Analyzing the correlation between body composition variables and cellular phase angle via computerized bioelectrical impedance analysis (BIA)

Background: Various diseases and pathological conditions, as well as inflammatory processes, either in isolation or in combination with advanced age, can lead to significant alterations in body composition. This study investigates the correlation between various body composition metrics and the cellular Phase Angle (PhA) through Computerized Bioelectrical Impedance Analysis (BIA) in a diverse age group of individuals ranging from 18 to 80 years. Objective: Our aim was to explore the interplay between body composition variables such as Body Mass Index, Lean Body Mass, Skeletal Muscle Mass, and their potential influence on the cellular integrity as indicated by PhA. Methods: Utilizing a dataset of 199 participants, we employed both linear and advanced machine learning models, including Random Forest regression, to analyze the predictive relationships and variable importance within our body composition metrics. Results: Initial analyses revealed strong correlations between mass-related measures and suggested complex relationships with the PhA, an indicator of cellular health and membrane integrity. The Random Forest model significantly outperformed simple linear regression in predicting PhA, emphasizing the nonlinear nature of these relationships and the importance of a comprehensive approach in body composition assessment. Conclusion: Our findings highlight the nuanced interactions between body composition variables and their collective impact on cellular health as measured by PhA. This study underscores the potential of utilizing PhA alongside traditional metrics for a more nuanced understanding of body composition and its implications for health. Future research should continue to leverage advanced statistical and machine learning techniques to further elucidate these complex relationships, with implications for nutritional, sports medicine, and biomedical fields. Initial analyses revealed strong correlations between mass-related measures and suggested complex relationships with the Phase Angle, an indicator of cellular health and membrane integrity. The Random Forest model significantly outperformed simple linear regression in predicting PhA, emphasizing the nonlinear nature of these relationships and the importance of a comprehensive approach in body composition assessment. Our findings highlight the nuanced interactions between body composition variables and their collective impact on cellular health as measured by PhA. This study underscores the potential of utilizing PhA alongside traditional metrics for a more nuanced understanding of body composition and its implications for health. Future research should continue to leverage advanced statistical and machine learning techniques to further elucidate these complex relationships, with implications for nutritional, sports medicine, and biomedical fields.