Article RETRACTED due to malpractice Hidden Iron Deficiency in High-Performance Adolescents: A Multi-Omics and Predictive Analytics Framework
DOI:
https://doi.org/10.47197/retos.v82.119281Keywords:
academic institutions, health and well-beingAbstract
Purpose: The research question of this study was to explore the multifaceted interaction between iron status, inflammation and aerobic performance in adolescent athletes as well as to establish a sophisticated, integrative model of early iron deficiency diagnosis and prediction of performance beyond conventional single-biomarker methods.
Methodology: The study utilized a longitudinal cohort design that included multidimensional data such as hematological (hemoglobin, ferritin), inflammatory (CRP, IL-6), physiological (VO 2max), nutritional (nutrition) and training load. Machine learning (Random Forest, XGBoost, neural networks) was used in conjunction with statistical analyses (multivariate regression, correlation analysis) to identify nonlinear relationships. To combine these variables into one predictive framework, a novel composite measure, the Iron Performance Index (IPI), was developed.
Findings: These findings revealed that hemoglobin and ferritin were major positive predictors of VO2max, while CRP and IL-6 showed significant negative associations, indicating the influence of inflammation on iron bioavailability and performance. The machine learning models had high predictive accuracy (up to 87%), high sensitivity (85.5%), and high specificity (95.2%). The most influential variables were ferritin, IL-6, VO2max, and hemoglobin, as identified by feature importance analysis. The IPI effectively measured the multidimensional reciprocality of the physiological system and better identified subclinical iron deficiency.
Conclusions: The results suggest that a dynamic interaction between iron metabolism and inflammation is the determinant of aerobic performance, not isolated biomarkers. The Iron Performance Index and the proposed integrative framework offer a more precise, holistic, and clinically meaningful method for monitoring athletes' health and optimizing performance, representing a significant improvement over traditional diagnostic practice.
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