Introduction
Complex diagnostic criteria, triggers and pathophysiology make the diagnosis of OTS challenging.1 2,3,4,5 Its estimated prevalence varies widely (15%–60%), and depends on the frequency, intensity of training and type of sport.1 6 7 Potential biomarkers of OTS include higher creatine kinase (CK), lower postexercise lactate, and blunted exercise-stimulated prolactin, growth hormone (GH), cortisol and adrenocorticotropic hormone (ACTH) responses.1 2 8–18 Basal hormones and other biochemical parameters are normal in OTS.16–18 There is currently no conclusive data on the causes, pathophysiology, early identification and prevention of OTS,1 2 10,19,20 as reinforced by a systematic review.10
We designed the Endocrine and Metabolic Responses on Overtraining Syndrome (EROS) study21–24 to try to elucidate the pathophysiology of OTS, identify new biomarkers and risk factors, and propose new tools for prevention and early diagnosis of OTS. We evaluated and compared clinical, biochemical and metabolic changes in athletes with OTS (OTS group), age-matched, body mass index (BMI)-matched and sex-matched healthy athletes (ATL group) and non-physically active control subjects (NPAC group). The inclusion of a second control group of non-athletes provided a context in which to interpret differences between OTS-affected athletes and healthy athletes. The simultaneous comparison of healthy athletes and non-athletes enhanced our understanding of the differences between the OTS-affected and healthy athletes, that is, differences that were also present when OTS-affected athletes were compared with non-athletes as a reference group, and differences between the OTS-affected and healthy athletes that were not present when the OTS-affected athletes were compared with non-athletes. Relative differences refer to the analysed parameters that were different from those typical of athletes, but not different from the general population.
The parameters evaluated in the EROS study revealed approximately 45 new markers in both the healthy and OTS-affected athletes.21–24 We hypothesised that levels of dysfunction in OTS vary widely, depending on the parameters evaluated, as we found different types of behaviours in OTS-affected athletes compared with healthy athletes and non-athletes, which could help to predict those at high risk for OTS.
First, the behaviours referred to as unaltered markers were similar for both the healthy athletes and non-athletes. Second, the markers were similar to those of healthy athletes, and different from those of non-athletes indicating the adaptation of these parameters to physical activity, which was maintained in OTS. Markers that disclosed these two types of behaviours were not helpful to detect imminent OTS.
Third, the markers were different from those of healthy athletes, but were similar to those of healthy non-athletes, while the markers of healthy athletes were different from those of non-athletes, indicating these parameters underwent adaptive changes to exercise, which seemed to be absent in the presence of OTS, likely as a loss of these adaptive processes. Fourth, the markers were different from those of healthy athletes, which were also different from those of non-athletes. This finding suggests the physiological adaptation to exercise was exacerbated in OTS with higher marker levels in healthy athletes than non-athletes, and even higher marker levels in OTS-affected athletes. It also may indicate a loss of adaptation to exercise, leading to overt dysfunction (ie, higher marker levels were found in healthy athletes than non-athletes, which were higher than those of OTS-affected athletes were). Parameters from the third or fourth types of behaviours were those that were potentially valuable for the detection of OTS.
The objective of this study was to identify new insights for OTS, by conducting a comprehensive joint analysis of data from the four arms of the EROS study (EROS-HPA axis, EROS-STRESS, EROS-PROFILE and EROS-BASAL).21–24
We set out to clarify the behaviours of each parameter, the predominant types of behaviours in OTS-affected athletes, the metabolic and biochemical changes caused by athletic training and their disruptions in OTS, and the metabolic and hormonal changes caused by OTS through post hoc analyses.
On the basis of these findings, we aimed to hypothesise novel risk factors for OTS, to elaborate predictive models to detect athletes at high risk or imminent OTS for an effective preventive approach and early identification of OTS, and to hypothesise novel mechanisms that underlie the pathophysiology of OTS, to be further elucidated.