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Keywords:

  • fracture;
  • clinical risk factors;
  • Korean;
  • cohort study;
  • hip circumference

Abstract

  1. Top of page
  2. Abstract
  3. Introduction
  4. Subjects and Methods
  5. Results
  6. Discussion
  7. Disclosures
  8. Acknowledgements
  9. References

Clinical risk factors (CRFs), either alone or in combination with bone mineral density, are used to determine the fracture risk for clinical assessment and to determine intervention thresholds. Because fracture risk is strongly affected by ethnicity and population-specific differences, we sought to identify Korean-specific CRFs for fracture, in combination with quantitative ultrasound (qUS) measurements of the radius and tibia. A total of 9351 subjects (4732 men and 4619 women) aged 40 to 69 years were followed for a mean of 46.3 ± 2.2 months. We obtained CRF information using a standardized questionnaire and measured anthropometric variables. Speed of sound at the radius (SoSR) and tibia (SoST) were measured by qUS. Fracture events were recorded using a questionnaire, and a height-loss threshold was used as an indicator of vertebral fracture. Relative risks were calculated by Cox regression analysis. A total of 195 subjects (61 men and 134 women) suffered low-trauma fractures. Older age, lower body mass index (BMI), and previous fracture history were positively associated with fracture risk in both sexes. Decreased hip circumference, lack of regular exercise, higher alcohol intake, menopause, and osteoarthritis history were further independent CRFs for fracture in women. However, neither SoSR nor SoST was independently associated with fracture risk. In this study, we identified the major Korean-specific CRFs for fracture and found that smaller hip circumference was a novel risk factor. This information will allow optimal risk-assessment targeting Koreans for whom treatment would provide the greatest benefit. © 2010 American Society for Bone and Mineral Research


Introduction

  1. Top of page
  2. Abstract
  3. Introduction
  4. Subjects and Methods
  5. Results
  6. Discussion
  7. Disclosures
  8. Acknowledgements
  9. References

Osteoporosis is skeletal disorder characterized by reduced bone strength that predisposes an individual to an increased fracture risk.1 Currently, there is no accurate measure of overall bone strength. Bone mineral density (BMD) measured using dual-energy X-ray absorptiometry (DXA), which is often used to measure bone mass, is used frequently for the diagnosis of osteoporosis because bone mass accounts for approximately 70% of bone strength.1 Central DXA has been considered the gold standard test used to diagnose and initiate intervention in osteoporosis for prevention of fracture.2 However, there are several problems when the BMD test is used alone.3 Central DXA is expensive and not portable and hence is not widely available in some communities, not accessible in primary-care settings, and not suitable as a mass screening modality in a community-based studies.4 For these reasons, several other techniques, such as quantitative ultrasound (qUS), peripheral DXA, and hand X-ray, have been developed.5 The advantages of the qUS method over X-ray-based techniques include low cost, portability, and no ionizing radiation exposure.6 Several studies have suggested that parameters measured by qUS may provide independent information on fracture risk,7–9 but as yet there are no clear guidelines indicating how qUS should be used with or without DXA.

More important, BMD tests alone are not optimal for the detection of individuals at high risk of fracture3 because osteoporotic fracture can occur in patients with any given T-score,10 even in individuals with normal BMD values, according to current World Health Organization (WHO) classifications.2 Fracture risk is multifactorial; thus many independent factors, including those related to the risk of fall, contribute to the risk over and above that reflected by BMD.5 Recently, a WHO scientific group proposed that the 10-year probability of fracture calculated using information on clinical risk factors (CRFs), with or without BMD data, should be used to express fracture risk for clinical assessment10 and to determine interventional thresholds.11 However, fracture risk is strongly affected by ethnicity,12 so fracture risk factors should be evaluated according to each ethnic and population group.4 To our knowledge, no large prospective study has previously investigated the CRFs of osteoporotic fracture in Koreans. We therefore sought to identify Korean-specific CRFs that predict fracture alone or in combination with qUS of the radius and tibia.

Subjects and Methods

  1. Top of page
  2. Abstract
  3. Introduction
  4. Subjects and Methods
  5. Results
  6. Discussion
  7. Disclosures
  8. Acknowledgements
  9. References

Study population

Two communities were selected for the Korean Health and Genome Study (KHGS) in 2001; the Ansung and Ansan cohorts represented rural and urban communities, respectively. This is an ongoing prospective study involving a biennial examination. Details of the KHGS and study methodology have been described elsewhere.13 In Ansan, a total of 5020 subjects (2523 men and 2497 women, 45.8% of 10,957 subjects initially contacted) were recruited by telephone calls that were made to 10,957 randomly selected local telephone numbers requesting cohort participation. In Ansung, sampling was based on a clustering sampling method using mailing, door-to-door visits, and telephone solicitations within five randomly selected local government regions (termed Myons in Korea) of the 11 such divisions in the district. After identifying 7192 eligible subjects, a total of 5018 subjects (2239 men and 2779 women; 70.0% of 7192 subjects initially contacted) were recruited. In both Ansan and Ansung, age and gender distributions of those enrolled were similar to those who refused to participate in the study.13–17

In brief, a total of 10,038 subjects aged from 39 to 70 years were recruited (5020 from the rural Ansung community and 5018 from the urban Ansan community). Of these, 687 subjects who had history of malignancy or had been received any drug that might affect bone metabolism, such as vitamin D, hormones, and medications for osteoporosis, for more than a 6-month period or within the previous 12 months were excluded. Thus a total of 9351 subjects (4732 men and 4619 women) were enrolled in the present study. All subjects participated voluntarily in the study, and written informed consent was obtained from all subjects. The study protocol was approved by the Ethics Committees of the Korean Health and Genomic Study of the Korean National Institute of Health and Ajou University School of Medicine.

Clinical risk factor assessment

Information on age and CRFs was obtained using a standardized questionnaire administered face to face. The investigated risk factors included dairy product consumption; exercise habits; cigarette smoking; alcohol consumption; menopausal state and age at menopause; maternal history of kyphosis; personal history of rheumatoid arthritis (RA), osteoarthritis, hypertension, diabetes mellitus, and thyroid disease; history of medication use, such as steroids, arthritis and thyroid medications, and herbal remedies; and history of previous fracture. Regular exercise was defined as engaging in any of a variety of activities for the purpose of exercise and was recorded as less than 30 or 30 or more minutes per day. Subjects were asked about cigarette smoking (current, past, or never) and alcohol consumption (units per week). Height, body weight, and waist and hip circumferences were measured when wearing light clothes. Body mass index (BMI) was calculated as weight divided by height squared (kg/m2), and weight was stratified into four categories: underweight < 18.5 kg/m2, normal 18.5 to 22.9 kg/m2, overweight 23.0 to 24.9 kg/m2, and obese ≥ 25.0 kg/m2.18

Fracture detection in personal histories and new low-trauma fracture event assessment

Nonvertebral fracture events were recorded at the first examination and biennially using a standardized self-administered questionnaire. Fractures clearly caused by high-trauma events were excluded. High-trauma events included motor vehicle accidents, violence, and falls from more than standing height of the individual. We included only fracture events at five sites (hip, wrist, humerus, rib, and pelvis) that occurred after the age of 40 years. Height loss of 4.0 cm or more during the follow-up period was regarded as indicative of a new vertebral fracture.19–21 A threshold of 4.0 cm has been shown to have a specificity of 98.3% and a positive predictive value of 63.9% for incident vertebral fractures.21

qUS measurements

qUS measurements at the radius and tibia were performed using the Omnisense 7000 device (Sunlight Medical, Ltd., Rehovot, Israel) with a handheld probe specifically designed for measurements of the axial speed of sound (SoS, m/s) along the surface of bone. Measurements of SoS were performed at the distal third of the radius (SoSR) and at the midshaft of the tibia (SoST). All measurements were performed three times on subjects' nondominant sides, and subjects were repositioned between measurements. The average of the three measurements was used as the final SoS value. Quality control assessments of the Omnisense device were performed daily using an SoS verification phantom provided by the manufacturer. Interobserver validity was tested by repeated qUS measurements on 15 healthy individuals tested by two different examiners. The correlation coefficient was 0.941, and the mean SoS comparison was t = 0.2, P = .84. Furthermore, the mean coefficients of variation (CVs) for measurement of radius and tibia were 0.22% and 0.19%, respectively. Intraobserver validity was assessed by repeated consecutive measurements of 15 healthy individuals within 3 days by the same operator. The mean CVs for radius and tibia measurements were 0.24% and 0.18%, respectively.

Statistical analysis

Baseline characteristics of subjects were expressed as means ± standard deviations (SDs) for continuous variables and as numbers (%) for categorical variables. The unpaired Student's t test and the chi-square test were used to compare simple differences in continuous variables and categorical variables, respectively, between the two groups. The Cox proportional hazards model was used to identify potential independent risk factors for osteoporotic fracture. Results were reported as relative risks (RRs) with 95% confidence intervals (CIs). Survival time was measured from the first interview until the first fracture date for each subject. Individuals who were lost to follow-up were censored, and their survival time was considered to be the sum of one-half the next mean follow-up period and the time between the first and the last visit. Nonfracture cases were censored at the date on which the latest information became available. First, crude univariate analyses were preformed, which were next adjusted for age. Second, we constructed four Cox multivariate models: In model I, variables found to be significant in age-adjusted univariate models were included in a multivariate model; in model II, variables in model I and SoSR were simultaneously adjusted; in model III, variables in model I and SoST were simultaneously adjusted; and in model IV, variables in model I and SoSR and SoST were simultaneously adjusted. We selected variables that were marginally significant (P < .10) in univariate analyses for subsequent multivariate analysis. All statistical analyses were conducted using SPSS for Windows Version 12.0 statistical software (SPSS, Chicago, IL, USA).

Results

  1. Top of page
  2. Abstract
  3. Introduction
  4. Subjects and Methods
  5. Results
  6. Discussion
  7. Disclosures
  8. Acknowledgements
  9. References

Baseline clinical characteristics

The average follow-up period of study participants was 46.3 ± 2.2 months (range 22.0 to 68.0 months). Of the 9351 subjects initially recruited at baseline, 7832 (83.8%) and 6870 (73.5%) participated in second- and third-round examinations, respectively. Baseline characteristics of study participants are listed in Tables 1 and 2.

Table 1. Variables Measured at Baseline, Number of Fractures During the Follow-up Period, and Associated Relative Risks According to the Baseline Variables in Men (n = 4732)Thumbnail image of
  • Values are means ± SDs or numbers (%). 95% CI = 95% confidence interval; DM = diabetes mellitus; HTN = hypertension; NA = not applicable; OA = osteoarthritis; RA = rheumatoid arthritis; Ref = reference group; RR = relative risk; SoSR = speed of sound of radius; SoST = speed of sound of tibia. Underweight (<18.5 kg/m2), normal (18.5 − 22.9 kg/m2), overweight (23.0 − 24.9 kg/m2), and obese (≥25.0 kg/m2).

  • aAge was adjusted as a categorical variable,

  • bP < .05,

  • cP < .10,

  • dRelative risk for fracture at an increment of 100 m/s.

  • Table 2. Variables Measured at Baseline, Number of Fractures During the Follow-up Period, and Associated Relative Risks According to the Baseline Variables in Women (n = 4619)Thumbnail image of
  • Values are means ± SDs or numbers (%). 95% CI = 95% confidence interval; DM = diabetes mellitus; HTN = hypertension; NA = not applicable; OA = osteoarthritis; RA = rheumatoid arthritis; Ref = reference group; RR = relative risk; SoSR = speed of sound of radius; SoST = speed of sound of tibia. Underweight (<18.5 kg/m2), normal (18.5 − 22.9 kg/m2), overweight (23.0 − 24.9 kg/m2), and obese (≥25.0 kg/m2).

  • aAge was adjusted as a categorical variable,

  • bP < .05,

  • cP < .10,

  • dRelative risk for fracture at an increment of 100 m/s.

  • Crude and age-adjusted relative risks for fracture

    During the follow-up period, 198 new low-trauma fractures were reported (62 in men and 136 in women). In men, there were 20 vertebral fractures, 3 hip fractures, 10 humerus fractures, 20 wrist fractures, 8 rib fractures, and 1 pelvic fracture. One patient suffered fractures at both the femur neck and humerus; hence 61 men were included in the fracture group. In women, there were 33 vertebral fractures, 5 hip fractures, 15 humerus fractures, 78 wrist fractures, 4 rib fractures, and 1 pelvic fracture. One woman suffered two wrist fractures, and another had fractures of both the wrist and humerus; hence 134 women were included in the fracture group. Fracture incidences per 1000 subjects were 4.0 in men and 9.1 in women. Fracture incidence rates per 1000 subjects according to age were 1.7, 5.4, and 6.4 for men aged 40 to 49, 50 to 59, and 60 to 69 years, respectively, and the corresponding fracture incidences in women were 2.5, 11.4, and 20.2, respectively.

    Univariate analyses with a crude model showed that age, weight, BMI, waist and hip circumferences, and history of previous fracture were associated with fracture risk in men (see Table 1). The SoST Q2 group had a significantly reduced fracture risk compared with the Q1 group; however, SoSR analysis did not show similar results. Lower BMI, previous fracture history, and SoST Q2 were significantly associated with increased fracture risk even after adjustment for age.

    Univariate analyses with a crude model showed age, weight, height, lower BMI, smaller waist and hip circumferences, the presence of menopause, increased time since menopause, previous fracture history, reduced dairy product consumption, lower levels of regular exercise, previous history of RA or osteoarthritis, previous hypertension or diabetes mellitus, and previous arthritis medication or herbal medication were associated with fracture risk in women (see Table 2). After adjustment for age, lower BMI, the lowest quartile of hip circumference, the presence of menopause, increased time since menopause, previous fracture history, reduced dairy product consumption, lower levels of regular exercise, higher alcohol intake, previous history of RA or osteoarthritis, and previous arthritis medication were associated with fracture risk. SoSR and SoST were significantly associated with fracture risk before adjustment for age (P < .001 for trend in both); however, these factors were not significant after adjustment for age (P = .421 and .680 for trend, respectively).

    Independent predictors of any new fracture

    Table 3 summarizes the results of Cox multivariate analysis in men. Age was found to be one of the key predictors of any new fracture; compared with the men aged 40 to 49 years, those aged 50 to 59 years had a 2.70-fold increased fracture risk, and those aged 60 to 69 years had a 3.23-fold increased risk. BMI also was marginally associated with fracture risk; obesity was a protective factor for fracture risk, with an RR of 0.56. Previous fracture history was the strongest predictor of future fracture, with an RR of 3.78. However, neither SoSR nor SoST was an independent predictive factor of fracture risk.

    Table 3. Risk Factors Predictive of Fracture in Men, Showing the Relative Risk (RR) for Each Variable According to Cox Multivariate Models
    VariableModel I RR (95% CI)Model II RR (95% CI)Model III RR (95% CI)Model IV RR (95% CI)
    • Note: Model I: Age, body mass index, and history of previous fracture were simultaneously adjusted for fracture risk. Model II: Variables that were used in model I and SoSR were simultaneously adjusted for fracture risk. Model III: Variables that were used in model I and SoST were simultaneously adjusted for fracture risk. Model IV: Variables that were used in model I, SoSR, and SoST were simultaneously adjusted for fracture risk. 95% CI = 95% confidence interval; Ref = reference group; SoSR = speed of sound of radius; SoST = speed of sound of tibia. Underweight (<18.5 kg/m2), normal (18.5–22.9 kg/m2), overweight (23.0–24.9 kg/m2), and obese (≥25.0 kg/m2).

    • a

      Age was adjusted as a categorical variable,

    • b

      P < .05,

    • c

      P < .10,

    • d

      Relative risk for fracture at an increment of 100 m/s.

    Age (years)a
     40–49RefRefRefRef
     50–592.70 (1.35–5.38)b2.58 (1.29–5.18)b2.55 (1.27–5.11)b2.55 (1.27–5.12)b
     60–693.23 (1.64–6.38)b3.00 (1.51–5.97)b3.04 (1.53–6.02)b2.91 (1.46–5.80)b
    P for trend.002.005.005.007
    Body mass index (kg/m2)
     Underweight1.36 (0.41–4.49)1.40 (0.42–4.64)1.36 (0.41–4.50)1.42 (0.43–4.71)
     NormalRefRefRefRef
     Overweight0.59 (0.31–1.14)0.60 (0.31–1.17)0.58 (0.30–1.12)0.59 (0.30–1.15)
     Obese0.56 (0.30–1.02)c0.53 (0.28–0.99)b0.51 (0.27–0.95)b0.51 (0.27–0.96)b
    P for trend.143.124.094.101
    History of previous fracture
     NoRefRefRefRef
     Yes3.78 (1.36–10.50)b3.96 (1.42–11.03)b3.79 (1.36–10.54)b3.88 (1.39–10.81)b
    SoSR (m/s)d0.91 (0.77–1.08)0.94 (0.79–1.12)
    SoST (m/s)d0.87 (0.71–1.06)0.87 (0.70–1.08)

    Table 4 summarizes the results of Cox multivariate analysis in women. Lower body weight, the presence of menopause, previous fracture history, a lack of regular exercise, higher alcohol intake, and a history of osteoarthritis were independent predictors of increased fracture risk. Advanced age also showed a trend toward increased fracture risk (P = .098 for trends). Smaller hip circumference was marginally associated with increased fracture risk (P = .071). As in men, neither SoSR nor SoST was an independent predictive factor of fracture risk in women.

    Table 4. Risk Factors Predictive of New Fractures in Women, Showing the Relative Risk (RR) for Each Variable According to Cox Multivariate Models
    VariableModel I RR (95% CI)Model II RR (95% CI)Model III RR (95% CI)Model IV RR (95% CI)
    • Note: Model I: Age, body mass index, menopausal state, history of previous fracture, hip circumference, weekly dairy product consumption, regular exercise duration, alcohol intake, and history of RA and OA were simultaneously adjusted for fracture risk. Model II: Variables that were used in model I and SoSR were simultaneously adjusted for fracture risk. Model III: Variables that were used in model I and SoST were simultaneously adjusted for fracture risk. Model IV: Variables that were used in model I, SoSR, and SoST were simultaneously adjusted for fracture risk. 95% CI = 95% confidence interval; OA = osteoarthritis; RA = , rheumatoid arthritis; Ref = reference group; SoS = speed of sound; SoSR = speed of sound of radius; SoST = speed of sound of tibia. Underweight (<18.5 kg/m2), normal (18.5–22.9 kg/m2), overweight (23.0–24.9 kg/m2), and obese (≥25.0 kg/m2).

    • a

      Age was adjusted as a categorical variable,

    • b

      P < .05,

    • c

      P < .10,

    • d

      Relative risk for fracture at an increment of 100 m/s.

    Age (years)a
     40–49RefRefRefRef
     50–591.73 (0.61–4.91)2.08 (0.66–6.53)2.18 (0.71–6.70)2.05 (0.65–6.44)
     60–692.46 (0.85–7.14)c2.95 (0.91–9.59)c2.96 (0.93–9.45)c2.86 (0.88–9.34)c
    P for trend.098.092.110.114
    Body mass index (kg/m2)
     Underweight2.54 (1.03–6.23)b2.61 (1.06–6.45)b2.44 (0.99–6.00)c2.61 (1.06–6.45)b
     NormalRefRefRefRef
     Overweight1.30 (0.77–2.19)1.24 (0.72–2.11)1.24 (0.73–2.10)1.24 (0.73–2.12)
     Obese1.29 (0.76–2.18)1.27 (0.74–2.16)1.25 (0.74–2.12)1.27 (0.74–2.17)
    P for trend.221.214.268.770
    Menopause
     NoRefRefRefRef
     Yes2.94 (1.02–8.46)b2.49 (0.79–7.82)2.57 (0.84–7.88)c2.48 (0.79–7.78)
    History of previous fracture
     NoRefRefRefRef
     Yes1.77 (1.07–2.91)b1.93 (1.17–3.18)b1.79 (1.08–2.95)b1.93 (1.17–3.18)b
    Hip circumference (cm)
     <89.7RefRefRefRef
     ≥89.80.68 (0.44–1.05)c0.69 (0.44–1.09)c0.70 (0.45–1.10)c0.69 (0.44–1.09)c
    Dairy product consumption
     ≤2/weekRefRefRefRef
     3–6/week0.81 (0.48–1.37)0.83 (0.48–1.43)0.87 (0.52–1.48)0.84 (0.49–1.44)
     ≥7/week0.80 (0.50–1.27)0.83 (0.51–1.35)0.81 (0.50–1.31)0.83 (0.51–1.36)
    P for trend.625.737.694.750
    Regular exercise
     <30 minRefRefRefRef
     ≥30 min0.59 (0.36–0.97)b0.57 (0.34–0.96)b0.56 (0.33–0.95)b0.58 (0.34–0.98)b
    Alcohol intake (unit/week)
     <1.82RefRefRefRef
     ≥1.822.19 (1.33–3.62)b2.09 (1.24–3.54)b2.03 (1.20–3.43)b2.07 (1.22–3.51)b
    History of RA
     NoRefRefRefRef
     Yes1.25 (0.72–2.17)1.20 (0.68–2.12)1.23 (0.70–2.15)1.17 (0.66–2.09)
    History of OA
     NoRefRefRefRef
     Yes1.69 (1.15–2.50)b1.73 (1.16–2.59)b1.70 (1.15–2.51)b1.73 (1.17–2.58)b
    SoSR (m/s)d0.97 (0.87–1.07)0.97 (0.87–1.08)
    SoST(m/s)d0.97 (0.86–1.09)0.97 (0.86–1.11)

    Hip circumference as a novel risk factor for nonvertebral fracture in women

    We further divided the fractures into vertebral and nonvertebral fractures and performed Cox multivariate analyses of the fracture data in women (Table 5). BMI and lower weekly dairy product consumption were independently associated with the risk of vertebral fracture, whereas menopause, history of previous fracture, a lack of regular exercise, higher alcohol intake, and a history of osteoarthritis were independently associated with the risk of nonvertebral fracture. Furthermore, increased hip circumference was a significant protective factor for nonvertebral fracture (P = .011) but not for vertebral fracture (P = .544). Gender-specific analysis was not performed because of the small number of male subjects. To identify factors associated with hip circumference, we performed stepwise multiple-regression analyses (Table 6). Age and years since menopause were negatively correlated with hip circumference, whereas BMI, waist circumference, and regular exercise were positively correlated with hip circumference.

    Table 5. Risk Factors Predictive of New Vertebral and Nonvertebral Fractures in Women, Showing the Relative Risk (RR) for Each Variable According to Cox Multivariate Models
    VariableVertebral fracture (n = 33) RR (95% CI)Nonvertebral fracture (n = 101) RR (95% CI)
    • 95% CI = 95% confidence interval; OA = osteoarthritis; RA = rheumatoid arthritis; Ref = reference group. Underweight (<18.5 kg/m2), normal weight (18.5–22.9 kg/m2), overweight (23–24.9 kg/m2), and obese (≥25 kg/m2).

    • a

      Age was adjusted as a categorical variable,

    • b

      P < .05,

    • c

      P < .10.

    Age (years)a
     40–49RefRef
     50–591.09 (0.11–11.01)2.04 (0.62–6.70)
     60–692.69 (0.25–28.55)2.57 (0.76–8.68)
    P for trend.158.250
    Body mass index (kg/m2)
     Underweight4.60 (1.16–18.30)b1.83 (0.53–6.28)
     NormalRefRef
     Overweight0.64 (0.22–1.87)1.66 (0.90–3.14)
     Obesity0.65 (0.25–1.69)1.69 (0.90–3.14)
    P for trend.255.326
    Menopause
     NoRefRef
     Yes1.57 (0.16–15.79)3.59 (1.06–12.19)b
    History of previous fracture
     NoRefRef
     Yes1.53 (0.52–4.50)1.80 (1.02–3.17)b
    Hip circumference (cm)
     <89.7RefRef
     ≥89.81.45 (0.56–3.76)0.53 (0.32–0.87)b
    Dairy product consumption
     ≤2/weekRefRef
     3–6/week0.42 (0.15–1.13)c1.09 (0.58–2.04)
     ≥7/week0.47 (0.21–1.07)c1.03 (0.58–1.85)
    P for trend.132.960
    Regular exercise
     <30 minRefRef
     ≥30 min0.69 (0.26–1.83)0.54 (0.30–0.98)b
    Alcohol intake (units/week)
     <1.82RefRef
     ≥1.820.34 (0.05–2.49)3.13 (1.84–5.33)b
    History of RA
     NoRefRef
     Yes1.21 (0.41–3.61)1.25 (0.65–2.38)
    History of OA
     NoRefRef
     Yes1.77 (0.80–3.88)1.68 (1.07–2.64)b
    Table 6. Stepwise Multiple Linear Regression Analyses of Hip Circumference in Women
    VariablesβP value
    1. Note: The variables (age, body mass index, waist circumference, years since menopause, presence of menopause, premature menopause, history of previous fracture, maternal history of kyphosis, dairy product consumption, regular exercise longer than 30 minutes/day, smoking, alcohol consumption, history of rheumatoid arthritis, and history of osteoarthritis) were included using the stepwise method, retaining only variables that were statistically significant.

    Age (years)−0.043.039
    Body mass index (kg/m2)0.705<.001
    Waist circumference (cm)0.089<.001
    Regular exercise longer than 30 min/day0.092<.001
    Years since menopause (years)−0.042.043
    Adjusted R20.632 

    Discussion

    1. Top of page
    2. Abstract
    3. Introduction
    4. Subjects and Methods
    5. Results
    6. Discussion
    7. Disclosures
    8. Acknowledgements
    9. References

    In a large prospective cohort of 9351 Korean individuals, we evaluated CRFs for osteoporotic fracture in men and women. Age, BMI, and history of previous fracture were identified as independent risk factors in both sexes, and menopausal state, smaller hip circumference, lack of regular exercise, higher alcohol intake, and personal history of osteoarthritis were identified as additional independent risk factors in women. Neither SoSR nor SoST was an independent predictor of future osteoporotic fractures in both men and women. Reasons for the differences in risk factors between men and women were not clear in the present study. The few significant predictive factors in men might be attributable to the small number of fracture cases, resulting in insufficient statistical power to detect modest effects. For example, the age-adjusted RR of increased hip circumference was similar in men and women (0.69 in men and 0.72 in women), but the fracture association was not significant in men.

    In this population, we identified a new and interesting risk factor—smaller hip circumference. A smaller hip circumference was associated with increased fracture risk in our population. Hip circumference is associated with bone structure in the pelvis, muscle mass in the gluteofemoral region, and subcutaneous fat mass; hence a smaller hip circumference may reflect a smaller pelvis size or reduced gluteofemoral muscle and/or fat masses.22 Recently, Lang and colleagues23 found that elderly women showed tissue changes in the proximal femoral region such as a loss of subcutaneous fat, decreased muscle cross-sectional area (CSA), and increased muscle adiposity. They also reported that these changes were related to declining physical function and increased disability in elderly women and were associated with increased risks of hip fracture. Similarly, in the present study, hip circumference had a tendency to decrease in elderly women and with increased time since menopause. In addition, the finding that hip circumference was positively correlated with regular exercise indicates that greater hip circumference might reflect good muscular function and coordination, which may, in turn, prevent falls. Because falls were associated far more with nonvertebral fractures than with vertebral fractures,24 we expected that smaller hip circumference would be related to an increased risk of nonvertebral fractures. As expected, despite the low fracture incidence, smaller hip circumference was a significant predictor of nonvertebral fractures in women (P = .011).

    Most CRFs for fracture identified in the present study also have been identified in several recent meta-analyses of other population-specific cohorts.11, 25–31 Age contributed to fracture risk independently of BMD, and changes in age were approximately sevenfold more important than changes in BMD in other ethnic cohorts.11 In the present study, crude RRs were 4.70 and 8.00 for women aged 50 to 59 and 60 to 69 years, respectively, compared with those aged 40 to 49 years. Many studies have indicated that a previous fragility fracture is an important risk factor for further fracture.30, 31 Fracture risk is approximately double in subjects with a prior fracture compared with those without a prior fracture.30 A similar increment was seen in our study population (RR = 1.80 in women). Other contributing risk factors, such as low BMI,27 higher alcohol consumption,29 lack of exercise,28 menopausal state, and osteoarthritis,25, 26 were consistent with the results of previous studies.

    Some variables known to be CRFs for fracture in other ethnicity-specific cohorts32–34 were not identified in the present study; these included maternal history of kyphosis (which reflected parental history of fracture),35 smoking, and RA. These differences may be due to the relatively small numbers of reported vertebral fractures and subjects who smoked or who had a history of maternal kyphosis. RA was previously identified as a significant risk for any fracture,33 but it was identified as a risk factor in the present study only after crude analysis or adjustment for age in our population. Compared with subjects without RA, more subjects with RA had received drugs that might affect bone metabolism (22.7% versus 11.2%, P < .001), and these patients were excluded according to the study criteria. It seems likely that this decision may have reduced any association between the occurrence of a new fracture and RA in enrolled subjects. Prior corticosteroid use also was known to be a risk factor for fracture33; however, it was not significant risk factor in the present study. This might be so because of the low prevalence of corticosteroid use in our population (0.1% in either gender). The possibility that subjects might have been unaware of the possible inclusion of corticosteroids in arthritis medications was not excluded. Indeed, history of arthritis medication was a significant risk factor in the age-adjusted univariate analysis among women (RR = 1.86). In addition, ethnic group differences and other population-specific variables may be responsible for discrepancies between the results of our study and those of previous studies in other cohorts.

    In the present study, neither SoSR nor SoST was predictive of osteoporotic fracture. By contrast, a meta-analysis demonstrated a relationship between reduced heel qUS values and fractures such as hip/humeral/forearm/wrist fractures.36 Several studies also have shown that heel qUS was useful for the identification of osteoporosis and osteoporotic fractures, especially nonvertebral fractures.37, 38 However, heel qUS was not measured in our study. The relationships between low qUS values at the radius or tibia and fracture risk have been inconsistent. One study reported odds ratios of 1.4 (95% CI 1.03–1.99) for SoSR and 1.2 (95% CI 0.87–1.66) for SoST in patients with a history of vertebral fractures.39 However, these results were obtained only after age adjustment in a cross-sectional study that included 109 women. Consistent with our study, other cross-sectional study involving age- and BMI-adjusted analyses of 513 women found that neither radius nor tibia SoS, except phalanx SoS, was useful to identify postmenopausal nonvertebral fractures.6 The heel measurements included both cortical and trabecular bones and have been shown to be strongly correlated with BMD measurements at the same site. In contrast, the Sunlight Omnisense equipment measures the cortical bone at the phalanx, distal radius, and tibia. These differences in bone sites studied and type of bone evaluated may explain why SoS values of radius and tibia were not predictive of osteoporotic fracture in the present study.40

    Fracture incidence per 1000 subjects in our study was 4.0 in men and 9.1 in women. Another study that included 59,644 individuals (14,887 men and 44,757 women) from 12 prospective population-based cohorts reported that the fracture incidence per 1000 subjects was 13.9 in men and 23.4 in women.27 The lower fracture incidence in our study might have resulted from the younger subjects in our population (51.8 versus 62.2 years for men and 52.2 versus 63.2 years for women) and differences owing to ethnicity. Asian-American individuals are known to have a reduced risk of osteoporotic fracture (RR = 0.32, 95% CI 0.15–0.66) compared with Caucasian individuals [RR = 1.0 (reference group)], Hispanic women (RR = 0.95, 95% CI 0.76–1.20), Native Americans (RR = 0.87, 95% CI 0.57–1.32), and black individuals (RR = 0.52, 95% CI 0.38–0.70). In another population-based study in Japan, including 33,418 men and 36,953 women, the fracture incidence per 1000 subjects was 2.6 in men and 7.2 in women,41 similar to the results of our study.

    Our study has several potential limitations. First, we did not measure BMD by DXA, and hence we could not investigate whether some risk factors were related to low BMD or whether CRFs in combination with BMD measurements might be more reliable predictors of osteoporosis fracture than were BMD measurements alone. However, many primary-care facilities do not have DXA equipment, so the use of BMD alone to predict and diagnose osteoporosis is restricted in primary-care settings. Second, we obtained information about age and risk factors using a standardized self-administered questionnaire; hence we could not obtain the past medical histories and medication histories of our population in detail. Third, we assumed a self-reported fracture as an indicator of fractures and a height loss threshold of 4.0 cm as an indicator of incident vertebral fractures,19–21 but we did not confirm such fractures by radiologic studies.

    In conclusion, this study has identified the major CRFs specific to Korean individuals that can be used to predict fracture risk. This information will allow optimal risk assessment to target Korean patients who would obtain the greatest benefit from intervention and will be particularly useful in primary-care settings and in clinics without ready access to instruments for measuring BMD.

    Acknowledgements

    1. Top of page
    2. Abstract
    3. Introduction
    4. Subjects and Methods
    5. Results
    6. Discussion
    7. Disclosures
    8. Acknowledgements
    9. References

    This work was supported by the National Genome Research Institute, Korean Center for Disease Control and Prevention (2001-2003-348-6111-221, 2004-347-6111-213, and 2005-347-2400-2440-215). Data analysis was supported by the Korea Health 21 R&D Project, Ministry of Health and Welfare, Republic of Korea (Project No. A010252).

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    1. Top of page
    2. Abstract
    3. Introduction
    4. Subjects and Methods
    5. Results
    6. Discussion
    7. Disclosures
    8. Acknowledgements
    9. References
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