Prediction of muscle fiber type from powermeter data, part 2

by Andrew R. Coggan, Ph.D.

In this prior blog entry:

http://www.trainingandracingwithapowermeter.com/2010/12/prediction-of-muscle-fiber-type-from.html

I briefly discussed some of the differences between the two major muscle fiber types found in human skeletal muscle (i.e., slow-twitch, or type I, and fast-twitch, or type II), and also indicated some ways in which knowledge of an athlete’s muscle fiber type distribution could, at least in theory, be helpful in optimizing their approach to training and racing. Continuing on from that introduction, in this entry I will describe one of two ways of estimating an individual’s fiber type based on data obtained using a powermeter.

Method #1: Estimation of muscle fiber type from muscle contractile properties

As first demonstrated by Gasser and Hill in 1924 (1), the force-velocity relationship of isolated muscle is non-linear – that is, as the speed of muscle shortening increases, force falls off quite rapidly at first, then more slowly thereafter. On a molecular basis, this is because it takes a finite amount of time for the head of the myosin protein to attach to the actin filament, generate force, and then detach before reattaching again at another binding site. Consequently, fewer and fewer such force-generating bonds can be formed as the myosin and actin filaments slide past each other at higher and higher speeds. However, because the myosin found in type II fibers can complete this cycle (and hydrolyze ATP) more rapidly than that found in type I fibers, force declines less rapidly as a function of contraction speed in type II than in type I fibers. The result is not only a higher maximal speed of shortening, but also a higher maximal power output. As well, the speed of shortening at which maximal power is developed is also higher in type II than in type I fibers. These functional differences are readily apparent in Figures 1 and 2 below, which are based on the data Gilliver et al. (2).

Figure 1. Force-velocity relationship of isolated type I and type II human muscle fibers.

Figure 2. Power-velocity relationship of isolated type I and type II human muscle fibers.

Unlike the force-velocity relationship found in isolated muscle, during cycling the relationship between force and velocity is essentially linear, as first shown by McCartney et al. (3) and as illustrated in Figure 3 below. This is apparently due to the complex interaction of multiple muscle groups, each having their own unique force-velocity relationship, acting over multiple joints. Consequently, rather than the positively-skewed curve found for isolated muscle, the power-velocity relationship is well-described by a parabolic function, as shown in Figure 4. (Note that the constants of the two equations differ slightly due to the way individual data points are weighted somewhat differently while calculating the linear and non-linear regressions.)

Figure 3. Force-velocity relationship during cycling.

Figure 4. Power-velocity relationship during cycling.

Despite these differences, the same general principles described above apply, i.e., the higher the percentage of type II fibers (especially when expressed as a fraction of total muscle area or volume) an individual has, the less of a decline in force they would be expected to exhibit as velocity (cadence) increases. Consequently, all else being equal they would be expected to have a higher maximal neuromuscular power and a higher optimal pedaling velocity. Indeed, this is precisely what both McCartney et al. (3) and Hautier et al. (4) found for n=2 and n=10 subjects of varying fiber type, respectively. Furthermore, Gardner et al. (5) have demonstrated that field-based tests performed using an SRM can provide force-velocity data comparable to that obtained under more controlled conditions, i.e., on an ergometer in a laboratory setting. Thus, at least in theory it should be possible predict someone's muscle fiber type distribution with reasonable accuracy from such field tests.

To derive an equation for doing so, I converted the cadence data of Hautier et al. (4) to circumferential pedal velocity (CPV in m/s; which is equal to (cadence * 2 * Pi * crank length in m)/60) and then calculated the regression of the % type I fiber area on the optimal pedal velocity (CPVopt, the pedal velocity at which maximal power is produced) (instead of the reverse as originally presented by Hautier et al. (4)). The result was:

% type I fiber area = 242.7 - 89.5 * CPVopt (m/s)

R^2 = 0.784

P = 0.001

S.E.E. = 2.8%

For the subject whose data are shown in Figures 3 and 4, this equation would predict a % type I area of just 26% (therefore 74% type II). This is consistent with their extremely high maximal power output as shown in Figure 4, as well as their extremely high maximal theoretical CPV (CPVmax), i.e., 4.84 m/s or 280 rpm on 175 mm cranks. (In fact, provided the relationship between force and velocity is truly linear, CPVopt will always be one-half of CPVmax.)

Cool! But how do I measure my own force-velocity or power-velocity relationship?

A detailed answer to this question is beyond the scope of this blog. In essence, however, doing so requires measuring power and cadence with high temporal resolution while performing a maximal effort against just the right amount of external resistance. If the resistance is too great, then significant fatigue may develop before a sufficient number of data points are obtained out/down the force-velocity line or up-and-over the peak of the power-velocity curve. Conversely, if the resistance is too small, then too few (or even no) data points will be obtained on the right-most half of the force-velocity line or on the ascending portion of the power-velocity curve. Perhaps the best suggestion I can make, then, is that people simply experiment, e.g., by performing maximal accelerations from a dead-stop using various gear ratios. Depending upon the conditions (e.g., outdoors vs. upon some form of trainer), this may entail the use of either very small gears (e.g., 39 x 23) or very large gears (e.g., 53 x 12). Regardless, the SRM should be set to record data as frequently as possible, to try to "capture" as many individual pedal strokes as possible. If an insufficient number of data points are obtained during a single effort to permit reliable determination of the force-velocity or power-velocity relationship, data from several short efforts can be combined. For example, the data shown in Figs. 3 and 4 were drawn from five different "gate starts" performed by an elite BMX cyclist, each one of which provided data for just 1-3 pedal strokes (recorded at 0.1 s intervals).

References

1. Gasser HS, Hill AV. The dynamics of muscular contraction. Proc Royal Soc B 1924; 96:398-427.

2. Gilliver SF, Degens H, Rittweger J, Sargeant AJ, Jones DA. Variation n the determinants of power of chemically-skinned human muscle fibers. Exp Physiol 2009; 94:1070-1078.

3. McCartney N, Heigenhauser GJF, Jones NL. Power output and fatigue of human muscle in maximal cycling exercise. 1983; 55:218-224.

4. Hautier CA, Linossier MT, Belli A, Lacour JR, Arsac LM. Optimal velocity for maximal power production in non-isokinetic cycling is related to muscle fiber type composition. Eur J Appl Physiol 1996; 74:114-118.

5. Gardner AS, Martin JC, Martin DT, Barras M, Jenkins DG. Maximal torque- and power-pedaling rate relationships for elite sprint cyclists in laboratory and field tests. Eur J Appl Physiol 2007; 101:287-292.

Prediction of muscle fiber type from powermeter data, part 1

by Andrew R. Coggan, Ph.D.

As many readers of this blog are undoubtly aware, the skeletal muscles of humans and other animals can be classified into various "types". A number of such classification schemes exist, but the most common approach is to characterize muscle fibers based on their speed of contraction, which is primarily determined by the isoform of myosin protein they express. Thus, in simplest terms muscle fibers can described as slow-twitch, or type I, or fast-twitch, or type II. In addition to being slower to contract (and relax), type I muscle fibers tend to be smaller, but have more mitochondria and are surrounded by more capillaries, than type II muscle fibers located within the same muscle. As a result of these (and other) differences, "tonically-active" type I fibers tend to be less powerful but more resistant to fatigue, whereas "phasically-active" type II fibers are generally more powerful but also fatigue more rapidly. (Note that many, if not all, muscle fiber properties mentioned in this blog entry change in response to exercise training. However, the inherent differences between type I and type II fibers, even if markedly diminished, will generally tend to remain.)

Given the above, it is perhaps not surprising that, at least at the elite level, endurance athletes tend to have more type I fibers than average, whereas athletes in sprint sports tend to have more type II fibers. For example, in 1976 Costill and coworkers obtained biopsy samples from the gastrocnemius (calf) muscle of 40 male and female international-caliber track-and-field athletes (1). Although the fiber type distribution of those competing in field events was notably quite unexceptional, the gastrocnemius of the distance (5000 m to marathon) runners was composed of ~70% type I and ~30% type II fibers, whereas that of the sprint (100 m) runners was ~25% type I and ~75% type II. (The gastrocnemius of the average untrained individual usually contains 55-60% type I and 40-45% type II fibers(2).) As a result of the study by Costill et al., as well as numerous others, it is now well-established that muscle fiber type distribution can be an important determinant of athletic performance.*

Presented with the above information, it is natural for any athlete to wonder about their own personal fiber type distribution – in fact, it was partially because of such curiousity that I first volunteered for a research study involving muscle biopsies approximately 30 y ago. The muscle biopsy procedure, however, is somewhat invasive, and although it is generally quite safe, it is not entirely without risks. As well, the variability in determining the percentage of type I and type II fibers based on a single biopsy can be quite large (3), meaning that multiple samples may need to be obtained (ideally from multiple muscles) to really “nail down” someone’s true fiber type distribution. Thus, few, if any, exercise physiologists would argue that it is worth having a biopsy performed simply to satisfy an athlete’s curiousity, or even in hopes of improving their performance by altering their approach to training, the tactics they use in races, the events they choose to enter, etc. On the other hand, if information regarding an individual’s muscle fiber type were more easily obtained, at least in theory it could prove valuable in this regard, and if nothing else, might help satisfy their curiousity.

The purpose of this series of blog entries, then, is to describe two equations for predicting an individual's muscle fiber type distribution based on data easily collected using a powermeter. Specifically, in part 2 I will discuss how to do so based on force-velocity (really, power-velocity) measurements. This method is the more precise of the two, but requires use of an SRM powermeter, as none of the other devices currently on the market appear to provide data with sufficient fidelity and temporal resolution to utilize this approach. Thus, in part 3 I will describe how to estimate fiber type based on measurement of fatigue resistance. Being based on a secondary characteristic (i.e., fatigability vs. contractile properties) of the different muscle fiber types, this method is less precise, but has the advantage of being available to all powermeter users, not just those who own SRM cranks.

*Interestingly, however, this influence seems to be less evident in cycling than in running. For example, in a study of road cyclists Burke et al. (4) found no difference in fiber type distribution of the v. lateralis (thigh) muscle between those who had achieved national or international success and those who had not. Along the same lines, Mackova et al. (5) found that although international caliber match sprint cyclists had a greater percentage of type II fibers in the v. lateralis than non-athletes, the difference observed was less than previously reported for track-and-field sprinters by Costill et al. (1). The reason for this is not known. It may, however, be because in road racing the dynamics of pack cycling would tend to disfavor those who have an extremely high percentage of type I fibers, whereas in track racing access to different gears on a bicycle would tend negate some of the advantage provided by having an extremely high percentage of type II fibers.

References

1. Costill DL, Daniels J, Evans W, Fink W, Krahenbuhl G, Saltin B. Skeletal muscle enzymes and fiber composition in male and female track athletes. J Appl Physiol 1976; 40:149-154.

2. Coggan AR, Spina RJ, Rogers MA, King DS, Brown M, Nemeth PM, Holloszy JO. Histochemical and enzymatic comparison of the gastrocnemius muscle of young and elderly men and women. J Geront 1992; 47:B71-B76.

3. Nygaard E, Sanchez. Intramuscular variation of fiber types in the brachial biceps and the lateral vastus muscles of elderly men: how representative is a small biopsy sample? J Anat Rec 1982; 203:451-459.

4. Burke ER, Cerny F, Costill D, Fink W. Characteristics of skeletal muscle in competitive cyclists. Med Sci Sports 1977; 9:109-112.

5. Mackova E, Melichna J, Havlickova L, Placheta Z, Blahova D, Semiginovsky B. Skeletal muscle characteristics of sprint cyclists and nonathletes. Int J Sports Med 1986; 7:295-297.

Crr - roller vs. field test results, part 2

by Andrew R. Coggan, Ph.D.

In this prior blog entry:

http://www.trainingandracingwithapowermeter.com/2010/06/crr-roller-vs-field-test-results.html

I described a comparison of the Crr data I had obtained using the regression method for five pairs of tire to the Crr values Al Morrison measured for the same tires in his well-known roller tests. Since that time, I have continued to collect additional data, and so thought it might be worth updating that prior report. Thus, without further ado:

Figure 1. Crr of various tires measured on the road and on rollers.

Note that, except for the Continental Supersonic (SS) and Michelin Pro Race 2 SC data, where n=1, the field test results are averages based on 3-6 tests performed on separate days. The average (+/- SD) coefficient of variation across days was 7.9 +/- 2.8%.