DEFINING SARCOPENIA USING MUSCLE QUALITY INDEX

C.-D. Lee, E. Dierickx

School of Nutrition and Health Promotion, Arizona State University, Phoenix, AZ;

Corresponding Author: Dr. Chong Lee, Arizona State University, School of Nutrition and Health Promotion, 500 North 3rd street, Phoenix, AZ 85004, Phone: 602-827-2282, Fax: 602-496-1873, Email: chong.lee@asu.edu

J Aging Res Clin Practice 2018;7:45-59
Published online March 29, 2018, http://dx.doi.org/10.14283/jarcp.2018.11

Abstract

Objectives: Although low muscle quality is a strong predictor of sarcopenia, defining sarcopenia using muscle quality remains unknown. This study investigated the cut-points to define sarcopenia using muscle quality index (MQI) in the young reference population. Methods: Fifty healthy young (20 to 29 years) and forty elderly adults (60 to 79 years) were recruited in this study. Dual-energy X-ray absorptiometry was used to assess appendicular skeletal muscle mass. Hand grip and leg dynamometers were used to measure muscle strengths in the arm and leg. Muscle quality in the arm (MQIArm, kg/kg) and leg (MQILeg, Nm/kg) were computed as muscle strength per lean mass in the arm and leg, respectively. Total muscle quality (MQITotal) was computed as the combination of MQIArm and MQILeg, while standardized muscle quality (MQIStd) was computed as the combination of z-scores in MQIArm and MQILeg. Sarcopenia was defined as ≤2 SD below from the mean values in the young reference group. Results: The cut-points for defining sarcopenia using MQIArm, MQILeg, MQITotal, and MQIStd in men were ≤8.37, ≤12.07, 22.06, and <-3.35, and in women were ≤10.09, ≤13.97, 28.22, and <-2.25, respectively. In the elderly adults, the frequencies of sarcopenia using MQIArm, MQILeg, MQITotal, and MQIStd were 15%, 27.5%, 32.5%, and 35%, respectively. Conclusion: This study establishes new values for defining sarcopenia using MQIs. The proposed new MQI cut-points may be a role in detecting sarcopenia across individual and population level.

Key words: sarcopenia, muscle quality index, muscle mass, muscle strength.

Introduction

Age-related skeletal muscle loss (sarcopenia) is a significant risk factor for falls (1), disability (2), and mortality (3) in the elderly men and women. With increasing life expectancy, sarcopenia is a major public health concern in the geriatric societies (4). Approximately 7 to 27.8% of the United States (US) men and 10 to 19.3% of US women, aged ≥ 60 years, suffer from sarcopenia (5-6), with increasing prevalence of sarcopenia in persons over 80 years of age (50%) (7) and in some cancer patients (68.9%) (8). Estimated medical costs associated with sarcopenia in the US is about 18 billion a year (5). Since effective clinical treatments for sarcopenia in the elderly populations are limited, preserving muscle mass and muscle strength at younger and middle ages is imperative to avoid the burden of this disease.
Skeletal muscle mass decreases about 40% between the ages of 20 and 80 years (9) with an annual decreasing rate of 1 to 2% after 50 years of age (10). Notably, persons with skeletal muscle loss and excess fat are at greater risk of physical disability and mortality (11). Although the primary causes of sarcopenia still remain unknown, early detection and subsequent treatment of sarcopenia is a key to prevent sarcopenia-related disability and mortality.
To address sarcopenia prevention strategies, establishing an accurate definition of sarcopenia should be in the first place. Baumgartner et al. first proposed cut-points to define sarcopenia using low muscle mass (12). Several investigators have shown that low muscle mass is associated with disability (2,12) and mortality (13). Other investigators have also shown that low muscle strength, not low muscle mass, is associated with disability (14) and mortality (15). Currently, sarcopenia has been defined as the combination of low muscle mass and weakness or slowness (16-20). However, the definition of sarcopenia still remains in dispute worldwide. The European Working Group on Sarcopenia in Older People (EWGSOP) (16) and the Asian Working Group for Sarcopenia (AWGS) consensus panels have defined sarcopenia using the combination of low muscle mass and low muscle function (17). The International Working Group on Sarcopenia (IWGS) (18) and the Society of Sarcopenia, Cachexia and Wasting Disorders (SCWD) (19) have defined sarcopenia using the combination of low muscle mass and low physical performance. The Foundation for the National Institutes of Health (FNIH) (20) defined sarcopenia using the combination of low muscle mass and low muscle strength. These inconsistent guidelines may lead to confusion to the public and clinical settings as a screening tool to determine sarcopenia patients.
From the methodological perspectives, the definition of sarcopenia should represent muscle quality, muscle strength per unit of muscle mass, rather than muscle mass or muscle strength. Some investigators have shown that intermuscular fat increases by 35.5-74.6% in men and 16.8-50% in women with aging (21), and an increase in fat mass is positively associated with muscle mass and muscle strength (22). Thus, the greater muscle strength or muscle mass associated with increment in fat may lack muscle quality. Although few studies have shown that muscle quality is a better indicator of functional capacity as compared with muscle mass or muscle strength (23), there is no standardized method to define sarcopenia using muscle quality. To fill this gap, we investigated the cut-points to define sarcopenia using the muscle quality indexes (MQIs) in the young reference adults.

Methods

Study Participants

Fifty young male and female adults (ages 20-29 years; m = 30, f = 20) and forty elderly male and female adults (ages 60 to 79 years; m = 16; f = 24) were recruited for the present study. The study was advertised by fliers, online posts, and University announcements within the Downtown Phoenix area. For the healthy young reference group, inclusion criteria were aged 20 to 29 years, body mass index (BMI) <30 kg/m2 (weight in kilograms divided by height in meters squared), ability to perform physical activity assessed by online physical activity readiness questionnaire (PAR-Q), no pregnant, no personal history of chronic diseases, and not taking any hypoglycemic and hypertensive medications. For the elderly persons, inclusion criteria were aged 60 or more, ability to perform physical activity assessed by online PAR-Q, with no personal history of heart disease, stroke, or cancer. Written informed consent was obtained from all subjects prior to study participation. The study was approved by the Institutional Review Board at Arizona State University. All participants were given a detailed description of the protocol prior to their participation.

Measurement Procedure

Body height and weight were measured using a standardized physician’s scale. Dual-energy X-ray absorptiometry (DXA) was used to assess body composition, and arm and leg skeletal muscle mass by a licensed technician (Lunar iDXA, GE Healthcare, Madison, WI). Appendicular skeletal muscle mass (SMS) was computed by combining lean tissues in both arms and legs, and relative muscle mass was computed as SMS divided by height in meters squared (AMS) or SMS divided by body mass index (AMSBMI).
The grip strength was measured using Takei Physical Fitness Test dynamometer (kg). The dominant hand was used with the subject standing and their arm at a position parallel to the floor. Grip strength was measured twice, and the average of two test scores was used for analysis. The leg strength was measured by isometric knee extension test (1 set of 3 repetitions) at an angle of 60 degrees using the CSMI Humac Norm Dynamometer test (Nm). An average of the highest two performance scores was used for analysis.
Muscle quality in the arm (MQIArm, kg/kg) was calculated as the grip strength (STRArm, kg), right arm, divided by the lean mass in the right arm (LMArm, kg). Muscle quality in the leg (MQILeg, Nm/kg) was calculated as the isometric leg strength (STRLeg, Nm), right leg, divided by lean mass in the right leg (LMLeg, kg). Total muscle quality (MQITotal) was computed as the combination of MQIArm and MQILeg. Standardized MQI (MQIStd) was computed as the combination of z-scores in both MQIArm and MQILeg. In the elderly persons, MQIStd was computed by the combination of z-scores in both MQIArm and MQILeg, using the sex-specific means and SDs from the healthy young reference group.

Statistical Analysis

General linear models were used to investigate mean differences for anthropometric, clinical measures, relative muscle mass and muscle quality indexes between men and women after adjustment for age and race. The normality assumptions for all outcome measures were justified by Shapiro-Wilk test or Kolmogorov-Smirnov test. Sex-specific cut-points for ASM, ASMBMI, MQIArm, MQILeg, MQITotal, and MQIStd were computed as >1 SD, 1 SD≥ to >2 SD, and ≤2 SD below from the mean values in the young reference group. Sarcopenia was defined as ≤2 SD below from the mean values in the young reference group. We also examined the sex- and race-adjusted partial Pearson correlations among of muscle mass, muscle strength, and muscle quality in both young and elderly adults, respectively. All statistical procedures were performed by Statistical Analysis Systems (SAS 9.4) software (SAS Institute, Cary, NC).

Results

In the young reference group, as shown in Table 1, men had greater BMI, SBP, grip strength, STRLeg, LMArm, and LMLeg than did women after adjustment for age and race (all p<0.001). There were no statistical gender differences in DBP, MQILeg, MQITotal, and MQIStd (all p>0.10), while women had greater MQIArm than did men (p<0.001). In the elderly persons, men had greater grip strength, LMArm, STRLeg, LMLeg, MQIStd than did women (all p<0.02). There were no statistical gender differences in BMI, SBP, DBP, MQIArm, MQILeg, and MQITotal (all p>0.35).

Table 1
Characteristics of the study participants in young and elderly adults

*Values are means. †Adjusted for age and race. LMArm, a lean mass in the arm; LMLeg, a lean mass in the leg; STRLeg, leg strength; MQIArm, muscle quality in the arm; MQILeg, muscle quality in the leg; MQITotal, a combination of MQIArm and MQILeg; MQIStd, a standardized MQI.

The sex-specific relative muscle mass (ASM and ASMBMI) and MQI (MQIArm, MQILeg, MQITotal, and MQIStd) cut-points are shown in Table 2. The ASM, ASMBMI, and MQI cut-points were classified as normal, low, and poor categories, corresponding to >1 SD, 1 SD≥ to >2 SD and ≤2 SD below from the sex-specific mean values in the young reference group. “Poor” categories were classified as sarcopenia. The cut-points for sarcopenia using ASM and ASMBMI in men were ≤7.75 kg/m2 and ≤0.96, and in women were ≤5.69 kg/m2 and ≤0.71, respectively. The cut-points for sarcopenia using MQIArm, MQILeg, MQITotal, and MQIStd in men were ≤8.37, ≤12.07, 22.06, and <-3.35, and in women were ≤10.09, ≤13.97, 28.22, and <-2.25, respectively.

Table 2
Cut-points to define sarcopenia using relative muscle mass and muscle quality indexes in young men and women

*Normal, low, and poor indicates >1 SD, 1 SD≥ to >2 SD, and ≤2 SD below from the sex-specific mean values in the reference group. ASM, appendicular skeletal muscle mass; ASMBMI, ASM divided by height in meters squared; MQIArm, muscle quality in the arm; MQILeg, muscle quality in the leg; MQITotal, total muscle quality; MQIStd, standardized muscle quality

In the elderly adults, the frequencies of sarcopenia using MQIArm, MQILeg, MQITotal, and MQIStd were 15% (n = 6), 27.5% (n = 11), 32.5% (n = 13), and 35% (n = 14), respectively. The sex- and race-adjusted partial Pearson correlations among muscle mass, muscle strength, and muscle quality are shown in Table 3. There was no association between muscle mass and muscle strength in both young (r = 0.19, p = 0.20) and elderly adults (r = 0.05, p = 0.75), but muscle mass was inversely associated with muscle quality in young (r = -0.48, p<0.001) and elderly adults (r = -0.73, p<0.001). There was a moderate association between muscle strength and muscle quality in young (r = 0.62, p<0.001) and elderly persons (r = 0.46, p<0.001).

Table 3
Sex- and race-adjusted Pearson partial correlations among muscle mass, muscle strength, and muscle quality in young and elderly adults

Muscle mass, a combined relative muscle mass in both arm and leg; Muscle strength, a combined z-scores of muscle strength in both arm and leg; Muscle quality, a combined z-scores of muscle quality in both arm and leg. *p<0.001

Discussion

Although the rising trend in the prevalence of sarcopenia and disabilities is a major public health concern in the US (5-6), the accurate definition of sarcopenia still remains in controversial. To our knowledge, we first define sarcopenia using MQI cut-points (MQIArm, MQILeg, MQITotal, and MQIStd) based on young reference group. Several investigators have proposed muscle quality index or muscle power index using the ratio of muscle strength to muscle mass or the ratio of muscle power to muscle mass (24-25). Barbat-Artigas et al. (24) proposed MQI cut-points using the ratio of grip strength to total skeletal muscle mass (kg/SMkg) based on young reference population. Using this ratio, they classified “poor” MQI cut-points in men and women as ≤1.36 and ≤1.35 kg/SMkg. The Concord Health and Ageing in Men Project (CHAMP) proposed lower and upper extremity muscle quality scores based on the lowest 20% of the distribution in men aged 70 to 90 years (25). Other investigators have also proposed muscle power index using the ratio of muscle power (W) to total skeletal muscle mass (SMkg) (24) or the ratio of muscle power to time (26). However, the feasibility of these indices in clinical settings as a screening tool to detect sarcopenia has not been well documented.
Muscle mass, muscle strength, and muscle quality are associated with physical function and disability, all of these are important factors to define sarcopenia (12, 14, 23, 24-25). The muscle quality represents muscle’s ability to function, which is the best marker of functional capacity when compared with muscle mass or muscle strength (23). The CHAMP study also showed that muscle quality or muscle strength, not muscle mass, was a strong predictor of physical function and disability (25). Several International Working Groups have defined sarcopenia using the combination of low muscle mass and weakness or slowness (16-20). Defining sarcopenia using muscle mass and muscle weakness may have some limitations without considering myosteatosis (intermuscular and intramuscular adipose tissue). For instance, muscle mass or muscle strength with myosteatosis is not a good indicator of functional capacity because aging is positively associated with an increase in fatty infiltration of muscle tissue (27-28). Some investigators have also shown that elderly men had about 59-127% more fat in quadriceps and hamstrings than did young men (27), with an annual increase of intramuscular fat by 18% (28). Interestingly, an increase in fat mass is positively associated with muscle mass and muscle strength but is negatively associated with muscle quality (22). In fact, a greater muscle mass or muscle strength with excess fat may lack muscle quality, which may misclassify sarcopenic patients to nonsarcopenic patients. Increasing muscle strength per unit of muscle mass, not by accumulating fat mass, is associated with muscle quality. Our findings also show that muscle mass is inversely associated with muscle quality in young and elderly persons, which is consistent with the findings from the US general population and the French women study (29-30). The muscle quality is a strong surrogate marker for sarcopenia because it quantifies the function of muscle mass and muscle strength as a single unit. Further studies are needed to determine whether muscle quality is a better marker for physical disability and mortality as compared with the combination of low muscle mass and low muscle function, which defined sarcopenia by International Working Groups (16-20). Also, more studies are needed to justify the optimal cut-points for MQIs, muscle strength, and muscle mass in relation to disability and mortality.
A strength of this study is that the MQIs are based on young healthy reference group. Our cut-points to define sarcopenia using ASM in men were slightly higher (0.5 kg/m2) than those cut-points by Baumgartner et al. (12), but our women’s cut-points were similar with the EWGSOP (16). In the ASMBMI cut-points, we observed that our ASMBMI cut-points were greater than the FNIH cut-points in both men and women, which may be due to methodological differences defining sarcopenia. For instance, the FNIH cut-points to define sarcopenia for ASMBMI were based on the elderly people (aged 70-90 years) using the mean values of the lowest 20% distribution, whereas our cut-points to define sarcopenia were based on young reference group (aged 20-29 years) using <2 SD from the mean values. Another strength of our study is that we used DXA, a criterion method, to estimate lean mass in both arms and legs. A limitation of our study is that our findings may limit generalizability due to small sample size. Further studies are needed to define MQI cut-points with a large sample size across different race and gender groups.
In summary, muscle quality is a significant risk factor for disability and mortality. Based on healthy young reference men and women, we establish a new definition of sarcopenia using muscle quality indexes. At the very least, our muscle quality indices may still be a role as a screening tool in detecting sarcopenia across individual and population level.

Acknowledgements: This study was supported by Mayo-Arizona State University Obesity Solution Grant. The authors thank the graduate students from the ASU and study participants for their important contributions.

Conflict of Interest: None

Ethical standard: This study was performed in accordance with the ethical standards by the Arizona State University review board and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.

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EFFECT OF INCREASED DAILY INTAKE OF PROTEIN, COMBINED WITH A PROGRAM OF RESISTANCE EXERCISES, ON THE MUSCLE MASS AND PHYSICAL FUNCTION OF COMMUNITY-DWELLING ELDERLY WOMEN

H. Mori¹, Y. Tokuda²

1. Diabetes Therapeutics and Research Center, University of Tokushima, , Tokushima, Tokushima, Japan; 2. Faculty of Health Science Department, Hyogo University, Hiraoka, Kakogawa, Hyogo, Japan

Corresponding Author: Dr. Hiroyasu Mori, Diabetes Therapeutics and Research Center, University of Tokushima, 3-18-15 Kuramoto, Tokushima, Tokushima 770-8503, Japan, Phone number: +81 886337587, Fax Number: +81 886337589, Email: hiroyam31@gmail.com

J Aging Res Clin Practice 2016;inpress
Published online December 1, 2016, http://dx.doi.org/10.14283/jarcp.2016.124

Abstract

Abstract: Background: In elderly women, significant loss of muscle mass due to declining levels of estrogen secretion is a health concern. Increasing the recommended dietary allowance of protein intake has been included as a general health guideline to prevent age-related sarcopenia. Objectives: To investigate effects of light-to-moderate resistance training combined with increased protein intake on the muscle mass, strength, and physical function of community-dwelling elderly women. Design: The 12-week training program combined weight-bearing and resistance band exercises, performed 3 times per week. Setting: Hyogo Prefecture, in either City K or Town H. Practical Intervention: Women were randomly allocated to three groups: exercise with protein intake adjusted to the recommended daily allowance (RDA) of 1.0–1.1 g/kg body weight/day (MP+EX group); exercise with protein intake adjusted above the RDA level at 1.2–1.3 g/kg body weight/day (HP+EX group); and a control group receiving classroom-based session on nutrition management, with protein intake adjusted to the RDA level (MP group). Measurements: Body weight and physical composition were measured by multi-frequency bioelectrical impedance analysis. Results: Exercise prevented decreases in muscle mass and strength and in performance of physical function tasks (p<0.05). Increasing dietary intake of protein above RDA level increased muscle mass (p<0.01), walking speed (p<0.01) and knee extensor strength (p<0.05). Conclusion: Adjusting protein intake to 1.2–1.3 g/kg body weight/day, in combination with light-to-moderate resistance training, can improve body composition and physical function in elderly women. The result of this study could be effective in lowering the incidence of age-related sarcopenia.

Key words: Community-dwelling elderly, exercise, muscle mass, protein intake, sarcopenia.

Introduction

Aging is associated with a decrease in skeletal muscle mass and strength which leads to limitations in physical activities, increased risk for falls and overall lowering of the quality of life of elderly people (1–7). A specific health concern is the significant loss of muscle mass in elderly women due to declining levels of estrogen secretion (8). The resulting loss of strength is one of the primary factors of the higher incidence of falls in older women, compared to older men. Increasing the recommended dietary allowance of protein intake has been included as a general health guideline to prevent age-related sarcopenia (9-11).
The Japanese recommendations for dietary reference intakes (DRIs) indicate a daily protein intake of 1.0–1.1 g/kg body weight/day for people aged >65 years (12). These DRIs do not include protein intake levels necessary for elderly individuals performing resistance exercises for the purpose of increasing skeletal muscle mass and physical function, knowledge required to optimize outcomes of exercise (13).
According to the Long-Term Care Prevention Service, which aims to increase musculoskeletal system function in the elderly, few elderly individuals practice high-intensity resistance exercises using large-scale training equipment. Moderate-intensity resistance exercises performed using body weight and resistance bands provide an easy way for elderly people to exercise for the purposes of increasing skeletal muscle mass and physical function to prevent sarcopenia. Accordingly, the aim of our study was to evaluate the effectiveness of adjusting total protein intake to levels of 1.2–1.3 g/kg body weight/day, a level exceeding the DRIs, on skeletal muscle mass and physical function in elderly women performing moderate intensity resistance exercises that used their own body weight and resistance bands, thereby preventing sarcopenia.

Methods

Participants

Participants were 151 Japanese women, ≥65 years old, living in Hyogo Prefecture, in either City K or Town H.
With the aim of using the DRI level of protein intake (1.0–1.1 g/kg/day12) as a baseline reference, a dietary survey was used to screen the daily protein intake of prospective participants. Based on this dietary survey, 26 participants were excluded as their total daily protein intake was <1.0 g/kg/day, and 74 participants for a daily protein intake >1.1 g/kg/day. Remaining participants were randomly allocated to three groups of 17 participants: an exercise intervention group with protein intake adjusted to the DRI of 1.0–1.1 g/kg/day (MP+EX group); an exercise intervention group with protein intake adjusted above the DRI at 1.2–1.3 g/kg/day (HP+EX group); and a control group who participated in classroom-based session on nutrition management, with their protein intake adjusted to the DRI of 1.0–1.1 g/kg/day (MP group). A stratified randomization strategy was used to achieve a comparable age distribution across groups. Over the study period, 4 participants withdrew due to family circumstances or poor physical conditioning, 2 participants from the HP+EX group and 2 participants from the MP+EX group. The information from these 4 participants was excluded from analysis, with the final study group formed of 15 participants in the HP+EX group, 15 participants in the MP+EX group, and 17 participants in the MP group (Figure 1).

Figure 1
Flow chart of study participation

Using participants with a pre-intervention protein intake of 1.0-1.1g/kg body weight/day, we randomly divided the participants into a high protein intake group of 1.2-1.3g/kg body weight/day (HP+EX group) and a moderate protein intake group of the usual 1.0-1.1g/kg body weight/day (MP+EX group) during an intervention of light-to-moderate intensity resistance exercise. We also established a moderate protein intake group of the usual 1.0-1.1 g/kg body weight.

Intervention design

A 12-week program was implemented. Groups HP+EX and MP+EX completed a standardized program of resistance exercise and a nutrition management, with MP group completed a classroom-based program on nutrition.

Measurement of body weight and physical composition

Body weight and physical composition were measured by multi-frequency bioelectrical impedance analysis (In Body 430, Bio Space, Seoul, Korea). The following parameters of body composition were obtained: body mass index (BMI), body fat percentage, lean body mass (LBM), and total limb muscle mass, which was calculated separately for the upper and lower limbs, and for the trunk.

Measurement of physical functions

The following measures of physical function were evaluated: grip strength, knee extension strength, 5-m maximum walking speed, and timed up-and-go (TUG). Grip and knee extension strength were measured by hand held dynamometry (T.K.K5401; Takei Instruments, Tokyo, Japan. μ-tus F-100; ANIMA., Tokyo, Japan). Measures were taken twice, and the maximum value recorded for analysis.

Nutritional survey

A nutritional survey was conducted to document: total energy intake (TEI), total and adjusted protein intake (g/kg/day), total fat intake, and total carbohydrate intake. The survey was complemented by food weighing methods (Excel Eiyou version 5.0; Kenpakusha, Tokyo, Japan). The nutrition questionnaire and weighing were completed for 5 consecutive days before the start of the program, and daily during the 12-week intervention.

Daily activity survey

Participants recorded their daily activity for three consecutive days before the start of the program, with the information used to calculate each participant’s regular physical activity level (PAL). Again, individual interviews were used to complete missing information. The estimated energy requirement (EER) to meet each individual’s PAL was calculated as follows: EER = basal metabolic rate × body weight (kg) × PAL (12).

Exercise intervention

The exercise intervention consisted of a series of body weight resisted and resistance band exercises, performed 3 times per week for 12 weeks. Twice per week, exercises were performed under supervision of an exercise specialist, with participants completing the third session at home.

Resistance exercises

Body weight resisted exercises consisted of the following: abdominal crunches, rising and sitting from a chair; leg extensions; standing heel kicks, and calf raises. Five exercises were also performed with elastic bands (REP BAND; Magister Corporation, Chattanooga, TN): arm curls, pull-ups, leg extensions, squats, and sit-ups. Elastic bands of five difference resistance were used, with the level of resistance individually adjusted using 1 maximum repetition (1RM) testing for the upper and lower limbs prior to the intervention. The resistance load was modified in standardized fashion over the 12-week program as follows: for weeks 1-4, participants performed 2 sets of 10 repetitions each of the body weight resisted exercises and 2 sets of 20 repetitions of the resistance band exercises, at a resistance load of 50% 1RM; for weeks 5-8, participants performed 2 sets of 15 repetitions each of the body weight resisted exercises and 2 sets of 15 repetitions each of the resistance band exercises at 60% 1RM; and for weeks 9-12, participants performed 3 sets of 15 repetitions each of the body weight resisted exercises and 3 sets of 12 repetitions each of the resistance band exercises at 70% 1RM.

Nutritional intervention

Total protein intake in the HP+EX group was adjusted to a daily level of 1.2–1.3 g/kg/day. Participants’ were advised to increase their protein intake in their standard meals (breakfast, lunch, and dinner). The total protein intake for participants in the MP+EX and MP groups was adjusted to 1.0–1.1 g/kg/day.

Nutritional management

Nutritional management was provided by a nutritionist, based on the DRIs (14) and results of the nutrition survey conducted before the intervention. Nutritional management was provided according to the following schedule: once to all participants prior to the onset of the study; as part of the group nutrition guidance sessions in intervention weeks 1-2; and on an individual basis, twice per week, for intervention weeks 3-12. At these individual sessions, protein intake was verified to ensure compliance with group-specific levels. Daily activity surveys were also reviewed to ensure sufficient TEI for all participants.

Statistical analysis

Individual changes in EER and TEI, pre- and post-intervention, were evaluated using paired t-test analysis. Between-group differences in outcome variables, measured pre- and post-intervention, were evaluated using two-way analysis of variance (ANOVA). Changes in measured outcomes of muscle mass, physical function, and nutrient intake were evaluated between groups (HP+EX, MP+EX and MP) and time (pre- and post-intervention) using a repeated measures ANOVA with group as an independent factor and time as the repeated factor. For identified main effects and interactions, multiple comparisons were performed using the Tukey post hoc analysis, with specific change in pre- and post-intervention values compared using an unpaired one-way ANOVA. All statistical analyzes were performed using IBM SPSS statistical software (IBM, Tokyo, Japan), with the level of significance defined as a p value < 0.05.

Results

Baseline measures of body composition and physical function are listed in Table 1. Pre-intervention, measures were comparable between groups. Adherence to the schedule of resistance exercise of three sessions per week for 12 weeks was comparable for the two exercise groups, with a mean (SD) number of sessions completed of 34.3±1.2, out of a possible maximum of 36, for the HP+EX group (95.3%) and 34.0±1.0 for the MP+EX group (94.4%).

Table 1
Physical characteristics of subjects pre-intervention

Mean value ± Standard deviation; The results of a non-paired one-way ANOVA showed no differences in the characteristics of the 3 groups pre-intervention; HP+EX group: Protein intake of 1.2-1.3 g/kg body weight/day during the 12-week period of exercise intervention; MP+EX group: Protein intake of 1.0-1.1 g/kg body weight/day during the 12-week period of exercise intervention; MP group: Protein intake of 1.0-1.1 g/kg body weight/day for the 12-week period; BMI: Body mass index; LBM: Lean body mass

Changes in measures of muscle mass pre- and post-intervention are reported in Table 2. A significant main effect of exercise on limb muscle mass was identified (p<0.05) with the largest change identified for the HP+EX group (p<0.001 compared to both the MP+EX and MP groups). A significant group X limb interaction was identified (p<0.05), with a significantly higher increase in muscle mass for the HP+EX group, compared to the MP group, for the upper limbs (p<0.05), trunk (p<0.001) and lower limbs (p<0.001). Comparing HP+EX and MP+EX groups, the magnitude of change in muscle mass was higher for the HP+EX group only for the lower limbs (p<0.05).

Table 2
Comparison of limb muscle mass and physical functions pre-/post-intervention

Mean value ± Standard deviation; Two-way ANOVA by time (Pre- and Post-intervation period) × group (HP+EX, MP+EX, MP group)* p <0.05, ** p <0.01,*** p <0.001; One-way ANOVA and post-hoc by Tukey test, Significant difference with MP group: †p <0.05, ††p <0.01, †††p <0.001, Significant difference with MP+EX group: ‡p <0.05, ‡‡p <0.01

Effects of the intervention physical function are reported in Table 2. The ANOVA identified significant effects of exercise on knee extensor strength and 5-m maximum walking speed, as well as knee extensor strength x TUG interaction. Change in knee extensor strength was higher for the MP+EX group, compared to the MP group (p<0.01), with the highest magnitude of change obtained by the HP+EX group (p<0.001 compared to MP; p<0.05 compared to MP+EX group). Findings were similar for 5-m maximum walking speed, with greater improvement in walking speed in the MP+EX compared to MP groups (p<0.05), and largest increase for the HP+EX group (p<0.001 compared to MP; p<0.01 compared to MP+EX group). The improvement in TUG was greater for the HP+EX compared to the MP group (p<0.01).
Nutritional intake, TEI, EER, and levels of total protein intake, adjusted protein intake/kg body weight, fat, and carbohydrates are listed in Table 3. Significant changes in TEI and protein intake (TEI, p<0.05; protein, p<0.001), as well as an interaction between adjusted protein intake and body weight (p<0.001) were identified. The magnitude of change was higher for the HP+EX group compared to the MP group (TEI, p<0.05; protein intake/kg, p<0.001) and the MP+EX group (TEI, p<0.05; protein intake/kg, p<0.001).

Table 3
Comparison pre-intervention and during intervention

Mean value ± Standard deviation, Two-way ANOVA by time (Pre- and post-intervation period) × group (HP+EX, MP+EX, MP group), * p <0.05, ** p <0.01, ***p <0.001; One-way ANOVA and post-hoc by Tukey test, significant difference with MP group: †p <0.05, †††p <0.001, Significant difference with MP+EX group: ‡p <0.05, ‡‡‡p <0.001

Discussion

In this study, we evaluated the effectiveness of providing an adjusted protein intake of 1.2-1.3 g/kg/day during a 12-week program of light-to-moderate intensity weight-bearing and resistance band exercises on the skeletal muscle mass and physical function of elderly women. The adjusted protein intake improved selected parameters of body composition and physical function, namely lower limb muscle mass, knee extension strength, and 5-m maximum walking speed. The mean protein intake for the HP+EX group was 11.4 ± 1.8 g/day, and increased by approximately 0.22 ± 0.02 g/kg/day over the 12-week program. Fat and carbohydrate intake were comparable between groups and TEI was never below EER.
Our current results support our a priori hypothesis of the necessity of increasing protein intake to optimize outcomes of exercise on muscle mass and physical function in the elderly. In fact, total limb muscle mass increased by 0.9±0.3 kg when combining increased protein intake and exercise (HP+EX), compared to 0.2±0.4 kg for exercise alone (MP+EX). Our measured effect of exercise alone is comparable to results reported by Kim et al. (15) of a 0.29 kg increase in total limb mass for elderly Japanese women completing a 12-week program. Kim et al. (15) combined resistance exercises with amino acid supplements, reporting an increase of approximately 0.34 kg in total limb muscle mass. Using an adjusted protein intake, our program surpassed these levels with a mean increase in total limb mass of 0.9 kg, with the most significant increase noted for the lower limbs. This lower limb bias may be explained by performance of a higher number of lower limb exercises. An important difference in our study, compared to Kim et al. (15), is that we graduated the workload of our program over the 12-weeks.
An important outcome of our study was the identification of a 0.2±0.7 kg decrease in total limb muscle mass and 0.2±1.0 kg decrease in knee extension strength for participants in the MP group over the study period. This emphasizes the need to adjust protein intake, at least to the minimum DRI of 1.0–1.1 g/kg/day, in combination with a light-to-moderate program of resistance training, to maintain muscle mass, with further adjustment in protein intake yielding more significant results.
According to Chevalier et al. (16), a dietary protein intake of 0.8 g/kg/day is insufficient for increasing the skeletal muscle mass of elderly persons and intake should be increased to levels of at least 1.2 g/kg/day. Several physiological reasons contribute to the increased requirement of protein intake in the elderly. Drummond et al. (17) reported a delay and/or decrease of the signaling responses to amino acids in skeletal muscle cells with age. Volpi et al. (18) further demonstrated that a higher proportion of the amino acids absorbed in the gastrointestinal tract of elderly persons is metabolized in the small intestine and liver. Although specific physiological measurements related to protein metabolism were not included in our study, our outcomes provide evidence of insufficient muscle protein synthesis during resistance exercise with a moderate adjustment of protein intake to levels of 1.0–1.2 g/kg/day and that a higher adjustment to levels of 1.2–1.3 g/kg/day are required to enhance effectiveness of resistance training on muscle mass and strength.
Our light-to-moderate level of resistance was sufficient to maintain muscle mass and strength in the MP+EX group. Levinger et al. (19) reported a 1.1-kg increase in skeletal muscle mass of elderly participants performing a 10-week program of high-intensity resistance exercises. In their review of research evidence for resistance training in elderly individuals, Miyachi et al. (20) indicated that an exercise intensity of 80% 1RM or above is required to increase the skeletal muscle mass with a single intervention of exercise. It is important to note that the participants in our study group had a relatively low PAL of 1.75; as none of our participants engaged in regular resistance training, prescription of a high-intensity program was not possible. Future research is required to more clearly elucidate the effectiveness of supplemented protein intake and different levels of resistance training for enhancing gains in muscle mass and strength. However, we do emphasize that our program of light-to-moderate resistance was effective, with a practice rate of approximately 95%, and can be safely implemented as a home program for elderly individuals who are relatively deconditioned.
The benefits of combining a light-to-moderate resistance training program with nutritional supplementation in lowering the risk for sarcopenia in elderly individuals is supported by previous research. In their review comparing the transient response of muscles to high- and low-intensity resistance training regimes, Mallinson et al. (21) reported an increase in muscle protein synthesis (and increased muscle protein reserves) with high-intensity resistance exercise, performed at 90% 1RM, while low-intensity resistance exercises, performed at 30% 1RM, stimulated muscle protein synthesis. Therefore, light-to-moderate intensity weight-bearing and resistance band resistance exercise can have benefits for elderly women, who can easily perform these exercises.
In our study, we assumed that a training resistance of 50–70% 1RM would have little stimulus on protein synthesis in skeletal muscle. As a result, we did not include a control group of adjusted high protein intake alone, with no participation in the program of exercise. Therefore, we are unable to determine if the increase in muscle mass for the HP+EX group results from the increase in protein intake or from the combination of increased protein intake and exercise. Future research is required to clarify the pathway of muscle mass increase associated to a combination of supplemented protein intake and exercise. Another important limitation of our study is the inclusion only of women to control for known sex-specific differences in age-related muscle atrophy and sarcopenia (22, 23). Future research will need to verify effectiveness of protein supplementation in elderly men. Moreover, we estimated muscle mass from BIA measurements. There are currently no population norms of BIA measurements for elderly women, and research is needed to establish norms compared to DEXA (24). Finally, the present study is that the sample size was too small, and the conclusion was not well supported by this data. For example, SD of total limb muscle mass is 1.9 and the change by intervention was 0.9kg in HP+EX group. Required sample size to make this change as significant is 37, which is more than twice of the actual number of sample.
Our study provides evidence of the benefit of increasing protein intake as one component of a resistance training program to improve muscle mass and strength in elderly women.

Funding: This work was supported by JSPS KAKENHI Grant Number 25750353. The sponsors had no role in the design and conduct of the study; in the collection, analysis, and interpretation of data; in the preparation of the manuscript; or in the review or approval of the manuscript.

Acknowledgements: None.

Conflict of Interest: All of the authors declare that they have no conflicts of interest regarding this paper.

Ethical standards: Ethics Committee of Hyogo University (approved # 12003).

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HIGHER APPENDICULAR AND TRUNK FAT MASS USING BIOELECTRICAL IMPEDANCE ANALYSIS ARE RELATED TO HIGHER RESTING BLOOD PRESSURE IN OLDER ADULTS

D. Takagi¹, M. Kageyama², S. Kojima³, Y. Nishida⁴

1. Department of Physical Therapy, Health Science University; 2. Department of Rehabilitation, Toyoda Eisei Hospital; 3. Department of Rehabilitation, Suzukake Healthcare Hospital; 4. Department of Physical Therapy, Seirei Christopher University.

Corresponding Author: Daisuke Takagi, Department of Physical Therapy, Health Science University: 7187 Kodachi, Fujikawaguchiko-Town, Yamanashi, 401-0380, Japan. TEL : +81 555-83-5299/FAX : +81 555-83-5298, Email : pt.takadai@gmail.com

Abstract

Background: Little is known about how fat mass and muscle mass in different parts of the body (e.g., appendages, trunk) using bioelectrical impedance analysis influences resting blood pressure in older adults. Objective: The purpose of the study was to clarify the association between resting blood pressure and muscle mass and fat mass in older adults using bioelectrical impedance analysis. Design: A cross-sectional study. Settings: A sample living independently in the community. Participants: The subjects were older adults between the ages of 65 and 85 years (n = 100). Measurements: Systolic, diastolic and mean arterial pressure was measured using an automatic hemodynamometer, and bioelectrical impedance analysis was used to estimate muscle mass and fat mass. Results: A positive correlation was observed between total fat mass, left and right arm fat mass, trunk fat mass, and left and right leg fat mass and resting systolic, diastolic and mean arterial pressure (p < 0.05), but this was not observed with any muscle mass (p > 0.05). In a multiple regression analysis adjusted for sex, systolic, diastolic and mean arterial pressure were independently predicted by total fat mass, left and right arm fat mass, trunk fat mass, and left and right leg fat mass (p < 0.05). Conclusions: These findings suggest that total, appendicular, and trunk fat mass, measured using bioelectrical impedance analysis, could aid in detecting the factors that increase blood pressure in clinical settings and even in daily life, thereby helping in controlling blood pressure.

Key words: Blood pressure, muscle mass, fat mass, bioelectrical impedance analysis, older adults.

Introduction

Ischaemic heart disease and stroke were the major leading causes of death in the world in 2012 (1). Age, particularly being over the age of 65 years, is one of the main risk factor for stroke and heart disease (2-3). 51% of deaths from stroke (cerebrovascular disease) and 45% of deaths from ischemic heart disease are attributable to high blood pressure (4). Hypertension leads to the development of arterial stiffness, and cardiovascular diseases, accordingly (5). The prevalence of hypertension in older adults aged 60 years or over is 67% (6), and the residual lifetime risk for hypertension in middle-aged and older adults is 90% (7). That is, the prevalence of hypertension in older adults is particularly high. If the prevention of hypertension which is related to developing about half the number of stroke (cerebrovascular disease) and ischemic heart disease (4) in older adults is possible, it may potentially lead to a decrease in the number of cardiovascular related deaths.

Many studies have investigated the risk factors associated with hypertension. For example, Han et al reported that subjects aged 60 years or older with sarcopenic obesity had a greater risk of hypertension; sarcopenic obesity was defined as an appendicular muscle mass/weight <1 standard deviation (SD) below the mean of a sample of healthy adults (aged 20–40 years) and a body mass index (BMI) of ≥25 kg/m2 (8). Park et al also suggested that sarcopenic obesity is associated with hypertension (9). Therefore, higher fat mass and lower muscle mass may lead to higher resting blood pressure. Moreover, another study found that central but not peripheral fat mass percentage was associated with high blood pressure in older adults (10). Pulse wave velocity has also been found to be an independent predictor of incident hypertension (11) and is related to appendicular muscle mass decline (12).

Fat mass and muscle mass can be measured using dual-energy x-ray absorption (DEXA) and CT scan. Measurement of fat mass and muscle mass using DEXA or CT scan is inconvenient in clinical settings or daily life. The bioelectrical impedance analysis (BIA) device is widely accepted as a safe, rapid, low cost, highly reliable, and valid technique to estimate muscle mass and fat mass (13-16), and the relationship between body lean mass, body fat, and visceral fat areas measured by DEXA and CT scans (17-19). However, little is known about how fat mass and muscle mass in different parts of the body (e.g., appendages, trunk) using BIA influences resting blood pressure in older adults. The measurement of BIA is more convenient compared with those of DEXA and CT scans, and clinicians and subjects can purchase BIA devices inexpensively for use in clinical settings or daily life. Clarifying the relationship between fat mass and muscle mass in the appendages and trunk using BIA and resting blood pressure could aid in detecting the factors that increase blood pressure in clinical settings and even in daily life will help control blood pressure, thereby preventing cardiovascular events.

The purpose of this study was to clarify the association between resting systolic blood pressure, diastolic blood pressure, mean arterial pressure, total muscle mass, total fat mass, appendicular muscle mass and fat mass, trunk muscle mass and fat mass in older adults in the community using BIA. We hypothesized that fat mass and muscle mass would influence on resting blood pressure.

Methods

Subjects

In this cross-sectional study, subjects were older adults (n = 100, male: 47, female: 53) visiting an outpatient internal medicine clinic and living independently in the community; their age was 65–85 years (74.9 ± 5.3). Subjects were excluded if 1) they had a pacemaker and/or 2) they had a systolic blood pressure of greater than 180 mmHg and/or diastolic blood pressure of greater than 100 mmHg (20). According to the medical records, 91 subjects had hypertension and had been using anti-hypertensive drugs, including Calcium antagonists, angiotensin II receptor antagonists, angiotensin converting enzyme inhibitors, α blockers, β blockers, αβ blockers, diuretics, renin inhibitors, angiotensin II receptor blocker/calcium channel blocker combination medication, angiotensin II receptor blocker/diuretics combination medication, and amlodipine besilate/atorvastatin calcium hydrate combination medication (Table 1). All subjects read and signed an informed consent form and this study was approved with the Ethics Committee of Seirei Christopher University.

Blood Pressure

Systolic and diastolic pressures were measured using an automatic hemodynamometer (HEM-907, Omron Health Care, Kyoto, Japan) after subjects sat for a 5-min rest period. Blood pressure was measured twice, and the average of the values was recorded as the systolic and diastolic pressures. Mean arterial pressure [(systolic blood pressure − diastolic blood pressure)/3 + diastolic blood pressure] were calculated. The measurements were recorded from the left arm at the height of the heart.

Muscle Mass and Fat Mass

BIA with an ioi 353S (Kobe Medi-care Co. Ltd) was used to estimate the total muscle mass, total fat mass, appendicular muscle mass and fat mass, and trunk muscle mass and fat mass. BIA is reliable and valid technique to estimate muscle mass and fat mass (15-16). Impedance values for seven segments (total muscle and fat mass, appendicular muscle and fat mass, trunk muscle and fat mass) were measured at 5 Hz, 50 Hz, and 250 Hz by the tetra-polar method using 8 touch electrodes. In the tetra-polar method, the current electrode (to send the electric current) and voltage electrode (to measure the impedance of human body) are separated and currents through each electrode are measured, thereby reducing contact resistance. Current and voltage electrodes are situated at both handle sensors and foot sensors of this device (eight electrodes in all). For the analysis, subjects stood upright barefoot on the device. Their body weight was automatically measured, and then we entered their name, age, sex, and height into the analyzer. Subjects grasped the handles, and their palms and soles of their feet were in contact with the current and voltage electrodes.

Statistical Analysis

A priori power analysis for correlation and linear multiple regression using G*power 3.1.9.2 (Correlation; Two tails, Correlation ρ H1 = 0.3, α = 0.05, Power = 0.8: Linear multiple regression; Effect size = 0.15, α = 0.05, Power = 0.8, three variables ) determined minimal sample size to be 82 and 77 subjects, respectively. Statistical evaluation was performed using JMP 11 software (SAS Institute Japan, Tokyo, Japan). The results were expressed as the mean ± SD and the significance was set at p < 0.05. Pearson correlations were used to evaluate the relationship between total muscle mass, total fat mass, appendicular muscle mass and fat mass, trunk muscle mass and fat mass and systolic blood pressure, diastolic blood pressure and mean arterial pressure. Moreover, associations between total muscle mass, total fat mass, appendicular muscle mass and fat mass, trunk muscle mass and fat mass with systolic blood pressure, diastolic blood pressure and mean arterial pressure were evaluated in a multiple regression analysis adjusted for the following multiple confounders: age and/ or sex.

Results

The characteristics of the study subjects are presented in Table 1. In the Pearson correlations, no correlation was observed between total muscle mass, left and right arm muscle mass, trunk muscle mass, left and right leg muscle mass and resting systolic blood pressure (p > 0.05; see Table 2), resting diastolic blood pressure (p > 0.05; see Table 2) and resting mean arterial pressure (p > 0.05; see Table 2). However, a positive correlation was observed between total fat mass, left and right arm fat mass, trunk fat mass, left and right leg fat mass and resting systolic blood pressure (p < 0.05; see Table 2), resting diastolic blood pressure (p < 0.05; see Table 2) and resting mean arterial pressure (p < 0.05; see Table 2). Age was not significantly associated with total fat mass, left and right arm fat mass, trunk fat mass, left and right leg fat mass (r = -0.07, r = -0.07, r = -0.06, r = -0.10, r = -0.08, r = -0.07, p > 0.05). In the multiple regression analysis adjusted for sex, systolic, diastolic and mean arterial pressure was independently predicted by total fat mass, left and right arm fat mass, trunk fat mass, left and right leg fat mass (p < 0.05; see Table 3).

Table 1 Characteristics of the study subjects (n = 100)

Values are expressed as means (SD) unless otherwise specified; BMI body mass index, SBP systolic blood pressure, DBP diastolic blood pressure; MAP mean arterial pressure, PP pulse pressure

Table 2 Correlation coefficient of blood pressure with muscle and fat mass (n = 100)

SBP systolic blood pressure, DBP diastolic blood pressure, MAP mean arterial pressure

Table 3 Multiple regression analysis of total fat mass, appendicular fat mass, and systolic, diastolic and mean arterial blood pressure after adjusting for sex (n= 100)

SBP systolic blood pressure, DBP diastolic blood pressure, MAP mean arterial pressure

Discussion

Although no correlation was observed between total muscle mass, appendicular muscle mass, trunk muscle mass and resting systolic, diastolic and mean arterial pressure, total fat mass, appendicular fat mass and trunk fat mass correlated positively with systolic, diastolic and mean arterial pressure when measured using BIA. Thus, we suggest that higher total fat mass as well as appendicular and trunk fat mass can be related to higher systolic, diastolic and mean arterial pressure in older adults in the community. These findings suggest that total, appendicular, and trunk fat mass, measured using BIA, could be useful to identify the factors that increase blood pressure, thereby helping in controlling blood pressure and preventing cardiovascular events.

With respect to the association between muscle mass and blood pressure, we did not consider whether the subjects had sarcopenia or not. Han et al defined sarcopenic obesity as an appendicular muscle mass/weight <1 standard deviation (SD) below the mean of a sample of healthy adults (aged 20–40 years) and a body mass index (BMI) of ≥25 kg/m2 (8). Hence specific muscle mass decreases may be necessary to influence blood pressure. In contrast, non-sarcopenic subjects (defined as decreasing fast twitch fibers) are more likely to have hypertension than sarcopenic subjects (21). Sadamoto et al reported that subjects with a higher ratio of fast twitch fibers had a higher blood pressure (22), and a lower ratio of slow twitch fibers raised resting blood pressure (23). The relationship between decreased fast twitch fibers and higher blood pressure may be complicated by other factors, but fat mass may be more modifiable than muscle mass with respect to blood pressure effects. Future studies may shed further light on these relationships as BIA measures the muscle mass of both fast and slow twitch fibers.

In this study, weak correlation was observed between fat mass and blood pressure. Blood pressure is determined

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by cardiac output by peripheral vascular resistance that may be confounded by multiple factors such as stroke volume, heart rate, vascular arterial wall elasticity, and blood viscosity etc. Therefore, the relationship between fat mass and blood pressure may be weak. BIA measurements are affected by meal or beverage consumed and exercise performed prior to the analysis (24-25). It has been also reported that consumption of food or fluid and exercise do not influence the measurement of body composition using BIA (26-27), but may cause fat mass to be under- or overestimated. Thus, lack of control for food or beverage consumption or exercise performed prior to BIA in our study may have led to the weak correlation between fat mass and blood pressure. Future studies are needed to further explore this phenomenon.

This study has some limitations. First, the sample size was small and the participants came from a small region. We measured fat mass, muscle mass, and blood pressure only among older Japanese adults, and our results may not be generalizable to people of other races and ages. Second, the cross-sectional design used in this study does not allow us to determine a causal relationship between fat mass and blood pressure. Therefore, the manner in which fat mass influences blood pressure remains unclear. Third, we did not modulate meal intake, hydration or exercise for subjects prior to BIA measurements. Therefore, we may have underestimated or overestimated the relationship between fat mass and blood pressure.

Funding: The sponsors had no role in the design and conduct of the study; in the collection, analysis, and interpretation of data; in the preparation of the manuscript; or in the review or approval of the manuscript.

Conflict of Interest: None to declare

Ethics Statement: All subjects read and signed an informed consent form and this study was approved with the Ethics Committee of Seirei Christopher University.

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