<b>Should we predict maximum oxygen uptake using fat-free mass or body mass in a non-athletic population</b>

<b>Background</b>: Our purpose was to assess whether fat-free mass (FFM) or body mass (M) is the more appropriate body-size variable to predict maximum oxygen uptake (VO_2max) (L⋅min⁻¹) in a non-athletic population.<b>Methods</b>: We adopted the well-known allometric/power function-model VO_2max=a·X^b⋅e to predict VO_2max (L⋅min⁻¹) using either FFM or M as the predictor variable. These allometric models can be linearised with a log-transformation, and linear regression (or ANCOVA) can then be used to estimate the unknown parameters.<b>Results</b>: Initially, ignoring the effect of age and sex, FFM was the strongest predictor of VO_2max. When we predicted Ln(VO_2max) using only Ln(FFM) adjusting for the effects of age and sex, the explained variance was R²=0.718 (AIC=-1882.5), with the FFM exponent b=0.658. However, when we predicted Ln(VO_2max) allowing body mass AND bodyfat% as separate covariates also adjusting for age and sex, the explained variance increased further to R²=0.733 (AIC=-2077.4), with the M exponent b=0.636. The difference in the explained variances, but more importantly, the difference in the AIC’s was 194.88, confirming the superiority of predicting VO_2max using separate M and bodyfat% covariates, i.e., adopting FFM alone was less successful compared to using M and bodyfat% separately. Our analyses identified a greater negatively weighted bodyfat% term that improved the prediction of VO_2max, found by our subsequent/final prediction models, to be due to excess central adiposity (waist circumference). Note that subsequent/final M and FFM exponents were both estimated to be b=0.67, confirming that VO_2max should be normalized using VO_2max (mL·M^−2/3·min⁻¹) or VO_2max (mL·FFM^−2/3·min⁻¹) rather than VO_2max (mL·FFM⁻¹·min⁻¹) as recommended by previous research.<b>Conclusion</b>: Incorporating FFM into equations to predict VO_2max fails to adequately explain the negative effect of excess central adiposity. However, by incorporating body mass (M) and BF% separately into the equations, a greater negatively weighted BF% coefficient is capable of compensating for this apparent omission.<b>研究背景</b>：本研究旨在评估在非运动人群中，去脂体重（fat-free mass, FFM）与体质量（body mass, M）何者为更合适的体成分变量，用于预测最大摄氧量（maximum oxygen uptake, VO₂max，单位：升·分钟⁻¹）。<b>研究方法</b>：本研究采用经典的异速生长/幂函数模型：VO₂max = a·Xᵇ·e，分别以去脂体重或体质量作为预测变量，对最大摄氧量（单位：升·分钟⁻¹）进行预测。此类异速生长模型可通过对数变换实现线性化，随后可借助线性回归（或协方差分析，Analysis of Covariance, ANCOVA）估算未知参数。<b>研究结果</b>：初始分析未纳入年龄与性别效应时，去脂体重是最大摄氧量的最强预测因子。当仅以对数转换后的去脂体重（Ln(FFM)）为自变量，并校正年龄与性别效应来预测对数转换后的最大摄氧量（Ln(VO₂max)）时，模型解释方差R²=0.718，赤池信息准则（Akaike Information Criterion, AIC）=-1882.5，去脂体重的幂指数b=0.658。而当同时纳入体质量与体脂百分比（bodyfat%, BF%）作为独立协变量，并同样校正年龄与性别效应来预测对数转换后的最大摄氧量时，模型解释方差提升至R²=0.733，AIC=-2077.4，体质量的幂指数b=0.636。两组模型的解释方差存在差值，更关键的是AIC差值达194.88，证实了同时纳入体质量与体脂百分比作为协变量的预测模型更优，即单独使用去脂体重的预测效果劣于分别使用体质量与体脂百分比的模型。本研究分析显示，后续最终预测模型中权重为负的体脂百分比项可改善最大摄氧量的预测效果，该效应源于过量中心性肥胖（腰围）。值得注意的是，后续最终模型中体质量与去脂体重的幂指数均被估算为b=0.67，证实最大摄氧量应按照VO₂max（毫升·体质量⁻²/³·分钟⁻¹）或VO₂max（毫升·去脂体重⁻²/³·分钟⁻¹）的方式进行标准化，而非既往研究所推荐的VO₂max（毫升·去脂体重⁻¹·分钟⁻¹）。<b>研究结论</b>：将去脂体重纳入预测最大摄氧量的方程，无法充分解释过量中心性肥胖带来的负面效应。但分别将体质量与体脂百分比纳入预测方程时，权重为负的体脂百分比系数可弥补这一缺失。