Age-related changes affect the muscle-skeletal system resulting in decline of muscle and bone mass, especially in women and the bone decline may result in a metabolic bone disease called osteoporosis. Osteoporosis is a common and costly disease, characterized by the decline of bone mineral density and changes on bone microarchitecture, which leads to weakness on the resistance of bones and higher risk of fractures in older women [1-3].

The main cause of osteoporosis are changes on hormonal system function, which results in decline on blood levels of oestrogen, and it may be or not associated with low ingestion of calcio on daily diet [2,4-6]. Beyond the bone mass decline, decrements on blood levels of oestrogen also may cause age-related muscle mass loss [1,3,7,8]. According to previous studies the relative muscle skeletal index (RMSI) reduces approximately 1.1 kg/decade [9]. And, functionally, the handgrip strength (HGS) and mobility performance (Timed-up-Go, TUG) reduces significantly in women over 50 years.

Previous studies suggested that the early identification of risk factors for the development of osteoporosis should reduce medical complications, which are associated with increased morbidity and mortality in older women [1-3]. With respect of this, investigations focused on find out how the aging, postmenopausal changes, muscle and bone loss may be associated and how these factors interfere on functional performance and strength are important to reduce the risk of falls, bone fractures and incapacity in older women [10]. Thus, the present study had the objective to identify the relationship among BMD, anthropometric characteristics, years of postmenopausal, strength, muscle mass and the performance on functional tests in up 10 years postmenopausal women.

Participants

Fifty-nine postmenopausal women up to 10 years since the last menstruation, all sedentary (no moderate-to-vigorous physical exercise practised more than once per week) and not under hormonal treatment participated of this study. Menopause was confirmed after cessation of menstrual cycles for more than 12 months, according to the recommendations of the World Health Organization (WHO). One woman was excluded because was diagnosed with osteoporosis.

All volunteers were recruited from the community of Ribeirão Preto/ Brazil. The study was approved by the local Ethics Committee (protocol number 174.828) and all the volunteers signed the consent form. The non-inclusion criteria were having: neurological, cardiorespiratory or rheumatic diseases, diabetes mellitus, smoking, vestibulopathy, history of bone fractures, orthopaedical illness and the score in Mini-Mental State Examination less than 23.

Procedures

All women underwent bone densitometry examination of femoral neck and whole-body by dual-energy X-ray absorptiometry – DXA (Discovery, Hologic Inc., Bedford, MA, USA) by a trained radiologic technician. The whole-body evaluation allowed to determine the RMSI, which was calculated by dividing appendicle skeletal muscle mass by height squared. The sarcopenia cut-off for women proposed by Baumgartner et al of 5.45 Kg/m2 was considered [5-7].

Strength was assessed by a handgrip (HGS, Jamar Hand Dynamometer) [4,10,11]. For the strength assessment the volunteers stood in upright position with their elbow flexed to 90o to perform maximum grip by right and left sides [4,10,11]. The test was performed three times for each side with 30-second rest interval between the trials. The average of all trials was considered. To avoid any influence of the circadian cycle on the data collection all tests were performed in the morning.

For the functional and mobility assessment the timed-up-go test (TUG) was performed [4]. For the TUG, the subjects had to stand up from a chair, walk 3 meters, turn around, return and sit down again on the chair [12,13]. Also, the five-time-sit-to-stand test (FTSTS) was performed [14,15]. For the FTSTS, the subjects were seated on a chair with their hips and knees flexed at 90 degrees. Thus, the subjects were instructed to cross their arms in front of the trunk. Upon hearing a verbal command, the subjects had to stand up and sit down five times, consecutively, as quickly as possible. TUG and FTSTS were performed three times, and the average of all trials were considered

Statistical analysis

For the statistical analysis the Shapiro-Wilk’s test was used to verify the normality, for the comparison between groups for used t-student test and Mann-Whitney test were used, and to correlate the variables the Pearson test and multiple regression analyses were conducted. The statistical package PASW (SPSS Inc., USA) was used for all statistical analyses. The significant level was set at p < 0.05.

The average age, BMI, post menopause time, and BMD of the sample were 53.8 ± 4,17 yr, 28,99 ± 4,93 kg.m^-2, 38,03 ± 16,91 months, and 0,78 ± 0,54 g.cm^-2. Table 1 shows the characteristics of the sample.

Table 1: Characteristics of the study sample, regarding clinical tests: HGS, TUG and FTSTS.

The results demonstrated that was a positive correlation between BMD and weight (r = 0.54, p < 0.05), BMD and BMI (r = 0.56, p < 0.05), BMD and RSMI (r = 0.38, p < 0.05). Weight was positively correlated with RSMI, HGS and BMI. BMI and RSMI was correlated (r = 0.71). Table 2 shows these results.

Table 2: Analyses of multiple linear regressions (Pearson test).

Highlight the mechanisms that predispose older postmenopausal women at risk of fractures may corroborate to create more efficient preventive strategies to prevent this. Although, aging is one of the most important risk factors for osteoporosis, recent studies have demonstrated that the most fractures in postmenopausal women occur in subjects with normal BMD or osteopenia [16,6]. With respect of this, the present study evaluated the relationship between BMD, anthropometric characteristics, years of postmenopausal, strength, muscle mass and the performance on functional tests in up 10 years postmenopausal women.

The osteoporosis is a multi-factorial bone diseases and it is controversial in the literature what are the anthropometric and physical characteristics that most influence BMD [10,17]. The mechanical load may lead to bone strengthening with mobility-induced weight-bearing stress [3]. Thus, our results are in accordance to previous studies that demonstrated that weight loss may increase the bone turnover, suggesting that body weight enhances the differentiation of osteoblasts and increase osteoblastic bone formation besides attenuates osteoclastic activity [13,14,15,18].

Previous studies suggested that low weight or BMI might be an important risk factor for lower BMD [2,4,10,16,17,19], pelvis and femur fractures [6] and the correlation is stronger over time for nonhispanic whites [18]. In this context, the femoral neck BMD varies each 0.16% per kilogramme change in weight [2]. Thus, the prevalence of osteoporosis for postmenopausal women decreases from 50% for BMI < 20 km/cm² to 29.8% for BMI > 25 km/cm² [20].

In addition, previous studies suggested that fat mass is associated with whole body BMD in older women, independent of lean mass [15]. The relationship between body composition and endocrine control is related to the production of peripheral gonadal hormones by adipose tissue. With respect to this, in obese subjects this hormonal changes may affect bone turnover and BMD, protecting the adverse effects of estrogen deficiency after menopause [3,10,14] and attenuating the bone loss. The leptins, the adipokine most studied in relation to bone, increase proliferation and differentiation of osteoblasts and they also regulate osteoclast development. Additionally, the secretion of boneactive pancreatic beta cell hormones like insulin, indirectly, amylin and preptin also seems more prevalent in obesity and they are direct stimulators to osteoblast growth and inhibiting osteoclastic bone resorption [10,21].

Other authors suggest that the weight body is significantly correlated with skeletal muscle mass because heavier subjects require greater muscle mass for movement and would be expected to have more muscle [9,19]. Our findings demonstrated that there is a moderate correlation between weight and RSMI, and a strong relation between BMI and RSMI. Consistent with the statement that lower strength or low muscle mass or RSMI may induce a lower contraction and mechanical stress on bone, a lower osteogenic stimulus in osteocytes resulting in a lower BMD development and possible higher risk of osteoporotic fracture in elderly people [4, 6,13,22- 24].

With respect to this, our results demonstrated that weight; BMI and RSMI have a moderate positive correlation with BMD in adult women. In this context, some authors reported that age, RSMI and fat mass collectively explained 38% variance of femoral neck BMD [6]. In addition previous observational data suggested that increasing lean mass could constitute a preventive measure against bone loss and possibly musculoskeletal aging [2,13].

Additionally it is unclear which measure should be used to sarcopenia [7,22], associated to the fact that decrease of muscle mass by itself seems an inconsistent predictor of mobility limitation [7] or functional ability [25]. The present study did not find correlation between physical performance and RMSI. Also, similarly to previous studies our finding did not find association between handgrip strength, FTSTS and TUG score with BMD in women [22].

Also, we found a weak correlation between RSMI and HGS. Consistent with some authors, modest reductions in skeletal muscle mass with aging do not cause functional impairment and disability [26], however others have demonstrated the clinical risk factors as a indicate of low BMD and fracture [27].

Our sample showed approximately 21% lower strength for right side and 17% for left side considering the values reported by Bohannon et al. [11], but the HGS were higher than 20 kgf, a cut off value that represents a good muscle strength condition in the clinical practice [7]. Regarding TUG test, the results are similar to those expected for the same age [12]. In the FTSTS, no volunteer spent more than 15 seconds to performance the FTSTS, which is considered a cut off value to double the risk of recurrent falls [15]. These functional performance results may be explained by the fact that the evaluated women did not present sarcopenia.

Our sample did not meet the muscle mass criteria below 5.45 kg/ m2 [6] for sacopenia, which may be explained because the cut-off reference value was not appropriate for the Brazilian population [28], or for adult post-menopausal population, or because early stage of sarcopenia are undetectable [7]. Previous studies supported the idea that intervention strategies designed to preserve muscle mass should be initiated by the fifth decade of life, because although the prevalence of sarcopenia increased from the third to sixth decades of life, it remained relatively constant thereafter [29]. Additionally, according to the European Working Group on Sarcopenia in Older People criteria also, a cohort Finnish study with 70–80 year-old home dwelling showed that 36% has osteopenia and only 0,9% of women has sarcopenia [30], while in osteoporotic Brazilian woman (69.7 ± 6.4 years), only 21% of sample is below criteria of sarcopenia [31].

Our data corroborate the studies about the effect of weight and BMI on BMD, however it is important to emphasize that higher BMI is significantly associated with a number of comorbidities, including asthma, emphysema, diabetes, reduced physical activity, co-medications, increased risk of falls, higher morbidity and economic costs associated with fractures because of a greater risk of non-union, postoperative complications, comorbidities, and slower rehabilitation [3,24,32,]. Furthermore, the BMI and hip fracture seems to have nonlinear correlation. Although the lower BMI is a higher risk factor for fracture, the higher BMI not seems a higher protect factor comparing with normal BMI, thus obesity should not be a protective factor for hip fracture risk [33]. Additionally, the increased prevalence of overweight in older US women appears unlikely to be accompanied by a significant reduction in osteoporosis prevalence [26] suggesting that protective factors to avoid BMD decrease should be further studied and recommendations on the benefits of BMI should be cautious for post-menopausal women.

Finally, the screening and prevention of bone loss and a specific orientation about weight body in younger postmenopausal women may be an effective way to prevent or delay fractures in postmenopausal women. Although the menopause occurs around 50 years old and the rate of BMD loss accelerates in the peri-menopause and in the first yeas after menopause [10], the women before 60 or 65 years old do not meet current national guidelines for osteoporosis treatment [10,34-38]. Additionally, recent studies have demonstrated that over 50% of postmenopausal women with incident fractures have a BMD higher than the diagnostic of osteoporosis and a substantial proportion of women with low-trauma fractures also have normal BMD [16]. Understanding the influence of different body composition on BMD or on fractures, as well as the importance of increasing lean mass as a strategy for preventing bone loss seem relevant.

Limitations of this study include the fact that we chose to use HGS, TUG and FTSTS tests to measure respectively muscle strength and performance because they could be easily applied in clinical practice. However, we do not know whether these results would be similar if we had used different evaluation methods to measure muscle strength and performance such as isokinetic or isometric strength tests for lower limb muscles, self-reference gait speed or power stair climbing test.

Based on the results, in the first 10 years of post-menopause, there was not a direct association between the decrease of BMD and sarcopenia in adult postmenopausal women. Despite the significant correlations among weight, BMI, RSMI and HGS were found it could not be proven that it is independently correlated with BMD. With respect to this, the most important finding of the present study is that it demonstrated that weight was the only factor to affect the BMD for enrolled post-menopause women.