The metabolic syndrome, which represents the constellation of metabolic abnormalities including central obesity, dyslipidemia, hypertension, insulin resistance, and glucose intolerance, has become one of the major global public health challenges. The syndrome has a high worldwide prevalence, after a severe increase during the past two decades (9). In addition, it predicts the development of type 2 diabetes (18), cardiovascular disease (CVD), and all-cause mortality (19) in nondiabetic individuals. Consequently, there is an urgent need for strategies to prevent an emerging global epidemic of the metabolic syndrome.
The first-line therapy for treatment of the metabolic syndrome is lifestyle modification, including an increase in physical activity (9). Previous cross-sectional studies have suggested an inverse association between aerobic fitness and several risk factors associated with the metabolic syndrome (21,28). Furthermore, a high level of aerobic fitness in adolescence is protective for the development of diabetes, hypertension, hypercholesterolemia (5), and obesity (10) in adulthood. In addition, aerobic fitness is a strong, independent predictor of metabolic syndrome incidence (5,20). Therefore, aerobic exercise, which results in higher levels of aerobic fitness, might play an important role in the primary and secondary prevention of the metabolic syndrome and associated chronic diseases.
Resistance training also has become part of the physical activity recommendations for preventing chronic diseases in adults (2). Some intervention studies suggest beneficial effects of resistance training on whole-body insulin action (1), body composition and central obesity (2), blood pressure (6), triglycerides, and HDL cholesterol blood levels (12). Consequently, biological pathways might exist to explain and support protective effects of resistance training and muscular strength on the development and treatment of the metabolic syndrome. Nevertheless, until now, few studies have examined the association between muscular strength and the metabolic syndrome.
Jurca et al. (15) examined this association in adult men using a cross-sectional design. They found that muscular strength was inversely associated with prevalence of the metabolic syndrome independently of aerobic fitness and several confounding variables. Further longitudinal analyses in adult men yielded rather comparable results (14). Muscular strength was inversely associated with incidence of the metabolic syndrome after extensive adjustment for confounders. This association was marginally nonsignificant after additional adjustment for aerobic fitness. These findings might indicate a protective effect of strength in the development of the metabolic syndrome that would be in addition to the benign effect of aerobic fitness in men. Until now, no studies have investigated the independent and combined association of muscular strength and aerobic fitness with the metabolic syndrome in women.
Studies examining the association between muscular strength, aerobic fitness, and the metabolic syndrome often use a binary definition of the syndrome (5,20,14,15). However, increasing evidence supports the use of a continuous metabolic syndrome risk score in epidemiological analyses, rather than a binary definition (16), because a) dichotomizing continuous outcome variables reduces statistical power to detect associations (25); b) CVD risk is a progressive function of several metabolic syndrome risk factors, eliminating the need to dichotomize these risk factors using cut points (16); and c) CVD and diabetes risk increase progressively with an increasing number of metabolic syndrome risk factors (17), eliminating the need to dichotomize the presence of the syndrome in case of, for example, three or more present risk factors (16).
Aerobic fitness and muscular strength are both inversely associated with central and general obesity (2,10). Furthermore, obesity predicts the development of several individual risk factors of the metabolic syndrome and of type 2 diabetes (13). Therefore, the association of aerobic fitness and muscular strength with the individual risk factors of the metabolic syndrome may (partially) be mediated by obesity indicators.
The aim of the present study was twofold. First, the independent and combined association of muscular strength and aerobic fitness with a validated continuous metabolic syndrome risk score (30) was examined in both male and female adults aged 18-75. Second, the relationship of muscular strength and aerobic fitness with the individual continuous risk factors of the metabolic syndrome, and the extent to which obesity mediates this relationship, was determined.
Data for this study were collected by the Flemish Policy Research Centre Sport, Physical Activity and Health (SPAH) between October 2002 and April 2004, for a cross-sectional survey on the relationship between physical activity, physical fitness, and several health parameters. For this purpose, the National Institute of Statistics randomly selected a community sample of 18- to 75-yr-old men and women in the Flemish part of Belgium. Subjects were asked to visit the SPAH examination center to provide a fasting blood sample and go through a medical examination, anthropometric measurements, physical fitness tests, and some questionnaires. Participants with complete data for all risk factors of the metabolic syndrome, muscular strength, aerobic fitness, and for all confounding variables used in the analyses, were included in the current study. Participants were excluded in cases of history or evidence of cardiovascular disease (abnormal heart auscultation or rest electrocardiogram; sudden death of father or brother before the age of 45, or of mother or sister before the age of 55; systolic blood pressure > 150 mm Hg or diastolic blood pressure > 100 mm Hg; use of antihypertensive or lipid-lowering drugs), diabetes mellitus, or if they did not achieve at least 85% of the age-predicted maximal heart rate during a maximal cycle ergometer test. Consequently, results are based on data of 571 men and 448 nonpregnant women, aged 18-75. The study was approved by the Ghent University ethics committee. Before participation, the purpose and procedures of the study were explained, and subjects gave their written informed consent.
Subjects were asked to fast from 11:00 p.m. the evening before visiting the laboratory. A sample of fasting blood was taken from an antecubital vein of subjects in supine position. All samples were stored at 4°C before they were transferred to the lab at 11:00 a.m. They were analyzed before 5:00 p.m. the same day. Triglyceride was analyzed using the lipase/glycerol kinase/glycerol phosphate oxidase enzymatic method. HDL cholesterol was analyzed using the homogeneous polyanion/cholesterol esterase/oxidase enzymatic method. Glucose was analyzed using the hexokinase method. Triglycerides, HDL cholesterol, and glucose were measured on an Olympus AU5400 analyzer (Olympus Diagnostica, Hamburg, Germany). The coefficients of variation between days were 1.4% (at 2.07 mM) for triglycerides, 3.9% (at 2.04 mM) for HDL cholesterol, and 2.0% (at 16.71 mM) for glucose.
A physician measured blood pressure in the left arm of subjects who had been seated for 10 min, using a NOVA Presameter mercury sphygmomanometer (Riester, Jungingen, Germany). Systolic and diastolic blood pressure were measured three times with 1-min intervals, and the mean of these three measurements was used in analyses.
Anthropometric measurements were taken by trained staff, with participants barefoot and in underwear. Waist circumference was measured using a metal tape (Rosscraft, Surrey, Canada) to the nearest 0.1 cm, at the narrowest level between lowest ribs and iliac crests, in standing position. Body weight was recorded with a digital scale (Seca 841, Seca GmbH, Hamburg, Germany) to the nearest 0.1 kg. Height was measured using a stadiometer (Holtain, Crymych, UK) to the nearest 0.1 cm. Body mass index (BMI) was calculated as [body weight (kg)/(height (m))2]. Percentage body fat (%BF) was evaluated by means of bioelectrical impedance analysis (BIA) on the right side of the body with a tetrapolar BIA analyzer (Bodystat 1500, Bodystat Ltd, Isle of Man, UK) in subjects who had been in supine position for 5 min with the arms and legs in abduction. Fat-free mass (FFM, kg) was calculated based on the %BF measured by means of BIA.
Continuous metabolic syndrome risk score.
A validated continuous metabolic syndrome risk score (cMSy) was calculated as described in more detail by Wijndaele et al. (30). In short, cMSy is based on the five risk factors in the National Cholesterol Education Program definition (11), namely, waist circumference, triglycerides, HDL cholesterol, blood pressure (systolic and diastolic), and fasting plasma glucose. Calculation involved three successive steps: a) all variables were normalized (log10) because of their nonnormal distribution, and a blood pressure index was computed by averaging systolic and diastolic blood pressure; b) principal component analysis with varimax rotation was applied to the individual risk factors to derive components that represented large fractions of the metabolic syndrome variance and, consequently, to give each risk factor the most appropriate weight in calculating cMSy. This analysis revealed two principal components with eigenvalue ≥ 1.0 in both genders; and c) cMSy was computed by summing both individual principal component scores, each weighted for the relative contribution of principal components 1 and 2 in the explained variance. The resulting cMSy was 0 ± 1.42 in men and 0 ± 1.41 in women. This score showed high validity in both genders (30).
A calibrated Biodex System Pro 3 dynamometer (Biodex Medical Systems, Inc., Shirley, NY) was used to measure isometric strength of the knee. Isometric measurements using this device are found to be reliable and valid (8). Strength of the right knee was measured in a standardized manner. In some subjects with a history of injury in the right knee, strength of the left knee was evaluated. Subjects were seated upright on the Biodex chair with the axis of the dynamometer corresponding to the knee-joint axis. Once positioned, the shoulder, waist, thigh, and lower right leg, 2 cm above malleolus medialis, were secured with straps. Participants were asked to cross their arms in front of the thorax during each exercise, to avoid compensatory movements. To measure peak torque of isometric extension at an angle of 60° flexion (0° is straight leg), subjects were instructed to extend their lower leg as hard as possible, regardless of its fixed position. For peak torque of isometric flexion at an angle of 60° flexion, they were asked to maximally flex their knee. During exercise, both the subject and the instructor were able to see the strength curve on the monitor. Subjects were given verbal encouragement to generate the highest possible plateau. Both the extension and flexion exercise were performed twice, and for each exercise, the highest torque measurement of both trials during plateau was included in analyses. Peak torque values were corrected for the effects of gravity.
Total knee strength was defined as the sum of peak torque of isometric extension and flexion at 60°, divided by FFM, because Pearson correlation coefficients between FFM and absolute total knee strength were higher compared with Pearson correlation coefficients found between body weight and absolute total knee strength in men (r = 0.56, P < 0.001 for FFM; r = 0.45, P < 0.001 for body weight) and women (r = 0.54, P < 0.001 for FFM; r = 0.26, P < 0.001 for body weight). To optimally scale absolute total knee strength for differences in FFM, allometric analyses were performed using log-linear adjustment models, to identify the most appropriate exponent for FFM in dividing absolute total knee strength by FFM (27). These analyses revealed an FFM exponent of b = 1.014 ± 0.061 in men and b = 0.975 ± 0.069 in women. Consequently, muscular strength was quantified as (absolute total knee strength)·FFM−1.014 (N·m·kgFFM−1.014) in men and as (absolute total knee strength)·FFM−0.975 (N·m·kgFFM−0.975) in women.
Aerobic fitness was determined by means of a maximal exercise test on an electrically braked Lode Excalibur cycle ergometer (Lode, Groningen, the Netherlands). The standardized exercise protocol started with a workload of 20 W, which was increased stepwise by 20 W·min−1. Participants were instructed to continuously cycle at approximately 70 rpm. They were verbally encouraged to reach a maximal level of exertion. Oxygen consumption was measured directly with breath-by-breath respiratory gas exchange analysis, using a Cortex Metalyser 3B analyzer (Cortex Biophysic GmbH, Leipzig, Germany), which generates highly reliable results (22). Heart rate was registered continuously with a Polar Smart Heart Rate Monitor Set (Polar Electro Oy, Kempele, Finland). Cardiac function during the test was monitored by means of a 12-channel electrocardiogram (Cardiolyzer Ultra, Cortex Biophysic GmbH, Leipzig, Germany). The exercise test was terminated when subjects were exhausted or when the physician stopped the test for medical reasons. A high correlation was found between peak oxygen uptake (V˙O2peak) and exercise duration (men: Pearson r = 0.85, P < 0.001; women: r = 0.87, P < 0.001).
For the current analyses, aerobic fitness was quantified as peak oxygen uptake divided by FFM, because Pearson correlation coefficients between FFM and absolute V˙O2peak were higher in comparison with those found between body weight and absolute V˙O2peak in both genders (men: r = 0.39, P < 0.001 for FFM; r = 0.17, P < 0.001 for body weight; women: r = 0.50, P < 0.001 for FFM; r = 0.20, P < 0.001 for body weight). Allometric analyses (27) revealed an FFM exponent of b = 0.820 ± 0.079 in men and b = 0.973 ± 0.072 in women. Therefore, aerobic fitness was quantified as V˙O2peak·FFM−0.820 (mL·kgFFM−0.820 min−1) in men and as V˙O2peak·FFM−0.973 (mL·kgFFM−0.973 min−1) in women.
Several confounding variables were included in analyses: age (yr), height (cm), education level (low, high), smoking status (current,former, never), and dietary intake (total energy (kcal), saturated fat (%), sugars (%), fiber (g·1000 kcal−1), and alcohol (%)). Education level was assessed using the Flemish Physical Activity Computerized Questionnaire (24), and data were reduced from 13 to 2 levels: secondary school or lower, and university or college. Smoking status was determined by means of the WHO Monica Smoking Questionnaire (29). To assess dietary intake, subjects completed a validated 3-d diet record (7). Diet records were analyzed using Becel Nutrition software (Unilever Co., Rotterdam, the Netherlands).
All analyses were carried out using the SPSS 12.0 statistical software package (SPSS, Inc., Chicago, IL). The metabolic syndrome risk factors were logarithmically transformed (log10) because of their positively skewed distribution, as shown by the Shapiro-Wilk test for normality. Differences between sexes were analyzed by means of independent-samples t-tests and Pearson χ2 tests. All further analyses were carried out in both genders separately.
Separate multiple linear-regression models were used to assess the association of the predictor variables muscular strength and aerobic fitness with cMSy. Model A contained muscular strength or aerobic fitness and was adjusted for all confounding variables: age, height, education level, smoking status, and dietary intake. Model B was additionally adjusted for the other predictor variable to test the independent association of both predictor variables with cMSy. Moreover, the interaction between muscular strength and aerobic fitness on cMSy, adjusted for the same confounding variables, was verified. To evaluate this interaction effect, muscular strength and aerobic fitness were mean centered, and the product of both mean-centered variables was computed for each subject. No further adjustment for adiposity was made in assessing the association between metabolic syndrome risk and muscular strength and aerobic fitness, because waist circumference is integral to the risk score for the metabolic syndrome.
In assessing the association of muscular strength and aerobic fitness with the individual metabolic syndrome risk factors, two models were applied. Model C contained both muscular strength and aerobic fitness and was adjusted for all confounding variables. Additional adjustment for obesity was made in model D to analyze the extent to which obesity mediates this relationship. Two measures of obesity were successively included in model D: waist circumference for central adiposity, and body mass index for general adiposity.
Muscular strength and aerobic fitness were entered as continuous variables in all analyses. Standardized β coefficients are provided, which express the number of standard deviations by which the outcome (cMSy or metabolic syndrome risk factors) changes as a result of a one-standard deviation change in the predictor (muscular strength or aerobic fitness). Because all of the standardized β values are measured in standard deviation units, they facilitate the comparison of the (relative) contribution of each predictor in the model. The minimal level of statistical significance was set at P < 0.05. A priori power analyses were conducted to create a strong study design. The analyses showed that N values of 168 and 156 in males and females, respectively, were sufficient to power the study at 0.8 for a detectable β of 0.15 for muscular strength and aerobic fitness, given the 0.05 level of significance, which confirms the adequacy of the present sample size of males (N = 571) and females (N = 448).
Descriptive statistics of subjects are shown in Table 1. Men showed higher BMI in comparison with women (P < 0.001). However, men also displayed higher FFM (P < 0.001). Furthermore, waist circumference, triglycerides, glucose, and systolic and diastolic blood pressure were higher in men (P < 0.001), whereas HDL cholesterol was higher in women (P < 0.001). Finally, sex differences were found for muscular strength and aerobic fitness, expressed in absolute terms, both being higher in men (P < 0.001). After scaling both variables for FFM, mean values for muscular strength and aerobic fitness in men were 4.50 ± 0.77 N·m·kgFFM−1.014 and 98.14 ± 20.19 mL·kgFFM−0.820 min−1, respectively. In women, scaled mean values were 4.84 ± 0.88 N·m·kgFFM−0.975 for muscular strength and 44.76 ± 8.64 mL·kgFFM−0.973 min−1 for aerobic fitness. No comparison between men and women was made for these scaled values because of the different FFM exponents between sexes for both variables.
Associations of muscular strength and aerobic fitness with cMSy.
Table 2 presents the individual and combined associations of muscular strength and aerobic fitness with cMSy. Muscular strength was negatively associated with cMSy in men (P < 0.05) and women (P < 0.001) after adjustment for age, height, education level, smoking status, and dietary intake (model A). Additional adjustment for aerobic fitness (model B) attenuated this association to a nonsignificant level (P > 0.05) in men. In women, however, the association between strength and cMSy remained almost equally strong in model B (P < 0.001). For aerobic fitness, an inverse relationship, adjusted for all confounding variables, was found with cMSy in both men and women (P < 0.001) (model A). These associations remained similar (P < 0.001) after additional adjustment for muscular strength (model B).
A model including muscular strength, aerobic fitness, an interaction term (muscular strength × aerobic fitness), and all confounding variables did not reveal an interaction of strength and aerobic fitness on metabolic syndrome risk in men (P = 0.207) or in women (P = 0.461).
Associations of muscular strength and aerobic fitness with the metabolic syndrome risk factors.
Table 3 shows the regression results for the individual continuous risk factors of the metabolic syndrome. Model C includes both muscular strength and aerobic fitness and is adjusted for all confounding variables. Model D is additionally adjusted for waist circumference. In men, no associations were found between muscular strength and any of the metabolic syndrome factors (P > 0.05). In women, strength was inversely associated with waist circumference and triglycerides (P < 0.001) and was positively associated with HDL cholesterol (P < 0.05) (model C). The association between strength and triglycerides in women was partially mediated by waist circumference but remained significant (P < 0.01). However, additional correction for waist circumference attenuated the association with HDL cholesterol to a nonsignificant level (P > 0.05) (model D).
Additional correction in model D for BMI, measuring general adiposity instead of waist circumference, yielded very comparable results for muscular strength (triglycerides: β = −0.137, P < 0.01; HDL cholesterol: β = 0.064, P > 0.05), indicating a comparable mediating effect of BMI and waist circumference on these relationships.
Table 3 also shows that high aerobic fitness was associated with a healthier profile for central adiposity and blood lipids in men (P < 0.001) (model C); this association remained equally significant (P < 0.001) after additional correction for waist circumference (model D). In women, high aerobic fitness was associated with a healthier profile for waist circumference (P < 0.01), HDL cholesterol (P < 0.001), and systolic and diastolic blood pressure (P < 0.01) (model C). Waist circumference displayed only a partially mediating effect on the association with HDL cholesterol in that it remained significant (P < 0.01). However, a stronger mediating effect was found for the associations with systolic and diastolic blood pressure, which were attenuated to a marginally nonsignificant level (P = 0.063 and P = 0.073, respectively) (model D).
Adjusting for BMI instead of waist circumference in model D revealed similar results for aerobic fitness in men (triglycerides: β = −0.185, P < 0.001; HDL cholesterol: β = 0.256, P < 0.001) and women (HDL cholesterol: β = 0.128, P < 0.05; systolic blood pressure: β = −0.072, P > 0.05; diastolic blood pressure: β = −0.074, P > 0.05).
This study examined the combined association of muscular strength and aerobic fitness with metabolic syndrome risk and the independent risk factors of the metabolic syndrome in both male and female 18- to 75-yr-old adults. The main findings of this study are that 1) muscular strength and aerobic fitness are independently and inversely associated with metabolic syndrome risk in women after extensive adjustment for confounding factors. In men, the inverse association between strength and metabolic syndrome risk was smaller and not independent of aerobic fitness, for which an independent inverse association with metabolic syndrome risk was found; 2) muscular strength and aerobic fitness are independently associated with a better profile for several individual risk factors of the metabolic syndrome in women, after extensive adjustment for confounding variables. In men, no independent associations were found for muscular strength, but aerobic fitness was strongly and inversely associated with a better profile for central adiposity and for blood lipids; and 3) most of the significant associations with the individual risk factors were only partially mediated by central and general adiposity indicators.
The inverse association between aerobic fitness and metabolic syndrome risk found in the present study is in line with the results of previous investigations (5,15,20). So far, only two studies have investigated the combined association between strength, aerobic fitness, and the metabolic syndrome, and both studies have done so in a male adult population. In a cross-sectional study, Jurca etal. (15) found an inverse association between muscular strength and prevalence of the metabolic syndrome in men, independent of aerobic fitness and after adjustment for age and smoking. In further longitudinal analyses, they found a significant inverse association between strength and incidence of metabolic syndrome; this association was marginally not significant when further adjusting for aerobic fitness (14). The present study did show an inverse association in men between strength and metabolic syndrome risk, adjusted for age, height, education level, smoking status, and dietary intake. However, this association was attenuated (P > 0.05) after further adjustment for aerobic fitness.
Differences in results for men between studies may be attributed to several factors. First, in both previous studies (14,15), muscular strength was scaled for differences in total body weight instead of FFM. FFM, however, is more strongly correlated with strength. Because fat mass (FM) is highly correlated with waist circumference (r = 0.88 for men and r = 0.87 for women in the present study, P < 0.001) and, to a lesser extent, also with several other risk factors of the metabolic syndrome (ranging from r = −0.19 for HDL cholesterol in men to r = 0.43 for systolic blood pressure in women of the present study, P < 0.001), dividing strength by total body weight (including FM and FFM) instead of FFM might (artificially) strengthen the inverse association found between metabolic syndrome and muscular strength. This hypothesis was confirmed by reconducting regression analyses in the present study, including muscular strength scaled for total body weight instead of FFM. These analyses revealed stronger associations between strength and metabolic syndrome risk, and these associations remained significant after further adjustment for aerobic fitness in both genders (data not shown). Second, results of the present study were more extensively adjusted for confounding variables in comparison with those of both previous studies (14,15). Finally, differences between studies in defining the metabolic syndrome, in characteristics and size of the study samples, and in measurement procedures for strength and aerobic fitness might also partially explain differences in study results.
Aerobic fitness was more strongly associated with metabolic syndrome risk in comparison with muscular strength, and correction for aerobic fitness attenuated the association between strength and metabolic syndrome risk in both genders. However, in women, the inverse association between strength and metabolic syndrome risk remained significant after correction for aerobic fitness. This may suggest a possible protective effect of strength in the development of the metabolic syndrome in addition to the benign effects of aerobic fitness on women. This protective effect may be a function of muscular strength, or it may be indicative of the benign effects of regular participation in resistance training, ameliorating both strength and metabolic homeostasis (14). Both hypotheses, however, support the promotion of resistance training in addition to aerobic fitness training as part of physical activity recommendations in lowering metabolic syndrome risk. Nonetheless, further longitudinal and intervention studies are needed to confirm these assumptions.
Biological pathways through which strength training might lower metabolic syndrome risk include improvements in triglycerides and HDL blood levels (12), blood pressure (6), central adiposity and body composition (2), and whole-body insulin action and glucose uptake (1). Insulin resistance is described as a central risk factor in the pathophysiology of the metabolic syndrome (9). Resistance training might protect against insulin resistance by both an increase in muscle quantity and an increase in skeletal muscle insulin action and glucose uptake per unit of muscle mass, indicating qualitative muscle adaptations (1). Pathways explaining the protective effects of endurance training closely resemble those suggested for resistance training (5). However, the independent associations of strength and aerobic fitness with metabolic syndrome risk, and with several individual risk factors found in women, suggest that both predictors may have unique benefits. Further research is needed to clearly identify mechanisms explaining these unique benefits.
Adiposity indicators only partially mediated most associations found between strength, aerobic fitness, and the independent risk factors of the metabolic syndrome. Bouléet al. (3), examining the association between aerobic fitness and the individual risk factors, found similar results, except for the association with HDL cholesterol in women that disappeared after controlling for total and abdominal adiposity in their study. The present results may indicate that biological pathways other than obesity are also responsible for the associations found. However, because obesity indicators in the present study depend on anthropometric measurements rather than imaging techniques such as dual-energy x-ray absorptiometry or magnetic resonance imaging systems, these results should be interpreted with caution (26).
To our knowledge, this is the first study to report an inverse association between strength and metabolic syndrome risk in women and to examine the combined relationship for muscular strength and aerobic fitness with metabolic syndrome risk in women. Furthermore, no previous studies had examined the association of muscular strength with a continuous risk score for the metabolic syndrome. Although the use of cMSy implies population-specific results, it is more appropriate than using categorical definitions of the metabolic syndrome for epidemiological analyses (16,25,30). Additional strengths of this study include the objective measurement of both strength and aerobic fitness by means of standardized maximal test protocols using highly reliable measurement devices (8,22). Furthermore, strength and aerobic fitness were corrected for differences in FFM by means of allometric scaling, the sample studied covered a wide age range, and extensive adjustment in regression analyses was made for confounding variables, including dietary intake. In previous epidemiological studies examining the association of aerobic fitness and muscular strength with the metabolic syndrome, adjustment for dietary intake was rare. However, diet may influence several risk factors of the metabolic syndrome, and men and women with higher aerobic fitness levels have been shown to have healthier diets than their less fit counterparts (4).
Some limitations of this study also should be addressed. First, the causality of relationships cannot be determined because of the cross-sectional design. However, the confounding influence of health status on level of aerobic fitness and muscular strength may have been partially eliminated by excluding subjects with a history/evidence of CVD and type 2 diabetes. Second, the present study sample may represent a more healthy group of adults, which may partially limit the external validity of the results. A more heterogeneous sample, including more unhealthy individuals and, therefore, more extreme values for all metabolic syndrome risk factors, would probably yield stronger associations. Finally, muscular strength was measured by means of lower body strength only. However, a study comparing knee strength and hand-grip strength in predicting mortality found similar risk estimates for both strength parameters (23). Future longitudinal studies in both genders are needed to investigate a cause-and-effect relationship between total body strength and cMSy, including aerobic fitness and all relevant confounding variables in analyses. Furthermore, more controlled experimental studies should be conducted to clearly identify the physiological mechanisms explaining these associations and the differences found between men and women.
In conclusion, the results of the present study show that muscular strength and aerobic fitness are independently and inversely associated with metabolic syndrome risk in women aged 18-75. This may indicate a protective effect of muscular strength on the metabolic syndrome in addition to the benign effect of aerobic fitness. Furthermore, muscular strength and aerobic fitness were associated with a better profile of several individual risk factors of the metabolic syndrome in women. Most of these associations were only partially mediated by central and general adiposity indicators.
Although cross-sectional, the present results support the inclusion of strength training in physical activity recommendations in addition to aerobic exercise, because both types of physical activity may provide unique benefits in the primary and secondary prevention of the metabolic syndrome. Moreover, resistance training might be a more attractive type of exercise for overweight and obese individuals, who are at a higher risk of developing the metabolic syndrome and who may be averse to endurance training (1).
The Flemish Policy Research Centre Sport, Physical Activity and Health is supported by the Flemish Government.
1. Andersen, J. L., P. Schjerling, L. L. Andersen, and F. Dela. Resistance training and insulin action in humans: effects of de-training. J. Physiol.
2. Banz, W. J., M. A. Maher, W. G. Thompson, et al. Effects of resistance versus aerobic training on coronary artery disease risk factors. Exp. Biol. Med.
3. Boule, N. G., C. Bouchard, and A. Tremblay. Physical fitness and the metabolic syndrome in adults from the Quebec family study. Can. J. Appl. Physiol.
4. Brodney, S., R. S. McPherson, R. A. Carpenter, D. Welten, and S. N. Blair. Nutrient intake of physically fit and unfit men and women. Med. Sci. Sports Exerc.
5. Carnethon, M. R., S. S. Gidding, R. Nehgme, S. Sidney, D. R. Jacobs, and K. Liu. Cardiorespiratory fitness in young adulthood and the development of cardiovascular disease risk factors. JAMA
6. Carter, J. R., C. A. Ray, E. M. Downs, and W. H. Cooke. Strength training reduces arterial blood pressure but not sympathetic neural activity in young normotensive subjects. J. Appl. Physiol.
7. Deriemaeker, P., D. Aerenhouts, M. Hebbelinck, and P. Clarys. Validation of a 3-day diet diary: comparison with a 7-day diet diary and a FFQ. Med. Sci. Sports Exerc.
8. Drouin, J. M., T. C. Valovich-McLeod, S. J. Shultz, B. M. Gansneder, and D. H. Perrin. Reliability and validity of the Biodex system 3 pro isokinetic dynamometer velocity, torque and position measurements. Eur. J. Appl. Physiol.
9. Eckel, R. H., S. M. Grundy, and P. Z. Zimmet. The metabolic syndrome. Lancet
10. Eisenmann, J. C., E. E. Wickel, G. J. Welk, and S. N. Blair. Relationship between adolescent fitness and fatness and cardiovascular disease risk factors in adulthood: the Aerobics Center Longitudinal Study (ACLS). Am. Heart J.
11. Expert Panel on the Detection, Evaluation, and Treatment of High Blood Cholesterol in Adults. Executive summary of the 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). JAMA
12. Fahlman, M. M., D. Boardley, C. P. Lambert, and M. G. Flynn. Effects of endurance training and resistance training on plasma lipoprotein profiles in elderly women. J. Gerontol. A. Biol. Sci. Med. Sci.
13. Han, T. S., K. Williams, N. Sattar, K. J. Hunt, M. E. J. Lean, and S. M. Haffner. Analysis of obesity and hyperinsulinemia in the development of metabolic syndrome: San Antonio Heart Study. Obes. Res.
14. Jurca, R., M. J. LaMonte, C. E. Barlow, J. B. Kampert, T. S. Church, and S. N. Blair. Association of muscular strength with incidence of metabolic syndrome in men. Med. Sci. Sports Exerc.
15. Jurca, R., M. J. LaMonte, T. S. Church, et al. Associations of muscle strength and aerobic fitness with metabolic syndrome in men. Med. Sci. Sports Exerc.
16. Kahn, R., J. Buse, E. Ferrannini, and M. Stern. The metabolic syndrome: time for a critical appraisal: joint statement from the American Diabetes Association and the European Association for the Study of Diabetes. Diabetes Care
17. Klein, B. E. K., R. Klein, and K. E. Lee. Components of the metabolic syndrome and risk of cardiovascular disease and diabetes in Beaver Dam. Diabetes Care
18. Laaksonen, D. E., H. M. Lakka, L. K. Niskanen, G. A. Kaplan, J.T. Salonen, and T. A. Lakka. Metabolic syndrome and development of diabetes mellitus: application and validation of recently suggested definitions of the metabolic syndrome in a prospective cohort study. Am. J. Epidemiol.
19. Lakka, H. M., D. E. Laaksonen, T. A. Lakka, et al. The metabolic syndrome and total and cardiovascular disease mortality in middle-aged men. JAMA
20. LaMonte, M. J., C. E. Barlow, R. Jurca, J. B. Kampert, T. S. Church, and S. N. Blair. Cardiorespiratory fitness is inversely associated with the incidence of metabolic syndrome - a prospective study of men and women. Circulation
21. LaMonte, M. J., P. A. Eisenman, T. D. Adams, B. B. Shultz, B. E. Ainsworth, and F. G. Yanowitz. Cardiorespiratory fitness and coronary heart disease risk factors - the LDS Hospital Fitness Institute cohort. Circulation
22. Meyer, T., T. Georg, C. Becker, and W. Kindermann. Reliability of gas exchange measurements from two different spiroergometry systems. Int. J. Sports Med.
23. Newman, A. B., V. Kupelian, M. Visser, et al. Strength, but not muscle mass, is associated with mortality in the Health, Aging and Body Composition Study Cohort. J. Gerontol. A. Biol. Sci. Med. Sci.
24. Philippaerts, R., L. Matton, K. Wijndaele, A. L. Balduck, I. De Bourdeaudhuij, and J. Lefevre. Validity of a physical activity computer questionnaire in 12- to 18-year old boys and girls. Int. J. Sports Med.
25. Ragland, D. R. Dichotomizing continuous outcome variables - dependence of the magnitude of association and statistical power on the cutpoint. Epidemiology
26. Stewart, K. J., A. C. Bacher, K. Turner, et al. Exercise and risk factors associated with metabolic syndrome in older adults. Am. J. Prev. Med.
27. Welsman, J. R., N. Armstrong, A. M. Nevill, E. M. Winter, and B. J. Kirby. Scaling peak VO2
for differences in body size. Med. Sci. Sports Exerc.
28. Whaley, M. H., J. B. Kampert, H. W. Kohl, and S. N. Blair. Physical fitness and clustering of risk factors associated with the metabolic syndrome. Med. Sci. Sports Exerc.
30. Wijndaele, K., G. Beunen, N. Duvigneaud, et al. A continuous metabolic syndrome risk score: utility for epidemiological analyses. Diabetes Care