Fitness and Fatness as Predictors of Mortality from All Causes and from Cardiovascular Disease in Men and Women in the Lipid Research Clinics Study

June Stevens1,2, Jianwen Cai3, Kelly R. Evenson2 and Ratna Thomas3

1 Department of Nutrition, School of Public Health, University of North Carolina, Chapel Hill, NC.
2 Department of Epidemiology, School of Public Health, University of North Carolina, Chapel Hill, NC.
3 Department of Biostatistics, School of Public Health, University of North Carolina, Chapel Hill, NC.

Received for publication December 7, 2001; accepted for publication June 19, 2002.


    ABSTRACT
 TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS
 DISCUSSION
 REFERENCES
 
The relative size of the effects of fitness and fatness on longevity has been studied in only one cohort. The authors examined this issue using data from 2,506 women and 2,860 men in the Lipid Research Clinics Study. The mean age was 46.6 years in women and 45.1 years in men at baseline (1972–1976). Fitness was assessed using a treadmill test, and fatness was assessed as body mass index calculated from measured height and weight. Participants were followed for vital status through 1998. Hazard ratios were calculated using proportional hazard models that included covariates for age, education, smoking, alcohol intake, and the dietary Keys score. Fitness and fatness were both associated with mortality from all causes and from cardiovascular disease. For mortality from all causes, the adjusted hazard ratios were 1.32 among the fit-fat, 1.30 among the unfit-not fat, and 1.57 among the unfit-fat women compared with fit-not fat women. Among men the same hazard ratios were 1.44, 1.25, and 1.49. There were no significant interactions between fitness and fatness in either men or women. The authors conclude that both fitness and fatness are risk factors for mortality, and that being fit does not completely reverse the increased risk associated with excess adiposity.

body weight; exercise test; mortality; obesity; physical fitness; survival analysis

Abbreviations: Abbreviation: MET, metabolic equivalent.


    INTRODUCTION
 TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS
 DISCUSSION
 REFERENCES
 
Decades of research have shown that obesity increases morbidity and mortality, and the toll on the nation’s health is enormous. In the United States in the 1990s, between 280,000 and 325,000 deaths per year were attributable to obesity (1). Although public interest in obesity is intense and billions of dollars are spent on weight loss efforts annually (2), most Americans have little success in maintaining a reduced weight or preventing weight gain with aging (3, 4). Therefore, research showing that fitness can reduce the impact of obesity on mortality and morbidity is of great public health importance. It is plausible that many persons who are not able to maintain a reduced weight could attain and maintain a higher level of fitness. If higher fitness levels in obese persons substantially reduced morbidity and mortality, this approach could provide improved health and longevity in these persons at increased risk.

We know of six published studies that have specifically examined the independent and combined effects of fatness and fitness on mortality (510). All of these studies used data from the same cohort: the Aerobics Center Longitudinal Study. This cohort was composed of clients who received medical examinations at the Cooper Institute for Aerobics Research in Dallas, Texas (6). The cohorts varied somewhat but, in a recent report (10), 95 percent were White and approximately 80 percent were college graduates. Fitness was measured using a maximal treadmill exercise test (6), and men in the lowest fitness quintile of each age group were compared with those in the four higher quintiles. The reports used different exclusion criteria, covariates, cutpoints, and analytic designs; however, each of these studies reported some similar findings. Although the trends were often not demonstrated to be statistically significant, low fitness was associated with a greater mortality risk compared with fatness, and fitness reduced the impact of fatness on mortality.

The reports from the Aerobics Center Longitudinal Study present convincing evidence that fitness is a more potent risk factor for mortality than is fatness, and that fitness attenuates the effect of obesity on mortality in this cohort of men. Evidence from other cohorts, especially ones that include women, is needed to learn if this association is present in other populations. Here, we report an analysis of data from a cohort of American women and men drawn from diverse geographic locations that examines the interrelated effects of fitness and body mass index on all-cause and cardiovascular disease mortality.


    MATERIALS AND METHODS
 TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS
 DISCUSSION
 REFERENCES
 
The Lipid Research Clinics cohort
This research used data from the Lipid Research Clinics first prevalence studies and the mortality follow-up studies. Participants were from eight geographically diverse centers in the United States: Houston, Texas; Oklahoma City, Oklahoma; Palo Alto, California; La Jolla, California; Cincinnati, Ohio; Iowa City, Iowa; Minneapolis, Minnesota; and Seattle, Washington. Recruitment used a standardized protocol in target populations defined by occupational groups, households in geographic zones, households with membership in a particular medical plan, or students and parents within school districts (11). The participants in the Lipid Research Clinics were sampled from defined groups but were not necessarily representative of the local populations.

The methodology and study design of the Lipid Research Clinics Study have been reported elsewhere in detail (1215). A two-stage procedure was used, with selected participants from a brief first visit participating in a more extensive second visit. Participants in the second visit consisted of a 15 percent random sample of all visit 1 participants and 100 percent of those with elevated plasma lipids. The response rate for both strata of the sample was 85 percent. It was during the second examination that fitness measures were obtained, and this examination provided the baseline measures for this study. The two examinations took place between 1972 and 1976, and the median time between an individual’s two visits was 96 days.

Data for this analysis were drawn from 7,589 men and women between the ages of 30 years and 75 years who were examined in one of the eight study centers. Unfortunately, the number of minority persons examined was too small to enable us to study them here separately (n = 534). Our previous work (16) and the work of others (17, 18) have indicated that the association between body mass index and mortality is likely different in African Americans compared with Whites. Therefore, we chose not to combine data from ethnic groups. To reduce confounding from preexisting illness (19, 20), we excluded participants who died in the first year of follow-up (n = 30), participants who reported a history of coronary heart disease or stroke (n = 558), and participants with a body mass index of less than 18.5 (n = 101). Because the heart rate response to exercise was used as an indicator of fitness, we excluded participants who were taking medication that might alter the heart rate (n = 43) and participants with inconsistent heart rates (n = 20). We excluded 172 participants with positive graded exercise test results that indicated possible cardiovascular disease. Another 414 participants were excluded because of contraindications for participating in the exercise test (e.g., aortic stenosis, congestive heart failure, excessive blood pressure at rest, R-on-T-type premature ventricular complexes, ventricular tachycardia, parasystolic focus, atrial flutter, atrial fibrillation, and congenital heart disease) (21). If the duration of the graded exercise test was less than 1 minute (n = 309), participants were excluded since a steady state for exercise was not reached. Also excluded were 42 participants who were missing data on height, weight, or the covariates used in our analyses. Thus, the analysis sample included 5,366 participants: 2,506 women and 2,860 men. Fifty-seven percent of the subjects examined at visit 2 were in the visit 1 random sample, and the remainder were from the visit 1 hyperlipemic sample.

Fatness measure
At the second visit, a detailed examination was conducted that included an interview, physical examination, graded exercise test, and collection of plasma samples. Height and weight were measured with the participant wearing light clothing and no shoes. Height was measured to the nearest 0.5 cm using a headboard and a vertical rule fixed to a wall. Weight was measured to the nearest 0.1 kg using a balance scale. Fatness was assessed as body mass index and calculated as weight in kg divided by height in meters squared. Quintile cutpoints were calculated using the visit 2 body mass index among participants who were recruited as part of the random sample drawn from the visit 1 participants.

Fitness measure
Cardiorespiratory fitness, now called "fitness" for the remainder of this report, was assessed as the time to produce a predicted maximal heart rate, based on age and training, during a standardized treadmill test (22). Participants were told to refrain from eating for 2 hours prior to testing, and most tests were performed in the morning. The test was conducted according to a Bruce protocol, as described in previous publications (2224). A total of seven 3-minute stages were used in which the speed and inclination were increased in a stepwise fashion as follows: stage 1 (1.7 miles per hour and 10 percent inclination); stage 2 (2.5 miles per hour and 12 percent inclination); stage 3 (3.4 miles per hour and 14 percent inclination); stage 4 (4.2 miles per hour and 16 percent inclination); stage 5 (5.0 miles per hour and 18 percent inclination); stage 6 (5.5 miles per hour and 20 percent inclination); and stage 7 (6.0 miles per hour and 22 percent inclination) (1 mile = 1.6 km).

The electrocardiogram was monitored continuously, and blood pressure was measured at the end of each stage. Heart rate was monitored continuously and was also recorded at the end of each stage, or earlier if the participant stopped during a stage. The test was stopped when participants reached 90 percent of their predicted maximal heart rate, based on age and physical training (22, 25). The test was terminated early if the participant was unable to continue because of chest pain, fatigue, dyspnea, or leg pain or because of abnormalities in the electrocardiogram (>=1 mm horizontal ST-segment change, major arrhythmias, or conduction defects), a decrease in systolic blood pressure, technical difficulties, or subjects’ being unwilling to continue. Otherwise, the test was stopped when the participant attained 90 percent of the predicted maximal heart rate and either maintained it for 1 minute, maintained it to the end of the stage, or exceeded the target heart rate by eight beats per minute, whichever occurred first (22). For these analyses, fitness was quantified as the duration of the exercise test in minutes. Quintile cutpoints were calculated using visit 2 fitness tests in participants who were recruited as part of the random sample drawn from visit 1.

Other measurements
Physical activity was assessed with two questions and categorized as 1) very active (persons reporting strenuous exercise three or more times per week); 2) moderately active (persons reporting strenuous activity less than three times per week); or 3) inactive (persons reporting no strenuous exercise) (26). Education was categorized as less than high school graduate, high school graduate, or more than high school. Cigarette smoking was categorized as currently smoking more than 20 cigarettes per day, currently smoking 20 cigarettes per day, currently smoking less than 20 cigarettes per day, former, or never. Participants were questioned on the type and amount of different types of alcoholic beverages they consumed in the past 7 days, and the average grams of ethanol per day were calculated. Dietary intake was assessed with a 24-hour recall, and a Keys score was calculated as described by Anderson et al. (27).

Vital status follow-up
The number of deaths was obtained by annual follow-up contacts with the cohort (predominantly by phone) until the end of 1987. At the end of 1987, the vital status was known for 99.6 percent of the cohort. After 1987, annual follow-up contacts were discontinued, and the cohort was followed by searching the National Death Index (1988–1991) and the Epidemiology Research Index (1992–1998). For this study, vital status information was complete through 1998. The cause of death was ascertained by nosologist’s codings of the death certificates for the entire follow-up. International Classification of Diseases, Ninth Revision, codes 390–459 identified cardiovascular disease deaths.

Statistical analysis
To account for the sampling scheme, we treated the data as a stratified random sample with two strata: hyperlipidemic persons and normolipidemic persons (including borderline hyperlipidemic persons). Because of the higher proportion of hyperlipidemic persons in the sample relative to the population, mortality rates were calculated by averaging across lipid strata using the inverse of the sampling probability as the weight. Associations between body mass index and fitness and mortality were examined using stratified Cox proportional hazards models, with the sampling strata (hyperlipidemic persons and normolipidemic persons) as the stratifying variable (28). These procedures enabled us to draw inferences to those screened at visit 1 (14, 29). Statistical Analysis System software (30) was used to conduct analyses.


    RESULTS
 TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS
 DISCUSSION
 REFERENCES
 
In the sample from the Lipid Research Clinics, the mean body mass index in men (27.4 kg/m2) was in the overweight range and was higher than that of women (24.9 kg/m2) (table 1). Among women, 65 percent were of normal weight with a body mass index of 18.5–24.9 kg/m2, 23 percent were overweight with a body mass index of 25.0–29.9 kg/m2, and 12 percent were obese with a body mass index of 30 or more kg/m2. Among men, 38 percent were of normal weight, 50 percent were overweight, and 12 percent were obese. The mean time on the treadmill was 7.3 minutes in women and 9.1 minutes in men. Metabolic equivalent (MET) values were extrapolated from published exercise intensities (21, 31) and yielded corresponding values of approximately 8.4 METs for women and 10.3 METs for men. These values represent the capacity required to walk on the treadmill at the corresponding speed and grade. Most participants stopped the exercise test beyond 90 percent of predicted maximal heart rate. In men, the median was 96.2 percent (interquartile range, 93.0–99.4 percent) and, in women, it was 95.6 percent (interquartile range, 92.3–99.4 percent). The majority of participants had at least a high school education, and over half the men had greater than a high school education. Among women, 33 percent were current smokers, while 35 percent of men were current smokers. According to our scoring system, 86 percent of women and 67 percent of men were in the lowest physical activity category.


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TABLE 1. Description of analysis sample at the second examination (1972–1976) in the Lipids Research Clinics Prevalence Study
 
The correlation between body mass index and fitness was not very high: r = –0.10 in men and r = –0.21 in women. The percentage of obese persons in a fitness category tended to decline as the fitness level increased (figure 1). Nevertheless, 7 percent of the women and 10 percent of the men in the most fit category were obese. We considered the possibility that obese persons in the most fit category could have a body mass index closer to the cutpoint (30 kg/m2) than obese persons in other fitness categories. However, examination of the mean body mass index among obese participants in each fitness quintile yielded no support for this notion.



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FIGURE 1. Percentage of participants classified as having normal weight (body mass index (BMI) = 18.5–24.9), being overweight (BMI = 25–29.9), and being obese (BMI >= 30) within quintiles (Q) of fitness levels, Lipid Research Clinics Study, 1972–1976.

 
The number of deaths and the age-adjusted death rates for each quintile of body mass index and fitness are shown in table 2. All-cause mortality rates were relatively stable across the first four body mass index quintiles and increased in the fifth quintile for both men and women. For cardiovascular disease mortality, rates generally increased in a monotonic fashion across the body mass index quintiles for both men and women. For fitness, both all-cause and cardiovascular disease death rates generally decreased with increasing fitness quintiles. Death rates for the highest body mass index quintile and the lowest fitness quintile were similar for all-cause mortality in women (7.8 vs. 7.6 per 100,000 person-years for both) and in men (11.5 vs. 12.3 per 100,000 person-years for both) and for cardiovascular disease mortality (2.8 per 100,000 person-years for both in women and 4.7 vs. 5.1 per 100,000 person-years in men).


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TABLE 2. All-cause and cardiovascular disease death rates by quintiles of body mass index and fitness for women and men, Lipid Research Clinics Study, 1972–1998*
 
A J- or U-shaped association has been found between body mass index and mortality in many studies (32). We examined the shapes of the associations between body mass index and mortality and between fitness and mortality. Examination of body mass index and fitness with quadratic terms and quadratic spline terms in separate models in men and women showed no improvement in the prediction of all-cause or cardiovascular disease mortality over the model with just the linear term. Therefore, where body mass index and fitness were used as continuous variables, they were used here in the linear form.

We examined possible confounding of body mass index by cigarette smoking by examining the body mass index-mortality association within smokers and nonsmokers. We used the full covariate-adjusted model stratified by gender and included indicators for former versus never smoking in the analyses of nonsmokers and indicators of smoking dose (<20, 20, >20 cigarettes per day) in the analyses of smokers. The quadratic term for body mass index was not significant in participants in either smoking category. In addition, the hazard ratios associated with body mass index were very similar across smoking categories, varying by less than 4 percent. In fact, in men, the coefficient associated with body mass index was slightly larger in the smoking than in the nonsmoking group, which is opposite to what would be expected if confounding were present. Hazard ratios associated with fitness were also very similar across smoking categories, differing by less than 1 percent. Two-way interactions between smoking status and body mass index and smoking status and fitness were not statistically significant. Further analyses were not stratified by smoking status but were adjusted for smoking status in five categories (as shown table 1).

Confounding by preexisting illness was tested by examining coefficients for body mass index and fitness after excluding participants who died in the first year versus the first 4 years of follow-up (20). The hazard ratios from the sets of analyses varied by less than 1 percent. Therefore, deaths in years 2–4 of follow-up were not excluded.

Table 3 shows associations between body mass index and all-cause and cardiovascular disease mortality, with and without adjustment for fitness (in the continuous form). In both women and men, the largest relative hazard was seen in the fifth quintile. In no case was the hazard ratio as high as 2. Adjustment of the association between body mass index and all-cause mortality and cardiovascular disease mortality with fitness generally tended to reduce the hazard ratios modestly.


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TABLE 3. Adjusted hazard ratios* by quintiles of body mass index and fitness for women and men, Lipid Research Clinics Study, 1972–1998
 
Table 3 also shows associations between fitness and all-cause and cardiovascular disease mortality in women and men. Associations are shown before and after adjusting for body mass index (in the continuous form), and the most-fit quintile was used as the reference. Lower levels of fitness were generally associated with higher levels of all-cause and cardiovascular disease mortality. In women, there was an increase in cardiovascular disease mortality in the lowest quintile of fitness with the hazard ratio over 3. Adjustment of the association between fitness and all-cause and cardiovascular disease mortality for body mass index tended to reduce the hazard ratios, but only slightly.

Figures 2 and 3 show the hazard ratios for participants categorized as fit-not fat (the reference), fit-fat, unfit-fat, and unfit-not fat. For this analysis, both fitness and fatness were categorized by using the highest risk quintile versus all others combined. In women, the risks of all-cause and cardiovascular disease mortality were significantly increased above the reference in all categories except for cardiovascular disease mortality in the fit-fat. In that group, the hazard ratio was 1.39, but the p value was over 0.1. In men, hazard ratios were also increased above the reference in each category, although the p value was not always less than 0.05. We tested specifically for differences in risk between the fit-fat group and the fit-not fat group, using the fit-fat group as the reference. None of these differences was statistically significant. Moreover, interactions between fitness and fatness were not statistically significant when tested using the two exposure variables in the continuous form, in quintiles or as dichotomous variables categorized as shown in figures 2 and 3.



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FIGURE 2. Relative hazard in women categorized by fitness level (quintile 1 vs. quintiles 2–5) and body mass index (quintiles 1–4 vs. quintile 5) adjusted for age, education, smoking, alcohol, and Keys score, Lipid Research Clinics Study, 1972–1998. *, p < 0.05.

 


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FIGURE 3. Relative hazard in men categorized by fitness level (quintile 1 vs. quintiles 2–5) and body mass index (quintiles 1–4 vs. quintile 5) adjusted for age, education, smoking, alcohol, and Keys score, Lipid Research Clinics Study, 1972–1998. *, p < 0.05; {dagger}, p < 0.06.

 
We also explored whether physical activity predicted all-cause and cardiovascular disease mortality, adjusting for fatness, age, education, smoking, alcohol, and Keys score. For women and men, being very active or moderately active was not significantly related to all-cause or cardiovascular disease mortality when compared with those who were not active. There were also no statistically significant interactions between physical activity and body mass index.


    DISCUSSION
 TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS
 DISCUSSION
 REFERENCES
 
In both women and men in the Lipid Research Clinics cohort, fitness was predictive of all-cause and cardiovascular disease mortality in models that included body mass index. Thus, an effect of fitness that was statistically independent of the level of fatness was confirmed. The effect of fatness independent of fitness was less clear. Body mass index quintiles tended to be associated with both mortality outcomes after controlling for fitness, but the effects were often smaller than those seen with fitness and often not statistically significant. In both women and men, the hazard ratios associated with fitness and fatness were not greatly reduced when both variables were included in the model.

The size of the associations of body mass index with mortality before adjustment for fitness was generally similar to those observed in other studies comparing categories with a lower bound of approximately 30 kg/m2 with a reference body mass index category (17, 33). For instance, in the Lipid Research Clinics Study (table 3), the relative hazard was 1.33 for all-cause mortality for women in the fifth body mass index quintile (>27.7 kg/m2) compared with that for women in the first quintile (18.5–21.0 kg/m2). In the Cancer Prevention Study I (33) and Cancer Prevention Study II (17) cohorts, the hazard ratios for all-cause mortality were 1.5 for comparisons of women with a body mass index between 30 and 35 (Cancer Prevention Study I) or between 30 and 32 (Cancer Prevention Study II) compared with the reference body mass indexes of 18.5–25 and 23.5–25, respectively. Associations between body mass index and mortality were somewhat larger than those observed here in the Nurses’ Health Study cohort (34) and somewhat smaller in the National Health and Nutrition Examination Survey Epidemiologic Follow-up Study (35).

The sizes of the associations of fitness with all-cause and cardiovascular disease mortality before adjustment for body mass index were generally weaker in Lipid Research Clinics men compared with those observed in other studies of men. In this study, the relative hazard comparing the lowest with the highest population-specific quintile of fitness for men was 1.63 for all-cause mortality and 1.65 for cardiovascular disease mortality. Data from the Aerobics Center Longitudinal Study indicate a relative risk for men of 2.0 for all-cause mortality and 2.7 for cardiovascular disease mortality in a comparison of the lowest quintile with all other quintiles of fitness, adjusting for age and year of examination (36). Other studies have shown a similar dose-response relation with fitness to all-cause and cardiovascular disease mortality (3743).

The effects of fitness on all-cause and cardiovascular disease mortality appeared to be larger in Lipid Research Clinics women than in men. This may have been influenced by the overall lower levels of fitness in Lipid Research Clinics women compared with Lipid Research Clinics men. The minutes to produce the predicted maximal heart rate in the lowest quintile of fitness ranged from 1.3 to 5.6 minutes in women, whereas the range was from 2.3 to 8.0 minutes in men. There are fewer published studies of fitness in women than in men, and the results as they are reported are not directly comparable with the work presented here. In the Lipid Research Clinics Study, the relative hazard comparing the lowest with the highest population-specific quintile of fitness for women was 2.1 for all-cause mortality and 3.4 for cardiovascular disease mortality. In the Aerobics Center Longitudinal Study, the relative risk for women was 2.8 for all-cause mortality and 2.2 for cardiovascular disease mortality in a comparison of the lowest quintile with all the other quintiles of fitness and adjusting for age and year of examination (36). In a study of women from Olmsted County, Minnesota, the risk of all-cause mortality was 0.75 for each MET increase in workload from the maximal exercise test (39).

The cutpoints chosen to form categories can have a large effect on the magnitude of the risk observed. We chose to form joint categories of fitness and fatness by drawing the cutpoint at the boundary of the highest risk quintile for both body mass index and fitness. This seemed reasonable, given the shape of the body mass index-mortality association here and the tradition of defining unfit as the least fit quintile. All of the previous studies (510) on this issue have defined unfit as the least fit quintile, and all but two (5, 8) compared the lowest fitness quintile with all the other quintiles combined when assessing relative risk. This method of categorization also has the advantage of providing a cutpoint for the two different measures that is equivalent in at least one aspect, that is, as a marker of the 20 percent at highest risk among the sample studied. Another advantage of these cutpoints is that they advanced the statistical power of the analysis of fit groups and fat groups in that all events were included, and the numbers of events in each of the four fitness-fatness categories were reasonably well distributed. Other methods of defining these groups could have been used, or both variables could have been examined in the continuous form. Analysis of variables in the continuous form is appealing in that no information is lost by grouping. A disadvantage is that it is more difficult to visualize the comparison of the relations of two continuous variables with an outcome, and categorization makes the associations easier to illustrate.

This study has several strengths and limitations. The cohort from the Lipid Research Clinics was not a representative sample of the US population, but nevertheless, the cohort was drawn from diverse groups, and the results have a broader generalizability than those from the Aerobics Center Longitudinal Study cohort. It is a strength of this work that analytic strategies were used that allowed inferences to be made to the original Lipid Research Clinics cohort, even though the analysis data set included an oversampling of hyperlipemic persons. The fitness measure used here was good, although the measure of fatness was somewhat less precise. The body mass index correlates with the percentage of body fat, with a correlation coefficient of approximately 0.7 in adults (44). The measure of physical activity used here was even more imprecise. The age- and gender-adjusted correlation between the three-item physical activity assessment used here and a 4-week history of physical activity was only 0.07 (45). An additional limitation of this work was the statistical power. Many of the trends seen here were assessed with relatively large confidence intervals.

In conclusion, this research showed that both high levels of fatness and low levels of fitness increased mortality from all causes and from cardiovascular disease. The effects of fitness were somewhat more consistent than the effects of fatness, but differences in the precision of the measurements of these two variables may have contributed to this finding. Overall, it did not appear that being fit entirely alleviated the effects of being fat or that being slender entirely alleviated the effects of being unfit. The public health message is that, to best reduce the risk of mortality, fit persons need to attain or maintain a normal body weight, and normal weight persons need to attain or maintain at least a moderate level of fitness.


    ACKNOWLEDGMENTS
 
This research was supported by grant U48/CCU409660 from the Centers for Disease Control and Prevention.

The authors thank Olivia Thomas for her assistance in the preparation of this manuscript.


    NOTES
 
Correspondence to Dr. June Stevens, Departments of Nutrition and Epidemiology, Campus Box 7461, University of North Carolina, Chapel Hill, NC 27599 (e-mail: June_Stevens{at}unc.edu). Back


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 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS
 DISCUSSION
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