Metabolic syndrome (MetS), the clustering of abdominal obesity, elevated blood pressure, elevated triglycerides, decreased HDL cholesterol (HDL-C), and elevated fasting plasma glucose, remains a major public health burden with the prevalence of the syndrome increasing in concert with obesity and sedentary lifestyles (2). MetS affects both children and adults and has been linked with clinical manifestations in cardiovascular disease (CVD) and type 2 diabetes (2,21,27). Consequently, means of prevention and treatment for MetS, particularly those commencing in childhood, are of interest (14,22,28).
Cross-sectional and longitudinal studies among children and adults have shown higher levels of cardiorespiratory fitness (CRF) to be associated with healthier cardiometabolic profiles, reduced clustering of metabolic risk factors, and decreased MetS (3,9,17–19,34). Longitudinal data suggest that CRF in childhood and adulthood can be used to predict adult MetS independent of adiposity levels (19,30). Muscular fitness, a unique fitness phenotype, is becoming increasingly recognized in health promotion and disease prevention guidelines (26,37,39,40) owing to its association with important chronic disease outcomes, independent of CRF.
Muscular fitness incorporates the phenotypes of muscular strength, muscular power, and muscular endurance. Recent data have shown low grip strength in adults to be associated with all-cause death, cardiovascular death, and CVD, exhibiting greater effects with mortality than hypertension (20). Past findings suggest that phenotypes of muscular fitness are associated with insulin sensitivity, beta cell function, and decreased CVD risk factors, independent of CRF (11,12). Muscular fitness has been shown to be inversely associated with MetS prevalence in adult cross-sectional (16) and longitudinal data (15). In childhood, muscular fitness has been cross-sectionally linked with cardiometabolic risk factors including waist circumference (24). Childhood waist circumference presented as one of the strongest predictors of subsequent MetS in adulthood in past longitudinal data (31). Previous data have investigated the relationship between muscular strength in adolescents and cardiovascular risk in young adulthood, independent of CRF (12). However, what is lacking is longitudinal evidence of the association between muscular fitness in childhood and MetS in adulthood, independent of CRF. Therefore, using 20-yr follow-up data from the Childhood Determinants of Adult Health (CDAH) Study, we aimed to determine the association of childhood muscular fitness, incorporating all three phenotypes of muscular strength, muscular power, and muscular endurance, with adult MetS.
MATERIALS AND METHODS
The CDAH study collected baseline data on a nationally representative sample of 8498 Australian schoolchildren in 1985 age between 7 and 15 yr. During this time, children age 9, 12, and 15 (N = 2726) received additional measurements of blood pressure, blood sampling, and more detailed fitness tests that included measures of muscular strength. Of those eligible from this subset at baseline, 741 participants (27.2%) attended one of 34 follow-up clinics held across Australia from 2004 to 2006 and had measures for MetS collected. Of these, four pregnant participants were excluded leaving an analysis subset of 737 participants. A flow chart of participation is given in Figure 1. In 1985, consent was obtained from both parent and child before inclusion in the study. At follow-up, written informed consent was obtained from the participant. The State Directors General of Education approved the baseline study, and the Southern Tasmania Health and Medical Human Research Ethics Committee approved the follow-up study.
Childhood muscular fitness
Muscular strength was measured as maximum voluntary contractile force in kilograms of the right grip, left grip, shoulder flexion, shoulder extension, and leg, using isometric dynamometers (Smedley Dynamometer, TTM, Tokyo, Japan). Each strength measure was repeated twice, with the maximum of the two attempts used in the analysis. Right and left grip strength was measured as participants held the dynamometer with one hand, supported it on the opposite shoulder, and gripped with maximum force. Shoulder strength in the form of shoulder flexion and extension was measured as participants held the dynamometers in front of their chest with both hands parallel to the ground and then either pulled or pushed with maximum effort trying to get their hands as far apart or as close together as possible. For leg strength, participants were asked to stand on the dynamometer with flat feet, with a straight back, and with their body flat against a wall. While holding a hand bar with an overhand grip, knees were flexed until an angle of 115° was measured, at which point the bar was attached to the dynamometer by a chain; participants then pulled the bar as far as possible by moving their body upward. To obtain a single measure of muscular strength, we performed principal component analysis to estimate the first principal component of the five measures of childhood muscular strength (see Table, Supplemental Digital Content 1, factor loadings from principal components factor analysis to derive the combined childhood muscular strength variable, http://links.lww.com/MSS/A686) (29). Childhood muscular power was measured as the best resulting distance in centimeters from two standing long jump tests that required a two-footed takeoff. Each child was encouraged to swing their arms and bend their knees to provide drive. Muscular endurance was measured as the number of correctly completed inclined push-ups in 30 s. Starting with two hands placed on the front edge of a chair shoulder width apart, with legs straight at a 90° angle to the body and arms fully extended, a correct push-up was defined as when their body was lowered until their chest touched the chair and then raised until the arms were back to a fully extended position. All muscular fitness phenotypes were adjusted for body weight (by regressing body weight on each phenotype and using the residuals) to create indices uncorrelated with body weight (29) and standardized for age and sex. Finally, a combined childhood muscular fitness score was created using the first principal component of all childhood muscular fitness phenotypes (see Table, Supplemental Digital Content 2, factor loadings from principal components factor analysis to derive the combined childhood muscular fitness variable, http://links.lww.com/MSS/A687).
Childhood clinical measurements
CRF in childhood was measured indirectly using a Monark 818E bicycle ergometer (Monark Exercise AB, Vansbro, Sweden) as physical work capacity at an HR of 170 bpm (PWC170) (8). PWC170 has previously shown to be highly correlated with maximal oxygen consumption (V˙O2max) (r = 0.83) and is a more appropriate test for field-based settings (10). This submaximal test incorporates three successive 3-min workloads that increased resistance stepwise. HR and watts, recorded in the final minute of each workload, were plotted and extrapolated to provide PWC170. Absolute CRF was adjusted for lean body mass in this study to provide a measure uncorrelated with lean body mass (29), as the absolute work load achieved is a function of muscle mass (5). Body mass was measured to the nearest 0.5 kg using regularly calibrated scales. Waist circumference was measured to the nearest 0.1 cm at the level of the umbilicus using a constant tension tape. Height was measured to the nearest 0.1 cm using a KaWe height tape. Childhood lean body mass was calculated using weight (kg) and estimates of percentage body fat derived from the sum of skinfolds. Triceps, biceps, subscapular, and suprailiac skinfolds were measured to the nearest 0.1 mm using Holtain calipers (Holtain, Crymych, UK). Body density was calculated from the log of the sum of four skinfolds using age-specific regression equations (7), and fat percentage was determined. Lean body mass was then calculated by subtracting fat mass from total body mass.
Adult clinic measurements
In adulthood, blood samples were collected from participants who observed a 12-h fast before the clinic. Fasting status was enquired from participants on arrival. Serum triglyceride, HDL-C, and glucose concentrations were measured enzymatically (23). Resting systolic and diastolic blood pressure readings were recorded after 5 min of quiet sitting using an OMRON HEM907 Digital Automatic Blood Pressure Monitor (Omron Healthcare Co., Ltd., Kyoto, Japan) with the mean of three recordings used for analysis. Adult waist circumference was measured at the narrowest point between the lower costal border and the iliac crest to the nearest 0.1 cm using a constant tension tape. A Leicester height measure (Invicta, Leicester, UK) was used to measure height, and Heine scales (Heine, Dover, NH) were used to measure weight in adult clinics. Adult body mass index (BMI) was calculated as weight in kilograms divided by height in meters squared. Participants self-reported medication use and previous physician-diagnosed conditions.
Adult MetS and continuous MetS score
MetS was determined using the harmonized definition (2), where MetS is diagnosed when at least three of the following five components are present: waist circumference (male ≥102 cm, female ≥88 cm), fasting plasma glucose (≥5.6 mmol·L−1 (≥100 mg·dL−1) or drug treatment for elevated blood glucose), serum triglycerides (≥1.7 mmol·L−1 (≥150 mg·dL−1) or treatment for elevated triglycerides), HDL-C (male <1.03 mmol·L−1 (<40 mg·dL−1), female <1.3 mmol·L−1 (<50 mg·dL−1), or drug treatment for this lipid abnormality), and blood pressure (≥130/85 mm Hg or treatment of previously diagnosed hypertension) (2). A continuous MetS (cMetS) score was created by aggregating age- and sex-specific standardized residuals of the five components in MetS (blood pressure, triglyceride, HDL-C, glucose, and waist circumference), as we have previously described (31). The cMetS score was computed from weighted principal components, as described by Wijndaele et al. (38), with a higher cMetS score representing a less favorable MetS profile.
All statistical analyses were performed using Stata (version 12.1; StataCorp, College Station, TX).
Participant baseline and follow-up characteristics for continuous variables are presented as mean and SD for normally distributed data and as median and interquartile range (25th, 75th percentile) for skewed data. N values and proportions are reported for categorical values.
Loss to follow-up
Comparison of baseline characteristics between participants and nonparticipants in adulthood was analyzed using t-tests for continuous variables and chi-square tests for categorical variables. Where continuous variables were not normally distributed, values were log transformed before analysis with geometric means reported. Nonparticipants were classified as those who had complete muscular fitness measures in childhood and either did not participate at follow-up or lacked a full set of MetS measures in adulthood.
Childhood muscular fitness and adult MetS
Each variable of muscular fitness was categorized into thirds. The relative risk (RR) and 95% confidence intervals of dichotomous MetS in adulthood were estimated for each childhood muscular fitness phenotype using Poisson regression with robust errors. Multivariable linear regression was used to determine the relationship between thirds of childhood muscular fitness phenotypes and adult cMetS score. In all analyses, the lowest third of each muscular fitness phenotype was used as the reference group. Three multivariable models with successive adjustment were considered. The first model was adjusted for childhood age, sex, and length to follow-up; the second model was adjusted for model 1 covariates and additionally for childhood CRF; and model 3 was adjusted for model 2 covariates and additionally for childhood waist circumference. Using model 3, we additionally considered interactions between muscular fitness phenotypes with age, sex, and CRF by fitting separate multiplicative interaction terms. We found no evidence of significant interaction. The above analyses were repeated for each component of MetS and cMetS. These associations were adjusted for age at baseline, sex, length to follow-up, and childhood CRF. For the cMetS component outcome of continuous triglycerides, values were log transformed and geometric means reported, owing to a skewed distribution.
Baseline and follow-up characteristics of the 737 participants are presented in Table 1. Mean (SD) length to follow-up was 19.9 yr (0.6), ranging from 18.7 to 21.0 yr. Males tended to have higher fitness phenotypes in childhood. In adulthood, males had a greater prevalence of MetS compared with females, as well as a higher cMetS score.
Loss to follow-up
Participants had on average a greater distance in standing long jump (151.9 cm vs 149.1 cm, P = 0.03) and higher combined muscular fitness score (0.07 vs −0.03, P = 0.02) at baseline compared with nonparticipants (see Table, Supplemental Digital Content 3, comparison of baseline characteristics of participants and nonparticipants, http://links.lww.com/MSS/A688). Nonparticipants had higher measures of BMI (18.8 kg·m−2 vs 18.5 kg·m−2, P = 0.02) and waist circumference (65.6 cm vs 64.8 cm, P = 0.04) and greater proportions of low socioeconomic position (9.5% vs 7.9%, P < 0.001) and were smokers (15.5% vs 11.9%, P = 0.06) compared with participants. No other differences were found between participants and nonparticipants at baseline.
Childhood muscular fitness and adult MetS
Table 2 shows the longitudinal association between childhood muscular fitness phenotypes and dichotomous MetS in adulthood. Participants with higher levels of the combined muscular fitness score, muscular strength, and muscular power in childhood had lower risk of adult MetS independent of CRF (model 2, highest vs lowest third: fitness score, RR = 0.20 (0.09, 0.47); strength, RR = 0.21 (0.09, 0.49); power, RR = 0.26 (0.12, 0.60)). Although the effect estimates for the combined muscular fitness score, muscular strength, and muscular power remained suggestive of a protective effect, when child waist circumference was included in the model, the main effects reduced substantially (range, 17%–41%) and confidence intervals crossed one for muscular power.
Similar results were observed for the cMetS score (Table 3). Higher levels of the combined muscular fitness, muscular strength, and muscular power in childhood were associated with a lower adult cMetS score, independent of CRF (model 2, highest vs lowest third: fitness score, β = −0.45 (−0.58, −0.33); strength, β = −0.46 (−0.59, −0.34); power, β = −0.36 (−0.49, −0.23), all Ptrend < 0.001). Although associations remained statistically significant after additional adjustment for child waist circumference (model 3), the effect estimates were reduced by 41% to 60%.
Tables in Supplemental Digital Content 4 (http://links.lww.com/MSS/A689) and Supplemental Digital Content 5 (http://links.lww.com/MSS/A690) show associations between the combined child muscular fitness score and the components of MetS. When the components were considered as continuous outcomes (see Table, Supplemental Digital Content 4, association between MetS components in adulthood and childhood muscular fitness levels, http://links.lww.com/MSS/A689), the combined childhood muscular fitness score (high vs low) was associated with significantly lower waist circumference (β = −9.03 cm, P < 0.001), diastolic blood pressure (β = −1.77 mm Hg, P = 0.03), triglycerides (β = −0.21 mmol·L−1, P < 0.001), and higher HDL-C (β = 0.10 mmol·L−1, P < 0.001) in adulthood. For the association between the combined muscular fitness score in childhood with dichotomous MetS components in adulthood (see Table, Supplemental Digital Content 5, RR of meeting abnormal levels of MetS components in adulthood based on childhood muscular fitness levels, http://links.lww.com/MSS/A690), all associations suggested a protective effect of higher muscular fitness levels in childhood on abnormal adult risk factor levels. Statistical significance was shown for waist circumference (high vs low fitness: RR = 0.20, (0.11, 0.36)), HDL-C (high vs low fitness: RR = 0.67, (0.49, 0.91)) and triglycerides (high vs low fitness: RR = 0.46, (0.28, 0.73)).
To determine whether the association between childhood muscular fitness and adult MetS is independent of adult muscular fitness, we additionally adjusted adult muscular strength and muscular power (see Document, Supplemental Digital Content 6, supplementary methods, http://links.lww.com/MSS/A691). The independent effect remained after adjustment of these adult factors (strength β = −0.30, SE = 0.08, P < 0.001; power β = −0.16, SE = 0.08, P = 0.04). Furthermore, to determine whether other childhood measures of MetS beyond waist circumference, systolic and diastolic blood pressure, triglycerides, and HDL-C (see Document, Supplemental Digital Content 6, supplementary methods, http://links.lww.com/MSS/A691) mitigated the association between the combined childhood muscular fitness score and the adult MetS, we performed a sensitivity analysis that adjusted for these factors in addition to those in model 3. In this model, the effect for the combined child muscular fitness score did not change and remained statistically significant. Moreover, child smoking status and socioeconomic position (see Document, Supplemental Digital Content 6, supplementary methods, http://links.lww.com/MSS/A691) were adjusted in addition to model 3 covariates. Although potential confounders, these additional covariates were not included in model 1 because these variables were missing for a proportion of our sample. It was found that additional adjustment for child smoking status and socioeconomic position did not further reduce the effect estimate for both the dichotomous and continuous MetS outcomes. To account for differential loss to follow-up in the combined muscular fitness score, waist circumference, and socioeconomic position that was observed in Supplemental Digital Content 3, http://links.lww.com/MSS/A688, we performed inverse propensity weighting as a sensitivity analysis to determine the likely effect on our reported findings. The weighted analysis returned a regression coefficient about 15% lower (β = −0.22, SE = 0.07); however, the results remained statistically significant (P = 0.002).
Our results address a current gap in the literature by showing that higher childhood levels of muscular strength, muscular power, and a combined muscular fitness score are associated with a reduced risk of dichotomous MetS and lower cMetS score in adulthood, independent of CRF. However, upon additional adjustment for waist circumference, the strength of the observed associations reduced. These results suggest that childhood waist circumference is a potential mediator of the association between childhood muscular fitness and both adult MetS outcomes, although the possibility that it is a confounder may also be true.
However, there is biological plausibility to suggest childhood waist circumference is mediating part of the effect of child muscular fitness on adult MetS outcomes via an indirect pathway of increased child muscular fitness leading to decreased child adiposity that is subsequently reducing adult MetS. This plausible mechanism is substantiated by our previous cross-sectional results in the childhood sample where muscular fitness phenotypes were inversely and independently related with waist circumference and BMI (24) as well as findings from intervention studies showing improvements to body composition among overweight and obese youth who underwent resistance training (32). The link between child adiposity and adult MetS is well substantiated (25,33), including previous data from CDAH that showed childhood waist circumference to be the strongest predictor of adulthood dichotomous MetS, with these associations remaining independent of changes in waist circumference that had occurred between childhood and adulthood (31). Our sensitivity analyses suggested that the remaining effect is not being mediated through adult muscular fitness levels or other childhood MetS risk factors in blood pressure and blood lipids. That child blood pressure and blood lipids are not mediating the association is no surprise given previous research found limited associations between muscular fitness phenotypes and these measures independent of BMI (24) and findings from the Bogalusa and Cardiovascular Risk in Young Finns studies (21) that showed child BMI to be as good a predictor of adult dichotomous MetS as child MetS status, plus other child MetS risk factors including blood pressure and lipids (21). Therefore, the remaining effect we observed may be mediated by other unmeasured factors or represents a direct independent effect of child muscular fitness.
The mechanism responsible for the remaining effect is unknown; however, pathways can be hypothesized with potential links via insulin-stimulated glucose uptake and whole-body energy expenditure through skeletal muscle glucose and triglyceride metabolism (36). Increased muscular fitness via resistance training has been shown to result in skeletal muscle growth and hypertrophy (1) and increased insulin sensitivity (13). However, this enhanced skeletal muscle insulin sensitivity resultant from resistance training is likely to be independent of muscle mass increases (13). The biological mechanism behind the positive effect of resistance training may be attributable to proteins in the insulin-signaling cascade (13), providing a rationale as to how muscular fitness, insulin sensitivity, and decreased MetS risk may be linked.
Previous results from cross-sectional childhood data in this and other cohorts have shown childhood muscular fitness to be associated with lower CVD and metabolic risk, independent of childhood BMI and CRF (24,35). The study by Magnussen et al. (24) also showed an interaction whereby the greatest effect of muscular power on clustered CVD risk was observed among those with the lowest CRF. This observation was not observed in our longitudinal analyses whereby muscular fitness and CRF appear to act independently on both MetS outcomes. Similar results were observed in cross-sectional analyses of adults in the Aerobics Centre Longitudinal Study cohort, where a muscular strength score had independent and joint inverse associations with CRF for dichotomous MetS (16). Furthermore, longitudinal findings from the Aerobics Centre Longitudinal Study cohort showed that the association between adult muscular strength and dichotomous MetS remained, yet was strongly reduced, when accounting for CRF (15). In the only other longitudinal study spanning childhood to adulthood, data from the European Youth Heart Study suggest that childhood muscular strength (baseline age of 15 yr) is associated with a modifiable CVD risk score in young adulthood (follow-up age = 21–27 yr) independent of both waist circumference and CRF (12). Although these results gave insight into the independent effect childhood muscular strength had on cardiometabolic outcomes, its effect on MetS was unable to be examined owing to low case numbers. Our current study expands on this existing literature by involving a longitudinal cohort with a long follow-up period, a greater sample size, and younger age at baseline. Furthermore, an expanded range of muscular fitness phenotypes was considered, although importantly looking at MetS risk via two MetS outcomes exclusively. Nevertheless, the key theme among these previous findings as well as our own is that an association between muscular fitness phenotypes and cMetS score, dichotomous MetS, or other cardiometabolic outcomes remains but is reduced upon further adjustment for a measure of adiposity or CRF.
Muscular conditioning activities in childhood have been advocated in the most recent physical activity guidelines (40). For children, these guidelines state that in addition to aerobic exercise, activities that aim to strengthen muscle and bone should be performed ≥3 d·wk−1. In adults, muscle-strengthening activities that involve major muscle groups should be performed on ≥2 d·wk−1 (40). Consistent with these recommendations, our results show some benefit of higher muscular fitness independent of CRF, thus reinforcing the importance of activities that promote muscular fitness in addition to increased CRF to reduce MetS risk in adulthood and improve one’s cardiometabolic health profile.
The strengths of this study include the long follow-up period of a large national sample. Moreover, this study included a complete and diverse set of muscular fitness phenotypes. The validity of field-based measures of muscular fitness phenotypes has been the subject of previous systematic reviews (4,6). Comparisons of field- and clinic-based measures in youth have found that the field-based measures of handgrip strength (6) and standing long jump are valid tests of muscular strength and power (4). Because these measures were collected in the CDAH study, they appear appropriate to test muscular fitness in childhood. Furthermore, the ability to assess the longitudinal association between childhood muscular fitness on two adult MetS outcomes, in the dichotomous and continuous definition, is a key strength in this study helping expand the application of these findings. Although the dichotomous definition is clinically relevant and accepted, the cMetS score provides a more statistically powerful quantitative score, representing a continuum of metabolic risk (38).
Despite the strengths of this study, there were also limitations. These include some differential loss to follow-up whereby nonparticipants had lower combined muscular fitness, higher adiposity, and were of lower socioeconomic position than the participants. Upon further analysis that weighted on these factors, these differences resulted in a slight overestimation of the effect in our results, though statistical significance remained. Furthermore, because of the observational study design, one must consider the effects of unmeasured and residual confounding. This study implies that at baseline, children with increased muscular fitness engaged in more muscular conditioning activities; however, we do not have data in support of this. Variations existed in our results in terms of statistical significance between MetS outcomes; these could be explained by the loss of statistical power associated with dichotomizing continuous variables (38). A weak or no effect for muscular endurance was observed in our analyses. These results may reflect a less important role of child muscular endurance for adult MetS outcomes but may have also been subject to increased measurement error compared with our other measures of muscular fitness, with questions raised in previous systematic reviews on the reliability of this measure (6).
In conclusion, these findings suggest that childhood muscular fitness predict adult MetS outcomes independently of childhood CRF, with part of this effect being potentially mediated through childhood waist circumference. These results suggest that increased childhood muscular fitness protects against adult MetS, and exercise that improves muscular fitness in combination with CRF might further reduce MetS risk in young adulthood.
We acknowledge the contribution of CDAH staff and volunteers, past and present, to this study. Furthermore, we acknowledge advice from Associate Professor Leigh Blizzard. Above all, we gratefully acknowledge the ongoing commitment of CDAH participants to the study.
This baseline study was supported by grants from the Commonwealth Departments of Sport, Recreation and Tourism, and Health; The National Heart Foundation; and the Commonwealth Schools Commission. The follow-up study was funded by grants from the National Health and Medical Research Council (NHMRC), the National Heart Foundation, the Tasmanian Community Fund, and Veolia Environmental Services. Sponsors included Sanitarium, ASICS, and Target. A. J. Venn (APP1008299) is supported by a NHMRC Research Fellowship and C. G. Magnussen (APP1037559) is supported by a NHMRC Early Career Fellowship. The funding bodies did not play a role in the study design, collection, analysis, and interpretation of data, in the writing of the manuscript, or the decision to submit the manuscript for publication.
The authors declare no conflict of interest.
The results of the present study do not constitute endorsement by the American College of Sports Medicine.
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