Low Energy Availability and Cardiovascular Disease

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The team plans to include 126 trained to elite female athletes from local sports clubs near the Liverpool area to participate in the study. Following ethical approval and informed consent from the participants, data will be collected during a one-time visit at Liverpool Hope University in one of the laboratories in the Health Science building. Following a single laboratory visit, the following will be collected: 1. Demographic data such as age, sports participation (training, competition duration, frequency etc.) and competitive level. 2. Anthropometric data including height, body mass and DEXA scans. 3. Genetic material (DNA) in cells collected from a buccal cheek smear swab test. Methylation analysis (study of cell function) on the buccal cells will be performed. 4. Venous blood samples to determine hormonal profile. For example, measuring for Cholesterol, Lipids, C-reactive protein (CRP), full blood count , BNP (Natriuretic Peptide), Troponin, Oestrogen, GH (growth hormone), Ferritin, Iron studies, B12 and Folate, urea and electrolytes, Liver Function Tests, Calcium, Magnesium, Luteinising hormone, follicle stimulating hormone and sex hormone binding globulin, Prolactin, Cortisol, Progesterone, Testosterone, Insulin, Insulin-like Growth Factor, Thyroid-stimulating hormone, Thyroxine, Triiodothyronine, Creatine kinase and Vitamin D levels. 5. Cardiovascular assessment to test heart and blood vessel health. For example, blood pressure, the stiffness of their arteries, and the thickness of blood vessel walls. Using assessments such as pulse wave velocity (PWV), Carotid Intima-Media Thickness Test (CIMT), Flow-mediated Dilation (FMD) and electrocardiogram (ECG). Pearson r correlation will be performed between Cardiovascular health and Low energy availability variables. 6. Nutritional assessment via 5-day photographic food diaries, weighed food records along with a short interview to ensure accurate readings. 7. Physical activity assessment with accelerometers for seven days to determine physical activity levels. The accelerometers will be dropped off and picked up after one week by the lead researcher, Mr Liam Pope. 8. Eating behaviour and mood tests using the Eating Disorder Examination Questionnaire (EDE-Q) and the Clinical Impairment Assessment (CIA) (12) to assess their eating behaviours. 9. Low energy availability test via the LEAF-Q (5) and calculation of LEA to check if participants are getting enough energy 10. A questionnaire to assess menstrual cycle health, status, and hormonal contraception use 11. Energy expenditure and resting metabolic rate assessment via indirect calorimetry to measure how much energy their bodies use and how much they burn at rest. In addition, metabolomic analysis, lipoprotein subclass analysis and methylation analysis on blood cells will be performed. Machine learning models will also be used to detect novel patterns of lipids/metabolites in the data. Multivariate analysis will be performed before the machine learning models. Two groups will be formed, comprising one group identified as a high LEA risk group and the other as a low LEA risk group. LEA risk status will be established via the Loukes et al. (1999) equation: Energy availability = (Energy intake (kJ) - Energy expenditure during exercise (kJ))/fat-free mass (kg) (5). Group allocation of participants will be based on the following classification: Low risk of LEA High: EA ≥45 kcal/kg LBM/d and high risk of LEA EA 30 kcal/kg LBM/d (3). To reduce the variability among the participant results concerning their menstrual cycle characteristics, all 126 selected volunteers will engage in a two-month menstrual cycle monitoring process before the testing for the main research study, following the methodological recommendations for female athlete research (7). This monitoring will occur in participants\' homes, utilising menstrual cycle tracking apps and ovulation testing kits that will be sent to them. To track menstrual cycles, volunteers will use a menstrual cycle tracking app to record the first and last day of menstruation for each cycle. Daily ovulation tests will also be conducted using urine to detect the mid-cycle surge in luteinising hormone. The occurrence of the mid-cycle surge in luteinizing hormone will be documented in the app, providing visual confirmation to the researcher. This will also serve as crucial information to identify each participant specific menstrual cycle phases. To ensure consistent testing, the research team will schedule all participants\' tests for the main project during their early luteal phase, specifically in phase three. This phase captures a medium oestrogen concentration while keeping progesterone levels low, confirmed by a positive luteinizing hormone surge captured by the ovulation kit. This strategic choice is made to measure hormone levels within a normal range for health assessment, making it easier to identify any potential hormonal imbalances not due to the typical hormonal fluctuations linked with different phases of the menstrual cycle. This approach ensures precise timing aligned with specific menstrual phases that help minimise the impact of cycle-related variations to enhance the main study's results reliability.