## 4 Discussion  
Anthropometric data: as one would expect, measures of body size and composition (like weight, body fat, and skinfold thickness) are strongly inter-related. For example, as weight increases, both the fat-free mass and the sum of skinfolds tend to increase. Metabolic data: indicators of metabolism, such as the resting metabolic rate (RMR) and VO\({}_{2}\) max, are closely related. Those with a higher RMR tend to have a higher VO\({}_{2}\) max. Substrates used at rest: the respiratory quotient (RQ) is an indicator of which fuel (fat or carbohydrate) is being predominantly metabolized. As the RQ increases, there is a shift from fat metabolism towards carbohydrate metabolism. \begin{table}
\begin{tabular}{l c c c c c c c c c c c c} \hline  & **Ant** & **Ant** & **Ant** & **Ant** & **Ant** & **Ant Data:** & **MetBi:** & **MetBi:** & & & & S.U. & S.U. & S.U. \\  & **Data:** & **Data:** & **Data:** & **Data:** & **Sum** & **of** & **RMR** & **VO\_Max** & **MetBiVCO** & **MetBi:** & **S.R. ** & **S.R. ** & **S.R. ** & **S.R. ** \\  & **Weight** & **Body** & **FM** & **R** & **S**infields** & **de** & **(**)** & **(**)** & **(**)** & **(**)** & **(**)** & **(**)** & **(**)** & **(**)** \\  & **(**ag**) & **Fat** (**\%**) & **(**ag**) & **(**deg**) & **(**)** & **(**)** & **(**)** & **(**)** & **(**)** & **(**)** & **(**)** & **(**)** & **(**)** \\ \hline
**Ant data: Weight** & 0.64 & 0.76 & 0.96 & 0.57 & -0.39 & -0.34 & -0.48 & -0.24 & 0.34 & 0.27 & -0.34 & -0.44 \\
**Ant data: Body fat** & 0.64 & -0.8 & 0.98 & 0.41 & 0.99 & -0.29 & -0.18 & -0.60 & -0.58 & 0.67 & 0.64 & -0.67 & -0.75 \\
**Ant data: FAT** & 0.76 & 0.98 & - & 0.56 & 0.96 & -0.33 & -0.23 & -0.475 & -0.56 & 0.64 & 0.50 & -0.64 & -0.63 \\
**Ant data: FFM** & 0.96 & 0.41 & 0.56 & - & 0.32 & -0.37 & -0.34 & -0.36 & -0.08 & 0.17 & 0.09 & -0.17 & -0.26 \\
**And data: Sum** & 0.57 & 0.97 & 0.96 & 0.32 & - & -0.26 & -0.15 & -0.60 & -0.64 & 0.71 & 0.69 & -0.71 & -0.79 \\
**St skilloffs** & -0.39 & -0.29 & -0.33 & -0.37 & -0.26 & -0.99 & 0.83 & 0.04 & -0.13 & 0.17 & 0.13 & 0.29 \\
**MetBi:** VO\_max & -0.38 & -0.18 & -0.28 & -0.34 & -0.15 & 0.99 & 0.99 & 0.83 & 0.04 & -0.11 & 0.06 & 0.50 & 0.62 & 0.14 \\
**MetBi:** VO\_2 & -0.48 & -0.68 & -0.65 & -0.38 & -0.64 & 0.83 & 0.74 & 0.74 & -0.11 & 0.06 & 0.50 & -0.64 & 0.66 & 0.75 \\
**MetBi:** RQ & -0.24 & -0.58 & -0.55 & -0.08 & -0.64 & 0.01 & -0.11 & 0.59 & -0.99 & -0.99 & 0.96 & 0.99 & 0.98 \\
**St.U.R** & -0.24 & -0.58 & -0.55 & -0.08 & -0.64 & 0.01 & -0.11 & 0.59 & -0.99 & -0.99 & 0.96 & 0.99 & 0.98 \\
**St.U.R** & -0.27 & 0.64 & 0.60 & 0.79 & 0.79 & 0.17 & 0.32 & -0.46 & 0.96 & 0.95 & 0.95 & -0.38 & -0.88 Interplay between different data types: there are relationships between body composition and metabolic measurements. For instance, as the body fat percentage increases, certain metabolic measures like \(\text{VCO}_{2}\) decrease. The data highlight the intricate relationships between body composition, metabolism, and the type of fuel our body uses at rest. However, it is crucial to remember that these are correlations, not causations, and the exact relationships can vary based on various factors, including genetics, diet, and activity levels. Studies on elite athletes often highlight the importance of nutritional guidance and its impact on body composition and performance. For instance, nutritional interventions can lead to significant changes in body weight and composition, such as increased lean body mass and fat mass, depending on the energy balance and dietary intake [40]. Additionally, the body composition of athletes is crucial in weight-sensitive sports, where maintaining optimal body weight and composition is essential for performance [41]. The strengths of this study are represented by the fact that, during the study period, we had the possibility to better develop a nutritional strategy to match the athlete's increased energy needs, based on periodic measurements of RMR undertaken with the athlete. This study has some obvious limitations related to the design of this case study.