ORIGINALARBEIT
The reproducibility of different metabolic markers for muscle fiber type distributions investigated by functional 31P-MRS during dynamic exerciseUntersuchungen zur Reproduzierbarkeit verschiedener metabolischer Marker für die muskuläre Fasertypenverteilung durch funktionelle 31P-MRS während einer dynamischen Belastungsübung

https://doi.org/10.1016/j.zemedi.2016.06.006Get rights and content

Abstract

Purpose

The objective of the study was to investigate the reproducibility of exercise induced pH-heterogeneity by splitting of the inorganic phosphate (Pi) signal in the corresponding 31P-MRS spectra and to compare results of this approach with other fiber-type related markers, like phosphocreatine/adenosine triphosphate (PCr/ATP) ratio, and PCr-recovery parameters.

Material and methods

Subjects (N = 3) with different sportive background were tested in 10 test sessions separated by at least 3 days. A MR-compatible pedal ergometer was used to perform the exercise and to induce a pH-based splitting of the Pi-signal in 31P-MR spectra of the medial gastrocnemius muscle. The PCr recovery was analyzed using a non-negative least square algorithm (NNLS) and multi-exponential regression analysis to estimate the number of non-exponential components as well as their amplitude and time constant. The reproducibility of the estimated metabolic marker and the resulting fiber-type distributions between the 10 test sessions were compared.

Results

The reproducibility (standard deviation between measurements) based on (1) Pi components varied from 2% to 4%, (2) PCr recovery time components varied from 10% to 12% and (3) phosphate concentrations at rest varied from 8% to 11% between test sessions. Due to the sportive activity differences between the 3 subjects were expected in view of fiber type distribution. All estimated markers indicate the highest type I percentage for volunteer 3 medium for volunteer 2 and the lowest for volunteer 1.

Conclusions

The relative high reproducibility of pH dependent Pi components during exercise indicates a high potential of this method to estimate muscle fiber-type distributions in vivo. To make this method usable not only to detect differences in muscle fiber distributions but also to determine individual fiber-type volume contents it is therefore recommended to validate this marker by histological methods and to reveal the effects of muscle fiber recruitments and fiber-type specific Pi concentrations on the intensity ratios between the splitted Pi-components.

Zusammenfassung

Ziel

Untersuchung der Reproduzierbarkeit der belastungsinduzierten pH-Heterogenität anhand der Aufspaltung des MR-Signals für anorganisches Phosphat (Pi) mittels 31P-MRS und deren Vergleich mit der Reproduzierbarkeit anderer, zur Abschätzung der Fasertypenverteilung genutzter metabolischer Marker, wie Phosphokreatin/Adenosintriphosphat (PCr/ATP) Quotient und kinetischen Parametern der PCr-Resynthese nach Beendigung der Übung.

Material und Methoden

Drei Probanden mit unterschiedlichem sportlichem Hintergrund wurden zu jeweils zehn aufeinanderfolgenden Messterminen mit einem zeitlichen Abstand von jeweils mindestens drei Tagen untersucht. Für die Durchführung der Belastungsübung zur Pi-Aufspaltung im medialen M. gastrocnemius wurde ein MR-kompatibles Pedal-Ergometer eingesetzt. Die Anzahl exponentieller Zeitkomponenten im zeitlichen Verlauf der PCr-Wiederherstellung wurde mit dem NNLS-Verfahren (Non Negative Least Square) und die Reproduzierbarkeit der Amplituden und Zeitkonstanten für mono-, bi- beziehungsweise tri-exponentielle Exponentialfunktionen durch eine nichtlineare Regressionsanalysen bestimmt. Die Reproduzierbarkeiten, die sich aus Abschätzungen der Fasertypenverteilung auf Grundlage der verwendete Marker ergaben, wurden miteinander verglichen.

Ergebnisse

Die Reproduzierbarkeit (Standardabweichung zwischen den Einzelmessungen) variierte basierend auf (1) den separierten Komponenten des Pi-Signals zwischen 2% und 4%, (2) den Zeitkomponenten für die PCr-Wiederherstellung zwischen 10% und 12% und (3) den PCr/ATP-Quotienten der Ruhespektren zwischen 8% und 11%. Die Abschätzung der Faserverteilung anhand der sportlichen Aktivität sowie für alle untersuchten Marker ergab die gleiche Reihenfolge mit absteigendem Typ-I-Anteil von Proband 3 über Proband 2 zu Proband 1.

Schlussfolgerungen

Die relativ gute Reproduzierbarkeit, mit der die Teilkomponenten des durch belastungsinduzierte Aufspaltung gebildeten Pi-Komponenten bestimmt wurden, zeigt das Potential dieser Methode für In-vivo-Bestimmungen der Fasertypenverteilung in Muskeln. Um mit dieser Methode nicht nur Unterschiede der muskulären Fasertypenverteilung verschiedener Muskeln sondern auch deren individuelle Faserverteilungen nicht-invasiv bestimmen zu können, sollte die Methode durch Vergleich mit histologischen Bestimmungen der Faserverteilung validiert und der Einfluss des Rekrutierungsverhaltens und fasertypspezifischer Pi-Konzentrationsunterschiede auf die Intensitätsverteilung der Pi-Komponenten untersucht werden.

Introduction

According to the most prominent myosin heavy chain (MHC) complement human muscle fibers are commonly classified biochemically into three types: predominantly oxidative slow fibers (type I), fast oxidative-glycolytic fibers (type IIa) and predominantly glycolytic, fast fibers (type IIx) [1], [2], [3]. Muscle fiber-type composition and cross sectional areas are major factors that determine power, force, and endurance of muscles and are frequently used therefore to assess the effectiveness of special training and rehabilitation programs as well as the potential of young talents for specific sportive disciplines and to diagnose muscle diseases.

So far, determining the distribution of fiber-type contents in a particular muscle relies mainly on collecting tissue samples by invasive biopsy. In professional sports, however, this procedure involves certain health risks for the athletes, that may affect practice time after the intervention, and it is limited considerable due to inhomogeneities in the fiber-type distribution within the muscle [4], [5]. Consequently, great interest exists to apply alternative, noninvasive methods that, e.g., can be based on metabolic fiber properties [6], [7].

Phosphorous magnetic resonance spectroscopy (31P-MRS) belongs to one of these methods as it enables noninvasive investigations of the metabolic processes in muscles [8]. Since phosphocreatine (PCr), adenosine triphosphate (ATP) and inorganic phosphate (Pi) contents [9], [10], [11], [12] in the resting muscle differ for type I and II muscle fibers, the fiber-type distribution will affect the measured metabolic concentration. Consequently, if these concentrations are identical for all fibers of the same type the resting muscle and not affected by other individual factors like diet, age or body mass [13], [14], mean fiber fractions should be derivable from spectroscopically determined concentrations of PCr, ATP and Pi [15], [16], [17], [18], [19], [20]. However, concerning the relation between muscle fiber-type and high energy phosphate contents partly contradictory results were reported. Due to methodological differences and the lack of absolute concentrations these relations are hardly portable [17], [18], [19], [20], [21]. Furthermore, single fiber investigations revealed inhomogeneous metabolic compositions of type II fibers: It was observed that IIa fibers contain similar ATP but higher PCr contents compared to type I fibers and that the PCr content was higher in pure IIx-fibers than in pure IIa-fibers [10], [12]. Moreover, considerable variations of PCr were detected within the same fiber-type [22]. Given these conditions, 31P-MRS estimated resting state concentrations may be useful to detect changes of muscle fiber-type contents, but a precise prediction of fiber-type compositions based on 31P-MRS estimated resting state concentrations seems to be rather uncertain.

Another source of information about fiber compositions of human muscles is the analysis of metabolic changes during exercise. It is well known that during intense exercise anaerobic glycolytic energy supply and the proton release are higher in type II fibers compared to type I fibers. Therefore, it might be reasonably assumed that pH-values decrease faster in type II than in type I fibers. This disparate pH-development should become apparent by a Pi peak splitting. Pi peak splitting in muscles has already been observed during high intense exercise [23], [24], [25], [26]. However, further possibilities like interstitial or mitochondrial spaces seem to be realistic concerning the nature of these pH-compartments [26]. Interstitial and mitochondrial spaces are more alkaline than cytoplasm with pH differences between 0.3 and 0.4 [27], [28], [29], [30] but are more likely too small to be responsible for the observed exercise induced Pi split. A small additional Pi signal 0.38 ppm downfield with approximately 7% of the Picytosolic intensity was observed using high-field MRS (7.0 T) [30] and assigned to the mitochondrial compartment (volume fraction <5% [31]). The assignment to mitochondrial Pi was supported by higher intensities in slow-twitch oxidative (type I) than in fast-twitch glycolytic (type II) muscle fibers and by the shorter T1-values of Piadditional, which are more specific for mitochondrial than for extracellular P. Moreover, the observed pH-heterogeneity can be explained by other muscles contaminating the spectroscopic signal [32]. Otherwise, a split of the Pi signal into multiple components was observed also in localized spectra [26] and it was demonstrated that high and low pH-components respond differently on non-depolarizing and depolarizing neuromuscular blocking agents with preferential effects on ST or FT fibers [24]. Therefore, muscle fiber-type heterogeneity seems to be a realistic explanation for the observed Pi peak split if different levels of activity can be excluded.

To be able to use functional 31P-MRS for fiber-typing several challenges have to be met [33]. One major problem is that all muscle fibers should ideally be recruited equally at the maximum level and the pH-splitting should be strong enough to separate high- from low-pH signal intensities. Signal contamination from less strained muscles has to be avoided by, e.g., applying spectroscopic localization methods. Moreover, fiber-type related concentrations of Pi and PCr are necessary to relate Pi intensity and partial volumes and the impact of Pi trapping processes on the spectroscopically visible Pi intensity has to be known.

To make functional 31P-MRS applicable for fiber-typing our study was designed to answer the following questions: (i) Is it possible to induce pH-splitting of the Pi peak reproducible, (ii) is it possible to quantitate high- and low-pH partial intensities for Pi in each case and (iii) how large is the variance for repeated measurements. This study intends therefore to prepare further large-scale studies to make this approach usable for non-invasive determinations of muscle fiber distributions.

To this end, we compared the results of 10 repeated measurements of three volunteers with different sportive activities suggesting different fiber contents [34]. Repetitive measurements of the same volunteer were investigated to estimate the reproducibility of the estimated Pi components. Different sportive active volunteers were chosen as different fiber-type contents were assumed and the reproducibility of the estimated Pi components may be affected by their intensity ratio.

The exercise level should be strong enough to activate all motor units but low enough to enable sufficient exercise times for an expected asymptotic approximation to minimum pH-values. To prove the consistency of our approach with other indicators of muscle fiber distribution we compared the position of volunteers in the ranking by Pi components, by fiber-type contents assumed by their sportive activity, by predictions from metabolic resting state concentrations as well as by amplitudes and time constants of the PCr recovery time courses [35], [36], [37].

Section snippets

Volunteers and exercise

Three male volunteers aged between 25 and 31 years were investigated. Their sportive activities were table tennis (volunteer 1, one per week), soccer/cycling (volunteer 2, formerly once per week/2–3 times per week) and triathlon (volunteer 3, 3 h per day and participation in professional competitive sport). Each volunteer gave his written informed consent prior to scanning and performing the ergometer exercise in the scanner. Volunteers were positioned supine with fully extended knees. The right

Measurements during rest

The PME, PDE, Pi, PCr and [Mg2+] resting concentration and the pH-value in the medial gastrocnemius (averaged over the 10 experiments) are presented in Table 1 for all three subjects. These data are in the range of values provided in the literature [20], [42], [47], [48], [49]. The fiber-type dependent PCr/β-ATP and Pi/PCr ratios [17] as well as the fiber fractions estimated by using the relation of Takahashi [19] are likewise listed in Table 1. Except for [PME] and [Mg2+], the metabolic

Discussion

The main objective of the present study was to investigate the reproducibility of the exercise induced pH-heterogeneity indicated by the split of the Pi signal and the degree of variation found in the intensity distribution of the formed Pi components. The split of the Pi peak into three components was observed for all three volunteers in all investigations. As indicated in Table 2 and Fig. 5b–d, the pH-differences between adjacent Pi peaks ranged from 0.16 (volunteer 3) to 0.49 (volunteer 1),

Conclusions

The aim of the study was to investigate the reproducibility of exercise induced Pi peak splitting as potential marker of muscle fiber distribution and as a precondition to perform more comprehensive studies including a high number of invasive biopsy excisions. As the reproducibility might be influenced by the fiber distribution three volunteers of different expected fiber distributions were investigated. Nevertheless we have compared the results with other potential markers of muscle fiber

Conflict of interest

There are no conflicts of interests to declare.

Acknowledgements

This study was supported by grants from the Deutsche Forschungsgemeinschaft (DFG Rz 10/5-1 and DFG RE 1123/11-1). P.H. and A.G. acknowledge support from the Competence Center for Interdisciplinary Prevention (KIP) at the Friedrich-Schiller-University Jena in cooperation with the Institution for Statutory Accident Insurance and Prevention in the Foodstuffs Industry and Catering Trade (BGN).

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