Literature DB >> 23497554

Association between body mass index and muscularity in healthy older Japanese women and men.

Hiroaki Kanehisa1, Tetsuo Fukunaga.   

Abstract

BACKGROUND: Body mass index (BMI), expressed as the ratio of body mass to height squared (kg/m(2)), involves not only fat but also lean mass. The present study aimed to clarify how BMI is associated with total muscle mass (TMM) in older Japanese women and men.
FINDINGS: Using a B-mode ultrasound apparatus, muscle thickness was measured at nine sites (forearm, upper arm anterior and posterior, thigh anterior and posterior, lower leg anterior and posterior, abdomen, and subscapular) for 346 women (BMI 16.40 to 33.11 kg/m(2)) and 286 men (BMI 16.86 to 31.18 kg/m(2)) aged 60.0 to 79.5 yrs. TMM was estimated using the product of the sum of the muscle thicknesses at the nine sites with height as an independent variable. For both sexes, the estimated TMM relative to height squared was significantly correlated with BMI (r = 0.688, P<0.0001 for women; r = 0.696, P<0.0001 for men), but the percentage of the estimated TMM in body mass was not.
CONCLUSION: These results indicate that, for older Japanese women and men, BMI is a simple and convenient index for assessing total muscularity.

Entities:  

Mesh:

Year:  2013        PMID: 23497554      PMCID: PMC3599217          DOI: 10.1186/1880-6805-32-4

Source DB:  PubMed          Journal:  J Physiol Anthropol        ISSN: 1880-6791            Impact factor:   2.867


Background

Body mass index (BMI) is widely used for assessing overweight and obesity [1]. However, the association between BMI and the percentage of body fat mass (%BF) in body mass is influenced by age, sex, and ethnicity [2-7]. Some studies have suggested that the diagnostic accuracy of BMI to detect excess body adiposity diminishes with increasing age of the person being assessed [8,9]. Romero-Corral et al. [9] found that the correlation between BMI and %BF was lower in older than in younger subjects, but the correlation between BMI and lean mass was similar across age groups. Micozzi and Harris [10] suggested that, based on anthropometric measurements, the ratios of body mass to height2 and height1.5 in men and women, respectively, are more closely correlated with estimates of body fat in younger than in older adults, and with estimates of muscle mass in older than in younger adults. This suggests that, for the elderly individual, BMI may be a simple and convenient index for assessing muscularity. This assumption has already been examined using populations aged 60 and over [10-13]. Iannuzzi-Sucich et al. [11] indicated that BMI is a strong predictor of skeletal-muscle mass in older Caucasian women and men. In their study, using dual X-ray absorptiometry (DXA), BMI was shown to account for 48% and 50% of the variance in appendicular skeletal muscle mass in women and men, respectively. However, in other studies, the r2 values between BMI and muscle mass were not so high (r2 = 0.22 to 0.40) [10,12,13]. The reasons for these lower r2 values are unknown, but it might be due to the fact that the previous studies used muscle girth or area estimated from the anthropometric data of the upper arm [10,13] or the upper arm and calf [12]. Gallagher et al. [4] reported that the middle-aged and older Japanese population had a higher %BF for any given BMI than did white and African-American populations. Considering this, whether the findings of Iannuzzi-Sucich et al. [11] can be applied to the elderly Japanese population remains unclear. Furthermore, it seems strange that the previous studies cited above correlated the measures obtained from a limited number limbs with BMI, which is an index representing the total body. To our knowledge, no study has examined how total muscle mass is associated with BMI in elderly individuals. Thus, the present study aimed to examine the association between BMI and muscle mass in elderly Japanese individuals of both sexes. To this end, we determined muscle thicknesses at nine sites of the body using B-mode ultrasonography. The product of the sum of muscle thicknesses at these nine sites with height was shown to have a high correlation with total muscle mass (TMM) in a sample of Japanese women and men [14]. The equation with the product of the two variables as an independent variable for predicting TMM, developed in the previous study [14], has been successfully used to examine age-related muscle loss in Japanese men and women aged 20 to 95 years [15]. Using of the prediction equation, therefore, the present study estimated TMM for an older population. We hypothesized that, for the elderly population, the estimated TMM relative to height squared would be significantly correlated with BMI, but its value relative to body mass would not.

Methods

Ethics approval

This study was approved by the Ethics Committee of the Graduate School of Arts and Sciences, University of Tokyo, Japan, and was consistent with the institutional ethics requirements for human experimentation in accordance with the Declaration of Helsinki. The subjects were fully informed of the purpose and risks of the experiment, and gave their written informed consent.

Subjects

A group of 346 women and 286 men aged 60.0 to 79.5 years voluntarily participated in this study. None of the subjects was or had been an athlete. Moreover, none was using walking sticks or other walking aids ,and all were functionally independent in daily life. In addition, no participant was on an extreme diet or using any major medications, such as chemotherapy, cardiac, respiratory, or antipsychotic drugs. The mean ± standard deviation (SD) for age, height, body mass, and BMI are presented in Table  1.
Table 1

Descriptive data on the measured variablesa,b

VariablesWomen, n = 346Men, n = 286
Age, years
69.4 ± 4.9
69.8 ± 4.7
Height, cm
150.1 ± 5.4
163.4 ± 6.0
Body mass, kg
53.6 ± 7.1
63.5 ± 8.
BMI, kg/m2
23.77 ± 2.77
23.76 ± 2.58
Muscle thickness, mm
 
 
Forearm
19.1 ± 3.4
22.6 ± 4.2
Upper arm anterior
28.4 ± 3.8
33.3 ± 4.6
Upper arm posterior
26.9 ± 4.7
32.8 ± 5.1
Thigh anterior
38.1 ± 5.6
42.1 ± 6.1
Thigh posterior
56.6 ± 7.0
61.9 ± 7.5
Lower leg anterior
25.9 ± 2.8
28.7 ± 3.2
Lower leg posterior
59.5 ± 4.6
66.0 ± 5.6
Subscapular
18.2 ± 4.3
21.1 ± 4.7
Abdomen
7.6 ± 1.7
10.4 ± 2.1
SMTc, mm
280.3 ± 21.4
319.8 ± 27.7
TMMd, kg
13.8 ± 2.6
20.8 ± 3.7
TMM/ht2,e kg/m2
6.10 ± 0.99
7.77 ± 1.15
%TMMf25.7 ± 3.132.7 ± 3.5

aValues are mean ± SD.

bAll listed variables except for age and BMI were significantly greater (P<0.0001) in men than in women.

cSum of the muscle thickness values at nine sites.

dEstimated total muscle mass.

eTMM relative to height squared.

fPercentage of TMM in body mass.

Descriptive data on the measured variablesa,b aValues are mean ± SD. bAll listed variables except for age and BMI were significantly greater (P<0.0001) in men than in women. cSum of the muscle thickness values at nine sites. dEstimated total muscle mass. eTMM relative to height squared. fPercentage of TMM in body mass.

Muscle thickness measurements

Muscle thickness was measured at nine sites (forearm, upper arm anterior and posterior, thigh anterior and posterior, lower leg anterior and posterior, abdomen, and subscapular) on the right side of the body, using a real time B-mode ultrasound apparatus (SSD-500, Aloka Co., Tokyo, Japan). The position of the subjects during the ultrasonographic measurements, the site selected for obtaining cross-sectional images, and determination of muscle thickness at each site were the same as those described in a previous study [15]. The sum of muscle thicknesses at the nine sites (SMT) was used to estimate TMM. TMM was estimated using the prediction equation developed by Sanada et al. [14]: TMM (kg) = 0.687 × SMT (cm) × body height (m) − 15.122 (R2 = 0.96, SEE = 1.1 kg). In addition to the absolute value, the estimated TMM relative to height squared (TMM/ht2, kg/m2) and the percentage of the estimated TMM in body mass (%TMM) were calculated, and used to examine how muscularity and the relative distribution of muscle mass within body mass are associated with BMI.

Statistics

Descriptive values are presented as mean ± SD. An unpaired Student’s t-test was used to test the differences between men and women in the measured variables. A simple linear regression analysis was used to calculate the coefficient of correlation between BMI and TMM/ht2 or %TMM. The probability level for statistical significance was set at P<0.05.

Results

All measured variables except for age and BMI were significantly greater in men than in women (Table  1). TMM/ht2 was significantly correlated with BMI in both women (r = 0.688, P<0.0001) and men (r = 0.696, P<0.0001) (Figure  1). However, %TMM was not significantly associated with BMI in either sex: r = −0.024 (P>0.05) for women and r = −0.029 (P>0.05) for men (Figure  2).
Figure 1

Relationship between BMI and TMM/ht.

Figure 2

Relationship between BMI and %TMM.

Relationship between BMI and TMM/ht. Relationship between BMI and %TMM.

Discussion

The current results support our hypothesis regarding the association between BMI and muscle mass, and indicate that, for the elderly person, BMI is an index that assesses total muscularity rather than the relative distribution of muscle mass within the total body. Based on the r2 values, BMI accounted for 47% and 48% of the variance in TMM/ht2 in women and men, respectively. These values are higher than those reported in previous studies using anthropometric approaches for predicting muscle size [10,12,13], and are comparable those in the study of Iannuzzi-Sucich et al. [11] who used DXA to determine appendicular skeletal muscle mass. In the present study, we estimated the total muscle mass by using a prediction equation developed by Sanada et al. [14]. Based on the report of Sanada et al. [14], the accuracy of the ultrasonography prediction model for estimating TMM is greater than that of anthropometric and bioelectrical impedance prediction models, and is similar to that of DXA prediction models and whole-body 40K counting. This may explain the aforementioned difference in the observed r2 between the current study and previous reports using anthropometric model. However, it should be noted that the number of subjects in our study categorized as underweight (BMI<18.50 kg/m2) or obese (30.00 kg/m2 or higher) [1] was small (23; 3.6%). The correlation coefficient of the associations between BMI and TMM/ht2 was found to be lower for subjects with BMI greater than 25 (women: r = 0.468, P<0.001, men: r = 0.448, P<0.0001) than for subjects with BMI less than 25 (women: r = 0.660, P<0.0001, men: r = 0.630, P<0.0001). This implies a limitation on the use of BMI as an index for assessing muscularity in elderly individuals with relation to the magnitude of BMI. Further studies examining individuals categorized as either underweight or obese are needed to clarify this finding.

Abbreviations

%BF: Percentage of body fat mass in body mass;BMI: Body mass index;TMM: Total muscle mass;SMT: Sum of muscle thicknesses at the nine sites;TMM/ht2: Estimated TMM relative to height squared;%TMM: Percentage of the estimated TMM in body mass

Competing interests

The authors declare that they have no competing interests.

Authors’ contributions

HK participated in study design, helped acquire funding from the Ministry of Education, Culture, Sports, Science and Technology, helped coordinate research activities, performed statistical analysis, and drafted the manuscript. TF participated in study design and coordination, and drafted the manuscript. Both authors read and approved the final manuscript.
  15 in total

1.  Body mass index as a measure of body fatness: age- and sex-specific prediction formulas.

Authors:  P Deurenberg; J A Weststrate; J C Seidell
Journal:  Br J Nutr       Date:  1991-03       Impact factor: 3.718

2.  The relationship between BMI and percent body fat, measured by bioelectrical impedance, in a large adult sample is curvilinear and influenced by age and sex.

Authors:  S Meeuwsen; G W Horgan; M Elia
Journal:  Clin Nutr       Date:  2010-03-31       Impact factor: 7.324

3.  The effect of sex, age and race on estimating percentage body fat from body mass index: The Heritage Family Study.

Authors:  A S Jackson; P R Stanforth; J Gagnon; T Rankinen; A S Leon; D C Rao; J S Skinner; C Bouchard; J H Wilmore
Journal:  Int J Obes Relat Metab Disord       Date:  2002-06

4.  Prevalence of sarcopenia and predictors of skeletal muscle mass in healthy, older men and women.

Authors:  Michele Iannuzzi-Sucich; Karen M Prestwood; Anne M Kenny
Journal:  J Gerontol A Biol Sci Med Sci       Date:  2002-12       Impact factor: 6.053

5.  Prediction and validation of total and regional skeletal muscle mass by ultrasound in Japanese adults.

Authors:  Kiyoshi Sanada; Charles F Kearns; Taishi Midorikawa; Takashi Abe
Journal:  Eur J Appl Physiol       Date:  2005-10-19       Impact factor: 3.078

6.  Decreased muscle mass and increased central adiposity are independently related to mortality in older men.

Authors:  S Goya Wannamethee; A Gerald Shaper; Lucy Lennon; Peter H Whincup
Journal:  Am J Clin Nutr       Date:  2007-11       Impact factor: 7.045

7.  Accuracy of body mass index in diagnosing obesity in the adult general population.

Authors:  A Romero-Corral; V K Somers; J Sierra-Johnson; R J Thomas; M L Collazo-Clavell; J Korinek; T G Allison; J A Batsis; F H Sert-Kuniyoshi; F Lopez-Jimenez
Journal:  Int J Obes (Lond)       Date:  2008-02-19       Impact factor: 5.095

8.  Body size, body composition and fat distribution: comparative analysis of European, Maori, Pacific Island and Asian Indian adults.

Authors:  Elaine C Rush; Ismael Freitas; Lindsay D Plank
Journal:  Br J Nutr       Date:  2009-02-10       Impact factor: 3.718

9.  Age variations in the relation of body mass indices to estimates of body fat and muscle mass.

Authors:  M S Micozzi; T M Harris
Journal:  Am J Phys Anthropol       Date:  1990-03       Impact factor: 2.868

10.  How useful is body mass index for comparison of body fatness across age, sex, and ethnic groups?

Authors:  D Gallagher; M Visser; D Sepúlveda; R N Pierson; T Harris; S B Heymsfield
Journal:  Am J Epidemiol       Date:  1996-02-01       Impact factor: 4.897

View more
  5 in total

Review 1.  Differential control of muscle mass in type 1 and type 2 diabetes mellitus.

Authors:  David Sala; Antonio Zorzano
Journal:  Cell Mol Life Sci       Date:  2015-06-20       Impact factor: 9.261

2.  Strong influence of dietary intake and physical activity on body fatness in elderly Japanese men: age-associated loss of polygenic resistance against obesity.

Authors:  Kumpei Tanisawa; Tomoko Ito; Xiaomin Sun; Ryuken Ise; Satomi Oshima; Zhen-Bo Cao; Shizuo Sakamoto; Masashi Tanaka; Mitsuru Higuchi
Journal:  Genes Nutr       Date:  2014-07-17       Impact factor: 5.523

3.  Autophagy-regulating TP53INP2 mediates muscle wasting and is repressed in diabetes.

Authors:  David Sala; Saška Ivanova; Natàlia Plana; Vicent Ribas; Jordi Duran; Daniel Bach; Saadet Turkseven; Martine Laville; Hubert Vidal; Monika Karczewska-Kupczewska; Irina Kowalska; Marek Straczkowski; Xavier Testar; Manuel Palacín; Marco Sandri; Antonio L Serrano; Antonio Zorzano
Journal:  J Clin Invest       Date:  2014-04-08       Impact factor: 14.808

4.  Factors associated with muscle mass in community-dwelling older people in Singapore: Findings from the SHIELD study.

Authors:  Siew Ling Tey; Samuel Teong Huang Chew; Choon How How; Menaka Yalawar; Geraldine Baggs; Wai Leng Chow; Magdalin Cheong; Rebecca Hui San Ong; Farah Safdar Husain; Shuyi Charmaine Kwan; Cynthia Yan Ling Tan; Yen Ling Low; Ngiap Chuan Tan; Dieu Thi Thu Huynh
Journal:  PLoS One       Date:  2019-10-09       Impact factor: 3.240

5.  Development & cross-validation of anthropometric predictive models to estimate the appendicular skeletal muscle mass in middle-aged women in Sri Lanka.

Authors:  Nirmala Rathnayake; Gayani Alwis; Janaka Lenora; Sarath Lekamwasam
Journal:  Indian J Med Res       Date:  2019-09       Impact factor: 2.375

  5 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.