Research Paper Volume 14, Issue 18 pp 7517—7526
Gender difference in appendicular muscle strength: determinant of the quality of life in the older Taiwanese
- 1 School of Nursing, National Yang Ming Chiao Tung University, Taipei 112304, Taiwan
- 2 Department of Nursing, Tri-Service General Hospital Songshan Branch, Taipei 105309, Taiwan
- 3 School of Public Health, National Defense Medical Center, Taipei 114201, Taiwan
Received: May 20, 2022 Accepted: September 1, 2022 Published: September 19, 2022https://doi.org/10.18632/aging.204297
How to Cite
Copyright: © 2022 Chen et al. This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Background: The loss of skeletal muscle mass by aging determines the health status and the quality of life (QoL).
Objective: To examine the relationships between appendicular muscle strength and the QoL of elderly adults in gender difference.
Methods: This was a cross-sectional study, in which 690 subjects who participated in older adults health examination in the health management center of Tri-Service General Hospital from 2018 to 2021. A structured questionnaire was used to collect basic demographic data. The 12-Item Short Form Survey (SF-12) was used to evaluate the QoL of subjects. Their grip strength and gait speed were measured, and Dual-energy X-ray absorptiometry was used to measure muscle mass and other body composition data. Multivariate regression analysis was used to examine the relationships between upper and lower limb muscle strength and the QoL of older adults.
Results: In men, legs muscle mass percentage (LegsMM%) (β = 3.67; 95% CI: 0.64–6.69; p = 0.018) and gait speed (β = 6.09; 95% CI: 3.88–8.30; p < 0.001) were positively associated with physical component summary (PCS) scores, and gait speed (β = 4.63; 95% CI: 2.66–6.60; p < 0.001) was also related to an improvement mental component summary (MCS) scores. In women, arms muscle mass percentage (ArmsMM%) (β = 6.50; 95% CI: 2.34–10.66; p = 0.002) and grip strength (β = 10.54; 95% CI: 6.27–14.81; p < 0.001) had the greatest effect on improving PCS scores, whereas grip strength (β = 7.58; 95% CI 4.00–11.17; p < 0.001) was also found to help improve MCS scores.
Conclusions: Men should focus on lower limb training, whereas females should focus on upper limb training to effectively improve their QoL. Appropriate exercise interventions should be designed for different genders for the promotion of the healthy aging policy.
Aging is an important issue in almost all developed countries. The global population of elderly individuals aged ≥65 years is expected to increase 28.9% from 771 million to 994 million from 2022 to 2030 . Currently, public health policy majorly focused on the improvement of elderly quality of life (QoL) .
Aging plays a pivotal role in the modification of body composition. Generally, skeletal muscle mass decreases and fat increases in the older adults . In older adults, changes in body composition are often associated with insufficient exercise, malnutrition, disability, obesity, and cardiovascular diseases . Among these changes, skeletal muscles play an important role in physical function maintenance, muscle mass decreases by 3–8% every 10 years after coming to apex at 30 years old, and this process deteriorates after 50 years old [5–7]. However, physical exercise averts detrimental process caused by aging in maintenance of skeletal muscles or physical function [8, 9].
Gender determines the body composition; men have a higher ratio of muscle to fat, contrary to women [10, 11]. Age compounds gender-dependent body composition, plummeting muscle mass in men, a case in point . Compared to loss of muscle mass in men, decrease of muscle strength carries more weighing values in women [12–16].
QoL is a complex concept that includes individual objective factors, subjective factors, and sociocultural effects . The QoL of elderly individuals can be assessed based on physical functional status and ability to independently complete activities of daily living . In addition to worsening health due to chronic diseases that occur with age, decrease in physical function is also common in elderly individuals, thereby affecting health status and QoL . Therefore, the relationship between age-related changes (such as body composition) and QoL have been widely researched [20–22]. Among the changes that may lead to negative effects on QoL, more attention should be paid to reduction in muscle mass and strength and increase in fat mass.
Decreased muscle mass and strength are important indicators for sarcopenia and dynapenia. Sarcopenia refers to decreased muscle mass, function, and strength and has adverse effects on health in elderly individuals, thereby affecting QoL [13, 23, 24]. On the other hand, dynapenia refers to age-related loss in muscle strength and presents as decreased muscle strength. This limits the mobility of elderly individuals, affects their QoL, and increases the risk of death. In addition, dynapenia occurs earlier than sarcopenia and is an important prognostic indicator of disability in elderly individuals [24–27]. It can be seen that muscle mass, muscle strength, and physical function are associated with the health and QoL of elderly individuals. A study pointed out that upper limb muscle strength plays an important role in many activities of daily living and may affect individual independence and QoL . Handgrip strength (HGS) measurement is considered to be a good indicator of overall muscle strength and function in elderly individuals [29, 30]. However, other studies pointed out that lower limb muscle strength is associated with QoL as lower limb muscle strength is a factor that determines elderly individuals’ level of independence in terms of daily living . Although the correlation between muscle strength and QoL of elderly individuals has been extensively studied, there are gender differences in body composition with aging and a lack of studies that systematically examine the correlation between body composition and the QoL of elderly individuals. Therefore, this study aimed to examine the relationship between upper and lower limb muscle strength and the QoL of elderly individuals and determine the presence in gender difference.
Materials and Methods
This study used a cross-sectional research design, and elderly individuals who were undergoing health examinations at Tri-Service General Hospital were recruited between March 2018 and October 2021. All the participants did not consume alcohol for at least 48 h, perform vigorous exercise for at least 12 h, or fasting for at least 12 h prior to the examination. Structured questionnaires were used to obtain basic demographic data from the study subjects. A total of 690 elderly individuals (297 males and 393 females) were included in the study.
Body weight was measured by a standard scale as subjects wore light clothing and no shoes; barefoot standing height was measured by a wall-mounted stadiometer; and body mass index (BMI) was calculated using the following formula: weight in kilograms/(height in meters)2.
Body composition measurements
Dual-energy X-ray absorptiometry (DXA) was used for measuring body composition, including fat and muscle masses. The participants were dressed in cotton robes without metal attachments and were placed in a supine position in the center of the scanning field with their palms facing downwards; arms lying away from their body, either straight or at a slight angle; feet in a neutral position; and face facing upwards with their chin in a neutral position. The scan took approximately 180 s to complete, and the dose of radiation per individual was 0.01 mGy (1.0 mrad). Muscle and fat mass percentage were calculated as follows: muscle mass percentage (MM%) = muscle mass/weight, appendicular muscle mass percentage (AMM%) = appendicular muscle mass/weight, arms muscle mass percentage (ArmsMM%) = arms muscle mass/weight, legs muscle mass percentage (LegsMM%) = legs muscle mass/weight, trunk muscle mass percentage (TrunkMM%) = trunk muscle mass/weight, fat mass percentage (FM%) = fat mass/weight, appendicular fat mass percentage (AFM%) = appendicular fat mass/weight, arms fat mass percentage (ArmsFM%) = arms fat mass/weight, legs fat mass percentage (LegsFM%) = legs fat mass/weight, and trunk fat mass percentage (TrunkFM%) = trunk fat mass/weight. The fat-to-muscle ratio (FMR) was calculated as follows: fat mass/muscle mass.
Muscle strength measurements
In the present study, HGS was used as an indicator of upper limb muscle strength and was assessed by using a Hydraulic Hand Dynamometer (JAMAR Plus+). Evaluation of grip strength was complied with standard principles that mentioned in several papers [31–33]; participants were asked to stand and used only the dominant hand with the elbow joint at 90°C and shoulder joint at 180°C of flexion. Additionally, the wrist and trunk were at neutral positions. Participants had their HGS measured three times and the best of the three attempts was recorded.
In the present study, gait speed was used as an indicator of lower limb muscle strength, and a 15-feet (4.572 m) unobstructed flat ground was used for gait speed testing. Two end lines were taped on the ground; one at the beginning and one at the end of the 15-feet ground. Participants were asked to walk as quickly and safely as possible, and a stopwatch was used to record the time.
The 12-Item Short Form Health Survey (SF-12) was used to assess each participant’s QoL. It consists of 12 questions covering 8 health domains: physical functioning (PF), social functioning (SF), role-physical (RP), role-emotional (RE), mental health (ME), energy/vitality (VT), bodily pain (BP) and general health perception (GH) [31, 32]. The questions were combined, scored, and weighted to create the physical component summary (PCS) and mental component summary (MCS) scores ranging from 0 (lowest level of health) to 100 (highest level of health). The PCS scores comprised of PF, RP, BP, and GH domains, and the MCS scores comprised of SF, RE, ME, and VT domains.
Categorical and continuous variables were presented as a number (proportion) and mean ± standard deviation. We used Student t-test and χ2 test (with Fisher’s exact test when appropriate) to compare the demographic data, anthropometric data, body composition, SF-12 domains, HGS and gait speed between male and female participants. Linear regression was used to test the effects of each independent variable on each dependent variable. To determine the independent association of body composition and muscle strength, we used multiple regression analysis with adjustment for selected demographic and anthropometric variables. Statistical analyses were performed using SPSS 22.0 (SPSS Inc., Chicago, IL, USA) and R 3.5.2 (R Project for Statistical Computing, Vienna, Austria). A p-value of <0.05 was considered as the level of statistical significance.
The basic clinical characteristics of the study population are summarized in Table 1. The participants in this study were aged 72.21 ± 6.88 years (male: 72.97 ± 7.73, female: 71.64 ± 6.11, p = 0.012). Males had significantly higher BMI and higher components of body composition, such as muscle mass than those in females, whereas fat mass were significantly higher in females exception for trunk fat mass. Males had higher SF-12 scores than females. In addition, males had significantly higher HGS and gait speed than those in females.
Table 1. Basic demographic and clinical data.
|Total (n = 690)||Male (n = 297)||Female (n = 393)||p-value|
|Age||72.21 ± 6.88||72.97 ± 7.73||71.64 ± 6.11||0.012*|
|Unmarried/Widow/Divorced||147 (21.8%)||21 (7.3%)||126 (32.8%)||<0.001*|
|Married/Cohabitant||526 (78.2%)||268 (92.7%)||258 (67.2%)|
|None||585 (86.9%)||207 (71.6%)||378 (98.4%)||<0.001*|
|Current/Former||88 (13.1%)||82 (28.4%)||6 (1.6%)|
|None||648 (96.3%)||267 (92.4%)||381 (99.2%)||<0.001*|
|>20 g per day||25 (3.7%)||22 (7.6%)||3 (0.8%)|
|Height||158.98 ± 7.97||165.33 ± 6.02||154.18 ± 5.53||<0.001*|
|Weight||61.71 ± 10.97||68.33 ± 9.91||56.70 ± 8.88||<0.001*|
|Body Mass Index||24.28 ± 3.50||24.95 ± 3.08||23.78 ± 3.71||<0.001*|
|Muscle mass||39.64 ± 7.96||47.31 ± 5.24||33.85 ± 3.52||<0.001*|
|Appendicular muscle mass||16.60 ± 3.90||20.32 ± 2.65||13.78 ± 1.73||<0.001*|
|Arms muscle mass||4.25 ± 1.16||5.35 ± 0.81||3.41 ± 0.52||<0.001*|
|Legs muscle mass||12.35 ± 2.79||14.97 ± 1.96||10.37 ± 1.30||<0.001*|
|Trunk muscle mass||19.44 ± 3.77||22.91 ± 2.64||16.81 ± 1.91||<0.001*|
|Fat mass||19.44 ± 6.25||18.10 ± 5.89||20.46 ± 6.33||<0.001*|
|Appendicular fat mass||7.14 ± 2.65||5.91 ± 1.97||8.08 ± 2.72||<0.001*|
|Arms fat mass||1.78 ± 0.74||1.46 ± 0.55||2.02 ± 0.77||<0.001*|
|Legs fat mass||5.36 ± 2.04||4.45 ± 1.50||6.06 ± 2.12||<0.001*|
|Trunk fat mass||11.49 ± 3.88||11.44 ± 4.00||11.53 ± 3.79||0.765|
|Fat-to-muscle ratio||0.51 ± 0.18||0.38 ± 0.11||0.60 ± 0.17||<0.001*|
|Physical component summary scores||68.26 ± 22.59||73.33 ± 20.05||64.45 ± 23.65||<0.001*|
|Physical functioning||77.00 ± 28.87||84.08 ± 24.06||71.69 ± 30.99||<0.001*|
|Role-physical||68.92 ± 43.29||75.61 ± 40.20||63.90 ± 44.87||<0.001*|
|Bodily pain||87.76 ± 20.41||91.00 ± 16.83||85.32 ± 22.44||<0.001*|
|General health||39.35 ± 24.05||42.65 ± 24.03||36.88 ± 23.80||0.002*|
|Mental component summary scores||78.32 ± 19.49||81.24 ± 18.23||76.13 ± 20.13||0.001*|
|Vitality||68.04 ± 28.01||70.66 ± 27.44||66.08 ± 28.31||0.036*|
|Social functioning||86.20 ± 22.20||87.80 ± 22.16||85.00 ± 22.19||0.105|
|Role-emotional||77.23 ± 37.03||82.01 ± 33.15||73.64 ± 39.36||0.004*|
|Mental health||81.82 ± 19.18||84.50 ± 16.58||79.82 ± 20.72||0.002*|
|Handgrip strength||27.72 ± 10.09||35.89 ± 8.64||21.55 ± 5.82||<0.001*|
|Gait speed||1.10 ± 0.33||1.13 ± 0.35||1.07 ± 0.32||0.024*|
|*p < 0.05.|
Linear regression model of demographic and anthropometric data with QoL
In Table 2, simple linear regression analysis identified the relevant variables associated with the SF-12 scores. PCS scores were negatively associated with females (p < 0.001) and age (p = 0.010), and positively associated with current or former smoking (p = 0.011). MCS scores were negatively associated with females (p = 0.001) and BMI (p = 0.019) and positively associated with married/cohabitant marital status (p = 0.015).
Table 2. Linear regression model of demographic and anthropometric data with QoL.
|β (95% CI)||p-value||β (95% CI)||p-value|
|>20g per day||4.12||(−4.91~13.15)||6.45||(−1.33~14.23)|
|*p < 0.05; Abbreviations: CI: Confidence interval; PCS: Physical component summary scores; MCS: Mental component summary scores.|
Linear regression model of body compositions and muscle strength with QoL
The body composition and muscle strength factors associated with the SF-12 PCS and MCS scores are shown in Table 3. PCS scores, after adjusting for sex, age, height, and smoking status, were positively associated with muscle mass percentage and muscle strength, exception for TrunkMM% (p = 0.206), but negatively associated with fat mass and FMR. MCS scores, after adjusting for sex, height, and marital status, were positively associated with muscle mass and muscle strength, exception for ArmsMM% (p = 0.065) and TrunkMM% (p = 0.170), but negatively associated with fat mass and FMR, exception for AFM% (p = 0.052) and LegsFM% (p = 0.216).
Table 3. The correlation of body compositions and muscle strength with QoL by linear regression model.
|Standardized β (95% CI)||p-value||Standardized Adj-β (95% CI)a||p-value||Standardized β (95% CI)||p-value||Standardized Adj-β (95% CI)b||p-value|
|MM%||4.68 (2.98~6.38)||<0.001*||2.77 (0.69~4.85)||0.009*||2.96 (1.48~4.44)||<0.001*||2.14 (0.33~3.95)||0.021*|
|AMM%||5.97 (4.29~7.65)||<0.001*||4.65 (2.36~6.94)||<0.001*||3.59 (2.12~5.06)||<0.001*||3.21 (1.23~5.18)||0.002*|
|ArmsMM%||5.41 (3.73~7.09)||<0.001*||3.58 (1.03~6.13)||0.006*||2.95 (1.47~4.42)||<0.001*||2.05 (−0.12~4.23)||0.065|
|LegsMM%||5.86 (4.18~7.54)||<0.001*||4.40 (2.27~6.52)||<0.001*||3.66 (2.19~5.13)||<0.001*||3.18 (1.34~5.01)||0.001*|
|TrunkMM%||3.20 (1.49~4.92)||<0.001*||1.25 (−0.68~3.18)||0.206||2.15 (0.66~3.63)||0.005*||1.19 (−0.51~2.88)||0.170|
|FM%||−5.04 (−6.73~−3.34)||<0.001*||−3.19 (−5.30~−1.08)||0.003*||−2.98 (−4.46~−1.50)||<0.001*||−2.16 (−4.02~−0.31)||0.022*|
|AFM%||−5.44 (−7.15~−3.74)||<0.001*||−3.93 (−6.36~−1.51)||0.002*||−2.95 (−4.45~−1.46)||<0.001*||−2.12 (−4.25~0.02)||0.052|
|ArmsFM%||−5.24 (−6.95~−3.53)||<0.001*||−3.40 (−5.65~−1.16)||0.003*||−3.51 (−4.99~−2.02)||<0.001*||−3.11 (−5.08~−1.15)||0.002*|
|LegsFM%||−5.09 (−6.81~−3.38)||<0.001*||−3.29 (−5.61~−0.97)||0.006*||−2.53 (−4.03~−1.02)||0.001*||−1.29 (−3.34~0.75)||0.216|
|TrunkFM%||−3.77 (−5.49~−2.05)||<0.001*||−2.10 (−3.94~−0.26)||0.026*||−2.47 (−3.96~−0.98)||0.001*||−1.70 (−3.31~−0.08)||0.040*|
|FMR||−5.53 (−0.21~−3.85)||<0.001*||−4.03 (−6.13 ~ 1.93)||<0.001*||−3.40 (−4.87~−1.93)||<0.001*||−2.92 (−4.76~−1.08)||0.002*|
|Handgrip strength||6.27 (4.62~7.92)||<0.001*||5.33 (2.75~7.91)||<0.001*||3.17 (1.71~4.63)||<0.001*||3.10 (0.95~5.25)||0.005*|
|Gait speed||7.14 (5.52~8.76)||<0.001*||6.56 (4.85~8.27)||<0.001*||4.95 (3.52~6.38)||<0.001*||4.87 (3.43~6.31)||<0.001*|
|aAdjusted for sex, age, height, and smoking status. bAdjusted for sex, height, and marital status. *p < 0.05. Abbreviations: MM%: muscle mass percentage; AMM%: appendicular muscle mass percentage; ArmsMM%: arms muscle mass percentage; LegsMM%: legs muscle mass percentage; TrunkMM%: trunk muscle mass percentage; FM%: fat mass percentage; AFM%: appendicular fat mass percentage: ArmsFM%: arms fat mass percentage; LegsFM%: legs fat mass percentage; TrunkFM%: trunk fat mass percentage; FMR: fat-to-muscle ratio.|
Linear regression model of body compositions and muscle strength with QoL stratified by gender
In Table 4, we checked the factors associated with the SF-12 scores stratified by gender. After adjusting for age, height, and smoking status in males, PCS scores were positively associated with AMM% (β = 3.37; 95% confidence interval (CI) 0.27~6.47; p = 0.034), LegsMM% (β = 3.67; 95% CI 0.64~6.69; p = 0.018) and gait speed (β = 6.09; 95% CI 3.88~8.30; p < 0.001) and negatively associated with ArmsFM% (β = −5.00; 95% CI −9.27~−0.73; p = 0.023) and FMR (β = −4.31; 95% CI −8.08~−0.54; p = 0.026). In females, the results were similar to the overall results shown in Table 3, except that TrunkFM% (p = 0.090) had no significant association with PCS scores, and the strongest predictor of PCS was HGS (β = 10.54; 95% CI 6.27~14.81; p < 0.001). In addition, the interaction between gender and ArmsMM% as well as HGS was statistically significantly different (p-interaction = 0.032, 0.001 respectively). Briefly, the factors of PCS are gender dependent; gait speed plays a more weighing role in male while handgrip strength, in female.
Table 4. The correlation of body compositions and muscle strength with QoL stratified by gender in linear regression model.
|Standardized Adj-β (95% CI)a||Standardized Adj-β (95% CI)a||Standardized Adj-β (95% CI)b||Standardized Adj-β (95% CI)b|
|MM%||2.09 (−0.82~5.01)||3.35 (0.42~6.27)*||0.428||1.49 (−1.13~4.12)||2.64 (0.15~5.13)*||0.547|
|AMM%||3.37 (0.27~6.47)*||5.75 (2.45~9.05)*||0.203||2.32 (−0.45~5.08)||4.05 (1.26~6.83)*||0.362|
|ArmsMM%||1.75 (−1.31~4.82)||6.5 (2.34~10.66)*||0.032*||1.48 (−1.24~4.19)||3.2 (−0.34~6.74)||0.387|
|LegsMM%||3.67 (0.64~6.69)*||4.87 (1.92~7.81)*||0.459||2.43 (−0.29~5.15)||3.72 (1.24~6.21)*||0.481|
|TrunkMM%||0.58 (−2.12~3.27)||1.87 (−0.85~4.60)||0.458||0.49 (−1.97~2.94)||1.7 (−0.63~4.04)||0.524|
|FM%||−2.78 (−5.90~0.34)||−3.51 (−6.36~−0.65)*||0.675||−1.11 (−3.96~1.74)||−2.80 (−5.25~−0.36)*||0.394|
|AFM%||−4.48 (−9.02~0.06)||−3.88 (−6.84~−0.92)*||0.981||−1.88 (−6.01~2.24)||−2.19 (−4.74~0.36)||0.915|
|ArmsFM%||−5.00 (−9.27~−0.73)*||−3.01 (−5.73~−0.29)*||0.541||−2.08 (−5.99~1.83)||−3.40 (−5.72~−1.08)*||0.623|
|LegsFM%||−3.75 (−8.32~0.81)||−3.27 (−6.07~−0.47)*||0.984||−1.59 (−5.74~2.55)||−1.21 (−3.62~1.20)||0.883|
|TrunkFM%||−1.89 (−4.43~0.65)||−2.27 (−4.89~0.34)||0.803||−0.74 (−3.06~1.59)||−2.42 (−4.65~−0.19)*||0.320|
|FMR||−4.31 (−8.08~−0.54)*||3.95 (−6.55~−1.36)*||0.446||−2.56 (−5.98~0.86)||−3.03 (−5.25~−0.80)*||0.319|
|Handgrip strength||2.07 (−1.00~5.14)||10.54 (6.27~14.81)*||0.001*||0.42 (−2.18~3.02)||7.58 (4.00~11.17)*||0.001*|
|Gait speed||2.09 (−0.82~5.01)||7.1 (4.55~9.66)*||0.462||4.63 (2.66~6.60)*||5.14 (3.06~7.21)*||0.693|
|aAdjusted for age, height, and smoking status. bAdjusted for height and marital status. *p < 0.05. Abbreviations: MM%: muscle mass percentage; AMM%: appendicular muscle mass percentage; ArmsMM%: arms muscle mass percentage; LegsMM%: legs muscle mass percentage; TrunkMM%: trunk muscle mass percentage; FM%: fat mass percentage; AFM%: appendicular fat mass percentage; ArmsFM%: arms fat mass percentage; LegsFM%: legs fat mass percentage; TrunkFM%: trunk fat mass percentage; FMR: fat-to-muscle ratio.|
After adjusting for height and marital status, in males, MCS scores were merely positively associated with gait speed (β = 4.63; 95% CI 2.66~6.60; p < 0.001). In females, the results were similar to the overall results shown in Table 3, and the strongest predictor of MCS was HGS (β = 7.58; 95% CI 4.00~11.17; p < 0.001), gait speed (β = 5.14; 95% CI 3.06~7.21; p < 0.001) came to the next. In addition, the interaction between gender and HGS was statistically significantly different (p-interaction = 0.001). Briefly, the factors of MCS are gender dependent; gait speed plays a more weighing role in male while handgrip strength, in female.
This study demonstrated a gender difference in SF-12, body composition, and muscle strength in the elderly Taiwanese population. Gait speed plays a more weighing role in male while handgrip strength, in female. This implied that the muscle mass and strength of the lower limbs in men significantly affect their QoL. However, in women, ArmsMM% and HGS have the greatest impact on the improvement of SF-12 PCS, and HGS is also helpful for improving MCS, showing that the muscle mass and strength of upper limbs of female play a key role in improving the QoL.
Previous research have reported that lower muscle mass and higher fat mass are key factors to affect mobility and cause disability, thereby affecting QoL, consistent with our results [3, 19, 20, 34–36]. Furthermore, muscle strength, yet rapidly decline with aging and gender dependence, carries more contribution than muscle mass to QoL . Another intriguing discovery is we found that PCS scores were positively associated with current or former smoking. This is probably because Taiwan’s law, Tobacco Hazards Prevention Act, has been stipulated that smoking is prohibited in all workplaces and public places since 2009, and it is necessary to move to outdoor designated smoking areas, which may increase the activity of smoking groups, thereby improving PCS scores. Reduction in muscle strength and mass will worsen QoL. Recent studies suggest that intervention of training program in high resistance and nutritional consultation based on individual needs are beneficial [20, 30]. In female, compared to gait speed, the handgrip strength shows higher β value in QoL, while gait speed, in male, implicating a differential intervention policy in promotion of QoL by gender difference. Therefore, our discovery may shed light on gender-dependent emphasis to precisely enhance QoL. Although we found gender differences in upper and lower limb muscle strength linking to QoL, further research is needed to elucidate mechanism behind biological significance.
Some studies propose no association between muscle mass and QoL, but we disclose the existence of association between them [38, 39]. Furthermore, the effect of muscle strength on QoL is more important than that of muscle mass, consistence with previous research, that is, muscle strength overwhelming muscle mass in determinants of QoL [39, 40]. If proper training in muscle strength is intervened, it would be conducive to QoL in older adults [9, 41].
This is a cross-sectional study, displaying the association between the body composition, muscle strength of upper and lower limbs and QoL. However, further cohort research is needed to elucidate cause and effect. The subjects of this study came from health examinations and they may have higher healthy literacy and cases source is needed to take into account in future research. Hence, these results may be applicable only to older adults who live in community-dwelling with relatively healthier status.
An increase in muscle percentage and strength in elderly individuals will improve physical and psychological QoL, whereas an increase in fat percentage will decrease QoL. As there are gender differences in upper and lower limb muscle strength, men should focus on lower limb training, whereas women should focus on upper limb training to effectively improve QoL. Therefore, gender dependent exercise interventions should be considered in aging.
Data curation, Mei-Jung Chen and Pi-Shao Ko; Formal analysis, Meng-Chang Lee and Pi-Shao Ko; Supervision, Sui-Lung Su; Validation, Shu Yu; Writing – original draft, Mei-Jung Chen; Writing – review and editing, Sui-Lung Su and Shu Yu.
Conflicts of Interest
The authors declare no conflicts of interest related to this study.
Ethical Statement and Consent
The study was reviewed and approved by the institutional ethics committee of Tri-Service General Hospital (TSGH-2-102-05-028). After providing an explanation regarding the study objectives, written informed consent was obtained from all participants. All clinical and biological samples were collected after obtaining patient consent.
This study was supported by grants from Tri-Service General Hospital (TSGH-E-109254 and TSGH-E-110258), Ministry of Science and Technology (MOST110-2314-B-016-008), and Medical Affairs Bureau, Ministry of National Defense (MND-MAB-110-041 and MND-MAB-110-042).
- 1. United Nations. Department of Economic and Social Affairs Population Division. World Population Prospects 2022. 2022.
- 2. Panda PK. Sustainable Development Plan for Aging World Population: Challenges and Way Forward. Research Anthology on Supporting Healthy Aging in a Digital Society. 2018; 26–36. https://doi.org/10.4018/978-1-6684-5295-0.ch007
- 3. Zamboni M, Mazzali G, Fantin F, Rossi A, Di Francesco V. Sarcopenic obesity: a new category of obesity in the elderly. Nutr Metab Cardiovasc Dis. 2008; 18:388–95. https://doi.org/10.1016/j.numecd.2007.10.002 [PubMed]
- 4. Sonn U, Rothenberg E, Steen B. Dietary intake and functional ability between 70 and 76 years of age. Aging (Milano). 1998; 10:324–31. https://doi.org/10.1007/BF03339795 [PubMed]
3rd, Khosla S, Crowson CS, O'Connor MK, O'Fallon WM, Riggs BL. Epidemiology of sarcopenia. J Am Geriatr Soc. 2000; 48:625–30. [PubMed]
- 6. Lexell J, Taylor CC, Sjöström M. What is the cause of the ageing atrophy? Total number, size and proportion of different fiber types studied in whole vastus lateralis muscle from 15- to 83-year-old men. J Neurol Sci. 1988; 84:275–94. https://doi.org/10.1016/0022-510x(88)90132-3 [PubMed]
- 7. Starling RD, Ades PA, Poehlman ET. Physical activity, protein intake, and appendicular skeletal muscle mass in older men. Am J Clin Nutr. 1999; 70:91–6. https://doi.org/10.1093/ajcn/70.1.91 [PubMed]
- 8. Taaffe DR. Sarcopenia--exercise as a treatment strategy. Aust Fam Physician. 2006; 35:130–4. [PubMed]
- 9. Zampieri S, Pietrangelo L, Loefler S, Fruhmann H, Vogelauer M, Burggraf S, Pond A, Grim-Stieger M, Cvecka J, Sedliak M, Tirpáková V, Mayr W, Sarabon N, et al. Lifelong physical exercise delays age-associated skeletal muscle decline. J Gerontol A Biol Sci Med Sci. 2015; 70:163–73. https://doi.org/10.1093/gerona/glu006 [PubMed]
- 10. Schorr M, Dichtel LE, Gerweck AV, Valera RD, Torriani M, Miller KK, Bredella MA. Sex differences in body composition and association with cardiometabolic risk. Biol Sex Differ. 2018; 9:28. https://doi.org/10.1186/s13293-018-0189-3 [PubMed]
- 11. Bredella MA. Sex Differences in Body Composition. Adv Exp Med Biol. 2017; 1043:9–27. https://doi.org/10.1007/978-3-319-70178-3_2 [PubMed]
- 12. Doherty TJ. The influence of aging and sex on skeletal muscle mass and strength. Curr Opin Clin Nutr Metab Care. 2001; 4:503–8. https://doi.org/10.1097/00075197-200111000-00007 [PubMed]
- 13. Hida T, Harada A, Imagama S, Ishiguro N. Managing sarcopenia and its related-fractures to improve quality of life in geriatric populations. Aging Dis. 2013; 5:226–37. https://doi.org/10.14336/AD.2014.0500226 [PubMed]
- 14. Evans WJ. Skeletal muscle loss: cachexia, sarcopenia, and inactivity. Am J Clin Nutr. 2010; 91:1123S–7. https://doi.org/10.3945/ajcn.2010.28608A [PubMed]
- 15. Cruz-Jentoft AJ, Landi F. Sarcopenia. Clin Med (Lond). 2014; 14:183–6. https://doi.org/10.7861/clinmedicine.14-2-183 [PubMed]
- 16. Carlsson M, Gustafson Y, Eriksson S, Håglin L. Body composition in Swedish old people aged 65-99 years, living in residential care facilities. Arch Gerontol Geriatr. 2009; 49:98–107. https://doi.org/10.1016/j.archger.2008.05.012 [PubMed]
- 17. Walker A. A European perspective on quality of life in old age. Eur J Ageing. 2005; 2:2–12. https://doi.org/10.1007/s10433-005-0500-0 [PubMed]
- 18. Tajvar M, Arab M, Montazeri A. Determinants of health-related quality of life in elderly in Tehran, Iran. BMC Public Health. 2008; 8:323. https://doi.org/10.1186/1471-2458-8-323 [PubMed]
- 19. Sun W, Aodeng S, Tanimoto Y, Watanabe M, Han J, Wang B, Yu L, Kono K. Quality of life (QOL) of the community-dwelling elderly and associated factors: a population-based study in urban areas of China. Arch Gerontol Geriatr. 2015; 60:311–6. https://doi.org/10.1016/j.archger.2014.12.002 [PubMed]
- 20. Trombetti A, Reid KF, Hars M, Herrmann FR, Pasha E, Phillips EM, Fielding RA. Age-associated declines in muscle mass, strength, power, and physical performance: impact on fear of falling and quality of life. Osteoporos Int. 2016; 27:463–71. https://doi.org/10.1007/s00198-015-3236-5 [PubMed]
- 21. Jyväkorpi SK, Urtamo A, Kivimäki M, Salomaa V, Strandberg TE. Association of midlife body composition with old-age health-related quality of life, mortality, and reaching 90 years of age: a 32-year follow-up of a male cohort. Am J Clin Nutr. 2020; 112:1287–94. https://doi.org/10.1093/ajcn/nqaa230 [PubMed]
- 22. Mikkola TM, Kautiainen H, von Bonsdorff MB, Salonen MK, Wasenius N, Kajantie E, Eriksson JG. Body composition and changes in health-related quality of life in older age: a 10-year follow-up of the Helsinki Birth Cohort Study. Qual Life Res. 2020; 29:2039–50. https://doi.org/10.1007/s11136-020-02453-1 [PubMed]
- 23. Go SW, Cha YH, Lee JA, Park HS. Association between Sarcopenia, Bone Density, and Health-Related Quality of Life in Korean Men. Korean J Fam Med. 2013; 34:281–8. https://doi.org/10.4082/kjfm.2013.34.4.281 [PubMed]
- 24. Silva Neto LS, Karnikowski MG, Osório NB, Pereira LC, Mendes MB, Galato D, Matheus LB, Matheus JP. Association between sarcopenia and quality of life in quilombola elderly in Brazil. Int J Gen Med. 2016; 9:89–97. https://doi.org/10.2147/IJGM.S92404 [PubMed]
- 25. Clark BC, Manini TM. What is dynapenia? Nutrition. 2012; 28:495–503. https://doi.org/10.1016/j.nut.2011.12.002 [PubMed]
- 26. Barbat-Artigas S, Dupontgand S, Fex A, Karelis AD, Aubertin-Leheudre M. Relationship between dynapenia and cardiorespiratory functions in healthy postmenopausal women: novel clinical criteria. Menopause. 2011; 18:400–5. https://doi.org/10.1097/gme.0b013e3181f7a596 [PubMed]
- 27. Pérez-López FR. Dynapenia and Sarcopenia During Female Midlife. In: Cano A. (eds), Menopause: A Comprehensive Approach. Springer: Cham. 2017; 317–31. https://doi.org/10.1007/978-3-319-59318-0_20
- 28. Özcan Kahraman B, Özsoy İ, Acar S, Özpelit E, Akdeniz B, Sevinç C, Savcı S. Pulmoner arteriyel hipertansiyonlu bireylerde hastalık şiddetinin üst ekstremite kas kuvveti, egzersiz kapasitesi ve günlük yaşam aktivitelerine etkisi [Effect of disease severity on upper extremity muscle strength, exercise capacity, and activities of daily living in individuals with pulmonary arterial hypertension]. Turk Kardiyol Dern Ars. 2017; 45:434–40. https://doi.org/10.5543/tkda.2017.24557 [PubMed]
- 29. Ling CH, Taekema D, de Craen AJ, Gussekloo J, Westendorp RG, Maier AB. Handgrip strength and mortality in the oldest old population: the Leiden 85-plus study. CMAJ. 2010; 182:429–35. https://doi.org/10.1503/cmaj.091278 [PubMed]
- 30. Marques LP, Confortin SC, Ono LM, Barbosa AR, d'Orsi E. Quality of life associated with handgrip strength and sarcopenia: EpiFloripa Aging Study. Arch Gerontol Geriatr. 2019; 81:234–9. https://doi.org/10.1016/j.archger.2018.12.015 [PubMed]
- 31. Keramidaki K, Tsagari A, Hiona M, Risvas G. Osteosarcopenic obesity, the coexistence of osteoporosis, sarcopenia and obesity and consequences in the quality of life in older adults ≥65 years-old in Greece. J Frailty Sarcopenia Falls. 2019; 4:91–101. https://doi.org/10.22540/JFSF-04-091 [PubMed]
- 32. Yoo JI, Choi H, Ha YC. Mean Hand Grip Strength and Cut-off Value for Sarcopenia in Korean Adults Using KNHANES VI. J Korean Med Sci. 2017; 32:868–72. https://doi.org/10.3346/jkms.2017.32.5.868 [PubMed]
- 33. Fırat Ozer F, Akın S, Soysal T, Gokcekuyu BM, Erturk Zararsız G. Relationship Between Dysphagia and Sarcopenia with Comprehensive Geriatric Evaluation. Dysphagia. 2021; 36:140–6. https://doi.org/10.1007/s00455-020-10120-3 [PubMed]
- 34. Kim S, Leng XI, Kritchevsky SB. Body Composition and Physical Function in Older Adults with Various Comorbidities. Innov Aging. 2017; 1:igx008. https://doi.org/10.1093/geroni/igx008 [PubMed]
- 35. Toselli S, Campa F, Spiga F, Grigoletto A, Simonelli I, Gualdi-Russo E. The association between body composition and quality of life among elderly Italians. Endocrine. 2020; 68:279–86. https://doi.org/10.1007/s12020-019-02174-7 [PubMed]
- 36. Bea JW, Going SB, Wertheim BC, Bassford TL, LaCroix AZ, Wright NC, Nicholas JS, Heymsfield SB, Chen Z. Body composition and physical function in the Women's Health Initiative Observational Study. Prev Med Rep. 2018; 11:15–22. https://doi.org/10.1016/j.pmedr.2018.05.007 [PubMed]
- 37. Balogun S, Winzenberg T, Wills K, Scott D, Jones G, Callisaya ML, Aitken D. Prospective associations of low muscle mass and strength with health-related quality of life over 10-year in community-dwelling older adults. Exp Gerontol. 2019; 118:65–71. https://doi.org/10.1016/j.exger.2019.01.008 [PubMed]
- 38. Hwang AC, Lee WJ, Peng LN, Liu LK, Lin MH, Loh CH, Chen LK. Unfavorable body composition and quality of life among community-dwelling middle-aged and older adults: What really matters? Maturitas. 2020; 140:34–40. https://doi.org/10.1016/j.maturitas.2020.05.024 [PubMed]
- 39. Beaudart C, Locquet M, Reginster JY, Delandsheere L, Petermans J, Bruyère O. Quality of life in sarcopenia measured with the SarQoL®: impact of the use of different diagnosis definitions. Aging Clin Exp Res. 2018; 30:307–13. https://doi.org/10.1007/s40520-017-0866-9 [PubMed]
- 40. Kim M, Kim J, Won CW. Association between involuntary weight loss with low muscle mass and health-related quality of life in community-dwelling older adults: Nationwide surveys (KNHANES 2008-2011). Exp Gerontol. 2018; 106:39–45. https://doi.org/10.1016/j.exger.2018.02.027 [PubMed]
- 41. Villareal DT, Aguirre L, Gurney AB, Waters DL, Sinacore DR, Colombo E, Armamento-Villareal R, Qualls C. Aerobic or Resistance Exercise, or Both, in Dieting Obese Older Adults. N Engl J Med. 2017; 376:1943–55. https://doi.org/10.1056/NEJMoa1616338 [PubMed]