Polygenic Profiles Are Associated with Multidomain Biochemical Adaptations Across a Competitive Season in Professional Football Players: A Longitudinal Observational Study

Background/Objectives: The physiological adaptations required to sustain elite football performance are influenced by both genetic background and dynamic biochemical responses, although their interaction across a full competitive season remains insufficiently characterized. This study aimed to examine the association between polygenic profiles and longitudinal biochemical adaptations in professional football players. Methods: Forty male professional football players competing in the Spanish league were monitored across two consecutive seasons. Blood samples were collected at six time points representing different phases of the competitive cycle. Biomarkers related to muscle metabolism, iron status, and hepatic function were analyzed. Polygenic profiles were calculated using Total Genotype Scores (TGS) for muscle performance, hepatic resilience, and metabolic efficiency. Associations were initially explored using Pearson correlations and subsequently evaluated using linear mixed-effects models accounting for repeated measurements within subjects. Results: Exploratory correlation analyses identified several associations between polygenic profiles and biochemical markers. Muscle performance TGS was inversely associated with serum iron (r = −0.36, p = 0.017) and positively associated with CK (r = 0.32, p = 0.041), Hb (r = 0.29, p = 0.046), and Hct (r = 0.33, p = 0.024). Hepatic resilience TGS showed inverse associations with ALT (r = −0.39, p = 0.012), urea (r = −0.51, p = 0.011), and BUN (r = −0.51, p = 0.011). Metabolic efficiency TGS was negatively associated with AST (r = −0.43, p = 0.044), ALT (r = −0.33, p = 0.025), and GGT across multiple time points (p = 0.001–0.013). However, although several nominal associations emerged in linear mixed-effects models accounting for repeated measurements, none remained statistically significant after false discovery rate correction. These findings should therefore be interpreted as exploratory and hypothesis-generating. Conclusions: Polygenic profiles may be associated with inter-individual variability in biochemical adaptations throughout a competitive season. These findings suggest the integration of genomic and biochemical data in precision athlete monitoring, while highlighting causal relationships and predictive applications require further investigation.