by gufeng
Level 1 Seller

5

(0)

$0

eduzhai > Helth Sciences > Medical >

- sky
- (0) Download
- 20211031

https://www.eduzhai.net Clinical Medicine and Diagnostics 2014, 4(3): 42-54 DOI: 10.5923/j.cmd.20140403.02 The Correlation of Body Mass Index, Age, Gender with Bone Mineral Density in Osteopenia and Osteoporosis: A Study in the United Arab Emirates Wasan A. M. Al Taie1,*, Abdulameer M. Rasheed2 1College of Education, American University in the Emirates, P.O. Box: 503000, Dubai, UAE 2Specialist Rheumatologists, Canadian Specialist Hospital, P.O. Box: 15881, Dubai, UAE Abstract The primary objectives of this research are to explore and evaluate the correlation between the Body Mass Index (BMI) and Bone Mineral Density (BMD), to gauge the correlation between age and BMD and to investigate the effect of gender on BMD. Methods: BMD was determined in the femoral neck and lumbar (L2-L4) regions for 210 men and women with an average age of (57.41 ± 9.73) using dual energy X- ray absorptiometry (DEXA). Subsequently, 116 participants were determined to have osteopenia, and 94 participants had osteoporosis. We analyzed the data by multiple regression and ANOVA models. Results: We found the highest percentages of osteopenia and osteoporosis, 48.3% and 44.7%, respectively in obese patients. The statistical analysis of each independent variables (age, gender and BMI) indicated that there were no significant correlations between the BMI and BMD in osteopenia and osteoporosis (p-value = 0.2001 and p-value = 0.4622), respectively. Moreover, the correlation of the independent variables (age, gender and BMI) together and the dependent variable (BMD) was significant (p-value = 0.034, P ≤ 0.05) of osteoporosis only, but the correlation was not significant between BMD and each individual variables separately, compatible with a diagnosis of osteopenia and osteoporosis. Furthermore, the most effective variable on the BMD was the BMI (p-value= 0.02) of osteopenia, while the age (p-value = 0.011) was the most effective variable on the BMD of osteoporosis when they were tested jointly. Conclusions: The BMD was influenced significantly by all independent variables (age, gender and BMI) together in the osteoporosis not in osteopenia, so all variables together are considered as risk factors of osteoporosis. However, this effect could not be implied in the osteopenia patients. Keywords Osteopenia, Osteoporosis, Obesity, Body mass index, Bone mineral density, Postmenopausal 1. Introduction Osteopenia and osteoporosis have become common global health problems affecting millions of individuals, particularly those who are middle-aged and elderly [1, 2]. It has been estimated that fracture incidence exceeds 50% at age 50 years among women, and 20% among men [3]. In the United States, more than 14 million postmenopausal white women have osteopenia [4], which leads to fractures and fractures lead to extensive health problems if not diagnosed and treated early [5]. Statistical data have shown that 4-6 million (13%-18%) postmenopausal women in the United States have osteoporosis [6], whereas osteoporosis occurs in more than one-third of the women over 65 years old in western societies [7]. High levels of osteopenia and osteoporosis have been detected in screening programs in * Corresponding author: wasan3072@yahoo.co.uk (Wasan A. M. Al Taie) Published online at https://www.eduzhai.net Copyright © 2014 Scientific & Academic Publishing. All Rights Reserved Dubai as of May 24 2007 [8]. Statistics show that an osteoporotic fracture occurs every three seconds, with one in three women and one in five men over 50 years old being at risk for osteoporotic fracture in the United Arab Emirates (UAE) [9]. Kiebzak et al. [10] have been revealed that osteoporotic- related hip fractures are predominant cause of death and are associated with a high incidence of disability in both men and women. Although aging and hormone deficiency in women and men are the major causes of primary osteopenia and osteoporosis [7], numerous physiological factors could increase the incidence of secondary osteopenia and osteoporosis, including the following: hypercortisolism, hyperthyroidism, long–term glucocorticoid use, diabetes mellitus, premature ovarian failure, hypogonadism, Cushing’s syndrome, prolonged heparin treatment, anticonvulsant treatment, prolonged immobilization, eating disorders such as anorexia nervosa and a family history of the osteopenia and osteoporosis, weight loss, and being European or Asian [11, 12]. Other lifestyle factors that present risk parameters of osteopenia and osteoporosis Clinical Medicine and Diagnostics 2014, 4(3): 42-54 43 include smoking, alcohol abuse, a diet low in dairy products or a calcium and vitamin D deficiency, excessive protein consumption, malabsorption, dieting and heavy consumption of caffeine in soft drinks and coffee [13, 14]. An important factor that affects bone mass and metabolism is physical activity [15]. Yamazaki et al. [16] demonstrated that walking exercise suppresses bone turnover in postmenopausal women with osteopenia and osteoporosis. Moreover, regular exercise and a stable weight in older age might delay the risk of fracture [17]. Al Attia et al. [18] studied the correlation between androgen levels and bone mineral density (BMD) in elderly Arab men and found that osteoporotic patients are significantly older than those with osteopenia or normal bone density. Moreover, several studies have reported that bone loss and low BMD are associated with advancing age and a low body mass index (BMI) which are considered as significant risk factors for osteoporosis in men, and menopausal, postmenopausal women [19-24]. Another serious health problem, especially for women in Arab countries, is obesity. The World Health Organization (WHO) estimated that more than 2.3 billion people will be overweight and 700 million will be obese by 2015, predominantly women [25]. Globally, the United Arab Emirates (UAE) is the fifth-ranked country in terms of obesity, particularly in women [26]. Statistics have shown that obesity and overweight in the UAE reached 25.6% in men and 39.9% in women [27]. Regarding studies on the relationships of osteopenia and osteoporosis with obesity, the results are inconsistent. Most work focuses on a positive correlation between obesity and increased bone density, especially in elderly women [28]. A high BMI is considered a protective factor on bone mineral density among elderly people [29, 30]. On the other hand, other researchers have mentioned that the risk of bone loss, osteopenia and osteoporosis are increased in overweight and obese older patients [31, 32]. Lin et al. [33] indicated that osteoporosis patients have a BMI higher than that of osteopenia patients. Age and female gender are other risk parameters in the increasing incidence of obesity and osteoporosis [34-36]. With this background and the global increase in obesity and osteoporosis; in this study, we investigated the effect of BMI, age and gender on BMD in 30-80-year old osteopenia and osteoporosis patients in the UAE. Research hypotheses Null hypotheses: Ho: β1 = 0, there is no statistically significant effect between the independent variables (age, gender and BMI) and the dependent variable (femoral neck and the lumbar spine L2-L4) of osteopenia and osteoporosis patients. Alternative hypotheses: H1: β1 # 0, there is statistically significant effect between the independent variables (age, gender and BMI) and the dependent variable (femoral neck and the lumbar spine L2-L4) of osteopenia and osteoporosis patients. The first hypothesis: Ho: There is no statistically significant effect of the BMI on BMD in the femoral neck and the lumbar spine of osteopenia and osteoporosis patients. The second hypothesis: Ho: There is no statistically significant effect of age on BMD in the femoral neck and the lumbar spine of osteopenia and osteoporosis patients. The Third hypothesis: Ho: There is no statistically significant effect of gender on BMD in the femoral neck and the lumbar spine of osteopenia and osteoporosis patients. 2. Patients and Method With the approval of the Canadian specialty hospital in Dubai which has Joint Commission International Accreditation (JCIA), the research was conducted on patients who attended the rheumatology clinic at the hospital from June 2010 till June 2012. A total of 210 patients aged 30-80 years, with an average age of (57.41 ± 9.73), were included in the study. All the patients were diagnosed as asymptomatic, with no history of fracture, and they did not suffer from other diseases including diabetes, hypertension and cardiovascular diseases. We excluded all the patients with other diseases, those who had had an organ transplant or who took medicine that might affect the BMD values. For each patient who suffered from bone pain and symptoms of osteopenia or osteoporosis, BMD was determined in the femoral neck and the lumbar spine (L2 - L4) using the dual energy X ray absorptiometry (DEXA) technique to diagnose the osteoporosis and osteopenia patients. According to the World Health Organization (WHO), osteopenia/osteoporosis is diagnosed based on the T-scores of BMD as follows: the T score of osteopenia is between -1 and -2.5, whereas the T score of osteoporosis is -2.5 or below [37, 38]. Among the 210 patients, there were 30 men and 180 women, and the BMD data were categorized into 116 patients with osteopenia and 94 patients with osteoporosis. The data of each disease were categorized based on patient age into the following four groups: group 1: 30-39; group 2: 40-49; group 3: 50-59; and group 4: 60 and above. The body weight of each patient was measured to the nearest 0.1 kg on an electronic beam scale, and the height was measured to the nearest 0.5 cm using a stadiometer. The body weight and height for each patient were measured twice per time point, and the average of the duplicate measures was calculated. The BMI was calculated using the following formula: the body weight in kg divided by the body height in meters square. The BMI was categorized by WHO as follows: underweight BMI is body weight of less than 18.5Kg/m2; normal weight BMI is body weight of 18.5-24.9 Kg/m2; overweight is body weight of 25-29.9 kg/m2; and obesity BMI is body weight of 30 Kg/m2 or more [39]. The data were analyzed without mention the names and personal details of 44 Wasan A. M. Al Taie et al.: The Correlation of Body Mass Index, Age, Gender with Bone Mineral Density in Osteopenia and Osteoporosis: A Study in the United Arab Emirates the patients using multiple regression, ANOVA and Bivariate correlation models using Statistics Package for Social Sciences (SPSS program, version 20) to measure the correlation between BMD with the BMI, age and gender in osteopenia and osteoporosis patients. This study did not include the control samples because the diagnostic test (DEXA) of the BMD is expensive. So this test was required from the patients who had bone pain and risk factors of osteoporosis after physical examination. 3. Results A detailed description of the osteopenia and osteoporosis patients is shown in Table (1). Among the 116 osteopenia patients, 86.2% were female and 13.8% were male, whereas of the 94 patients with osteoporosis, 85.1% were female and 14.9% were male. The average of the BMI values of the osteopenia and osteoporosis patients was 29.05 ± 4.193 and 27.4 ± 5.363, respectively, which implies that most of the patients were under overweight category in osteopenia and osteoporosis. The average of the BMD of the osteopenia and osteoporosis patients was -1.664 ± 0.111 and -3.185 ± 0.438, respectively. Approximately 39.7% of the osteopenia participants were 50-59 years old, whereas 57.4% of the osteoporosis participants were 60 years old and above. More than 37.9% and 48% of the osteopenia patients were overweight and obese, respectively. As shown in Table (1), 19.2% of the patients were overweight and 44.7% of the osteoporosis patients were obese. A descriptive data are represented in Tables (1) and (2). Table 1. Distribution of the patients according to Gender, Age, and BMI (Osteopenia and Osteoporosis Patients) Patients Osteopenia Independent Variables Gender Age (year) BMI Group Male Female 30-39 40-49 50-59 60 + Underweight Normal Overweight Obese Number 16 100 4 25 46 41 3 13 44 56 Percentage 13.8% 86.2% 3.4% 21.6% 39.7% 35.3% 2.6% 11.2% 37.9% 48.3% Gender Male Female 14 80 30-39 6 Age 40-49 15 Osteoporosis (year) 50-59 60 + 19 54 Underweight 10 BMI Normal Overweight 24 18 Obese 42 14.9% 85.1% 6.4% 16.0% 20.2% 57.4% 10.6% 25.5% 19.2% 44.7% Table 2. Association between Bone Mineral Density (BMD) and the independent variables (BMI and Age) (Osteopenia and Osteoporosis Patients) Patients Osteopenia Independent variables BMI Age (year) Groups Underweight Normal Overweight Obese 30-39 40-49 50-59 60 + Classification of bone mineral density (BMD) Normal No. % Low Bone Desity No. % Total 0 0 3 2.6 3 0 0 13 11.2 13 0 0 44 37.9 44 0 0 56 48.3 56 0 0 4 3.4 4 0 0 25 21.6 25 0 0 46 39.7 46 0 0 41 35.3 41 Underweight 0 0 10 10.6 10 BMI Normal 0 Overweight 0 0 0 24 18 25.5 19.2 24 18 Osteoporosis Obese 0 0 42 44.7 42 30-39 0 0 6 6.4 6 Age 40-49 0 0 15 16.0 15 (year) 50-59 0 0 19 20.2 19 60 + 0 0 54 57.4 54 Clinical Medicine and Diagnostics 2014, 4(3): 42-54 45 To test the validity of research hypotheses, we used linear multiple regression and ANOVA. The purposes for using multiple regressions were to determine the regression coefficients between the independent variables, BMI, age and gender and the dependent variable (BMD) of the osteopenia and osteoporosis patients. Cohen guidelines [40] were used to interpret the correlation among the independent variables (BMI, age and gender) looking at r- value as follows: 0.1 to 0.29 is weak, 0.30 to 0.49 is medium and 0.50 to 1.0 is large or strong correlation. ANOVA analysis is useful to study the influence of all variables jointly on the dependent variable (BMD) of the osteopenia and osteoporosis patients, looking at the p-value (sig.). In order to test the hypothesis for the model as a whole, we have to look at F-statistic and compare it with the tabulated Fstatistic, if the calculated F- statistic is greater than the tabulated with two degree of freedom meaning the null hypothesis will be rejected and alternative hypothesis will be accepted The results of these statistical analyses were presented in following tables in this section. Null hypotheses: Ho: β1 = 0, there is no statistically significant effect between the independent variables (age, gender and BMI) and the dependent variable (femoral neck and the lumbar spine L2-L4) of osteopenia and osteoporosis patients. Alternative hypotheses: H1: β1 # 0, there is statistically significant effect between the independent variables (age, gender and BMI) and the dependent variable (femoral neck and the lumbar spine L2-L4) of osteopenia and osteoporosis patients. The Pearson correlation analysis was performed to obtain the strength and direction of the relationship (positive or negative) among the independent variables considered together. As shown in Table (3), there was a significant positive correlation between age and BMI in both osteopenia and osteoporosis patients (p = 0.005) and (p = 0.000) respectively. Although there exists a positive correlation between the age and BMI in both patients, but the degree of correlation was weak of osteopenia and strong of osteoporosis patients. There was a significant negative correlation but moderate between age and gender (p-value = 0.000) of osteopenia patients meaning that they were inversely related, while no significant of osteoporosis patients (p-value=0.031). Moreover, there was no significant correlation between BMI and gender in both patients and between age and gender in osteoporosis. However, the results indicated that three independent variables (age, gender and BMI) were significantly associated with each other and all could be considered as risk factors in the diagnosis of osteopenia and osteoporosis. In order to explore the actual relationship between two independent variables of interest while statistically controlling for a third variable, and partial correlations was carried out with the following variables (age, gender and BMI) of both osteopenia and osteoporosis patients as shown in Table (4). *Correlation value lies between [-1, 1]. Values close to -1 show a negative correlation meaning that they are inversely related and values closer to one shows positive correlation which implies that they are directly related [41]. From Table (4), there was no significant correlation between gender and BMI. However, there was a moderate increase in the strength of the correlation from 0.014 with the influence of age in Table (3) to 0.114 without the influence of age of osteopenia patients, and there was a moderate decrease in the strength of the correlation from 0.196 in Table (3) to 0.067 in Table (4) without the effect of age of osteoporosis patients. This suggests very weak correlation between gender and BMI and the age influences moderately on the correlation between BMI and gender. In the case of controlling gender, there was significant correlation between age and BMI in both osteopenia and osteoporosis patients. The gender does not affect significantly on the correlation between age and BMI, however, there was a small increase in the strength of correlation from 0.241 in Table (3) to 0.265 in Table (4) of the osteopenia patients and there was a small decrease from 0.656 in Table (3) to 0.641 in Table (4) of the osteoporosis patients. That means, gender does not clearly affect on the correlation between age and BMI in both patients. The correlation between the age and the gender was significant and there was a small increase in the strength of correlation from 0.370 in Table (3) to 0.385 in Table (4) , meaning the BMI does not clearly affect on the correlation between age and gender of the osteopenia patients. Table 3. Correlation of the independent variables (Age, Gender and BMI) for Osteopenia and Osteoporosis patients Correlationsb / Osteopenia Patients Independent variables Pearson Correlation Age Sig. (1-tailed) Pearson Correlation BMI Sig. (1-tailed) Pearson Correlation Gender Sig. (1-tailed) Age 1 0.241** 0.005 -0.370** 0.000 BMI 0.241** 0.005 1 0.014 0.442 Gender -0.370** 0.000 0.014 0.442 1 **. Correlation is significant at the 0.01 level (1-tailed). b. Number of osteopenia patients = 116 Correlationsc / Osteoporosis Patients Independent variables Age BMI Pearson Correlation 1 Age Sig. (2-tailed) 0.656** 0.000 Pearson Correlation 0.656** 1 BMI Sig. (2-tailed) 0.000 Pearson Correlation 0.223* Gender Sig. (2-tailed) -0.031 0.196 0.058 **. Correlation is significant at the 0.01 level (2-tailed). *. Correlation is significant at the 0.05 level (2-tailed). c Number of osteoporosis patients = 94 Gender 0.223* 0.031 0.196 0.058 1 46 Wasan A. M. Al Taie et al.: The Correlation of Body Mass Index, Age, Gender with Bone Mineral Density in Osteopenia and Osteoporosis: A Study in the United Arab Emirates Table 4. Partial Correlation of independent variables (Age, Gender and BMI) for osteopenia and osteoporosis patients Correlations between Gender & BMI / Osteopenia Patients Control Variables Gender BMI Correlation 1.000 0.114 Gender Significance (1-tailed) . 0.112 df 0 113 Age Correlation 0.114 1.000 BMI Significance (1-tailed) 0.112 . df 113 0 Correlations between Gender & BMI / Osteoporosis Patients Control Variables BMI Gender Correlation 1.000 0.067 BMI Significance (2-tailed) . 0.520 df 0 91 Age Correlation 0.067 1.000 Gender Significance (2-tailed) 0.520 . df 91 0 Correlations between Age & BMI/ Osteopenia Patients Control Variables BMI Age Correlation 1.000 0.265 BMI Significance (1-tailed) . 0.002 Gender df Correlation 0 0.265 113 1.000 Age Significance (1-tailed) 0.002 . df 113 0 Correlations between Age & BMI / Osteoporosis Patients Control Variables Age BMI Correlation 1.000 0.641 Age Significance (2-tailed) . 0.000 Gender df Correlation 0 0.641 91 1.000 BMI Significance (2-tailed) 0.000 . df 91 0 Correlations between Age & Gender/ Osteopenia Patients Control Variables Age Gender Correlation 1.000 -0.385 Age Significance (1-tailed) . 0.000 BMI df Correlation 0 -0.385 113 1.000 Gender Significance (1-tailed) 0.000 . df 113 0 df = degree of freedom Table 5. Multiple Regression Analysis and ANOVA of the independent variables (age, gender and BMI) and Dependent Variable (BMD) Model Summaryb / Osteopenia Patients Model 1 R 0.172a R Square 0.029 Adjusted R Square 0.003 Std. Error of the Estimate 0.33290 a. Predictors: (Constant), Age, BMI, Gender b. Dependent Variable: BMD Model Summaryb / Osteoporosis Patients Model 1 R 0.302a R Square 0.091 Adjusted R Square 0.061 Std. Error of the Estimate 0.42422 a. Predictors: (Constant), AGE, Gender, BMI b. Dependent Variable: BMD ANOVAb / Osteopenia Patients Model Sum of Squares df Mean Square F Regressio n 0.376 3 0.125 1.132 1 Residual 12.412 112 0.111 Total 12.788 115 a. Predictors: (Constant), Age, BMI, Gender b. Dependent Variable: BMD Sig. .340a ANOVAb / Osteoporosis Patients Model Sum of Squares df Mean Square F Regression 1.622 3 0.541 3.005 1 Residual 16.197 90 0.180 Total 17.819 93 a. Predictors: (Constant), AGE, Gender, BMI b. Dependent Variable: BMD Sig. .034a Clinical Medicine and Diagnostics 2014, 4(3): 42-54 47 Coefficientsa / Osteopenia Patients Unstandardized Standardized Model 1 Coefficients B Std. Coefficients Beta t Sig. Error Constant -1.396 .318 -4.387 .000 BMI -.007 .006 -.115 -1.194 .235 Gender .048 .096 .050 .499 .619 Age -.003 .004 -.082 -.797 .427 a. Dependent Variable: BMD Coefficientsa / Osteoporosis Patients Unstandardized Standardized Model 1 Coefficients B Std. Coefficients Beta t Sig. Error Constant -3.347 .366 -9.136 .000 BMI .026 .011 .315 2.367 .020 Gender .134 .124 .110 1.080 .283 Age -.013 .005 -.346 -2.584 .011 a. Dependent Variable: BMD Multiple regression and ANOVA conducted as illustrated in Table (5) to assess the correlation between the independent variables together and dependent variable through comparing the contribution of each independent variable on the BMD depending on the beta values. In osteopenia, ANOVA table indicates that the regression model predicts the effect of the independent variables (age, gender and BMI) together on the dependent variable (BMD) was not significant because p-value= 0.340, which was greater than 0.05. So we accepted the null hypothesis for osteopenia. For osteoporosis, the effect of the independent variables (age, gender and BMI) together on the dependent variable (BMD) was significant because p-value= 0.034, which was less than 0.05, So we rejected the null hypothesis and accepted the alternative hypothesis , compatible with a diagnosis of osteoporosis. The next thing we need to know was which of the independent variables (age, gender, BMI) included in the model contributed more of the dependent variable (BMD). However, the largest beta coefficient of the osteopenia was 0.115 for BMI, whereas the largest beta coefficient was -0.346 for age of the osteoporosis patients. This means that BMI makes the strongest contribution to the dependent variable (BMD) than other independent variables (age and gender) of osteopenia patients, while age makes more contribution to the dependent variable (BMD) than other independent variables (BMI and gender) of the osteoporosis patients. Additionally, the beta coefficients of BMI and age were negative indicating that these two variables were inversely related with BMD of osteopenia patients, while only age was inversely related with BMD of osteoporosis as beta coefficients of age is negative. Furthermore, it could be observed from the same table that the independent variables (age, gender & BMI) are not making significant contribution on the BMD of osteopenia patients because Sig. values for all variables were greater than 0.05. In case of osteoporosis, the Sig. values of BMI (0.02) and age (0.011) were less than 0.05. Thus, BMI and age were significantly influence on the dependent variable (BMD). Moreover, the beta coefficient of age was negative indicating that the age was inversely related with BMD of osteoporosis patients. In order to test three hypotheses and examine the impact of individual independent variables (age, gender and BMI) on the dependent variable (BMD) and study the degree (positive or negative) of correlation between the variables considered. One way ANOVA analysis and Bivariate correlation were carried out as shown in the following tables. 3.1. The First Hypothesis Ho: There is no statistically significant effect of the BMI on BMD in the femoral neck and the lumbar areas in the osteopenia and osteoporosis patients From Table (6), the p-value= 0.2001 of osteopenia and p-value = 0.4622 of osteoporosis were greater than 0.01, which indicates that there is no significant correlation between the BMI and BMD in both diseases. From Table (7), there exists a negative correlation between BMI and BMD, which implies that the body mass index was not directly related to the cause of osteopenia. In the case of osteoporosis, although there was a positive correlation between the variables considered BMI and BMD but the degree of correlation was weak. This means that increasing in BMI will result in increase case of osteoporosis. So, we accepted the null hypothesis Table 6. ANOVA of BMI and BMD of Osteopenia and Osteoporosis Patients ANOVA / Osteopenia Patients Source SS df MS F Regression 0.84 1 0.184 1.661 Residual 12.604 114 0.111 Total 12.788 115 SS = Sum of Squares Df = degree of freedom MS = Mean Square p-value 0.200 ANOVA / Osteoporosis Patients Source SS df MS F Regression 0.105 1 0.105 0.545 Residual 17.714 92 0.193 Total 17.819 93 SS = Sum of Squares Df = degree of freedom MS = Mean Square p-value 0.462 48 Wasan A. M. Al Taie et al.: The Correlation of Body Mass Index, Age, Gender with Bone Mineral Density in Osteopenia and Osteoporosis: A Study in the United Arab Emirates Table 7. Correlation between BMI and BMD of Osteopenia and Osteoporosis Patients Correlations / Osteopenia Patients BMI Pearson Correlation 1 BMI Sig. (1-tailed) N 116 BMD Pearson Correlation Sig. (1-tailed) -.134 .076 N 116 Correlation is significant at the 0.01 level BMD -.134 .076 116 1 116 Correlations / Osteoporosis Patients BMD Pearson Correlation 1 BMD Sig. (1-tailed) N 94 Pearson Correlation .077 BMI Sig. (1-tailed) .231 N 94 Correlation is significant at the 0.01 level BMI .077 .231 94 1 94 3.2. The Second Hypothesis Ho: There is no statistically significant effect of age on BMD in the osteopenia and osteoporosis patients. Table 8. ANOVA of Age and BMD of Osteopenia and Osteoporosis Patients ANOVA / Osteopenia Patients Source SS df MS F Regression 0.204 1 0.204 1.848 Residual 12.584 114 0.110 Total 12.788 115 SS = Sum of Squares df = degree of freedom MS = Mean Square p-value 0.177 ANOVA / Osteoporosis Patients Source SS df MS F Regression 0.423 1 0.423 2.236 Residual 17.396 92 0.189 Total 17.819 93 SS = Sum of Squares df = degree of freedom MS = Mean Square p-value 0.138 BMD Age Table 9. Correlation between Age and BMD of Osteopenia and Osteoporosis Patients Correlations / Osteopenia Patients BMD Pearson Correlation 1 Sig. (1-tailed) N 116 Pearson Correlation -.126 Sig. (1-tailed) .088 N 116 Age -.126 .088 116 1 116 BMD AGE Correlations / Osteoporosis Patients BMD Pearson Correlation 1 Sig. (1-tailed) N 94 Pearson Correlation -.154 Sig. (1-tailed) .069 N 94 Age -.154 .069 94 1 94 The results indicated in Table (8) that the age did not have an effect on bone mineral density in the osteopenia and osteoporosis patients because p-value = 0.177 of osteopenia and p-value = 0.138 of osteoporosis were greater than 0.05. As shown in Table (9), the correlations between age and BMD were negative in both diseases, which help us to conclude that age and BMD were inversely related in both osteopenia and osteoporosis patients. Correlation matrix shows that there was no strong correlation to prove that age is a factor in causing osteopenia and osteoporosis. Therefore, the null hypothesis is accepted. 3.3. The Third Hypothesis Ho: There is no statistically significant effect of gender on BMD in the osteopenia and osteoporosis patients. Table 10. ANOVA of Gender and BMD of Osteopenia and Osteoporosis Patients ANOVA / Osteopenia Patients Source SS df MS F Regression 0.070 1 0.070 0.623 Residual 12.718 114 0.112 Total 12.788 115 SS= Sum of Squares df = degree of freedom MS = Mean Square p-value 0.431 ANOVA / Osteoporosis Patients Source SS df MS F Regression 0.275 1 0.275 1.439 Residual 17.545 92 0.191 Total 17.819 93 SS = Sum of Squares df = degree of freedom MS = Mean Square p-value 0.233 Clinical Medicine and Diagnostics 2014, 4(3): 42-54 49 BMD Gender Table 11. Correlation between Gender and BMD of Osteopenia and Osteoporosis Patients Correlations / Osteopenia Patients BMD Pearson Correlation 1 Sig. (1-tailed) N 116 Pearson Correlation .074 Sig. (1-tailed) .216 N 116 Gender .074 .216 116 1 116 BMD Gender Correlations/ osteoporosis patients BMD Gender Pearson Correlation 1 .124 Sig. (1-tailed) .117 N 94 94 Pearson Correlation .124 1 Sig. (1-tailed) .117 N 94 94 As shown in Table (10), the p-values of osteopenia and osteoporosis were greater than 0.05 as 0.431 and 0.233, respectively. We accepted the null hypothesis, which means that the gender did not have effect on the BMD. On the other hand, the results in Table (11) indicated that there was a positive weak correlation between the gender and the BMD. This helps us to conclude that the cases of osteopenia and osteoporosis are related to the gender of the patients, as they were found to be more common in the females compared to males. *Correlation value lies between [-1, 1]. Values close to -1 show a negative correlation meaning that they are inversely related and values closer to one shows positive correlation which implies that they are directly related [41]. 4. Discussion Osteopenia and osteoporosis affect millions of individuals. Although many studies have been conducted to study the factors that influence the development of osteopenia and osteoporosis, especially the association between the BMI and BMD, the controversy is still continuous and the results are inconsistent. Therefore, further investigations and studies are necessary to prove or deny these hypotheses. In our study, we continue this investigation and selected three independent variables (age, gender and BMI) in order to assess their influences together and individually on the bone density. We found the highest percentages of osteopenia and osteoporosis, 48.3% and 44.7%, respectively, in obese patients. That means, when the BMI increases, the case of osteoporosis and osteopenia increases. The statistical analysis of each independent parameter indicated that there were no significant correlations between the BMI and BMD in osteopenia and osteoporosis (p-value = 0.2001 and p-value = 0.4622), respectively. That means there was a weak association between the BMI and BMD. Our explanation for this finding is that our patients had different BMI values, so we did not analyze the effect of only obesity on BMD only. Additionally, we did not separate the BMD data of the neck from that of the lumbar spine (LS. BMD), so we did not the study the effect of BMI separately on each area (neck and lumbar) as explained by AL-Maitah [42], who found a positive correlation between LS.BMD and neck BMD (R = 0.534, P ≤ 001) and positive correlation between lumbar BMD and BMI which is consistent with our data in term of direction of the relationship between the independent variable (BMI) and dependent variable (BMD). Our results indicated that the individual variable BMI did not affect significantly on BMD of osteopenia (p-value = 0.200, p≤ 0.01) and of osteoporosis (p-value = 0.462, p≤0.01). Moreover, the correlation was weak and negative in osteopenia and weak positive in osteoporosis, but the strength of the relationship between BMI and BMD in osteopenia was greater than in the case of osteoporosis. Likewise in testing the contribution of independent variables jointly on the bone density (BMD), the most effective variable was BMI of osteopenia, while age was the most effective variable of osteoporosis as shown in Table (5). Our results indicated that the overweight and obesity do not have a protective effect on the bones in osteoporosis patients only as they are consistent with some studies in the literature [43]. Some of the previous studies mentioned the inverse relationship between body weight, BMI and osteoporosis [44-46]. The relationship between obesity and osteoporosis was explained by the suggestion that a high level of the obesity hormone “adiponectin” in the blood might increase the fragility of skeletons and the risk of fractures because of reduced muscle strength and lower muscle mass. Additionally, a high adiponectin level in the blood might increase functional aging [47, 48]. Zhao et al. [49] concluded in a study conducted on obese Chinese and Caucasian populations with osteoporosis that increasing fat mass does not have a valuable effect on the protection of bone mass. Many hypotheses have been used to explain the negative effect of obesity on bone metabolism. One of these hypotheses mentioned that obesity might increase the incidence of osteopenia and osteoporosis because of obstruction of intestinal calcium absorption by a high fat intake leading to reduced calcium availability for bone formation [50]. Another hypothesis mentioned that increased weight and body fat in postmenopausal women are related to decreased endogenous estrogen levels, which reduced the osteoblast count and accelerate bone loss [51-53]. Others studies support our results which illustrate that increased body weight and high BMI are positively correlated to high BMD [54], especially in postmenopausal women [55]. Contradictory, obese people lose bone slower than individuals with normal body weight [56, 57]. As a result, obesity has a protective effect on bone mass [58], as well as, overweight and obesity decrease the risk of osteoporosis [59]. 50 Wasan A. M. Al Taie et al.: The Correlation of Body Mass Index, Age, Gender with Bone Mineral Density in Osteopenia and Osteoporosis: A Study in the United Arab Emirates The identical results have been supported by Barrera et al. years old was 82.8%. In general, osteoporosis is highly [60] who confirmed that high BMI has a protective effect on prevalent in India, and 30 million women are diagnosed with femoral neck BMD among elderly patients. Obesity and osteoporosis [77]. Unni et al. [78] assessed the bone mineral osteoporosis have a similar pathophysiological linkage [61]. density in 105 Indian women above 40 years of age, finding Several hypotheses have been proposed to explain the that BMD decreases with increasing age and increases in protective effect of obesity on bone strength [62]. One obese women. Larijani, et al. [79] measured spinal BMD in hypothesis is that bone mass increases with increasing body women in 40-49-, 50-59, and 60-69- year old groups and mass to accommodate a greater mechanical loading on bone found that BMD levels were lower by 3%, 1%, and 4%, [63]. Adipose tissues produce estrogen in obese respectively, than in the previous age decades. As a result of postmenopausal women, which inhibits bone resorption by age, postural changes at the spinal column might contribute osteoclasts, as a result increasing bones mass [64]. Sarkis et in the explanation of the relationship between obesity and al. [65] explained the direct and indirect protective effect of obese osteoporotic women [80, 81]. Hsu et al. [82] illustrated endocrine changes on bone metabolism in healthy obese in their research that a high percentage of body fat is the women in the general population. Another hypothesis major risk for osteoporosis, osteopenia, and non-spine associate’s obesity with insulin resistance, characterized by a fractures, independently of body weight, physical activity, high plasma insulin level that causes overproduction of and age. Our data indicated that gender has no effect on ovarian androgen and estrogen and underproduction of sex BMD in osteopenia and osteoporosis (p-values = 0.431 and hormone-binding globulin by the liver These abnormal 0.233) respectively. However, the correlation between the changes might cause an elevated sex hormone level and gender and BMD was positive and weak of both diseases. increased bone mass because of reduced osteoclas activity Moreover, multiple regressions of the three variables and stimulated osteoblast action [66]. Migliaccio et al. [67] together showed in Table (5) that the gender has lowest illustrated that obesity could be considered as a protective contribution on dependent variable (BMD) than other factor against bone loss and fractures if obesity is addressed variables (age and BMI) with weak correlation in both by body mass index or body weight or it could be considered diseases. According to the literature, older women suffer as a risk factor for osteoporosis if obesity reflects the from osteoporosis more than men for two reasons, as percentage of body fat. Weight reduction might contribute to follows: 1) the bones of women are smaller and have less bone loss [68, 69]. Our results indicated that the incidences mass than the bones of men; and 2) estrogen production in of osteopenia and osteoporosis were high in the 50-59 years women decreases significantly at menopause. Older men age group (39.7%) and in the + 60 years age group (57.4%), have osteoporosis because of a gradual and slight decrease respectively; however, there was no significant correlation in the production of androgen and testosterone [2]. Our between age and BMD as an individual variable for both findings are consistent with AL Attia et al. in terms of gender osteopenia and osteoporosis patients with negative weak and obesity that studied osteoporosis in men in a tertiary association in both cases. This result was consistent with hospital in the UAE and concluded that osteoporosis is a AL-Maithah in term of direction of the relationship between disease of obese females [83]. the independent variable (age) and dependent variable (BMD) [42]. From our results, the multiple regression showed a significant correlation between age and BMD (p- value = 5. Conclusions and Recommendations 0.011, less than 0.05) when the three factors were used The prevalence of osteopenia, osteoporosis and obesity is together in osteoporosis only. That means, the age was increasing globally. We could conclude that three making a statistically significant contribution on the independent variables (age, gender and BMI) are dependent variable (BMD) than other variables (gender and significantly associated with each other. Although the BMD BMI) with weak correlation in the osteoporosis. Thus, this was influenced significantly by all independent variables risk of osteoporosis is increased with increasing the age. The (age, gender and BMI) together in the osteoporosis not in literature emphasized the effect of age on bone mass, osteopenia, the BMI makes more contribution to the BMD mentioning that the risk of vertebral fracture in women than age and gender of osteopenia and age makes more above age 50 to 55 years is increased with low body weight contribution to the BMD than gender and BMI of the and low BMI [70, 71] and the risk of any fracture exceeds osteoporosis. Moreover, BMD was not significantly 50% among women and 20% among men at 50 years of age correlated with each independent variable individually in [72]. This finding supported our results and is consistent osteopenia and osteoporosis. That means, all variables with previous studies that indicated that the incidence of together are considered as risk factors of osteoporosis. But osteopenia and osteoporosis in men and women, especially this effect could not be implied in the osteopenia patients. in postmenopausal women, is increased in older age [73-75]. We recommend early diagnosis of osteopenia and Balaguer and Olmos [76] found that the prevalence of osteoporosis for obese and non-obese men and women to osteopenia and osteoporosis in women 70-75 years old was prevent morbidity and mortality. Exercise and healthy 29.6% and 27.6%, respectively and the occurrence of nutrition at an early and older age are necessary to avoid the osteoporosis in postmenopausal women younger than 75 obesity and bone diseases. Clinical Medicine and Diagnostics 2014, 4(3): 42-54 51 6. Disclosure Statement .org/10.4065/77.5.453. There is no conflict of interest regarding the publication of this article. ACKNOWLEDGEMENTS We thank Dr. Semiyu Aderibigbe and Dr. Alexander Arguelles for reviewing and editing the paper. [13] Sampson H.W. (2003) Alcohol and Other Factors Affecting Osteoporosis Risk in Women. In: National Institute on Alcohol Abuse and Alcoholism. Available at: http://pubs.nia aa.nih.gov/publications/arh26-4/292-298.htm (accessed 11 June 2014). [14] Tucker K.L., Morita K., Qiao N., Hannan M.T., Cupples L.A., Kiel D.P. (2006) “Colas, but not other carbonated beverages, are associated with low bone mineral density in older women: The Framingham Osteoporosis Study”. Am J Clin Nutr 84(4): 936-942. REFERENCES [1] Guyton C., and Hall J. E. (1997) Human Physiology and Mechanisms of Diseases, 6th edition, W. B. Saunders Company, pp 643. [2] Tortora G. J., and Derrickson B. (2006) Principle of Anatomy and Physiology, 11th edition, John Wiley and Sons, Inc. pp189-190. [3] Van Staa T.P., Dennison E.M., Leufkens H.G., and Cooper C. (2001) “Epidemiology of fractures in England and Wales”. Bone 29(6):517-522. [4] Melton L.J., Chrischilles E.A., Cooper C. Lane A.W., and Riggs B. L. (2005) “How Many Women Have Osteoporosis?”. Journal of Bone and Mineral Research 20(5): 886–892. DOI: 10.1359/jbmr.2005.20.5.886. [5] Osteoporosis. In: BergRL, Cassells JS (eds.) “The Second Fifty Years: Promoting Health and Preventing Disability”. National Academy Press, Washington, DC, pp. 76–100. [6] K. Asomaning ER. Bertone-Johnson, P.C. Nasca , F. Hooven , and P.S. Pekow (2006) “The association between body mass index and osteoporosis in patients referred for a bone mineral density examination”, J Womens Health ( Larchmt), 15(9): pp1028-34. [7] Reinhardt R.A., Payne J.B, Maze C.A., Patil K.D, Gallagher S.J., and Mattson J.S. (1999) “Influence of estrogen and osteopenia/osteoporosis on clinical periodontitis in postmenopausal women”. J Periodontol 70 (8): pp.823-8. [8] Badsha H., “Osteoporosis UAE”; Dr. Humeira Badsha Medical Center, United Arab Emirates. (October 22nd 2007); available at: http://humeirabadsha.blogspot.com/2007/10/ost eoporosis-uae.html (accessed 15 November 2012). [9] Tawil T. (19 October 2011) Majority of Osteoporosis Cases Preventable, Say UAE Doctors. In: Dubib.com. Available at: http://www.dubib.com/news/15960_majority-of-osteoporsiscases-preventable-say-uae-doctors#.UKRxg4ZEGSo (accessed 15 November 2012). [15] Watkins J. (2010) “Physical activity helps reduce bone loss: Structure and Function of the Musculoskeletal System”, 2nd edition; Hardback, pp. 408. ISBN-13: 9780736078900. [16] Yamazaki, S., Ichimura S., Iwamoto J., Takeda T. and Toyama Y. (2004) “Effect of walking exercise on bone metabolism in postmenopausal women with osteopenia/ osteoporosis”. Clinical Rheumatology 23(5): 383-389. DOI: 10.1007/s10067-004-0881-z. [17] Nguyen T.V., Sambrook P.N., and Eisman J.A. (1998) “Bone loss, physical activity and weight change in elderly women: The Dubbo Osteoporosis Epidemiology Study”. J Bone Miner Res. 13(9): 1458-67. [18] AL Attia H. M., Jaysundaram K. and Saraj F. (2010) “Lack of biochemical hypogonadism in elderly Arab males with low bone mineral density disease”. Rheumatology International 30(3): 365-367, DOI: 10.1007/s00296-009-0970-z. [19] Fawzy T., Muttappallymyalil J., Sreedharan J., Ahmed A., Alshamsi S. O. S., Bin Bader Al Ali M. S., and Al Balsooshi K. A. (2011) “Association between Body Mass Index and Bone Mineral Density in Patients Referred for Dual-Energy X-Ray Absorptiometry Scan in Ajman, UAE”. Journal of Osteoporosis, vol. 2011, Article ID 876309. DOI: 10.4061/2011/876309. [20] De Laet C., Kanis J.A., Odén A., Johanson H., Johnell O., Delmas P., Eisman J.A., Kroger H., Fujiwara S., Garnero P., McCloskey E.V., Mellstrom D., Melton L.J. 3rd, Meunier P.J., Pols H.A., Reeve J., Silman A., and Tenenhouse A.(2005) “Body mass index as a predictor of fracture risk: a meta-analysis”, Osteoporos Int. 16: 1330-8. [21] Poiana, M. Carsote, V. Radoi, C. Ene, D. Paun, and G. Voicu (2012) “The Body Mass Index and Bone Mineral Density Age Related Correlations – A study in 347 women. Osteoporos Int. Vol.23 (Suppl 2): S85- S386. [22] Ravn P., Cizza G., Bjarnason V.H., Thompson D., Daley M., Wasnich R.D., Mc Clung M., Hosking D., Yates A.J., and Christiansen C. (1999) “Low body mass index is an important risk factor for low bone mass and increased bone loss in early postmenopausal women. Journal of Bone and Mineral Research 14(9):1622-7. [10] Kiebzak G.M., Beinart G.A., Perser K., Ambrose C.G., Siff S.J., and Heggeness M.H., (2002) Undertreatment of Osteoporosis in Men With Hip Fracture. Arch Intern Med. 162(19):2217-2222. DOI:10.1001/archinte.162.19.2217. [23] S. I. Sofia Inez Iqbal, L. S. Mørch, M. Rosenzweig, and F. Dela, (2005) “The outcome of bone mineral density measurements on patients referred from general practice”, Journal of Clinical Densitometry 8(2): 178–182. [11] Marshall W. J., and Bangert S. K. (2008) “Clinical Chemistry”, 6th edition, Mosby Elsevier, pp:295,296. [12] Fitzpatrick L.A. (2002) Secondary Causes of Osteoporosis. Mayo Clinic Proceedings 77(5): 453–468. DOI: http://dx.doi [24] S. Morin, J.F. Tsang, W. D. Leslie (2009) “Weight and body mass index predict bone mineral density and fractures in women aged 40 to 59 years”. Osteoporos Int. 20(3): 363-70. [25] Center for strategic health care development (2009) “Obesity

... pages left unread,continue reading

Please select stars to rate!

Same seriesView all >

Popular documents

## Document pages: 13 pages