Low energy availability (EA) is related to adverse physiological effects including hormonal disruption. PURPOSE: To evaluate in-season changes in EA and to assess biomarkers related to EA, macronutrient intake, body composition (BC), and exercise energy expenditure (EEE). METHODS: Prior to preseason and weeks 2, 4, 8 & 12, female collegiate soccer players (N=11) underwent blood draws to assess thyroid hormones, leptin (LEP), growth hormone (GH), IGF-1, total cortisol (TC) and prolactin (PRL), and BC tests to determine fat free mass (FFM) and percent body fat (%BF). Heart rate monitoring was used to assess EEE/kg during all training. Energy intake (EI), protein (PRO), carbohydrate (CHO) and FAT per kg were tracked via 3-day diet logs. EA was calculated as EI_AVG-EEE_AVG/FFM for each time block. RM-MANOVAs with univariate follow-ups assessed change in energy status, BC and EEE. Area under the curve (AUC) was calculated for biomarkers, EA, macronutrients and BC. Pearson-product correlations assessed AUC relationships with significance set at P<.05. Trends were considered P<0.1. RESULTS: Time main effects were seen for all macronutrients, EA, EI and EEE, with the highest values seen during preseason (P<.05). Time main effects were seen with increases in FFM and declines in %BF (P<.05). EA correlated with FFM (r=-67), GH (r=.63), PRL (r=.65) and FreeT₄ (r=.69). %BF correlated with TC (r=.70) and LEP (r=.71), with a trend for T₄ (r=.55). FFM correlated with PRO (r=-.65), with trends for FAT (r=-.57), IGF-1 (r=.58), FreeT₄ (r=-.53) and CORT (r=-.57). PRO correlated with GH (r=.73), PRL (r=.75) and FreeT₃ (r=.61), with a trend for EEE (r=.53). FAT correlated with GH (r=.65), PRL (r=.76) and FreeT₄ (r=.60), with a trend for IGF-1 (r=-.57). CHO correlated with LEP (r=.60) and PRL (r=.62). EEE correlated with LEP (r=-.63) and trended with PRL (r=.56). CONCLUSIONS: EA was reportedly highest in preseason and declined as the season progressed, despite increases in FFM. Adherence and accuracy challenges with self-reported EI limits the feasibility of this method in teams. Associations between BC, EEE and markers of stress/metabolism point to the efficacy of biomarker monitoring as a method to assess metabolic status and recovery in athletes, thus enabling in-season adjustments to training and nutrition. Funding by Quest Diagnostics