The relative risks and their standard errors were assigned to the midpoint of each interval, and linear interpolation was used to estimate the intermediate values. Although this author favors weighting by person-years of experience, all of the above procedures should yield good estimates.
Bootstrap resampling was used to estimate the standard errors for the pooled relative risks and significance levels for the differences in relative risk (10). Specifically: 1) sampling was done with replacement to create a bootstrap data set of 7 fitness and 16 physical activity cohorts; 2) the weighted average of the relative risk for fitness studies, physical activity studies, and the difference between fitness and physical activity studies was determined at each 5% percentile between 0 and 100; and 3) these steps were repeated 1,000,000 times to estimate the standard error for relative risks and their differences. The number of bootstrap iterations will not affect the estimated significance level other than to increase the precision of its estimate. Two-tailed significance levels were computed as 2*minimum (p, 1 − p), for which p is the proportion of times that the relative risks were less than one or relative risk differences were less than zero.
Five of the seven fitness cohorts show their greatest decrease in relative risk occurring between the lowest- and second-lowest fitness categories (4,4,18,47,53). The pooled results, displayed in Figure 2, indicate that all percentiles above the 20th are at significantly less risk than the percentiles of least fit individuals. Between the 30th and 100th percentile of the distribution, each percent increase in fitness was associated with a −0.0038 ± 0.0009 reduction in relative risk (P < 0.001).
Relative to the least fit or active percentiles, the relative risk reduction is significantly greater for fitness than physical activity at all percentiles ≥ 25th (Table 2, Fig. 2). After the initial sharp decline in the fitness curve, the risk reduction associated with increasing levels of fitness and increasing levels of physical activity are parallel (i.e., the regression line for fitness between the 30th and 100th percentile (−0.0038 ± 0.0009) has a different intercept but similar slope to the regression line for physical activity (i.e., −0.0024 ± 0.0009 between the 1st and 100th percentile, P = 0.28 for difference, and −0.0031 ± 0.0006 when restricted to within the 30th and 100th percentile, P = 0.51). The significant differences between the physical activity and cardiorespiratory fitness curves persist when the analyses are restricted to CVD endpoints (Fig. 3).
Similar results were obtained whether the analyses were based on the person-years of experience (null hypothesis of relative risk = 1) or the standard error of the relative risk estimates (null hypothesis of ln(relative risk)=0). Table 2 presents the significance levels for ln(relative risk) at increasing levels of physical activity or cardiorespiratory fitness. Cardiorespiratory fitness is again associated with a precipitous drop in risk below the 25th percentile of fitness and a gradual, graded risk reduction thereafter. Physical activity shows a gradual, graded reduction in ln(relative risk) from the least to most active individuals. Also as previously demonstrated, the risk reduction associated with fitness after the initial drop stabilized (i.e., after the 25th percentile) parallels that observed with physical activity. Specifically without interpolation, ln(relative risk) is reduced −0.0058 ± 0.0029 per fitness percentile (P < 0.001) between the 30th and 100th percentiles, which is not significantly different (P = 0.11) from the reduction per percentile of activity between 1% and 100% (−0.0034 ± 0.0007, P < 0.001). The corresponding regression slopes for the analyses of interpolated values were −0.0057 ± 0.0033 (P < 0.001) per fitness percentile and −0.0048 ± 0.0008 (P < 0.001) per physical activity percentile. which again are not significantly different from each other (P = 0.38).
Our findings suggest that cardiorespiratory fitness and physical activity have significantly different relationships to CVD (or CHD) risk. Although physical activity increases fitness and may be an appropriate therapy for the unfit, inactivity may not be the principal cause for being unfit when subclinical disease or genetics may be involved. In sedentary subjects, maximal heritability estimates of at least 50% are reported for cardiorespiratory fitness, which may be an underestimate because the estimate includes the attenuating effects of measurement error (5). Formulating physical activity recommendations on the basis of fitness studies may inappropriately demote the status of cardiorespiratory fitness as a risk factor while exaggerating the public health benefits of moderate amounts of physical activity.
The differences observed between the cardiorespiratory fitness and physical activity curves could not be ascribed to either age or disease endpoint. Although the mean age during the midpoint of follow-up was about 5 yr older in the physical activity cohorts than the fitness cohorts, within the 16 physical activity cohorts, there was no significant association between midpoint follow-up age and relative risk at any percentile (analyses not displayed).
It is true that CVD was more frequently reported for the fitness than physical activity cohorts. However, the risk reduction remains significantly greater for fitness than physical activity when the analyses are restricted to the subset of studies reporting CVD endpoints (Fig. 3). At 25% and above, there were significant reductions in relative risk (P < 0.01) for both fitness and activity, with the reduction in relative risk being significantly greater for fitness than for activity (P < 0.025).
This is not to say that some of the difference in shape between the fitness and activity curves couldn’t arise by the way the samples were partitioned into intervals. However, this assumes that the cutoff points used to define the lowest interval (i.e., the referent group) for the physical activity studies were not result driven. Specifically, it assumes that the investigators would obscure, by accident or design, a reduction in risk among the least active individuals. It is more reasonable to assume that several of the physical activity studies defined broadly the interval for the referent group because there was no trend within the interval. Moreover, a broad referent group would increase statistical power to detect significant reductions in risk for other categories.
The analyses presented in this paper suggest that cardiorespiratory fitness and physical activity have significantly different relationships to combined CVD and CHD risk. The reductions in relative risk are nearly twice as great for cardiorespiratory fitness than physical activity. Individuals below the 25th percentile of the fitness distribution are at substantially higher risk than those at higher percentiles. Being unfit warrants consideration as a risk factor, distinctly from inactivity, and is worthy of screening and intervention. At question is: when is it appropriate to screen for and intervene upon low levels of physical fitness, irrespective of physical activity status?
This work was supported in part by grants HL-58621, HL-45652, and HL-55640 from the National Heart Lung and Blood Institute and was conducted at the Lawrence Berkeley National Laboratory (Department of Energy DE-AC03-76SF00098 to the University of California).
Address for correspondence: Paul T. Williams, Life Sciences Division, Lawrence Berkeley National Laboratory, 1 Cyclotron Road, Berkeley, CA 94720; E-mail: PTWilliams@LBL.gov.
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