By Tej Pandya
“RED-S is a complex, multifactorial syndrome that it is often missed”
This was the key message from the 2018 BASEM Conference – the largest attendance for the spring meeting since its inception. We were treated to a wide variety of SEM practitioners (with equal split between male and female presenters) who collectively shared their knowledge. Below are the highlights!
RED-S can present in many ways with a wide variety of complications
RED-S occurs as a result of insufficient caloric intake to cover energy expenditure through training and energy required to maintain basal metabolic rate; leading to relative energy deficiency. This can disrupt many endocrine axes that manifests in females as menstrual dysfunction. Endocrine dysfunction also has a knock on effect on bone health, and therefore there is an increased risk of stress fractures. Key screening questions when assessing athletes include libido, menstrual abnormalities, mood, number of infections and sleep pattern. It is important to note while RED-S has been more widely studied in females, male athletes who follow low calorie restricted nutrition diets (such as endurance cyclists) are also at risk of developing RED-S.
RED-S can be corrected, but needs buy in from the athlete and the wider team
Correcting RED-S can often be tricky due to the beliefs of the athletes, coaches and performance team such as restricting carbohydrate intake and maintenance of low body weight for performance advantage. Often external factors such as post match interviews and doping may lead to delayed post exercise fuelling. A key way of getting “buy in” from the athlete is by communicating the performance consequences of RED-S: increased injury risk and decreased endurance performance.
Machine learning can be used to predict common biochemical patterns
Chris Kelly and Dr Tommy Wood from the US presented work from their clinic showing how a screening questionnaire can be used to predict diseases such as H Pylori and anaemia. Furthermore, filter feature selection has the potential to calculate which questions are more closely associated with biochemical patterns. The potential advantage of this could lead to patient stratification and increased workflow efficiency and quicker synthesis of large data sets. This is certainly an exciting area of medicine with potentially huge implications for practice.
Tej Pandya is a medical student at the University of Manchester and currently President of the Manchester Sports and Exercise Medicine Society (@semsocuk). All enquiries can be directed to tej1pandya@hotmail.com
