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MUSCLE LOSS IS ASSOCIATED WITH RISK OF ORTHOSTATIC HYPOTENSION IN OLDER MEN AND WOMEN

 

M.J. Benton, A.L. Silva-Smith, J.M. Spicher

University of Colorado Colorado Springs, Colorado Springs, CO, USA.
Corresponding author: Melissa J. Benton, PhD, RN, Helen & Arthur E. Johnson Beth-El College of Nursing & Health Sciences, University of Colorado Colorado Springs, 1420 Austin Bluffs Parkway, Colorado Springs, CO 80918, phone: +1-719-255-4140, email: mbenton@uccs.edu

J Frailty Aging 2020;in press
Published online December 23, 2020, http://dx.doi.org/10.14283/jfa.2020.72


Abstract

Background: Muscle provides a reservoir for water to maintain fluid volume and blood pressure, so older adults may be at risk for orthostatic hypotension due to muscle loss with age. Objectives: To evaluate the association between muscle loss with age and postural blood pressure. Design: Longitudinal comparison of overnight changes in hydration, postural blood pressure, and strength. Setting: Community field study. Participants: Sixty-nine men and women (76.0 ± 0.8 years) with low (Low) or normal (Normal) muscle based on the Lean Mass Index. Measurements: Body composition was measured with bioelectrical impedance analysis. Postural blood pressure was measured sequentially (lying, sitting, standing). Strength was measured with a handgrip dynamometer, Arm Curl test, and Chair Stand test. Results: On Day 1, Low had less hydration and a significant drop in postural systolic blood pressure compared to Normal (lying to standing: -11.06 ± 2.36 vs. +1.14 ± 2.20 mmHg, p < 0.001). Overnight, both groups lost significant total body water, while fluid volume was unchanged. On Day 2, both groups experienced significant drops in postural systolic blood pressure, although the drop in Low was more profound and significantly greater than Normal (lying to standing: -16.85 ± 2.50 vs. -3.89 ± 2.52 mmHg, p = 0.001). On both days, Normal compensated for postural changes with increases in postural diastolic blood pressure not observed in Low. Only Low experienced significant overnight decreases in all strength measures. Conclusions: In older men and women, muscle loss with age is accompanied by loss of hydration and less stable early morning postural systolic blood pressure that increase risk for orthostatic hypotension and can also increase risk for falls.

Key words: Orthostatic hypotension, postural blood pressure, lean mass index, hydration, strength.


 

Introduction

Orthostatic hypotension (OH) increases risk for cognitive impairment, disability, and mortality (1-4). Generally, among older adults, the prevalence of OH is greater than 20% (5). However, among those with a history of falls, prevalence can exceed 50% (6). Finally, frailty and OH are associated, and among frail older adults, prevalence can be as high as 66% (4).
The criteria for OH are a decrease in postural systolic blood pressure of ≥ 20 mmHg or a decrease in postural diastolic blood pressure of ≥ 10 mmHg within three minutes of standing (7). Older adults who meet this criteria have been found to be weaker, slower, and have diminished physical function compared to those without OH (8). Notably, these characteristics are diagnostic of frailty (9), and related to sarcopenia or muscle loss with age (10).
Muscle provides a reservoir for water to maintain fluid volume and blood pressure (11), so older adults with low muscle mass may be at risk for OH, especially when oral intake is diminished, such as the overnight period. We previously demonstrated that older women with less muscle mass also have less total body water and intramuscular water compared to those with greater muscle mass, and these differences are reflected by more unstable systolic and diastolic postural blood pressure that meets the criteria for OH (12). By comparison, men have greater muscle mass, experience less loss with age, and have a lower prevalence of OH (2, 13). Nonetheless, men are at risk for OH and to our knowledge, the association between muscle, hydration, and postural blood pressure has not yet been evaluated among older men. Therefore, the objective of this study was to answer the question: Is muscle loss with age associated with risk of OH in both older men and women? To do so, we compared overnight changes in hydration, postural blood pressure, and strength in community-dwelling older men and women.

 

Methods

Participants

Men and women were recruited who were age 65 years or older, non-smokers, able to stand up independently, and able to ambulate independently or with an assistive device such as a cane or walker. Because our variable of interest was hydration, participants were excluded if they were currently using diuretic medications, or had any condition that could influence hydration including fever, nausea, vomiting or diarrhea; hemodialysis or peritoneal dialysis; or had been hospitalized within the last month.

Ethics

The university Institutional Review Board approved the study and all participants gave written informed consent prior to enrollment.

Design

Participants completed two identical measurement sessions in their own homes on consecutive days. Baseline (Day 1) measurements were completed in a euhydrated state (normal food and fluid intake) between 10:00 am – 4:00 pm when older adults are most well hydrated based on their 24-hour fluid consumption patterns (14). The second (Day 2) measurement session was completed the next morning, within 30 minutes of waking with participants in a fasted state (no food or fluids for at least 8 hours). All data were collected by the same researcher.

Measurements

Lean mass and hydration were measured using multifrequency bioelectrical impedance analysis (Quadscan 4000, Bodystat, UK). Participants remained supine for at least 5 minutes prior to measurement. Bioelectrical impedance analysis is valid, reliable, and accurate despite hydration status (15).
Postural blood pressure was measured using a digital blood pressure monitor (Omron Healthcare, Japan). Three sequential measurements were taken (lying, sitting, standing). The initial (lying) measurement was taken after completion of lean mass and hydration measurement, so participants had been resting for at least 5 minutes. The second (sitting) measurement was taken 1 minute after sitting upright with both feet flat on the floor. The third (standing) measurement was taken 1 minute after standing erect.
Handgrip strength was measured to the nearest 0.1 kg using a digital grip dynamometer (Takei Scientific Instruments, Japan). Participants sat with the device in their dominant hand, their arm supported on a stable surface, their wrist in a neutral position, and their elbow at a 90° angle. They squeezed the device one time as hard as possible for 3 seconds. Handgrip strength has been validated using manual upper extremity muscle strength testing as the criterion measure (16), and reliability has been established in multiple studies with intra-class correlation coefficients exceeding 0.80 (17). Moreover, handgrip dynamometry has strong predictive validity for cognitive, physical, and functional decline in older adults (18).
Upper and lower body strength was measured using the Arm Curl and Chair Stand tests. For the Arm Curl, participants sat with a 5-lb (women) or 8-lb (men) dumbbell in their dominant hand. They repeatedly raised and lowered it through a full range of motion for 30 seconds. For the Chair Stand test, participants remained seated with both arms folded across their chest. They repeatedly stood up to a fully erect position and sat down again for 30 seconds. Criterion validity for the 30-second Arm Curl and Chair Stand tests has been determined using laboratory measurement of maximal upper and lower body strength (chest and leg press), and test-retest reliability has been well established with intra-class correlation coefficients exceeding 0.80 (19-21).

Grouping for Analysis

Participants were grouped by lean mass relative to height (kg/m2) using the Lean Mass Index (LMI). Low muscle mass (Low) was defined as women <15.0 kg/m2 and men <19.0 kg/m2, and normal muscle mass (Normal) was defined as women ≥15.0 kg/m2 and men ≥19.0 kg/m2, 22).

Statistical Analysis

Data were analyzed using SPSS version 25 (IBM, USA). Analysis of variance (ANOVA) was used to identify individual between-group differences, and repeated measures ANOVA was used to evaluate between and within-group differences over time (Day 1 vs. Day 2 measurements). An additional multivariate analysis using age as a covariate was conducted to assess the influence of age on postural blood pressure changes. Chi-square analysis was used to evaluate between-group differences in gender, and Spearman correlation analysis was used to evaluate the influence of gender on postural blood pressure changes. Significance was determined as p < 0.05 and data were reported as mean ± standard error with 95% confidence intervals. Effect sizes were calculated as eta squared (η2) and interpreted as small (≥ 0.01), medium (≥ 0.06), and large (≥ 0.14) effects. Sample size calculation determined that 64 participants were adequate for a two-group ANOVA with a significance level of 0.05, 80% power, and a medium effect size.

 

Results

Sixty-nine men (n = 37) and women (n = 32) completed the study. Overall, they were 76 ± 0.8 years of age with an average body mass index (BMI) of 26.0 ± 0.5 kg/m2 (Table 1). There were no differences between genders for age, BMI, or resting (lying) blood pressure, and correlation analysis identified no influence of gender on postural blood pressure changes, so men and women were combined for analysis based on LMI. In total, 34 participants met the criteria for Low muscle mass. Nineteen (55%) were males and 15 (45%) were females.

Table 1
Participant characteristics at baseline (Day 1)

Note: Data reported as Mean ± SE, [95% CI], (η2) = effect size (eta squared); Low = Low muscle group; Normal = Normal muscle group; M = Male; F = Female; *Between-group differences in co-morbidities calculated using Fisher’s Exact Test.

At baseline (Day 1), participants in the Low group were significantly older, with lower body mass and BMI. They also had less lean mass that was accompanied by significantly less total body water, fluid volume, and intramuscular water. Although resting systolic blood pressure did not differ between groups, those in the Low group had significantly lower resting diastolic blood pressure, as well as significantly lower systolic and diastolic blood pressure when repositioned to sitting and standing postures (Table 1). In addition, during postural changes the Low group experienced a significant decrease in systolic blood pressure compared to the Normal group, which remained relatively stable (lying to standing: -11.06 ± 2.36 vs. +1.14 ± 2.20 mmHg, p < 0.001, η2 = 0.18) (Figure 1A). At the same time, a difference in diastolic blood pressure was observed. Specifically, the Normal group compensated for postural changes by increasing diastolic blood pressure, while the Low group did not (lying to standing: +5.14 ± 1.38 mmHg vs. +0.56 ± 1.49 mmHg; p = 0.027, η2 = 0.07). When age was included as a covariate, between-group differences in postural systolic blood pressure were somewhat attenuated (adjusted p = 0.003), while between-group differences in postural diastolic blood pressure were no longer significant.

Figure 1
Postural blood pressure changes on Day 1 (A) when participants were normally hydrated and Day 2 (B) when participants had fasted overnight. On both days, participants with low muscle had significant drops in systolic BP (Day 1: -11.06 ± 2.36 mmHg, p < 0.001; Day 2: -16.85 ± 2.50 mmHg, p < 0.001) that were not observed in those with normal muscle (Day1: +1.14 ± 2.20 mmHg; Day 2: -3.89 ± 2.52 mmHg). In contrast, participants in the normal muscle group compensated for postural changes with increases in diastolic BP (Day 1: +5.14 ± 1.38 mmHg, p = 0.027; Day 2: +5.37 ± 1.17 mmHg, p = 0.009) that were not observed in the low muscle group (Day 1: +0.56 ± 1.49 mmHg; Day 2: -0.03 ± 1.67 mmHg)

 

Overnight, both groups lost significant but similar amounts of total body water (p < 0.001), although only the Normal group lost significant amounts of intramuscular water (p = 0.038). Fluid volume remained stable in both groups (Table 2, Figure 2). This change in hydration was manifested as significant (p < 0.001) overnight decreases in both body mass (Low: -0.85 ± 0.07 kg; Normal: -0.97 ± 0.26 kg) and lean mass (Low: -1.08 ± 0.13 kg; Normal: -1.09 ± 0.16 kg). However, there were no between-group differences in any of these overnight changes. By comparison, significant between and within-group differences were observed in postural blood pressure (Figure 1B). On Day 2, systolic blood pressure decreased significantly (p < 0.001) during postural changes from lying to standing in both groups. However, the decrease in the Low group was even more profound than on Day 1 and significantly greater than that observed in the Normal group (lying to standing: -16.85 ± 2.50 vs. -3.89 ± 2.52 mmHg, p = 0.001, η2 = 0.18). Furthermore, as was observed on Day 1, the Normal group again compensated for postural changes with an increase in diastolic blood pressure that was significantly greater than the Low group that again remained stable (lying to standing: +5.37 ± 1.17 vs. -0.03 ± 1.67 mmHg, p = 0.009, η2 = 0.17). When age was included as a covariate, between-group differences in postural blood pressure were again somewhat attenuated (systolic blood pressure: adjusted p = 0.004; diastolic blood pressure: adjusted p = 0.040). Overall, on Day 2, 44.1% (n = 15) of the Low group met the criteria for OH (decrease in postural systolic blood pressure of ≥ 20 mmHg or decrease in postural diastolic blood pressure of ≥ 10 mmHg) compared to only 8.6% (n = 3) of the Normal group (p = 0.001).

Table 2
Overnight changes in mass, hydration, and strength in participants with Low and Normal muscle

Note: Data reported as Mean ± SE, [95% CI], (η2) = effect size (eta squared); Low = Low muscle group; Normal = Normal muscle group

Significant overnight changes in strength that favored older adults in the Normal group also occurred. On Day 1, handgrip strength was similar between groups (Low: 24.5 ± 1.6 kg; Normal: 22.5 ± 1.6 kg) (Table 1). Overnight, a significant between-group difference was observed (Table 2). Participants in the Low group experienced a significant decrease in handgrip strength (-2.42 ± 0.48 kg; p = 0.001) that was not observed in the Normal group (-0.76 ± 0.77 kg). A similar pattern was also observed in lower body strength. For lower body strength measured as the Chair Stand test, both groups were initially similar (Low: 11.6 ± 0.8 repetitions; Normal: 10.6 ± 0.7 repetitions). Overnight, a significant decrease in lower body strength was observed in the Low group (-1.42 ± 0.41 repetitions, p = 0.001) that was not observed in the Normal group (-0.43 ± 0.22 repetitions), and that resulted in a statistically significant difference between groups (p = 0.034). Finally, Arm Curl scores were initially similar between groups (Low: 14.5 ± 0.5 repetitions; Normal: 14.8 ± 0.8 repetitions). Overnight, a significant decrease was observed in both groups (Low: -2.67 ± 0.40 repetitions; p < 0.001; Normal: -0.91 ± 0.37 repetitions, p = 0.017), although the decrease in the Low group was statistically greater than that observed in the Normal group (p = 0.002).

Figure 2
Overnight changes in hydration between participants with Low and Normal muscle. Both groups lost similar and significant amounts of total body waster (p < 0.001), while only the Normal group lost significant amounts of intramuscular water (p = 0.038). Fluid volume remained stable in both groups

 

Discussion

To our knowledge, this is the first study to evaluate postural blood pressure changes in men and women using muscle as the criterion for evaluation. Based on our findings, muscle loss with age is associated with risk for OH in both men and women. Although the average drop in postural systolic blood pressure of 17 mmHg that we observed in participants with low muscle mass was less than the ≥ 20 mmHg decrease needed to meet the definition of OH, the differences between older men and women with low compared to normal muscle mass were statistically significant with large effect sizes. Hence, we believe our findings reflect a clear association with muscle loss, especially as we found a statistically greater prevalence of participants that met the diagnostic criteria for OH among participants with low muscle mass (44.1%) compared to those with normal muscle mass (8.6%). Furthermore, we found no relationship with gender, indicating that both men and women are equally susceptible despite differences in absolute and relative lean mass.
Poor nutrition, which increases the risk for muscle loss and frailty in older adults, has previously been found to be associated with OH (23). This is consistent with the differences in body composition observed among our participants, in which those with low muscle were also observed to have significantly less fat and an overall lower BMI compared to those with normal muscle. Some previous research has demonstrated a negative association between BMI and OH, such that older adults with OH had lower BMI levels than those without OH (4, 24). However, average BMI values were in the overweight category and in one study, differences were not statistically significant (4). The association between OH and BMI is not clear. In other previous reports of normal (25) and overweight (2) older adults with and without OH, there were no differences based on BMI. Furthermore, in a comparison of OH prevalence among robust, pre-frail, and frail older adults, the prevalence of OH and participant age increased significantly with level of frailty, but there was no difference in BMI, which was in the overweight category for all participants (26). We believe the link between frailty and OH may lie in the influence of lean (muscle) mass. This is a gap in the literature and should be explored.
In addition to greater instability in postural blood pressure, participants with low muscle experienced greater overnight losses of strength compared to those with normal muscle. When loss of strength, especially in the lower body, is accompanied by severe early morning drops in postural blood pressure, this increases risk for falls in the early morning when between 30 to 50% of falls are reported to occur (27). Falls are of concern among older adults with OH, who have a greater than 50% higher risk of a first fall than older adults without OH (28). Treatment of OH is frequently driven by concerns regarding potential injuries due to falls. Unfortunately, first line treatment often focuses on medication reduction, including cardiovascular medications such as antihypertensives (29). This creates a burden for patients who must choose between the risks associated with OH and potential risks associated with discontinuance of medications. Cardiovascular medications optimize blood pressure control and reduce the risk of stroke, myocardial infarction, renal dysfunction, and complications of diabetes (30). Furthermore, abrupt discontinuation of blood pressure-lowering medications, can place patients at risk for an acute stroke or cardiovascular event (31). Non-pharmacological strategies for management of OH are available, but evidence indicates that they are minimally effective (32), and not generally acceptable to older adults with OH (33).
Increasing muscle mass may represent a novel strategy for OH that has not previously been considered. Although frailty is associated with OH (4), we can find no studies in which body composition has been included in the assessment of older adults with OH. Nonetheless, decreased fluid volume and deconditioning are recognized factors in the etiology of OH (34). Muscle, as a repository for body fluids, enhances fluid volume. As observed in our participants, those with greater muscle had significantly greater reserves of water. Furthermore, there was a non-selective loss of fluid overnight of approximately one liter that did not differ between those with low and normal muscle mass. This non-selective fluid loss is consistent with what we previously observed in older women (12). Our interpretation is that individuals with limited fluid reserves due to reduced amounts of muscle cannot compensate for fluids losses during periods of low intake. Hence, in our study, when muscle tissue apparently “donated” intramuscular water to maintain overall fluid volume during the overnight period, those with low muscle mass were seen to be at greater risk for unstable postural blood pressure and loss of strength compared to those with greater muscle mass and the fluid reserves that accompany it.
Resistance training may represent a feasible non-pharmacologic approach to blood pressure management in the context of OH. However, evidence is limited. We identified only one resistance training program for patients with OH by Zion and colleagues (35), and it did not successfully improve blood pressure. However, the duration was only 8 weeks and elastic bands were used for training. While elastic bands can provide adequate resistance to stimulate muscle hypertrophy, their use in research has been limited. Krause and colleagues reported use of elastic bands to increase muscle mass in healthy older adults, but the training program was 12 weeks long and all training was supervised (36). Unfortunately, Zion and colleagues did not measure body composition (35), but it seems likely that their shorter, unsupervised program was not sufficient to increase muscle mass and therefore had no effect on blood pressure. Evidently, more research is needed.
We recognize that the fact that we did not control for medications other than diuretics may be considered a study weakness. However, we also recognize that use of medications is increasing, especially use of multiple medications. In the United States, approximately 90% of older adults report use of at least one medication, while approximately 40% report polypharmacy (use of 5 or more medications) (37, 38). For the current study, we used a pragmatic approach with the intent of evaluating older adults under real-world conditions in their own homes, and those conditions include use of regularly prescribed medications. Furthermore, previous research demonstrates that medications do not have a significant influence on OH (39, 40). In older men and women no association has been found between OH and either the number or type of medications, including antihypertensives, diuretics, antipsychotics, antidepressants, and drugs for Parkinson’s disease (39, 40). Nonetheless, there are numerous other types of medications that may influence OH that have not been evaluated.
In conclusion, our findings support a role for muscle in maintaining stable postural blood pressure and decreasing risk for OH. Although more research is evidently needed, in these older adults, muscle loss with age was accompanied by loss of hydration and less stable early morning systolic blood pressure that may increase risk for falls. Resistance exercise to increase muscle mass may provide a novel therapeutic strategy that should be explored.

Acknowledgements: The authors thank Andrew Quinonez for assistance with graphic design to format the figures for publication.
Funding: No funding was received for this study.
Conflicts of Interest: All authors declare no conflict of interest.
 

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ACCURACY OF BODY MASS INDEX VERSUS LEAN MASS INDEX FOR PREDICTION OF SARCOPENIA IN OLDER WOMEN

 

M.J. BENTON, A.L. SILVA-SMITH

 

Helen & Arthur E. Johnson Beth-El College of Nursing & Health Sciences, University of Colorado Colorado Springs, Colorado Springs, CO.
Corresponding author: Melissa J. Benton, PhD, RN, FACSM, FGSA, Helen & Arthur E. Johnson Beth-El College of Nursing & Health Sciences, University of Colorado Colorado Springs, 1420 Austin Bluffs Parkway, Colorado Springs, CO  80918, phone: 719-255-4140, email: mbenton@uccs.edu

J Frailty Aging 2018;7(2):104-107
Published online January 8, 2018, http://dx.doi.org/10.14283/jfa.2018.1

 


Abstract

 We compared accuracy of body mass index (BMI) versus lean mass index (LMI) to predict sarcopenia in 58 community-dwelling women (74.1±0.9 years).  Lean mass was measured with multi-frequency bioelectrical impedance analysis, and strength was measured with Arm Curl test, Chair Stand test, and handgrip dynamometry.  Sarcopenia was defined as low LMI.  When categorized by BMI, normal women had less absolute lean mass (37.6±1.0 vs. 42.6±0.9 kg; P<0.001) and less relative lean mass (14.1±0.2 vs. 16.1±0.2 kg/m2; P<0.001) compared to overweight/obese women, but no differences in strength.  When categorized by LMI, normal women had more absolute lean mass (44.0±0.7 vs. 35.7±0.7 kg; P<0.001), more relative lean mass (16.2±0.2 vs. 13.8±0.2 kg/m2; P<0.001), and greater upper body strength (16.7±0.9 vs. 14.2±0.6 arm curls; P<0.05) compared to women with low LMI.  BMI failed to accurately predict low values of lean mass and strength.  For clinical assessment, calculation of LMI rather than BMI is appropriate.

Key words: Aging, sarcopenia, body mass index, lean mass index.


 

Introduction

Sarcopenia is characterized by loss of muscle with age (1).  Loss of muscle is typically balanced by gains in fat, so body weight and body mass index (BMI) remain stable (2).  Hence, sarcopenia may not be clinically evident until loss of function occurs and older adults are already at risk for disability and falls (3). Sarcopenia is also linked to frailty. All five frailty criteria proposed by Fried and colleagues (4) are either influenced by or have an influence on muscle.
BMI is widely used for clinical assessment of obesity, but fails to differentiate between lean and fat tissue, particularly in older women (5).  As an alternative, the lean mass index (LMI) has been proposed for clinical assessment of lean mass (6).  Calculated as lean mass relative to height (kg/m2), population level cut points have been identified for men and women ages 20-80 years (7).  Use of LMI normalizes lean mass between individuals by providing a relative measure of lean mass compared to height, just as use of BMI normalizes body weight by providing a relative measure of weight compared to height.
The LMI may be a feasible alternative to BMI that can be used to assess changes in muscle and risk for sarcopenia and frailty in aging adults.  This study compared the accuracy of BMI and LMI for prediction of sarcopenia.  Older women were selected as our population of interest because they have less muscle over the lifespan (8) and are at greater risk for developing sarcopenia.

 

Methods

The study was approved by the local Institutional Review Board. Healthy women aged 65 years and older were recruited from the community.  All were non-smokers, could stand up independently, and ambulated independently or with an assistive device.  Exclusion criteria were hospitalization within the past month or current fever, nausea, vomiting, or diarrhea.  Participants were tested in their homes, between 10:00 am and 4:00 pm, and verified they were eating and drinking normally before measurement.
Body composition was measured with multi-frequency bioelectrical impedance analysis (Bodystat Quadscan, Isle of Man, UK).  Participants lay supine for at least 5 minutes to equalize body fluids before testing.  Electrodes were placed on their right hand and foot using standardized positioning (9).  The manufacturer’s proprietary equations were used to calculate absolute (kg) lean mass.  BMI cut points were determined using World Health Organization recommendations for normal weight (≤ 24.9 kg/m2) and overweight (≥25.0 kg/m2).  For analysis, overweight and obese (≥30.0 kg/m2) were combined into one category.  LMI cut points for normal (≥ 14.9 kg/m2) and low (< 14.9 kg/m2) muscle were based on the range for women identified by Coin and colleagues (7).  Sarcopenia was defined as low relative (to height) lean mass.
Strength was measured with the 30-second Arm Curl and 30-second Chair Stand tests, using standardized procedures (10).  For the Arm Curl test, participants held a 5-lb (2.3 kg) dumbbell in their dominant hand and repeatedly raised and lowered it for 30 seconds.  Upper body strength was scored as the number of full curls completed.  For the Chair Stand test, participants crossed their arms over their chest and repeatedly stood up and sat down for 30 seconds. Lower body strength was scored as the number of full stands completed.
A subgroup (n = 30) completed handgrip strength measurement using a digital handgrip dynamometer (Smedley III, Takei Scientific Instruments, Niigata, Japan) designed to detect 0.1 kg differences in strength.  Grip width was adjusted to the second position, corresponding to 5.1 cm recommended for measurement of maximal grip strength (11).  Participants held the dynamometer in their dominant hand, with their arm on a stable surface, their wrist in a neutral position, and their elbow at 90 degrees flexion.  They squeezed the dynamometer one time with maximal force for 3 seconds.  Grip strength was scored as the greatest force (kg) exerted.

Statistical Analysis

Data were analyzed using SPSS version 24.0 and reported as mean and standard error (SE).  Significance was set at p < 0.05.  Participant characteristics were analyzed using descriptive statistics and between-group differences were compared using analysis of variance (ANOVA).  Four participants with BMI greater than two standard deviations above the mean were removed from final analysis.

 

Results

Sixty-two women (73.8 ± 0.9 years; BMI: 27.2 ± 0.7 kg/m2; LMI: 15.6 ± 0.3 kg/m2) completed testing.  Final data were available for 58 women (74.1 ± 0.9 years) after removal of the four outliers.  Overall, mean BMI was 26.1 ± 0.5 kg/m2 (overweight) and mean LMI was 15.2 ± 0.2 kg/m2 (normal).  Prevalence of normal BMI was 41% (n = 24) while prevalence of overweight/obese BMI was 59% (n = 34).  Prevalence of low LMI (41%) was identical to that of normal BMI, while prevalence of normal LMI (59%) was identical to that of overweight/obese BMI.
When categorized by BMI (Table 1) there was no difference in age, although normal BMI women had less (p ≤ 0.001) body mass, absolute lean mass, and relative lean mass compared to overweight/obese women (Figure 1a).  There were no differences in strength.

Table 1 Participant characteristics based on body mass index (BMI) and lean mass index (LMI). Sub-group comparisons were analyzed between normal and overweight/obese BMI and low and normal LMI

Table 1
Participant characteristics based on body mass index (BMI) and lean mass index (LMI). Sub-group comparisons were analyzed between normal and overweight/obese BMI and low and normal LMI

Data reported as Mean ± Standard Error; *Significant difference between sub-groups (p < 0.05); †Significant difference between sub-groups (p < 0.01); ‡Significant difference between sub-groups (p < 0.001)

 

Figure 1a Relationship between body mass index (BMI) and lean mass index (LMI). The dotted line represents the cut point between normal (≤ 24.9 kg/m2) and overweight/obese BMI (25.0 kg/m2). Women in the normal BMI category had significantly less relative lean mass than women in the overweight/obese BMI category

Figure 1a
Relationship between body mass index (BMI) and lean mass index (LMI). The dotted line represents the cut point between normal (≤ 24.9 kg/m2) and overweight/obese BMI (25.0 kg/m2). Women in the normal BMI category had significantly less relative lean mass than women in the overweight/obese BMI category

 

Figure 1b Relationship between lean mass index (LMI) and body mass index (BMI). The dotted line represents the cut point between normal (≥ 14.9 kg/m2) and low LMI. Women with normal lean mass were predominantly in the overweight/obese BMI category, while women with normal BMI predominantly had low lean mass

Figure 1b
Relationship between lean mass index (LMI) and body mass index (BMI). The dotted line represents the cut point between normal (≥ 14.9 kg/m2) and low LMI. Women with normal lean mass were predominantly in the overweight/obese BMI category, while women with normal BMI predominantly had low lean mass

When categorized by LMI (Table 1), normal women were younger (p < 0.05), had greater (p ≤ 0.001) BMI, absolute lean mass, and relative lean mass, and more (p < 0.05) upper body strength compared to women with low LMI (Figure 1b).

 

Discussion

To our knowledge, this is the first study to compare BMI versus LMI for accurate assessment of lean mass and risk for sarcopenia in older women.  Our principle finding was that women with normal, healthy BMI had relative lean mass similar to women with low LMI and met the criteria for sarcopenia.  Furthermore, use of BMI failed to capture loss of upper body strength that was apparent when women were categorized by LMI.
Clinical assessment of lean mass is important to capture changes that lead to sarcopenia and frailty, both of which increase risk for falls, hospitalization, and mortality in the elderly (12, 13).  If, as it has been proposed, sarcopenia is a biological precursor of frailty (14), then assessment of sarcopenia can provide early assessment of risk for frailty.  Although the European Working Group on Sarcopenia in Older People (EWGSOP) definition of sarcopenia includes loss of strength (15), loss of lean mass alone predicts mortality in healthy older adults (16) and clinical populations (6).  Hence, for clinical purposes, assessment of lean mass alone is a feasible strategy consistent with long-term risk.  Based on our findings, use of the LMI for assessment may also reflect early changes in strength before they are manifested by actual loss of function.
Although only loss of upper body strength was observed in our participants, this may be due to differences in deposition of muscle mass.  In older women, upper body skeletal muscle can be 20-30% less than lower body muscle (8), so the upper body may be more susceptible to early loss of strength.  This is supported by evidence that with age, lower initial lean mass is associated with greater declines in strength (17).
However, handgrip strength was not statistically different between participants with low LMI and normal LMI, possibly due to the smaller sample size for this measure.  The low LMI group did demonstrate reduced grip strength, but it did not achieve significance.  Alternately, grip strength has been positively associated with lower extremity function, measured by chair stands (18).  Consistent with this, we observed no significant differences in handgrip strength or lower body strength (measured by chair stands) between participants categorized by either BMI or LMI.
Another consideration is that muscle strength is lost at a faster rate than muscle mass (17). Average handgrip strength for our participants was below the 20-lb cut point for women identified by the EWGSOP (15). If handgrip strength was diminished generally, it is possible that differences based on either BMI or LMI would not be significant.  Additionally, there can be significant variation between handgrip instruments (19).  Although we standardized our procedure according to recommendations (11), use of the Smedley III digital dynamometer may have influenced findings.  However, we believed the more precise digital measurement was desirable compared to hydraulic devices that provide displays in 2-kg increments that must be interpreted by the tester.  Variance among devices may be a limitation of grip strength measurement not shared by other field tests such as the Arm Curl, where the set-weight hand-held dumbbell does not vary.  Although handgrip strength is a reliable measure of individual change over time as long as the same device is used (19, 20), as a criterion measure to define sarcopenia, it may be problematic.
Limitations include our relatively small sample size and use of women only.  However, we used previously validated LMI cut points (7) which strengthens our analysis.  Further research is needed to strengthen our current findings and extend them to men.

 

Conclusion

LMI is appropriate for clinical assessment of sarcopenia, and older women with normal BMI are at greater risk than overweight and obese women.  Clinicians can use this information to guide assessment and counselling prior to adverse outcomes such as falls.

 

Acknowledgements: We wish to thank Andrea M. Hutchins, PhD, RD, FAND for editorial assistance in the preparation of this manuscript.
Conflict of Interest: The authors have no conflict of interest to disclose

 

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