Article Category: Original scientific article
Published Online: Jun 13, 2023
Page range: 121 - 128
Received: Nov 20, 2022
Accepted: Mar 29, 2023
DOI: https://doi.org/10.2478/sjph-2023-0017
Keywords
© 2023 Eva Peklaj et al., published by Sciendo
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.
Insufficient energy intake in athletes leads to physiological disorders, which may result in health issues (1). In 2014, the IOC group of experts defined relative energy deficiency syndrome in sport (RED-S) as impaired body functioning due to a relative energy deficit (2). RED-S can result from low-energy nutritional intake and/or excessive exercise. According to scientific data, it is assumed that the key pathophysiological mechanism of RED-S is insufficient energy availability (EA) to support bodily functions and maintain optimal health and performance during physical activity. Melin et al. proposed the concept of different energy availability (EA) for men and women according to their physiological and clinical characteristics (3). It is currently considered that EA<30 kcal/kg FFM/day represents the threshold below which unfavorable physiological changes likely occur in both genders. EA under this threshold may lead to physiological dysfunctions with serious health and performance consequences, and is termed as low EA (LEA) (3). We currently do not have specific cut-off values for LEA in young athletes (younger than 18 years). However, studies show that athletes with long-term LEA might develop nutritional deficiencies, chronic fatigue, and an increased risk for infectious diseases (3, 4). In addition, they can develop physiological alterations, affecting metabolic rate, protein synthesis, growth, and development, cardiovascular and emotional health, and gastrointestinal, endocrine, reproductive, skeletal, renal, and nervous systems disorders (2, 4,5,6). Psychological disorders can be either the cause or consequence of LEA (3, 5, 7).
Our recent study revealed that young athletes (14–21 years), in general, have more health-related symptoms of RED-S than elite athletes (above 21 years) (8). The period of adolescence is a critical period for athletes’ physical development, including altered body composition, metabolic and hormonal fluctuations, maturation of organ systems, and establishment of nutrient stores, and thus RED-S-related health disorders should be considered very seriously with regard to this age group (9,10). In terms of nutrition, the period of adolescence is also a critical life period for establishing personal relationships with food (11, 12). RED-S is a medical disorder and it is therefore essential to detect and if possible, prevent its consequences as soon as possible. This requires good knowledge of RED-S causes and symptoms by an athlete’s support team. As there is little information regarding the RED-S in young adolescent athletes, the main aim of our study was to explore nutrition-related risk factors for RED-S using exploratory factor analysis and to compare two age subgroups of athletes (middle adolescents 14–17 years vs. late adolescents 18–21 years) to evaluate potential differences regarding the nutritional risks for RED-S.
Our retrospective study targeted young Slovenian athletes (14–21 years) who had undergone comprehensive nutritional assessment between the years 2015 to 2020. The study was approved by the Commission for Medical Ethics of the Slovenian Ministry of Health (number 0120-21/2019/8).
The research inclusion criteria were that athletes had a nutritional assessment as part of their sports’ medical examination. We screened 150 athletes’ records and 118 met the inclusion criteria, of whom 61 were female and 57 were male. Eighty-four (46 females, 38 males) were in the middle adolescent group (14–17 years), and 34 (15 females, 19 males) were in the late adolescent group (18–21 years).
We have retrospectively analysed nutritional questionnaire which were completed by the athletes themselves and tailored to detect RED-S-related health disorders and performance problems. The questionnaire was divided into five parts. In the first part we enquired about medical conditions and medication intake. The second part included questions regarding nutritional status, the third part about eating habits, the fourth part was an assessment of athletics performance, and the fifth part included a standard three-day weighted nutritional diary, a physical activity diary, and a customized standard food frequency questionnaire (13).
All the athletes underwent a nutritional assessment by a clinical dietitian (14), body composition measurement before practice using the bioimpedance method (Bodystat Quadscan 4000) and laboratory analysis. Data about previous medical conditions was collected from the medical records of athletes that were available during the nutritional assessment. Energy and nutrient intake were analysed using the Open Platform for Clinical Nutrition (OPKP,
RED-S-related disorders were assessed using questionnaires, body composition measurements, and laboratory analysis (see the parameters in Table 2). Disordered eating was detected by assessing a nutritional diary, a food and meal frequency questionnaire, and a nutritional assessment interview. We also obtained the growth histories in last year of the young athletes. If their height was two standard deviations or more below the mean for children of that sex and chronologic age, we defined them as athletes with growth disorder (19). After the nutritional examination, we used the RED-S CAT tool (20), to diagnose RED-S. Therefore, athletes with LEA and RED-S-related disorders were diagnosed with RED-S.
One of purposes of this study was to find potential nutritional risk factors for RED-S (other than LEA). After receiving the questionnaires and three-day food diaries the intakes of energy, protein, carbohydrates, fats, dietary fibre, iron, and calcium were calculated. The amount of carbohydrates and proteins in meals 1–4 h before training, during training and 1h after practice was calculated and questions regarding weight loss and desire to lose weight were analysed.
The results are presented as means, with the standard deviations (SD) for parametric data. The prevalence was calculated as the number of participants above/below the threshold for each assessment, divided by the total number of participants who completed that assessment. The normality of the distribution was analysed using the Shapiro-Wilk test. For data that was not normally distributed we used the Mann-Whitney test to evaluate the statistical significance. We used a Cronbach’s alpha level of p ≤0.05 as a threshold to accept our hypothesis. For data analysis and statistics, we used the Python libraries NumPy, SciPy, and Pandas, and used the Seaborn library for visualizations.
The demographic data, anthropometric characteristics, presence of chronic illness, hours of training per week and type of sports are presented in Table 1.
Anthropometric characteristics and disease states of athletes.
| Average height [cm] | 167.8±5.6 | 176.4±12.6 | 168.6±5.9 | 179.1±7.1 |
| Average body mass [kg] | 57.5±10 | 65.8±13 | 58.1±8.3 | 74.3±8.7 |
| Average BMI [kg/cm2] | 20.4±2.7 | 20.9±2.1 | 20.4±2.5 | 22.9±2.6 |
| Body fat [%] | 20.7±4.7 | 12.8±5.7 | 17.3±3.5 | 9.6±3.5 |
| FFMI [kg/cm2] | 16.1±1.8 | 18.1±1.7 | 16.5±1.1 | 20.4±2.4 |
| Chronic illness [number of athletes] | Asthma (5), Chronic gastritis (4), Hashimoto thyroiditis (2), Atopic dermatitis (1) | Asthma (5) |
||
| Hours training per week [h] | 16.9±0.6 | 19.2±1.0 | ||
| Type of sport [number of athletes] | ||||
The detailed information on prevalence of RED-S-related health disorders and performance problems is available in Table 2.
Prevalence of RED-S-related health disorder.
| Primary amenorrhea (F) | 3 | 43 | 6.5% | 0 | 15 | 0% | ||||||
| Secondary amenorrhea (F) | 13 | 33 | 28.2% | 5 | 10 | 35.7% | ||||||
| Other disorders (F) | 18 | 28 | 39.1% | 5 | 10 | 33.3% | ||||||
| Low testosterone (M) | 1 | 37 | 2.6% | 0 | 19 | 0% | ||||||
| Several viral infections | 22 | 24 | 47.8% | 15 | 23 | 39.5% | 5 | 10 | 33.3% | 4 | 15 | 21.1% |
| Lymphopenia | 1 | 28 | 3.4% | 1 | 18 | 5.2% | 1 | 10 | 9.1% | 0 | 12 | 0% |
| Leukopenia | 3 | 26 | 10.3% | 1 | 19 | 5% | 2 | 10 | 16.7% | 1 | 11 | 8.3% |
| Stress fractures | 3 | 43 | 6.5% | 0 | 38 | 0% | 0 | 15 | 0% | 0 | 19 | 0% |
| Osteoporosis/Osteopenia | 3 | 43 | 6.5% | 0 | 38 | 0% | 0 | 15 | 0% | 0 | 19 | 0% |
| T3<3.1 pmol/l | 0 | 7 | 0% | 0 | 3 | 0% | 0 | 2 | 0% | 0 | 1 | 0% |
| T4<12 pmol/l | 0 | 7 | 0% | 1 | 2 | 33.3% | 1 | 1 | 50% | 1 | 0 | 100% |
| Glucose<4 mmol/l | 1 | 18 | 5.2% | 0 | 16 | 0% | 1 | 7 | 12.5% | 1 | 10 | 9.1% |
| TSH < 0.5 mU/l | 0 | 9 | 0% | 0 | 5 | 0% | 0 | 2 | 0% | 0 | 1 | 0% |
| TSH>4.3 mU/l | 2 | 7 | 22.2% | 0 | 5 | 0% | 0 | 2 | 0% | 0 | 1 | 0% |
| Ferritin<30 µg/l | 7 | 21 | 25% | 3 | 16 | 15.8% | 2 | 7 | 22.2% | 1 | 11 | 8.3% |
| Iron<10µmol/l | 2 | 24 | 7.7% | 2 | 15 | 11.7% | 1 | 9 | 10% | 0 | 12 | 0% |
| Haemoglobin < 120 g/l | 0 | 30 | 0% | 0 | 20 | 0% | 0 | 12 | 0% | 0 | 12 | 0% |
| Eating disorders | 7 | 39 | 15.2% | 1 | 37 | 2.6% | 3 | 12 | 20% | 0 | 19 | 0% |
| Disordered eating | 30 | 16 | 65.2% | 12 | 21 | 44.7% | 7 | 8 | 46.7% | 7 | 12 | 36.8% |
| Psychological problems | 29 | 17 | 63.0% | 8 | 29 | 21.6% | 6 | 9 | 40% | 1 | 18 | 5.3% |
| Cholesterol<4 mmol/l | 7 | 14 | 33.3% | 8 | 9 | 47.1% | 3 | 3 | 50% | 3 | 8 | 27.3% |
| Cholesterol >5.2 mmol/l | 1 | 20 | 4.8% | 0 | 17 | 0% | 2 | 4 | 33.3% | 2 | 9 | 18.2% |
| HDL-cholesterol<1.45 mmol/l | 2 | 6 | 25% | 3 | 6 | 33.3% | 1 | 2 | 33.3% | 0 | 2 | 0% |
| LDL-cholesterol>2.59 mmol/l | 3 | 5 | 37.5% | 4 | 6 | 40% | 1 | 2 | 33.3% | 1 | 1 | 50% |
| Triglycerides>2.62 mmol/l | 0 | 8 | 0% | 0 | 8 | 0% | 0 | 3 | 0% | 0 | 2 | 0% |
| Gastrointestinal disorders | 13 | 33 | 28.2% | 0 | 38 | 0% | 3 | 12 | 20% | 3 | 16 | 15.8% |
| Digestion disorders (gastritis, constipation, diarrhoea, gastroesophageal reflux) | 4 | 42 | 8.7% | 0 | 38 | 0% | 1 | 14 | 6.7% | 1 | 18 | 5.3% |
| Growth disorders | 4 | 42 | 8.7% | 6 | 32 | 15.8% | 1 | 14 | 6.7% | 0 | 19 | 0% |
| Tiredness | 35 | 11 | 76.1% | 24 | 14 | 63.2% | 12 | 3 | 80% | 13 | 6 | 68.4% |
| Muscle cramps | 8 | 38 | 17.4% | 13 | 25 | 34.2% | 1 | 14 | 6.7% | 8 | 11 | 42.1% |
| Other problems (Concentration, coordination, strength…) | 33 | 13 | 71.7% | 21 | 17 | 55% | 10 | 5 | 66.7% | 12 | 7 | 63.2% |
| Recurring injury | 13 | 33 | 28.2% | 3 | 35 | 7.9% | 1 | 14 | 6.7% | 5 | 14 | 26.3% |
| Maintaining focus | 18 | 28 | 39.1% | 5 | 33 | 13.2% | 2 | 13 | 13.3% | 0 | 19 | 0% |
Figure 1 presents the distribution of all RED-S-related problems. To inspect distributions in health, and performance-related problems separately, refer to Figure 2 and Figure 3 respectively. All figures indicate that middle female adolescents had the highest number of RED-S-related problems (health disorders and performance problems). In addition, the highest risk of developing RED-S was found in adolescent female athletes. Middle adolescents had 2.6 (0.2) health-related problems while late adolescents had 1.9 (0.3).
Figure 1.
Distribution of number of RED-S-related health disorders and performance problems.
Figure 2.
Distribution of number of RED-S-related health disorders.
Figure 3.
Distribution of number of RED-S-related performance problems.
Clinical LEA (EA<30 kcal/kg FFM) was identified in 52.5% of middle female adolescents, 26.5% of middle male adolescents, 40% late female adolescents and 36.8% of late male adolescents. Subclinical LEA (female: 30 kcal/kg FFM<EA< 45kcal/kg FFM) was found in 28.3% female and 42.1% male middle adolescents and 40% female and 57.9% male late adolescents. In addition to identifying LEA, several potential nutritional risks for RED-S were detected, as presented in Table 3.
Number of RED-S-related health disorders and performance problems in nutrition-related risk factors. Rows in the first column are the potential nutrition-related risk factors, and the numbers in the following columns are the numbers of RED-s related (with standard deviation) in athletes that have the particular nutritional risk (YES column) or don’t have this risk (NO column).
| 4.2±1.9 | 3.3±2.0 | 0.05 | 4.9±2.0 | 4.2±2.0 | 0.20 | 3.0±0.8 | 2.6±1.6 | 0.32 | 4.6±1.8 | 3.5±1.9 | 0.02 | 2.2±1.0 | 3.0±2.0 | 0.50 | |
| 3.6±2.0 | 3.2±1.9 | 0.32 | 4.4±2.0 | 3.9±2.1 | 0.43 | 2.6±1.3 | 2.8±1.8 | 0.51 | 4.0±2.0 | 3.1±1.7 | 0.09 | 2.8±1.7 | 3.2±2.5 | 0.70 | |
| 4.0±2.1 | 3.5±1.9 | 0.40 | 4.5±2.2 | 4.3±2.0 | 0.76 | 2.7±1.5 | 2.6±1.5 | 0.90 | 4.6±1.8 | 3.7±1.9 | 0.17 | 2.3±2.3 | 3.0±1.9 | 0.58 | |
| 3.5±2.0 | 3.7±1.8 | 0.45 | 4.4±2.1 | 4.0±1.9 | 0.48 | 2.4±1.3 | 3.5±1.7 | 0.03 | 3.9±2.0 | 3.5±1.7 | 0.56 | 2.6±1.8 | 5.0±1.8 | 0.03 | |
| 4.6±1.8 | 3.3±1.9 | 0.002 | 5.8±1.4 | 4.0±2.0 | 0.006 | 3.5±1.4 | 2.4±1.4 | 0.02 | 5.2±1.8 | 2.4±1.4 | 0.003 | 3.8±1.7 | 2.7±1.9 | 0.13 | |
| 4.6±1.8 | 3.3±1.9 | 0.002 | 5.8±1.4 | 4.0±2.0 | 0.006 | 3.5 ± 1.4 | 2.4±1.4 | 0.02 | 5.2±1.8 | 2.4±1.4 | 0.003 | 3.8±1.7 | 2.7±1.9 | 0.13 | |
| 3.5±2.0 | 5.5±0.7 | 0.09 | 4.3±2.0 | 5.5±0.7 | 0.26 | 2.6 ± 1.5 | / | / | 2.8±1.9 | 5.0 | 0.19 | 3.7±1.9 | 6.0 | 0.25 | |
| 3.3±1.9 | 4.5±1.9 | 0.01 | 4.2±2.0 | 4.8±2.1 | 0.18 | 2.5 ± 1.5 | 3.7±1.3 | 0.03 | 3.5±1.9 | 4.9±1.7 | 0.005 | 2.9±1.9 | 2.8±1.9 | 0.96 | |
| 3.4±2.0 | 4.5±0.9 | 0.05 | 4.3±2.1 | 4.5±0.6 | 0.84 | 2.5 ± 1.4 | 4.5±1.3 | 0.01 | 3.7±2.0 | 4.6±1.0 | 0.10 | 2.9±1.9 | 4.0 | 0.54 | |
| 3.4±1.8 | 3.6±2.0 | 0.81 | 4.2±1.6 | 4.4±2.2 | 0.63 | 2.3± 1.5 | 2.7±1.5 | 0.40 | 3.5±2.0 | 3.8±1.9 | 0.46 | 3.2±1.4 | 2.8±2.1 | 0.47 | |
| 3.6±2.0 | 3.3±1.9 | 0.48 | 4.5±2.1 | 4.0±1.9 | 0.38 | 2.6 ± 1.3 | 2.7±1.8 | 0.97 | 3.9±2.0 | 3.5±1.9 | 0.45 | 3.0±1.8 | 2.8±2.2 | 0.68 | |
This retrospective analysis represents the first study on RED-S-related health disorders in the group of young Slovenian athletes using the IOC diagnostic tool RED-S CAT (20). Young athletes have always been presumed to be a healthy population. However, this study showed that they could easily compromise their health with their lifestyles. From a medical point of view, the results of our study are worrying, because as many as 87% of athletes in this population have at least one of the RED-S-related health disorders, and 85% have athletic performance-related problems. The reason is probably the athletes’ high prevalence of clinical LEA (40%) and subclinical LEA (39%). In addition, middle adolescent athletes had a greater nutritional risk than late adolescents (p=0.02).
In comparison to the study of Rogers et al., we found that middle female adolescent athletes included in our study have more health-related disorders consistent with RED-S (92%) than the females in their study (80%) (Rogers et al., 2021). A similar range of health disorders is present in the male group, found in 82% of such athletes in our study.
We did not find any data for young male athletes in the literature. Interestingly, the prevalence of RED-S health disorders in male athletes in our study is in the same range as in the female group in Rogers et al.’s study (21). However, the prevalence of LEA among athletes is comparable in both groups. In Logue et al. the clinical LEA was 22–58% (22), and in our previous study the prevalence was 40% (8). Similar results were found in middle adolescent endurance runners (14–17 years), as LEA was detected in 30% of males and 60% females (23). The prevalence in our study was high, at 52.5% in females and 36.8% in males.
We also identified some potential nutritional risk factors for RED-S that should be investigated in the prospective cohort studies as potential risk factors for this condition. In the middle adolescent group nutritional risk factors are low carbohydrate (CHO) intake below 3g/kg/day and skipping meals 1–3 hours before and one hour after practice (Table 3). Additional nutritional risk factors are the desire to lose weight and history of weight loss in the past year. CHO intake lower than 6–10 g/kg/day for athletes exercising moderately to high intensity (24) is commonly reported in athletes with LEA (25, 26). We observed that athletes in the middle adolescent group with a CHO intake below 3g/kg/day have more RED-S-related health disorders than late adolescents (p=0.02). The results in Table 3 also showed that middle female adolescents often struggle with body weight and image issues, even through they are not yet elite athletes (11). The desire to lose body weight and weight loss in the past year are significantly related to RED-S in this group of athletes, although it is very well known that unsafe weight management practices can compromise athletic performance and negatively affect health (2). Athletes choose different strategies for losing weight: not eating, limiting energy or excluding food groups from the diet, engaging in pathological weight control behaviors and restricting fluids. These athletes often respond to pressures of the sport or activity, coaches, peers, or parents by adopting negative body images and unsafe practices to maintain an ideal body composition (28). We did not find any studies on skipping meals before, during and after practice and the prevalence of RED-S problems. Our study showed that athletes who skipped meals one hour before and one hour after practice had more RED-S problems.
None of the athletes included in our study were diagnosed with RED-S before our examination despite health or performance-related problems, most likely due to a lack of awareness of these health problems. This highlights an urgent need for systematic RED-S syndrome screening among adolescent athletes (14). However, establishing a diagnosis of RED-S can be challenging, as the symptoms can be subtle (2, 5, 20). A simple screening tool for RED-S syndrome would be of great help. The development, validation and implementation of such a tool remain tasks for the future in order to help decrease the prevalence of RED-S-related health disorders.
It should be noted that our study was retrospective and access to some laboratory analysis results and body composition data was limited. Because of the unavailability of indirect calorimetry, we did not assess metabolism-related disorders (such as low RMR). Moreover, the determination of menstrual dysfunction was based only on a self-reported questionnaire. We also did not use a validated questionnaire to assess disordered eating and eating disorders. Finally, we only had a small cohort of athletes training in aesthetic sports.
In conclusion, young athletes have always been considered as a healthy population. However, our study shows that young athletes are at risk of easily compromising their health when their nutritional strategy and energy availability is insufficient. From a clinical point of view, the results of our study are concerning, because as many as 87% of athletes in this population have at least one of the RED-S-related health disorders, and 85% have athletic performance-related problems. In addition, middle adolescents have a greater nutritional risk than late adolescents.
Our study clearly indicates that nutritional screening, examination, and treatment for young athletes should already start in early adolescence, as this population of athletes is most at risk of developing RED-S-related health disorders, which are expected to influence their development and future sports performance. We urgently need a simple nutritional screening tool to identify athletes at risk of developing – or who have already developed – RED-S.