses of incident hypertension, diabetes, the metabolic syndrome, and hypercholesterolemia included 4392, 4464, 4272, and 4126 participants, respectively. The study was approved by the institutional review board and written informed consent was obtained from all participants.

Fitness Assessment. All medically eligible participants underwent a graded symptom-limited maximal exercise test according to a modified Balke protocol.¹⁵ Participants were ineligible for the following reasons: history of ischemic heart disease, use of cardiovascular medications other than antihypertensives, blood pressure (BP) greater than 160/90 mm Hg, or febrile illness. The test included up to nine 2-minute stages of progressively increasing difficulty. Participants were encouraged to exercise as long as possible to maximal exertion. At the end of each stage, heart rate and BP were measured.

Fitness was determined based on the duration of the treadmill test. Participants in the lowest quintile (women: 0.2 to ≤6.4 minutes; men: 1.9 to ≤10 minutes) of sex-specific duration of testing were classified as in the low fitness category, participants in the 20th to 60th percentile (women: 6.4-8.6 minutes; men: 10-12 minutes) were classified as moderately fit, and participants above the 60th percentile of test duration were classified as highly fit. These cutpoints were selected to emphasize the group at highest risk (<20th percentile) and have been used in previous studies of the association between fitness and mortality.^5,16 Resting heart rate was measured in the seated position. Heart rate increase from resting to maximum was calculated as the difference between the standing heart rate prior to the treadmill test and the maximum heart rate during the test. At each stage, participants were asked to rate their level of exertion on the Borg scale.¹⁷ Metabolic equivalent (MET) rate of energy expenditure at peak exercise (equal to a multiple of oxygen consumption of 3.5 mL × kg⁻¹× min⁻¹) was estimated.

Fitness testing was conducted according to the same protocol at year 7 (1992-1993) in 2478 participants from the Chicago, Birmingham, and Oakland study sites (participants from Minnesota were not included because of a protocol violation).¹⁸ In this subset of participants, changes in fitness were calculated as the difference in test duration between the year 7 and baseline examinations. Fitness change was categorized by approximate quintiles in the full sample, the lowest 20% decreased duration (−12 to −2.4 minutes), the middle 20th to 80th percentile (−2.4 to 0.4 minutes) remained stable, and the uppermost 20% increased duration (0.4-8.9 minutes).

Other Measurements. Participants were asked to fast for at least 12 hours prior to examination and avoid smoking or engaging in heavy physical activity for at least 2 hours before the examination. Laboratory measurements of glucose and lipids^19- 20 were collected according to standardized CARDIA procedures¹⁴ and processed at central laboratories. After 5 minutes of rest, BP was measured from participants in the seated position 3 times at 1-minute intervals; the average of the last 2 measurements was used. Age, race, education, cigarette smoking status, medication use, and first-degree family history of hypertension, diabetes, and premature (age <60 years) myocardial infarction were ascertained by interview. Height, weight, and waist circumference (the average of 2 measurements at the minimum waist girth) were measured on participants in light examination clothes and no shoes. Body mass index (BMI) was calculated as the ratio of weight (kilograms) to standing height (meters) squared (kg/m²); obesity was defined as BMI of at least 30. Self-reported physical activity was assessed by a validated interview–administered questionnaire; activity scores were computed by multiplying the frequency of participation by intensity of the activity.²¹

CVD Risk Factors. The incidence of each CVD risk factor was identified as a diagnosis of that condition at any follow-up examination among the sample free from that condition at baseline. Diabetes was defined as a fasting glucose level of at least 126 mg/dL (7.0 mmol/L) at examinations 7, 10, or 15 (glucose was not assayed in years 2 and 5) or the self-reported use of oral hypoglycemic medications or insulin at any examination. Hypertension was defined as systolic BP of at least 140 mm Hg, diastolic BP of at least 90 mm Hg, or antihypertensive medication use.²²

Participants were classified as having the metabolic syndrome according to the National Cholesterol Education Program/Adult Treatment Panel III definition of at least 3 of the following: glucose level at least 110 mg/dL (6.1 mmol/L); high BP (systolic BP ≥130 or diastolic BP ≥85 mm Hg); waist circumference greater than 88 cm (women) or greater than 102 cm (men); triglyceride level at least 150 mg/dL (1.70 mmol/L); high-density lipoprotein (HDL) cholesterol level less than 50 mg/dL (1.30 mmol/L) in women or less than 40 mg/dL (1.04 mmol/L) in men.²³ Participants who reported using diabetes or hypertension control medications were classified as having high glucose or high BP components of the metabolic syndrome. Hypercholesterolemia was defined as low-density lipoprotein (LDL) level of at least 160 mg/dL (4.14 mmol/L) at any follow-up examination.²³

Baseline characteristics of the population were estimated by fitness category for men and women. Trends in covariates by fitness (ie, test duration) were estimated using F tests. Person-time was calculated from the baseline examination until each risk factor developed or until the last examination, whichever came first. Incidence rates (per 1000 person-years) were calculated using Poisson regression. The proportional hazards assumption was confirmed using log-log survival plots. We used Cox proportional hazards regression to evaluate the risk of developing each risk factor by fitness category (using 2 indicator variables) and per-minute decrement in test duration.

We calculated the proportion of CVD morbidity in the population attributable to low fitness (PAR) with the following formula: P_e × [1 – (1/HR_adj)], where P_e is the proportion of persons with the CVD risk factor (eg, hypertension) who are in the low fitness category and HR_adj is the multivariable adjusted hazards ratio (HR) for low fitness and that CVD risk factor.²⁴

Next, we tested the association between fitness change over 7 years and the development of CVD risk factors using proportional hazards regression. To evaluate whether the associations were similar across race, sex, and baseline obesity, we included multiplicative interaction terms between each covariate of interest and fitness and conducted stratified analyses. The presence of interaction was determined by the significance of the interaction term and stratum-specific estimates that differed markedly from each other. Because results were consistent across race and sex, we present pooled analyses. However, due to evidence of effect modification by baseline obesity, we present additional estimates specific to strata of baseline obesity.

In a secondary analysis, we performed a propensity analysis²⁵ in which we used a logistic regression model to predict high vs moderate or low fitness. The following variables were selected to calculate the score using forward stepwise modeling (P<.05): age, race, sex, education, BMI, cigarette smoking, family history of premature myocardial infarction, self-reported physical activity, waist circumference, and total cholesterol level. We then evaluated the association between fitness and each CVD risk factor adjusted for the predicted propensity score in a proportional hazards model. Statistical significance for all analyses was denoted at P<.05. All analyses were conducted using the SAS statistical software, version 8.02 (SAS Inc, Cary, NC).

At baseline, less fit women (Table 1) and men (Table 2) were slightly older than those in the moderate and high fitness categories, had a lower education level, and CVD risk factors were often less favorable. Exercise test parameters also varied by fitness category (Table 3). Most associations were consistent with expectations and similar across sex.

Hypertension, diabetes, the metabolic syndrome, and hypercholesterolemia developed in 648 (rate = 13/1000 person-years), 156 (rate = 2.8), 556 (rate = 10.2), and 477 (rate = 11.7) persons, respectively. Low and moderate fitness were strongly (3- to 6-fold risk elevation) associated with the development of hypertension, diabetes, and the metabolic syndrome and modestly associated with an increased risk for developing hypercholesterolemia (40% increase) (Figure 1). Following adjustment for baseline BMI, low and moderate fitness remained associated with a doubling in risk for developing each CVD risk factor except hypercholesterolemia, which attenuated to nonsignificance (low vs high, P = .13).

The number of persons in the low fitness category who developed hypertension, diabetes, the metabolic syndrome, and hypercholesterolemia was 244 (38%), 74 (47%), 202 (36%), and 99 (21%), respectively, and the adjusted PAR of developing each risk factor due to low fitness was 21%, 28%, 21%, and 4%. There was a linear relation between fitness and CVD risk factors that remained significant following adjustment for baseline BMI. The HRs of developing hypertension, diabetes, the metabolic syndrome, and hypercholesterolemia were 1.19 (95% CI, 1.14-1.24; P<.001), 1.12 (95% CI, 1.03-1.22; P = .01), 1.16 (95% CI, 1.11-1.21; P<.001), and 1.07 (95% CI, 1.02-1.12; P = .01) per-minute decrement in test duration, respectively. When we adjusted additionally for weight change over follow-up, the magnitude and significance of the observed associations did not change.

The distribution of fitness varied by obesity status. Mean test duration was 10.2 minutes (95% CI, 10.1-10.3 minutes) among participants who were not obese and 6.7 minutes (95% CI, 6.5-6.9 minutes) among obese participants (P<.001). Only 518 (13%) nonobese participants were in the low fitness category compared with 352 (68%) obese participants. Similar differences were observed for moderate (1425 [36%] vs 148 [29%], not obese vs obese), and highly fit (2022 [51%] vs 19 [4%], not obese vs obese) participants (all P<.001). Obesity emerged as an effect modifier of the association between fitness and the development of diabetes and the metabolic syndrome. Low fitness was only associated with the development of diabetes and the metabolic syndrome among participants who were not already obese at baseline (Table 4). Patterns were similar to those in the total population, including evidence of a dose response.

In the subset of 2478 participants who repeated the treadmill test, duration decreased, on average, 1 minute (95% CI, −1.1 to −0.9 minute) between examinations. As expected due to regression to the mean, participants in the higher fitness category at baseline experienced significantly greater declines in test duration (−1.4 minutes; 95% CI, −1.5 to –1.3) compared with participants in the moderate category (−0.9 minutes; 95% CI, −1.0 to −0.8), or low fitness category (−0.1 minutes; 95% CI, −0.3 to 0). A detailed description is published elsewhere.¹⁸

The rates of hypertension, diabetes, the metabolic syndrome, and hypercholesterolemia in this subpopulation were 12.0 (n events = 362), 2.6 (n = 86), 10.0 (n = 306), and 12.5 (n = 293) per 1000 person-years, respectively. After adjustment for age, sex, race, smoking status, and family history of diabetes, improving fitness (increasing duration) was associated with an approximate 50% risk reduction for developing diabetes and the metabolic syndrome, but was not associated with the incidence of hypertension or hypercholesterolemia (Figure 2). Each minute of increase in treadmill test duration was associated with nonsignificant reductions in the HRs for developing hypertension (HR, 0.99; 95% CI, 0.93-1.04; P = .63) and hypercholesterolemia (HR, 0.99; 95% CI, 0.93-1.05; P = .70), a modest reduction in the risk of developing diabetes (HR, 0.89; 95% CI, 0.80-1.00; P = .05), and a significant reduction in the metabolic syndrome (HR, 0.88; 95% CI, 0.83-0.93; P<.001).

In the subset of participants who performed the treadmill test at baseline and 7 years after baseline, participants gained, on average, 7 kg (95% CI, 6.7-7.2 kg) over 7 years and 12.7 kg (95% CI, 12.3-13.1 kg) over 15 years. Change in fitness was inversely correlated with weight gain over 7 years (r = −0.37) and 15 years (r = −0.25) (both P <.001). Following adjustment for baseline BMI and weight change over 15 years, the HR attenuated to marginal significance for diabetes (HR, 0.89; 95% CI, 0.78-1.01 per 1-minute increase; P = .06) and the metabolic syndrome (HR, 0.96; 95% CI, 0.90-1.02 per 1-minute increase; P = .20).

In the total study population (n = 4487), propensity scores were generated from demographic, behavioral, and clinical measurements. Scores ranged from 0 to 0.98. Propensity score–adjusted HRs for developing incident CVD risk factors (Table 5) did not differ appreciably from multivariable adjusted results. The risk of developing hypertension, diabetes, and the metabolic syndrome remained double among participants in the low fitness category compared with the high fitness category.

Cardiorespiratory fitness in young men and women, estimated by the duration of a maximal treadmill exercise test, was inversely associated with the risk of developing hypertension, diabetes, metabolic syndrome, and hypercholesterolemia in middle age. Our findings were only partly attributable to body mass and weight maintenance, suggesting that fitness plays an important independent protective role in the development of cardiovascular risk factors. However, among those who became obese earlier in life (possibly during childhood or adolescence), fitness does not protect against developing diabetes or metabolic syndrome. Increasing treadmill test duration between visits was associated with a lower risk for developing both diabetes and the metabolic syndrome, suggesting that 2 very important risk factors for coronary heart disease and mortality may be modified by improved fitness over time.

Persons who are physically fit maintain a more favorable caloric balance and lower body weights, both of which protect against the development of CVD risk factors. Previous observational and trial results demonstrate that leaner persons and persons who lose weight are at markedly lower risk for developing hypertension,^26- 27 diabetes,²⁸ the metabolic syndrome,²⁹ and lipid disorders.³⁰ A recent meta-analysis of 44 studies of the effect of physical fitness on BP reported BP declines with fitness training in both lean and obese study subjects. Mechanisms suggested to account for these observations are reduced systemic vascular resistance, decreased cardiac output, and decreased plasma noradrenaline concentrations.³¹

Fitness promotes muscle insulin sensitivity,¹² insulin-mediated transport of glucose from blood to muscles,³² improved autonomic nervous system function,³³ and lower heart rates, which each decrease the risk of developing diabetes, independent of body mass. Increased lipoprotein lipase activity in active skeletal muscle, which results in an enhanced clearance rate of plasma triglycerides; increased transport of lipids and lipoproteins from the peripheral circulation and tissues to the liver; and enhanced HDL cholesterol, are mechanisms by which lipids may improve with fitness.^10,34 The relatively modest association between fitness and high LDL cholesterol levels in this study may be attributable to the greater contribution of genetics and diet than fitness to LDL levels.

Our results must be interpreted in light of several limitations. First, in this study we used treadmill test duration as an estimate of fitness in lieu of direct measurements of maximum oxygen consumption per unit time (O₂). Previous research has demonstrated a strong correlation (r = 0.92) between test duration on a symptom-limited test and O₂.³⁵ Next, it is possible that our measure of improving fitness, increasing duration of the exercise treadmill test 7 years later, may simply reflect increased familiarity with the apparatus among participants using it for at least the second time. Finally, because physical fitness reflects the combination of genetics and physical activity,³⁶ fitness may not represent a truly modifiable behavior. However, the strong association between self-reported participation in physical activity and test duration suggests that we are at least in part capturing the role of physical activity participation on health risk.

Our findings demonstrate the importance of low cardiorespiratory fitness in young adulthood as a risk factor for developing cardiovascular comorbidities in middle age. Previous work has demonstrated that engaging in a regular exercise program can improve fitness.³⁷ If the association between fitness and CVD risk factor development is causal, and if all unfit young adults had been fit, there may have been 21% to 28% fewer cases of hypertension, diabetes, and metabolic syndrome. Given the current obesity epidemic and observations of a decline in daily energy expenditure in the population,³⁸ improving cardiorespiratory fitness in young men and women and developing public health policies that encourage physical activity should be important health policy goals. Substantial public health benefits may be achieved in the prevention of CVD morbidity by reversing adverse trends in the general level of fitness in the population.