|Year : 2019 | Volume
| Issue : 1 | Page : 61-67
Low bone mineral density and its risk factors in an urban adult population of South India
Pawan Kumar Sharma1, Shravani Sriram1, Anantha Krishna1, Atul Gandhi2, Enakshi Ganguly1
1 Department of Community Medicine, Mediciti Institute of Medical Sciences, Medchal District, India
2 Department of Statistics, Tata Institute of Social Sciences, Mumbai, Maharashtra, India
|Date of Web Publication||18-Feb-2019|
Dr. Enakshi Ganguly
Department of Community Medicine, Mediciti Institute of Medical Sciences, Ghanpur, Medchal Mandal District, Telangana - 501 401
Source of Support: None, Conflict of Interest: None
BACKGROUND AND OBJECTIVES: Low bone mineral density (BMD) is widely prevalent in Indian populations. Very few studies that reported risk factors for low BMD and osteoporosis did not explore its relationship with age and sex. Objectives were to determine the prevalence of low BMD and osteoporosis in urban adults, and study the age- and sex-wise trends of changing BMD.
METHODOLOGY: BMD of 521 healthy adults aged 20 years or more was tested using quantitative ultrasound of right tibia, and T-scores thus obtained indicated normal or low BMD. Multivariate analysis was done to calculate odds ratios for risk factors for low BMD and osteoporosis.
RESULTS: Eighty-four percent participants had low BMD. Low BMD increased significantly with increasing age in both genders. An increase in age per standard deviation (SD) was associated with four-fold increase in risk of low BMD for both women (odds ratio [OR]: 4.83, 95% confidence interval [CI]: 2.61–8.92) and men (OR: 4.14, 95% CI: 2.44–7.05). Age per SD (OR: 2.28, 95% CI: 1.37–3.81) and age–gender interaction (OR: 1.09, 95% CI: 1.02-1.17) was positively associated with osteoporosis. Increasing age by one SD was associated with seven-fold increase (OR: 7.35, 95% CI: 3.51–15.40) in risk of osteoporosis among women.
CONCLUSIONS: Low BMD is highly prevalent in South Indian urban population. Low BMD and osteoporosis were positively associated with increasing age. Loss of BMD appeared to begin at young ages thereby indicating the need for early institution of prevention measures.
Keywords: Bone mineral density, gender, osteopenia, osteoporosis, prevalence, T-score
|How to cite this article:|
Sharma PK, Sriram S, Krishna A, Gandhi A, Ganguly E. Low bone mineral density and its risk factors in an urban adult population of South India. Int J Health Allied Sci 2019;8:61-7
|How to cite this URL:|
Sharma PK, Sriram S, Krishna A, Gandhi A, Ganguly E. Low bone mineral density and its risk factors in an urban adult population of South India. Int J Health Allied Sci [serial online] 2019 [cited 2022 Jan 23];8:61-7. Available from: https://www.ijhas.in/text.asp?2019/8/1/61/252450
| Introduction|| |
Osteoporosis, caused by severely low bone mineral density (BMD), is an increasingly important public health problem globally, with nearly 200 million people suffering from the disease. The complications of the disease, including spontaneous fractures and vertebral collapse, affected approximately 9 million people worldwide in 2000., Literature indicates that 40% women and 15%–30% men with osteoporosis face the risk of fragility fractures;, this risk increases beyond 50 years of age in both sexes., While direct estimates of increasing incidence of low BMD are missing, osteoporotic hip fractures statistics showed significantly associated morbidity and mortality in the developed countries a decade earlier and were projected to increase from 1.7 million in 1990 to 2.6 million by 2025., The current literature reports the prevalence of osteoporosis to be lower in Western countries than in Asia. With the rise in life expectancy and greater proportions of aged population in Asian countries, primarily China and India, the problem has escalated causing a shift of focus from Europe and US to Asian countries., The challenge of treating low BMD is greater in these countries because most patients with osteoporotic complications, predominantly rural, are underdiagnosed and undertreated conservatively at home.,,
Studies on estimating BMD in Indian populations, using different X-ray radiometry tools and criteria, have reported osteoporosis prevalence ranging from 8.5% among men to about 53% among postmenopausal women. These studies have shown a steady decline in BMD with increasing age. Several risk factors for higher prevalence of osteoporosis among women, as compared to men, have been documented that include greater longevity, low calcium intakes, vitamin D deficiency, genetic predisposition, lack of diagnostic facilities, and poor knowledge of bone health. Other age-dependent factors, including early menopause and reduced estrogen production after menopause, pose additional risk to older women.
Previous studies that have examined age-related changes in BMD among premenopausal women are conflicting; while some cross-sectional studies reported no age differences in BMD of the lumbar spine, or the femoral neck, others found age differences in BMD to exist. Age at the onset of bone loss among women is also not well described, and estimates have ranged widely suggesting the onset of trabecular bone loss between ages 20 and 40 years., Cross-sectional data from women indicate that bone loss of both the femoral neck and the lumbar spine may begin as early as late adolescence or as late as age 39 years in the femoral neck and age 49 years in the lumbar spine. In men, bone loss has been suggested to begin in the early twenties, which is also the age for attaining peak bone mass. The importance of attaining greater peak bone mass before the onset of bone loss is receiving increasing public health attention because greater peak bone mass is considered to be a means of attenuating the effects of postmenopausal bone loss among women. On the contrary, higher bone mass too has been associated with greater rates of bone loss. The current global research focusing on the relationship between peak bone mass, the rate of subsequent bone loss, and the time over which that loss is sustained will be crucial for evaluating the public health interventions aimed at increasing or sustaining peak bone mass.
India is experiencing a demographic shift from rapid urbanization and increasing proportion of aging population, where the risk for osteoporosis and its complications are compounding fast. There is extreme shortage of community-based studies in India that examine the influences of age and gender on BMD. Therefore, the present study was designed to identify the current disease burden with respect to the changes in population pyramid. The objectives of the present paper were to determine the prevalence of low BMD and osteoporosis in urban adults of Hyderabad city and its surrounding urban areas and to examine the trends of changing BMD among different ages and genders.
| Methodology|| |
Study population and setting
The present study was done among the urban and peri-urban population of Andhra Pradesh (now Telangana). Of 31 districts in the state, the study area covered three districts selected in the Southern part of the state, namely, Ranga Reddy, Medak, and Nalgonda. The total population in the study area was 7,749,334.
Forty-five percent population was of adult males, whereas 43% was of adult females. The adult female: male ratio was 945/1000 males. About 82.96% adult males were literate, while female literacy was 79.79%. The main languages spoken were Telugu, English, and Hindi. The population had easy access to different health-care services in the region that had 50 government tertiary care hospitals, 494 large and small private hospitals, and numerous day clinics run by doctors of different systems of medicine as well as registered medical practitioners.
Study design and duration
The institutional ethics committee provided approval for the study. This cross-sectional study was conducted from April through August 2013.
Based on the previous published estimate of 35% prevalence of osteoporosis in a South Indian urban area, the required sample for the prevalence estimation was calculated to be 423, including 10% oversampling. We, however, included all participants, who presented for the camps and were willing to undergo screening, to state risk factors with sufficient power, whose previous estimates were unavailable for sample size estimation.
Selection of participants and study site
All apparently healthy adults aged 20 years and more presenting to MediCiti Hospital were contacted for participating in the study. In addition, three camps were also set up in adjoining districts, where camp attendees of the required age were screened for low BMD. A study clinic was set up in the hospital premises while screening camps were held at identified radiology centers, where detailed information regarding the study was provided. Written informed consent was obtained from those willing to participate.
Inclusion and exclusion criteria
Participants having a history of surgery in either lower limb or those on antiepileptic or antitubercular drugs were excluded. Pregnant women and participants with a history of chronic use of steroids and known rheumatoid arthritis were also excluded from the study.
A questionnaire was designed to collect information on age, sex, socioeconomic condition, and preexisting medical conditions and medication history.
Testing bone mineral density
Quantitative ultrasound (QUS) of the right tibia was performed in all participants using a multisite ultrasonometer (BeamMed Sunlight MiniOmni Ultrasonometer, USA) to calculate the BMD of the right lower limb. QUS is portable, radiation-free, and relatively inexpensive for providing a proxy for BMD; in addition, it can assess properties of bone quality. QUS employs speed of sound (SOS) detection that is directly related to the elasticity and density of the bone and has been shown to be comparable to dual energy X-ray absorptiometry (DEXA) (gold standard) while providing reliable T-scores and cost-effective for community screening. T-score describes the bone mass of the patient compared to the peak bone mass of the normal young adult reference population.
Participants were supine with lower right leg exposed. Midpoint of the tibia was marked halfway between apex of patella and medial malleolus. The probe was placed over the skin at this point after scrubbing and application of ultrasonic coupling gel. Three consecutive measurements were taken for each participant over the right tibia with interim repositioning, and the skin was scrubbed before each measurement. Last two measurements were averaged to minimize potential repositioning errors. Left tibia was used for measurement in 11 participants who had right limb edema. The World Health Organization recommendations for T-score, based on densitometer readings, were used for classifying normal and low BMD, where participants having T-score >−1 were considered normal while <−1 were categorized as low BMD; T-score − 1 to − 2.5 was considered osteopenia; and <−2.5 was considered osteoporosis.
Quality of QUS measurements was assured using several procedures. A phantom provided by the manufacturer was used to check the stability of the densitometer daily at all centers. All bone density measurements were performed in a temperature-controlled environment at 25°C to minimize temperature variations. A limb-positioning device was used to help target placement of the right tibia, ensuring reproducibility and limiting movement. The coefficient of variation for SOS, calculated by dividing the root mean square of individual SDs by mean SOS, was 0.22% indicating excellent precision of QUS measurement in the study.
Data entry and analysis
Data were entered and analyzed using SPSS version 21. 0 (IBM Corp, Armonk, NY, USA). The mean (± standard deviation [SD]) for continuous normally distributed variables was reported. T-test was used to study the differences between groups. Age quartiles were formed for men and women, and ANOVA was used to report BMD differences within quartiles. We conducted a logistic regression to examine if age alone or age and gender taken together could have predicted low BMD and osteoporosis in this population. Variables with P < 0.05 in univariate analysis were entered in the logistic regression model. Odds ratios and P values were reported. P <0.05 was considered statistically significant.
| Results|| |
Of 540 people invited, 521 agreed to participate giving a response rate of 96.4%. The mean age (±SD) of the participants was 44.13 (±12.76) years. The mean ages of men (44.16 ± 11.99 years) and women (44.09 ± 13.73 years) were similar.
The number of participants detected with low BMD (84.26%) was higher than those with normal BMD (15.73%) [Table 1]. The prevalence of low BMD increased steadily from lower to higher age groups both among men and women, with almost 100% participants of above 60 years of age having low BMD [Table 2]. The mean age of women having low BMD (46.37 ± 13.41 years) was significantly higher than those with normal BMD (32.11 ± 8.13 years); similar pattern was observed in men, 45.88 ± 11.65 years among low BMD and 34.82 ± 9.28 years among normal BMD. About 10.22% women had osteoporosis, while 73.77% had osteopenia. Among men, 5.74% had osteoporosis and 78.71% had osteopenia. Overall, 7.67% population was osteoporotic while 76.58% showed osteopenia [Table 1].
|Table 2: Distribution of normal and low bone mineral density by age and sex|
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Men and women showed a trend of being increasingly osteoporotic with increasing age [Table 1]. Women showed lower BMD over men till 40 years of age, after which both sexes showed osteoporosis at a very similar rate, with the rates equaling beyond 60 years of age. The rate of losing BMD with age was higher among men of younger ages compared with women of similar age [Figure 1].
|Figure 1: Mean bone mineral density in 10-year age groups in women and men. Women have a lower mean bone mineral density than in all age groups except 30–39 years where men show a lower mean bone mineral density|
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Age quartiles for women and men were formed. The mean T-scores for lowest through highest quartiles for women and men are shown in [Table 4]. The interquartile differences in mean T-scores are also shown [Table 4]. The interquartile differences in mean T-scores, calculated using ANOVA, were significant both in women (F = 50.53 and P < 0.001) and men (F = 19.26 and P < 0.001). Men had lower BMD than women in younger age quartiles [Figure 2].
|Figure 2: Mean bone mineral density in age quartiles among women and men. The mean bone mineral density in the first and second quartiles of age among women is higher than men, while in the third and fourth quartiles, men show higher mean bone mineral density. The differences in mean bone mineral density in different age quartiles were significant for both, women and men|
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We also examined the differences in mean T-scores among young adults (18–40 years), older adults (41–59 years), and elderly (60 years and above). The differences in mean T-scores across the three categories were significant in the overall population and in women and men as well [Table 3].
|Table 3: Mean T-scores of participants stratified by age categories and gender|
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The variables age per SD, gender, and age and gender interaction were entered into logistic regression. Separate models were formed for low BMD and osteoporosis. Age per SD (odds ratio [OR]: 4.14, 95% confidence interval [CI]: 2.44–7.05) alone was a significant risk factor for low BMD. An increase in age per SD was associated with four-fold increase in the risk of low BMD for both women (OR: 4.83, 95% CI: 2.61–8.92) and men (OR: 4.14, 95% CI: 2.44–7.05). For osteoporosis, age per SD (OR: 2.28, 95% CI: 1.37–3.81) as well as age and gender interaction (OR: 1.09, 95% CI: 1.02–1.17) showed positive association, whereas gender (male) (OR: 0.01, 95% CI: 0.000–0.59) was negatively associated. Increasing age by one SD was associated with seven-fold increase (OR: 7.35, 95% CI: 3.51–15.40) in the risk of osteoporosis among women and two-fold increase (OR: 2.28, 95% CI: 1.37–3.81) in men [Table 5].
| Discussion|| |
The prevalence of osteoporosis in our study was found to be low, while that of low BMD was high. Women had higher prevalence of osteoporosis over men. Low BMD as well as osteoporosis increased with increasing age in this population. The mean BMD was lower in men of younger age groups than in women of similar age groups, while women in the third and fourth quartiles of age had significantly lower mean BMD than men in those quartiles.
In large community-based studies, comparatively lower prevalence of osteoporosis has been reported from Western countries than from Asian countries. A study from Canada conducted on 10,061 community dwellers aged 25 years and above found 15.8% and 6.6% prevalence of osteoporosis among women and men, respectively, which were close to our findings. A Spanish study on 115 postmenopausal women aged 49–85 years reported 50.4% prevalence of osteoporosis and 29.6% prevalence of osteopenia, similar to the high prevalence seen among women in our study. A study from the Netherlands, however, reported a lower prevalence of osteopenia and osteoporosis at 27.3% and 4.1%, respectively, among 5896 perimenopausal women aged 46–54 years.
In India, high prevalence of low BMD was reported in women aged above 25 years from the Northern state of Jammu. The incidence of osteoporosis among 158 women studied by Sharma et al. in 2006 was 20% while that of osteopenia was 37%, with highest number of women in age group of 55–64 years. Such differences from our results can be attributed to nutritional and environmental factors, wherein lower calcium intake and lower sun exposure among North Indian hilly populations could affect BMD adversely. In another study among Indian women aged 30–60 years from the low-income groups, high prevalence of osteopenia (52%) and osteoporosis (29%) was reported, which was attributed to possible nutritional inadequacy. Another study involving 200 peri- and postmenopausal women aged 45 years and more also reported 53% prevalence of low BMD in general population in the north Indian state of Haryana. These findings were inconsistent with our results where the prevalence of osteoporosis was lower and that of osteopenia was higher. Other South Indian studies have also reported low prevalence of osteoporosis along with high osteopenia prevalence among apparently healthy populations, which is possibly due to environmental factors coupled with low Vitamin D status. A recent study of 200 men, aged 50 years and above, from an urban area reported 8.5% prevalence of osteoporosis, which is slightly higher than in our population. These differences can be explained based on the younger age of the participants in our study. The use of Western referent data for classifying BMD in Indian population has been debated widely wherein it has been suggested that using cutoffs for Western populations tend to overdiagnose low BMD in the Indian population and consequently, lower ICMR cutoffs have been prescribed, particularly for screening younger populations. Since most community-based studies, including ours, have used the Western cutoffs for younger populations as well, it might have influenced the prevalence data. Mikuls et al. too could report differences in osteoporosis prevalence using different cutoffs, using Caucasian referent data, 33% of patients had osteopenia and 5% were osteoporotic, whereas with the use of African-American normative data, 55% were osteopenic and 16% were osteoporotic. These findings emphasize the need for using local cutoff values.
An increase in number of low BMD individuals with age was observed for women in our population as compared with men. This is consistent with several studies which have found that although bone loss continues with increasing age in both sexes, the rate of loss is significantly higher among postmenopausal women compared with premenopausal women and with men belonging to same age groups as well. Low BMD is clearly attributed to loss of the protective effect offered by estrogen during postmenopausal stage among women, whereas in men, these influences are absent explaining the steady rate of bone loss across all ages. Upon dividing the population into gender-dependent age quartiles, we found the mean differences in T-scores across each quartile to be significant among women. For men, we found that the mean T-score was not different in the second and third quartiles, whereas the lowest and highest quartiles were significantly different from the second and third quartile, respectively. Research has indicated that the protective effect exerted by estradiol in men continues to wane steadily beyond the age of maximum bone deposition and starting of fat accumulation which is usually around 30 years. This offers a plausible explanation for the observed differences in BMD between men and women in younger age groups in our population, where we found men having lower BMD than women. This also explains the greater magnitude of bone loss among men in younger age groups compared with young women.
Increasing age per SD was found to be significantly associated with low BMD and osteoporosis among men and women in our study. Women showed a stronger association (seven-fold risk) of osteoporosis risk with age, compared with men (four-fold risk). Age × gender interaction was also a significant risk factor for osteoporosis. We expected to find significant age × gender interactions toward increasing the risk for low BMD, which has been previously shown by Daly et al. among Swedish men and women aged 50 years and above. The absence of this association may be explained by the large age range of our population, and the protective effect offered by the male gender as well.
There are few limitations. First, the sample size was not derived using sufficient statistical power to identify risk factors. Second, recruitment of participants was uneven across sites with no clear rural-urban demarcation. Third, QUS was performed at four different radiology centers, where quality was assured by calibration of BMD readings for cross-sectional consistency through regular scanning of phantoms supplied by the manufacturer;, and scan methodology and training of radiology technicians were standardized according to protocols. Further, owing to the cross-sectional nature of our analysis, results may be influenced by selection bias resulting from eligibility criteria and screening processes, providing no evidence for temporal relationships. Finally, our results are based on the use of Western referent data for classifying low BMD that may have contributed to greater magnitude of low BMD in this population.
| Conclusions|| |
Low BMD is highly prevalent in South Indian urban population. Women were affected with osteoporosis and osteopenia earlier than men. Significant associations were found with increasing age in both genders. This indicates the need for instituting prevention measures at earlier ages.
Declaration of patient consent
The authors certify that they have obtained all appropriate patient consent forms. In the form, the patients have given their consent for their images and other clinical information to be reported in the journal. The patients understand that their names and initials will not be published and due efforts will be made to conceal their identity, but anonymity cannot be guaranteed.
Financial support and sponsorship
Conflicts of interest
There are no conflicts of interest.
| References|| |
Johnell O, Kanis JA. An estimate of the worldwide prevalence and disability associated with osteoporotic fractures. Osteoporos Int 2006;17:1726-33.
Gullberg B, Johnell O, Kanis JA. World-wide projections for hip fracture. Osteoporos Int 1997;7:407-13.
Cooper C, Campion G, Melton LJ 3rd
. Hip fractures in the elderly: A world-wide projection. Osteoporos Int 1992;2:285-9.
Smeets-Goevaers CG, Lesusink GL, Papapoulos SE, Maartens LW, Keyzer JJ, Weerdenburg JP, et al.
The prevalence of low bone mineral density in Dutch perimenopausal women: The Eindhoven perimenopausal osteoporosis study. Osteoporos Int 1998;8:404-9.
Tenenhouse A, Joseph L, Kreiger N, Poliquin S, Murray TM, Blondeau L, et al.
Estimation of the prevalence of low bone density in Canadian women and men using a population-specific DXA reference standard: The Canadian multicentre osteoporosis study (CaMos). Osteoporos Int 2000;11:897-904.
Dhanwal DK, Cooper C, Dennison EM. Geographic variation in osteoporotic hip fracture incidence: The growing importance of Asian influences in coming decades. J Osteoporos 2010;2010:757102.
Mithal A, Bansal B, Kyer CS, Ebeling P. The Asia-Pacific regional audit-epidemiology, costs, and burden of osteoporosis in India 2013: A report of international osteoporosis foundation. Indian J Endocrinol Metab 2014;18:449-54.
Teotia SP, Teotia M. Nutritional bone disease in Indian population. Indian J Med Res 2008;127:219-28.
] [Full text]
Gupta A. Osteoporosis in India – The nutritional hypothesis. Natl Med J India 1996;9:268-74.
Agrawal NK, Sharma B. Prevalence of osteoporosis in otherwise healthy Indian males aged 50 years and above. Arch Osteoporos 2013;8:116.
Aggarwal N, Raveendran A, Khandelwal N, Sen RK, Thakur JS, Dhaliwal LK, et al.
Prevalence and related risk factors of osteoporosis in peri – And postmenopausal Indian women. J Midlife Health 2011;2:81-5.
Khadilkar AV, Mandlik RM. Epidemiology and treatment of osteoporosis in women: An Indian perspective. Int J Womens Health 2015;7:841-50.
Reyes Balaguer J, Moreno Olmos J. Prevalence of osteopenia and osteoporosis in postmenopausal women. Aten Primaria 2005;35:342-5.
Bainbridge KE, Sowers MF, Crutchfield M, Lin X, Jannausch M, Harlow SD, et al.
Natural history of bone loss over 6 years among premenopausal and early postmenopausal women. Am J Epidemiol 2002;156:410-7.
Hansen MA. Assessment of age and risk factors on bone density and bone turnover in healthy premenopausal women. Osteoporos Int 1994;4:123-8.
Mazess RB, Barden HS. Bone density in premenopausal women: Effects of age, dietary intake, physical activity, smoking, and birth-control pills. Am J Clin Nutr 1991;53:132-42.
Riggs BL, Melton Iii LJ 3rd
, Robb RA, Camp JJ, Atkinson EJ, Peterson JM, et al.
Population-based study of age and sex differences in bone volumetric density, size, geometry, and structure at different skeletal sites. J Bone Miner Res 2004;19:1945-54.
Mazess RB. On aging bone loss. Clin Orthop Relat Res 1982;165:239-52.
Matkovic V, Jelic T, Wardlaw GM, Ilich JZ, Goel PK, Wright JK, et al.
Timing of peak bone mass in Caucasian females and its implication for the prevention of osteoporosis. Inference from a cross-sectional model. J Clin Invest 1994;93:799-808.
Mazess RB, Barden H. Bone density of the spine and femur in adult white females. Calcif Tissue Int 1999;65:91-9.
Kulak CA, Bilezikian JP. Osteoporosis: Preventive strategies. Int J Fertil 1998;43:56-64.
Davis JW, Grove JS, Ross PD, Vogel JM, Wasnich RD. Relationship between bone mass and rates of bone change at appendicular measurement sites. J Bone Miner Res 1992;7:719-25.
World Health Organization – WHO Criteria for Diagnosis of Osteoporosis. Available from: http://www. 4bone
health.org/education/world-health-organization-criteria-diagnosis-osteoporosis/. [Last accessed on 2016 Aug 15].
Sharma S, Tandon VR, Mahajan A, Kour A, Kumar D. Preliminary screening of osteoporosis and osteopenia in urban women from Jammu using calcaneal QUS. Indian J Med Sci 2006;60:183-9.
] [Full text]
Shatrugna V, Kulkarni B, Kumar PA, Rani KU, Balakrishna N. Bone status of Indian women from a low-income group and its relationship to the nutritional status. Osteoporos Int 2005;16:1827-35.
Bala S, Prabha ML, Krishna TP. Prevalence and risk factors of low bone mineral density with quantitative ultrasonography among South Indian postmenopausal women. Int J Community Med Public Health 2016;3:1735-40.
Mikuls TR, Saag KG, Curtis J, Bridges SL Jr., Alarcon GS, Westfall AO, et al.
Prevalence of osteoporosis and osteopenia among African Americans with early rheumatoid arthritis: The impact of ethnic-specific normative data. J Natl Med Assoc 2005;97:1155-60.
Jhaveri S, Upashani T, Bhadauria J, Biswas S, Patel K. Current clinical practice scenario of osteoporosis management in India. J Clin Diagn Res 2015;9:RC04-8.
Daly RM, Rosengren BE, Alwis G, Ahlborg HG, Sernbo I, Karlsson MK, et al.
Gender specific age-related changes in bone density, muscle strength and functional performance in the elderly: A-10 year prospective population-based study. BMC Geriatr 2013;13:71.
[Figure 1], [Figure 2]
[Table 1], [Table 2], [Table 3], [Table 4], [Table 5]