Overtraining Syndrome as a Complex Systems Phenomenon

Abstract

The phenomenon of reduced athletic performance following sustained, intense training (Overtraining Syndrome, and OTS) was first recognized more than 90 years ago. Although hundreds of scientific publications have focused on OTS, a definitive diagnosis, reliable biomarkers, and effective treatments remain unknown. The present review considers existing models of OTS, acknowledges the individualized and sport-specific nature of signs/symptoms, describes potential interacting predisposing factors, and proposes that OTS will be most effectively characterized and evaluated via the underlying complex biological systems. Complex systems in nature are not aptly characterized or successfully analyzed using the classic scientific method (i.e., simplifying complex problems into single variables in a search for cause-and-effect) because they result from myriad (often non-linear) concomitant interactions of multiple determinants. Thus, this review 1) proposes that OTS be viewed from the perspectives of complex systems and network physiology, 2) advocates for and recommends that techniques such as trans-omic analyses and machine learning be widely employed, and 3) proposes evidence-based areas for future OTS investigations, including concomitant multi-domain analyses incorporating brain neural networks, dysfunction of hypothalamic-pituitary-adrenal responses to training stress, the intestinal microbiota, immune factors, and low energy availability. Such an inclusive and modern approach will measurably help in prevention and management of OTS.

Keywords: exercise; genome; hypothalamic-pituitary-adrenal axis; metabolism; network; overreaching; stress.

FIGURE 1

Training states that an athlete may experience throughout a competitive season. Although states and transitions are difficult to define and identify specifically, this figure is based on well-established concepts (Kuipers and Keizer, 1988; Fry and Kraemer, 1997; Kreider et al., 1998a; Armstrong and VanHeest, 2002; Meeusen et al., 2013).

FIGURE 2

The primary hormone systems that respond to external and internal stress. The hypothalamic-pituitary-adrenocortical (HPA) axis represents releasing factors, produced by the hypothalamus (CRH in conjunction with AVP) and pituitary gland (ACTH), which lead to responses within the adrenal cortex and other peripheral organs/tissues. The sympathetic-adrenal medullary (SAM) axis represents the sympathetic branch of the autonomic nervous system. Abbreviations: ACTH, adrenocorticotropic hormone; CRH, corticotropin-releasing hormone; AVP, arginine vasopressin.

FIGURE 3

Idealized representation of HPA axis responses across months of training, as envisioned by Steinacker and colleagues in 2004 (Steinacker et al., 2004). During training with positive adaptations, ACTH and cortisol levels increase in response to the stress of training. During overreaching, the cortisol response is blunted whereas the ACTH response is augmented. Overtraining is characterized by decreased ACTH and cortisol responses. Figure modified and redrawn from (Steinacker et al., 2004). The original data sources were (Lehmann et al., 1993; Snyder, 1995; Wittert, 1996; Urhausen et al., 1998; Lehmann et al., 1999; Steinacker et al., 2004).

FIGURE 4

Proposed complex systems assessment of the Overtraining Syndrome (OTS). The classic scientific method of reductionist analysis is ineffective when studying OTS because multiple factors interact simultaneously. Potential predisposing factors for OTS (illustrated here as multicolored nodes) are linked to each other in a non-linear web (lower left quadrant). The nodes of this web may be host characteristics (e.g., hereditary capabilities, training adaptations) or may arise from the environment (e.g., training intensity/volume, nutrition, and non-exercise life stressors). A complex systems analytical approach explores and reveals the inherent interaction patterns of predisposing factors (upper left quadrant) in athletes who exhibit OTS. By calculating the probability that each interaction pattern will result in OTS, a risk profile is developed for each athlete (upper right quadrant); this profile is applied by modifying high-risk factors (e.g., reduced training load, increased rest/recovery, increased dietary carbohydrate). Varying node sizes and intensity of connecting lines between nodes represent variability in the impact of factors (nodes) and strength of interactions (bold lines). The cyclical nature of this method accommodates both positive adaptations (e.g., improved strength and endurance) and counterproductive maladaptations (e.g., OTS); it also implies that athletes should be reevaluated regularly throughout a training cycle or season to update the risk profile.

FIGURE 5

Trans-omics analyses improve the precision of detecting individual responses in complex systems. Single or panel biomarker analyses are limited in their ability to predict outcomes associated with complex systems that contain redundant, multi-functional variables. Trans-omics (integrative-omics) analysis permits a broad landscape view of thousands of contributing factors and patterns that lead to global outcomes (e.g., phenotype A vs. phenotype B) which otherwise would be undetectable by single biomarkers.