Nutritional intake and anthropometric characteristics are associated with endurance performance and markers of low energy availability in young female cross-country skiers

The relationship between energy availability (EA) and performance is not yet fully understood, although the negative consequences of low energy availability (LEA) on health are quite well documented, especially with females [1]. Most importantly, there is a significant gap in knowledge between short-term laboratory studies and long-term field-based studies examining LEA [2]. Although actual cutoff values are individual, LEA is often defined as < 30 kcal·kg FFM⁻¹·d⁻¹ based on laboratory studies in sedentary women [3]. LEA may suppress endocrine function, impair bone health, and increase the risk of illness while leaving inadequate amounts of energy for recovery and training adaptations, which all have negative consequences for performance development [1,2,4]. Changes in hormone levels, menstrual disturbances, and increased incidence of bone stress injuries are typical symptoms and signs of LEA [1,2,4]. Regrettably, relatively little is known about EA in cross country (XC) skiers who are prone to several LEA risk factors such as high exercise energy expenditure (EEE) [5] and the performance benefits of lean body composition (i.e. more fat free mass (FFM) and less fat mass (FM)) [6].

In addition to EA, the macronutrient composition of a diet can also have a significant impact on performance and health. Carbohydrates (CHO), in particular, are known to be a critical macronutrient for performance in endurance sports such as XC skiing [5,7]. Indeed, XC ski races and key training sessions are conducted at intensities that mostly depend on CHO based fuels [5,8]. To maintain capacity for high working intensities between hard training sessions and competitions, consumption of high amounts of CHO is necessary [9]. Unfortunately, previous studies have shown that both elite [10] and young female skiers [11] struggle to meet the current CHO recommendations for endurance athletes. In contrast, the recommendations for protein and fat intake are met by most XC skiers [10,11], which supports recovery, training adaptations, and normal body functions [12].

Females, and especially females under 20 years of age, who compete in endurance and/or lean sports (e.g. running, XC skiing), are especially prone to LEA associated health and performance problems [1,13]. Regrettably, most studies that have focused on the macronutrient requirements in athletes have been conducted with male participants resulting in a significant gap in knowledge about macronutrient needs in female athletes [14]. Therefore, the aim of this study was to investigate whether EA and macronutrient intake in field-based conditions are associated with the changes in performance, anthropometric characteristics, blood markers, training volume, and/or questionnaire-determined risk for LEA. In addition, the study aimed to clarify which of the above-mentioned variables explained maximal oxygen uptake (VO_2max), lactate threshold, upper body endurance performance, and lower limb explosive performance, i.e. the factors that contribute to performance in XC skiing [15].

Table 1 presents the mean EI, EEE, EA, and macronutrient intake from the food and training logs (the mean of 12 d). The mean EA (37.4 ± 9.1 kcal·kg FFM⁻¹·d⁻¹) and CHO (4.8 ± 0.8 g·kg⁻¹·d⁻¹) intake were below the recommendation while protein (1.8 ± 0.3 g·kg⁻¹·d⁻¹) and fat intake (1.4 ± 0.4 g·kg⁻¹·d⁻¹) were at recommended range.

BM, height, body mass index (BMI), FFM, FM, and F% increased significantly during the follow-up year (Table 2), while VO_2max in relation to BM decreased (Table 2). The results of other performance variables and blood hormone concentrations remained similar between M₁ and M₂. Nine athletes had LEAF-Q score ≥ 8 at both measurement points. Five of the athletes had score of ≥ 8 at both M₁ and M₂, four athletes only in M₁, and four athletes only in M₂. One subject had self-reported amenorrhea at both M₁ and M₂. This participant had LEA (26.4 kcal·kg FFM⁻¹·d⁻¹), a high LEAF-Q score (12 at M₁ and 15 at M₂), and her concentrations of several metabolic hormones were below the lower limit of 95% confidence interval (CI) for the group mean. Namely, concentrations of insulin at M₁ (4.0, CI: 5.8–11.5) free T3 at M₁ (3.7 pmol·l⁻¹, CI: 4.2–5.2 pmol·l⁻¹) and at M₂ (3.0 pmol·l⁻¹, CI: 4.2–5.1 pmol·l⁻¹), free T4 at M₁ (10.0 pmol·l⁻¹, CI: 12.5–14.5 pmol·l⁻¹) and at M₂ (8.2 pmol·l⁻¹, CI: 12.5–14.0 pmol·l⁻¹), TSH at M₁ (0.9 mU·l⁻¹, CI: 1.9–3.1) and at M₂ (1.1 mU·l⁻¹, CI: 2.0–2.8 mU·l⁻¹), and leptin at M₂ (14.2 ng·L⁻¹, CI: 23.2–37.0 ng·L⁻¹) were below 95% CI.

On average, participants trained 601 ± 101 (454–811) hours during the follow-up year. Training volume was not associated with anthropometric or nutritional variables. Participants who had higher training volume generally performed better in the DP test at M₂ (r = 0.49, p = 0.033) and had higher absolute VO_2max (L·min⁻¹) at M₂ (r = 0.57, p = 0.18). In addition, training volume showed a statistical trend toward higher relative VO_2max (ml·kg⁻¹·min⁻¹) at M₂ (r = 0.42, p = 0.092), and toward increase in DP test time (r = 0.43, p = 0.069) and relative VO_2max (r = 0.40, p = 0.11) from M₁ to M₂.

BM at M₂ was negatively associated with CHO intake (τ_b = 0.33, p = 0.036), FM at M₂ with protein intake (τ_b = 0.43, p = 0.006) and F% at M₂ with both CHO (Figure 1c) and protein intake (r = -0.50, p = 0.017; r = -0.66, p = 0.001, respectively). There were no associations between nutritional intake and changes in anthropometric characteristics or between nutritional intake and hormone concentrations. In contrast, EA and CHO intake were negatively associated with the LEAF-Q score at M₂ (r = 0.44, p = 0.042; r = 0.47, p = 0.026, respectively).

The present study aimed to clarify how 12-day mean EA and macronutrient intake in field-based situations are associated with performance, anthropometric characteristics, hormonal concentrations, LEAF-Q points, and/or training volume. The results indicated that young female XC skiers with higher CHO and protein intake had higher VO_2max and VO₂ at OBLA in relation to their BM as well as better DP performance. Nevertheless, anthropometric characteristics and higher training volume seemed to be the most important factors explaining endurance performance. The results also indicated that lower EA and CHO intake were associated with higher LEA risk (high LEAF-Q score).

The 12-day mean EA (37.4 ± 9.1 kcal·kg FFM⁻¹·d⁻¹) was at a suboptimal level in light of the EA recommendation for female athletes [3]. In addition, four (17 %) of the participants had LEA. These values are comparable to those previously reported from a shorter follow-up period [11]. On average, the young female XC skiers appear to have EA that is above the “historical” LEA threshold of 30 kcal·kg FFM⁻¹·d⁻¹ [3]. Notably, this threshold is based on the short-term laboratory interventions, that showed impaired endocrine [27] and bone turnover markers [28]. Unfortunately, the minimum EA level that can be maintained over longer periods (weeks, months, or years) without negative effects on health and performance is currently unknown and likely individual [2]. Therefore, we do not know with certainty whether the mean EA observed in the present study ultimately affects long-term health and/or performance.

EA assessment in field conditions have several methodological challenges that can reduce the validity of the observations [2,29]. Consequently, researchers have recommended the use of surrogate markers of LEA (i.e. blood parameters and/or questionnaires) for screening purposes [2,29]. As such, the assessment of LEAF-Q scores and blood hormone analyses were included in the study protocol. As expected, lower EA was associated with a higher risk of LEA determined by LEAF-Q score, and indeed, all of the athletes with LEA, as determined by food diaries, had a LEAF-Q score ≥ 8 at the end of the follow-up period. These findings suggest that food and training logs may provide important information regarding the risk of LEA if the recording period is long enough.

Blood hormone concentrations remained similar from M₁ to M₂ and were not associated with other variables. This could be due to the fact that hormonal disturbances are typically reported as a result of LEA [27,30], which was avoided by most participants in the present study. Indeed, the only amenorrheic athlete had insulin, free T3, free T4, TSH, and leptin concentrations that were among the lowest of the entire group (below the lower limit of 95% CI). Previous studies have shown decreased insulin, free T3, and leptin are associated with LEA and amenorrhea, while the results for free T4 and TSH are variable [30]. In addition to decreased levels of metabolic hormones, the amenorrheic participant was identified as having LEA determined by food and training logs and was classified as high risk for LEA based on LEAF-Q. This case suggests that food and training logs, LEAF-Q, and measurement of specific hormones could all be successfully used for screening purposes in athletes with long-term LEA. Nevertheless, this conclusion is limited because we did not perform a clinical diagnosis to exclude other possible causes of amenorrhea. In addition, CVs of the hormone concentrations were relatively high and thus the interpretations should be made with caution.

As in previous studies in female XC skiers [10,11], CHO intake (4.8 ± 0.8 g·kg⁻¹·d⁻¹) was lower than recommended for endurance athletes training 1–3 h per day (6–10 g·kg⁻¹·d⁻¹ [12]), while protein (1.8 ± 0.3 g·kg⁻¹·d⁻¹), and fat intake (31 ± 4 of total EI) were within the recommended range (protein 1.2–2.0 g·kg⁻¹·d⁻¹; fat 20–35% of total EI [12]). Interestingly, athletes with higher CHO and protein intake tended to have better relative VO_2max, VO₂ at OBLA, and DP test time in the end of the follow-up period, which suggest that eating practices that include higher amounts of these macronutrients might be beneficial for long-term performance adaptations. This finding is not surprising as the intensities used in XC skiing competitions and key training sessions are reliant on CHO based fuels [5,8], and relatively high CHO intake is needed to replenish muscle glycogen stores between training sessions [9]. In addition, adequate CHO intake is important to minimize the risk of illness [31] and overtraining [32]. Protein, in turn, plays an essential role as a substrate for recovery and trigger for adaptation after exercise [33], thus adequate amount of protein is needed for recovery and training adaptation. Given that high load endurance training [34], whole body training [35], adolescence [36], energy deficiency [37] and low CHO availability [38] may increase protein requirements, a protein intake in the upper range or slightly above current recommendations [12] may have been beneficial for the participants in the present study.

Despite the associations between nutrition and performance, the strongest factors explaining better performance were related to leaner body composition and higher training volume. Training volume during the one-year follow-up explained 24% of the variation in the DP test time at the end of the study, while 25% of the annual changes of the DP test time were explained by changes in F%. This is consistent with the findings of Jones et al. (2021), who reported that changes toward leaner body composition favor the development in XC ski performance [6]. In particular, increased upper-body strength and lean mass can lead to improved DP performance [6,39]. In addition, it is known that a higher training volume is an important factor that differentiates elite XC skiers from national level athletes [40], which highlights the importance of high training volume for performance development.

Lower F% was beneficial for relative VO_2max and explained 44% of the variation, while F% alone explained 47% of the variation in relative VO₂ at OBLA. Although mean relative VO_2max and VO₂ at OBLA decreased and BM, FM, and F% increased during the follow-up, changes in VO_2max and VO₂ at OBLA could not be explained by changes in anthropometric characteristics. Interestingly, lower BM, FM, and F% were associated with higher EA and macronutrient intake but there were no associations between nutrition and changes in anthropometric characteristics. Together these findings suggest that associations between nutrition, anthropometric characteristics, and maximal and submaximal aerobic performance have likely developed over a longer period of time than what was investigated. Nonetheless, the better performing athletes tended to have higher macronutrient intakes, which may have promoted their development over the years. Notably, the results of this study suggest that restricting EA or macronutrient intake is not an effective way to modify BM and/or body composition in young female endurance athletes. In addition, the results highlight that the adequacy of an athletes´ dietary intake cannot be determined based on BM or body composition [1,41,42].

Although problems with the validity and reliability of assessing dietary intake and EA in field conditions [2,29] can be considered as the main limitation of the present study, the methods used to minimize methodological errors were the strength of the study. As real-world EA assessment has been criticized as a short recording period only gives a snapshot of an athlete’s dietary and training practices [2,19], the present study aimed to provide an overall picture of nutritional and training practices by analyzing a total of 12 days of nutrition and training data from different parts of the training year. Although misreporting may limit the validity of the food logs, comprehensive written and verbal instructions, weighted food logs and highly motivated participants increased the validity of the present data. To increase the validity of EEE assessment, laboratory-based associations of EE, VO₂, and heart rate were utilized [2]. Unfortunately, for practical reasons, we could not measure body composition using the gold standard method DXA and the use of the less precise bioimpedance method is a minor limitation of the study. To minimize potential confounding factors, the measurement was always performed after an overnight fast and after the participants had visited the toilet. In addition, we were not able to standardize the phase of the menstrual cycle, which may have influenced on the bioimpedance analysis as well as on the other variables investigated. Additionally, while this study lacks assessment of competition performance, all performance parameters included are considered important for assessing XC skier development [15]. Furthermore, a recent study by Talsnes et al. (2021) showed that laboratory-based VO_2max incremental running test and roller ski performance tests have a strong positive correlation with competition performance [43].

Anthropometric characteristics, especially F%, and training volume appeared to be the most important factors explaining different indicators of a young female XC skier’s performance. In addition, lower CHO and protein intake were associated with higher BM, FM, and F% as well as with lower VO_2max and DP performance. Taken together, these findings suggest that although lean body composition may be beneficial for endurance performance, it should not be sought by restricting dietary intake. Indeed, athletes with lower CHO intake and EA not only had poorer performance, but also an increased risk of self-reported physiological symptoms of LEA. These findings highlight the importance of adequate EA and macronutrient intake to support performance and overall health.