Journal of Womens Health, Issues and Care ISSN: 2325-9795

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Research Article, J Womens Health Issues Care Vol: 2 Issue: 2

Age Typical Associations between Skeletal Muscle Mass and Bone Mass among Healthy Women

Sylvia Kirchengast1* and Johannes Huber2
1Department of Anthropology, University of Vienna, Althanstrasse 14, A-1090 Vienna, Austria
2Clinic for Gynecology and Obstetrics, Medical University of Vienna, University Währinger Gurtel 18-20 A-1090 Vienna, Austria
Corresponding author : Sylvia Kirchengast
Department of Anthropology, University of Vienna, Althanstrasse 14, A-1090 Vienna, Austria
Tel: ++43 1 4277 54712; Fax: ++43 1 4277 9547
E-mail: [email protected]
Received: January 15, 2013 Accepted: April 12, 2013 Published: April 17, 2013
Citation: Kirchengast S, Huber J (2013) Age Typical Associations between Skeletal Muscle Mass and Bone Mass among Healthy Women. J Womens Health, Issues Care 2:2. doi:10.4172/2325-9795.1000104

Abstract

Age Typical Associations between Skeletal Muscle Mass and Bone Mass among Healthy Women

Human body composition changes through the process of ageing. Beside a well documented increase in fat mass, bone mass and muscle mass decline progressively, resulting in pathologically deceased skeletal muscle mass, i.e. sarcopenia and osteoporosis, in the worst case. Both reduced bone density and reduced muscle mass have dramatic consequences, such as impaired functional performance, increased risk of falls, and consequently, an increased risk of fragility fractures.

Keywords: Bone density; Bone mass; Body composition; Sacropenia

Keywords

Bone density; Bone mass; Body composition; Sacropenia

Introduction

Human body composition changes through the process of ageing. Beside a well documented increase in fat mass, bone mass and muscle mass decline progressively [1-4], resulting in pathologically deceased skeletal muscle mass, i.e. sarcopenia and osteoporosis, in the worst case. Both reduced bone density and reduced muscle mass have dramatic consequences, such as impaired functional performance, increased risk of falls, and consequently, an increased risk of fragility fractures [5-9]. Among elderly bone fractures, first of all of the femoral neck result often in helplessness, and in the loss of independence [7]. Reduced bone density and skeletal muscle mass are, therefore, main risk factors for impaired functional performance, increased physical disability, and consequently, reduced health related quality of life during old age. This is especially true of women. The term sarcopenia from the Greek “Poverty of flesh“, for pathologically reduced skeletal muscle mass was introduced by Rosenberg more than twenty years ago [10-12]. However, it is not always true that sarcopenia and the loss of skeletal muscle appear only during old age. The decline of skeletal muscle mass starts very early, during the third decade of life, and progresses thereafter [13,14]. Genetic predisposition, hormonal changes, reduced physical activity, a sedentary lifestyle, smoking and nutritional factors lead to reduced mitochondrial function, impaired muscle protein synthesis and impairments in the growth hormone/ IGF-I pathway [15-21].
Skeletal muscle mass is interrelated with bone mass throughout life. Consequently, muscle loss and bone loss with increasing age are interrelated [22-24]. The relationship between muscle function and bone tissue is illustrated by the mechanostat theory [25,26], and the so called functionally unified muscle bone system [27]. The muscle bone relationship is based on the common embryogenesis of muscle and bone tissue, which are regulated and controlled by the same hormones and genes [28]. Furthermore, muscle contractions induce tension in the bone, which in turn, activates bone modeling via osteocyte mechanoreceptors [25]. Consequently, a healthy skeleton is adapted to mechanical stress as muscle tension leads to an increase in bone mass and strength. While the functional muscle bone unit was intensively described for children, adolescents and young adults [27,29,30], only few studies focused on the muscle-bone unit during adulthood and old age [31-34]. Therefore, the aim of the present study was to analyze age typical associations between soft tissue body composition and bone mass, and bone density among healthy adult women.

Materials and Methods

Subjects
This study is based on a data set of 950 Viennese women ageing between 20 and 92 years. Considering all inclusion and exclusion criteria, 781 female subjects ranging in age from 20 to 92 years (x=50.3 ± 14.4) were finally enrolled in the present study. All subjects were recruited by newspaper advertising, broadcasting or via snowball system, and originated from Vienna or neighboring Lower Austria. The examination took place at the Menox-out-patientdepartment for the treatment of climacteric symptoms in Vienna. The examination started with an extensive documentation of individual medical history, history of diseases, current and past medication and reproductive history. Age at menarche and age at menopause of the female subjects was determined retrospectively. It is well known that the reliability declines with increasing number of years, since menarche and menopause [35], however, it was the only possibility to get any information regarding age at menopause. All subjects with acute diseases, hip or knee replacements, or a history of chronic or metabolic bone disease, physical disabilities, and a treatment with drugs that may influence lean soft tissue and bone mass, such as cortisone treatment, hormone replacement therapy or hormonal contraceptives were excluded from further analyses. Furthermore, smoking was a strict exclusion criterion. Actual habitual physical activity was evaluated by means of the physical activity questionnaire by Baecke et al. [36]. Life time physical activity was assessed by an extended interview, documenting life time history of regular physical activities from multiple sources, including occupational physical activity and leisure time physical activity. Persons who were or had been professional athletes were excluded from this analysis, because the high level of physical activity is associated with improved muscle mass and bone density even during old age [37]. All older subjects of the present sample lived in private homes and not in geriatric homes for elderly people. Therefore, the sample consists exclusively of healthy and mobile subjects. This selection was necessary because the aim of the study was to analyze the association patterns between soft tissue body composition, and bone mass and bone density. Therefore, a homogeneous sample with as few as possible covariates, which may influence soft tissue body composition and bone mass, was chosen. Socioeconomic status was estimated by marital status, educational level, profession, family income and the area living in Vienna. All subjects belonged to the typical social middle class of urban Vienna. Therefore, socioeconomic parameters were not considered in the further analyses. Beside the objectives of the study, the right to withdraw at any time was explained. Strictly confidential was ensured. The study was conducted in compliance with “Ethical principles for medical research, involving human subjects” of Helsinki Declaration, and was approved by the Menox bioethical committee.
Anthropometrics and body composition analyses
Body height was measured to the nearest 0.5 cm using a standard anthropometer, and body weight to the nearest 0.1 kg on a balance beam scale. Relative weight status was determined by using the body mass index (BMI) kg/m2. Body composition analysis was performed by dual-energy-x-ray absorptiometry (DXA), using a Hologic Delphi W scanner (Hologic Inc. Bedford, MA, USA; software version 12.4.2). Body composition was acquired from whole body scan, following manufacturer for position, scan acquisition and analysis. According to Sakai et al. [38] DXA (Delphi W) measures total bone mineral content (BMC) and bone mineral density (aBMD), total mass, absolute fat mass, relative fat mass (fat%), and lean soft tissue mass, with a precision (coefficient of variation, CV) of 0.1%, 1.5%, 1.1%, 0.5%, 1.1% and 0.8%, respectively. All scans were obtained by the same person. Daily scanning of the manufacturer-supplied phantom confirmed no instrumentation drift. All subjects fitted into the scan field because none was too tall or too obese.
Skeletal muscle mass was described by 3 different parameters: beside the total lean body mass determined by DXA scans, appendicular skeletal muscle mass (ASM), to describe the muscle mass of the limbs, and relative appendicular skeletal muscle mass (RASM) were calculated. Appendicular skeletal muscle mass (ASM) was determined by combining the lean tissue mass of the regions of the arms and legs, excluding all other regions from analyses [39]. ASM was calculated because it allows to defined sarcopenia, according to Baumgartner et al. [5]. Since skeletal muscle mass is significantly related to stature height, the relative ASM (RASM) was calculated as follows: ASM was divided by the square of height (metres squared). For the classification of sarcopenia, the definition proposed by Baumgartner et al. [5] was used. According to Baumgartner et al. [5], an adjusted ASM greater than 2 standard deviations below the sex specific mean from a young healthy reference population was classified as sarcopenia. For the present paper, normative levels for adjusted ASM were taken from Baumgartner et al. [5] for non Hispanic whites. According to this study, the cut-off values for sarcopenia were 7.26 kg/m2 for men and 5.45 kg/m2 for women. These cut-offs are still used, although DXA technology in 1997 was quite different from recent technologies [40,41]. In 2003, Newman et al. [42] proposed an alternative method to potentially derive sarcopenia cutpoints. According to this new method, the following cutpoints were defined: 7.23 kg/m2 for men and 5.67 kg/m2 for women. These cutpoints are very similar to the original Baumgartner cut-offs [5]. Therefore the Baumgartner cut-offs are still used as correct up to now [40].
Statistical analyses
Statistical calculations were performed by using SPSS for Windows Program Version 18.0 (Microsoft corp.). The sample was divided according to age into five subsamples. Age group one comprised 89 women younger than 30 years, representing young adults. 241 women ranging in age between 31 and 50 years, representing preand perimenopausal women were classified as age group 2. Age group 3 comprised 299early postemopausal women ageing between 51 and 60 years. 110 elderly women ageing between 61 and 75 years belonged to age group 4. Age group 5 comprised 42 old aged women who were older than 75 years. After calculation of descriptive statistics (means, SDs), the Kolmogorov-Smirnov test was applied to test the somatometric and body composition variables for normal distribution. According to the results of the Kolmogorov Smirnov test, a normal distribution could be assumed for all somatometric and body composition variables. Chi-squares and oneway ANOVA (Duncun tests) were performed to test age group differences, with respect to their statistical significance. Partial correlations were used to analyze the relationship between aBMD, as well as BMC and body composition parameters for each age group separately. To analyse the impact of age, body height, fat mass and lean body mass on aBMD, as well as BMC multiple regression analyses, were performed. A probability P value of less than 0.05 was considered significant.

Results

Characteristics of study population
The mean age at menarche was 13.1 ± 1.6 years. 471 (60.7%) of the probands were postmenopausal at the time of investigation. The mean age at menopause was 48.4 ± 4.7 years. 1.4% of the subjects were classified as underweight, i.e. BMI below 18.50 kg/m2. 39.8% corresponded to the definition of normal weight BMI between 18.50 and 24.99 kg/m2, and 39.8% to the definition of overweight (BMI between 25.00 and 29.99 kg/m2). 18.9% of the subjects were classified as obese (BMI above 30.00 kg/m2), The highest BMI was 34.25 kg/m2.
Age group differences in somatometric and body composition parameters
Table 1 summarizes the somatometric parameters. Body height decreased significantly with increasing age group. Body weight, BMI, absolute fat mass and fat percentage increased significantly up to age group 4 and decreased for age group five. Bone mass (BMC) and bone density (aBMD), but also muscle mass decreased significantly with increasing age.
Table 1: Age differences in somatometric parameters, body composition, bone density and weight status (descriptive statistics, ANOVA).
Sarcopenia and bone
Age group typical prevalence of sarcopenia is presented in figure 1. The highest prevalence (38.1%) of sarcopenia was found among women older than 75 years. All other age groups exhibited very similar prevalence rates of sarcopenia between 30.1 and 33%. Statistically significant differences between the five age groups could not be observed. Bone mass (BMC) differed significantly between sarcopenic and non sarcopenic women. Bone mass of non sarcopenic women was significantly higher than that of sarcopenic women. This was true of all age groups, with the exception of women ageing between 61 and 75years (Table 2). Regarding bone density (aBMD), sarcopenic women showed always lower values, significant differences however, were only found for age groups 2 and 3.
Figure 1: Prevalence of sarcopenia according to age group.
Table 2: Bone mass and between sarcopenic and non-sarcopenic women for each age group separately (student t-tests).
Predictors of aBMD and BMC
Table 3 presents partial correlations corrected for body height of bone mass (BMC) and bone density (aBMD), with body composition parameters. BMC correlated significantly positively with nearly all anthropometric and soft tissue body composition parameters. This was true of all age groups. Bone mineral density (aBMD) correlated significantly positively, with all parameters of muscle mass among women younger than 61 years. No significant correlations between bone parameters and soft tissue body composition were found among women older than 60 years. To test the impact of age, body height, fat mass and skeletal muscle masses on aBMD and BMC multiple regression analyses were performed. In these analyses, only absolute values and no indices were included. Multiple regression analyses indicated that body height, fat mass and skeletal fat mass were significantly positively associated with BMC, while age was significantly negatively associated with BMC. Regarding bone mineral density, a significant positive association was found with skeletal muscle mass, a significant negative association with age. Body height and fat mass had no significant impact on aBMD (Table 4).
Table 3: Partial correlations (Body height =constant) of total bone mineral density (aBMD), bone mass (BMC) and body composition parameters.
Table 4: Multiple regression models for predicting aBMD and BMC.

Discussion

Skeletal muscle mass and bone mass, as well as bone density, are interrelated. This bone-muscle relationship is mainly viewed in the context of the mechanostat theory [43,44], and the theory of the functional muscle bone unit [27,29,30]. These theories imply that bones and skeletal muscle respond to varying mechanical strains modulated by systemic effects, such as hormones. It is well documented that bone and skeletal muscle share a common embryogenesis,s and are regulated and controlled by the same hormones and genes [28,45,46]. Therefore, age related bone density reduction and skeletal muscle mass decline, but are two sides of the same coin because bone and muscle share genetic, but also environmental, paracrine and endocrine influences throughout life [46]. The functional muscle bone unit was mainly focused on among children and adolescents [26,27,47,48], however, several studies documented the bone-muscle relationship through adult life [31,33,34,49,50]. Lima et al. [50] described a strong relationship between lean body mass and bone mineral density in older women. Genaro et al. [49] found an association between a low amount of lean body mass and reduced bone mass and bone density. A positive association between fat mass, as well as lean body mass and bone mineral density (aBMD), at all skeletal sites, was documented by Wang et al. [51] and Gjesdal et al. [52]. In general, the skeletal muscle is one of the most powerful determinants of bone strength and bone density [53].
In the present study, the age typical association patterns between soft tissue body composition parameters, and bone density among women ageing between 20 and 92 years were analyzed. In particular, the muscle bone relationship through adult age was focused on.
We have to state that the present study has certain limitations. One shortcoming is the cross sectional design. Another minor problem is that bone mineral density was determined by DXA measurements, and not by Computer tomography (CT). Therefore, areal BMD and not volumetric BMD were determined. But nevertheless, areal BMD is the most widely used densiometric parameter. Furthermore, whole body aBMD and BMC were used and not proximal femur or lumbar spine aBMD. This fact represents a further limitation of the study because whole body BMC and aBMD have not the ability to predict fragility fractures as site specific aBMD locations. Another important limitation of the present study is the way sarcopenia is defined. According to the EWGSOP [41], sarcopenia is a syndrome characterized by progressive and generalized loss of skeletal muscle mass and strength. Consequently, diagnosis of sarcopenia requires documentation of low muscle mass–as done by this study–but also documentation of low muscle strength or low physical performance. Both muscle strength as well as physical performance, however, was not documented in the present study. This weakness of data collection may explain the quite surprising result that around a third of healthy Viennese women younger than 60 years corresponded to the definition of sarcopenia based on cutoff values of AMS/height2 only. Even among women younger than 30 years, more than 30% corresponded to the definitions of sarcopenia, according to Baumgartner et al. [5]. The prevalence of sarcopenia remains more or less stabile up to age group 61 to 75 years. Among women older than 75 years, the prevalence of sarcopenia was 38%. This result is similar to that of Berger and Doherty [54], who described a prevalence of sarcopenia of 20 to 50% among women ageing 65 years and above. The prevalence of sarcopenia among young women, however, is still incredible high. As pointed out above, this high prevalence may be an artefact of the classification system of sarcopenia used in the present study. Although the cutoff values of Baumgartner et al. [5] are still used, there is no doubt that defining sarcopenia is still a challenging task. Recently the EWGSOP [41], presented a new approach to define sarcopenia and to diagnose sarcopenia, including the determination of muscle strength. However, as pointed out above, muscle strength– was not measured in the present study.
Despite the definition problems, sarcopenic women showed always significantly lower bone mass and bone mineral density than non-sarcopenic women in the present sample. Furthermore, the main focus of this study was not sarcopenia itself, but the analysis of age typical association patterns between bone mass, as well as bone density and soft tissue body composition parameters, in particular, muscle mass. The main finding was that the bone-soft tissue relationship changed with increasing age. While bone mass and bone density correlated significantly positively with skeletal muscle mass from the youngest age group (< 30years) up to age group 51 to 60 years, no significant correlations between bone mineral density skeletal muscle mass was found among women older than 60 years. Bone mass, in contrast, correlated significantly positively with skeletal muscle mass among all age groups. Absolute and relative fat mass correlated significantly with bone mass only, while no significant relationship between absolute/relative fat mass and bone density were documented. The positive relationship between bone mass, as well as bone density and skeletal muscle mass among women younger than 60 years, were in accordance with those described by Gjesdal et al. [52], Lima et al. [50] and Gennaro et al. [49]. Gjesdal et al. [52] and Lim et al. [55] demonstrated a strong influence of fat mass, but also lean body mass on BMD. The age typical muscle-bone correlation patterns were corroborated by the multiple regression analyses. Skeletal muscle mass was a predictor of bone mass (BMC) and bone mineral density, independently of age.
The results of the present study demonstrate a strong association between skeletal muscle mass and bone mass and bone density during adulthood; however, according to the results of partial correlation analysis, the relationship between muscle mass and bone density was not statistically significant among women older than 60 years. Bone mass, in contrast, was significantly associated with fat mass, as well as muscle mass among all age groups. The basis for the association patterns between skeletal muscle mass and bone mass, as well as bone density takes place during adolescence and young adulthood [53]. Adolescent girls are characterized by higher estrogen levels, but lower testosterone levels. During this phase of life in girls, bone mass tends to increase more rapidly in relation to muscle area, and the increase in bone mass is mainly due to increased endosteal apposition [53]. During young adulthood, the peak bone mass and muscle strength is achieved. After that, bone loss and muscular tissue decline starts and enhances with increasing age. Beside various environmental and lifestyle factors, the decline in bone and muscle mass is influenced by hormonal factors. In women, the rapid decrease of ovarian estrogen levels through menopausal transition accelerates the decline in bone mass and bone density [16,17,56].

Conclusions

A significant relationship between skeletal muscle mass and bone mass as well as bone density, an indicator of the functional muscle bone unit were found among healthy adult women. Soft tissue body composition parameters are significantly related with bone mineral density and bone mass, up to the age of 60 years. Among women older than 60 years, only bone mass was significantly related to skeletal muscle mass. Furthermore, it could be shown that for the diagnosis of sarcopenia, the only sarcopenia definition based on appendicular skeletal mass is not appropriate.

Acknowledgements

The authors are gratefully indebted to the Menox outpatient department for its kind cooperation. Special thanks go to the participants for the cooperation. The project was financially supported by the Hochschul-Jubiläumsfond (Project number: 1667/2006).

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