Evidence for a Causal Role of Low Energy Availability in the Induction of Menstrual Cycle Disturbances during Strenuous Exercise Training

AbstractThis study aimed to investigate (1) the accuracy of resting metabolic rate (RMR) prediction equations in female rugby players on a group and individual level; and (2) whether individual differences in the accuracy of prediction equations is associated with muscle damage or energy availability.RMR was assessed in 14 female provincial and club rugby players (Age: 20–34 years, FFM: 47–63 kg, FM: 15–37%) training a minimum of twice per week. Participants attended the laboratory following an overnight fast and having avoided strenuous exercise for 24 hours. RMR was measured over 30 minutes by indirect calorimetry, and taken as the 10 minutes with the lowest variation. Body composition was assessed by air displacement plethysmography, muscle damage indicated by creatine kinase (CK) and risk of low energy availability assessed by the Low Energy Availability in Females Questionnaire. Accuracy of RMR prediction equations relevant to the general population and athletes were assessed including the Harris Benedict (1919), Cunningham (1980) and Ten Haaf FFM (2014) based equations.Measured RMR was 1748 ± 146 kcal/day (range: 1474–2010 kcal/day). Predicted RMR determined by the Harris-Benedict equation (1601 ± 120 kcal/day) was significantly lower than measured RMR (p < 0.001), whereas predicted RMR using the Cunningham (1753 ± 146 kcal/day, p = 0.89) and the Ten Haaf (1781 ± 115 kcal/day, p = 0.33) equations did not differ from measured RMR. On an individual level, 50% (n = 7), 86% (n = 12) and 79% (n = 11) of participants fell within 10% of the measured RMR value when RMR was predicted by Harris-Benedict, Cunningham and Ten Haaf equations respectively. CK values were 182 ± 155U/L (range: 25–490U/L). When correlations of the whole group were studied, the difference between predicted and measured RMR was not associated with CK (r = 0.13). However, in the two individuals who fell outside the 10% range of that predicted by the Cunningham equation, one above and one below, CK values were 428U/L and 166U/L respectively. Muscle damage (as indicated by a high CK value) could therefore be one potential explanation for the higher measured RMR in the individual who was above the Cunningham predicted value.In this cohort of female rugby players, the Cunningham equation showed the best accuracy on a group and individual level, suggesting this may be the most suitable prediction equation for this population. Further studies with larger sample sizes and investigating underlying reasons for why RMR measured values may differ from predicted values are needed.

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Exercise Training in the Normal Female: Effects of Low Energy Availability on Reproductive Function

Endocrinology of Physical Activity and Sport - Contemporary Endocrinology ◽

10.1007/978-3-030-33376-8_11 ◽

2020 ◽

pp. 171-191

Author(s):

Anne B. Loucks

Keyword(s):

Exercise Training ◽

Reproductive Function ◽

Low Energy ◽

Normal Female ◽

Energy Availability ◽

Low Energy Availability

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Clinical application of monitoring indicators of female dancer health, including application of artificial intelligence in female hormone networks

10.1101/2021.09.27.21264119 ◽

2021 ◽

Author(s):

Nicola Keay ◽

Martin Lanfear ◽

Gavin Francis

Keyword(s):

Artificial Intelligence ◽

Menstrual Cycle ◽

Clinical Application ◽

Low Energy ◽

List Type ◽

Energy Availability ◽

Clinical Tool ◽

Female Hormone ◽

Network Function ◽

Low Energy Availability

AbstractObjectivesThe purpose of this study was to assess the effectiveness of monitoring professional female dancer health with a variety of subjective and objective monitoring methods, including application of artificial intelligence (AI) techniques to modelling menstrual cycle hormones and delivering swift personalised clinical advice.MethodsFemale dancers from a ballet company completed a published online dance-specific health questionnaire. Over the study period, dancers recorded wellbeing and training metrics, with menstrual cycle tracking and blood tests. For menstrual cycle hormones AI-based techniques modelled hormone variation over a cycle, based on capillary blood samples taken at two time points. At regular, virtual, clinical interviews with each dancer, findings were discussed, and personalised advice given.Results14 female dancers (mean age 25.5 years, SD 3.7) participated in the study. 10 dancers recorded positive scores on the dance health questionnaire, suggesting a low risk of relative energy deficiency in sport (RED-S). 2 dancers were taking hormonal contraception. Apart from 1 dancer, those not on hormonal contraception reported current eumenorrhoeic status. The initiative of monitoring menstrual cycles and application of AI to model menstrual cycle hormones found that subclinical hormone disruption was occurring in 6 of the 10 dancers reporting regular cycles. 4 of the 6 dancers who received personalised advice, showed improved menstrual hormone function, including one dancer who had planned pregnancy.ConclusionsMultimodal monitoring facilitated delivery of prompt personalised clinical medical feedback specific for dance. This strategy enabled the early identification and swift management of emergent clinical issues. These innovations received positive feedback from the dancers.Summary boxesWhat are the new findings?Monitoring female dancers with a variety of interactive methods – dance specific questionnaire, online tracking and blood testing – together with individual clinical discussion, facilitates comprehensive, personalised support for dancer health.The clinical application of artificial intelligence (AI) techniques to endocrine function provides the finer detail of female hormone network function.This novel approach to monitoring dynamic hormone function enabled the detection of subtle female hormone dysfunction as a result of changes in training and nutrition patterns, which occurred before change in menstruation pattern from menstrual tracking.This multifaceted clinical approach was also effective and helpful in supporting dancers restore full hormone network function through personalised training and nutritional strategies.How might this study impact on clinical practice in the future?Personalised, dance specific health advice based on subjective and objective measures can support sustainable individual dancer health.Clinical application of artificial intelligence (AI) to menstrual cycle hormones can provide a dynamic and complete picture of hormone network function, without the need to do daily blood tests to measure all four key menstrual cycle hormones.This AI approach to modelling hormones enables early detection of subtle, subclinical endocrine dysfunction due to low energy availability in female exercisers. This clinical tool can also facilitate the close clinical monitoring of the restoration of full hormone network function in recovery from low energy availability.Using AI to model female hormones can be an important clinical tool for female athletes, including those athletes where it is difficult to distinguish between perimenopause symptoms and those associated with low energy availability.

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Exercise Training in the Normal Female: Effects of Low Energy Availability on Reproductive Function

Endocrinology of Physical Activity and Sport ◽

10.1007/978-1-62703-314-5_11 ◽

2013 ◽

pp. 185-205 ◽

Cited By ~ 1

Author(s):

Anne B. Loucks

Keyword(s):

Exercise Training ◽

Reproductive Function ◽

Low Energy ◽

Normal Female ◽

Energy Availability ◽

Low Energy Availability

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Body Composition, Dietary Intake and the Risk of Low Energy Availability in Elite-Level Competitive Rhythmic Gymnasts

Nutrients ◽

10.3390/nu13062083 ◽

2021 ◽

Vol 13 (6) ◽

pp. 2083

Author(s):

María Villa ◽

José G. Villa-Vicente ◽

Jesus Seco-Calvo ◽

Juan Mielgo-Ayuso ◽

Pilar S. Collado

Keyword(s):

Body Composition ◽

Dietary Intake ◽

Body Fat ◽

Low Energy ◽

Energy Availability ◽

Future Health ◽

Elite Level ◽

Low Energy Availability ◽

Rhythmic Gymnasts ◽

Recommended Level

The aim of this study was to analyze dietary intake and body composition in a group of elite-level competitive rhythmic gymnasts from Spain. We undertook body composition and nutritional analysis of 30 elite gymnasts, divided into two groups by age: pre-teen (9–12 years) (n = 17) and teen (13–18 years) (n = 13). Measures of height, weight, and bioimpedance were used to calculate body mass index and percent body fat. Energy and nutrient intakes were assessed based on 7-day food records. The two groups had similar percentages of total body fat (pre-teen: 13.99 ± 3.83% vs. teen: 14.33 ± 5.57%; p > 0.05). The energy availability values for pre-teens were above the recommended values (>40 kcal/FFM/day) 69.38 ± 14.47 kcal/FFM/day, while those for the teens were much lower (34.7 ± 7.5 kcal/FFM/day). The distribution of the daily energy intake across the macronutrients indicates that both groups ingested less than the recommended level of carbohydrates and more than the recommended level of fat. Very low intakes of calcium and vitamin D among other micronutrients were also noted. The main finding is that teenage gymnasts do not consume as much energy as they need each day, which explains their weight and development. Moreover, they are at a high risk of developing low energy availability that could negatively impact their performance and future health.

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