Development of the Beta-Weighted Lacto-Glycemic Equation (β-LGE): A Dual-Application Machine Learning Framework for...

Background and Objective: Maximal oxygen uptake (VO₂max) is a fundamental indicator of cardiorespiratory fitness in sports medicine, essential for athletic profiling and training prescription. However, traditional direct measurements require exhaustive physical testing, which induces considerable physiological stress, limits testing frequency, and increases the risk of injury. To address this problem, this study aimed to eliminate the need for exhaustive protocols by developing a highly precise, non-invasive digital prediction model for VO₂max utilizing readily available anthropometric data and acute metabolic biomarkers (blood glucose and lactate kinetics). Methods: To overcome the limitations of a small initial empirical sample (n = 16) and prevent model overfitting, the original dataset was statistically augmented to create a robust synthetic cohort (n = 200) using Multivariate Normal Distribution and k-Nearest Neighbors (k-NN) algorithms. Three different machine learning models (Multiple Linear Regression [MLR], Random Forest [RF], and Support Vector Regression [SVR]) were trained using such parameters as sex, height, weight, baseline/pre-exercise, and net (Δ) glucose and lactate concentrations. Evaluation of the performance of the models included tenfold cross-validation and Bland-Altman analysis as a measure of clinical agreement. Results: Among the three algorithms used, the highest correlation coefficient (R² = 0.939) was observed for SVR, along with the lowest error metrics (RMSE = 1.442 mL/kg/min, MAPE = 2.78%). Moreover, SVR showed remarkable performance in predicting VO₂max of female (R² = 0.890) and male (R² = 0.702) athletes separately. Also, Bland-Altman analysis proved almost zero-bias estimation with 95% limits of agreement ranging between -4.40 and 4.43 mL/kg/min. In order to bypass the black-box problem of complex algorithms for practical application in the field, the MLR model was used for the development of the Beta-Weighted Lacto-Glycemic Equation (β-LGE). Conclusions: In this work, a unique dual-model approach was introduced, with the SVR algorithm serving as a high-performance backend of a digital tool for sport technologists and Beta-Weighted LGE providing a practical calculation formula for coaches. This innovative approach allowed for a completely non-exhaustive profiling of an athlete's VO₂max using only minimally invasive metabolic measurements.