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Correspondence: Address to Conrad P. Earnest, PhD, Department for Health Sport, Health, and Exercise Science, University of Bath, Eastwood 22-23 3.4, Bath, United Kingdom BA2 7AY.
To examine the relationship between estimated maximal cardiorespiratory fitness (CRF) and metabolic syndrome (MetSyn).
Patients and Methods
We performed a cross-sectional analysis of 38,659 Aerobics Center Longitudinal Study participants seen between January 1, 1979, and December 31, 2006, to examine CRF levels defined as low (lower 20%), moderate (middle 40%), and high (upper 40%) of age- and sex-specific distributions vs National Cholesterol Education Program–derived MetSyn expressed as a summed z-score continuous variable. We used a general linear model for continuous variables, the χ2 test for distribution of categorical variables, and multiple linear regression for single and cumulative MetSyn scores adjusted for body mass index, smoking status, alcohol intake, and family history of cardiovascular disease.
Results
We observed significant inverse trends for MetSyn vs CRF in both sexes (P for trend <.001). The CRF associations vs individual components were as follows: waist circumference–men: β=−.14, r2=0.78; women: β=−.04, r2=0.71; triglycerides–men: β=−.29, r2=0.18; women: β=−.17, r2=0.18; high-density lipoprotein cholesterol–men: β=.25, r2=0.17; women: β=.08, r2=0.19; fasting glucose–men: β=−.09, r2=0.09; women: β=.09, r2=0.01; systolic blood pressure–men: β=−.09, r2=0.09; women: β=−.01, r2=0.21; and diastolic blood pressure–men: β=−.07, r2=0.12; women: β=−.05, r2=0.14. All associations except for systolic blood pressure (both sexes) and glucose (women) are significant (P<.001).
Conclusion
Cardiorespiratory fitness demonstrated a strong inverse relationship with MetSyn in both sexes, with the strongest single associative component being waist circumference.
Although a number of earlier works laid the groundwork for its conception, Reaven presented the original hypothesis for this clustering of characteristics, referring to them as syndrome X. In his original treatise, Reaven proposed that insulin resistance was the mediating component and root cause of dyslipidemia and hypertension.
Recommendations for blood pressure measurement in humans and experimental animals, part 1: blood pressure measurement in humans: a statement for professionals from the Subcommittee of Professional and Public Education of the American Heart Association Council on High Blood Pressure Research.
As with many diseases and syndromes, positive lifestyle behaviors, such as physical activity and nutrition, play an important role in the development and progression of each component of MetSyn.
Epidemiological studies have demonstrated a significant, inverse, independent association among physical activity, maximal cardiorespiratory fitness (CRF), and both the composite MetSyn score and each component taken singularly.
National Cholesterol Education Program (NCEP) Expert Panel on Detection, Evaluation, and Treatment of High Blood Cholesterol in Adults (Adult Treatment Panel III) Third Report of the National Cholesterol Education Program (NCEP) Expert Panel on Detection, Evaluation, and Treatment of High Blood Cholesterol in Adults (Adult Treatment Panel III) final report.
Insulin sensitivity index, acute insulin response, and glucose effectiveness in a population-based sample of 380 young healthy Caucasians: analysis of the impact of gender, body fat, physical fitness, and life-style factors.
Although these results may be intuitive, we propose that the categorical nature of the MetSyn assessment may not fully elucidate the cumulative benefits afforded by fitness and the consequent improvements within each component category. Supporting this premise is the concept that MetSyn represents a constellation of risk factors influenced by multiple physiologic systems. Likewise, exercise also affects multiple physiologic systems that may not be fully explained during the analysis of an intervention aimed at improving the syndrome as a whole. Further, the categorical nature of MetSyn does not allow for intraindividual response differences within each component. Thus, the global effects of exercise may be blunted if one views each component individually, in which, despite improvements in a particular MetSyn feature, those who do not show enough improvement to move out of a qualifying category are not “given credit” for their improvement. For example, National Cholesterol Education Program (NCEP) Adult Treatment Panel (ATP) III MetSyn guidelines categorize men as qualifying for higher risk if their waist circumference is 102 cm or greater. Consequently, if an individual presents with a waist circumference of 120 cm and reduces said circumference to 103 cm through behavioral change, they are still scored as positive for that particular risk component.
One way to avoid this complication is to examine the MetSyn score as a summed z score as recently proposed by Brage et al.
In brief, individual components of MetSyn were statistically normalized and expressed as z scores. A MetSyn score is then computed as the mean of these z scores.
The use of such a score may have particular utility when examining the effectiveness of clinical interventions for those participants who are at a higher risk of type 2 diabetes and CVD. We examine the role of CRF as it relates to MetSyn in the Aerobics Center Longitudinal Study (ACLS).
Patients and Methods
Study Population
We performed a cross-sectional analysis of participants from the ACLS by examining the association between MetSyn and estimated maximal CRF. Study participants came to the Cooper Clinic (Dallas, Texas) for periodic preventive health examinations. We initially considered 47,398 participants with complete data and having medical examinations from January 1, 1979, to December 31, 2006. We excluded participants with a history of CVD (myocardial infarction or stroke; n=740), cancer (n=2294), underweight (body mass index [BMI], a measure of weight in kilograms divided by the square of the height in meters, <18.5; n=528), or abnormal electrocardiography results (n=4199) and those who did not achieve at least 85% of age-predicted maximum heart rate during exercise testing (n=978). This analysis includes 38,659 individuals (20-90 years old; 8492/22% women) who were predominantly white, well educated, and within the middle to upper socioeconomic strata. All participants gave written, informed consent to participate in the study, which was approved annually by The Cooper Institute Institutional Review Board.
Clinical Examination
Details of the ACLS clinical examination are detailed elsewhere.
Briefly, examinations were completed after an overnight fast and included an extensive physical examination inclusive of BMI, waist circumference measured at the umbilicus, and resting blood pressure measured with the patient in the seated position using standard auscultation methods after 5 minutes of sitting quietly and the averaging of 2 or more readings separated by 2 minutes.
Recommendations for blood pressure measurement in humans and experimental animals, part 1: blood pressure measurement in humans: a statement for professionals from the Subcommittee of Professional and Public Education of the American Heart Association Council on High Blood Pressure Research.
Hypertension was defined as systolic blood pressure of 140 mm Hg or higher and/or diastolic blood pressure of 90 mm Hg or higher or by physician diagnosis. Triglycerides, total cholesterol, high-density lipoprotein cholesterol (HDL-C), and fasting plasma glucose were measured using automated techniques in accordance with the standards of the Centers for Disease Control and Prevention lipid standardization program. Hypercholesterolemia and diabetes were defined as a total cholesterol concentration of 240 mg/dL or higher (to convert to mmol/L, multiply by 0.0259) and a fasting glucose concentration of 126 mg/dL or higher (to convert to mmol/L, multiply by 0.0555), respectively, or by previous physician diagnosis.
Participants completed a standardized questionnaire on medical history, including a personal history of myocardial infarction, stroke, hypertension, diabetes, and cancer; parental history of premature CVD, defined as myocardial infarction, coronary artery bypass, angioplasty, or angina at younger than 50 years; smoking status; alcohol intake; and physical activity. Physical inactivity was defined as reporting no physical activity during leisure time 3 months before the examination.
Cardiorespiratory Fitness
We assessed fitness using a modified Balke maximal treadmill exercise test.
Test end points included volitional exhaustion or termination for medical reasons. We have reported that total exercise test time correlates highly (r≥0.92) with measured maximal oxygen uptake in men and women.
To standardize interpretation of exercise test performance, maximal metabolic equivalents (METs; 1 MET=3.5 mL/kg per minute of oxygen uptake) were estimated on the basis of the final treadmill speed and grade.
Maintaining consistency with previous ACLS reports, fitness was classified as low, moderate, and high corresponding to the lower 20%, the middle 40%, and the upper 40%, respectively, of the age- and sex-specific distributions for treadmill exercise duration.
We calculated MetSyn according to the NCEP ATP III criteria by creating a continuous score on the basis of the mean individual component z scores comprising MetSyn.
National Cholesterol Education Program (NCEP) Expert Panel on Detection, Evaluation, and Treatment of High Blood Cholesterol in Adults (Adult Treatment Panel III) Third Report of the National Cholesterol Education Program (NCEP) Expert Panel on Detection, Evaluation, and Treatment of High Blood Cholesterol in Adults (Adult Treatment Panel III) final report.
We checked all variables for distribution normality before the analysis, subsequently transforming several variables for skewness. Natural logarithms were applied to BMI, triglycerides, glucose, and systolic blood pressure. Square roots were applied to total cholesterol and HDL-C. However, untransformed data summarizing participant characteristics are presented for ease of interpretation. The mean levels of continuous variables were compared using analysis of the variance, whereas χ2 tests compared the distribution of categorical variable values. Multiple linear regression models were used to examine the association among fitness and single and clustered MetSyn scores using 3 models. Model 1 was adjusted for age and examination year. Model 2 was adjusted for age, examination year, BMI, smoking (current smoker or not), alcohol intake (>14 drinks per week for men and >7 drinks per week for women or not), and family history of CVD. A limitation to the ACLS data set is a lack of data on medications and diet information, which may bias our findings. In model 3, we adjusted for age, examination year, BMI, physical activity (active or inactive), smoking (current smoker or not), alcohol intake (>14 drinks per week for men and >7 drinks per week for women or not), hypercholesterolemia, hypertension and diabetes (present or not for each), and family history of CVD (present or not).
Analysis of covariance was used to assess differences in single and clustered MetSyn components across levels of fitness (low, moderate, and high) after adjusting for confounders. Analysis of variance post hoc analyses were conducted for each between-group comparison (low vs moderate, moderate vs high, and low vs high) using Bonferroni adjustments. Quadratic regression analyses were performed to examine the reduction in the MetSyn score as a summed z score vs estimated CRF (ie, METs; Figure 1). Data analyses were performed using PWSA statistical package, version 18.0 (SPSS Inc), and all P values are 2-sided with an α level of .05.
Figure 1Data represent the adjusted mean (SEM) for men (left) and women (right) for averaged metabolic syndrome summed z scores on the basis of 3 statistical models. Model 1 was adjusted for age and examination year. Model 2 was adjusted for age, examination year, body mass index, smoking (current smoker or not), alcohol intake (>14 drinks per week for men and >7 drinks per week for women or not), and family history of cardiovascular disease. Model 3 was adjusted for age, examination year, body mass index, physical activity (active or inactive), smoking (current smoker or not), alcohol intake (>14 drinks per week for men and >7 drinks per week for women or not), hypercholesterolemia, hypertension and diabetes (present or not for each), and family history of cardiovascular disease. ∗P<.001; ∗∗P=.02; †P=.04; ††P=.008.
Demographic characteristics (Table 1), independent MetSyn composite scores for men (Table 2) and women (Table 3), and composite MetSyn scores (Figure 2) are presented.
Table 1Characteristics of the Study Population, Aerobics Center Longitudinal Study, 1979-2006
Differences between sexes and fitness categories were examined by analysis of the variance and χ2 tests for continuous and categorical variables, respectively.
b SI conversion factor: To convert total cholesterol to mmol/L, multiply by 0.0259.
c Differences between sexes and fitness categories were examined by analysis of the variance and χ2 tests for continuous and categorical variables, respectively.
d P<.001.
e Values were natural log-transformed before analysis, but nontransformed values are presented.
f Values were square root–transformed before analysis, but nontransformed values are presented.
SI conversions factors: To convert HDL-C to mmol/L, multiply by 0.0259; to convert triglycerides to mmol/L, multiply by 0.0113; to convert glucose to mmol/L, multiply by 0.055.
Values are expressed as adjusted mean (SEM). Model 1 was adjusted for age and examination year. Model 2 was adjusted for age, examination year, body mass index, smoking (current smoker or not), alcohol intake (>14 drinks per week for men and >7 drinks per week for women or not), and family history of cardiovascular disease. Model 3 was adjusted for age, examination year, body mass index, physical activity (active or inactive), smoking (current smoker or not), alcohol intake (>14 drinks per week for men and >7 drinks per week for women or not), hypercholesterolemia, hypertension and diabetes (present or not for each), and family history of cardiovascular disease.
Values were natural log-transformed before analysis, but nontransformed values are presented.
Model 1
124.9±0.2
121.5±0.1
120.2±0.1
<.001
<.001
<.001
<.001
Model 2
121.9±0.2
121.0±0.1
121.2±0.1
.02
.003
.04
.71
Model 3
121.5±0.2
120.9±0.1
121.3±0.1
.85
.14
.02
>.99
Diastolic blood pressure (mm Hg)
Model 1
85.1±0.2
82.4±0.1
80.0±0.1
<.001
<.001
<.001
<.001
Model 2
82.6±0.2
82.0±0.1
80.8±0.1
<.001
.002
<.001
<.001
Model 3
88.2±0.2
81.9±0.1
81.0±0.9
<.001
.19
<.001
<.001
a HDL-C = high-density lipoprotein cholesterol.
b SI conversions factors: To convert HDL-C to mmol/L, multiply by 0.0259; to convert triglycerides to mmol/L, multiply by 0.0113; to convert glucose to mmol/L, multiply by 0.055.
c Values are expressed as adjusted mean (SEM). Model 1 was adjusted for age and examination year. Model 2 was adjusted for age, examination year, body mass index, smoking (current smoker or not), alcohol intake (>14 drinks per week for men and >7 drinks per week for women or not), and family history of cardiovascular disease. Model 3 was adjusted for age, examination year, body mass index, physical activity (active or inactive), smoking (current smoker or not), alcohol intake (>14 drinks per week for men and >7 drinks per week for women or not), hypercholesterolemia, hypertension and diabetes (present or not for each), and family history of cardiovascular disease.
d Values were natural log-transformed before analysis, but nontransformed values are presented.
e Values were square root–transformed before analysis, but nontransformed values are presented.
SI conversion factors: To convert HDL-C to mmol/L, multiply by 0.0259; to convert triglycerides to mmol/L, multiply by 0.0113; to convert glucose to mmol/L, multiply by 0.0555.
Results expressed as adjusted mean (SEM). Model 1 was adjusted for age and examination year. Model 2 was adjusted for age, examination year, body mass index, smoking (current smoker or not), alcohol intake (>14 drinks per week for men and >7 drinks per week for women or not), and family history of cardiovascular disease. Model 3 was adjusted for age, examination year, body mass index, physical activity (active or inactive), smoking (current smoker or not), alcohol intake (>14 drinks per week for men and >7 drinks per week for women or not), hypercholesterolemia, hypertension and diabetes (present or not for each), and family history of cardiovascular disease.
Values were natural log-transformed before analysis, but nontransformed values are presented.
Model 1
118.3±0.5
113.8±0.3
111.9±0.2
<.001
<.001
<.001
<.001
Model 2
114.2±0.5
112.9±0.3
112.9±0.2
.04
.90
.12
>.99
Model 3
113.7±0.5
112.8±0.2
113.1±0.2
.38
.50
>.99
>.99
Diastolic blood pressure (mm Hg)
Model 1
79.8±0.3
77.2±0.2
75.4±0.1
<.001
<.001
<.001
<.001
Model 2
77.2±0.3
76.7±0.2
76.1±0.1
.004
.54
.01
.02
Model 3
76.9±0.3
76.7±0.2
76.2±0.1
.052
>.99
.16
.60
a HDL-C = high-density lipoprotein cholesterol.
b SI conversion factors: To convert HDL-C to mmol/L, multiply by 0.0259; to convert triglycerides to mmol/L, multiply by 0.0113; to convert glucose to mmol/L, multiply by 0.0555.
c Results expressed as adjusted mean (SEM). Model 1 was adjusted for age and examination year. Model 2 was adjusted for age, examination year, body mass index, smoking (current smoker or not), alcohol intake (>14 drinks per week for men and >7 drinks per week for women or not), and family history of cardiovascular disease. Model 3 was adjusted for age, examination year, body mass index, physical activity (active or inactive), smoking (current smoker or not), alcohol intake (>14 drinks per week for men and >7 drinks per week for women or not), hypercholesterolemia, hypertension and diabetes (present or not for each), and family history of cardiovascular disease.
d Values were natural log-transformed before analysis, but nontransformed values are presented.
e Values were square root–transformed before analysis, but nontransformed values are presented.
Figure 2Data present the quadratic regression of individual metabolic syndrome summed z scores and accompanying 95% CIs vs estimated cardiorespiratory fitness (ie, metabolic equivalents [METs]) from graded exercise testing for men (A) and women (B).
In men, we observed a significant trend of lower MetSyn scores across fitness groups regardless of analytical model (Figure 2, P for trend <.001 for all models). Between-group post hoc assessments demonstrated significant differences for low vs moderate, low vs high, and moderate vs high fitness levels (P<.001 for all models).
In women, we observed a significant trend of lower MetSyn scores across fitness groups. Models 1 and 2 demonstrated the same level of significance (P for trend <.001), whereas model 3 was slightly lower (P for trend <.007). Between-group post hoc assessments revealed significant differences for low vs moderate, low vs high, and moderate vs high fitness levels (model 1, P < .001). Model 2 was also significant for each comparison for low vs moderate (P=.04), low vs high (P<.001), and moderate vs high (P=.008) fitness levels. Model 3 revealed only a significant difference between the low- and high-fit participants (P=.02).
Waist Circumference
In men, we observed significant trends of a smaller waist circumference across fitness groups (P for trend <.001 for all models) with significant post hoc differences for the low vs moderate, low vs high, and moderate vs high fitness levels (P<.001 for all models and comparisons).
In women, we observed significant trends of smaller waist circumference across fitness groups (P for trend <.001 for all models). Contrasting with men, we observed several post hoc differences. In model 1, we observed significant differences for low vs moderate, low vs high, and moderate vs high fitness levels (P<.001 for all). For models 2 and 3, we found no significant differences between the low and moderate fitness groups. In model 2, the comparisons between the low vs high and moderate vs high groups remained consistent (P<.001); however, model 3 revealed the low vs high fitness group to be slightly different (P=.004).
Triglycerides
In men, we observed a significant trend of lower triglyceride concentrations across fitness groups (P for trend <.001 for all models). Our post hoc assessments further revealed significant differences for low vs moderate fitness levels, low vs high fitness levels, and moderate vs high fitness levels (P<.001 for all models and comparisons).
In women, we observed a significant trend of lower triglyceride concentrations across fitness groups (P for trend <.001 for all models). In contrast to men, we observed several post hoc analysis differences. In model 1, we observed significant differences for low vs moderate, low vs high, and moderate vs high fitness levels (P<.001 for all). When using models 2 and 3, we found no significant differences between the low and moderate fitness groups, whereas the low vs high and moderate vs high group comparisons revealed the same level of significance (P<.001).
High-Density Lipoprotein Cholesterol
In men, we observed a significant trend of higher HDL-C concentrations across fitness groups (P for trend <.001 for all models). Between-group post hoc assessments were low vs moderate, low vs high, and moderate vs high fitness levels for model 1 (P<.001 for all), with a similar pattern noted for model 2 (low vs moderate group difference, P=.20). No significant difference was noted for the low vs moderate groups in model 3.
In women, we observed a significant trend of a higher HDL-C concentration across fitness groups (P for trend <.001 for all models). Contrasting men, we observed several differences for our post hoc analyses. In model 1, we observed significant differences for low vs moderate, low vs high, and moderate vs high fitness levels (P<.001 for all). However, when using models 2 and 3, we found no significant differences between the low and moderate fitness groups, whereas the low vs high and moderate vs high comparisons remained significant (P<.001).
Fasting Blood Glucose
In men, we observed a significant trend of lower glucose concentrations across fitness groups (P for trend <.001 for all models). Between-group post hoc analyses further revealed significant differences for low vs moderate, low vs high, and moderate vs high fitness levels (P<.001 for all models and comparisons).
In women, we only observed a significant trend for model 1 (P for trend <.001). Accordingly, our post hoc assessment between groups further revealed significant differences for low vs moderate, low vs high, and moderate vs high fitness levels (model 1, P<.001 for all comparisons).
Systolic Blood Pressure
In men, we observed a significant trend of lower systolic blood pressures across fitness groups for model 1 (P for trend <.001) and model 2 (P for trend <.02). Between-group post hoc assessments in model 1 are low vs moderate, low vs high, and moderate vs high fitness levels (P>.99). For model 2, significant between-group differences were noted for low vs moderate (P=.003) and low vs high (P=.04) fitness levels. In model 3, only the low vs high fitness group comparison was significant (P<.02).
Women also had a significant trend for model 1 (P for trend <.001) and model 2 (P for trend =.004) but not for model 3 (P=.052). For our post hoc comparison, all between-group comparisons were significant for model 1 (P<.001 for all). No significant between-group comparisons were noted for model 2 or model 3.
Diastolic Blood Pressure
In men, we observed a significant trend of lower diastolic blood pressures across fitness groups (P for trend <.001 for all models). Our post hoc assessment between groups further revealed significant differences for model 1: low vs moderate, low vs high, and moderate vs high fitness levels (P<.001 for all models). For model 2, all between-group comparisons were also found to be significant: low vs moderate (P<.002), low vs high (P<.001), and moderate vs high (P<.001). For model 3, no significant differences were observed for the low vs moderate group comparison. However, the low vs high and moderate vs high groups were both significant (P<.001).
Women also had a significant trend for model 1 (P for trend <.001), model 2 (P for trend = .004), and model 3 (P for trend = .05). Within model 1, significant between-group differences were noted for low vs moderate, low vs high, and moderate vs high fitness levels (P<.001 for all models). Within model 2, significant differences were only noted between the low and high fitness groups (P=.01) and the moderate and high fitness groups (P=.02).
Discussion
The primary finding from our current study is the observation of a significant inverse relationship between fitness and MetSyn for both men and women regardless of the analytical model we used (Figure 1). For men, each model demonstrated significant group differences among each fitness category. In women, models 1 and 2 demonstrated the same pattern as men; however, model 3 did not reveal a significant difference between the moderate and high fitness groups. This latter finding may be in part due to the degree of adjustment in model 3, which is covaried for hypercholesterolemia, hypertension, and diabetes, the smaller sample of women in this study, and subtle differences for factors that influence insulin resistance between the sexes because of the use of oral contraceptives.
Insulin sensitivity index, acute insulin response, and glucose effectiveness in a population-based sample of 380 young healthy Caucasians: analysis of the impact of gender, body fat, physical fitness, and life-style factors.
When considering model 3, readers should note that the medications we adjusted for are important to the calculation of MetSyn and should be interpreted accordingly. For the purposes of our discussion, we focus on model 2, yet present information from all 3 models in our tables and Figure 2 so that the readers can make their own interpretations of our findings.
Numerous cross-sectional and prospective studies have examined the relationship among physical activity, fitness, and MetSyn and have thoroughly been reviewed elsewhere.
We are able to extend previous findings on the basis of a more than 4-fold increase in the number of participants included in our current study. An important distinction in discussing these reports is the separation of terms used to evaluate various findings. In our current analysis, we report on the effects of fitness while covarying our analysis (model 3) for physical activity. However, it can be argued that fitness is largely genetic, though trainable with exercise, and physical activity is a practiced behavior that does not always carry with it higher levels of fitness. In an effort to separate these 2 distinctions, Wareham et al
examined a small mixed-sex cohort for the prevalence of MetSyn, in whom physical activity level and fitness, predicted by a submaximal exercise test, were measured. Although the authors reported that fitness revealed a stronger relationship with MetSyn, they also demonstrated that physical activity, adjusted for fitness, also plays a significant role. Nonetheless, some caution should be exercised when interpreting these findings given the submaximal nature of the maximum oxygen consumption (o2max) assessment.
In a larger study using more robust measures of CRF via measured o2max, Lakka et al
reported that men who engaged in moderate-intensity (≥4.5 METs) leisure time physical activity of less than 1.0 hour per week were 60% more likely to have MetSyn compared with their counterparts engaging in more than 3.0 hours per week after adjusting for covariates, including o2max. Further analysis of this same cohort also revealed that men with a o2max less than 29.1 mL−1 ∙ kg−1 ∙ min−1 were almost 7 times more likely to have MetSyn than those with a o2max of 35.5 mL−1 ∙ kg−1 ∙ min−1 or greater. Therefore, physical activity and fitness appear to play a role in the prevalence of MetSyn. Although others have reported similar findings, the association between physical activity and MetSyn is much steeper in those individuals who are less fit.
An examination of model 3 from our current analysis supports this finding given its adjustment for physical activity. On the basis of these results, several clinical considerations should be examined.
The foremost consideration is the long-held fact that fitness can be improved and the subsequent health effects are quite notable. For example, Blair et al
have reported that less fit individuals substantially improve their risk for all-cause and CVD mortality by improving their fitness and moving into a higher fitness category. In their study, Blair et al found that changing fitness categories was synonymous with a 2- to 4-minute (1- to 2-MET) improvement in time to exhaustion on a standardized treadmill test. This finding is clinically important because each 1-MET improvement in fitness carries with it a 13% and 15% reduction in all-cause and CVD risk mortality, respectively.
An advantage to using estimated METs and not directly measuring o2max is body mass. In essence, those with higher METs and excess adiposity will have a reduced oxygen consumption value during o2max testing. Estimation of METs potentially avoids this controversy. Further, the minimal requirement for changing fitness category should be emphasized because such an improvement is easily achievable by most individuals participating in exercise training at a low to moderate fitness intensity.
Effects of different doses of physical activity on cardiorespiratory fitness among sedentary, overweight or obese postmenopausal women with elevated blood pressure: a randomized controlled trial.
recently examined changes in fitness vs development of CVD risk factors, inclusive of MetSyn, in a cross-sectional study and found that participants maintaining or improving fitness had a lower risk of developing each outcome even after adjusting for possible confounders inclusive of fatness and fitness for each. Our premise is clinically supported by trials examining the prevalence of MetSyn in those undertaking exercise training.
In the Diabetes Prevention Program, individuals with type 2 diabetes undergoing treatment with increased physical activity and dietary modification had a 41% reduction in developing MetSyn accompanying weight loss.
Putting the Diabetes Prevention Program into practice: a program for weight loss and cardiovascular risk reduction for patients with metabolic syndrome or type 2 diabetes mellitus.
reported that 30% of participants classified as having MetSyn in the Heritage Family Study and undertaking 20 weeks of aerobic exercise no longer qualified as having MetSyn after exercise training. These findings are supported by others.
Exercise training amount and intensity effects on metabolic syndrome (from Studies of a Targeted Risk Reduction Intervention through Defined Exercise).
have extended the body of research on exercise training and MetSyn by examining the exercise dose in men and women assigned to a 6-month sedentary control group or 3 exercises doses: (1) low amount and moderate intensity (equivalent to walking approximately 19 km per week), (2) low amount and vigorous intensity (equivalent to jogging approximately 19 km per week), or (3) high amount and vigorous intensity (equivalent to jogging approximately 32 km per week). The authors of this study reported that although low-amount, moderate-intensity exercise and high-amount, vigorous-intensity exercise improved MetSyn relative to inactive controls, the low-amount, vigorous-intensity group had no such relationship. This latter study presents an interesting, though counterintuitive, finding, possibly suggesting that short-duration, higher-intensity exercise may not be effective in reducing MetSyn. However, 2 exercise trials have found that interval training, which is short-duration, high-intensity exercise by nature, has a greater effect in reducing MetSyn in men.
Earnest CP, Lupo M, Thibodeaux J, et al. Interval training in men at risk for insulin resistance. Int J Sports Med. In press. http://dx.doi.org/10.1055/s-0032-1311594.
Although we have attempted to focus our discussion on the effect of fitness and MetSyn as a singular component, it would be remiss not to briefly address our findings regarding individual MetSyn component scores. Our most noteworthy finding is the strong, inverse association between fitness and waist circumference in both men (models 2 and 3; r2=0.78) and women (models 2 and 3; r2=0.71; Table 4). This is an important observation because Reaven’s hypothesis suggests that insulin resistance is the mediating cause of obesity, whereas in contrast the NCEP definition of MetSyn implies that central adiposity has the central role of driving insulin resistance because overweight and obese individuals have a greater prevalence of MetSyn than their nonobese counterparts.
The metabolic syndrome: prevalence and associated risk factor findings in the US population from the Third National Health and Nutrition Examination Survey, 1988-1994.
Although our findings preclude a thorough discussion surrounding the mechanisms of action involved with visceral adiposity and MetSyn, the topic has been thoroughly reviewed elsewhere.
Briefly, however, it has been suggested by some that MetSyn alone may not adequately predict CVD and that abdominal obesity (hence, “dysfunctional adipose tissue”) may be more essential for clinical purposes.
Moreover, regardless of the clinical significance of MetSyn vs abdominal obesity, all of our statistical models reveal a strong relationship between fitness and waist circumference. In addition, weight loss is not essential for reducing waist circumference via exercise participation and, therefore, should be encouraged in participants in need of reducing MetSyn risk, regardless of weight loss.
Table 4Standardized Regression Coefficients Examining the Association Between Cardiorespiratory Fitness and Single and Clustered Metabolic Syndrome Components in Men (N=30,167) and Women (N=8492)
Model 1 was adjusted for age and examination year. Model 2 was adjusted for age, examination year, body mass index, smoking (current smoker or not), alcohol intake (>14 drinks per week for men and >7 drinks per week for women or not), and family history of cardiovascular disease. Model 3 was adjusted for age, examination year, body mass index, physical activity (active or inactive), smoking (current smoker or not), alcohol intake (>14 drinks per week for men and >7 drinks per week for women or not), hypercholesterolemia, hypertension and diabetes (present or not for each), and family history of cardiovascular disease.
Values were natural log-transformed before analysis.
0.16
−.13
0.014
<.001
0.21
−.01
0.000
.60
0.26
.01
0.000
.46
Diastolic blood pressure (mm Hg)
0.10
−.16
0.021
<.001
0.14
−.05
0.001
<.001
0.20
−.04
0.001
.006
Metabolic syndrome z score
0.27
−.27
0.063
<.001
0.44
−.05
0.002
<.001
0.48
−.03
0.001
.002
a HDL-C = high-density lipoprotein cholesterol; METs = metabolic equivalents.
b Model 1 was adjusted for age and examination year. Model 2 was adjusted for age, examination year, body mass index, smoking (current smoker or not), alcohol intake (>14 drinks per week for men and >7 drinks per week for women or not), and family history of cardiovascular disease. Model 3 was adjusted for age, examination year, body mass index, physical activity (active or inactive), smoking (current smoker or not), alcohol intake (>14 drinks per week for men and >7 drinks per week for women or not), hypercholesterolemia, hypertension and diabetes (present or not for each), and family history of cardiovascular disease.
c Values were natural log-transformed before analysis.
d Values were square root–transformed before analysis.
Limitations of the current study include a focus on participants who were primarily white, well educated, and of middle to upper socioeconomic status. The results may not apply to other racial groups. However, the homogeneity of our sample strengthens the internal validity of our findings by reducing potential confounding by unmeasured factors related to socioeconomic status, such as income, education, or prestige. Overall, our study found that fitness is strongly and inversely related to MetSyn. The strength of these associations persists to waist circumference, which, regardless of hypotheses focusing on insulin resistance or central adiposity, demonstrates the effectiveness of fitness on the overall MetSyn score and the one individual component most highly associated with the development of MetSyn, type 2 diabetes, and CVD mortality risk.
Acknowledgments
We thank Cooper Clinic physicians and technicians and staff at the Cooper Institute.
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Grant Support: This study was supported by National Institutes of Health grants AG06945 , HL62508 , and R21DK088195 and the Spanish Ministry of Education grant EX-2010-1008 .
Potential Competing Interests: Dr Blair receives book royalties (<$5000 per year) from Human Kinetics; honoraria for service on the scientific/medical advisory boards for Alere, Technogym, Santech, Clarity, and Jenny Craig; and honoraria for lectures and consultations from scientific, corporate, educational, and lay groups. He has received research grants from the National Institutes of Health , The Coca-Cola Company , Department of Defense , and Body Media . Dr Church receives honoraria for lectures from scientific, educational, and lay groups. Dr Church has a book entitled Move Yourself: The Cooper Clinic Medical Director’s Guide to All the Healing Benefits of Exercise. Dr Church has received research funding from the American Heart Association and the National Institutes of Health as well as unrestricted research funding from Coca-Cola . Dr Church has overseen study sites for large pharmaceutical trials funded by Sanofi Aventis, Orexigen, Arena, and Amylin . Dr Church is a member of the Jenny Craig Medical Advisory Board and has served as a consultant to Technogym, Trestle Tree, Vivus, Lockton-Dunning, and Neuliven Health. In addition, he serves as the senior medical adviser for Catapult Health.
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