Bone health among older adults in Taiwan

Accepted Manuscript Title: Bone Health among Older Adults in Taiwan Authors: Wei-Jen Wang, Kuan-Liang Kuo, Chen-Kun Liaw, Tai-Yin Wu, Wei-Chu Chie, Jer-Min Chen PII: DOI: Reference:

S0167-4943(17)30019-5 http://dx.doi.org/doi:10.1016/j.archger.2017.01.003 AGG 3433

To appear in:

Archives of Gerontology and Geriatrics

Received date: Revised date: Accepted date:

6-6-2016 31-12-2016 9-1-2017

Please cite this article as: Wang, Wei-Jen, Kuo, Kuan-Liang, Liaw, Chen-Kun, Wu, Tai-Yin, Chie, Wei-Chu, Chen, Jer-Min, Bone Health among Older Adults in Taiwan.Archives of Gerontology and Geriatrics http://dx.doi.org/10.1016/j.archger.2017.01.003 This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

Bone Health among Older Adults in Taiwan Wei-Jen Wang, MD1; Kuan-Liang Kuo, MD, PhD1; Chen-Kun Liaw, MD, PhD2, 3, 4; Tai-Yin Wu, MD, PhD1, 5; Wei-Chu Chie, MD, PhD5; Jer-Min Chen, MD1 1Department of Family Medicine, Renai Branch, Taipei City Hospital, 10F, No. 10, Sec. 4, Ren-Ai Rd., Taipei City 106, Taiwan 2Department of Orthopaedics, Shin Kong Wu Ho-Su Memorial Hospital, No. 95, Wen-Chang Rd., Taipei City 111, Taiwan 3National Taiwan University School of Medicine, No.1, Sec.1, Ren-Ai Rd., Taipei City 100, Taiwan 4School of Medicine, Fu-Jen Catholic University, No. 510, Zhong-Zheng Rd., Xin-Zhuang Dist., New Taipei City 242, Taiwan 5Institute of Epidemiology and Preventive Medicine and Department of Public Health, College of Public Health, National Taiwan University, 5F, No. 17, Hsu-Chow Rd., Taipei City 100, Taiwan E-mail address for the authors Wei-Jen Wang: [email protected] Kuan-Liang Kuo: [email protected] Chen-Kun Liaw: [email protected] Tai-Yin Wu: [email protected] Wei-Chu Chie: [email protected] Jer-Min Chen: [email protected] Correspondence to: Tai-Yin Wu, MD. PhD. E-mail address: [email protected] Telephone Number: +886 (2) 2709-3600#1055 Fax Number: +886 (2) 2703-9053 Postal address: 10F, No. 10, Sec. 4, Ren-Ai Rd., Taipei City 106, Taiwan

Highlights 

This study reports bone health in older people in Taiwan.



Underweight (OR = 9.80) and lack of dairy products/calcium supplements (OR = 3.68) were the main risk factors for osteoporosis in community-dwelling older people in Taiwan.



The key questions of underweight and dietary pattern can be used to screen older people at high risk for osteoporosis in busy clinical settings.

Bone Health among Older Adults in Taiwan Abstract Background and Purpose: There has been much discussion about the risk factors for osteoporosis, but studies involving elderly population in Taiwan are minimal. We aimed to describe variables related to osteoporosis among community dwelling older people in Taiwan. Methods: This is a cross-sectional study. The 671 participants were randomly selected from 3680 examinees of the annual Senior Citizens Health Examination in year 2010. Participants were interviewed with a detailed questionnaire, and 91 of them were invited for dual-energy X-ray absorptiometry (DXA). Predictor variables included age, gender and clinical risk factors for osteoporosis. The main outcome was osteoporosis confirmed by DXA. Results: The mean age of the participants was 75.7 ± 6.4 years old. Overall, the most prevalent variables for osteoporosis were height loss in adulthood (41.0%), lack of dairy products or calcium supplements (32.0%) and insufficient physical activity (10.4%). In multivariate models, we found that underweight (OR = 9.80) and lack of dairy products/calcium supplements (OR = 3.68) were the main variables for osteoporosis. In the subgroup analysis involving only women, underweight (OR = 14.60) was the main variable.

Discussion: Among community-dwelling older people in Taiwan, osteoporosis was mainly associated with underweight and lack of dairy products or calcium supplements. Conclusion: We suggest using the key questions of underweight and dietary pattern in clinical settings to identify high risk people who are candidates for further BMD exam.

Keywords Older people, osteoporosis, risk factors

1. Introduction The risk factors for osteoporosis differ ethnically and geographically (Barrett-Connor et al., 2005). Yet our understanding about this multifactorial disease is incomplete. The prevalence of osteoporosis increases with age. Research about the risk factors of osteoporosis in older people in Taiwan is minimal (Shaw, 1993; Tsai, 1997). The assessment of clinical risk factors has an important role in determining the underlying bone disease and individualizing treatment decisions. For primary care physicians, there is a need to develop a simple and easy method to quickly screen population at high risk of osteoporosis for further necessary referral and action plan. Currently, there are several risk assessment tools to help select asymptomatic people for measurement of BMD (Cadarette et al., 2001), such as the World Health Organization (WHO) Fracture Risk Assessment Tool (FRAX) (International Osteoporosis Foundation (IOF), 2015; Kanis et al., 2008) and the Osteoporosis Self-assessment Tool for Asians (OSTA) (Koh et al., 2001). However, FRAX requires online calculation that might not always be feasible in clinical and community settings whereas the usage of OSTA is limited to Asian women. Purpose There is a need to identify risk factors for osteoporosis among Taiwanese older

people and to select the population at risk for further assessment and early treatment. We aimed to report risk factors for osteoporosis in elderly population in Taiwan. 2. Materials and Methods 2.1. Participants This is a hospital-based, cross-sectional study conducted in 2010. The Taipei City Government provides free Senior Citizens Health Examination each year. Residents aged over 65 and native Taiwanese over 55 years can sign up for the exam in several local hospitals. Our inclusion criteria were ambulatory community-dwelling senior citizens in Taipei City, Taiwan, who participated in this health exam in Taipei City Hospital, Renai Branch in year 2010. Exclusion criteria included inability to provide reliable data, cognitive function impairment, failure to communicate face to face, and unwillingness to join the study. We identified 3680 health examinees in year 2010. By drawing paper lots, we recruited 671 (18.2%) participants for a detailed questionnaire interview (the questionnaire group). Among them, 91 (13.6%) residents were randomly selected by drawing paper lots and further invited for BMD test using dual-energy X-ray absorptiometry (DXA) (the BMD test group), which served as the gold standard of diagnosing osteoporosis. Fig. 1 described the design of the study. Oral and written informed consent was obtained. The detailed method of this study was described in

our previous works (Wu et al., 2013a; Wu et al., 2013b). 2.2. Measurements The Senior Citizens Health Examination included: a brief questionnaire of basic demographic characteristics, lifestyle habits, past medical and personal history, physical examination, laboratory tests, and screening for mood disorders. The detailed questionnaire collected further information about frailty status, medications, long-term medical conditions, and risk factors for osteoporosis using the One-Minute Osteoporosis Risk Test (IOF, 2015). The One-Minute Osteoporosis Risk Test was proposed by International Osteoporosis Foundation (IOF) to evaluate risk factors for osteoporosis. It has 19 questions in total and was intended for use in the general population. The more risk factors a respondent have, the greater chance to develop osteoporosis. The presence of any significant risk factor should warrant further referral for DXA exam. In this study, the predictor variables included age, gender, and risk factors for osteoporosis collected by using the One-Minute Osteoporosis Risk Test (IOF, 2015). Parental low impact fracture represented either of the participants’ parents has osteoporosis or fracture after a minor fall, which is a fall from standing height or less. Frequent falls was defined as falling more than once in the last year. Underweight was defined as body mass index (BMI) less than 19 kg/m2. Long-term corticosteroid use

was interpreted as taking oral corticosteroid for more than three consecutive months. Excessive drinking was characterized by drinking more than two standard drinks of alcohol per day. One standard drink equals 10g of pure alcohol (Kalinowski and Humphreys, 2016). Insufficient physical activity was described as daily physical activity less than 30 minutes. No time spent outdoors was interpreted as less than 10 minutes per day. Each variable scored one point and the number of variables were added up to get the total score. Our main outcome variable, osteoporosis, was based on DXA results. The International Society for Clinical Densitometry (ISCD) and the National Osteoporosis Foundation (NOF) suggested that the diagnosis of osteoporosis should be made by using the lowest T-score of the lumbar spine (L1-L4), total proximal femur, or femoral neck (Cosman et al., 2014; International Society for Clinical Densitometry (ISCD), 2013). In our study, we defined “overall osteoporosis” accordingly. Either spine, total hip or femoral neck T-score≦-2.5 was considered osteoporosis (Kanis et al., 1994; World Health Organization, 1994). This study was approved by the Institutional Review Board of Taipei City Hospital, Taiwan [TCHIRB-990204]. 2.3. Statistical Analysis Statistical Package for the Social Sciences (SPSS) (version 22.0 for Windows,

SPSS Inc., IBM Company, Chicago, USA) was used for statistical analysis. A p-value (P) <0.05 for 2-tailed tests was considered statistically significant. Analysis of variance (ANOVA), chi-square tests and multivariate logistic regression were used as appropriate. Variables entered in the model were shown in Table 4. In subgroup analysis, we stratified our participants according to age (young old (<75 years old) or old old (≥75 years old)) and gender. 3. Results We enrolled a total of 671 participants aged 55 to 94. The baseline characteristics of the subgroups are summarized in Table 1, corresponding to the BMD test group (N = 91), the questionnaire only group (N = 580), and the rest of the health examinees (N = 3009). The mean age was 74.3 (standard deviation (SD) 5.5), 75.8 (SD 6.6), and 75.4 (SD 6.8) years old for the three groups, respectively. Female participants accounted for 69.2%, 45.5%, and 50.1% of these groups, respectively. The three groups were similar with regard to age, having chronic disease, having self-rated good health and lifestyle habits but differed in gender (P < 0.01) and the proportion having depressive disorder (P = 0.05). Regarding all the examinees who participated in the Health Examination, we found that some lifestyle habits such as smoking or drinking are more likely to develop in men than in women, as shown in Appendix Table 1.

Table 2 presents variables that are more likely in participants who have osteoporosis than those who do not. Other than age, the most common variables are lost more than 3 cm in height in adulthood (41.0%), lack of dairy products/calcium supplements (32.0%) and insufficient physical activity (10.4%). There was no significant gender difference in the total number of variables (2.77 (SD 1.26) vs. 2.77 (SD 1.38)). However, women predominantly had a history of low impact fracture and parental low impact fracture (P < 0.01 and P = 0.06, respectively), lost more than 3 cm in height in adulthood (P < 0.01), underweight (P = 0.07), and rheumatoid arthritis (P = 0.03). By contrast, men predominantly had a history of smoking (P < 0.01), excessive drinking (P < 0.01), and a lack of dairy products or calcium supplements (P < 0.01). The distribution of the total scores is shown in Fig. 2A. We found that the total numbers of risk factors were similar in men and women. Regarding age, there was significant difference in the total number of osteoporosis variables between the two age groups (2.90 (SD 1.31) vs. 2.63 (SD 1.32); P = 0.01). In the young olds, a history of parental low impact fracture was a predominant variable (P < 0.01). By contrast, old olds mainly had a history of height reduction in adulthood (P < 0.01) and insufficient physical activity (P = 0.01) (Fig. 2B). Table 3 and Table 4 illustrate variables associated with osteoporosis (N = 91). In

univariate model (Table 3), osteoporosis was positively associated with underweight (odds ratio (OR) = 7.38; 95% confidence interval (CI) = 1.39-39.15). In multivariate logistic regression analysis (Table 4), underweight (OR = 9.80, 95% CI = 1.09-88.59) and a lack of dairy products or calcium supplements (OR = 3.68, 95% CI = 1.09-12.44) were both significant variables associated with osteoporosis. Subgroup analysis involving only women showed that underweight (OR = 14.60, 95% CI = 1.24-172.55) was an important variable associated with osteoporosis. However, the confidence interval was wide and care needs to be taken when interpreting this result. The relationship between osteoporosis and lack of dairy products/calcium supplements was not statistically significant, probably due to the small sample size of this population. 4. Discussion In this study, we observed that the most crucial risk factors for osteoporosis among community-dwelling older people were underweight and lack of dairy products or calcium supplements. Although women have higher bone loss rate at the time of menopause (Richelson et al., 1984), the rate slows down years later. By about age 65, men and women are at the same rate of losing bone mass. Older people are our main research objects, so it is not surprising that we observed that the total scores of variables are similar in men

and women. However, age related bone loss is only one part of the risk for osteoporosis. Other factors are also important. Some lifestyle habits, such as excessive drinking, smoking and lacking dairy products/vitamin D supplement, are considered risk factors to bone health. Such habits are more common in men (Appendix Table 1), which might explain the similarity of total scores between men and women. With regard to gender differences (Table 2), men were more likely to drink excessively and smoke. On the other hand, underweight, height loss in adulthood, a history of low impact fracture and rheumatoid arthritis were more likely to be seen in women. The above findings are similar to that of other studies (Ahlmen et al., 2010; Centers for Disease and Prevention, 2009; Dawson and Archer, 1992). Women are usually more concerned about their own health (Bertakis et al., 2000). Hence, it is plausible that women are more motivated to consume dairy products/calcium supplements than men. Some variables increase with age, such as insufficient physical activity and height loss (Table 2), partly explaining why total risk scores were higher in the older group (Fig. 2B). The relationship between both weight, BMI and osteoporosis in women has been consistently reported (Michaelsson et al., 1996). Our study observed that underweight was related to lower BMD, not only in women but also in men. Compared to OSTA,

which was a simple self-assessment tool designed for Asian women only (Koh et al., 2001; Yang et al., 2013), our study has extended this view also to older men. Body weight is a reflection of nutritional status and is positively associated to BMD. Two proposed mechanisms clarify how body mass affects BMD. Body fat has been suggested as a source of peripheral conversion of androstenedione to the metabolically active form of estrogen, and hence provides an indirect protection from bone loss (Felson et al., 1993). The other potential mechanism suggests that heavy individuals reach a higher peak BMD in their early adulthood, so they have less chance to develop osteoporosis in their old age (Felson et al., 1993). The Dietary Guidelines Advisory Committee (DGAC) report in 2005 indicated significant positive relationship between dairy product intake and BMD which is compatible with our results (Heaney, 2009; U.S. Department of Health and Human Services and U.S. Department of Agriculture, 2005). Dairy products contain an appropriate amount of calcium which is a key element of bone structure. Not only calcium but also phosphate and protein in dairy foods are beneficial to organic bone matrix formation and mineralization. (Bonjour, 2011a, b) It may explain the important role that dairy food play in bone health (Bonjour et al., 2013). Several previous studies discussed the relationship between height loss and osteoporosis. One study suggested that a loss of more than 3 cm of height is related to

low BMD (Sanila et al., 1994). However, the population was limited to postmenopausal women with rheumatoid arthritis and the generalizability of this finding remains inconclusive. In another study regarding elderly women, no strong association between height loss and osteoporosis was found (Dargent-Molina et al., 2000). Our study failed to observe an association between height loss and osteoporosis in community-dwelling older people in Taiwan as well. Many studies found an association between height loss and osteoporotic fracture but not directly related to osteoporosis (Siminoski et al., 2005). A possible explanation is that height loss in adulthood might represent overall musculoskeletal aging but not merely osteoporosis (Wu et al., 2013b). Therefore, elderly people with height loss are prone to falls and fractures. Another possible explanation is that examinees didn’t remember accurately their actual height when they were young. Previous study had shown that self-report height loss might lead to overestimation of true height loss (Birrell et al., 2005). In clinical practice, clinicians should always confirm the accuracy of reported height loss objectively before considering further evaluation. Health problems in older people are complicated and influenced by several other bio-psycho-social factors, so confounding factors must always be taken into account. This, in our opinion, explains why lacking dairy products/calcium supplements makes significant difference in multivariate analysis but not univariate analysis.

Our study has several strengths. Our participants were from community and were generally healthier. They may reflect the general Taiwanese older population more accurately. The completion of the questionnaire was assisted by a trained staff, so misunderstanding of the contents was minimized. We tested BMD of three different areas and selected the lowest T-score for analysis to avoid under-diagnosis of osteoporosis. There are some limitations. Causality cannot be inferred due to our observational design. The size of our study population was limited, so the interpretation of the result should be more careful. Besides, we enrolled our participants in the capital Taipei City and they may not be representative of rural people. Examinees who participated in the regular health check-ups tend to have better health literacy (healthy volunteer bias). Our data was collected mainly by questionnaire interview and recall bias remains. Our questionnaire was designed according to the items of One-Minute Osteoporosis Risk Test (IOF, 2015), thus calcium supplementation and dairy food consumption were asked altogether. However, they might represent two different factors. Reporting the prevalence of variables for osteoporosis helps to identify people at risk for early intervention and prevention of future osteoporotic fractures. We suggest that by asking the key questions of “underweight” and “intake of dairy products/calcium supplements” in clinical practice, we can select the population most

in need for DXA referral and initiate further necessary steps. 5. Conclusions We observed that in older Taiwanese population, underweight and lack of dairy products/calcium supplements were the main variables for osteoporosis. In the busy clinical scenario, these can be used as sentinel questions to identify high risk people who are candidates for further BMD exam, instead of other time consuming, extensive survey. Further studies are needed to confirm the validity of our proposal. Acknowledgements We thank the assistants Ginger Yeh and Hsiang-Yu Liao for their generous help in data collection. We thank assistant Hua Chung for his managerial support. Funding This research was funded by the research grants of the Department of Health, Taipei City Government and Taipei City Hospital.

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Table Table 1. Comparability of the 3 groups who participated in the Senior Citizens Health Examination in a Taipei City Hospital in 2010 (N = 3680) Characteristics

BMD test groupa (N = 91) N

Demographic Characteristics Age (years) (mean, (SD)) Gender (female) Education (elementary and over) Living in the nearby district Having a partner Living alone Job before retirement (civil servant) Perceived economic status (good)

%

Questionnaire only b

Health examination only groupc (N = 3009) N %

group (N = 580) N %

P-value

74.32 63 86 58 68 5 32 79

5.5 69.2 94.5 63.7 74.7 5.5 35.2 86.8

75.79 264 556 309 424 85 264 475

6.6 45.5 95.9 53.4 73.1 14.7 45.7 82.8

75.4 1507 2842 1610 2268 368 -

6.8 50.1 94.5 53.6 75.4 12.2 -

0.30 <0.01 0.43 0.16 0.50 0.03 0.06 0.16

General Health Depressive disorders (BSRS-5 score ≥1) Having chronic disease Self-rated health (good)

65 77 75

71.4 84.6 85.2

337 494 454

58.1 85.2 80.2

1691 2462 -

56.2 81.8 -

0.05 0.13 0.27

Lifestyle Habits Teeth brushing Fruit and vegetables everyday Riding a motorcycle or driving a car

86 74 8

94.5 81.3 8.8

525 442 100

90.5 76.2 17.2

2685 2325 542

89.2 77.3 18.0

0.19 0.55 0.07

Abbreviations: BMD, bone mineral density; BSRS, brief symptom rating scale; SD, Standard deviation. a Participants received BMD test, measured by dual-energy X-ray absorptiometry, detailed questionnaire interview, and ordinary health check-up b Participants received both detailed questionnaire interview and ordinary health check-up. c Participants received only ordinary health check-up.

Table 2. Variables for osteoporosis in the questionnaire group (N = 671). Variables

P-valuea

Men (N = 344)

Women (N = 327)

N (%)

N (%)

N (%)

Under 75 years old (N = 334) N (%)

Aged over 40 Parental low impact fracture Parental kyphosis Low impact fracture Frequent falls or fear of falling Lost more than 3 cm in height after the age of 40 Underweight Long-term corticosteroid use Having rheumatoid arthritis Having over-reactive thyroid/parathyroid glands Excessive drinking Smoking

671 (100%) 45 (6.7%) 64 (9.5%) 24 (3.6%) 25 (3.7%) 275 (41.0%) 49 (7.3%) 2 (0.3%) 19 (2.8%) 29 (4.3%) 17 (2.5%) 32 (4.8%)

344 (100%) 17 (4.9%) 31 (9.0%) 2 (0.6%) 10 (2.9%) 122 (35.5%) 19 (5.5%) 0 5 (1.5%) 12 (3.5%) 17 (4.9%) 29 (8.4%)

327 (100%) 28 (8.6%) 33 (10.1%) 22 (6.7%) 15 (4.6%) 153 (46.8%) 30 (9.2%) 2 (0.6%) 14 (4.3%) 17 (5.2%) 0 3 (0.9%)

0.06 0.55 <0.01 0.25 <0.01 0.07 0.15 0.03 0.28 <0.01 <0.01

334 (100%) 32 (9.6%) 40 (12.0%) 11 (3.3%) 12 (3.6%) 106 (31.7%) 18 (5.4%) 1 (0.3%) 7 (2.1%) 16 (4.8%) 7 (2.1%) 19 (5.7%)

337 (100%) 13 (3.9%) 24 (7.1%) 13 (3.9%) 13 (3.9%) 169 (50.1%) 31 (9.2%) 1 (0.3%) 12 (3.6%) 13 (3.9%) 10 (3.0%) 13 (3.9%)

<0.01 0.03 0.69 0.86 <0.01 0.06 1.00 0.25 0.55 0.47 0.27

Insufficient physical activity No dairy products/calcium supplements No time spent outdoors/vitamin D supplements (Men only) Impotence or lack of libido (Women only) Menopause before the age of 45 (Women only) Missed periods for over 12 months (Women only) Removed ovaries before age 50, without HRT

70 (10.4%) 215 (32.0%) 65 (9.7%) -

36 (10.5%) 127 (36.9%) 32 (9.3%) 148 (43.0%) -

34 (10.4%) 88 (26.9%) 33 (10.1%) 75 (22.9%) 11 (3.4%) 20 (6.1%)

0.97 <0.01 0.74 -

25 (7.5%) 109 (32.6%) 26 (7.8%) 55 (38.5%) 41 (21.5%) 6 (3.1%) 13 (6.8%)

45 (13.4%) 106 (31.5%) 39 (11.6%) 93 (46.3%) 34 (25.0%) 5 (3.7%) 7 (5.1%)

0.01 0.74 0.10 0.15 0.45 0.79 0.54

2.77 (1.32)

2.77 (1.26)

2.77 (1.38)

0.98

2.63 (1.32)

2.90 (1.31)

<0.01

Total score (1 point for each “Yes”)(mean(SD)) a

P-value comparing different genders. P-value comparing different age groups. c HRT: Hormone replacement therapy b

Over 75 years old (N = 337) N (%)

P-valueb

Total (N = 671)

Table 3. Variable comparison between osteoporotic and non-osteoporotic participants of the BMD test group using univariate analysis (N = 91) Osteoporosis variables

Parental low impact fracture Parental kyphosis Low impact fracture Lost more than 3 cm in height after the age of 40 Underweight Having over-reactive thyroid/parathyroid glands Insufficient physical activity No dairy products/calcium supplements No time spent outdoors/vitamin D supplements (Women only) Menopause before the age of 45

Osteoporotic participantsa (N =30)

Non- Osteoporotic participantsa (N =61) (men=27,

(men=1, women=29)

women=34)

3 (10.0%) 5 (16.7%) 2 (6.7%) 19 (63.3%) 6 (20.0%) 2 (6.7%) 3 (10.0%) 12 (40.0%) 4 (13.3%) 16 (55.2%)

8 (13.4%) 7 (11.5%) 1 (1.6%) 28 (45.9%) 2 (3.3%) 4 (6.6%) 4 (6.6%) 18 (29.5%) 5 (8.2%) 25 (73.5%)

OR

0.74 1.54 4.29 2.04 7.38 1.02 1.58 1.59 1.72 0.44

95% CI

0.18-3.00 0.45-5.34 0.37-49.27 0.83-5.00 1.39-39.15 0.18-5.90 0.33-7.58 0.64-3.97 0.43-6.95 0.15-1.27

P-value

0.48 0.35 0.25 0.09 0.01 0.65 0.42 0.22 0.34 0.10

Abbreviations: BMD, Bone mineral density, measured by dual-energy X-ray absorptiometry; OR, odds ratio; CI, confidence interval. a

Using overall T-score: either spinal, total hip or femoral neck T-score was less or equal to -2.5

Table 4. Multivariate analysis of the variables for osteoporosis in the BMD test group (N = 91). Subgroup OR

Total (N = 91) 95% CI P-value

Only women (N = 63) OR 95% CI P-value

Male gender

0.03

0.003-0.30

<0.01

-

-

-

Age >75 years Parental low impact fracture Parental kyphosis Low impact fracture Lost more than 3 cm in height after the age of 40 Underweight Having over-reactive thyroid/parathyroid glands

1.20 0.88 1.74 3.67 1.76 9.80 0.54

0.38-3.86 0.11-6.80 0.27-11.01 0.20-68.70 0.58-5.31 1.09-88.59 0.07-4.45

0.76 0.90 0.56 0.39 0.32 0.04 0.57

1.02 1.00 2.62 5.22 1.67 14.60 0.64

0.29-3.59 0.10-9.78 0.38-18.18 0.25-110.28 0.51-5.44 1.24-172.55 0.07-6.30

0.98 1.00 0.33 0.29 0.40 0.03 0.70

Insufficient physical activity No dairy products/calcium supplements No time spent outdoors/vitamin D supplements (Women only) Menopause before the age of 45

1.41 3.68 1.45 -

0.04-46.46 1.09-12.44 0.19-10.94 -

0.85 0.04 0.72 -

3.41 1.67 0.35

0.94-12.30 0.20-13.63 0.10-1.20

0.06 0.63 0.10

Abbreviations: BMD, Bone mineral density, measured by dual-energy X-ray absorptiometry; OR, odds ratio; CI, confidence interval. Outcome variable: osteoporosis (either spinal, total hip or femoral neck T-score was less or equal to -2.5)

Appendix Table 1. Comparison of different gender in all examinee who participated in the Senior Citizens Health Examination in a Taipei City Hospital in 2010 (N = 3680). Variables

P-valuea

Total (N = 3680)

Men (N = 1846)

Women (N = 1834)

N (%)

N (%)

N (%)

Alcohol drinking Cigarette smoking (regular) Betel nut chewing Regular exercise Drinking milk everyday

897(24.4%) 116(3.2%) 23(0.6%) 2426(65.9%) 2041(55.5%)

722(39.1%) 105(5.7%) 14(0.8%) 1327(71.9%) 1018(55.1%)

175(9.5%) 11(0.6%) 9(0.5%) 1099(59.9%) 1023(55.8%)

<0.01 <0.01 0.40 <0.01 0.72

Living alone Depressive disorders (BSRS-5 score ≥1)b Falling ≥ 2 times in recent 6 months Taking sedative or sleeping pills

458(12.4%) 2093(56.9%) 294(8.0%) 813(22.1%)

175(9.5%) 896(48.5%) 118(6.4%) 316(17.1%)

283(15.4%) 1197(65.3%) 176(9.6%) 497(27.1%)

<0.01 <0.01 <0.01 <0.01

Trouble seeing the floor clearly Physical examination Central obesityc Hypertensiond Laboratory Tests Preprandial hyperglycaemiae

758(20.6%)

334(18.1%)

424(23.1%)

<0.01

1495(40.6%) 1073(29.2%)

641(34.7%) 486(26.4%)

854(46.6%) 587(32.0%)

<0.01 <0.01

1922(52.3%)

1018(55.2%)

904(49.3%)

<0.01

9(0.2%) 491(13.3%) 219(6.0%)

5(0.3%) 385(20.9%) 99(5.4%)

4(0.2%) 106(5.8%) 120(6.5%)

1.00 <0.01 0.14

f

Hypoalbuminemia Renal insufficiencyg Elevated liver enzymeh a

P-value comparing different genders. BSRS: brief symptom rating scale c Defined as abdominal girth of ≥80 cm in females and ≥90 cm in males d Defined as systolic blood pressure ≥140 mmHg or diastolic blood pressure ≥90 mmHg e Defined as blood sugar ≥100 mg/dL f Defined as serum albumin <3.5 mg/dL g Defined as either serum Cr ≥1.4mg/dL or BUN ≥25 mg/dL b

h

Defined as either AST or ALT ≥43mg/dl

Figure 1. Flowchart of the study design (* BMD: Bone mineral density).

Figure2A. The percentage of participants with different total osteoporosis variables scores (grouped by gender)

Figure2B. The percentage of participants with different total osteoporosis variables scores (grouped by age)