Neuromuscular function in different stages of sarcopenia

    Neuromuscular function in different stages of sarcopenia Tobias Morat, Kevin J. Gilmore, Charles L. Rice PII: DOI: Reference:

S0531-5565(16)30109-7 doi: 10.1016/j.exger.2016.04.014 EXG 9829

To appear in:

Experimental Gerontology

Received date: Revised date: Accepted date:

17 December 2015 14 April 2016 18 April 2016

Please cite this article as: Morat, Tobias, Gilmore, Kevin J., Rice, Charles L., Neuromuscular function in different stages of sarcopenia, Experimental Gerontology (2016), doi: 10.1016/j.exger.2016.04.014

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ACCEPTED MANUSCRIPT Neuromuscular function in different stages of sarcopenia Tobias Morata,b, Kevin J. Gilmorea, Charles L. Ricea,c a

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Canadian Centre for Activity and Aging, School of Kinesiology, Faculty of Health Sciences, Arthur & Sonia Labatt Health Sciences, Rm. 411D, London, ON, Canada, N6A 5B9; emails: [email protected]; [email protected] b, 1

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German Sport University Cologne, Institute of Movement and Sport Gerontology, Cologne, Germany; Am Sportpark Muengersdorf 6, 50933 Cologne, Germany; email: [email protected] c

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Department of Anatomy and Cell Biology, Schulich School of Medicine and Dentistry, The University of Western Ontario, London, ON, Canada, N6A 5C1

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Corresponding author:

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Tobias Morat, German Sport University Cologne, Institute of Movement and Sport Gerontology, Cologne, Germany; Am Sportpark Muengersdorf 6, 50933 Cologne, Germany; email: [email protected]

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Permanent address; see acknowledgements

ACCEPTED MANUSCRIPT Abstract:

Keywords:

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This study applied the screening tool developed by the European Working Group on Sarcopenia in Older People (EWGSOP) on seniors aged over 65 years and concurrently tested various laboratory-based indices of neuromuscular function. Twenty-four healthy and independent living older adults (9 men, 15 women) with a mean age of 79.1 ± 5.8 years participated. Based on gait speed, handgrip strength and muscle mass all subjects were categorized into one of the three conceptual sarcopenia stages (pre-sarcopenia, sarcopenia, severe sarcopenia). Maximal strength of dorsiflexors in the left leg was measured and voluntary activation was assessed by the interpolated twitch technique. In addition, isometric evoked contractile properties were recorded. Skeletal muscle mass was assessed by ultrasound from nine sites. There were roughly equal number of subjects in each sarcopenic category, and age was not different among the 3 groups. There were no differences in handgrip strength and skeletal muscle mass index among the 3 groups. Gait speed was significantly slower (p<0.01) in the severe sarcopenic subjects compared to the pre-sarcopenic group. With no differences in voluntary activation among the groups, the maximal voluntary contractions (MVCs) for severe sarcopenic subjects were 29% lower (p=0.02) and with 19% slower (p=0.02) voluntary rates of torque development (RTD) compared to sarcopenic subjects. Furthermore, the severe groups was 34% lower (p=0.04) with 36% slower (p=0.02) RTD compared to pre-sarcopenic subjects. Peak twitch tension was 54% lower (p<0.01) in the severe group compared with the pre-sarcopenic group. Maximal twitch RTD were 40% (p=0.03) slower for the severe group compared to the sarcopenia group, and 51% slower (p=0.03) compared with the pre-sarcopenia group, but when normalized to peak torques there were no statistical differences. The laboratory tests found neuromuscular differences among the 3 groups which generally supported the classification scheme and helped to illustrate some key factors that could explain differences in functional capacities. These initial findings support the assumption that this categorization is relevant for identifying older adults with different neuromuscular properties. However, further studies are needed to provide more insight into the specific neuromuscular changes in the three sarcopenia stages, and how these changes relate to functional capacity. Such studies could ultimately contribute to identifying optimal interventions to improve neuromuscular functioning.

Older adults; isometric strength; twitch properties; dorsiflexion; tibialis anterior; muscle mass

Highlights    

Sarcopenia screening tool applied to 24 subjects (9 males, 15 females) aged ~80y Categorized as pre-sarcopenic (n=8), sarcopenic (n=9), and severe sarcopenic (n=7) Independent of age, some dorsiflexor neuromuscular tests were category-dependent This screening tool has merit and should be explored further

ACCEPTED MANUSCRIPT 1. Introduction

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The original definition of sarcopenia (age-related loss of muscle mass by Rosenberg (1989)), has been modified over the last ~25 years to include diminished muscular function such as strength and power (Baumgartner, 1998; Cooper et al., 2012). Furthermore, in the last ten years, different working groups have attempted to find a more functional definition (European Society for Clinical Nutrition and Metabolism Special Interest group (ESPEN-SIG), European Working Group on Sarcopenia in Older People (EWGSOP), International Working Group on Sarcopenia (IWGS); (see also recent reviews comparing the various approaches (Lee et al., 2014; Santilli et al., 2014; McKinnon et al., 2015)). These discussions concluded that definitions based on muscle mass and function should be favoured compared with definitions based only on muscle mass (Cruz-Jentoft et al., 2014). In addition, the EWGSOP provided an algorithm to screen all older adults aged over 65 years for levels of sarcopenia. The algorithm uses measures of gait speed, handgrip strength and muscle mass (Cruz-Jentoft et al., 2010). Although several studies have used this algorithm (Ali & Garcia, 2014; Cooper et al., 2012; Lourenco et al., 2015; Spira et al., 2015a, 2015b; Volpato et al., 2012), none has separated the screened subjects into one of the three proposed conceptual stages: “pre-sarcopenia”, “sarcopenia” and “severe sarcopenia” (Cruz-Jentoft et al., 2014).

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This categorization into the three stages fundamentally eliminates age per se in the definition of sarcopenia and recognizes the importance of biological age as it relates to functional capacity. Indeed, comparisons among older adults of the same age, but with large differences in functional capacity, perhaps partly related to activity status, has been a confounding variable in studies on human aging (Bonder & Dal Bello-Haas, 2009; Chmelo et al., 2015; Laurentani et al., 2003; Venturelli et al., 2015). Although the concept seems sensible and valid it is unclear what underlying neuromuscular factors may be involved and whether they support this classification scheme that is based on easily applied field test measures of gait speed and handgrip strength in the initial assessment. It is well recognized that there are many factors contributing to sarcopenia at various levels and importantly in relation to functional measures, sarcopenia is strongly influenced by changes in the peripheral nervous system expressed at the motor unit level (Berger & Doherty, 2010; Hepple & Rice, 2015; McKinnon et al., 2015; Power et al., 2014a). However, at present it is unclear which neuromuscular deficits are associated with the three stages of sarcopenia as defined by the EWGSOP. Therefore, the aim of our study was to use selected laboratory-based measures of neuromuscular function to explore group differences. Aging results in a decline in strength and this decline is primarily attributed to a loss of muscle mass (Frontera et al., 2000; Kent-Braun & Ng, 1999; Klein et al., 2001), however, altered neural drive may also play a role. The influence of both mechanisms is highly dependent on the age of the subject, but also on specific underlying aspects of the neuromuscular system. The present study was designed to compare maximal voluntary torque and activation and peripheral parameters in an important lower limb muscle group (dorsiflexors) in the three sarcopenic subgroups with similar age to examine whether underlying aspects of the neuromuscular system are related to the main functional tests of the screening algorithm. Furthermore, identifying underlying factors of sarcopenia using this categorization model could help direct effective intervention strategies.

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Dorsiflexor (DF) function with an emphasis on the tibialis anterior (TA) was the muscle selected for laboratory testing. This representative lower limb muscle group has an important role during gait (Khanmohammadi et al., 2016) and DF function is related to balance (Billot et al., 2010; Fujimoto et al., 2013), as well as falls in aging (Hall et al., 1999; Kemoun et al., 2002; Tinetti et al., 1988; Whipple et al., 1987; Wolfson et al., 1995). Furthermore, DF central voluntary activation is high in both young and old adults, and many studies in aging have explored neuromuscular properties and function of this muscle group providing comparable data in a wide range of subjects in health, aging and disease (Allen et al., 2014, 2015; Clark & Taylor, 2011; Connelly et al., 1999; Kent-Braun & Ng, 1999; Klein et al., 2013; Maddocks et al., 2014; McKinnon et al., 2015; McNeil et al., 2005; Power et al., 2014a, 2015; Simoneau et al., 2005; Singh-Peters et al., 2007).

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2. Methods 2.1. Subjects

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Twenty-four older adults (9 men, 15 women) with a mean age of 79.1 ± 5.8 years (mean body height: 1.64 ± 0.08m; mean body mass: 70.3 ± 12.5kg) participated. Subjects were recruited from the Canadian Centre for Activity and Aging (CCAA), in London, Ontario. All members of the CCAA have completed an annual fitness appraisal that includes the Senior Fitness Test (SFT, Rikli & Jones, 2013). The SFT comprises six tests (chair stand test, arm curl test, 6-minute walk or alternatively two-minute step test, chair sit-and-reach test, back scratch test, eight-foot up-andgo test). Within the test manual gender-specific normal ranges for different age groups are given. If a person attained values that were below these normal ranges in at least two out of the six tests of the SFT, he or she was chosen as a potential candidate for further testing. Based on the SFT results, 63 older adults were qualified for participation in this study. Fourteen potential subjects were not available because they were no longer current members of the CCAA, and four were not interested in taking part in our study. Twenty-five potential subjects were excluded because of unstable diabetes (n=4), neurological and neuromuscular diseases (n=3), arterial hypertension (n=5), heart diseases (n=6), artificial joints (n=3) and disc prolapse during the last year (n=4). All remaining 24 subjects had no self-reported neuromuscular or musculoskeletal disorders that would affect their gait or the ability to perform strong muscle contractions during handgrip and dorsiflexion testing. The local institutional Human Research Ethics Board granted ethical approval and the study followed the principles set out in the Helsinki declaration (2008). All subjects provided informed verbal and written consent to participate. 2.2. Measurements To categorize subjects into one of the three conceptual sarcopenia stages (Cruz-Jentoft et al., 2010), all 24 subjects were measured for gait speed, handgrip strength and muscle mass following the guidelines of the algorithm. 2.2.1. Gait speed

ACCEPTED MANUSCRIPT Subjects were asked to walk at their normal gait speed on a 4-m course twice, and the average of the two trials in [m/s] was used for further analysis (Laurentani et al., 2003). A threshold value of 0.8m/s was used for sarcopenia screening purposes as recommended by Cruz-Jentoft et al. (2010).

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2.2.2. Handgrip strength

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Maximal isometric handgrip strength was measured with a Smedley S Dynamometer (TTM, Tokyo, 100kg). Subjects were standing comfortably, elbow flexed to 90°, with forearm and wrist in a neutral position. They were instructed to perform three maximal trials with each hand and the highest value out of these six trials was used for further analysis. The threshold values for handgrip strength (for men: <30kg; for women: <20kg) recommended by Cruz-Jentoft et al. (2010) were applied for the sarcopenia classification. 2.2.3. Muscle mass estimation using ultrasound

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For estimating the muscle mass of the subjects, the muscle thickness (MT) at nine specific sites (lateral forearm, anterior and posterior arm, anterior and posterior thigh, anterior and posterior leg, abdomen and scapula) on the right side of the body were imaged (as described by Abe et al., 1994) using a Vivid i BT-06, 2D (B) mode portable ultrasound (US) system (Model 2420015; General Electric Healthcare, UK) with a 12L-RS probe (5141337; 6-13MHz). This method was validated by Abe et al., (2015). To generate a muscle mass estimate, the MRI-validated sexspecific equations by Sanada et al. (2006) were used from the US data. The scanning head was prepared with water-soluble transmission gel and the scanner was placed perpendicular to the tissue interface at the specific body sites. MTs (distance from the adipose tissue-muscle interface to the muscle-bone interface) were directly measured from the screen using electronic calipers following the protocol by Sanada et al. (2006). 2.2.4. Evoked contractile properties and maximal strength of dorsiflexors Subjects were seated in a custom isometric dorsiflexion dynamometer to test maximal voluntary and electrically evoked contractile properties of the DF muscles (Marsh et al., 1981) in the left leg. The ankle was positioned and maintained (with inelastic Velcro straps, wrapped over the dorsum of the foot; McNeil et al., 2005) at 30° of plantar flexion, with both hip and knee angles at 90°. A C-shaped brace was firmly placed over the distal aspect of the left thigh just proximal to the patella to secure the leg and foot in the device. Subjects were asked to perform at least three isometric dorsiflexor maximal voluntary contractions (MVCs) with a rest period of at least 3 minutes between trials to minimize fatigue effects. Subjects were verbally exhorted and provided visual feedback to perform their MVCs as fast and hard as possible. Each MVC was held for 3-5s. To assess the voluntary activation of the dorsiflexors, the interpolated twitch technique (ITT) was used during the second and third MVC attempts (Belanger & McComas, 1981; Todd et al., 2004). This technique involved electrical stimulation of the fibular nerve, using a bipolar bar-type electrode pressed firmly over the nerve distal to the fibular head. The amplitude of the twitch was monitored as the current intensity was increased incrementally, and when twitch torque reached a plateau, the current was increased a further 15% to be certain that the stimulation was supramaximal. A single 100-μs stimulus

ACCEPTED MANUSCRIPT (Digitimer stimulator, model DS7AH, Digitimer, Welwyn Garden City, UK) was delivered ~1s

before, during the plateau portion of the MVC (superimposed twitch, Ts) and ~1s after completion (resting twitch, Tr) of the MVC. All torque signals were collected and sampled online at 500Hz using Spike2 software (version 7.11, Cambridge Electronic Design, Cambridge, UK).

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Neuromuscular variables have been measured on the left side to compare with other studies currently done in the same lab and other labs that routinely test the left (usually non-dominant side), but we followed the US estimation equations by Sanada et al. (2006) which were done and evaluated only on the right side of the body. Therefore, to determine whether there were side-toside differences we performed US on both lower limbs and compared the results.

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Physical activity of all subjects was recorded using the Physical Activity Scale for the Elderly (PASE; Washburn et al., 1993; 1999; Washburn & Ficker, 1999). 2.3. Data Analyses and Statistics

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To acquire a skeletal muscle mass index (SMI), the estimated muscle mass was divided by the subject’s squared body height and the threshold values recommended by Cruz-Jentoft et al. (2010) of 7.23 kg/m² for men and 5.67 kg/m² were used to identify low muscle mass.

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During the dorsiflexor MVCs, voluntary activation was calculated as a percentage using the following equations: [1 − (Ts/Tr)] × 100. Peak torque of the three MVC attempts was taken as the maximal torque for each subject, and analyzed off-line to determine maximal voluntary isometric torque (MVC [Nm]), maximal voluntary rate of torque development (maximal MVC RTD [Nm/s]) and normalized (to MVC torque) MVC RTD [s-1]. MVC strength was normalized to body mass [Nm/kg] as well as to TA muscle thickness of the left anterior leg [Nm/cm]. Furthermore, twitches were analyzed off-line for: peak twitch tension (Pt) [Nm], twitch time to peak tension (twitch TPT [ms]), maximal twitch rate of torque development (twitch RTD) [Nm/s], normalized (to Pt) twitch RTD [s-1], twitch half-relaxation time (HRT) [ms], twitch negative RTD (twitch -RTD) [Nm/s] and normalized (to Pt) twitch -RTD [s-1]. All data are presented as means (M) ± standard deviations (SD). All statistics were analyzed using the Statistical Package for the Social Sciences (version 22.0; IBM SPSS, Chicago, IL). With the Shapiro-Wilk test, the normal distribution of the data was inspected statistically. Mauchly’s sphericity test and Levene’s test were used to test sphericity and homogeneity of variance assumptions, respectively. A one-way analysis of variance (ANOVA) with “group” for the three sarcopenia stages was used to examine significant mean differences in normally distributed variables (body mass, BMI, physical activity, muscle thickness, handgrip strength, SMI, MVC RTD, TPT, voluntary activation, Pt, maximal twitch RTD, twitch HRT, twitch -RTD, normalized twitch -RTD, TA normalized strength). If the tests for parametric statistics failed then a Kruskal Wallis test (age, height, gait speed, MVC, normalized MVC RTD, twitch TPT, normalized twitch RTD, strength normalized to body mass, TA muscle thickness) was used as an ANOVA alternative. If a significant group difference was detected, a post hoc analysis with a modified Bonferroni correction was performed to identify the specific significant differences between the three stages. Left and right leg US data were normally distributed and a dependent t test was used to explore significant differences between left and right. For the calculation of correlations, Pearson’s (normally distributed variables) or Spearman’s rho correlation coefficient

ACCEPTED MANUSCRIPT were calculated. Eta squared (η2) was calculated to evaluate small (η2=0.02), middle (η2=0.13) or large (η2=0.26) effects. An alpha <0.05 was considered statistically significant.

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3. Results 3.1. Sarcopenia screening

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Using the algorithm proposed by the EWGSOP, subjects were separated into the three sarcopenia stages based on their results in muscle mass, gait speed and handgrip strength. Eight subjects showed only low muscle mass, but normal gait speed and handgrip strength, and were thus categorized as being pre-sarcopenic. Nine subjects displayed low muscle mass in combination with either low gait speed or low handgrip strength (defined as being sarcopenic), and seven subjects had results below the threshold points in all three measurements, placing them in the severe sarcopenia category (see Figure 1).

Figure 1. Categorization of the subjects into the three conceptual sarcopenia stages by using the algorithm proposed by the EWGSOP with threshold values used by Cruz-Jentoft et al., (2010). m = men; w = women; a muscle mass above the cut-off value (double line) results in “no sarcopenia”, low muscle mass (solid line) results in pre-sarcopenia, low muscle mass in combination with either low gait speed but normal handgrip strength (big dashes) or low handgrip strength but normal gait speed (short dashed lines) results in sarcopenia (long dashed lines); low muscle mass together with low gait speed and low handgrip strength (dotted lines) results in severe sarcopenia.[figure 1 as 2-column fitting image]

3.2. Subject characteristics Subject characteristics are presented in Table 1. Because of only 3 males in each group we did not formally test for sex differences. No significant differences between the three sarcopenia groups were found for mean age, height, body mass, BMI and physical activity. Furthermore, there were no significant differences in the muscle thickness ultrasound measures used to

ACCEPTED MANUSCRIPT estimate the skeletal muscle mass index. In addition, there was no significant difference for US measures between left and right legs.

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1.6 ± 0.4 2.2 ± 0.6 2.4 ± 0.5 3.4 ± 0.9 4.1 ± 0.6 2.4 ± 0.2 4.5 ± 0.8 0.8 ± 0.1 1.4 ± 0.2

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Severe sarcopenia (n=7) 3/4 82.7 ± 4.1 1.60 ± 0.08 66.1 ± 11.2 25.7 ± 3.1 112.4 ± 57.0

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1.6 ± 0.4 2.4 ± 1.0 2.0 ± 0.5 3.4 ± 0.8 4.6 ± 0.9 2.5 ± 0.1 4.6 ± 0.7 0.7 ± 0.2 1.4 ± 0.2

1.6 ± 0.3 2.3 ± 0.4 2.0 ± 0.6 3.2 ± 0.5 4.4 ± 0.8 2.4 ± 0.4 4.2 ± 0.7 0.7 ± 0.1 1.3 ± 0.2

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Lateral forearm Anterior arm Posterior arm Anterior thigh Posterior thigh Anterior leg Posterior leg Abdomen Subscapula

Sarcopenia (n=9) 3/6 78.3 ± 6.4 1.64 ± 0.07 73.6 ± 11.4 27.3 ± 3.8 109.9 ± 40.0

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Male/Female [number] Age [years] Height [m] Mass [kg] BMI [kg/m²] PASE score

Pre-sarcopenia (n=8) 3/5 76.4 ± 6.8 1.67 ± 0.08 71.3 ± 15.0 25.6 ± 4.5 135.7 ± 52.9

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Table 1. Anthropometric subject characteristics, physical activity level and ultrasound muscle thickness [cm] measurements of the 9 different body sites

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Values for age, height, body mass, BMI , muscle thickness by ultrasound and PASE score are Means ± Standard deviations; BMI = Body Mass Index; PASE = Physical Activity Scale for the Elderly; η2= eta squared. All anatomic measures obtained by ultrasound are in cm.

3.3. Sarcopenia stages

The results of the sarcopenia screening measurements (gait speed, handgrip strength and skeletal muscle mass) separated by sarcopenia stage are shown in Table 2. Results indicated a significant mean difference for gait speed (H(2)=13.187; p<0.01) with a significant group difference between pre-sarcopenia and severe sarcopenia (p<0.01), but no differences between presarcopenia and sarcopenia (p=0.41), with a trend between sarcopenia and severe sarcopenia (p=0.07). There were no significant differences in handgrip strength and skeletal muscle mass index, but the effect size for handgrip strength was moderate to large.

Table 2. Sarcopenia measurements

Gait speed [m/s] Handgrip strength [kg] Skeletal muscle mass index, SMI [kg/m²]

male female total male female total

Sarcopenia threshold values1 0.8

Presarcopenia (n=8) 0.87 ± 0.07

0.82 ± 0.10

Severe sarcopenia (n=7) 0.71 ± 0.04*

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37.0 ± 6.0 22.0 ± 2.9 27.6 ± 8.4 5.58 ± 1.48 3.44 ± 0.51 4.24 ± 1.41

29.3 ± 5.9 22.2 ± 3.4 24.6 ± 5.3 4.49 ± 1.33 3.53 ± 0.64 3.85 ± 0.96

25.3 ± 1.5 17.0 ± 2.3 20.6 ± 4.8 4.56 ± 0.29 3.94 ± 0.51 4.20 ± 0.52

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ACCEPTED MANUSCRIPT 1 Threshold values as defined by Cruz-Jentoft et al. (2010), values are Means ± Standard deviations; η2= eta squared; *significant difference (p<0.01) compared with the pre-sarcopenic group

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3.4. Dorsiflexor contractile properties

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Representative torque-time traces recorded during voluntary and electrically-evoked contractions are presented in Figure 2.

Figure 2. Representative torque-time traces recorded during voluntary and electrically-evoked contractions for an A presarcopenic, B sarcopenic and C severe sarcopenic person (MVC = maximal voluntary contraction; stim = electrical stimulation of the fibular nerve). [figure 2 as 1-column fitting image]

Dorsiflexion contractile properties are presented in Table 3. The MVCs for severe sarcopenic subjects were 29% lower (p=0.02) with 19% slower (p=0.02) MVC RTD compared to sarcopenic subjects; and MVCs were 34% lower (p=0.04) with 36% slower (p=0.02) MVC RTD compared to pre-sarcopenic subjects (MVC: H(2)=8.793; p=0.01; MVC RTD: F(2)=4.308; p=0.03; see Figure 3). Voluntary activation revealed no significant differences among groups. Maximal twitch RTD were 40% (p=0.03) slower for the severe sarcopenia group compared to the sarcopenia group, and were 51% slower (p=0.03) compared with the pre-sarcopenia group (see Figure 2). Peak twitch tension also showed a significant group difference (F(2)=7.487; p<0.01), however, based on post hoc test results there was significance only between the severe sarcopenia and the pre-sarcopenia (-54%, p<0.01) groups. The means of MVC, MVC RTD and maximal twitch RTD of the dorsiflexors of subjects in the sarcopenic and severe sarcopenic group are presented as percentages of the pre-sarcopenic group in Figure 3. When RTD (MVC RTD, twitch RTD, twitch -RTD) were normalized to MVC and Pt, there were no significant differences. Groups were not significantly different in twitch TPT, twitch HRT and twitch -RTD.

ACCEPTED MANUSCRIPT Table 3. Dorsiflexor neuromuscular properties in the three different sarcopenia groups

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Severe sarcopenia (n=7) 24.0 ± 3.9* # 95.2 ± 1.3 95.4 ± 36.9* # 3.9 ± 1.1 3.6 ± 1.3* 113.5 ± 23.4 69.2 ± 23.2* # 20.2 ± 3.1 153.8 ± 31.1 -114.2 ± 52.8 -38.3 ± 27.1

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MVC [Nm] Voluntary activation [%] Maximal MVC RTD [Nm/s] Normalized MVC RTD [s-1] Peak twitch tension [Nm] Twitch TPT [ms] Maximal twitch RTD [Nm/s] Normalized twitch RTD [s-1] Twitch HRT [ms] Twitch -RTD [Nm/s] Normalized twitch -RTD [s-1]

Sarcopenia (n=9) 33.9 ± 6.6 95.7 ± 1.4 121.0 ± 29.8 3.6 ± 0.8 6.1 ± 1.8 108.8 ± 14.1 114.9 ± 30.3 21.5 ± 6.8 139.2 ± 38.2 -138.0 ± 60.5 -24.9 ± 9.1

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Pre-sarcopenia (n=8) 36.5 ± 14.4 96.2 ± 1.5 150.0 ± 41.3 4.3 ± 1.2 7.9 ± 2.9 115.5 ± 15.1 139.8 ± 61.2 17.7 ± 1.9 119.3 ± 22.4 -147.0 ± 86.0 -19.0 ± 8.4

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Percentage of the pre-sarcopenia group

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Values are Means ± Standard deviations; MVC = maximal voluntary contraction; RTD = rate of torque development; TPT = time to peak tension; HRT = half relaxation time; -RTD = negative peak relaxation rate; η2= eta squared; *significant difference (p<0.05) compared with the pre-sarcopenia group; #significant difference (p<0.05) compared with the sarcopenia group

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Figure 3. Means of maximal voluntary contraction (MVC), maximal rate of torque development during maximal voluntary contraction (MVC RTD), and maximal twitch rate of torque development (twitch RTD) of the dorsiflexors of subjects in the sarcopenic (shaded bars) and severe sarcopenic (dotted bars) group expressed as percentages of the pre-sarcopenic group. * significant group difference between the severe sarcopenic and the sarcopenic group (p<0.05); #significant group difference between the severe sarcopenic and the pre-sarcopenic group (p<0.05) [figure 3 as a 1.5-column fitting image]

ACCEPTED MANUSCRIPT 3.5. TA left leg muscle thickness and normalized strength

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When normalized to body mass there was still a significant group difference (H(2)=7.083; p=0.03; see Table 4) in strength with significantly lower normalized strength (-26%) in the severe sarcopenia group in comparison to the pre-sarcopenia group (p=0.03). The TA muscle thickness of the left leg did not show any significant group differences (see Figure 4 and Table 4) and when strength was normalized to TA muscle thickness there was a trend towards a reduction in strength normalized to MT of the TA from pre-sarcopenia to the severe sarcopenia group (F(2)=3.003; p=0.07).

Figure 4. Ultrasound pictures with the TA muscle thickness [cm] of older women in the three sarcopenia stages: a) 71 years old, pre-sarcopenia; b) 69 years old, sarcopenia; c) 77 years old, severe sarcopenia. [figure 4 as a 2-column fitting image]

Table 4. TA muscle thickness of the left leg and normalized dorsiflexion strength

TA muscle thickness [cm] Strength normalized to body mass [Nm/kg BM] Strength normalized to TA muscle thickness [Nm/cm]

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Pre-sarcopenia (n=8) 2.6 ± 0.5 0.5 ± 0.1

Sarcopenia (n=9) 2.4 ± 0.3 0.5 ± 0.1

Severe sarcopenia (n=7) 2.4 ± 0.4 0.4 ± 0.1*

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12.6 ± 5.5

13.5 ± 3.1

10.4 ± 2.3

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Values are Means ± Standard deviations; BM = body mass; TA = tibialis anterior; η2= eta squared; *significant difference (p<.05) compared with the pre-sarcopenia group;

3.6. Relationships among age, gait speed, handgrip strength and neuromuscular properties As a whole group (N=24), age was significantly negatively related to dorsiflexor MVC (r(22)=0.43, p=0.04, see Fig. 5), and handgrip strength was significantly and positively related to TA

ACCEPTED MANUSCRIPT MVC (r(22)=0.60, p<0.01, see Fig. 6) and to SMI (r(22)=0.43, p=0.04). All other parameters tested showed no significant correlations or trends in this cohort.

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Figure 5. Relationship between age and maximal voluntary contraction (MVC) of the dorsiflexors of subjects in the three sarcopenia stages (pre-sarcopenia = black diamonds, sarcopenia = grey squares, severe sarcopenia = white triangles); black line = trend line of all data with r=-0.43. [figure 5 as 1.5-column fitting image]

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4. Discussion

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Figure 6. Relationship between handgrip strength and maximal voluntary contraction (MVC) of the dorsiflexors of subjects in the three sarcopenia stages (pre-sarcopenia = black diamonds, sarcopenia = grey squares, severe sarcopenia = white triangles); trend line of all data with r=0.60. [figure 6 as 1.5-column fitting image]

This study investigated differences in DF neuromuscular function among three different stages of sarcopenia as defined using the screening algorithm proposed by the EWGSOP. We screened 24 adults over 65 years and all subjects could be assigned to one of three sarcopenia categories. Differences among these sarcopenia groups were analyzed based on additional assessments of neuromuscular functioning (maximal strength, voluntary activation, isometric evoked contractile properties) of DFs. These measures are important to help appreciate the relevance of underlying neuromuscular factors that may explain the functional tests used in the screening algorithm. Voluntary strength measures assess the whole system including central activation and muscle contractile capabilities, and stimulated twitch and related contractile properties assess intrinsic contractile function of the dorsiflexor group. These measures support a basis for the categorization model but also highlight the importance of further validation to help direct improved screening measures and to provide appropriate interventions aimed at minimizing the degree of sarcopenia. Despite the modest sample size available (N=24) following pre-screening of 63 adults, the application of the algorithm based on gait speed, handgrip strength and muscle mass, resulted in an almost equal allocation of the subjects into the three sarcopenia stages of pre-sarcopenic (n=8), sarcopenic (n=9) and severe sarcopenic (n=7). There were 15 females and nine males approximately equally distributed among the three groups. Other studies have shown similar results using this categorization to assess sarcopenia (Ali & Garcia, 2014; Cooper et al., 2012; Lourenco et al., 2015; Spira et al., 2015a; 2015b; Volpato et al., 2012), but none has used the

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threshold values outlined by Cruz-Jentoft et al. (2010) to place individuals into one of the three sarcopenia categories. In addition, our results showing no significant differences in muscle thickness (and estimated muscle mass) between the three different sarcopenia groups are in line with the development of the sarcopenia definition during the last several years. Loss of muscle mass alone is insufficient to explain changes and differences regarding muscle strength, power, function and physical performance in sarcopenic older adults (e.g. see McKinnon et al., 2015) and the screening algorithm identifies older adults who may have neural as well as muscular deficits. Hence, it is important to further identify specific sarcopenia related neuromuscular factors that may explain differences in sarcopenia.

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The three groups did not differ in age, height, body mass (including muscle mass estimates), and physical activity scores, but although handgrip strength also did not differ among the groups, MVC of the dorsiflexors was less in the severe sarcopenia group compared both with the presarcopenia and the sarcopenia group despite no differences in voluntary activation among the groups. When normalized to body mass MVC strength differences persisted only between the sarcopenia and the severe sarcopenia groups, but when normalized specifically to muscle thickness of the TA, no differences in MVC strength were apparent. However, from functional perspectives, gait speed was slower in the severe sarcopenia group compared with the presarcopenia group. Besides lower MVC values, the severe sarcopenic group had slower MVC RTD, lower peak twitch tension and slower maximal twitch RTD compared with less sarcopenic persons. In addition, there was a moderate to large effect size for slowing of HRT across the 3 groups. These results fundamentally are equivalent with results of age-related changes shown in other studies in this muscle group (Allen et al., 2014; Connelly et al., 1999; McKinnon et al., 2015; McNeil et al., 2007; Power et al., 2014a; Simoneau et al., 2005). However, when RTDs were normalized to MVCs or Pts, there were no significant differences between the three sarcopenia groups. This is in line with former studies comparing young and old regarding normalized MVC RTD (Kent-Braun et al., 2002; Klaas et al. 2008; Thelen et al., 1996) and normalized twitch RTD (Kent-Braun et al., 2002) in the dorsiflexors. Thelen et al. (1996) compared young (~23y) with old (~72y) subjects and found significant differences in absolute RTD, but no differences when normalized to torque. Their mean normalized RTD (7.1s-1) were higher than in our study (3.9s-1) which can be due to their lower ages and different testing device in non-sarcopenic subjects (Thelen et al., 1996). Despite the lack of differences on a relative scale (normalized to torque), on an absolute scale it is clear that contractile quantity both when the central nervous system (CNS) is engaged during an MVC and when only intrinsic muscle capacity is tested (Pt), is lower in those categorized as more sarcopenic. Because overall voluntary activation, strength normalized to body mass and muscle thickness were not different among groups this indicates that the intrinsic quality of the muscle is an important feature in explaining differences. It should be noted that we did not attempt to distinguish non-contractile tissue from contractile tissue using US in the muscle mass estimates. Furthermore, several contractile quality variables related to rates of activation were lower on an absolute scale (Fig. 3) and may help explain the ultimate strength differences achieved under both voluntary and stimulated conditions. Several reasons have been suggested contributing to muscle weakness and age-related muscle slowing of contractile properties reflected in the variables outlined in Table 3. These include a greater proportion of Type I fibres and higher proportion of slow myosin heavy chains (MHC; Power et al., 2014a; 2014b; 2013; Russ et al., 2012), alterations in connective and

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structural tissues (Russ et al., 2012), or diminished excitation-contraction coupling (ECC) processes (Manring et al., 2014). Details of the proposed relationship of each factor to the tested parameters summarized in Table 3 can be found in a review by Hunter et al (1998). In addition to intrinsic muscle and connective tissue factors noted above, age-related remodelling of motor units (MUs) ultimately leading to a loss of MUs or related alterations in MU stability, may be important components in explaining sarcopenia (Hepple & Rice, 2015).

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Most all of these factors have been explored only as they relate to adult aging and have not been systematically tested in relation to degree of sarcopenia. Although the results of our study showed that higher age was significantly correlated with lower absolute MVC (see Fig. 5), age was not different among the 3 sarcopenic categories. Therefore it is unclear at this time whether MU numbers or other MU properties that seem to be age-dependent would be different depending on the severity of sarcopenia. In addition, in a small cohort of subjects, Venturelli et al. (2015) compared two muscle groups (elbow flexors and quadriceps) in 8 young adults to two groups of adults aged between ~85-90 years; one mobile (8) and one immobile (8). They concluded muscle function (measuring maximal voluntary contraction, electrically evoked resting twitch force, physiological cross-sectional area) was affected by age and exacerbated by disuse, however, neither advanced age nor disuse per se related to intrinsic muscle function (assessed in vitro by muscle biopsies to measure single fibre-specific tension). However, subjects were compared by age and not degree of sarcopenia.

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In studies on aging it is important to recognize the impact of physical activity (McKinnon et al., 2015). In our study, there were no significant differences regarding the amounts of physical activity (PASE score) among the 3 sarcopenia categories and there was no significant correlation between physical activity and any of the other parameters we measured (including age). In comparison, a study by Logan et al. (2013) who tested 297 older community-dwelling adults (age range 60 to 88 years) in the same region of Canada also using the PASE score, reported mean scores that were slightly lower. Their younger subgroup (mean age: 72.1 years) with 122 persons showed an average PASE score of 129 compared to our 24 persons (mean age: 79.1 years) with an average score of 119 points. There are limitations to questionnaires in assessing physical activity which are marginally improved with the use of accelerometers (Bastone et al., 2014; Ozemek et al., 2013; Trayers et al., 2014). Therefore without perhaps laboratory based tests of fitness, the impact of physical activity status on sarcopenia is not clear especially using this screening tool. Our correlation analysis in which we combined the screening measures (handgrip strength, muscle mass, gait speed) with the neuromuscular properties displayed a significant high correlation (r=0.60) between dorsiflexor MVC and handgrip strength. These relationships may not be surprising based on prior studies (Bohannon, 2015; Samuel & Rowe, 2012; Trombetti et al., 2015), but do support a basis for the categorization tests. Because MVC has both a muscular (sarcopenia) and neural (dynapenia) components, further studies are needed to identify the relative influence of various contributing factors.

5. Conclusions

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In summary, the data presented here demonstrate that the separation into the three stages of sarcopenia (pre-sarcopenia, sarcopenia, severe sarcopenia) by using the EWGSOP’s definition and classification did not result in any significant differences in age, or any anthropometric characteristics, physical activity, muscle mass and handgrip strength. The laboratory-based tests found some neuromuscular differences among the three groups in the DFs which generally supported the classification scheme and helped to identify key factors that could explain differences in functional capacities. Gait speed was slower in the severe sarcopenia group compared with the pre-sarcopenia group, whereas absolute maximal RTD of the weaker voluntary MVC was lowest in the severe sarcopenia group, and the sarcopenia group was lower than the pre-sarcopenia group. Our assessment indicates that the algorithm has merit based on the modest-sized dataset. Indeed, although handgrip for example was not different statistically, the trend was in the anticipated direction and the effect size was moderate using eta-squared (0.17). Handgrip strength which is easily administered, has been shown consistently to reflect overall strength capacity (Bohannon, 2015; Samuel & Rowe, 2012) and in this study it was correlated with DF strength (Fig 6).

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The loss of strength and overall slowing of contractile capacity strongly implies that greater sarcopenia leads to greater power loss, and indeed many studies indicate that power is the more important component than absolute strength in relating to functional incapacities. Incorporating a power test (Reid & Fielding, 2012) might be a valuable addition to the screening algorithm, and it also could reflect fatigability which is an important component of aging and physical capacity related to sarcopenia (Wallace et al., 2016). Finally, the laboratory-based tests reported here indicate the importance of the integrity and function of the neuromuscular system in sarcopenia. Thus, age-related remodeling of motor unit structure and function (Hepple & Rice, 2015) may affect some of the tasks used in the screening algorithm and warrant further study. A final benefit of a sarcopenic categorization model allows for longitudinal monitoring and assessment of interventions aimed at minimizing sarcopenia.

Acknowledgements

Funding Source: T. Morat was supported by a postdoctoral scholarship by the Robert Bosch Foundation, Stuttgart, Germany, and the study was supported by NSERC to C. Rice. We thank K. Shoemaker, for the use of his ultrasound unit and C. Moore for assistance with ultrasound imaging, C. Fitzgerald, S. Belfry and all the instructors from the Canadian Centre for Activity and Aging for their support and W. Zijlstra for his feedback regarding our manuscript. Especially we thank the subjects for volunteering their valuable time and interest.

Disclosures The authors have no conflicts of interest to disclose.

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