athletes (mid vertical Panels, A) and adult sedentary (last 3 vertical Panels, S) subjects. R2 = determination coefficient. The equations at the bottom are the result of step-wise linear regression analysis of VO2max with the variables indicated in the text.

Stepwise linear multiple regression analysis of VO2max (F < 0.05 to enter, F > 0.1 to exclude) against all variables measured in group A (age, anthropometry, resting and maximal heart rates, resting and maximal blood pressures, flexibility, grip strength and type of sport in which they participate), confirmed that FFM contributes importantly (standardized beta Sβ = 0.63) to the variance in Vo2max and that together with the value of the aerobic component of the sport in which they participate (Sβ = 0.35), their flexibility (Sβ = 0.24), their resting (Sβ = −0.22) and maximal exercise heart rates (Sβ = 0.17), they accounted for 48% of the variance in VO2max (R2 = 0.48, R = 0.69) in this group of adult athletes (see Figure 1, Panel A3). A similar step wise multiple regression analysis of VO2max in the sedentary group revealed that flexibility was weakly and not significantly correlated with VO2max (R2 = 0.23, Figure 1 Panel S3). Neither FFM, nor resting or maximal heart rates were related to the variation in VO2max values found in these sedentary subjects.

2) Young compared to adult athletes

Young athletes (group Y) had less fat (4.5 vs. 10.5 Kg), less lean (58.9 vs. 67 Kg FFM), were less flexible (22 vs. 26.2 cm reach) but had higher resting (76.5 vs. 68.8 bpm) and maximal (190.5 vs. 181.2 bpm) heart rates than the adult athletes (group A).

Linear regression analysis in young (Y) athletes showed stronger correlation of log VO2max with log FFM (R2 = 0.53, vs. 0.30) and higher regression coefficient (0.9 vs. 0.78) than in group A.

In addition, stepwise multiple linear regression analysis of VO2max in this group of young athletes showed significant contributions of FFM (Sβ = 0.75), the MET intensity of the sport they participate in (Sβ = 0.26), their resting (Sβ = −0.23) and maximal (Sβ = 0.18) heart rates to the variance of VO2max. Together these variables accounted for 65% (R2 = 0.65, R = 0.8) of the VO2max variance in young athletes. Flexibility did not correlate with VO2max in these young athletes compared to its small contribution to the correlation in older athletes (group A).

5. Discussion

In sedentary (S) subjects VO2max does not depend on the fat free (and muscle) mass, but was found to vary weakly (likely a non-causal correlation) with mid trunk flexibility, since both are known to be affected by the level of daily physical activity. However, residual variance is high and prediction unreliable. Skin-fold based FFM does not relate to the maximally metabolically active cell mass in these sedentary subjects. Variability in the relationship of skinfold thickness to body density [21] due to differences in internal and surface fat, differences in bone density and in muscle proportion in the FFM can contribute to the high variance in the VO2max vs. FFM relationship estimated from skinfolds in sedentary subjects. Heterogeneity in the proportion of active muscle within the FFM [5] , muscle heterogeneity in fiber type, mitochondrial and capillary densities [4] [9] may also preclude any proportionality between FFM and maximally active muscle mass in sedentary subjects. Furthermore, peak VO2 in sedentary subjects may be determined by factors other than maximal oxygen extraction capacity of muscles such as those influencing maximal oxygen deliveries (stroke volume, heart rate, flow distribution, blood oxygen capacity). In addition, pain or local fatigue may often determine the end of the test and the peak VO2 reached in this group. The mass of muscles involved, local muscle mechanics, fatigue or pain, are known to influence VO2max in various types of exercise tests [22] (such as arms or legs, running or cycling, etc.) and these may contribute to the large unaccounted variability of peak VO2 values in this sedentary group when tested using the treadmill walking Bruce protocol [17] . Neural sympathetic regulation limits blood flow distribution to active muscles resulting in VO2max during two legs biking lower than 2 times the VO2max during one leg biking ( [23] . Variability in vascular sympathetic tone of active muscles may similarly induce large variability in peak O2 uptake found in sedentary subjects. Because of these reasons, it is unlikely that in these sedentary subjects, peak oxygen uptake reflects the full capacity of oxygen extraction by muscles. In addition, oxygen delivery and extraction systems may not be adequately matched in untrained sedentary as they are in active subjects [6] .

In athletic young (Y) subjects, FFM, heart rate reserve (maximal ? rest) and type of training are found to be the major determinants of VO2max and account together for about 2/3 of the variance in VO2max. In young athletes, log VO2max varies in almost direct proportion (0.9:1.0) to log FFM. The nearly proportional scaling of VO2max with FFM in young active subjects suggests that in the growing young subjects differences in muscle cell size, cell number, capillary and mitochondrial density are in proportion to differences in FFM (mostly muscle) resulting in a maximal oxygen consumption capacity per unit of FFM (VO2max /FFM) nearly independent of the absolute value of the FFM [20] .

In adult (A) athletes VO2max variability is highly dependent on fat free mass (mostly muscle), their type of training as reflected by the relative MET intensity of the sport they participate in, their extent of aerobic training as reflected by their low resting heart rates, the relative effort during the test, as revealed by the maximal heart rates reached and also correlated with mid trunk flexibility (likely a correlative non causal relationship due to their mutual dependence on daily physical activity levels). The unaccounted VO2max variance is higher and prediction is less reliable than in young (Y) active subjects. In this group A, VO2max variability was thus found to depend mostly on characteristics of the oxygen delivery and extraction systems and to a lesser extent on a non-causally related factors such as mid trunk flexibility. In these active mature adults (A), differences in log VO2max scale as a fraction (0.78) of differences in log FFM. This could indicate that although differences in muscle cell size and number may be proportional to those in FFM, the average maximal cell metabolic rate (VO2max /FFM) is not independent of FFM but rather decreases at the larger FFM probably due to reduced maximal muscle capillary and mitochondrial densities in the larger subjects [9] , which may limit their fractal surface exchanges. This age related difference suggests that muscle adaptive responses to training may not be identical in mature and in growing subjects.

6. Conclusion

In summary, in young athletes and to a lesser extent in adult athletes, peak VO2 relates well to FFM indicating that FFM reflects well the mass of maximally active muscles. By contrast in sedentary adults, peak VO2 is unrelated to FFM, probably because oxygen delivery and local factors (neural pain and local fatigue) rather than oxygen extraction determine the mass of muscles activated in sedentary subjects. In addition, skinfold estimates of FFM in sedentary subjects are more variable as they are influenced by fat distribution and changes in bone density and in the proportion of muscle in the FFM.

Acknowledgements

We are grateful to Mr. G. Telahoun and to Mr. A. Hammouda for their help in this study and to the Dept of Physiology, Kuwait University for support.

Funding

Department of Physiology, Faculty of Medicine, Kuwait University.

Conflict of Interests

The authors are members of the Department of Physiology, Kuwait University, which funded this research.

Conflicts of Interest

The authors declare no conflicts of interest regarding the publication of this paper.

Cite this paper

Alkandari, J.R. and Nieto, M.B. (2019) Peak O2 Uptake Correlates with Fat Free Mass in Athletes but Not in Sedentary Subjects. Health, 11, 40-49. https://doi.org/10.4236/health.2019.111005

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