An assessment of the relation between bone mineral density and clinic-demographic properties and life quality during postmenopausal period

Abstract

OBJECTIVES:

This study was conducted to assess the relation between bone mineral density (BMD) and clinic-demographic properties and life quality during postmenopausal period.

METHODS:

The study group consisted of 172 postmenopausal women who applied to the physical therapy and rehabilitation outpatient clinic at a training and research hospital in Ankara, the capital of Turkey. The Survey Form, The 36-item Short Form Health Survey (SF-36) and the FRAX ${\texttrademark}$ were used as data collection tools in this study.

RESULTS:

The osteoporosis and osteopenia frequencies were respectively 28.5% and 42.4% in this study. The 10-year major osteoporotic fracture risk was 5.15% and the femur fracture risk was estimated as 0.9%. In this study, there was a positive and significant relation found between the L ${}_{1\text{--}4}$ and FN T-score and SF-36 scale score average ( $p<$ 0.05). There was a significant relation between the body mass index and SF-36 subscale ‘mental health’ ( $p<$ 0.05). In this study, the most significant determinant of life quality was exhibited as the “L ${}_{1\text{--}4}$ T-score” ( $\beta=$ 55.509, $p=$ 0.000).

CONCLUSIONS:

We determined that approximately one of every four women had osteoporosis and as the BMD dropped, the life quality of the women declined. Hence, we think that improving the awareness of health professionals working in this field is essential.

Keywords

Postmenopausal osteoporosis quality of life bone mineral density

1. Introduction

Osteoporosis is a metabolic bone disease characterized by low bone density and deterioration of bone micro architecture leading to an escalation of bone fragility [1]. The National Osteoporosis Foundation (NOF) reported that 200 million people were experiencing osteoporosis problem in the world. Furthermore, the women in postmenopausal period made up 30% of these effected women and one of every three women at the age of 50 and over would experience bone fractures caused by osteoporosis in any period of their life [2]. Genetic factors and race/ethnicity have a strong influence on peak bone density. Physiological, environmental and modifiable lifestyle factors can also play a significant role. These factors include adequate nutrition and body weight and level of physical activity [3]. Oka and her colleagues researched Japanese women in the postmenopausal period; increased risk of osteoporosis was found in women with low body mass index (BMI), high birth rate, family history of kyphosis and hip fracture, diabetic mellitus, liver disease, chronic disease such as thyroid, and fracture [4].

The osteoporosis prevalence of the women over the age of 50 was 12.9% in the FRACTURK study conducted in Turkey to uncover the osteoporosis and fracture risks. It was projected in the same study that 8.6% of the women in this age group would experience bone fractures within a 10 year period [5]. Osteoporosis can lead to physical, psychological and social outcomes that could cause important negative effects on the wellness stage and life quality of individuals. Pain, remission in dorsal muscle force, loss of flexibility, bone malformations and bone fractures experienced in osteoporosis affect patients’ life quality negatively [6, 7]. Patients go through long term treatments due to bone fractures developed simultaneously and this leads to workforce loss and imposes a heavy load on the country’s economy [8].

Life quality is a broad term influenced by the physical health and psychological state of individuals as well as their social life and relationships with the environment [9]. The effect of osteoporosis on life quality started to draw attention during the recent years and life quality has been assessed frequently in clinical studies. Life quality is assessed by various generic scales or disease-specific scales. the most frequently used generic scales in the literature to assess life quality are the Medical Outcomes Study (MOS), the Health Assessment Questionnaire (HAQ), the 36-item Short Form Health Survey (SF-36), the Nottingham Health Profile (NHP) and the Sickness Impact Profile [10, 11, 12]. The most frequently used life quality scales specific to osteoporosis are the Quality of Life Questionnaire of the European Foundation for Osteoporosis (QUALEFFO), the Osteoporosis Quality of Life Questionnaire (OQLQ), the Osteoporosis assessment questionnaire (OPAQ) and the Osteoporosis Functional Disability Questionnaire (OFDQ) [7]. The SF-36 is a generic scale that has been adopted in many countries and used commonly [13]. In this study, the generic the SF-36 was used since the women with various BMD levels participated in the study.

This study aimed to uncover the clinic-demographic risk factors affective on BMD during postmenopausal period and to assess the relation between BMD and life quality.

2. Method

2.1 Population and sampling

This study was conducted between January 2014 and June 2014 at a physical therapy and rehabilitation outpatient clinic and research hospital in Ankara, Turkey. The target population of the study consisted of women under the age of 65 who applied to the physical therapy clinic of the hospital during a one year period and were going through menopause and whose DXA measurements were taken (N $=$ 1224). The sample size of the research was determined to be 172 postmenopausal women according to the formula of “estimation of sampling number when the target population is known” (Chicago, IL, USA). G * Power 3.1 was used for power analysis in the study (power of 95%, $\alpha=$ 0.05, the effect size $=$ 0.34). The women who did not have their menstruation for one year were accepted as going through natural menopause and the women who went under bilateral-oophorectomy were accepted as going through surgical menopause [14]. The women who had an orthopedic, rheumatologic and neurologic problem that could affect their life quality, had auditory or mental disability and were over the age of 65 and did not consent for participating in the study were not accepted to the study. The ethics committee approval was obtained (approval date/number: December 16, 2013/1209). A legal permission was granted by the hospital management. In accordance with the Declaration of Helsinki, all participants gave written consent, which included permission to be interviewed.

2.2 Data collection

Interviewers used three different forms, which are outlined below.

2.2.1 The Survey Form

This form, by researchers, consists of 36 questions to define the individual characteristics of women and BMD results [14, 15, 16]. The physical activity practiced for minimum three days and 30 minutes a week was accepted as ‘the presence of physical activity’ [15]. Consumption of more than two cups of coffee was accepted as ‘the presence of coffee consumption’ [16]. Moreover, the age under 45 was accepted as early menopausal age, the ages between 45 and 55 were accepted as normal menopausal age and the age over 55 was accepted as late menopausal age [14].

2.2.2 The World Health Organization’s (WHO) Fracture Risk Assessment Questionnaire – FRAX ${}^{\texttrademark}$

The FRAX questionnaire was developed by the WHO to assess the fracture risk in patients in 2008. It is based on the individual patient models combining the fracture risk related with clinic risk factors and FN BMD measurements. It is a web-based algorithm assessing a 10-year fracture risk. The FRAX questionnaire consists of 12 question items namely age, height, weight, past fracture story, femur fracture story of parents, cigarette smoking, glucocorticoid use, the presence of rheumatoid arthritis disease and secondary osteoporosis, alcohol consumption and BMD result [17]. In this study, the 10-year possibility of experiencing femur fracture and a major osteoporotic fracture by the women was estimated in percentages by the FRAX™ questionnaire adapted to Turkey.

2.2.3 The 36-item Short Form Health Survey (SF-36)

The SF-36 developed by Ware is used in clinical practices and research for measuring the positive and negative aspects of health. The SF-36 consists of 36 question items. The scale has eight sub-dimensions namely “physical functionality”, “physical role restrictions”, “pain”, “general health perception”, “energy and vigor”, “social functionality”, “mental role restrictions” and “mental health” [13]. Koçyigit et al. has conducted the reliability and validity studies of the SF-36. Pre-test and post-test reliability of the scale was 0.94 and its internal consistency was 0.92 [18]. The alpha-Cronbach value was 0.91 in our study. A high score received in the scale points to improved life quality (min $=$ 0, max $=$ 100) [18].

2.3 Application

The height and weight measurements of the women, who applied to the outpatient clinic and conformed to the research criteria, were taken by the researcher by using a standard height meter and a scale. In the study, BMI was estimated by using the formula body mass index $=$ weight (kg)/height ${}^{2}$ (m ${}^{2}$ ). The women who had their BMI lower than 18.50 was accepted as thin, who had their BMI between 18.50 and 24.99 were accepted as normal, between 25 and 29.9 as overweight, between 30 and 39.99 as obese and 40 and over as morbid obese [19]. A radiology technician took BMD measurements of the women by using a Hologic brand DXA device. The screening device’s voltage was 67 kv, its current was 1500 Ma and its screening time under 20.0 $\mu$ Gy of dose was approximately 3 minutes. Both of the region’s BMD (g/cm2), T and Z scores were measured and assessed.

Figure 1.

BMD categorization in accord with the FN and L ${}_{1\text{--}4}$ DXA averages.

T Score $=$ Measured BMD $-$ young adult average

BMD/young adult normal SD

Z Score $=$ Measured BMD $-$ Same age group’s

average BMD/same age group’s SD

SD $=$ Standard deviation

The T score of the individuals were recorded in the study based on the results of the FN and L ${}_{1\text{--}4}$ DXA measurements. The women whose T score were under $-$ 2.5 and lower were accepted as having osteoporosis and the ones between $-$ 1 and $-$ 2.5 were accepted as having osteopenia and the ones between $-$ 1 and over were accepted as normal [20]. The data collection forms of the women who had DXA measurements were completed with a face to face interview by researchers.

Table 1

The relationship between the DXA result and some properties of women ( $n=$ 172)

Properties	Normal		Osteopenia		Osteoporosis		Analysis*
	$n$	%	$n$	%	$n$	%	X ${}^{2}$	$p$
Educational status
Literate and $\uparrow$	15	35.7	16	3.1	11	26.2	9.336	0.156
Elementary school	25	28.4	40	45.5	23	26.1
High school	6	25.0	13	54.2	5	20.8
University and $\uparrow$	4	22.2	4	22.2	10	55.6
Cigarette smoking
Yes	5	21.7	12	52.2	6	26.1	2.937	0.568
No	41	30.8	52	39.1	40	30.1
I quit	4	25.0	9	56.3	3	18.8
Coffee consumption
Yes	11	18.6	24	40.7	24	40.7	8.108	0.017
No	39	35.5	49	43.4	25	22.1
Physical activity
Yes	16	38.1	14	33.3	12	28.6	2.644	0.267
No	34	26.2	59	45.4	37	28.5
BMI
Normal	9	25.0	15	41.7	12	33.3	3.828	0.430
Overweight	19	35.2	18	33.3	17	31.5
Obese	22	26.8	40	48.8	20	24.4
Lactation period
Less than 12 months	8	13.3	31	51.7	21	35.0	9.770	0.008
12 months and $\uparrow$	36	36.6	40	40.0	24	24.0
Menopause age
45 years old $\downarrow$	16	23.5	30	44.1	22	32.4	2.097	0.350
45 years old and $\uparrow$	34	33.3	42	41.2	26	25.5
Menopause period
5 years $\downarrow$	16	34.0	19	40.4	12	25.5	0.811	0.667
5 years and $\uparrow$	34	27.2	54	43.2	37	29.6
OP story in the family
Yes	10	12.5	38	47.5	32	40.0	21.985	0.000
No	40	43.5	35	38.0	17	18.5

*Chi-square analysis was made ( $p<$ 0.05).

Table 2

The correlation between the BMD results and fracture risk

BMD	L ${}_{1\text{--}4}$ T-score		FN T-score
	$r$	$p$	$r$	$p$
Major osteoporotic fracture risk	$-$ 0.436	0.000*	$-$ 0.571	0.000*
Femoral fracture risk	$-$ 0.504	0.000*	$-$ 0.654	0.000

*Pearson correlation analysis was used. $p<$ 0.05.

2.4 Statistical analysis

The data were analyzed by using the SPSS (Statistical Package for Social Sciences) version 20.0. The frequencies, percentages and average data of the variables were estimated. Subsequently, a chi square ( $\chi^{2}$ ) analysis was made to show the relationship between the variables, and Pearson correlation analysis was made for the continuous variables. A multiple regression analysis was made to determine the degree of influence of the variables with statistically significant correlation on life quality. The correlation coefficient ( $r$ ) was assessed as a weak relationship between 0.00–0.24; as a mediocre relationship between 0.25–0.49; a strong relationship between 0.50–0.74; and as a very strong relationship between 0.75–1.00 [21]. A $p$ value of $<$ 0.05 was assessed as significant in the study.

3. Results

The average age of the women in the study scope was specified as 56.54 $\pm$ 5.24 (min $=$ 41, max $=$ 65). Considering the averages of the FN and L ${}_{1\text{--}4}$ DXA results, it was designated that BMD of 42.4% of the women was at osteopenia level, BMD of 29.1% of the women were at normal level and BMD of 28.5% of the women was at osteoporosis level (Fig. 1). The relationship between the DXA results and some properties of the women are shown in Table 1. There was a significant difference found between coffee consumption habit, lactation period and osteoporosis story in the family and the DXA results ( $p<$ 0.05). It was determined that there was no significant difference found between the educational status, cigarette smoking and physical activity state, BMI, menopausal entry and menopause period of the women and their DXA results ( $p>$ 0.05). The 10-year major osteoporotic fracture risk of the women was 5.15% and their femoral fracture risk was 0.9%. There was a negative correlation between the FN and L ${}_{1\text{--}4}$ T scores and the 10-year femoral fracture and major osteoporotic fracture experience risk ( $p<$ 0.05) (Table 2). In other words, as the FN and L ${}_{1\text{--}4}$ T scores dropped, the 10-year femoral fracture and a major osteoporotic fracture experience risk increased.

Table 3
The correlation between the DXA result and the SF-36 subscales

SF-36 subscales	L ${}_{1\text{--}4}$ T-score		FN T-score		BMI		Age
	$r$	$p$	$r$	$p$	$r$	$p$	$r$	$p$
Physical functionality	0.693	0.000*	0.508	0.000*	$-$ 0.036	0.615	$-$ 0.147	0.203
Physical role restrictions	0.654	0.000*	0.504	0.000*	0.075	0.705	$-$ 0.088	0.104
Social functionality	0.664	0.000*	0.456	0.000*	$-$ 0.062	0.712	$-$ 0.054	0.109
Mental role restrictions	0.510	0.000*	0.360	0.000*	0.039	0.603	$-$ 0.027	0.103
Mental health	0.194	0.011*	0.201	0.008*	$-$ 0.167*	0.003	$-$ 0.069	0.204
Energy/vigor	$-$ 0.089	0.245	$-$ 0.028	0.714	$-$ 0.048	0.701	$-$ 0.015	0.110
Pain	0.705	0.000*	0.495	0.000*	$-$ 0.040	0.604	$-$ 0.092	0.310
General health perception	0.640	0.000*	0.491	0.000*	$-$ 0.090	0.703	$-$ 0.116	0.290
Total	0.722	0.000*	0.533	0.000*	$-$ 0.009	0.912	$-$ 0.098	0.202

*Pearson correlation analysis was used. $p<$ 0.05.

Table 4

The influence levels of the variables having the highest correlation on SF-36

	Life quality SF-36 (dependent variable)
Independent variables	$\beta$	Significance level (P)
Lomber vertebra (L ${}_{1\text{--}4})$	55.509	0.000
T-score
Femoral neck T-Ssore	20.885	0.042

Multiple regression analysis was used.

3.1 Results on life quality

Life quality of the women was assessed by the SF-36 scale (min $=$ 0, max $=$ 100). The SF-36 scale total score average in our study was 55.92 $\pm$ 21.07. There was a positive and significant relationship found between the L ${}_{1\text{--}4}$ and FN T scores and the SF-36 scale score average ( $p<$ 0.05) (Table 3). There was a positive correlation found ( $p<$ 0.05) between the L ${}_{1\text{--}4}$ and FB T scores and the SF-36 subscale score averages in “functionality”, “physical role restrictions”, “social functionality”, “mental role restrictions”, “mental health”, “pain” and “general health perception” ( $p<$ 0.05) (Table 3). There was no statistically significant correlation found between age and SF-36 ( $p>$ 0.05). There was a statistically significant correlation found between BMI and the SF-36 subscale of “mental health” ( $p<$ 0.05). The parameters which exhibited the highest correlation between life quality and the SF-36 were contained in the multiple-regression model to define the influence level in the study. As a result of this analysis, it was determined that the most significant determinant of life quality was exhibited as the L ${}_{1\text{--}4}$ T score ( $\beta=$ 55.509, $P=$ 0.000).

4. Discussion

During menopausal period, bone resorption rate exceeds bone mineral formation rate depending on estrogen hormone inadequacy and therefore bone turnover takes place in the destruction direction [22]. In our study encompassing the women at the postmenopausal period, the osteoporosis rate was set as 28.5% (Fig. 1). It was anticipated that 30% of the women at the postmenopausal period in Europe and the United States of America had osteoporosis [2]. In a study conducted on Japanese women, it was specified that 15% of them had osteoporosis [4]. Based on the FRACTURK study results, the osteoporosis prevalence of the women over the age of 50 was 12.9% in Turkey [5]. Considering the regional studies assessing BMD in women at the postmenopausal period, Demir et al. reported the osteoporosis prevalence as 16.2% and Kutlu et al. reported it as 14.9% [23, 24]. Comparing our study with the other studies, the osteoporosis observance frequency was higher.

The 10-year major osteoporotic fracture experience risk was 5.15% and femoral fracture experience risk was 0.9% in our study. Kutlu et al. found the 10-year major osteoporotic fracture risk as 0.5–12% and femoral fracture risk as 0–10%. Franek et al. mentioned the major osteoporotic fracture risk as 10–20% in their study and the femoral fracture risk as 5–10% [24, 25]. In another study, the risk of major osteoporotic fracture was found to be 7.1% and hip fracture risk to be 1.3% [4].

Moreover, we uncovered in our study that as the FB and L ${}_{1\text{--}4}$ T scores dropped, the 10-year femoral fracture and a major osteoporotic fracture experience risk increased (Table 2). It is crucial to predefine the fracture risk clinically in postmenopausal women to reduce osteoporosis-dependent morbidity, mortality and health spending. We think that designation of the risk in advance in patients by the easily applied FRAX device is crucial for taking measures before the fractures occur. Furthermore, since the individuals’ bone density is set by their peak bone mass (PBM) and subsequent bone loss level understanding the factors responsible for keeping the PBM at a high level is critically important for preventing fractures in advanced ages. Genetics, physical activity level and nutrition play a role to determine the peak bone mass changes [26]. In this context, adequate calcium intake by nutrition and sustaining physical activity since early ages is important for reaching PBM.

It is common knowledge that when 150 mg of caffeine is consumed daily, urine calcium excretion increases approximately 5 mg [16]. In our study, it was remarkable that approximately half of (40.7%) the women consuming coffee regularly had osteoporosis. The conducted studies showed that osteoporosis increased in women who consumed coffee regularly [27, 28]. Moreover, it was set that approximately one of every four women who had more than 12 months of lactation period during their final pregnancy had osteoporosis. There are conducted studies in line with our study [29, 30]. An average of 280–400 mg of calcium is consumed daily during the lactation period [31]. BMD can reach a normal level within 6–18 months when lactation terminates or diminishes and menstruation restarts [32]. It was not able to be proven that calcium and vitamin D supplements administered during lactation prevented bone loss. The calcium and vitamin D amounts suggested during pregnancy and lactation by the National Institutes of Health (NIH) are the same as the ones in non-pregnant women at the same age. Daily calcium amount is 1300–3000 mg for 14–18 years of age and 1000–2500 mg for 19–50 years of age. At the same time, daily intake of calcium for postmenopausal women was 1300 mg, is provided with ideal dietary means [33]. According to the guideline recommendation of the Endocrine Society published in 2011, vitamin D amount to be taken daily during pregnancy and lactation is 1500–2000 IU [34]. At the same time, in a meta-analysis study, it has been reported that 800 IU of vitamin D intake in people over 65 years of age found 30% hip fracture and 14% vertebral fracture reduction [35].

It was envisaged that VDR gene, collagen 1 $\alpha$ gene, estrogen receptor gene, interleukin 6 (IL-6 gene and transforming growth factor $\beta$ (TGF- $\beta$ ) could be effective for osteoporosis development genetically [36]. In our study, four women out of every 10 women who had an osteoporosis story in their family had osteoporosis. Similarly conducted studies reported that women who had an osteoporosis story in their family had osteoporosis more commonly than those who did not have it [4, 37]. In addition, there was no significant difference found between the educational status, cigarette smoking habit, physical activity, BMI, menopause age and period and BMD. In the study of Oka et al. compared BMD with normal women and women with osteoporosis, the use of alcohol and cigarettes is higher. It is also noteworthy that the majority of women who exercise regularly in the same study are in the osteoporosis group [4]. In this regard, there is a need for more comprehensive studies to determine the risks of osteoporosis.

Osteoporosis is an important health problem in the world and affects life quality negatively by leading to pain and impairment of physical functions and mobility [38]. In our study, the SF-36 scale total score average was set as 55.92 $\pm$ 21.07. As the L ${}_{1\text{--}4}$ and FN T scores dropped, life quality worsened as well. We found out that especially the L ${}_{1\text{--}4}$ and FN T scores diminished, life quality declined in terms of physical functionality, physical role restriction, social functionality, mental role restrictions, mental health, pain and general health perception. This result illustrated that as the L ${}_{1\text{--}4}$ and FN T scores dropped the women were affected in negative manner physically, mentally and socially. We think that this situation led to pain caused by osteoporosis, movement-restriction developed based on pain and social isolation due to movement restrictions. Kutlu et al. purported in their study that as life quality deteriorated, T score dropped and in addition, there was a significant relationship between the sub-dimensions of physical function, social activity, general health and mental health and T score [24]. Jung et al. and Garip et al. reported in their study that there was a correlation between BMD values and life quality [39]. Paker et al. mentioned in their study that it affected life quality of postmenopausal women with osteoporosis and low FN BMD however there was no relationship found between the L ${}_{1\text{--}4}$ BMD values and life quality [41]. In the study of Sezer et al., there was no correlation found between BMD and life quality [37].

Whereas advanced age is a factor deteriorating life quality in general sense, the majority of the conducted studies reported that advanced age especially increased vertebral fracture prevalence and affected life quality negatively [37, 42]. In our study, on the other hand, there was no relationship found between life quality and advanced age.

In our study, there was a negative correlation found between BMI and the SF-36 subscale “mental health”. It was seen that as BMI increased, life quality declined in terms of “mental health”. It is common knowledge that weight increase leads to fitness restriction and damage in osteoarticular structure and respiratory problems and as a result of this, it affects life quality negatively due to deterioration occurring in social, cultural and behavioral elements of life. In the study of Marchesini et al., it was determined that high levels of BMI deteriorated life quality distinctively [43]. In the study of Papaionnau et al., it was reported that increased BMI affected life quality negatively [44]. We think that it is essential to do what is necessary for weight control in the treatment program developed for osteoporosis patients during the postmenopausal period when weight gain increases.

We included the variables having the highest correlation with life quality in the regression model in the study in order to reveal the influence levels of the parameters determining life quality in postmenopausal osteoporosis. As a result of this analysis, it was seen that the primary determinant of life quality in women with postmenopausal osteoporosis was L ${}_{1\text{--}4}$ T score found as a result of DXA. Low BMD causes osteoporosis and in addition, it is one of the significant factors increasing fracture risk developed based on osteoporosis. Even if there is no pain and fractures in women with osteoporosis, the fear for developing fractures in the future affects anxiety for falling and fracture development and self-confidence negatively; and it leads to the individuals to feel themselves sad and under stress in general and this in turn affects life quality negatively [6, 45].

In conclusion, it is seen that protection, early diagnosis and treatment is crucial in osteoporosis. However, in addition to BMD monitoring and the routine treatment for osteoporosis, we think that a better life quality level will be achieved by designating the risk factors affecting the BMD level and by developing the risk factors positively in accord with life style of the persons in addition to the genetic factors. Furthermore, we think that an effective assessment of life quality of individuals is crucial for designating strategies in clinic treatments of individuals with low BMD hence improving awareness of health professionals working in these fields is essential. It is also suggested that future studies should be conducted in a wider population and that long-term assessment of the relationship between osteoporosis and quality of life should be pursued with further monitoring.

There were some limatitions in our study. The first one was relatively small number of subjects. For this reason, the results of the study cannot be generalized. Secondly, due to the inclusion of illiterate women in the study, the data collection forms were filled in by interviews by the researchers. In the meantime, long-term follow-up on the quality of life was not possible due to the absence of a monitoring step in the study design.

Footnotes

Acknowledgments

The authors would like to thank all the participants and hospital staff who participated in this study.

Conflict of interest

None to report.

References

World Health Organization. Assessment of fracture risk and its application to screening for postmenopausal osteoporosis. Technical Report Series 843 WHO, Geneva 1994.

International Osteoporosis Foundation. [homepage on the internet] Epidemiology. [updated 2015; cited 2016 Oct 30] Available from: http://www.iofbonehealth.org/epidemiology.

Hernlund

Svedbom

Ivergard

Compston

Cooper

Stenmark

McCloskey

Jönsson

Kanis

. Osteoporosis in the European Union: Medical management, epidemiology and economic burden. A report prepared in collaboration with the International Osteoporosis Foundation (IOF) and the European Federation of Pharmaceutical Industry Associations (EFPIA). Arch Osteoporos 2013; 8: 136.

Oka

Ohira

Suziki

Yoshida

Koide

Tanaka

Tatsuno

. Fracture risk assessment tool (FRAX) and for the diagnosis of osteoporosis in Japanese middle-aged and elderly women: Chiba bone survey. Endocr J 2017.

Tuzun

Eskiyurt

Akarirmak

Saridogan

Senocak

Johansson

, et al. Turkish Osteoporosis Society. Incidence of hip fracture and prevalence of osteoporosis in Turkey: The FRACTURK study. Osteoporosis Int 2012; 23: 949-55.

Lips

van Schoor

. Quality of life in patients with osteoporosis. Osteoporos Int 2005; 16: 447-55.

Romagnoli

Carnevale

Nofroni

D’Erasmo

Paglia

De Geronimo

, et al. Quality of life in ambulatory postmenopausal women: The impact of reduced bone mineral density and subclinic vertebral fractures. Osteoporos Int 2004; 15: 975-80.

Camur

Kılınc

Sonmez

Çelik

Oç

. Osteoporotik Kalca Kırıklarının Maliyet Analizi. Turk J Osteoporos 2015; 21: 118-21 (in Turkish).

Madureira

Ciconelli

Pereira

. Quality of life measurements in patients with osteoporosis and fractures. Clinics (Sao Paulo) 2012; 67: 1315-20.

10.

Hunt

McEwen

McKenna

. Measuring health status: A new tool for clinicians and epidemiologist. J R Coll Gen Pract 1985; 35: 185-88.

11.

Bergner

Robbitt

Carter

, et al. The sickness impact profile: Development and final revision of a health status measure. Med Care 1981; 19: 787-805.

12.

Brazier

Harper

Jones

NMB

, et al. Validating the SF-36 health survey questionnaire: New outcome measures for primary care. BMJ 1992; 305: 160-4.

13.

Ware

Sherbourne

. The MOS 36-item short-form health survey (SF-36): I, Conceptual framework and item selection. Med Care 1992; 30: 473-83.

14.

World Health Organization. Research on the Menopause in the 1990s. [homepage on the internet]. [cited 2016 Oct 30]. Available from: http://whqlibdoc.who.int/trs/WHO_TRS_866.pdf.

15.

Bakanligi TCS. Turkiye Halk Sagligi Kurumu. [homepage on the internet]. Yetiskinlerde Fiziksel Aktivite. [updated 2016 Feb 7; cited 2016 Mar 30]. Available from: http://thsk.saglik.gov.tr/fiziksel-aktivite/725-yeti%C5%9Fkinlerde-fizikselaktivite.html (in Turkish).

16.

Morselli

Neuenschwander

Perrelet

Lippuner

. Osteoporosis diet. Ther Umsch 2000; 57(3): 152-60.

17.

World Health Organization. Fracture Risk Assessment Tool. [updated 2011, cited 2016 Sep 7]. Available from: http://www.shef.ac.uk/FRAX/.

18.

Kocyigit

Aydemir

Fişek

Olmez

Memis

. Kısa Form-36 (KF-36)’nın Turkce versiyonunun guvenirligi ve gecerligi. İlac ve Tedavi Dergisi 1999; 12(2): 102-6 (in Turkish).

19.

World Health Organization. [homepage on the internet] Global Database on Body Mass Index. [updated 2017, cited 2017 Feb 7]. Available from: http://apps.who.int/bmi/index.jsp?introPage=intro_3.html.

20.

WHO. Assessment of osteoporotic fracture risk and its role in screening for postmenopausal osteoporosis. WHO Technical report series, Geneva 1994.

21.

Aksakoglu

. Saglıkta arastırma teknikleri ve analiz yontemleri. Aksakoglu G, ed. DEU Rektörlük Basımevi 2006; 283-90. (in Turkish).

22.

Garnero

Shih

Gineyts

, et al. Comparison of new biochemical markers of bone turnover in late postmenopausal osteoporotic women in response to alendronate treatment. J Clin Endocrinol Metab 1994; 79: 1693-1700.

23.

Demir

Haberal

Geyik

Baskan

Ozturkoglu

Karacay

Deveci

. Identification of the risk factors for osteoporosis among postmenopausal women. Maturitas 2008; 60: 253-6.

24.

Kutlu

Civi

Pamuk

. Postmenopozal Kadınlarda Osteoporoz Sıklıgıve FRAX™ SkalasıKullanılarak 10 Yıllık Kırık Riskinin Hesaplanması. Turk J Phys Med Rehab 2012; 58: 126-35 (in Turkish).

25.

Franek

Wichrowska

Gozdowski

Puzianowska-Kuznicka

. WHO fracture risk calculator (FRAX™) in the assessment of obese patient with osteoporosis. Endokrynol Pol 2009; 60: 82-7.

26.

Soyka

Fairfield

Klibanski

. Hormonal determinants and disorders of peak bone mass in children. J Clin Endocrinol Metab 2000; 85(11): 3951-63.

27.

Costa

da Silva

Brito

Nascimento

Barbosa

Batista

, et al. Osteoporosis in primary care: An opportunity to approach risk factors. Rev Bras Reumatol Engl Ed 2016; 56: 111-6.

28.

Vestergaard

Hermann

Gram

, et al. Evaluation of methods for prediction of bone mineral density by clinical and biochemical variables perimenopausal women. Maturitas 2001; 40: 211-20.

29.

Dursun

Akin

Dursun

Sade

Korkusuz

. Influence of duration of total breast-feeding on bone mineral density in a Turkish population: Does the priority of risk factors differ from society to society? Osteoporos Int 2006; 17: 651-5.

30.

Yazici

Korkmaz

Erkan

Korkmaz

Erdem Baki

Alcelik

, et al. The effect of breast-feeding duration on bone mineral density in postmenopausal Turkish women: A population-based study. Arch Med Sci 2011; 7: 486-92.

31.

Kovacs

. Calcium and bone metabolism in pregnancy and lactation. J Clin Endocrinol Metab 2001; 86: 2344-8.

32.

Kalkwarf

Specker

. Bone mineral changes during pregnancy and lactation. Endocrine 2002; 17: 49-53.

33.

Sheu

Center

. Osteoporosis in post menopausal women Key aspects of prevention and treatment. Medicine Today 2017; 18(4): 29-38.

34.

Ross

Manson

Abrams

Aloia

Brannon

Clinton

, et al. The 2011 report on dietary reference intakes for calcium and vitamin D from the Institute of Medicine: What clinicians need to know. J Clin Endocrinol Metab 2011; 96: 53-8.

35.

Bischoff-Ferrari

Willett

Orav

, et al. Apooled analysis of vitamin D dose requirements for fracture prevention. N Engl J Med 2012; 367: 40-49.

36.

Ralston

. Genetics of osteoporosis. Ann N Y Acad Sci 2010; 1192: 181-9.

37.

Sezer

Tomruk-Sutbeyaz

Kibar

Köseoglu

Aras

. Postmenopozal osteoporozda yaşam kalitesinin belirtecleri. Turk J Phys Med Rehab 2009; 12: 19-25.

38.

Cortet

Roches

Logier

, et al. Evaluation of spinal curvatures after a recent osteoporotic vertebral fracture. Joint Bone Spine 2002; 69(2): 201-208.

39.

Jung

Park

Seo

Lee

Kim

Shin

Kang

Park

. Quality of life in patients with osteoporotic vertebral compression fractures. J Bone Metab 2017; 24: 187-196.

40.

Garip

Eser

Sayın

Bodur

Çavuşoğlu

. Pain and quality of life in postmenopausal osteoporotic women without vertebral fractures. Gaziantep Med J 2015; 21(2): 99-103.

41.

Paker

Bugdaycı

Dere

Tekdos

Erbil

Dere

. Relatıonshıp between bone densıty and qualıty of lıfe ın postmenopausal osteoporosıs. Turkish Journal of Geriatrics 2012; 15: 19-23.

42.

Oleksik

Lips

Dawson

Minshall

Shen

Cooper

, et al. Health-related quality of life in postmenopausal women with low BMD with or without prevalent vertebral fractures. J Bone Miner Res 2000; 15: 1384-92.

43.

Marchesini

Solaroli

Baraldi

Natale

Migliorini

Visani

, et al. Health-related quality of life in obesity: The role of eating behaviour. Diabetes Nutr Metab 2000; 13: 156-64.

44.

Papaionnau

Kennedy

Ioannidis

, et al. Determinants of health-related quality of life in women with vertebral fractures. Osteoporosis Int 2006; 17: 355-63.

45.

Bianchi

Orsini

Saraifoger

, et al. Quality of life in postmenopausal osteoporosis. Health and Quality of Life Outcomes 2005; 3: 78.