This study examined the relationships between fatigue-related blood biomarkers, training load distribution, and performance changes in elite triathletes across a structured training season. Twenty athletes (11 international-level and 9 national-level) were monitored. Exercise stimuli were quantified using objective and subjective metrics during the general and specific preparatory periods, with each microcycle classified as heavy, moderate, or low according to accumulated weekly load. Baseline and follow-up blood samples were collected to assess urea and creatine kinase (CK), and an incremental cycling test with gas analysis was performed at the beginning and end of the training period to evaluate changes in physiological performance. Statistical analyses assessed pre-post differences, while correlation analyses explored associations between training load, biomarker responses, and performance outcomes. Urea concentrations increased significantly during severe (p < 0.001; ES: 2.1) and moderate (p < 0.001; ES: 1.2) load microcycles compared with baseline, whereas CK showed no meaningful associations across the season. Greater accumulated subjective training load was associated with larger increases in urea (rho = 0.67; p < 0.05), whereas a more favorable balance between the objectively monitored training load and the athlete`s perceived exertion—characterized by lower perceived effort relative to the external load—was associated with smaller urea elevations (rho = -0.55; p<0.05). In addition, improvements in relative power at VO2max and VT2 were negatively associated with increases in urea ((rho = -0.65; p < 0.05) (rho = -0.68; p < 0.05) respectively). Despite phases of comparable exercise stimuli, no differences in biomarker responses were observed between national- and international-level triathletes. These findings suggest that longitudinal monitoring of urea may provide useful information to help identify potential states of excessive training load in athletes. However, the substantial inter-individual variability observed indicates that isolated biomarker measurements are insufficient to identify non-adaptive responses. Therefore, an individualized, longitudinal, and holistic monitoring framework integrating objective and subjective training load measures is required to better understand training-induced adaptations and fatigue.
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