{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,30]],"date-time":"2026-07-30T09:31:37Z","timestamp":1785403897229,"version":"3.56.0"},"reference-count":36,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2026,7,24]],"date-time":"2026-07-24T00:00:00Z","timestamp":1784851200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100004242","name":"Princess Nourah bint Abdulrahman University","doi-asserted-by":"publisher","award":["PNURSP2026R145"],"award-info":[{"award-number":["PNURSP2026R145"]}],"id":[{"id":"10.13039\/501100004242","id-type":"DOI","asserted-by":"publisher"}]},{"award":["PNURSP2026R145"],"award-info":[{"award-number":["PNURSP2026R145"]}],"id":[{"id":"https:\/\/ror.org\/05b0cyh02","id-type":"ROR","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Metabolites"],"abstract":"<jats:p>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 (\u0394) glucose and lactate concentrations. Evaluation of the performance of the models included tenfold cross-validation and Bland\u2013Altman 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\u2013Altman analysis proved almost zero-bias estimation with 95% limits of agreement ranging between \u22124.40 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 (\u03b2-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\u2019s VO2max using only minimally invasive metabolic measurements.<\/jats:p>","DOI":"10.3390\/metabo16080525","type":"journal-article","created":{"date-parts":[[2026,7,27]],"date-time":"2026-07-27T08:15:56Z","timestamp":1785140156000},"page":"525","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Development of the Beta-Weighted Lacto-Glycemic Equation (\u03b2-LGE): A Dual-Application Machine Learning Framework for Non-Exhaustive Maximal Aerobic Capacity Estimation"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7384-4760","authenticated-orcid":false,"given":"\u00d6mer","family":"\u00d6zer","sequence":"first","affiliation":[{"name":"Department of Physical Education and Sport, Faculty of Sport Science, Bandirma Onyedi Eylul University, Balikesir 10200, T\u00fcrkiye"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9292-5419","authenticated-orcid":false,"given":"Ahmet","family":"Kurto\u011flu","sequence":"additional","affiliation":[{"name":"Department of Coaching Education, Faculty of Sport Science, Bandirma Onyedi Eylul University, Balikesir 10200, T\u00fcrkiye"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Musa","family":"T\u00fcrkmen","sequence":"additional","affiliation":[{"name":"Department of Physical Education and Sport, Institute of Health Science, Inonu University, Malatya 44280, T\u00fcrkiye"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7422-9543","authenticated-orcid":false,"given":"Bekir","family":"\u00c7ar","sequence":"additional","affiliation":[{"name":"Department of Recreation, Faculty of Sport Science, Gazi University, Ankara 06560, T\u00fcrkiye"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7470-1921","authenticated-orcid":false,"given":"Jaros\u0142aw","family":"Muracki","sequence":"additional","affiliation":[{"name":"Institute of Physical Culture Sciences, Department of Physical Culture and Health, University of Szczecin, 70-453 Szczecin, Poland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ali","family":"Tatl\u0131c\u0131","sequence":"additional","affiliation":[{"name":"Department of Physical Education and Sport Teaching, Faculty of Sport Science, Sel\u00e7uk University, Konya 42060, T\u00fcrkiye"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1100-4301","authenticated-orcid":false,"given":"Safaa M.","family":"Elkholi","sequence":"additional","affiliation":[{"name":"Department of Rehabilitation Sciences, College of Health and Rehabilitation Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,7,24]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"313","DOI":"10.1007\/s40279-013-0029-x","article-title":"High-Intensity Interval Training, Solutions to the Programming Puzzle Part I: Cardiopulmonary Emphasis","volume":"43","author":"Buchheit","year":"2013","journal-title":"Sports Med."},{"key":"ref_2","unstructured":"Coulson, M., and Archer, D. (2011). Practical Fitness Testing: Analysis in Exercise and Sport, Bloomsbury Publishing."},{"key":"ref_3","first-page":"e61124","article-title":"Assessment of Maximal Oxygen Uptake (VO2 Max) in Athletes and Nonathletes Assessed in Sports Physiology Laboratory","volume":"16","author":"Srivastava","year":"2024","journal-title":"Cureus"},{"key":"ref_4","unstructured":"Bundy, M., and Leaver, A. (2012). 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