Muscle fiber typology is associated with the incidence of overreaching in response to overload training

Does overreaching from endurance-based training impair sleep: A systematic review and meta-analysis

BACKGROUND

Overreaching is often linked to a deterioration in sleep quality, yet a comprehensive review is lacking. The aim of this systemic review and meta-analysis was to synthesise the literature and quantify the effect of overreaching from endurance-based training on sleep quality.

METHOD

The review was conducted following the PRISMA guidelines. The final search was conducted in May 2023 using four electronic databases (Web of Science Core Collection, MEDLINE, Cochrane Central Database, SPORTDiscus). Studies were included for a qualitative review, while random-effects meta-analyses were conducted for objective and subjective sleep.

RESULTS AND DISCUSSION

The search returned 805 articles. Fourteen studies were included in the systematic review; Three and eight articles were eligible for the meta-analyses (objective and subjective, respectively). On average, the overreaching protocols were sixteen days in length (6 to 28 days) and included exercise modalities such as cycling (number of studies [k] = 5), rowing (k = 4), triathlon (k = 3), running (k = 2), and swimming (k = 1). Actigraphy was the only form of objective sleep measurement used across all studies (k = 3), while various instruments were used to capture subjective sleep quality (k = 13). When comparing objective sleep quality following the overreaching intervention to baseline (or a control), there was a significant reduction in sleep efficiency (mean difference = -2.0%; 95% CI -3.2, -0.8%; Glass' Δ = -0.83; p < 0.01). In contrast, when comparing subjective sleep quality following the overreaching intervention to baseline (or a control), there was no effect on subjective sleep quality (Glass' Δ = -0.27; 95% CI -0.79, 0.25; p = 0.08). Importantly, none of the included studies were judged to have a low risk of bias. While acknowledging the need for more high-quality studies, it appears that overreaching from endurance-based training can deteriorate objective sleep without influencing the perception of sleep quality.

PROTOCOL REGISTRATION

This protocol was registered in The International Prospective Register of Systematic Reviews (PROSPERO) on 21st November 2022, with the registration number CRD42022373204.

Match Running Performance in Australian Football Is Related to Muscle Fiber Typology

PURPOSE

To examine the association between muscle fiber typology and match running performance in professional Australian football (AF) athletes.

METHODS

An observational time-motion analysis was performed on 23 professional AF athletes during 224 games throughout the 2020 competitive season. Athletes were categorized by position as hybrid, small, or tall. Athlete running performance was measured using Global Navigation Satellite System devices. Mean total match running performance and maximal mean intensity values were calculated for moving mean durations between 1 and 10 minutes for speed (in meters per minute), high-speed-running distance (HSR, >4.17 m·s-1), and acceleration (in meters per second squared), while intercept and slopes were calculated using power law. Carnosine content was quantified by proton magnetic resonance spectroscopy in the gastrocnemius and soleus and expressed as a carnosine aggregate z score (CAZ score) to estimate muscle fiber typology. Mixed linear models were used to determine the association between CAZ score and running performance.

RESULTS

The mean (range) CAZ score was -0.60 (-1.89 to 1.25), indicating that most athletes possessed a greater estimated proportion of type I muscle fibers. A greater estimated proportion of type I fibers (ie, lower CAZ score) was associated with a larger accumulation of HSR (>4.17 m·s-1) and an increased ability to maintain HSR as the peak period duration increased.

CONCLUSION

AF athletes with a greater estimated proportion of type I muscle fibers were associated with a greater capacity to accumulate distance running at high speeds, as well as a greater capacity to maintain higher output of HSR running during peak periods as duration increases.

Can muscle typology explain the inter-individual variability in resistance training adaptations?

Considerable inter-individual heterogeneity exists in the muscular adaptations to resistance training. It has been proposed that fast-twitch fibres are more sensitive to hypertrophic stimuli and thus that variation in muscle fibre type composition is a contributing factor to the magnitude of training response. This study investigated if the inter-individual variability in resistance training adaptations is determined by muscle typology and if the most appropriate weekly training frequency depends on muscle typology. In strength-training novices, 11 slow (ST) and 10 fast typology (FT) individuals were selected by measuring muscle carnosine with proton magnetic resonance spectroscopy. Participants trained both upper arm and leg muscles to failure at 60% of one-repetition maximum (1RM) for 10 weeks, whereby one arm and leg trained 3×/week and the contralateral arm and leg 2×/week. Muscle volume (MRI-based 3D segmentation), maximal dynamic strength (1RM) and fibre type-specific cross-sectional area (vastus lateralis biopsies) were evaluated. The training response for total muscle volume (+3 to +14%), fibre size (-19 to +22%) and strength (+17 to +47%) showed considerable inter-individual variability, but these could not be attributed to differences in muscle typology. However, ST individuals performed a significantly higher training volume to gain these similar adaptations than FT individuals. The limb that trained 3×/week had generally more pronounced hypertrophy than the limb that trained 2×/week, and there was no interaction with muscle typology. In conclusion, muscle typology cannot explain the high variability in resistance training adaptations when training is performed to failure at 60% of 1RM. KEY POINTS: This study investigated the influence of muscle typology (muscle fibre type composition) on the variability in resistance training adaptations and on its role in the individualization of resistance training frequency. We demonstrate that an individual's muscle typology cannot explain the inter-individual variability in resistance training-induced increases in muscle volume, maximal dynamic strength and fibre cross-sectional area when repetitions are performed to failure. Importantly, slow typology individuals performed a significantly higher training volume to obtain similar adaptations compared to fast typology individuals. Muscle typology does not determine the most appropriate resistance training frequency. However, regardless of muscle typology, an additional weekly training (3×/week vs. 2×/week) increases muscle hypertrophy but not maximal dynamic strength. These findings expand on our understanding of the underlying mechanisms for the large inter-individual variability in resistance training adaptations.

The Relevance of Muscle Fiber Type to Physical Characteristics and Performance in Team-Sport Athletes

PURPOSE

The aim of this systematic review was to (1) determine the muscle fiber-type composition (or muscle fiber typology [MFT]) of team-sport athletes and (2) examine associations between MFT and the physical characteristics and performance tasks in team-sport athletes.

METHODS

Searches were conducted across numerous databases-PubMed, SPORTDiscus, MEDLINE, and Google Scholar-using consistent search terms. Studies were included if they examined the MFT of team-sport athletes. Included studies underwent critical appraisal using the McMasters University critical appraisal tool for quantitative research.

RESULTS

A total of 10 studies were included in the present review, wherein the MFT of athletes was measured from 5 different team sports (soccer, rugby union, rugby league, handball, and volleyball). There was large variability in the MFT of team-sport athletes both within (up to 27.5%) and between sports (24.0% relative difference). Male football players with a higher proportion of type II fibers had faster 10- and 30-m sprint times, achieved a greater total distance sprinting (distance at >6.67 m·s-1), and a greater peak 1-minute sprint distance.

CONCLUSIONS

MFT varies considerably between athletes both within and between different team sports. The results from some studies suggest that variation in MFT is associated with high-intensity running performance in a football match, as well as 10- and 30-m sprint times. Further experimental studies should focus on how determination of the MFT of team-sport athletes could be utilized to influence talent identification, team selection, and the individualization of training.

Individualized Endurance Training Based on Recovery and Training Status in Recreational Runners

PURPOSE

Long-term development of endurance performance requires a proper balance between strain and recovery. Because responses and adaptations to training are highly individual, this study examined whether individually adjusted endurance training based on recovery and training status would lead to greater adaptations compared with a predefined program.

METHODS

Recreational runners were divided into predefined (PD; n = 14) or individualized (IND; n = 16) training groups. In IND, the training load was decreased, maintained, or increased twice a week based on nocturnal heart rate variability, perceived recovery, and heart rate-running speed index. Both groups performed 3-wk preparatory, 6-wk volume, and 6-wk interval periods. Incremental treadmill tests and 10-km running tests were performed before the preparatory period ( T0 ) and after the preparatory ( T1 ), volume ( T2 ), and interval ( T3 ) periods. The magnitude of training adaptations was defined based on the coefficient of variation between T0 and T1 tests (high >2×, low <0.5×).

RESULTS

Both groups improved ( P < 0.01) their maximal treadmill speed and 10-km time from T1 to T3 . The change in the 10-km time was greater in IND compared with PD (-6.2% ± 2.8% vs -2.9% ± 2.4%, P = 0.002). In addition, IND had more high responders (50% vs 29%) and fewer low responders (0% vs 21%) compared with PD in the change of maximal treadmill speed and 10-km performance (81% vs 23% and 13% vs 23%), respectively.

CONCLUSIONS

PD and IND induced positive training adaptations, but the individualized training seemed more beneficial in endurance performance. Moreover, IND increased the likelihood of high response and decreased the occurrence of low response to endurance training.

Markers of Low Energy Availability in Overreached Athletes: A Systematic Review and Meta-analysis

BACKGROUND

Overreaching is the transient reduction in performance that occurs following training overload and is driven by an imbalance between stress and recovery. Low energy availability (LEA) may drive underperformance by compounding training stress; however, this has yet to be investigated systematically.

OBJECTIVE

The aim of this study was to quantify changes in markers of LEA in athletes who demonstrated underperformance, and exercise performance in athletes with markers of LEA.

METHODS

Studies using a ≥ 2-week training block with maintained or increased training loads that measured exercise performance and markers of LEA were identified using a systematic search following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. Changes from pre- to post-training were analyzed for (1) markers of LEA in underperforming athletes and (2) performance in athletes with ≥ 2 markers of LEA.

RESULTS

From 56 identified studies, 14 separate groups of athletes demonstrated underperformance, with 50% also displaying ≥ 2 markers of LEA post-training. Eleven groups demonstrated ≥ 2 markers of LEA independent of underperformance and 37 had no performance reduction or ≥ 2 markers of LEA. In underperforming athletes, fat mass (d =  - 0.29, 95% confidence interval [CI] - 0.54 to - 0.04; p = 0.02), resting metabolic rate (d =  - 0.63, 95% CI - 1.22 to - 0.05; p = 0.03), and leptin (d =  - 0.72, 95% CI - 1.08 to - 0.35; p < 0.0001) were decreased, whereas body mass (d =  - 0.04, 95% CI - 0.21 to 0.14; p = 0.70), cortisol (d =  - 0.06, 95% CI - 0.35 to 0.23; p = 0.68), insulin (d =  - 0.12, 95% CI - 0.43 to 0.19; p = 0.46), and testosterone (d =  - 0.31, 95% CI - 0.69 to 0.08; p = 0.12) were unaltered. In athletes with ≥ 2 LEA markers, performance was unaffected (d = 0.09, 95% CI - 0.30 to 0.49; p = 0.6), and the high heterogeneity in performance outcomes (I² = 84.86%) could not be explained by the performance tests used or the length of the training block.

CONCLUSION

Underperforming athletes may present with markers of LEA, but overreaching is also observed in the absence of LEA. The lack of a specific effect and high variability of outcomes with LEA on performance suggests that LEA is not obligatory for underperformance.

High-throughput muscle fiber typing from RNA sequencing data

Background

Skeletal muscle fiber type distribution has implications for human health, muscle function, and performance. This knowledge has been gathered using labor-intensive and costly methodology that limited these studies. Here, we present a method based on muscle tissue RNA sequencing data (totRNAseq) to estimate the distribution of skeletal muscle fiber types from frozen human samples, allowing for a larger number of individuals to be tested.

Methods

By using single-nuclei RNA sequencing (snRNAseq) data as a reference, cluster expression signatures were produced by averaging gene expression of cluster gene markers and then applying these to totRNAseq data and inferring muscle fiber nuclei type via linear matrix decomposition. This estimate was then compared with fiber type distribution measured by ATPase staining or myosin heavy chain protein isoform distribution of 62 muscle samples in two independent cohorts (n = 39 and 22).

Results

The correlation between the sequencing-based method and the other two were r_ATPas = 0.44 [0.13–0.67], [95% CI], and r_myosin = 0.83 [0.61–0.93], with p = 5.70 × 10^–3 and 2.00 × 10^–6, respectively. The deconvolution inference of fiber type composition was accurate even for very low totRNAseq sequencing depths, i.e., down to an average of ~ 10,000 paired-end reads.

Conclusions

This new method (https://github.com/OlaHanssonLab/PredictFiberType) consequently allows for measurement of fiber type distribution of a larger number of samples using totRNAseq in a cost and labor-efficient way. It is now feasible to study the association between fiber type distribution and e.g. health outcomes in large well-powered studies.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13395-022-00299-4.

The Muscle Typology of Elite and World-Class Swimmers

PURPOSE

To examine whether the muscle typology of elite and world-class swimmers could discriminate between their best distance event, swimming stroke style, or performance level.

METHODOLOGY

The muscle carnosine content of 43 male (860 [76] FINA [Fédération Internationale de Natation] points) and 30 female (881 [63] FINA points) swimmers was measured in the soleus and gastrocnemius by proton magnetic resonance spectroscopy and expressed as a carnosine aggregate Z score (CAZ score) to estimate muscle typology. A higher CAZ score is associated with a higher estimated proportion of type II fibers. Swimmers were categorized by their best stroke, distance category (sprinters, 50-100 m; middle distance, 200-400 m; or long distance, 800 m-open water), and performance level (world-class, world top 10, or elite and world top 100 swimmers outside of the world top 10).

RESULTS

There was no significant difference in the CAZ score of sprint- (-0.08 [0.55]), middle- (-0.17 [0.70]), or long-distance swimmers (-0.30 [0.75], P = .693). World-class sprint swimmers (all strokes included) had a significantly higher CAZ score (0.37 [0.70]) when compared to elite sprint swimmers (-0.25 [0.61], P = .024, d = 0.94). Breaststroke swimmers (0.69 [0.73]) had a significantly higher CAZ score compared to freestyle (-0.24 [0.54], P < .001, d = 1.46), backstroke (-0.16 [0.47], P = .006, d = 1.42), and butterfly swimmers (-0.39 [0.53], P < .001, d = 1.70). Furthermore, within the cohort of breaststroke swimmers, there was a significant positive correlation between FINA points and CAZ score (r = .728, P = .011); however, this association was not evident in other strokes.

CONCLUSION

While there was no clear association between muscle typology and event distance specialization, world-class sprint swimmers possess a greater estimated proportion of type II fibers compared to elite sprint swimmers, as well as breaststroke swimmers compared to freestyle, backstroke, and butterfly swimmers.

“I Want to Create So Much Stimulus That Adaptation Goes Through the Roof”: High-Performance Strength Coaches' Perceptions of Planned Overreaching

Functional overreaching (FOR) occurs when athletes experience improved athletic capabilities in the days and weeks following short-term periods of increased training demand. However, prolonged high training demand with insufficient recovery may also lead to non-functional overreaching (NFOR) or the overtraining syndrome (OTS). The aim of this research was to explore strength coaches' perceptions and experiences of planned overreaching (POR); short-term periods of increased training demand designed to improve athletic performance. Fourteen high-performance strength coaches (weightlifting; n = 5, powerlifting; n = 4, sprinting; n = 2, throws; n = 2, jumps; n = 1) participated in semistructured interviews. Reflexive thematic analysis identified 3 themes: creating enough challenge, training prescription, and questioning the risk to reward. POR was implemented for a 7 to 14 day training cycle and facilitated through increased daily/weekly training volume and/or training intensity. Participants implemented POR in the weeks (~5–8 weeks) preceding competition to allow sufficient time for performance restoration and improvement to occur. Short-term decreased performance capacity, both during and in the days to weeks following training, was an anticipated by-product of POR, and at times used as a benchmark to confirm that training demand was sufficiently challenging. Some participants chose not to implement POR due to a lack of knowledge, confidence, and/or perceived increased risk of athlete training maladaptation. Additionally, this research highlights the potential dichotomy between POR protocols used by strength coaches to enhance athletic performance and those used for the purpose of inducing training maladaptation for diagnostic identification.

Determinants of Performance in Paced and Maximal 800-m Running Time Trials

PURPOSE

We aimed to identify the underpinning physiological and speed/mechanical determinants of different types of 800-m running time trials (i.e., with a positive or negative pacing strategy) and key components within each 800-m time trial (i.e., first and final 200-m).

METHODS

Twenty trained male 800-m runners (800-m personal best time (min:s): 1:55.10 ± 0:04.44) completed a maximal 800-m time trial (800MAX) and one pacing trial, whereby runners were paced for the first lap and speed was reduced by 7.5% (800PACE) relative to 800MAX, while the last lap was completed in the fastest time possible. Anaerobic speed reserve, running economy, the velocity corresponding with VO2peak (VVO2peak), maximal sprint speed (MAXSS), maximal accumulated oxygen deficit and sprint force-velocity-power profiles were derived from laboratory and field testing. Carnosine content was quantified by proton magnetic resonance spectroscopy in the gastrocnemius and soleus and expressed as a carnosine aggregate Z-score (CAZ-score) to estimate muscle typology. Data were analysed using multiple stepwise regression analysis.

RESULTS

MAXSS and vVO2peak largely explained the variation in 800MAX time (r2 = 0.570; P = 0.020), while MAXSS was the best explanatory variable for the first 200-m time in 800MAX (adjusted r2 = 0.661, P < 0.001). Runners with a higher CAZ-score (i.e., higher estimated percentage of type II fibres) reduced their last lap time to a greater extent in 800PACE relative to 800MAX (adjusted r2 = 0.413, P < 0.001), while better maintenance of mechanical effectiveness during sprinting, a higher CAZ-score and vVO2peak was associated with a faster final 200-m time during 800PACE (adjusted r2 = 0.761, P = 0.001).

CONCLUSION

These findings highlight that diversity in the physiological and speed/mechanical characteristics of male middle-distance runners may be associated with their suitability for different 800-m racing strategies in order to have the best chance of winning.

Relationships between Lower Limb Muscle Characteristics and Force-Velocity Profiles Derived during Sprinting and Jumping

PURPOSE

This study aimed to identify the relationships between lower limb muscle characteristics and mechanical variables derived from the vertical (jumping) and horizontal (sprinting) force-velocity-power (FVP) profiles.

METHODS

Nineteen subelite male rugby league players performed a series of squat jumps and linear 30-m sprints to derive the vertical and horizontal FVP profiles, respectively. The theoretical maximal force (F0), velocity (V0), and power (Pmax) were derived from both the vertical (i.e., vF0, vV0, and vPmax) and the horizontal (i.e., hF0, hV0, and hPmax) FVP profiles. Vastus lateralis (VL), biceps femoris long head, and gastrocnemius medialis (GM) and lateralis muscle fascicle length, pennation angle, and thickness were measured using B-mode ultrasonography. Magnetic resonance imaging was used to calculate volumes of major lower limb muscles, whereas proton magnetic resonance spectroscopy was used to quantify the carnosine content of the GM to estimate muscle fiber typology.

RESULTS

Variation in vPmax was best explained by GM muscle fiber typology (i.e., greater estimated proportion of Type II fibers) and VL volume (adjusted r2 = 0.440, P = 0.006), whereas adductor and vastus medialis volume and GM muscle fiber typology explained the most variation in hPmax (adjusted r2 = 0.634, P = 0.032). Rectus femoris and VL volume explained variation in vF0 (r2 = 0.430, P = 0.008), whereas adductor and vastus medialis volume explained variation in hF0 (r2 = 0.432, P = 0.007). Variations in vV0 and hV0 were best explained by GM muscle fiber typology (adjusted r2 = 0.580, P < 0.001) and GM muscle fiber typology and biceps femoris short head volume (adjusted r2 = 0.590, P < 0.001), respectively.

CONCLUSION

Muscle fiber typology and muscle volume are strong determinants of maximal muscle power in jumping and sprinting by influencing the velocity- and force-oriented mechanical variables.

Overtraining Syndrome (OTS) and Relative Energy Deficiency in Sport (RED-S): Shared Pathways, Symptoms and Complexities

The symptom similarities between training-overload (with or without an Overtraining Syndrome (OTS) diagnosis) and Relative Energy Deficiency in Sport (RED-S) are significant, with both initiating from a hypothalamic-pituitary origin, that can be influenced by low carbohydrate (CHO) and energy availability (EA). In this narrative review we wish to showcase that many of the negative outcomes of training-overload (with, or without an OTS diagnosis) may be primarily due to misdiagnosed under-fueling, or RED-S, via low EA and/or low CHO availability. Accordingly, we undertook an analysis of training-overload/OTS type studies that have also collected and analyzed for energy intake (EI), CHO, exercise energy expenditure (EEE) and/or EA. Eighteen of the 21 studies (86%) that met our criteria showed indications of an EA decrease or difference between two cohorts within a given study (n = 14 studies) or CHO availability decrease (n = 4 studies) during the training-overload/OTS period, resulting in both training-overload/OTS and RED-S symptom outcomes compared to control conditions. Furthermore, we demonstrate significantly similar symptom overlaps across much of the OTS (n = 57 studies) and RED-S/Female Athlete Triad (n = 88 studies) literature. It is important to note that the prevention of under-recovery is multi-factorial, but many aspects are based around EA and CHO availability. Herein we have demonstrated that OTS and RED-S have many shared pathways, symptoms, and diagnostic complexities. Substantial attention is required to increase the knowledge and awareness of RED-S, and to enhance the diagnostic accuracy of both OTS and RED-S, to allow clinicians to more accurately exclude LEA/RED-S from OTS diagnoses.

The influence of exercise training volume alterations on the gut microbiome in highly-trained middle-distance runners

The aim of this study was to determine the influence of training volume alterations on diversity and composition of the gut microbiome in a free-living cohort of middle-distance runners. Fourteen highly-trained middle-distance runners (n = 8 men; [Formula: see text]O_2peak = 70.1 ± 4.3 ml·kg·min^-1; n = 6 women, [Formula: see text]O_2peak: 59.0 ± 3.2 ml·kg·min^-1) completed three weeks of normal training (NormTr), three weeks of high-volume training (HVolTr; a 10, 20 and 30% increase in training volume during each successive week from NormTr), and a one-week taper (TaperTr; 55% exponential reduction in training volume from HVolTr week three). Faecal samples were collected before and immediately after each training phase to quantify alpha-diversity and composition of the gut microbiome. A three-day diet record was collected during each training phase and a maximal incremental running test was completed after each training phase. Results showed no significant changes in nutritional intake, alpha-diversity, or global microbial composition following HVolTr or TaperTr compared to NormTr (p's > 0.05). Following HVolTr, there was a significant decrease in Pasterellaceae (p = 0.03), Lachnoclostridium (p = 0.02), Haemophilus (p = 0.03), S. parasagunis (p = 0.02), and H. parainfluenzae (p = 0.03), while R. callidus (p = 0.03) significantly increased. These changes did not return to NormTr levels following TaperTr. This study shows that the alpha-diversity and global composition of the gut microbiome were unaffected by changes in training volume. However, an increase in training volume led to several changes at the lower taxonomy levels that did not return to pre-HVolTr levels following a taper period.

Crossing the Golden Training Divide: The Science and Practice of Training World-Class 800- and 1500-m Runners

Despite an increasing amount of research devoted to middle-distance training (herein the 800 and 1500 m events), information regarding the training methodologies of world-class runners is limited. Therefore, the objective of this review was to integrate scientific and best practice literature and outline a novel framework for understanding the training and development of elite middle-distance performance. Herein, we describe how well-known training principles and fundamental training characteristics are applied by world-leading middle-distance coaches and athletes to meet the physiological and neuromuscular demands of 800 and 1500 m. Large diversities in physiological profiles and training emerge among middle-distance runners, justifying a categorization into types across a continuum (400–800 m types, 800 m specialists, 800–1500 m types, 1500 m specialists and 1500–5000 m types). Larger running volumes (120–170 vs. 50–120 km·week⁻¹ during the preparation period) and higher aerobic/anaerobic training distribution (90/10 vs. 60/40% of the annual running sessions below vs. at or above anaerobic threshold) distinguish 1500- and 800-m runners. Lactate tolerance and lactate production training are regularly included interval sessions by middle-distance runners, particularly among 800-m athletes. In addition, 800-m runners perform more strength, power and plyometric training than 1500-m runners. Although the literature is biased towards men and “long-distance thinking,” this review provides a point of departure for scientists and practitioners to further explore and quantify the training and development of elite 800- and 1500-m running performance and serves as a position statement for outlining current state-of-the-art middle-distance training recommendations.

CORP: quantification of human skeletal muscle carnosine concentration by proton magnetic resonance spectroscopy

Noninvasive techniques to quantify metabolites in skeletal muscle provide unique insight into human physiology and enable the translation of research into practice. Proton magnetic resonance spectroscopy (¹H-MRS) permits the assessment of several abundant muscle metabolites in vivo, including carnosine, a dipeptide composed of the amino acids histidine and beta-alanine. Muscle carnosine loading, accomplished by chronic oral beta-alanine supplementation, improves muscle function and exercise capacity and has pathophysiological relevance in multiple diseases. Moreover, the marked difference in carnosine content between fast-twitch and slow-twitch muscle fibers has rendered carnosine an attractive candidate to estimate human muscle fiber type composition. However, the quantification of carnosine with ¹H-MRS requires technical expertise to obtain accurate and reproducible data. In this review, we describe the technical and physiological factors that impact the detection, analysis, and quantification of carnosine in muscle with ¹H-MRS. We discuss potential sources of error during the acquisition and preprocessing of the ¹H-MRS spectra and present best practices to enable the accurate, reliable, and reproducible application of this technique.

Decreased energy availability during training overload is associated with non-functional overreaching and suppressed ovarian function in female runners

Low energy availability (EA) suppresses many physiological processes, including ovarian function in female athletes. Low EA could also predispose athletes to develop a state of overreaching. This study compared the changes in ad libitum energy intake (EI), exercise energy expenditure (ExEE), and EA among runners completing a training overload (TO) phase. We tested the hypothesis that runners becoming overreached would show decreased EA, suppressed ovarian function and plasma leptin, compared with well-adapted (WA) runners. After 1 menstrual cycle (baseline), 16 eumenorrheic runners performed 4 weeks of TO followed by a 2-week recovery (131 ± 3% and 63 ± 6% of baseline running volume, respectively). Seven-day ExEE, EI, running performance (RUN_perf) and plasma leptin concentration were assessed for each phase. Salivary estradiol concentration was measured daily. Urinary luteinizing hormone concentration tests confirmed ovulation. Nine runners adapted positively to TO (WA, ΔRUN_perf: +4 ± 2%); 7 were non-functionally overreached (NFOR; ΔRUN_perf: -9 ± 2%) as RUN_perf remained suppressed after the recovery period. WA increased EI during TO, maintaining their baseline EA despite a large increase in ExEE (ΔEA = +1.9 ± 1.3 kcal·kg fat free mass (FFM)^-1·d^-1, P = 0.17). By contrast, NFOR showed no change in EI, leading to decreased EA (ΔEA = -5.6 ± 2.1 kcal·kg FFM^-1·d^-1, P = 0.04). Plasma leptin concentration mid-cycle and luteal salivary estradiol concentration decreased in NFOR only. Contrasting with WA, NFOR failed to maintain baseline EA during TO, resulting in poor performance outcomes and suppressed ovarian function. ClinicalTrials.gov no. NCT02224976. Novelty: Runners adapting positively to training overload (TO) increased ad libitum energy intake, maintaining baseline EA and ovarian function through TO. By contrast, NFOR runners failed to increase energy intake, showing suppressed EA and ovarian function during TO.