Biomarker-guided optimization of monitoring and performance in para-athletes: Establishing precision exercise in Brazil

Para-athletes represent one of the most biologically heterogeneous populations in elite sport. Different impairments are associated with distinct physiological, metabolic, neuromuscular and endocrine characteristics that influence responses to training, recovery and athletic performance. Despite this heterogeneity, current athlete-monitoring strategies remain largely adapted from non-disabled athletic populations and rely on generalized interpretation of biomarkers. Creatine kinase (CK) is one of the most frequently used biomarkers for monitoring exercise-induced muscle stress and recovery; however, its interpretation is limited by substantial inter-individual variability and the absence of impairment-specific reference values. Therefore, this study aims to investigate CK as a precision biomarker for monitoring training adaptation, recovery and athletic performance in para-athletes. A multicenter study will be conducted through the State of São Paulo. Approximately 144 elite para-athletes (men and women), representing different impairment classifications, will be recruited. Participants will undergo body composition (DXA) and acute exercise session, quality of life, sleep quality, mood state and mental health evaluation. Blood samples will be collected at baseline condition and after maximal incremental test. Serum concentration of CK, glucose, insulin, triacylglycerol (TAG), total cholesterol, adiponectin, leptin, TNF-α, IL-6, and IL-10 using enzyme-linked immunosorbent assay (ELISA) commercial kits. Statistical analyses will be performed using Stata 14.0 and GraphPad Prism 10. Data normality will be assessed using the Shapiro–Wilk test. Generalized linear mixed models will be used to evaluate the effects of impairment classification, sex and repeated measurements on CK responses. Multivariable regression analyses will identify independent determinants of CK variability, and receiver operating characteristic (ROC) curve analysis will assess the ability of CK to discriminate training adaptation and recovery outcomes. Statistical significance will be established at p < 0.05.