Background We recently identified novel risk factors for EAMC in distance runners, including age, gender, racing distance, a history of chronic diseases, medication, training and competition load, and running injuries. The relationships between these risk factors for EAMC are over-simplified, and complex path relationships between these risk factors and EAMC have not been investigated.

Objective The objective of the analysis was to explore the complex relationships among the risk factors associated with EAMC by means of path analysis, in order to estimate direct as well as indirect path relationships between the risk factors and EAMC. In addition, we sought to explore total effects by aggregating the direct and indirect effects.

Design and Settings 41 698 runners (21 km and 56 km), who were part of a 2-year prospective cohort study.

Type of Study Path analysis of prospective data.

Main Outcome Measurements Indirect, direct and total path estimates of the predictors of EAMC in distance runners.

Results Body Mass Index (BMI) and age were significant indirect predictors of EAMC (p<0.0001), while training and competition load, a history of chronic disease, medication use for chronic disease, and a history of running injuries were significant direct predictors for EAMC (p<0.0001). Gender was a significant direct as well as indirect predictor of EAMC (P<0.0001) and thus the total effect gives the overall impact of gender as a predictor of EAMC.

Conclusions Using a path analysis approach, novel risk factors for EAMC (indirect and direct relationships) were identified, indicating that the risk factors associated with EAMC are complex. Indirect risk factors do not impact directly on EAMC, but rather through their effects on direct risk factors. These data enable clinicians to better understand these complex relationships of risk factors and can apply this to knowledge in the prevention, diagnosis and management of EAMC in athletes.