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An exploratory comparison of vertebral fracture prevalence andrisk factors among native Japanese, Japanese American, andCaucasian women
Huang, Chun, Ph.D.
University of Hawaii, 1994
V·M·I300N. ZeebRd.AnnArbor,MI48106
AN EXPLORATORY CO~ARISONOF VERTEBRAL FRACTURE
PREVALENCE AND RISK FACTORS AMONG
NATIVE JAPANESE, JAPANESE-AMERICAN,
AND CAUCASIAN WOMEN
A DISSERTATION SUBMITTED TO THE GRADUATE DIVISION OF THEUNIVERSITY OF HAWAII IN PARTIAL FULFILLMENT OF THE
REQUIREMENTS FOR THE DEGREE OF
DOCTOR OF PHll.OSOPHY
IN
BIOMEDICAL SCIENCES (BIOSTATISTICS-EPIDEMIOLOGy)
DECEMBER 1994
By
Chun Huang
Dissertation Committee:
F. DeWolfe Miller, ChairpersonPhilip D. RossKirk R. SmithJohn S. GroveChai Bin Park
Ming Pi Mi
.-_._-----------_.__ . ------
ACKNOWLEDGEMENTS
This dissertation research was made possible through the generous support of the Hawaii
Osteoporosis Foundation. I am grateful to the Hawaii Osteoporosis Center for providing
me the data and environment I need to finish my doctoral dissertation. I would like to
thank all the staff of the Hawaii Osteoporosis Center, especially Dr. Philip D. Ross, Dr.
James W. Davis, Dr. Richard D. Wasnich, and Mr. Carl K. Kamimoto, for their
invaluable academic advice and excellent logistic support. I would also like to extend a
special thanks to Dr. Saeko Fujiwara at the Radiation Effects Research Foundation,
Hiroshima, Japan for her substantial contribution and assistance through the entire period
of the research.
Thanks are also due to my wife, Ying, and my son, Yangyang, for their constant support
and tolerating lost evenings and weekends over a period of several years.
iii
ABSTRACT
In this cross-sectional population-based study, the prevalence of vertebral fractures in
elderly women was compared among native Japanese in Hiroshima, Japanese-Americans
in Hawaii, and North American Caucasians in Minnesota. Compared with Japanese
American women, the age-adjusted odds ratios for native Japanese women were
significantly and consistently greater than 1.0 (range from 1.6 to 2.6, depending on
fracture definition), while the age-adjusted odds ratios for Caucasian women living in
Minnesota were closer to 1.0 (range from 0.5 to 1.5, depending on fracture definition).
These data indicated that the age-adjusted overall prevalence of vertebral fracture among
Japanese-American women was quite different from the prevalence in Japan, but more
similar to the prevalence in the U.S., suggesting non-genetic factors may have some
impact on vertebral fractures. Spine bone mineral density (BMD), a major predictor of
vertebral fracture prevalence in this study, was found to be lower among native Japanese
than among Japanese-American women even after adjusting for age. This difference was
mainly due to the differences in body size and menstrual history between the two
populations. On the average, native Japanese women were shorter and lighter, and tended
to have a later menarche, an earlier menopause, and a shorter period between menarche
and menopause. In linear regression analysis, age, weight and menstrual history (age at
menopause or years between menarche and menopause) were found to be significantly
associated with BMD, and thus might contribute to fracture risk indirectly through their
effects on BMD. However, this study also shows that age and menstrual history provided
complementary information about fracture risk and explained additional difference in
iv
fracture prevalence after adjusting for BMD, suggesting that BMD is a major but not a
sole risk factor for vertebral fractures. Age-related and menopause-related mechanisms
may also play an important role in spine fracture independent of BMD. The observed
differences in vertebral fracture prevalence and related risk factors between native
Japanese and Japanese-American women may be evidence for environmental effects, such
as changes in nutrition and lifestyle.
v
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TABLE OF CONTENTS
Acknowledgements ....,.................................. 11l
Abstract . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .ivList of Tables viiiList of Figures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xList of Abbreviations and Symbols . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xii
Chapter 1: Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
1.1 The Epidemiology of Vertebral Fracture 11.1.1 Definition of Vertebral Fracture . . . . . . . . . . . . . . . . . . . . . . . . 21.1.2 Prevalence and Incidence 41.1. 3 Risk Factors for Vertebral Fracture .. . . . . . . . . . . . . . . . . . . . . 7
1.2 Objectives . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10
Chapter 2: Methodology 12
2.1 Study Subjects . . . . . . . . . . . . . . . . . . . . . . . . . . . . 122.1.1 Background 122.1.2 Subjects from the Hawaii Osteoporosis Study . . . . . . . . . . . . . .. 122.1.3 Subjects from the Adult Health Study. . . . . . . . . . . . . . . . . . . . 142.1.4 Subjects from the Rochester Osteoporosis Study 14
2.2 Spine Radiographs, Vertebral Measurements, and Assessment ofSpine Deformity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15
2.2.1 Spine Radiographs 152.2.2 Vertebral Measurements . . . . . . . . . . . . . . . . . . . . . . . . . . . . 162.2.3 Assessment of Spine Deformity 17
2.3 Measurement and Conversionof Spine Bone Mineral Density 182.4 Statistical Analyses 19
2.4.1 Agreement of Prevalent Fracture Definitions 192.4.2 Comparisonof Vertebral Fracture Prevalence between
Native Japanese, Japanese-Americans, and Caucasians. . . . . . . . .. 222.4.3 Comparisons of Native Japanese and Japanese-American Women ... 23
Chapter 3: Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26
3.1 Evaluation of Agreement between Different Definitions ofPrevalent Vertebral Fractures . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26
3.1.1 Agreement between Fracture Definitions . . . . . . . . . . . . . . . . .. 263.1.2 Bias and Prevalence Effects on Agreement . . . . . . . . . . . . . . . . . 29
3.2 Prevalence and Distribution of Vertebral Fractures among Native Japanese,Japanese-Americans, and Caucasians 31
vi
3.2.1 Vertebra-specific Prevalence 313.2.2 Age-specific Prevalence . . . . . . . . . . . . . . . . . . . . . . . . . . . . 323.2.3 Logistic Regression Analysis ... . . . . . . . . . . . . . . . . . . . . . . 33
3.3 A Comparison of Characteristics of Native Japanese andJapanese-American Women 34
3.3.1 Anthropometry 363.3.2 Gynecological History 363.3.3 Smoking and Alcohol Use 393.3.4 Effect of Radiation Exposure . . . . . . . . . . . . . . . . . . . . . . . . . 403.3.5 Determinants of Bone Mineral Density . . . . . . . . . . . . . . . . . . . 41
3.4 Determinants of Spine Fracture Prevalence . . . . . . . . . . . . . . . . . . . . 45
Chapter 4: Discussion and Conclusion 51
4.1 Agreement between Vertebral Fracture Definitions . . . . . . . . . . . . 514.1.1 The Rational for the Agreement Analysis 514.1.2 Measures of Agreement, Underlying Assumptions,
and Assessment of Agreement 524.1.3 Additional Information Supplied by Ppos' Pneg, PI, and BI . . . . . . .. 564.1.4 Population, Diagnosis Cutoff, and Agreement . . . . . . . . . . . . . .. 594.1.5 Significant Test 60
4.2 Vertebra- and Age-specific Prevalence of Vertebral Fractures 604.2.1 Vertebra-specific Prevalence 604.2.2 Age-specific Prevalence 634.2.3 Logistic Regression Analysis . . . . . . . . . . . . . . . . . . . . . . . . . 67
4.3 Predictors of Spine BMD and Genetic-Environmental Interaction .. . . .. 674.3.1 Potential Predictors of Spine BMD: Multiple Regression Analyses
Based on Japan and Hawaii Populations 674.3.2 Effects of Environmental and Genetic Factors. . . . . . . . . . . . . .. 80
4.4 Predictors of Spine Fractures: Logistic Regression AnalysesBased on Japan and Hawaii Populations 91
4.5 Potential Limitations of the Study 934.6 Conclusions 95
Appendix A: Tables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 98Appendix B: Figures .125References .146
vii
LIST OF TABLES
2.1 Nomenclature and Definitions Used to Diagnose Prevalent Fracture ..... 98
3.1 Agreement between Prevalent Fracture Definitions(Study Unit: Individual Woman) .. . . . . . . . . . . . . . . . . . . . . . . . . . 99
3.2 Agreement between Prevalent Fracture Definitions(Study Unit: Individual Vertebra) 100
3..3 Comparison of Overall Agreement between Different Populations, Study Units,and Diagnosis Cutoff 101
3.4 Indices of Bias and Prevalence (Study Unit: Individual Woman) 102
3.5 Indices of Bias and Prevalence (Study Unit: Individual Vertebra) 103
3.6 Spine Fracture Prevalence (cases per 100 women) by Diagnosis Criterion,Age, and Population 104
3.7 Age-adjusted Odds Ratios 105
3.8 Comparison of Basic Characteristics between Japanese-American andNative Japanese Women 106
3.9 Proportion of Women by Number of Live Births and theWomen's Year of Birth 107
3.10 Proportion of Artificial Menopause among Japanese-American and NativeJapanese Women by Birth Year 108
3.11 Proportion of Current Smoking and Alcohol Use among Japanese-Americanand Native Japanese Women by Birth Year 109
3.12 Linear Regression Analyses of the Association between JAPAN,BIRTH YEAR and Continuous Variables. . 110
3.13 Logistic Regression Analyses of the Association between JAPAN,BIRTH YEAR and Binary Variables 111
3.14 Multiple Linear Regression Analysis: Effect of Age and Body Sizeon Spine BMD (L2-IA) 112
viii
3.15 Effect of Cause of Menopause on Spine BMD 112
3.16 Multiple Linear Regression Coefficients for Potential Predictors ofSpine BMD (L2-IA) 113
3.17 Final Linear Regression Models for Spine BMD 114
3.18 Age-adjusted Odds Ratios Based on Fracture Definition PV2 115
3.19 Age-adjusted Odds Ratios Based on Fracture Definition PV2A 117
3.20 Age-adjusted and BMD-adjusted Odds Ratios Based onFracture Definition PV2 .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 119
3.21 Age-adjusted and BMD-adjusted Odds Ratios Based onFracture Definition PV2A . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 122
ix
LIST OF FIGURES
Figure
1.1 Diagram Illustrating the Determinants of Fracture Risk 125
2.1 Classification of Vertebral Fracture 126
3.1 Vertebra-specific Prevalence (Based on PVI-PV6) in Hawaii 127
3.2 Vertebra-specific Prevalence (Based on PV1-PV6) in Japan 128
3..3 Vertebra-specific Prevalence (Based on PVI-PV6) in Minnesota 129
3.4 Vertebra-specific Prevalence (Based on PV1A-PV6A) in Hawaii 130
3.5 Vertebra-specific Prevalence (Based on PVIA-PV6A) in Japan 131
3.6 Vertebra-specific Prevalence (Based on PVIA-PV6A) in Minnesota 132
3.7 Vertebra-specific Prevalence of Different Types of Fracture 133
3.8 Age-specific Prevalence (Based on PV2, PV4, PV6) of Spine Fractureby Study Population . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .134
3.9 Age-specific Prevalence (Based on PV2A, PV4A, PV6A) of Spine Fractureby Study Population . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .135
3.10 Age-specific Prevalence of Single and Multiple Vertebral Fracture ..... 136
3.11 Mean Height by Birth Year 137
3.12 Mean Weight by Birth Year 138
3.13 Mean Body Mass Index by Birth Year 139
3.14 Mean Age at Menarche by Birth Year 140
3.15 Mean Age at Menopause by Birth Year 141
3.16 Mean Years between Menarche and Menopause by Birth Year 142
3.17 Mean Lactation Period per Child by Birth Year 143
x
3.18 Mean Total Lactation Period by Birth Year 144
3.19 Mean Spine BMD by Age 145
xi
----- ._--- --
AHS
A,M,P
BI
BMA
BMC
BMD
BMI
CI
DZ
Gy
HHP
HOC
HOS
JA
L
MC
MZ
N
NJ
OR
LIST OF ABBREVIATIONS AND SYMBOLS
Adult Health Study (Hiroshima, Japan)
Anterior, medial, and posterior vertebral heights
Byrt's Bias Index
BMC/bone width
Bone mineral content
Bone mineral density
Body mass index
Confidence interval
Dizygotic (twins)
Gray: an unit for measuring absorbed doses of radiation
Honolulu Heart Program (Honolulu, Hawaii, USA)
Hawaii Osteoporosis Center (Honolulu, Hawaii, USA)
Hawaii Osteoporosis Study (Hawaii, USA)
Japanese-Americans
Lumbar vertebra
Mayo Clinic (Rochester, Minnesota, USA)
Monozygotic (twins)
Sample size
Native Japanese
Odds ratio
xii
PI
PABAK
Ppos,Pneg
Byrt's prevalence-adjusted bias-adjusted kappa
Proportion of agreement expected by chance
Byrt's Prevalence Index
Overall proportion of agreement
Cicchetti and Feinstein's indices of positive and negative
agreement
PVI-PV6 See Table 2.1
PVIA-PV6A See Table 2.1
QCT
R2
RERF
ROS
SD
SE
T
Z
K
Quantitative Computed Tomography
Coefficient of multiple determination
Radiation Effects Research Foundation (Hiroshima, Japan)
Rochester Osteoporosis Study (Minnesota, USA)
Standard deviation
Standard error
Thoracic vertebra
Z score
Cohen's Kappa
Scott's 7l"
Note: The symbols and notation used for calculating coefficients/indices of agreement are
summarized in Section 2.4.1.
xiii
CHAPfERl
INTRODUCTION
1.1 THE EPIDEMIOLOGY OF VERTEBRAL FRACTURE
Vertebral fracture is believed to be one of the most common consequences of
osteoporosis. It has been estimated that there are more than 500,000 cases of vertebral
fractures every year in the United States (Riggs and Melton, 1986). The major
consequences of vertebral fracture include back pain, kyphosis, and loss of height.
Osteoporotic fractures including vertebral fractures are now recognized as a major public
health problem in both developed and developing countries.
This chapter reviews some important epidemiologic characteristics of vertebral fracture.
First, some important issues pertaining to deftnition of vertebral fracture will be
addressed, and the most frequently used techniques for detecting vertebral deformity will
be briefly described. Then, the incidence and prevalence of spine fracture as well as the
difficulties in making inter-population comparisons will be outlined. Finally, the known
risk factors for fractures will be summarized with emphasis on vertebral fracture. This
chapter will be concluded with the statement of the study objectives.
1
1.1.1 DEFINITION OF VERTEBRAL FRACTURE
Unlike most fractures elsewhere in the skeleton, which are painful and clinically obvious,
a significant proportion of vertebral fractures are asymptomatic and often detected long
after they have occurred. Vertebral fractures often occur spontaneously or result from
minimal trauma such as a cough. It has been reported that only 7% of vertebral fractures
in women are due to high energy trauma (Kanis and McCloskey, 1992). The term
deformity rather than fracture is commonly used in osteoporosis studies since the cut-off
point between normal variation in vertebral shape and a fracture has not been well
defined.
Vertebral fractures may be detected by either semi-quantitative (visual) or quantitative
(morphometric) techniques. The former has been widely used in earlier studies.
However, poor reproducibility has been reported for this method since it relies on
subjective assessments and often exhibits high intra- and inter-observer variation. This
has motivated several attempts to develop quantitative, objective criteria for the definition
of vertebral fracture and thus permit standardization of methodology for comparisons
between studies ( Adami et al., 1992; Hedlund and Gallagher, 1988).
Many morphometric approaches have been proposed for identifying prevalent vertebral
fractures. Only the three most commonly used approaches are reviewed here because
they will be used in this study. The simplest method only requires the absolute height of
2
the vertebrae (anterior, middle, and posterior), but this approach may be influenced by
stature. One way to compensate for differences in body size is dividing the anterior (A)
and middle (M) heights by the posterior height (P) of the S31'1e vertebra and dividing the
posterior height by the posterior height of an adjacent vertebra. However, this approach
would lose its sensitivity if the posterior height is also reduced. When multiple crush
fractures are present at the vertebra below and the vertebra above, the posterior heights
of the adjacent vertebrae cannot be used. Another alternative approach is to divide all
vertebral heights(A, M, P) by the corresponding dimensions of T4 in the same
individual. Since this approach places great reliance on the normality of T4, serious
practical drawbacks may occur if T4 itself is deformed or not easily visible on
radiographs (Adami, 1992).
One difficulty in deciding whether a vertebra is fractured results from the variation in
shape and size of vertebral bodies both within the spine and between individuals. It has
been recommended that definitions of vertebral fractures (deformities) shouldbe vertebra
specific and based on the population under study(Davies et aI., 1989; Ross, 1991; Ross
et aI., 1991c). Once the normal means and standard deviations are estimated for each
vertebra, comparison to normal values can be performed according to some arbitrarily
assigned cut-off value.
The cutoff points for defining prevalent fractures are usually based on multiples of
standard deviation (SD) below the normal means. A criterion based on 2 SD below the
3
normal mean has been criticized for its low specificity (Kanis and McCloskey, 1992).
Currently, the most widely adopted cut-off points are 3 SD or 4 SD below the normal
mean value. Using stricter criteria (e.g. 4 SD) may improve the specificity, but often
miss vertebral deformities of clinical importance. Thus, high specificity is usually
achieved at the expense of low sensitivity, and vice versa. At present, it is impossible to
validate any criteria since sensitivity and specificitycan not be quantified without a 'gold
standard'. However, agreement between fracture definitions can be assessed without
knowing the 'truth'. There are many possible ways to define a vertebral fracture, but
there have been few, if any, studies exploring how well various morphometric criteria
agree with each other.
1.1.2 PREVALENCE AND INCIDENCE
Although vertebral fracture is a cardinal manifestation and one of the most common
consequences of osteoporosis, relatively little is known of its incidence and prevalence
for at least two reasons. First, a considerable proportion of vertebral fractures are
asymptomatic and thus cannot be diagnosed clinically without radiographs. Second, there
is no general agreement on the radiological definition of a vertebral fracture (Cooper et
al., 1993; Kanis and Pitt, 1992). In order to generate valid estimates of incidence or
prevalence of vertebral fracture, population-based longitudinal or cross-sectional studies
with radiological assessment of vertebral fracture are required.
4
To date, there have been few reports on incidence of vertebral fracture, primarily due
to the lack of prospective data. Melton et al. have calculated incidence rates for vertebral
deformities from prevalence data of 762 women living in Rochester, MN. The estimated
incidence rates of a first vertebral deformity rose from 5.8 per 1000 person-years among
50-54 years old to 37.7 per 1000 person-years in women 85-89 years of age, and the
overall age-adjusted incidence rate among these Rochester women aged 50 and over was
17.8 per 1000 person-years. When these data are projected to the population of white
women living in the United States, the overall incidence among those aged 50 and over
is estimated to be 16.7 per 1000 person-years, which suggests that about 518000 white
women will develop vertebral deformities for the first time each year in the United States
(Melton et aI., 1993b). The estimated incidence rates vary with the criteria utilized. In
Rochester, the incidence of clinically diagnosed vertebral fractures was only 35 % of that
estimated by morphometric methods (Copper et aI., 1992; Melton et aI., 1993). In a
serial radiographic study among 758 randomly selected Dutch women aged 45-64, 37
incident vertebral fractures ( defmed as 1. a new deformation became apparent, 2. a
wedge deformation changed into a crush deformation, or 3. the antero-posterior ratio of
a wedge deformation decreased by 0.2 or more) were observed at thoracolumbar spine
(TI2-L5) over 9 years of followup. The estimated incidence rate was 5.42 per 1000
person-years (van Hemert, 1989; van Hemert et al, 1990). Hanna et al.(1986), based on
radiologist's assessment, reported an overall incidence rate for thoracic spine deformities
of 0.32 per 1000 person-years in Finnish women (range from 0.06611000 person-years
for those aged 15-44 to 1.828/1000 person-years for those over 65 years of age).
5
Irrespective of the source of the data, vertebral fracture incidence rate increases
approximately exponentially with age (Fujiwara et aI., 1991; Kanis and McCloskey,
1992).
Data on the prevalence of vertebral fracture are availablefrom several population studies,
but the apparent prevalence is clearly dependent on the criteria utilized and the region
of the spine studied. In some studies, for example, prevalent vertebral fractures were
identified based only on the anterior dimension because most vertebral fractures
(deformities) results in a decrease in anterior height of the vertebral body. However,
these criteria could miss some fractures (deformities) not involving change in anterior
height. According to the reviews by Cooper et al. (1993) and by Kanis and McCloskey
(1992), the prevalence of vertebral fracture among postmenopausal women in
industrialized countries range from 2.9% in a Finnish study to 27% among residents of
Rochester, MN. As with incident data, most cross-sectional studies also suggest that
prevalence of vertebral fracture rises with age among women (Cooper et al., 1993).
Among white women living in Rochester, MN, for example, the observed prevalence
rose with age from about 11% in women 50-59 years old to 54% in those 80 years of
age and over (Melton et aI., 1993).
Comparison of incident and prevalent data between studies is very difficult due to the
lack of standardization of methodology. This lack of accurate and comparable
information on vertebral fractures has seriously limited our ability to study the
6
epidemiology of spine fractures. At present, it is not possible to specify exactly the
incidence or prevalence of vertebral fractures because no 'gold standard' exists.
However, it is possible and logical to make comparisons of prevalence or incidence
between studies based on the same criteria.
Comparison of hip fracture incidence among native Japanese, Japanese Americans, and
American Caucasians has been reported by Ross et al.(1991b). However, comparison of
spine fracture prevalence between these three populations has been hampered by
differences in methodology. Ross (1991) suggested using each population's own vertebra
specific mean and standard deviation values as reference. This method allows us to make
inter-population comparisons, which may improve our understanding of risk factors for
vertebral fractures.
1.1.3 RISK FACTORS FOR VERTEBRAL FRACTURE
The two primary determinants of osteoporotic fractures are bone strength and propensity
to trauma (Melton, 1993). All risk factors influence fracture risk through their effects
either on bone strength or on propensity to trauma or both. It should be noted, however,
that the relative contribution of these two aspects to fracture risk varies depending upon
the type of fracture. As mentioned earlier, osteoporotic fractures of the spine differ from
most other age-related fractures in that they are not typically associated with high energy
trauma. This suggests that the role played by trauma-related factors is much less
7
important in vertebral fracture than in other types of fracture (such as hip and Colles'
fractures) (Wasnich et al., 1989). Figure 1.1 illustrates the potential mechanisms related
to fractures.
Bone strength at any time is determined not only by both bone mass (bone quantity) but
also by a variety of qualitative aspects (bone quality). Decreased bone mass is often
considered the single most important risk factor, but this is probably because it has been
recognized the longest, and understood best (Heaney, 1993a). The potential contribution
of bone quality to fracture has been recognized widely only during the last decade.
Although much of the evidence is still indirect or from studies in vitro, the available data
suggest an important role for bone quality (Heaney, 1993b). Aspects of bone quality
involve bone architecture, amount of fatigue damage, characteristics of bone matrix, and
degree of mineralization. In vertebrae, two major qualitative defects which may increase
fracture risk are the loss of trabecular connectivity and the accumulation of unremodelled
fatigue microdamage with aging (Heaney, 1992). Identifying bone quality factors would
enhance our understanding of the pathogenesis of vertebral fracture. However, whether
this will lead to more accurate determination of fracture risk in an individual remains
uncertain (Parfitt, 1993a). One major problem is that the qualitative aspects of bone can
only be measured directly by invasive weans such as bone biopsy (Cooper, 1993).
Several variables, such as age and prior fractures, have been found to contribute to
fracture risk independently of bone mass measurement (Kanis and McCloskey, 1992).
8
Assuming these variables are surrogate indicators for bone quality, it is still unknown
which qualitative aspect(s) of bone they represent.
Until recently, much osteoporosis research has focused on the determinants of bone mass
and bone loss, probably because reduced bone mass is a major risk factor for
osteoporotic fractures and it can be measured easily using non-invasive densitometric
methods with excellent precision. Factors which have been reported to be related to bone
mass include body size (weight, height, body mass index, etc.), reproductive variables
(age at menarche, age at menopause, duration between menarche and menopause, cause
of menopause, lactation, parity, etc.), life style (smoking, drinking, nutrition, etc.) and
medication use (Cumming et aI., 1985; Ross, 1994). However, the associations between
bone mass and some of these variables are uncertain. Some studies yielded inconsistent
and even conflicting results. More research is needed to expand our understanding of the
underlying pathogenesis of vertebral fracture. To date, most studies of risk factors for
low bone mass and vertebral fracture were restricted to relatively homogeneous
populations in western countries. Our understanding of the causes of vertebral fractures
could further be advanced by comparing potential risk factors between populations with
different fracture prevalence or incidence. Inter-population comparisons not only increase
the dispersion of variables available for analysis, but also offer clues to the observed
discrepancy in fracture prevalence or incidence. Valuable information could be obtained
from migrant studies since comparison between migrants and their original population
9
allows us to explore potential environmental effects (such as change in life-style) with the
genetic component held relatively constant (Ross et al., 1989).
1.2 OBJECTIVES
The main objectives of this dissertation will be the comparisons of vertebral fracture
prevalence and related risk factors among native Japanese, Japanese-American, and
Caucasian women.
More specifically, this study will focus on the following epidemiological issues of
vertebral fracture:
(1) agreement between some commonly used definitions of vertebral fractures and the
implications for estimating fracture prevalence,
(2) differences in prevalence of vertebral fractures among native Japanese, Japanese
American, and Caucasian women,
(3) identification of risk factors for low bone mineral density (BMD) and/or vertebral
fracture and differences in distributions of these risk factors between native Japanese and
immigrant Japanese-American women,
10
(4) ability of putative risk factors to explain differences in BMD and vertebral fracture
prevalence between native Japanese and Japanese American women.
11
CHAPfER2
METHODOLOGY
2.1 STUDY SUBJECTS
2.1.1 BACKGROUND
All subjects in this study were female participants of three on-going longitudinal studies:
(1) the Hawaii Osteoporosis Study (HaS) conducted by the Hawaii Osteoporosis Center
(HOC) in Hawaii, USA, (2) the Adult Health Study (AHS) conducted by the Radiation
Effects Research Foundation (RERF) in Hiroshima, Japan, and (3) the Rochester
Osteoporosis Study (ROS) conducted by Mayo Clinic (MC) in Rochester, Minnesota,
USA. The sample size for each specific analysis varied depending upon the number of
participants at each particular examination, the number of missing values, and the
restrictions or assumptions made for the analysis. The sample size, restrictions, and
assumptions are specified where appropriate.
2.1.2 SUBJECTS FROM THE HAWAII OSTEOPOROSIS STUDY
Male subjects of HaS were recruited from the cohort of the Honolulu Heart Program
(HHP), which is a prospective cohort study of coronary heart disease and stroke among
male Japanese-Americans born between 1900-1919 and living on the Hawaiian island of
12
Oahu in 1965. Briefly, the U.S. National Heart, Lung and Blood Institute established the
Honolulu Heart Study in 1964, which was renamed Honolulu Heart Program in 1981.
The Oahu Japanese-American population was considered as the target population because
it had been very stable since 1924. 12,417 eligible men were identified through the
World War II Selective Service roster and 11,148 of them were located on Oahu. Using
1960 census data, it had been estimated that 14,426 eligible Japanese-American men
resided on Oahu. The World War II Selective Service record had succeeded in
identifying about 86% of eligible participants. Further details on the recruitment of the
original HHP cohort have been described elsewhere (Heilbrun et aI., 1985; Worth and
Kagan, 1970).
After excluding those who refused to answer the questionnaire, or refused to take the
examination, or died before the examination, a total of 8,006 men participated in the first
examination during 1965-1968, and 7,498 men took the second examination two years
later. In 1970, a 30 percent random sample of these 7,498 men was selected to
participate in the first lipoprotein examination. In 1980, 1,685 surviving men of this
random sample, and their wives, if also of Japanese ancestry, were invited to participate
in the longitudinal epidemiologic study of osteoporosis, the Kuakini Osteoporosis Study,
which was renamed Hawaii Osteoporosis Study (HOS) in 1990. A total of 1,379 men
(81 % of 1,685) and 1,105 wives participated in the first exam during 1980-1981. The
Japanese-American subjects of the present study were based only on these 1105 female
participants (Heilbrun et aI., 1985; Worth and Kagan, 1970).
13
2.1.3 SUBJECTS FROM THE ADULT HEALTH STUDY
In 1958, about 120,000 subjects, including exposed atomic bomb survivors living in
Hiroshima or Nagasaki and nonexposed controls, were recruited for the Life Span Study
based on the 1950 Japanese National Census data. The Adult Health Study (AHS) cohort
consists of approximately 20,000 participants of the Life Span Study, who have been
f~llowed through biennial health examinations since 1958. About 4,000 AHS participants
in Hiroshima underwent medical examination during the 1987-1989 examination cycle.
A sex-age-radiation-stratified sample was selected at random from these 4,000
participants living in Hiroshima. From this sample (640 men and 960 women), 309 men
and 884 women were finally recruited. Only the 884 women are included in the present
study, representing native Japanese subjects (Fujiwara et al., 1991, 1994; Radiation
Effects Research Foundation, 1992).
2.1.4 SUBJECTS FROM THE ROCHESTER OSTEOPOROSIS STUDY
Identification of the subjects for ROS was based on the medical records linkage system
of the Rochester Epidemiology Project. More than half of the Rochester population is
identified annually by this system and the large majority are attended in any 3-year
period, including both free-living and institutionalized individuals. During 1979-1981,
541 female residents were contacted and 38 of them were ineligible. Of the remainder,
304 (60%) consented to participate. Only those aged 50 years and over (N=201) had
14
radiographs of spine and were included in this study. Another sample was drawn in the
same way during 1982-1984. 1020 eligible women aged 50 and over were identified, of
whom 561 (55%) agreed to participate and were included in the present study. The
second sample had the same spine radiographs as the first sample. The subjects from
these two samples were combined and the combined sample (N =762) represents about
9% of Rochester women of the same age group. According to 1980 census data, 98%
of the Rochester population was Caucasian (Melton et aI, 1993b).
2.2 SPINE RADIOGRAPHS, VERTEBRAL MEASUREMENTS, AND
ASSESSMENT OF SPINE DEFORMITY
2.2.1 SPINE RADIOGRAPHS
In HOS, lateral radiographs were performed with the subject lying on her side, with
knees bent. All radiographs were obtained using a tube-to-film distance of 105 cm.
Thoraco-lumbar spine radiographs, which generally include all vertebrae below the level
of T8, were performed with the X-ray tube positioned approximately over level of L2.
Films of the thoracic (T3-T12) were centered approximately at the level of T8 (Ross et
aI., 1991c).
In AHS, lateral lumbar radiographs were taken with the subject lying on her side, with
knees bent, using a tube-to-film distance of 100 cm centered at about L3. Thoracic films
15
were taken with the subject standing, using a tube-to-film distance of 180 em, centered
at about T8 (Ross et al., 1994).
In ROS, lateral radiographs were obtained at a source-to-film distance of 122 cm. The
thoracic film was centered over T7 while the lumbar film was centered over L2 (Melton
1993).
2.2.2 VERTEBRAL MEASUREMENTS
At each research center, the anterior(A), medial(M), and posterior(p) heights of each
vertebral body were measured with the aid of a microcomputer-linked digitizing pad. In
Hawaii, the points indicating the border of the vertebral centrum were chosen based on
the procedure described by Gallagher et at. (1988), and Spencer et aI. (1990). In
Hiroshima, the vertebral heights were measured at the same locations on the vertebral
border as described for HOS except measurements were made based on a penciled outline
of the vertebra rather than individual points. The method used in Minnesota was also
similar to the one used in Hawaii, but the medial height was estimated by the average
of the right and left medial heights instead of a single measure at the center of the
vertebra.
16
2.2.3 ASSESSMENT OF SPINE DEFORMITY
Three sets of objective diagnostic criteria for prevalent vertebral fracture identification
were evaluated or used in this study (Table 2.1). All three criteria were based on
absolute or relative vertebral height reduction. Each set included four fracture definitions.
The first one was based on the measure of the anterior dimension only and used -3 SD
as the cutoff. The second one was based on the measures of all three dimensions and
used -3 SD as the cutoff. The third and the fourth definitions are similar to the first two
definitions, except -4 SD was used as the cutoff instead of -3 SD.
The first set of diagnostic criteria (pV1, PV2, PV1A, PV2A) simply required the original
absolute heights of the vertebrae (i.e., A, M, P). The criteria in the second set (PV3,
PV4, PV3A, PV4A), however, were based on the ratio of the original vertebral
dimensions to a reference dimension. The anterior height and the medial height were
divided by the posterior height of the same vertebra and, the posterior height was divided
by the posterior height of the adjacent vertebra above. Like the second set, the third set
of criteria (pV5, PV6, PV5A, PV6A) was also based on the ratio between vertebral
heights, but the reference dimensions were the corresponding vertebral dimensions of
vertebra T4. All of these three sets of diagnostic criteria, and codes used for brevity in
this dissertation are summarized in Table 2.1.
17
There are various ways to categorized spine fractures. In this study prevalent vertebral
fractures were further classified into three types: crush, wedge, and endplate fracture
(Figure 2.1). Crush fracture is characterized by the reduction in all three vertebral
dimensions (i.e. A, M, and P). Wedge fractures involve a reduction primarily in anterior
height (but usually also have reduction in medial height) while endplate fractures involve
a decrease only in medial height (Melton et aI., 1988). In the present study, vertebrae
with posterior heights falling below the respective normal limits (-3 SD or -4 SD) were
defined as crush fractures. Of the remaining vertebrae, those with anterior heights below
the normal limits were classified as wedge fractures, and vertebrae with only medial
heights below the normal limits were classified as endplate fractures.
2.3 MEASUREMENT AND CONVERSION OF SPINE BONE MINERAL DENSITY
For the subjects of AHS and HOC, spine bone mineral density (BMD) was estimated by
the average of BMD measured on L2-IA. No information on BMD is available for the
subjects of ROS. BMD measured on a fractured vertebra between L2-IA was excluded
from the calculation of mean BMD. If all three vertebrae (L2-IA) were fractured, then
the mean BMD was set to missing and therefore was excluded from the following data
analyses.
In Hiroshima, all BMD were measured on a Dualomex densitometer (Chugai
Pharmaceuticals, Tokyo), while in Hawaii, some of the BMD were measured on a Lunar
18
DP3 (Madison WI) densitometer and the others were measured on a Hologic QDR-1000
(Waltham, Massachusetts) densitometer. In order to make BMD measurements
comparable, thirty-three native Japanese women aged 57-82 were measured on both
Dualomex and Hologic densitometers in Hiroshima and these values were used to build
a linear regression model with R2=0.91. Similarly, 65 women aged 27-88 were
measured on both Lunar and Hologic densitometers in Hawaii and their BMD measures
were used to fit another regression model with R2=0.96. Both BMD values of the native
Japanese subjects measured on Dualomex densitometer and BMD values of the Japanese
American subjects measured on Lunar densitometer were converted to the Hologic
densitometer scale by the respective linear regression models before BMD data were
analyzed.
2.4 STATISTICAL ANALYSES
In the present study, all statistical analyses were conducted on ffiM PC using SAS
software version 6.04 or version 6.08.
2.4.1 AGREEMENT OF PREVALENT FRACTURE DEFINITIONS
In this study, overall proportion of agreement (Po)' Cohen's Kappa, Scott's 7f', and Byrt's
prevalence-adjusted bias-adjusted kappa (pABAK) (Byrt et aI., 1993; FIeiss, 1981;
Wickens, 1989; Zwick, 1988) were used as the measures of agreement between different
19
definitions of prevalent vertebral fractures. The potential effects of 'bias' and
'prevalence' on Kappa were assessed separately by Byrt's Bias Index (HI) and Prevalence
Index (PI) (Byrt et al., 1993), in conjunction with Cicchetti and Feinstein's indices of
positive and negative agreement(PPDS and Pneg) (Cicchetti and Feinstein, 1990).
In addition, BI, PPDS' and Pneg were also used to estimate the potential bias associated with
fracture definitions and to examine the consistency of the fracture definitions in terms of
positive and negative diagnosis.
In agreement analyses, two fracture definitions were compared only when they involved
measurements (either original or ratios) on the same vertebral dimensions and had the
same cutoff in terms of standard deviation (e.g., PVl vs PV3, PV2 vs PV4). Using both
individual woman and individual vertebra as a study unit, the agreement analyses were
conducted for each of the study populations separately. Since most spine fractures occur
after age fifty, all subjects less than 50 years old were not included in the analyses.
The symbols and notation used for calculating these coefficients and indices under the
situation of binary classification are summarized as follows:
Definition AYes No Total
Definition B Yes a b gl
No c d g2
Total f1 f2 N
20
~ _._._--_._---
The overall proportion of agreement is
p = a+do N (2-1)
Each of three coefficients of agreement (Kappa, 7r, and PABAK) used in this study can
be expressed in the form
(2 -2)
where P, denotes the proportion of agreement expected by chance. The three different
agreement coefficients can be distinguished by their definition of Pe.
In Byrt's PABAK, P, is assumed to be 0.5. Under this assumption, PABAK can also be
expressed as
PABAK=2PO-1
Which is merely a linear transformation of Po.
In calculation of Scott's 7r, P, takes the form
In Cohen's Kappa, P, is defmed as
f1g1+ f 2 g 2p =---=-=----=:........::e N 2
(2 -3)
(2 -4)
(2 -5)
Byrt's Bias Index is defmed to be equal to the difference in proportions of 'Yes' for the
two fracture definitions under evaluation and is estimated as
21
BI= a+b _ a+c = b-cN N N
(2 -6)
Byrt's Prevalence Index is defmed as the difference between the probability of 'Yes' and
the probability of 'No' and is estimated as
PI= a-dN
(2 -7)
Indices of positive and negative agreement proposed by Cicchetti and Feinstein (1990)
reflect the observed proportion of positive and negative agreement separately. They can
be calculated as
(2 -8)
(2 -9)
2.4.2 COMPARISON OF VERTEBRAL FRACTURE PREVALENCE BETWEEN
NATIVE JAPANESE. JAPANESE-AMERICANS. AND CAUCASIANS
Prevalence of vertebral fractures among each of the three study populations were
calculated separately using the fracture definitions described earlier in 2.2.3. Both
vertebra-specific prevalence based on individual vertebrae and age-specific prevalence
based on individual women were examined. For analyses based on individual people,
22
each person counted only once regardless of the number of vertebral fractures in an
individual. Site distribution and prevalence of the three different types of vertebral
fractures, as defined in 2.2.3, were also investigated.
Logistic regression analysis was used to explore differences in prevalence of vertebral
fracture among the three study populationsafter adjusting for age. Hawaii population was
chosen as the reference population so that the fracture prevalence of migrants could be
compared with the prevalence of both original population and American Caucasian. All
subjects included in the analysis were at least 50 years old, which made the study specific
to post- and peri-menopausal women.
2.4.3 COMPARISONS OF NATIVE JAPANESE AND JAPANESE-AMERICAN
WOMEN
Major goals of the study were 1) evaluate the magnitude of any differences in spine
BMD and/or vertebral fracture prevalence between the HOS and RERF populations, and
2) investigate the ability of potential determinants to explain such differences. Toward
this end, distributions of potential predictors were compared between populations.
Comparisons were made on body size (height, weight, body mass index), reproductive
and menstrual history, current smoking and alcohol use. Weight (Kg) and height (cm)
were measured during the 1978-1980 examination cycle in Japan and the 1981-1982
examination cycle in Hawaii (first HOS exam). Body mass index (BMI) was calculated
23
as weight(kg)/height(mf. Information on other variables were obtained about the same
time using structured interview in Hawaii and a structured mail survey in Hiroshima.
Study subjects were questioned about their birth date, age at menarche, age at natural
menopause, cause of menopause (natural or artificial), number of live births, total
lactation period, current smoking and alcohol habits. One exception was the information
on menopause for native Japanese subjects. Since about half of Japanese women were
premenopausal in 1978-1980, data on menopause obtained from 1989-1991 examination
were used in the present study.
Using line plots and contingency tables, mean values of continuous variables or
proportions of categorical variables were compared between the two study populations
after adjustment for birth year group. The age-adjusted differences between native
Japanese and Japanese-American women and possible interactions between populations
and birth year were tested using multiple regression (if continuous variable) or logistic
regression (if categorical variable). A binary indicator variable labelled JAPAN was
included in all regression models to indicate membership in the RERF population.
The effects of the potential risk factors on spine BMD were investigated using multiple
regression. The effects of risk factors on vertebral fracture prevalence (based on PV2 and
PV2A) was explored using logistic regression after adjustment for spine BMD and age.
Again, Japanese-American women living in Hawaii were chosen as reference group.
Potential interactions among the independent variables were also explored based on both
24
statistical and biological considerations (i.e. the interaction term being tested should make
sense biologically). In the present study, only a small proportion of subjects were less
than 50 years old and all of them were excluded from regression analyses. This restricted
the main study population to post- and peri-menopausal women who are believed to have
higher risk of osteoporotic fractures.
In order to eliminate the potential influence of early environmental components that
operated before migration, all Japanese-American women living in Hawaii but born in
Japan were also excluded from the analysis.
25
CHAPTER 3
RESULTS
3.1 EVALUATION OF AGREEMENT BETWEEN DIFFERENT DEFINITIONS OF
PREVALENT VERTEBRAL FRACTURES
3.1.1 AGREEMENT BETWEEN FRACTURE DEFINITIONS
The results of agreement analyses presented in Table 3.1 and Table 3.2 show that the
overall proportion of agreement (Po) for most comparisons between fracture definitions
was 95% or better (range from 87.2% to 98.9%) when using individual women as the
study unit, and was 99% or better (range from 98.0% to 99.8%) when using individual
vertebrae as the study unit. The observed proportion of negative agreement estimated by
Pneg was even higher than the corresponding overall proportion of agreement (Po)'
However, the observed proportion of positive agreement estimated by Ppaswas about 10
20% lower than the corresponding overall proportion of agreement (Po) when the
analyses were based on individual women, and the difference between Ppas and Po was
even larger for the analyses based on individual vertebrae.
Chance-corrected agreement between fracture definitions was also explored using three
coefficients of agreement: Cohen's kappa, Scott's 1r, and Byrt's PABAK (Table 3.1 and
Table 3.2). Kappa runs from -PJ(I-PJ to 1 depending upon the marginal proportions.
26
Kappa ~ 0 when observed agreement is greater than or equal to chance agreement, and
kappa ~ 0 when observed agreement is less than or equal to chance agreement. If
agreement is perfect kappa=l, while kappa=O if the agreement is totally by chance
(Fleiss, 1981; Liebetrau, 1983). Landis and Koch suggested that, for most purposes,
kappa values greater than 0.75 or so represent excellent agreement beyond chance, and
kappa values between 0.40 and 0.75 represent good agreement beyond chance (Fleiss,
1~81). Most kappa values listed in Table 3.1, where the study unit was individual
women, and in Table 3.2, where the study unit was individual vertebrae, fell between
0.70 to 0.90 (range from 0.579 to 0.926). Scott's 7r, which could be regarded as the
value of kappa calculated when marginals are homogeneous (f1=gl and f2=g2, see
Chapter 2), were almost always equal to the corresponding kappa listed both in Table 3.1
and in Table 3.2. Byrt's PABAK, as a linear transformation of Po, may range from -1
to 1. In the present study, the excellent agreement between fracture definitions was also
reflected by the high PABAK values.
Although all indices and coefficients of agreement used in this study indicated good to
excellent agreement for all pairs of fracture definitions being compared, there were
differences in the degree of agreement. In Table 3.1 and Table 3.2, for example,
comparisons based on the Japan population which involved either PV4 or PV4A show'
a poorer agreement. This observation could be attributed to the effectof bias as discussed
below.
27
In general, both observed and chance-corrected indices and coefficients of agreement
based on the Hawaii population were greater in magnitude than those based on the Japan
or Minnesota populations, except a few comparisons which showed a similar agreement.
This was consistent with line plots of vertebra-specific prevalence in Figure 3.1-Figure
3.6. The shape and magnitude of line plots based on different definitions looked more
similar to each other for HOS than the line plots for the ROS and AHS populations.
When focusing on one specific study population, the observed proportion of positive
agreement (Ppas) for individual women was generally higher than that for individual
vertebrae. On the other hand, the observed proportion of negative agreement (Pneg) and
the overall proportion of agreement (Po) for individual woman was consistently lower
than the corresponding Pneg and Po for individual vertebra. The PABAK values for
individual women, as expected, were also lower than those for individual vertebrae, since
PABAK was merely a linear transformation of Po, and so provided exactly the same
information as Po did (Byrt et al., 1993). However, no consistent pattern was observed
for the difference between Cohen's kappa and Scott's 7f based on individual women and
those based on individual vertebrae.
Both -3 SD and -4 SD were explored as the diagnosis cutoffs in this study. All six
measures of agreement based on -4 SD diagnosis cutoff were slightly greater than, or at
least similar to the corresponding measures based on -3 SD cutoff whether individual
women or vertebrae were used as study unit. These results are summarized in Table 3.3.
28
3.1.2 BIAS AND PREVALENCE EFFECTS ON AGREEMENT
Among many proposed agreement measures, Cohen's kappa is probably the most
frequently used chance-corrected coefficient of agreement. However, several 'paradoxes'
in its interpretation have been recognized (Byrt et al., 1993; Feinstein and Cicchetti,
1990). Difficulty occurs because kappa, as a single omnibus index, does not take into
account the effects of bias and prevalence (Byrt et aI., 1993). As presented in Table 3.1
and Table 3.2, there were differences of at least 10-20% between Ppos and Pneg• However,
neither Po nor kappa could reflect these differences. Although Ppos and Pneg were
originally proposed to independently evaluate two aspects of the information contained
in kappa (Cicchetti and Feinstein, 1990), they are unable to account for the effects of
bias. Byrt et al, (1993) derived PABAK (prevalence-adjusted bias-adjusted kappa), BI
(bias index), and PI (prevalence index) to decompose kappa into three components
reflecting observed agreement, bias and prevalence. The values of BI and PI based on
individual women and vertebrae are presented in Table 3.4 and Table 3.5.
Both BI and PI take values from -1 to +1. Formula 3-1 shows how kappa is related to
PABAK and is affected by BI and PI (Byrt et aI., 1993).
(3-1)
29
As pointed out by Byrt et aI. (1993), "unless PABAK=I, the larger the absolute value
of BI, the larger is K (for PI constant), and the larger the absolute value of PI, the
smaller is K (for BI constant). If both bias and prevalence effects are present, then the
result may be that K is larger or smaller than PABAK, depending on the relative size of
BI and PI".
As shown in Table 3.4 and Table 3.5, the absolute PI values mostly fell between 0.7 to
0.9 for individual women and were even larger for individual vertebrae. This was the
chief reason why the kappa values were considerably lower than PABAK values. On the
other hand, the absolute values of BI were so small that their effects on kappa were
negligible.
In addition to the effect on kappa, BI may also provide valuable information on the
existence of potential bias associated with the process of vertebral measurement. In Table
3.4 and Table 3.5, the greatest values of BI were consistently observed for comparisons
involving PV4 and PV4A for the Japan population. This suggested that the biases
associated with PV4 and PV4A in the Japan population should be further investigated,
though their effect on kappa might be negligible. Figure 3.2 and Figure 3.5 showed that
the vertebra-specific prevalence curves for PV4 and PV4A differed considerably from
the other five curves for the Japan population in prevalence around T8 to TlO. Although
none of the six curves in Figure 3.2 and Figure 3.5 was the 'gold standard', we may still
infer the presence of biases associated with PV4 and PV4A, since only the curves
30
associated with PV4 and PV4A had a very high 'peak' around T8 to TIO and this was
only observed in the Japan population. Without additional information for making further
inference about the cause of the bias, any inter-population comparisons of prevalence
based on PV4 or PV4A should be interpreted with caution.
3.2 PREVALENCE AND DISTRIBUTION OF VERTEBRAL FRACTURES
AJ\10NG NATIVE JAPANESE, JAPANESE-AMERICANS, AND CAUCASIANS
3.2.1 VERTEBRA-SPECIFIC PREVALENCE
Vertebra-specific prevalence of fracture based on various definitions (PV1-PV6, PV1A
PV6A) are shown in Figure 3. l-Figure 3.6. In general, all definitions yielded similar
profiles showing that the highest prevalence occurred in the region of TIl through L1
regardless of the study population. Another noticeable 'peak' of prevalence was observed
around T8. For the most part, the definitions involving the measurements of all three
dimensions tend to yield higher prevalence than those only based on measurement of
anterior dimension, suggesting that fracture definitions based on anterior dimension alone
may miss some fractures, such as endplate fracture, and therefore underestimate the
prevalence. As expected, prevalence associated with -3 SD diagnostic cutoff is always
higher than the corresponding prevalence associated with -4 SD cutoff. Although
vertebra-specific prevalence varies between populations, differences in the age
31
distribution preclude direct comparison, and the sample size does not allow adjustment
for age at each vertebral site.
Vertebra-specific prevalence of spine fractures was further investigated by type (i.e.,
wedge, endplate, and crush fractures). Figure 3.7, which was based on -4 SD cutoff,
shows that anterior wedge fracture was most common, endplate fracture was less so, and
crush fracture was least common in all three populations. The bimodal distribution of
fractures, with peaks around TI2 and T8, appeared to be determined predominantly by
the site distribution of wedge fractures. Compared with the Hawaii population and
Minnesota population, crush fracture was very uncommon in the Japan population and
occurred only in the lumbar region.
3.2.2 AGE-SPECIFIC PREVALENCE
Table 3.6 shows the overall age-specific prevalence of vertebral fractures, which counts
individuals with either single or multiple fractures. In general, the overall prevalence of
vertebral fracture based on all definitions increased dramatically and near exponentially
with age in all three study populations. Age-specific prevalence curves associated with
various fracture definitions based on all three dimensions are shown in Figures 3.8 and
3.9. It can be seen that spine fracture prevalence for the Japan population is consistently
higher after age seventy, compared to the corresponding prevalence for Hawaii and
Minnesota populations. Like the overall prevalence, the prevalence of both single and
32
multiple fractures also increasedwith age. Using PV2 definitions, Figure 3.10 shows that
the prevalence of single vertebral fractures was similar to that of multiple fractures
before age seventy. The observed fluctuations for fracture prevalence curves is probably
due to the small number of cases in each category after age stratification.
3.2.3 LOGISTIC REGRESSION ANALYSIS
Using HOS as the reference group, age-adjusted odds ratios estimated by logistic
regression and the corresponding confidence intervals are shown in Table 3.7. The odds
ratios for the Japan population were consistently and significantly greater than 1.0 (range
from 1.6 to 2.6, depending on fracture definition), suggesting that the prevalence of
vertebral fractures in native Japanese women were greater for any given definition than
their Japanese-American counterparts. In the Japan population, the values of odds ratios
for various definitions were similar except odds ratios based on definitions PV4 and
PV4A, which were considerably higher than the other odds ratios. As pointed out in
Section 3.1.2 , this might be due to the bias associated with PV4 and PV4A in the Adult
Health Study in Japan. In contrast, the magnitude of odds ratios for the Minnesota
population seemed to be dependent upon the diagnostic cutoffs. Most odds ratios based
on -3 SD cutoff were greater than or equal to 1.0, and only one of them was significant.
On the other hand, most odds ratios based on -4 SD cutoff were less than or equal to 1.0
and half of them were statistically significant. Consequently, it appears that Caucasian
33
women living in Minnesota may have a lower prevalence of severe (more than 4 SD
below the mean) vertebral fractures, compared to Japanese-Americans living in Hawaii.
3.3 A COMPARISON OF CHARACTERISTICS OF NATIVE JAPANESE AND
JAPANESE-AMERICAN WOMEN
The results reported in Section 3.2 indicated that native Japanese and Japanese-American
women differ in age-adjusted prevalence of vertebral fractures, though they share the
similar genetic factors. To explore the causes of this difference, some potential risk
factors including BMD, body size, reproductive factors, and life-style variables were
compared between native Japanese and Japanese-American women. The difference in
BMD between these two populations and the ability of the other variables to explain this
difference was also investigated.
Table 3.8 summarizes the basic characteristics of the Japan and Hawaii study
populations. At this crude level of comparison (not adjusted for age), some differences
between the two study populations could be observed. On the average, the native
Japanese subjects were about six years younger than Japanese-American subjects at the
time of the most recent exam, while the mean BMD for native Japanese women was
significantly (about 2.5%) lower than the mean BMD for Japanese-American women.
Compared to Japanese-Americans, native Japanese subjects tended to have lower weight
and body mass index, but no significant difference was found for height. With regard to
34
reproductive variables, the comparisons indicated that on the average native Japanese
women had a later menarche, a slightly earlier natural menopause (not significant), and
thus a shorter duration between menarche and natural menopause, compared to their
Japanese-American counterparts. The proportion of women with at least one live' birth
was similar in two study populations. Among those with lactation experience, native
Japanese women had a much shorter average of total lactation period, but a longer,
though not significant, average lactationperiod per child. Artificial menopause was found
to be much more common among Japanese-American women than native Japanese
women (29.0 %vs %14.0). No significant differences in the overall proportion of current
alcohol use was found between the two study populations, but the overall proportion of
current smokers was significantly higher among native Japanese women.
Since the unadjusted comparisons could be confounded by age and/or cohort effects, the
comparisons were also repeated after adjusting for birth year. The adjusted results are
presented in Figure 3.11-Figure 3.19 and Table 3.9-Table 3.11. In addition, linear
regression and logistic regression analyses were used to evaluate the magnitude and
significance of the observed differences, and to test for statistical interactions after
adjusting for birth year or age (Table 3.12 and Table 3.13). For this purpose, an
indicator variable, JAPAN, was used (JAPAN=1 if native Japanese, JAPAN=O if
Japanese-American) .
35
3.3.1 ANTHROPOMETRY
In general, mean height and mean weight for both native Japanese and Japanese
American women increased with later year of birth, but the rate of increase with birth
year was greater for Japanese-Americans than for native Japanese. For most birth
cohorts, native Japanese women were 1.5-2 cm shorter and 1.5-2 Kg lighter than
Japanese-American women. Mean body mass index fluctuated around 23 kg/m' among
native Japanese, but increased with successive Japanese-American birth cohorts (Figure
3.11-Figure 3.13). Different rates of change between populations for these
anthropometric variables were also reflected in the corresponding regression models
(Table 3.12), where the interaction effects were found to be significant. The increasing
trend in height and weight with birth year were statistically significant in both study
populations (see footnote c for Table 3.12). But the change in body mass index with birth
year was significant only among Japanese-Americans. The significant negative quadratic
term in the regression model for body weight suggests that for later birth cohorts, weight
increases more slowly than for earlier birth cohorts (Table 3.12).
3.3.2 GYNECOLOGICAL HISTORY
Compared to Japanese-American women, a higher mean age at menarche and a lower
mean age at menopause were consistently observed in native Japanese women regardless
of birth year. Consequently, the mean duration between menarche and menopause were
36
about two or more years shorter in native Japanese women. For both study populations,
mean duration between menarche and menopause increased with birth year as the result
of the decreasing trend in age at menarche and increasing trend in age at menopause
(Figure 3.14-Figure 3.16). The estimated differences (indicated by the coefficient for the
dummy variable JAPAN) in mean age at menarche, mean age at menopause, and mean
duration between menarche and menopause were 1.9 years, 0.8 years, and 2.3 years
respectively and all of these differences were highly significant after adjusting for birth
year (Table 3.12). Based on the fitted regression models, an estimated 0.55 years
decrease in mean age at menarche and an estimated 0.95 years increase in mean age at
menopause were expected for every 10 year increment in birthdate. Both observed and
estimated secular trend suggested the existence of a significant birth cohort effect on both
age at menarche and age at menopause.
Among those with lactation experience, both total lactation period and average lactation
period per child (calculated as total lactation period in months divided by number of live
births) were compared between the two study populations. In both study populations, the
mean lactation period, either the cumulative or the average duration per child, decreased
dramatically with birth year, but this decrease was more pronounced for Japanese
Americans (Figure 17 and Figure 18). The decreasing trends with birth year were
significant for both total lactation period and average lactation period per child in both
study populations (see footnote c for Table 3.12). A 'crossover' interaction was
observed; two regression lines for the two study populations crossed within the observed
37
-----------------------
range of birth year. As a result, the difference in lactation period between the two study
populations was sometimes positive and sometimes negative depending upon the birth
year.
A summary of the proportions of native Japanese and Japanese-American women
stratified by the number of live births and by birth cohort is shown in Table 3.9. The
ca~egory of three or more live births had the highest proportion of women for all
Japanese-American birth cohorts. However, this fairly stable distribution was not seen
among native Japanese. For example, the highest proportion fell in the category of three
or more live births among earlier Japanese birth cohorts. In the later birth cohorts,
however, it was found in the category of two live births. The relative frequency of
women without any live birth was similar for both study populations within birth year
strata, and decreased with successive birth cohorts.
The proportion of artificial menopause remained constant at about 30%among birth year
strata of Japanese-American women born between 1905-1930. In contrast, a steady
increase in proportion of artificial menopause was observed among native Japanese
women, from 5.5% for those born in 1910-1915 to 28.6% for those born in 1935-1940
(Table 3.10). The apparent high proportions of artificial menopause observed in 1935
1939 birth cohort could be in part due to the existence of premenopausal women.
However, the interaction between birth year and membership of study population was
found to be significant in logistic regression analysis even after excluding the birth
38
cohorts which might include premenopausal women (Table 3.13). In consistent with our
observation, the fitted model also suggested that birth year had little influences on
proportion of artificial menopause among Japanese-Americans, as indicated by the very
small coefficient for birth year. For native Japanese, the effect of birth year, reflected
by the sum of the coefficients of birth year and the interaction term, became much
bigger, suggesting an increasing trend in proportion of artificial menopause with birth
year.
3.3.3 SMOKING AND ALCOHOL USE
The overall proportion of current smoking was significantly higher in native Japanese
women than in Japanese-American women (Table 3.8). However, as shown in Table
3.11, the difference differed depending on the year of birth. The proportion of smoking
increased with birth cohorts among Japanese-Americans, but decreased with birth cohorts
among native Japanese. This observed interaction was tested by logistic regression and
was found to be statistically significant (Table 3.13).
The proportion of current alcohol users was similar among both populations (Table 3.8).
After adjustment for year of birth, the observed difference in proportion ranged from 2 %
to 4% depending upon the birth cohorts. A definite increasing trend in proportion of
alcohol use with successive birth cohorts was observed among Japanese-American
women, but no clear trend was found among native Japanese women (Table 3.11). A
39
consistent result was achieved by logistic regression analysis (Table 3.13). Considering
the effect of birth year and interaction simultaneously, the model also indicated that the
proportion of alcohol use increased with successive Japanese-American birth cohorts but
not with native Japanese birth cohorts (note that the coefficient reflecting the influence
of birth year on alcohol use among native Japanese is 0.034-0.0329 = 0.0011, which is
almost equal to zero).
3.3.4 EFFECT OF RADIATION EXPOSURE
Many non-genetic factors could be responsible for the observed difference between the
two study populations, and radiation exposure is obviously among those of consideration
since many native Japanese subjects living in Hiroshima were exposed to the atomic
bomb radiation in August 1945 and afterwards. The estimated radiation dose for native
Japanese subjects ranged from 0 for those far from the atomic bomb hypocenter, to a
maximum of 6 Gy for those exposed at locations near the hypocenter. Mean and standard
deviation of radiation dose of native Japanese subjects was 0.586 Gy and 0.889 Gy,
respectively. Assuming the exposure dose was zero for all Japanese-American subjects
living in Hawaii, the potential effect of radiation exposure on the variables being
compared was explored using multiple regression when the outcome variable was
continuous, or using logistic regression when the outcome variable was binary. After
taking into account the influence of birth year, membership of study population, and their
interaction (if significant), it was found that radiation exposure had significant association
40
-------_. -----~--
with height, age at menopause, artificial menopause, smoking and alcohol use. On the
average, an increase in radiation dose by one standard deviation (0.889 Gy) was
associated with 0.49 em decrease in height and 0.41 years decrease in age at menopause.
Corresponding to one standard deviation increase in radiation dose, the odds ratios for
artificial menopause, smoking, and alcohol use were 1.44, 1.22, and 0.82 respectively
based on logistic regression analysis. No significant association was found between
radiation exposure and other variables. However, adjustment for radiation dose could not
entirely explain the observed differences between populations in the variables being
compared. In other words, the magnitudes and significance of associations for
membership of study population, birth year, and their interaction changed little even after
the adjustment for radiation.
3.3.5 DETERMINANTS OF BONE MINERAL DENSITY
As mentioned earlier, the native Japanese subjects were about 5 years younger on the
average, but their mean BMD was lower than that of their Japanese-American
counterparts living in Hawaii. Figure 3.19 shows the age-specific difference in mean
lumbar BMD. It can been seen that mean BMD decreased with age in both study
populations and mean BMDs in all age group were lower among native Japanese women
than Japanese-American women. Using linear regression, it was found that the age
adjusted average difference in BMD was 0.048 g/cm2 between the two study populations,
which meant the mean BMD for Japanese women was about six percent lower than the
41
mean BMD for Japanese-American women if only adjusting for age (Table 3.14). In
addition to age, several differences in potential BMD predictors have been observed
between the two study populations and could also be responsible for the observed
difference in BMD. This was further investigated by multiple linear regression analysis.
Various measures of body size (such as weight, height, and body mass index) have been
reported to be associated with BMD, but there was some discrepancy in the literature
regarding the optimum method of adjusting for body size. Some authors use weight
and/or height, while others prefer body mass index. Table 3.14 shows the age-adjusted
effects of weight, height, and body mass index on lumbar BMD. Although body mass
index (BMI) was significantly associated with BMD, weight and height together seemed
to explain more variation in BMD in this study and account for more difference in BMD
between the two study populations, as indicated by the greater R2 and the smaller
coefficient for JAPAN. Therefore, weight and height were selected as body size
measures for adjustment as potential confounders in all regression analyses of other
variables. It is worth noting that among weight and height, weight explained more
variance of spine BMD. The magnitude of the mean difference in BMD, which was
estimated by the coefficient for JAPAN, decreased by about one-fourth as the result of
adjustment for weight and height.
As described earlier, Japanese-American women living in Hawaii had a much higher
proportion of artificial menopause, compared to native Japanese women. The linear
42
regression model presented in Table 3.15 implies that the effect of cause of menopause
on BMD depends upon the study population. Among native Japanese subjects, there was
no meaningful difference in BMD between natural menopause and artificial menopause.
Among Japanese-American subjects, however, those with artificial menopause tended to
have a higher BMD. This could be explained, at least in part, by the difference of
postmenopausal estrogen use between Japan and the United States. It is known that the
proportion of postmenopausal estrogen use was almost zero among native Japanese
women regardless of whether they had natural menopause or artificial menopause. In
addition, only about one-fourth of artificial menopause was due to bilateral oophorectomy
or uterine operation plus bilateral oophorectomy (unpublished data). Thus, other things
being equal, we would anticipate little differential effect of cause of menopause among
native Japanese subjects. On the other hand, postmenopausal estrogen use was much
more popular in the United States and was almost a routine treatment following any
operations which could lead to artificial menopause and estrogen deficiency. Thus,
among Japanese-American subjects, we would expect a higher mean BMD for those with
artificial menopause than for those with natural menopause since a larger proportion of
women who experienced artificial menopause were expected to use estrogen after the
operation, but a smaller percentage of the women with natural menopause were expected
to use estrogen. Since interaction effect is symmetrical, the same regression model also
implied that the average difference in BMD between the two study populations was a
function of cause of menopause. Among women with artificial menopause, the estimated
mean difference was about 0.068 g/cm2, while the corresponding difference among
43
women with natural menopause was about 0.019 g/cm'. Since no information on estrogen
use was available in this study and the information on date of artificial menopause was
not comparable between the two study populations, only the effect of natural menopause
and the duration between menarche and natural menopause were further investigated in
the present study. This approach may also avoid the potential confounding of some
possible endocrine problems, which could be the reason for artificial menopause.
Table 3.16 contains regression results for evaluating the effects of reproductive variables,
smoking, alcohol use, and radiation exposure on BMD. After adjustment for covariates,
age at menopause and years between menarche and menopause, were found to be
significantly and positively associated with lumbar BMD, while total lactation period and
average lactation period per child were found to be significantly and inversely associated
with lumbar BMD. The analyses were repeated after either age at (natural) menopause
or years between menarche and (natural) menopause was also adjusted as a covariate with
age, weight, height, and population; and none of the variables related to lactation,
number of live births, current smoking, current alcohol use, and radiation exposure were
found to be significant or have meaningful influence on the difference in BMD between
the two study populations. In addition, the results also suggested that height could be
dropped from the regression models since its influence on the average difference in BMD
was small enough to ignore and its effect on BMD was no longer significant after
adjustment for age at (natural) menopause or years between menarche and (natural)
menopause.
44
Table 3.17 shows two final regression models with adjustment for population, age, and
weight plus either age at menopause or years between menarche and menopause. When
the analysis was restricted to women with natural menopause, the age-adjusted difference
in mean BMD between native Japanese and Japanese-American women was 0.031. The
results presented in Table 3.17 indicated that the age-adjusted difference in mean BMD
was reduced by about half as the result of adjustment for weight and age at menopause
and the difference essentially disappeared when adjusting for years between menarche
and menopause instead of age at menopause. Both regression models suggested that
weight and age at menopause were two major factors responsible for the age-adjusted
difference in BMD between the two study populations. Years between menarche and
menopause is a function of both age at menarche and age menopause. Adjusting for this
variable appears to explain more of the difference in mean BMD than adjusting for age
at menopause alone, suggesting that age at menarche may have some influence on the
difference in BMD, though its effect on BMD was not statistically significant.
3.4 DETERMINANTS OF SPINE FRACTURE PREVALENCE
In the preceding section, potential predictors of BMD were compared between native
Japanese and immigrant Japanese-American women, and the ability for these factors to
explain the observed difference in spine BMD was also examined. The roles played by
these factors in osteoporosis have been investigated by many authors. However, almost
all studies have focused on the relationship between these factors and bone mass,
45
------ ---~------------
probably because reduced bone mass is a major risk factor of osteoporotic fractures. Few
studies have investigated their effects on fracture risk through mechanisms other than
affecting bone mass. In the present study, these factors were examined in terms of their
influence on fracture risk to see if they also contribute to the observed difference in
fracture prevalence independently of BMD.
As was shown earlier in Section 3.2, prevalence of vertebral fracture was higher among
native Japanese subjects compared to Japanese-American subjects of the same age with
age-adjusted odds ratios of 1.8 for definition PV2 and 1.6 for PV2A. It can been seen
from Figure 3.8 and Figure 3.9 that the absolute difference in prevalence between the
two study populations tends to be greater in older age groups.
The results oflogistic regression analysis presented in Table 3.18 and Table 3.19 show
that spine BMD, height, age at menopause, and duration between menarche and
menopause had 'significant' effects on prevalent vertebral fractures after adjusting for age
and population. The age-adjustedeffects of weight and body mass index were marginally
significant. Since BMD was known to be a major risk factor, and therefore a potential
confounder, and some variables might influence the fracture prevalence through their
effects on BMD, BMD was included in subsequent models to explore 1) the effect of
adjusting for BMD on other variables in the model and 2) the effect of BMD on vertebral
fracture after controlling for other variables.
46
In the models presented in Table 3.20 and Table 3.21, age and BMD showed consistent
and independent effects on vertebral fracture prevalence. After adding BMD to the
model, the effect of height became smaller and non-significant. In addition, the direction
of the effect of weight and body mass index has been changed as the result of adjusting
for BMD, though their effects were biologically very small and only marginally
significant. Model 12 and Model 13 indicate that age at menopause and duration between
menarche and menopause had both statistically significant and biologically meaningful
effects on vertebral fracture prevalence even after adjusting for the effects of age and
BMD.
A binary indicator variable labelled JAPAN was included in all logistic regression models
to indicate the membership of study population (JAPAN=1 if native Japanese,
JAPAN =0 if Japanese-American). It was used to estimate the remaining difference in
log odds of prevalent vertebral fracture between the two study populations which could
not be attributed to other factors included in the model. When BMD was added to
Models 3 to 15 listed in Table 3.18 and Table 3.19, the reduction in the estimated odds
ratio associated with JAPAN suggested that BMD was among the variables that were
responsible for the difference in prevalence of vertebral fracture between the two study
populations. Comparing all models in Table 3.20 and Table 3.21 with Model 2 in Table
3.18 and Table 3.19, it is evident that after adjusting for age and BMD, duration between
menarche and menopause explained more of the difference in fracture prevalence between
native Japanese and Japanese-Americans than other variables, as indicated by the smaller
47
magnitude of odds ratio associated with JAPAN. Other variables including height,
weight, body mass index, smoking history, alcohol use, radiation, number of live births,
total lactation period, and average lactation period per child were reassessed by adding
each of them, in tum, to Model 13, that contained JAPAN, BMD, age, and duration
between menarche & menopause. However, none of them showed either significant or
meaningful effects on vertebral fracture prevalence, nor did they influence the magnitude
of associations for other independent variables in the model. Therefore, Model 13 in
Table 3.20 and Table 3.21 could be considered as the 'best fmal model'. First order
interactions between the independent variables in Model 13 were also evaluated, but none
of them were significant and therefore no interaction terms were incorporated in the
model.
The difference in prevalence of vertebral fracture between the two study populations was
reduced substantially and became non-significant after allowing for the effects of AGE,
BMD, and DURATION BETWEEN MENARCHE AND MENOPAUSE, suggesting that
BMD and duration between menarche and menopause jointly accounted for almost all of
the difference in age-adjusted prevalence when comparing native Japanese with their
Japanese-American counterparts. However, as discussed later in Chapter 4, when AGE
and DURATION BETWEEN MENARCHE & MENOPAUSE or AGE and AGE AT
MENOPAUSE were simultaneously included in the model, it implicitly incorporated
information about the number of years since menopause, which could be an important
determinant of the dependent variable (either BMD in linear regression models or
48
prevalent spine fractures in logistic regression models), because bone loss is accelerated
after menopause. Recognition of the potential effect of time since menopause has
important implication for making inference on underlying biological mechanisms based
on regression models.
The results presented in Table 3.20 and Table 3.21 indicated that both age at (natural)
menopause and duration between menarche and (natural) menopause were predictors of
prevalent spine fracture, which were independent of the effect of BMD. The fact that the
latter explain more difference in fracture prevalence between the two study populations
suggested that age at menarche might also contribute to the observed difference in
fracture prevalence, although its effect on spine fracture was not significant.
It is worth noting that radiation dose, a factor of greatest interest to investigators at
RERF, does not show a significant effect on vertebral fracture prevalence in any model.
Furthermore, forcing radiation into the final model did not alter the magnitudes of
association for other variables (see Model 14 of Table 3.20 and Table 3.21). It should
be noted, however, that variables that failed to show significant effects on prevalent
fracture might still have indirect effects. It was pointed out earlier that radiation exposure
was associated with earlier age at menopause and increased risk of artificial menopause,
which might in tum affect BMD. Similarly, the fact that weight and cause of menopause
do not have 'direct' impact on prevalent fracture does not rule out their contribution to
49
risk. In contrast, it does suggest that their effects on prevalent fracture (if any) were
mediated by BMD.
Except the models involving lactation period, all other logistic regression models showed
that the odds ratios corresponding to JAPAN were greater when using PV2 as the
diagnostic criterion than when using PV2A as the diagnostic criterion. This observation
suggests that the difference in prevalence level of vertebral fracture was smaller when
the outcome was limited to severe fractures, and became larger when including less
severe fractures.
50
CHAPfER4
DISCUSSION AND CONCLUSION
4.1 AGREEMENT BETWEEN VERTEBRAL FRACTURE DEFINITIONS
4.1.1 THE RATIONALE FOR THE AGREEMENT ANALYSIS
In epidemiologic studies, the subjects are often classified into two categories according
to whether given characteristics (e.g., fracture in present study) are present or absent.
The validity of a given method or procedure for the binary classification can be evaluated
by sensitivity and specificity. The former reflects the ability of a method or procedure
to identify correctly those who have the given characteristic, while the latter reflects the
ability of a method or procedure to identify correctly those who do not have the given
characteristic (Mausner and Kramer, 1985). When information about the actual presence
or absence of the characteristic is available, then sensitivity and specificity can be
estimated directly to evaluate the validity. For many clinical assessments, however, there
is no 'gold standard' reference measurement available for evaluating the accuracy of
binary classifications based on a specific method. Without knowing the 'truth', we are
left with reproducibility or agreement as the only measure of the classification
performance of the method under evaluation (Maclure and Willett, 1987; Sackett et al.,
1991; Thompson and Walter, 1988). Ifa high agreement is observed, then we could infer
that the two classification methods tend to classify the subjects in a similar, and possibly
51
valid, way, which would allow us to predict the results of one study from another study
even though different classification methods (such as fracture definitions) are used. If the
agreement is low, on the other hand, the usefulness of the classification methods would
be limited. In addition to the magnitude of agreement, agreement analyses can also
provide information about the pattern of the agreement and disagreement. For example,
we could investigate whether band c in a fourfold concordance table (see the notation
in.Chapter 2) differ substantially, which could lead to investigations of potential reasons
for such differences.
4.1.2 MEASURES OF AGREEMENT. UNDERLYING ASSUMPTIONS. AND
ASSESSMENT OF AGREEMENT
To date, many indices or coefficients of agreement have been proposed for measuring
the agreement (Fleiss, 1981; Zwick, 1988), but no single method is perfect in the sense
that "no single omnibus index of agreement can be satisfactory for all purposes." (Byrt
et aI., 1993; Cicchetti and Feinstein, 1990; Feinstein and Cicchetti, 1990). In this study,
overall proportion of agreement(Po), Cohen's Kappa, Scott's 1r, and Byrt's PABAK were
used as the measures of agreement.
The overall proportion of agreement is the simplest and most frequently used index of
agreement. However, it has been criticized as a potentially misleading index of
agreement since chance agreement may occur even if there is no systematic tendency for
52
two observers or two methods to classify the same individuals similarly (Thompson and
Walter, 1988). Without correction for chance agreement, simple proportion of agreement
(Po) tends to overestimate the agreement unless 'no chance agreement' could justifiably
be assumed. On the other hand, Goodman and Kruskal argue that chance-expected
agreement need not cause much concern. Even among those who prefer the chance-
corrected measures of agreement, there is still controversy over the way of making the
correction (Fleiss, 1981).
Zwick (198g) examines three widely used chance-corrected coefficients, which are
Cohen's kappa, Scott's 1r, and the S coefficient of Bennett et al. Since PABAK is
mathematically the same as the S coefficient, though the derivation is different (Byrt et
aI., 1993), all assumptions made on the S coefficient apply equally to PABAK and vice
versa.
As mentioned earlier, all three agreement coefficients can be expressed in the same form
as formula 2-2. The difference lies in the assumptions made for calculating P; Under the
assumption of independence between fracture definitions, the proportion of chance
agreement (PJ for each of the three coefficients can be expressed as
2
Pe=E ».»;i=l
53
(4 -1)
where, h., is the hypothesized marginal proportion of cases assigned to category i by
definition 1 and h+i is the corresponding proportion for definition 2. P, for PABAK and
S is derived by assuming that the hypothesized marginal proportions are both
homogeneous (~+=h+i=hJ and uniform (hi+"=h+i=hi=1I2). It can be shown
algebraically that P, for PABAK and S is equal to 1/2 in the case of binary classification.
However, the assumption of uniformity has been criticized because it may lead to
underestimates of P, in some circumstances and therefore overestimate the agreement.
Scott's 7r was derived based only on the assumption of homogeneity to overcome the
defects of S related to the assumption of uniformity. In this case, 1lj was estimated by (Pi+
+ p+i)l2, where Pi+ and P+i are the observed marginal proportions of cases assigned to
category i by definition 1 and definition 2, respectively. Cohen criticized Scott's 1rand
argued that "One source of disagreement between a pair of judges is precisely their
proclivity to distribute their judgements differently over the categories." Therefore,
instead of assuming hi+=h+i=hi. the observed marginal proportion, Pi+ and P+i, were
used directly to calculate the proportion of chance agreement (i.e., assume ~+ =Pi+ and
h+i=p+i) for kappa. While kappa is considered to be an improvement on alternative
agreement measures (such as 1r and S), its assumption does not seem appropriate in some
situations. Note that 1r = kappa if the observed marginals are homogeneous. In the
present study, the values of 1r were very close to values of kappa since the marginal
proportions are approximately homogeneous. If, in addition, the observed marginals are
uniform, PABAK=1r=kappa (Feinstein and Cicchetti, 1990; Zwick, 1988).
54
Different agreement measures require different assumptions and probably give us
different pictures. How, then, should agreement between different fracture definitions be
assessed? Which of the agreement measures should be applied in a given problem? The
answer lies in the justification of the assumptions associated with the measure(s) of
agreement to be used. In practice, however, it is not easy to decide how the chance
correction should be incorporated into the agreement measure (Fleiss, 1981). Instead of
relying on a single agreement measure, several indices and coefficients of agreement
were evaluated in this study. Since all of them indicated that the agreement between
fracture definitions was generally good, it seems reasonable to conclude that each pair
of fracture definitions listed in the first column of Table 3.1 and Table 3.2 tend to
classify the fracture status similarly. This tentative conclusion appeared to be consistent
with the observed vertebra-specific prevalence. For the most part, the difference in
vertebra-specific prevalence between two definitions being compared is usually less than
0.5% for Hawaii population and less than 1.0% for Japan and Minnesota population,
except those involving PV4 or PV4A in the Japan population (Figure 3.1-Figure 3.6).
However, the difference in age-specific prevalence was obviously larger than for the
corresponding vertebra-specific prevalence (Table 3.6). For example, the observed
differences between three-dimension definitions with the same cutoff (-3 SD or -4 SD)
were greater than 10% in some older age groups. This discrepancy between vertebra
specific and age-specific prevalence was at least in part due to differences in sample size
for calculating vertebra-specific and age-specificprevalence. When sample size is small,
the estimates of prevalence tend to be unstable and could vary considerably just by
55
chance. For the same number of discordant classifications, the difference in prevalence
based on the definitions being compared would be considerably larger when the
denominator is small. Vertebra-specific prevalence was calculated without adjusting for
age and its denominator was approximately equal to the number of participants. In
contrast, after stratifying by age, age-specific prevalence was calculated based on a much
smaller denominator, and thus was much more sensitive to the influence of discordant
classifications. It is worth noting that the agreement between the non-age-adjusted crude
prevalence based on fracture definitions with the same cutoff (-3 SD or -4 SD) was quite
good (except the comparisons involving PV4 and PV4A for the Japan population) because
of the larger denominators (Table 3.6). These observations suggest that factors other than
agreement of underlying fracture definitions may also contribute to apparent differences
between fracture prevalence.
4.1.3 ADDITIONAL INFORMATION SUPPUED BYP~~
ANDBI
Several authors have pointed out that use of a single agreement measure can be
misleading. No matter how the single summary measure is constructed, some information
will be lost inevitably. For example, a reported value of kappa can mean many different
things because kappa only measures the degree of the agreement, but does not reflect the
character of the agreement. Different patterns of diagonal in a four-cell concordance table
may yield the same kappa value (Byrt et aI., 1993; Cicchetti and Feinstein, 1990;
Wickens, 1989). To avoid any obscuring or deceptive effects associated with summary
56
measures of agreement (such as Poand kappa), Ppas' Pneg, PI, and BI were also examined
in this study.
It was noted earlier that Ppos was about 10-20% lower than the corresponding Po and Pneg
was slightly higher than the corresponding Po. This information about differences
between Ppos and Pneg would be lost if only Po had been reported, because the value of Po
is a weighted sum of the values for Ppos and Pneg(Cicchetti and Feinstein, 1990). Using
the notation given in Chapter 2, the relationship between these three indices can be
expressed as
(4 -2)
As can be seen, the weights are obtained from the marginal proportions of positive and
negative readings. In this study, the prevalence of fracture based on each definition was
much lower than the prevalence of nonfracture (i.e., f1 < < f2, gl < < g2)' Since the
weight for Pneg is much larger than that for Ppos and Pneg is greater than Ppas' the effect of
a low Ppos was obscured in Po. Because Ppos measures the agreement of fracture
definitions for positive classifIcation (fracture), it reflects the degree of agreement
between the numerators of the prevalence estimated by different fracture definitions,
Since the prevalence being compared has the same denominator, the comparability of
prevalence would depend on the difference in the numerator. Thus, Poshould be used as
a measure of overall (both positive and negative) agreement between fracture definitions,
57
while Ppos could be used as a measure of consistency between prevalence based on
different fracture definitions (especially when Ppas is quite different from Po)'
The prevalence effect expressed by PI has been reported in Chapter 3 to be partly
responsible for the difference between PABAK and kappa. This interpretation also applies
to the difference between Po and kappa, since PABAK is merely a linear transformation
of Po. The difference between kappa and Po (or PABAK) could also be attributed to the
downward adjustment made by the chance correction of kappa for the disparities in Ppos
and Pneg• When Po is high, but either Ppos or Pneg is low, kappa will 'penalize' the
inequality by making a downward adjustment. Although this 'penalty' has been
considered to be desirable for a single summary measure of agreement, the reason for
the penalty would be obscured if Ppos and Pneg were not reported with kappa (Byrt et aI.,
1993; Cicchetti and Feinstein, 1990).
In the present study, BI has been used both for assessing the effect of bias on kappa and
for identifying potential bias associated with a specific classification method. In addition,
BI could also be used for exploring the pattern of agreement /disagreement, which might
reflect systematic differences in classification between definitions. Like the McNemar
bias index of (b-c)/(b+c) (Feinstein, 1985), the value ofBI helps to evaluate whether one
definition tends to identify more fractures than another definition. For example, the
consistent negative signs of BI values for PV4 vs PV6 and PV4A vs PV6A listed in
Table 3.4 and Table 3.5 suggested that PV4 and PV4A consistently classified more study
58
units as fracture than PV6 and PV6A did, though part of the difference in the Japan
population could be attributed to bias.
4.1.4 POPULATION, DIAGNOSIS CUTOFF, AND AGREEMENT
In Chapter 3, all agreement measures based on the Hawaii population were reported to
be greater than those based on the other two populations (Table 3.3). This may be in part
due to better quality control in the HOS, where measurements of all vertebral fractures
were verified before data analysis. In any case, poor agreement between fracture
definitions does not necessarily mean the definitions themselves are quite different in
terms of classification; measurement errors could result in poor agreement.
A better agreement between fracture definitions was achieved when using -4 SD cutoff
instead of -3 SD (Table 3.3). Both positive and negative agreement were slightly
increased. One possibility is that the deformities with Z < -4 were so severe that they
could be easily identified by all fracture definitions. In contrast, some of the less severe
fractures are only identified by the less strict definitions and are missed by the more
strict definitions. Another possibility is that measurement error can cause some normal
vertebrae to be misclassified as fracture by the less strict -3 SD definitions, but to be
classified as non-fracture by the more strict definitions. However, the magnitude of
measurement error may not be large enough to cause misclassificationeven when the less
strict -4 SD definitions were used.
59
4.1.5 SIGNIFICANT TEST
It is possible to test the agreement measures, such as kappa and 7r, for significance
(Wickens, 1989; Zwick, 1988). However, this seems inappropriate since the agreement
measures, such as kappa, are usually used where the agreement is significant. The
purpose of agreement analysis is to estimate the degree of agreement, not to test the null
hypothesis of no agreement (Fisher and van Belle, 1993; Maclure and Willett, 1987).
Therefore, no significant tests for agreement measures were reported in this study.
4.2 VERTEBRA- AND AGE-SPECIFIC PREVALENCE OF VERTEBRAL
FRACTURES
4.2.1 VERTEBRA-SPECIFIC PREVALENCE
As shown in Figure 3.1 to Figure 3.6, the prevalence of vertebral fractures varied
according to the location within the spine. A substantial proportion of fractures occurred
between T7 and L2 with peak prevalence at the thoracolumbar junction (around T12) and
in the midthoracic region (around T8). This was mainly due to predominant occurrence
and bimodal distribution of anterior wedge fractures in the same region. The increased
frequency of vertebral fractures, especially anterior wedge fracture, in the midthoracic
and thoracolumbar junction were related to the anatomic and biomechanical
characteristics of the spine. The thoracic spine is subject to compressive forces following
60
----- -----------------
the line of gravity, which is anterior to the thoracic vertebrae. Since the normal thoracic
kyphosis is greatest at around the level of T8, flexion would induce the greatest
compressive loading at this level. The thoracolumbar junction is also subject to increased
compression force due to the articulation between the relatively rigid thoracic spine,
which is 'stabilized' by the thoracic cage, and the freely mobile lumbar segment, where
most of the flexion occurs (Cooper et aI., 1992; Hedlund et aI., 1989; Melton et at,
1988). Anterior wedging may further change the anatomic shape of the spine, in tum
producing additional compression of the vertebral bodies (Meltonet al., 1988; White and
Panjabi, 1990). Since vertebrae tend to move together as a group, the forces sufficient
to cause fractures may act on a group of vertebrae simultaneously and result in multiple
fractures (Melton et aI., 1988). This may account in part for the observation that
vertebral fractures are more frequent in specific regions than others.
The predilection of vertebral fractures for the midthoracic and thoracolumbar regions has
been observed consistently in other studies based on either prevalent or incident cases,
and regardless of sex, ethnic group, region/nation, population, diagnostic criterion, and
number of fractures (Cooper et aI., 1992; Hedlund et aI., 1989; Itoi et al., 1990;
Kleerekoper et aI., 1992; Krelner and Nielsen, 1982; Mann et aI., 1992; Mellstrom,
1993; Melton et aI., 1989; Melton et aI., 1993b; Patel et al., 1991; Sauer et aI., 1991;
Smet et aI., 1988). One possible explanation of this observation is that some underlying
anatomic and biomechanical characteristics discussed above are common to all human
beings. It is worth noting that similar distributions of vertebral fractures were seen in
61
both native Japanese women and Japanese-American women, suggesting that social and
environmental factors probably had little influence on these common characteristics.
Figure 3.7 shows that endplate fractures (based on -4 SD cutoff) could occur at any level
between T4 and LS, but occurred most commonly in the lumbar region. This observation
coincides with the previously reported findings (Itoi et al., 1990; Smet et al., 1988).
Smet et al. (1988) speculated that the thicker lumbar vertebral cortex and the normal
lumbar lordosis may resist anterior wedging. Therefore, the loss of trabecular bone may
only result in central compression.
Like anterior wedge fractures, crush fractures were also most commonly seen in the
midthoracic spine and about the thoracolumbar junction in Hawaii and Minnesota
populations (Figure 3.7). A similar observation has been reported by Hedlund et al.
(1989). However this was not seen in Japan population, in which crush fractures
occurred only at L1 and L3. In addition, crush fractures seemed more frequent among
Japanese-Americans and Caucasian women, compared to native Japanese women (Figure
3.7). This could be partly due to the difference in age distribution. In this study, the
average age of the women with crush fractures was almost always greater than seventy
years old, regardless of the fracture location within the spine. Among the three study
populations, about 27% of native Japanese women (AHS) were more than seventy years
old, compared with 39% in the ROS and 49% in the HOS. Since crush fractures seem
to occur predominantly after age seventy, the observed lower vertebra-specific prevalence
62
_.__ .._--_..-._._---- ---
of crush fractures among native Japanese women could be due to the confounding effect
of age. Obviously, other potential confounding factors, such as BMD, may also
contribute to and help account for the difference, though they could not be investigated
fully in this study because of limited statistical power.
4.2.2 AGE-SPECIFIC PREVALENCE
In the present study, overall fracture prevalence, single fracture prevalence, and multiple
fracture prevalence were estimated separately by age group. Age variation in disease
frequency is nearly universally observed. Age is a proxy for many age-related biological
mechanisms and the change in fracture prevalence with age reflects their joint effects on
fracture risk.
It has long been recognized that bone strength and fragility are closely related to BMD
(Christiansen et aI., 1993; Delmas, 1993; Melton 1993). Age is by far the most
important empirical determinant of bone mass (Riggs and Melton, 1986). Among
postmenopausal women, the effect of menopause is also partly captured by age. A strong
inverse relationship between age and bone mass have been observed consistently in a
number of cross-sectional and longitudinal studies (Gallagher et aI, 1987; Geusens et aI.,
1986; Hannan et aI., 1992; Mazess et aI., 1987; Mazess et al., 1990; Nilas and
Christiansen, 1987; Nilas et aI., 1988; Pacifici, 1993; Parfitt, 1988; Pouilles et al., 1994;
Sambrook et aI., 1987b; Schaadt and Bohr, 1988; Steiger et al., 1992; Vico et aI., 1992;
63
Wasnich et al, 1989). In women, age-related and menopause-related bone loss is the
major determinant of BMD in later life after the peak bone density was achieved in early
life. It has been reported that by age 90 years, women have lost 20% of their peak
cortical bone mass and 40-50% of their peak trabecular bone mass (Melton et aI., 1988).
Age-related bone loss may reflect the joint effect of several processes associated with
aging, such as decreased calcium absorption, age-related changes in some hormones(e.g.
estrogen, vitamin D, and parathyroid hormone), impaired coupling between bone forming
osteoblasts and bone resorbing osteoclasts, decreased physical activity and intensity of
mechanical loading (Gennari, 1993; Heaney, 1993c; Pacifici and Avioli, 1993; Riggs and
Melton, 1986). The accelerated bone loss beginning at or shortly before menopause has
been well documented and regarded as one of the most important factors in the
development of osteoporosis in women (Barzel, 1988; Geusens et aI., 1986; Richelson
et aI., 1984; Ross, 1994).
Considerable evidence indicates that age is also a risk factor of osteoporotic fracture
independent of BMD. This suggests that age may actually be a surrogate indicator of
additional age-related skeletal and non-skeletal factors not fully captured by the
measurement ofBMD (Hui et aI., 1988; Kanis, 1990; Wasnich, 1993). It is well known
that bone strength depends on both bone quantity and bone quality. There is some
evidence to suggest that bone strength declines with age independently of changes in bone
mass or density (parfitt, 1993b), suggesting age-related changes in bone quality also
contribute to the reduced bone strength with age. Vertebrae are comprised of
64
-_ .._- _._-- ._-
predominantly trabecular bone. With aging, vertebrae undergo progressive bone loss,
which influences not only bone quantity (the amount of mineralized tissue) but also bone
quality (the architectural structure and possibly the strength of mineral tissue)(Meunier,
1990; Mosekilde, 1994; Parfitt, 1992). The bone quality decreases as the result of loss
of critical trabecular connectivity and the accumulation of unremodelled fatigue
microdamage with aging (Schnitzler, 1993).
Among non-skeletal factors, fall-related trauma is the most common cause of fractures
among elderly persons and plays an important role in fracture pathogenesis (Melton,
1993; Parfitt, 1993b). It is well known that the liability to fall increases with age
(Cummings et aI., 1985; Downton, 1993). At least half of the falls among elderly
persons are related to definable organic dysfunction, and the proportion increases with
advancing age (Melton, 1993). A number of age-related risk factors for falls have been
identified including diminished gait and postural control, gait changes, muscular
weakness, decreased reflexes, reduced vestibular function, poor vision, postural
hypotension, confusion, and dementia. In addition, some age-related diseases have also
been reported to be associated with falls, such as Parkinson's disease and stroke
(Downton, 1993; Melton, 1993; Rubenstein and Josephson, 1993).
The observed increase of vertebral fracture prevalence with age might be explained, at
least in part, by the age-related decrease in bone strength and increase in frequency of
falls. Among age-related risk factors, age-related bone loss including postmenopausal
65
bone loss may play a very important role in explaining the increase in vertebral fracture
risk with age. It has been reported that bone mass may account for 75-85% of the
variance in ultimate strength of bone tissue (Christiansen et al., 1993). More and more
investigators consider low bone mass a necessary condition for an osteoporotic fracture
(Melton, 1993; Parfitt, 1993b). Since the extent of premenopausal trabecular bone loss
and the accelerated postmenopausal trabecular bone loss is much greater than the extent
of cortical bone loss (Riggs and Melton, 1986) and vertebrae are mostly composed of
trabecular bone, the prevalence of vertebral fractures may be influenced to a considerable
extent by age-related and menopause-related bone loss. In contrast, age-related liability
to fall seems to have much less contribution to the age-related vertebral fractures since
most fractures appear to result from loading of the vertebral column during normal daily
activities rather than a trauma (Melton, 1988; Melton et al., 1989).
In the current study, a rapid increase in prevalence of vertebral fractures was observed
from about 60-65 years old regardless of study population and fracture definition. This
rapid increase occurred about 10-15 years later than the mean age at menopause. One
likely explanation for this observation is that bone has a substantial safety margin. For
many women, even after the menopause, it may still take several years before their bone
mass is reduced to some critical value (fracture threshold), which is considered a
necessary condition for an osteoporotic type fracture (Parfitt, 1993b).
66
4.2.3 LOGISTIC REGRESSION ANALYSIS
Using HOS as the reference group, age-adjusted odds ratios were estimated by logistic
regression. By comparing the estimated odds ratios for native Japanese women with those
for Caucasian women, it can be seen that Japanese migrants manifested prevalence that
were more similar to that of the host country rather than that of the original country.
This is a strong indicator for the presence of environmental determinants of vertebral
fracture.
4.3 PREDICTORS OF SPINE BMD AND GENETIC-ENVIRONMENTAL
INTERACTION
4.3.1 POTENTIAL PREDICTORS OF SPINE BMD: MULTIPLE REGRESSION
ANALYSES BASED ON JAPAN AND HAWAII POPULATIONS
Any differences in risk factors for vertebral fracture between native Japanese and
Japanese migrants could be responsible for the observed difference in vertebral fracture
prevalence between the two study populations. As was shown earlier in Chapter 3, native
Japanese and immigrant Japanese-American women differed in several aspects including
spine BMD. It is generally agreed that low bone mass is a major risk factor for
osteoporotic fractures, and many other factors affect fracture risk through their effects
on bone mass. In the present study, multiple linear regression analysis was used to
67
explore the relationship between spine BMD and other factors and to identify the factors
that were responsible for the difference in BMD.
A number of studies have shown a positive association between BMD and body size, as
measured by weight, body mass index etc. (Birkenhager et at, 1991; Clark et at, 1991;
Edelstein and Barrett-Connor, 1993; DeSimone et at, 1989; Kelly et aI., 1991; Kin et
at, 1991, 1993; Lanham et at, 1990; Liel et at, 1988; Nordin et aI., 1993; Picard et
at, 1988; Pouilles et at, 1994; Slemenda et at, 1990; Sowers et at, 1991b; Stillman
et aI., 1991; Vico et aI., 1992). In the present study, weight was found to be positively
and significantly associated with BMD after adjustment for study population, age, height,
cause of menopause, and the interaction between cause of menopause and study
population. At least, two mechanisms have been proposed to explain the weight effect:
1) increased circulating estrogens due to peripheral aromatization of adrenal androgens
by adipose tissue, and 2) additional strain of weightbearing. Both mechanisms could
independently contribute to bone density (Edelstein and Barrett-Connor, 1993).
Adipose tissue contains enzymes which convert serum androgens to estrogens. Since this
is a major source of estrogens for postmenopausal women, investigators have
hypothesized that women with more adipose tissue tend to produce more estrogen, which
in turn.may reduce postmenopausal bone loss (Edelstein and Barrett-Connor, 1993; Vico
et at, 1992; Wasnich et al., 1989). This hypothesis was supported by the finding that
68
. ----_ ....._ .._.- .._-_.._--
the association between obesity and BMD was observed among postmenopausal women
but was not seen among perimenopausal women (Ribot et al. 1987).
There is considerable evidence to suggest that mechanical loading is also essential for
maintaining the quality and quantity of bone. Although the biologic basis for the
osteogenic response to loading is still largely unknown, it is generally agreed that" 1) In
the absence of mechanical loading and/or gravitational force there is a rapid loss of bone,
2) When stress on a bone is increased beyond that to which it has adjusted, there is
usually an increase in bone mass"(Drinkwater, 1993). Several studies have reported that
body size variables were more highly correlated with BMD at weight-bearing sites than
at non-weight-bearing sites, suggesting that there is a mechanical component related to
the weight effect on BMD (Edelstein and Barrett-Connor, 1993; Liel et al., 1988).
Edelstein and Barrett-Connor (1993) recently examined the relative contribution of eight
measures of body size on BMD and found that among women total weight explained the
largest amount of adjusted BMD variance at all weight-bearing sites. Body mass index
was also an excellent predictor of BMD at the spine, although it accounted for less
variance of BMD. Height was not independently related to BMD. The results of the
present study are in accord with their fmdings. Weight appeared to account for more of
BMD variance than body mass index and consistently contributed to BMD before and
after adjustment for other variables. In contrast, height explained much less BMD
69
variance, as compared with weight and body mass index, and was no longer significant
after adjusting for other variables in the model.
The inverse association between age and BMD was nearly universally observed in all
bone-mass-related studies including the present study. Several potential mechanisms
explaining the age-related bone loss has been discussed earlier in Section 4.2.2 and will
therefore not be repeated here. It will be noted only that in addition to current
chronological age, age at the time of menarche and/or menopause may provide additional
information about influences of sex hormones, which is difficult to estimate directly.
Several authors have reported that age at menarche and age at menopause were predictors
of bone mass (Fox et aI., 1993; Gardsell et aI., 1991; Kin et aI., 1993; Kritz-Silverstein
and Barrett-Connor, 1993; Pouilles et aI., 1994; Rosenthal et aI., 1989; Seimiya et aI.,
1993; Smith, 1967). Others have found that duration between menarche and menopause
and time elapsed since menopause were associated with bone mass (Georgiou et aI.,
1987; Kritz-Silverstein and Barrett-Connor, 1993; Nordin et aI., 1993; Pouilles et aI.,
1994; Vico et aI., 1992). It should be noted that duration between menarche and
menopause is a linear combination of menarche age and menopause age. Similarly, time
elapsed since menopause can be expressed as a linear combination of menopause age and
current chronological age. Thus, the apparent difference in regression models does not
necessarily mean the underlying biological mechanism is different. Actually, to a large
extent, these fmdings may reflect the same biological effect: the impact of change in sex
hormone level on bone mass. Initiation of menses at puberty results from a surge of
70
estrogen, which also stimulates bone growth. On the other hand, cessation of menses at
onset of ovarian failure is accompanied by estrogen deficiency, which leads to
postmenopausal bone loss. These changes in estrogen level appear to affect the spine
more than other skeletal sties (Slemenda, 1993).
In the present study, duration between menarche and natural menopause and age at
natural menopause were found to have significant associations with spine BMD in
separate regression models after adjustment for age, weight, and height. When current
chronological age and years between menarche and menopause were simultaneously
included in the same model, it is necessary to recognize that the apparent effect of years
between menarche and menopause reflected not only the influence of total period of
higher estrogen exposure before menopause, but also the impact of total period of
estrogen deficiency after menopause. For a postmenopausal woman of given age, the
longer the duration between menarche and menopause, the shorter the time elapsed since
menopause. As pointed out by Fox et al., the current BMD is, to a substantial extent,
influenced by the estrogen deficiency after menopause. Therefore, the time elapsed since
menopause probably has much more influence on the BMD of postmenopausal women,
compared to the effect of duration between menarche and menopause (Fox et al., 1993).
However, for postmenopausal women, the current bone mass is largely determined by
the peak bone mass achieved in early life and age-related and menopause-related bone
loss. A longer duration between menarche and menopause indicates longer exposure to
estrogen, which may increase the bone gain and decrease the age-related bone loss during
71
early life. There is some evidence to suggest that peak bone mass attained in early life
may also be responsible for the lower BMD found in patients with fracture (Johnston,
1993; Seeman et aI., 1993). Although there is some discrepancy in the literature
regarding the relative contribution of bone mass gained in early life and postmenopausal
bone loss, it is likely that both mechanisms contribute to the current bone mass (Seeman
et aI., 1993). Thus, it should be clear that both duration between menarche and
menopause and the time elapsed since menopause may have impact on current BMD,
irrespective of which variables is incorporated in the model. Similar explanation should
be given when age at menopause and the current age are simultaneously included in the
model, since a later menopause for postmenopausal woman of given age implies not only
a longer exposure to estrogen but also a shorter period of estrogen deficiency.
Several studies have reported an inverse relationship between age at menarche and BMD
(Fox et aI., 1993; Kin et aI., 1993; Kritz-Silverstein and Barrett-Connor; 1993; Rosenthal
et aI., 1989; Seimiya et aI., 1993; Smith, 1967). It has been hypothesized that an early
menarche age would stimulate bone growth earlier and afford a protective effect on BMD
(Fox et aI., 1993). However, other studies failed to demonstrate the effect of age at
menarche on bone mass (Dequeker et aI., 1991; Hansen, 1994; Sowers et al., 1991c).
Kritz-Silverstein and Barrett-Connor (1993) found that in separate regression models, the
proportion of spine BMD explained by age at menarche (0.35%) was about 10 times
smaller than that explained by age at menopause (3.13 %) or years between menarche and
menopause (3.96%) after adjustment for age and other covariates. This finding suggested
72
that the influence of age at menarche on postmenopausal BMD, if any, was very weak.
The present study was consistent with this finding. We found that in separate regression
models, age at menopause and duration between menarche and menopause have
significant associations with spine BMD, but the effect of menarche age on BMD was
not strong enough to reach statistical significance. However, age at menarche may be a
stronger predictor among premenopausal women.
Another two reproductive factors explored in this study were lactation and number of live
births. Changes in maternal hormones and calcium requirement during pregnancy and
lactation have raised the question of whether pregnancy and lactation influence BMD
(Feldblum et aI., 1992; Fox et at, 1993; Hoffman et aI., 1993; Sowers, et aI., 1991a).
A number of studies have been reported on the relationship between BMD and number
of pregnancies or number of births. It is unfortunate that these studies have produced
contradictory results. Some have reported a positive association (Aloia et aI., 1983; Fox
et aI., 1993; Murphy et aI., 1994; Sambrook et aI., 1987a), whereas others have found
either an inverse association (Dequeker et aI., 1991; Hreshchyshyn et aI., 1988a; Kesson
et aI., 1947) or no association (Dequeker et aI., 1991; Hansen, 1994; Johnell and
Nilsson, 1984; Kritz-Silverstein et aI., 1992; Lindquist et aI., 1981; Picard et al., 1988;
Shaw, 1993; Sowers et al., 1985; Wasnich et aI., 1983). Data regarding the relationship
between BMD and lactation have also yielded inconsistent and conflicting results. Several
studies suggested a beneficial effect (Aloia et aI., 1983; Feldblum et aI., 1992; Hansen
et aI., 1991; Hreshchyshyn et aI., 1988a; Lamke et aI., 1977; Stevenson et aI., 1989),
73
but others demonstrated a detrimental effect (Atkinson and West, 1970; Drinkwater and
Chesnut, 1991; Goldsmith and Johnston, 1975; Lissner et al., 1991) or failed to show
any association between lactation and BMD (Fox et al., 1993; Hansen, 1994; Johnell and
Nilsson, 1984; Kritz-Silversteinet al., 1992; Shaw, 1993; Sowers et al., 1985; Wasnich
et aI., 1983).
A number of factors must be considered in evaluating the results of these studies. One
problem has been failure to control for potential confounding factors, such as age, time
since menopause, body size etc. (Kritz-Silverstein et aI., 1992). Many of the previous
studies were based on premenopausal or even currently lactating women, and thus could
only evaluate short-term effects. The present study focused on postmenopausal women
and found that the spine BMD was neither associated with number of live births nor
related to lactation after adjustment for other covariates.
Our results are in accord with those of Kritz-Silverstein et al. (1992), who recently
reported that among postmenopausal women, reproductive history (number of
pregnancies and number of live births) and breast feeding were not significantly
associated with BMD at the wrist, radius, hip, and spine after adjustment for potential
confounding factors. Similar results were reported by Shaw (1993) among 266 Taiwan
women aged 15 to 83 years. In another study focusing on elderly women, Fox et al.
(1993) also found no associationbetween duration of breast-feeding and radial BMD. In
contrast to the present study, they found that the number of births was positively related
74
to distal radius BMD, although no significant association was demonstrated between
parity and proximal radius BMD. A study by Berning et al. (1993) also focused on
postmenopausal women, but yielded conclusions different from those reported by Fox et
al. Using Quantitative Computed Tomography (QCT), they found that the total duration
of lactation rather than parity was associated with trabecular BMD of the spine. In an
animal experiment using monkeys, Lees et al. (1993) also demonstrated that pregnancy
did not affect spine bone mineral content (BMC) but lactation could cause spine bone
loss. Right now, there is no consistent and convincing explanation for the different results
of these studies. Several studies have suggested that the effect of lactation might be
transient (Kalkwarf et aI., 1993; Kent et aI., 1990; Lamke et aI., 1977; Sowers et aI.,
1993). The data regarding the effect of parity on postmenopausal BMD were far from
conclusive. More detailed studies will be needed to determined if parity and lactation
have long-term effects on postmenopausal BMD.
Current smoking and alcohol use were the only two life-style factors considered in the
present study and neither of them were found to be related to spine BMD. As with parity
and lactation, the association between smoking or alcohol use and BMD is also uncertain.
Many studies on cigarette smoking suggested that smokers tend to have lower bone mass
and higher risk of fracture (Aloia et aI., 1985; Daniell, 1976; Hansen et aI., 1991; Kelly
et aI., 1991; Kin et aI., 1993; KraIl and Dawson-Hughes, 1991; Paganini-Hill et aI.,
1981; Seeman et aI., 1983; Slemenda et aI., 1989; Slemenda et aI., 1990; Stevenson et
aI., 1989; Williams et aI., 1982); however, others failed to show evidence of an
75
independent detrimental effect of smoking on bone mass (Cheng et al., 1991; Johnell and
Nilsson, 1984; Lissner et al., 1991; McDermott and Witte, 1988; Picard et al., 1988;
Shaw, 1993; Sowers et al., 1985). Several hypotheses have been proposed, but the exact
underlying mechanism remains largely unknown. Available evidence suggests that the
apparent effect of smoking could be mediated by other factors such as body weight
(Lindquist and Bengtsson, 1979; McDermott and Witte, 1988; Willett et aI., 1983) or
age at menopause (Brambilla and McKinlay, 1989; Jick and Porter, 1977; Kaufman et
al., 1980; Lindquist and Bengtsson, 1979; McKinlay et al., 1985). In addition, smoking
may influence the metabolism of estrogens (Jensen et aI., 1985; Michnovicz et al.,
1986). However, it would be premature to conclude that smoking operates through these
mechanisms. Indeed, results vary from study to study. A good example is the fmdings
from the Framingham Osteoporosis Study, in which BMD for 458 elderly non-estrogen
taking postmenopausal women was assessed at radius, ultradistal radius, femur, and
spine. Neither smoking between ages 20-30 (near the time of peak bone mass) nor recent
smoking (10 years preceding the BMD measurement) were found to have adverse effects
on BMD measured at any sites (Kiel et al., 1993).
An adverse effect of alcohol use on BMD has also been observed in several studies, but
again not in all studies (Laitinen, 1993). A toxic effect of alcohol on cultured osteoblasts
is probably by far the most convincing and the most frequently cited evidence. Although
a variety of potential mechanisms have been suggested by animal experiments, human
epidemiological studies have yielded conflicting results. According to Laitinen's recent
76
review (1993), most human studies were based on men rather than women. Studies
concerning the effect of alcohol on postmenopausal BMD were more limited. Some of
the human studies have been criticized as poorly controlled. Actually, a number of
human studies based on the newer, more precise bone measurement methods have shown
no difference in BMD at different sites among alcoholics (Laitinen, 1993). Kelly et al.
(1991) even found alcohol consumption had a protective effect on BMD, though tobacco
consumption had an adverse effect on BMD in men. As pointed by Cummings et al.
(1985), some positive findings may be mediated or confounded by poor nutrition,
reduced body weight, cigarette smoking, liver disease or other factors. Among the few
studies involving premenopausal and/or postmenopausal women, Stevenson et al. (1989)
reported up to 13% lower BMD of proximal femur in premenopausal women with more
than two standard drinks per day (> 140g/week) as compared with women with less than
one standard drink per day « 70 g/week). Others have found no association between
alcohol use and bone mass (Cheng et aI., 1991; Shaw, 1993; Slemenda et aI., 1990). At
least two studies have shown alcohol consumption was positively associated with bone
mass (Angus et aI., 1988; Hansen et al., 1991). One limitation of the present study is
that only current status of smoking and drinking (yes/no) were used for analysis. This
is mainly because among female subjects in HOS, no information on 'age of starting
smoking' and 'age of starting drinking' is available for estimating the cumulative
exposure.
77
In sum, in the present study, weight and age at natural menopause or duration between
menarche and natural menopause were found to be predictors for spine BMD. Their
effects were independent of age. These results were supported by the fact that body size
(measured by body weight or body mass index etc.) and menopause-related variables
(such as age at menopause, duration between menarche and menopause, and time elapsed
since menopause) have been consistently related to BMD in many studies, but the data
on the association between bone mass and other possible risk factors (e.g., smoking) was
conflicting and inconclusive. Our results of regression analysis, however, do not rule out
the possibility that some 'non-significant' variables may still affect spine BMD indirectly
via the variables in the model.
The total effect of a factor on bone mass is the sum of its direct and indirect effect(s).
Depending on the underlying biological mechanism, a factor mayor may not have a
'direct' effect on bone mass. Moreover, a factor may have more than one indirect effect
on bone mass. In multiple regression analysis, which is by far the most frequently used
method for exploring the relationship between bone mass and the suspected risk factors,
only 'direct' effects are estimated, provided appropriate confounding factors and
intervening variables have been controlled. The potential indirect effects of many factors
have essentially been overlooked by most, if not all, of the investigators. Of course, both
direct and indirect effects are relative in a sense that they are dependent on the model
specification. Stated in another way, not including the intervening variable(s) in the
model would make the effect of the independent variable on dependent variable 'direct';
78
but it does not mean there are no intervening variables in the real world (Davis, 1985).
The important thing to bear in mind is that tracing out direct and indirect effects not only
tell us the total effect but also contribute to our causal understanding of the underlying
biological process.
There is some evidence suggesting that obesity is positively associated with childbearing
and parity (Forster et aI., 1986; Heliovaara and Aromaa, 1981; Newcombe, 1982; Noppa
and Bengtsson, 1980). As mentioned earlier, several studies have found smoking was
related to early age at menopause (Brambilla and McKinlay, 1989; lick and Porter,
1977; Kaufman et aI., 1980; Lindquist and Bengtsson, 1979; McKinlay et aI., 1985).
With regard to the relationship between parity/pregnancy and age at menopause, some
authors reported a positive association (McKinlay et aI., 1972; Soberon et aI., 1966;
Stanford et aI., 1987), while others found no association (Brambillaand McKinlay, 1989;
Goodman et aI., 1978; Masters and Johnson, 1966). None of these studies are
conclusive, and the underlying mechanismcould be very complicated. As pointed out by
Weg (1987), any factor that influences the reproductive history of the woman is a
potential modifier of age at menopause. Few published studies have examined the
potential indirect influence of these processes on bone mass. Additional research is
necessary to study not only the magnitudes of the relationships, but also how the
underlying causal system works.
79
4.3.2 EFFECTS OF ENVIRONMENTAL AND GENETIC FACTORS
It is well known that most diseases are neither purely genetic nor purely environmental
in etiology, but dependent on their interaction. The etiology of osteoporosis and related
fractures may also be explainable in terms of the genetic-environmental interaction
concept.
As was shown earlier, both body size and duration between menarche and menopause/age
at menopause were independent predictors of spine BMD. These variables also changed
with birth year. The observed secular trends are strong indicators for the presence of
environmental effects, since these rapid changes with successive birth cohorts over a
period of a few decades are difficult to explain on the basis of genetic factors. For
example, the rapid trend towards increasing adult height among the native Japanese after
World War II have been attributed to non-genetic factors (Susser, 1987). Probably the
increase in adult height among both native Japanese and Japanese-American women was
mainly due to the improvement in nutrition and living conditions and changes in life style
during the last few decades. The same holds true for the increase in weight, although age
effects may also play a role in the cross-sectional profiles of weight vs birth year which
were used in the analyses here.
One important observation of the present study is the temporary increase in age at
menarche among native Japanese subjects born in late-1920s and early-1930s (Figure
80
3.14). A similar fluctuation was also observed as reduced adult height among the same
group of birth cohorts in Hiroshima (Figure 3.11). These 'abrupt' changes implied
changes in their determinants which must reflect a transient environmental cohort effect.
The birth cohorts between late-1920and early-1930s consists of those women who shared
the common experience of World War II and atomic bomb exposure during childhood
and adolescence. Their menarche should have occurred during or immediately after the
wartime. Thus, it is most likely that the poor nutrition, disease, and physical and
psychological stresses of that period were jointly responsible for the observed transient
fluctuation in secular trend. Our results are in accord with those by Hoel et al. (1983),
who also found a similar temporary reversal in the time trend of age at menarche based
on approximately 21,000 atomic bomb survivors in Hiroshima and Nagasaki, suggesting
that our fmding is not related to sampling error or selection bias.
A secular trend toward an earlier age of menarche has been observed in many
populations. Most authors agree that nutrition, economic status, and urbanization are
among the most likely explanations for the observed downward trend (Goodman et al.,
1983; Wyshak and Frisch, 1982). The fact that girls of lower socioeconomic status living
in developing countries tend to have a later menarche suggests an important role for
environmental factors. The reversal time trend observed in Bangladesh as the result of
adverse economic conditions and malnutrition also indicates that age at menarche is
influenced by environmental factors (Wyshak and Frisch, 1982). In the present study, we
found a rate of decline in age at menarche of 0.55 years per decade, which was
81
approximately twice as great as the corresponding estimates of 0.2-0.3 years per decade
in Europe and the United States (Wyshak and Frisch, 1982). Our data are supported by
the results of Hoel et aI. (1983), who also found that age at menarche of Japanese women
living in Hiroshima and Nagasaki decreased more rapidly with year at birth, as compared
with the secular trend in the United States. Similar findings were also reported for Latin
American populations (Goodman et aI., 1983). Goodman et aI. (1983) compared the
downward trends towards earlier menarche between Oriental groups and Caucasians
living in Hawaii and found that age at menarche among Japanese and Chinese declined
at a rate of 0.5 years per decade, which was more than twice as rapid as that among
Caucasians (0.2 year per decade). Since these three ethnic groups were comparable in
both current socioeconomic status and climatic environment, past nutritional differences
and perhaps some cultural factors were thought to be responsible for the different rate
of change in age at menarche. In a recent study, Merzenich et aI. (1993) found that fat
intake was associated with accelerated menarche, while increased sports activity was
associated with a delay in menarche. These data are consistent with fmdings by other
authors (Kissinger and Sanchez, 1987; Meyer et aI., 1990; Moisan et aI., 1990) and
again indicate that nutritional and life-style habits can impact the onset of menarche.
In contrast to the downward secular trend in age at menarche, there has been a steady
increase in mean age at menopause in both native Japanese and Japanese-American
women. According to Hoel et aI. (1983), the median age at natural menopause for
Japanese women living in Hiroshima and Nagasaki increased 1.2 years from the 1880-
82
1899 birth cohort to the 1910-1914 birth cohort. Our data indicated that this increasing
trend continued in the succeeding birth cohorts. Since age at menarche has been falling,
it has been postulated that the age at menopause has also increased over the last 100
years. However, to date a number of studies based on populations in Western countries
have not provided finn evidence for this hypothesis. A review by Gray indicated that the
median age at menopause in Western industrialized societies has been remarkably
constant, around 50 years (Khaw, 1992). Little information is available on the mean or
median age of menopause in non-Westernized populations. Among the few studies from
developing countries, a very low median age at menopause has been observed in New
Guinea. One group of malnourished New Guinea women with mean height of 144.5 em
and mean weight of 40.22 kg were found to have a much earlier menopause (median age
= 43.6 years), while another group of better nourished New Guinea women with mean
height of 153.8 em and mean weight of 51.14 kg were found to have a later menopause
(median age = 47.3 years). The low median menopausal age of 44.0 years observed in
Punjab, India also points to poor nutrition as the probable explanation for premature
menopause (Weg, 1987). In the present study, the observed secular trends in age at
menopause among both native Japanese and Japanese-American women were consistent
with gain in height and weight over the last few decades, suggesting that non-genetic
factors, such as changes in nutrition, living conditions, health care, and life style habits
could possibly postpone the onset of menopause.
83
It is well known that not only natural menopause but also artificial menopause have
significant influences on bone mass (Cann et aI., 1980; Dalen et aI., 1974; Horsman et
aI., 1977; Hreshchyshyn et aI., 1988b; Kritz-Silverstein and Barrett-Connor, 1993;
Rezakovic et aI., 1981). What is noteworthy is the similarity in proportion of artificial
menopause between Japanese immigrants and American women. As was shown earlier,
successive generations of Japanese-Americans had constant proportions of artificial
menopause with minor variation from 27.9 to 30.8 percent. These data are in accord with
those of MacMahon and Worcester (1966), who reported that about 25-30% of U.S.
women who survive to the end of the menopausal age period have had their menopause
as the result of an operation. By contrast, native Japanese women had a very low
proportion of artificial menopause in the earlier birth cohorts, but it increased steadily
with successive generations. The relative frequency of artificial menopause is determined
by both medical and cultural factors, and perhaps other factors. Thus, our results could
be explained as another indication that the Japanese immigrants, at least to a certain
degree, have shifted towards the host population and departed from the population of
origin in terms of environment, life style, and culture.
Unlike a typical migrant study, data on the host population were not always available in
the present study. However, comparing Japanese immigrants only with native Japanese
could still help identify environmental factors with the genetic component held relatively
constant. As presented earlier, immigrant Japanese-American women differed from their
native Japanese counterparts in several aspects. Of particular interest are those
84
differences in body size, cause of menopause, age at menopause, and age at menarche,
since these variables explained much of the difference of age-adjusted spine BMD
between the two study populations.
On the average, immigrant Japanese-American women were taller, heavier, and had a
higher body mass index than Japanese women of similar birth year. The difference in
age-adjusted spine BMD between native Japanese and Japanese-Americans was reduced
by adjustment for body size, especially for weight. Similar fmdings have been reported
by other authors. Ross et al. (1989) compared the proximal radius BMC and BMA
(BMC/bone width) for Japanese-American men and women with those for their
counterparts living in several areas of Japan. They found that Japanese-Americans had
greater values of BMC and BMA, and a larger body size, relative to native Japanese.
They also noticed that the differences in BMC and BMA were reduced after body size
was taken into account, suggesting environmental factors may be responsible for some
of the observed difference in bone mass. Kagan et al. (1974) reported that Japanese men
living in California and Hawaii were similar in height and weight, but taller and heavier
than native Japanese in Hiroshima. The genetic markers of immigrant Japanese
Americans, as indicated by blood group patterns, appeared to be similar to those of
indigenous Japanese, but different from those in a typical Caucasian population.
Substantial differences were noted between native Japanese and their immigrant
counterparts living in Hawaii and California for environmental factors, such as diet
composition and smoking patterns. In a recent study, Kin et al. (1993) found that US-
85
born Japanese-American women had the highest age-adjusted BMD at spine, femur neck,
Ward's triangle, trochanter, and total body; native Japanese had lowest age-adjusted
BMD at these sites; and BMD levels among Japan-born Japanese-American were in
between. Furthermore, the US-born Japanese-American women had BMD values
equivalent to those of white normals at the spine and femur. In accord with other studies,
they also found US-born and Japan-born Japanese-Americans had larger body size than
native Japanese, which explained some of the differences in BMD. In a somewhat similar
study, Nomura et al. (1989) reported that among Japanese-Americans living in Hawaii,
US-born men and women tended to have higher BMC at the calcaneus, distal radius, and
proximal radius, compared to those of Japan-born men and women. As reported here,
the differences in body size were among the variables which accounted for the observed
difference in BMC between study populations. Recently we also found age-adjusted spine
BMD was also significantly higher among US-born Japanese women living in Hawaii
than their Japan-born counterparts (unpublished data).
In the present study, Japanese-American women were also found to have a lower mean
age of menarche, a higher mean age of natural menopause, and thus a longer duration
between menarche and natural menopause, relative to native Japanese women living in
Hiroshima. On the other hand, the averages for age at menarche among Japanese
American subjects in the present study were similar to those estimated among U.S. white
women of similar birth year (Hoel et al., 1983). Goodman et at. (1978) also reported that
both mean age at menarche and mean age at menopause were strikingly uniform among
86
Caucasian, Japanese, Chinese, and Hawaiian women living in Hawaii. All of these
fmdings suggested that, like the observed secular trends, the differences between native
Japanese and Japanese-American women in age at menarche and menopause were at least
partly due to the nutritional and other environmental differences. As was shown earlier,
the difference in spine BMD was further reduced as the result of adjustment for duration
between menarche and menopause.
From the foregoing discussion, it should be evident that environmental factors may
influence bone mass indirectly through their effects on body size and duration between
menarche and menopause, and thus contribute to differences in BMD between native
Japanese and Japanese-Americans. However, this does not rule out the importance of
genetic contributions to bone mass. The evidence of genetic influence on bone mass
includes: (1) higher correlations in BMD and BMC were observed in monozygotic (MZ)
twins as compared to dizygotic (DZ) twins; (2) offspring studies have shown that
daughters of women with osteoporosis tend to have lower spine bone mass thandaughters
of women with normal bone mass; (3) bone turnover was found to correlate more
strongly in MZ than in DZ twin pairs, which was supported by the observation that
longitudinal changes in BMD correlated more strongly in MZ than DZ twin pairs
(Dequeker et aI., 1987; Flicker et aI., 1993; Kelly et aI., 1993; Krall and Dawson
Hughes, 1993; Lutz, 1986; McKay and Bailey, 1993; Seeman et aI., 1989; Slemenda et
aI., 1991; Tylavsky et aI., 1989). However, the relative contributions of genetic and
environmental effects are poorly understood at present.
87
Several studies indicated that genetic factors contributed about 80%-90% of the total
variance in BMD. These findings implied that only a small amount of the variance in
BMD could be due to environmental influences. Some other studies, however, have
shown that up to 40 % of the variance in BMD could be attributed to environmental
effects, such as dietary calcium, physical fitness and strength (Kelly, et al., 1990). These
conflicting fmdings could be explained either by the difficulty of separating genetic
effects from environmental effects in twin and offspring studies and/or by the existence
of interaction between genetic factors and environmental factors.
To date, most of the evidence of genetic effects on bone mass comes from twin or
offspring studies. Measures of heritability were frequently overestimated in these studies
due to the fact that environmental covariances were often greater for MZ than for DZ
twins and that parents and offspring might share some common environmental factors
(Flicker et aI., 1993; Krall and Dawson-Hughes, 1993; Slemenda et aI., 1991). In a
recent study, Krall and Dawson-Hughes(1993) reported that only 46-62%, instead of 80
90%, of variance in BMD was attributable to heredity after adjustment for age, body
size, and important environmental factors.
Interaction between genetic and environmental factors provides another possible
explanation for the conflicting observation that the cumulative contributions of genetic
and environmental factors apparently explain more than 100% of observed variance of
BMD. It has been suggested that BMD reflects the interplay between genetic and
88
environmental factors (Kelly et aI., 1990). Some investigators have proposed that certain
behaviors, such as food preferences and smoking, may be strongly influenced by an
individual's genetic make-up (Kelly et aI., 1990; Slemenda et al., 1992). Others believe
that certain individuals may be more susceptible to specific environmental or life-style
factors, according to their genetic constitution (Kelly et aI., 1993). Another hypothesis
suggested that environmental factors interact to allow or prevent full expression of BMD
genotype (Kelly et al., 1990).
No matter to what extent genetic and environmental factors contribute to the variance of
BMD, and how they interact with each other, there is little doubt that both genetic and
environmental factors have significant influences on BMD. As discussed earlier,
environmental factors, such as nutritional factors, could have significant impact on body
size, which in tum might affect bone mass. However, environmental effects cannot
eliminate the fundamental importance of genetic influence (Heaney 1993a). Similarly,
nutritional and other environmental factors may also influence bone mass through their
effects on the cumulative estrogen exposure between menarche and menopause. But again
their effects may be only responsible for the variation in duration between menarche and
menopause around an individual's genetically determined level. For example, the median
age at menopause in most Western industrialize societies was remarkably constant around
50 years with a wide range between 35-59 years (Khaw, 1992). It has been suggested
that the consistency of median age at menopause implied a built-in genetic plan, and the
89
wide range was a reflection of the multiple environmental factors that could modify the
genetic potential (Weg, 1987).
Although both genetic and environmental factors are involved, only environmental factors
including life-style factors are open to intervention. Factors increasing the body size
and/or duration between menarche and menopause have important implications since the
former have a greater impact on weight-bearing bones than non-weight-bearing bones(
Edelstein and Barrett-Connor, 1993) and the latter have a stronger influence on
cancellous bones than cortical bones (Vico et aI., 1992). Together, these observations
suggest that factors closely associated with body size and duration between menarche and
menopause may have significant contributions to spine BMD, which supports our
findings. In the present study, body size and duration between menarche and menopause
were found to be important determinants of spine BMD" Since Japanese tend to have a
smaller body size and lower estrogen levels both pre- and post-menopausally than
Caucasian (Khaw, 1992; Kin et aI., 1993; Shimizu et aI., 1989), it may be desirable to
amplify the positive environmental influence among Japanese women so as to attain their
full genetic potential with respect to bone density.
90
4.4 PREDICTORS OF SPINE FRACTURES: LOGISTIC REGRESSION
ANALYSES BASED ON JAPAN AND HAWAII POPULATIONS
The discussion thus far has been concerned with the influence of environmental and
genetic factors on BMC or BMD (either directly, or through their effects on body size
or reproductive variables). As pointed out earlier, until recently, almost all studies have
focused on the relationship between bone mass and age, body size, life-style, or
reproductive variables. Few investigators have adequately studied these factors in terms
of their effects on fracture risk independent of their effects on bone mass. In the present
study, associations with prevalent spine fracture were analyzed by logistic regression,
using age, spine BMD, and variables related to body size, reproductive history, smoking
and drinking as possible predictor variables. In separate logistic regression models, age
at natural menopause and duration between menarche and natural menopause were found
to be significantly associated with prevalent spine fracture after adjustment for age and
BMD. No significant associations were found between prevalent spine fracture and other
reproductive variables, factors regarding body size, smoking, alcohol use, and radiation
exposure after multiple adjustment.
It is known that spine BMD is a major determinant of spine fracture. As noted earlier,
age and duration between menarche and natural menopause or age and age at natural
menopause had significant associations with spine BMD. In the current logistic regression
analyses, these variables were found to be significantly associated with prevalent
91
vertebral fracture after adjustment for spine BMD, suggesting that age-related and
menopause-related mechanisms had independent effects on spine fractures that were
distinct from BMD. Amongpostmenopausal women, changes in bone quantity with aging
and rapid postmenopausal bone loss must inevitably result in decrease in bone quality,
since changes in bone architecture occur with the reduction in BMD. Age-related
osteocyte death may also lead to hypermineralization and brittleness of bone (Schnitzler,
1993). As discussed earlier, the irreversible loss of trabecular connectivity and the
accumulation of unremodelled fatigue damage with age also have significant influences
on bone quality. All of these changes in bone quality and the corresponding decrease in
bone strength could partly be captured by age and age at natural menopause or age plus
duration between menarche and menopause since they are age- or menopause-related.
This is supported by the observation that bone strength decreases with age faster than
bone mass. However, Parfitt has argued that this observation may not justify the
conclusion that there is a density-independent component of bone strength because the
contribution of bone quality to fracture risk is likely captured by BMD measurement
(Parfitt, 1992, 1993a). In contrast, Kanis (1990) stressed that although bone mineral
measurements, as a major fracture determinant, could give an estimate of fracture risk,
the precision of this estimate could be improved by considering additional data, such as
age, which is presumably an index of other skeletal and extraskeletal factors not captured
by the measurement of density.
92
The results of the present study are consistent with those of Kanis (1990), Melton et at.
(1993a), and Ross et aI. (1991a), who reported an independent effect of age on spine
fracture. Our findings are also in accord with those of Gardsell et at. (1991), who found
that age at menopause had effect on fracture risk that was independent of age and BMC.
4.5 POTENTIAL LIMITATIONS OF THE STUDY
This study has several potential limitations. The results presented in this thesis are cross
sectional, which usually does not establish the temporal sequence of events necessary for
making causal inferences. The technique of vertebral dimension measurement might not
be completely consistent among the three study populations, which could in tum bias the
estimates of difference in fracture prevalence between study populations. In the present
study, means and standard deviations of vertebral dimensions were calculated separately
for each population. Thus, if there were any systematic differences in vertebral
measurement among the three study populations, they should be cancelled out when
calculating the standard Z scores. However, there might be some undetected sources of
bias which could not be adequately controlled by this method.
In this study, comparison of possible risk factors between native Japanese and Japanese
American migrants strongly suggested the presence ofenvironmental determinants, which
explained much of the differences in both BMD and prevalence of spine fractures
observed between the two study populations. As mentioned earlier, these fmdings were
93
supported by several other migrant studies. However. inferences made based on migrant
studies may be subject to potential biases and limitations. Migrants are often self-selected
and may differ from the original population in age, level of education, occupation,
religion, social and economic factors, and, most importantly, general health status
(Khoury et aI., 1993). Efforts were made in the present study to adjust for some
important variables and their contribution to the observed differences between the migrant
and original populations was evaluated. In addition, all Japanese-American women born
in Japan were excluded from the comparisons to eliminate the potential effects of early
environmental components that operated before migration. However, whether unmeasured
factors (such as general health status) differed between the two populations and to what
extent they might bias the study results is unknown.
Another potential source of bias was the postmenopausal use of estrogen and thiazide,
which are known to influence bone mass. In this study, it was not possible to adjust for
medication use. However, we expect most if not all effects of these medications on
fracture risk to operate through BMD. Therefore, differences in medication use should
have had little influence on the comparison of prevalent spine fractures since spine BMD
was adjusted in the logistic regression analyses.
94
4.6 CONCLUSIONS
This study has focused on several epidemiological aspects of vertebral fractures and has
shown that:
1. In general, each pair of vertebral fracture definitions compared in this study showed
good overall agreement. However, the comparability of vertebral fracture prevalence was
also influenced by factors other than the agreement of fracture definitions. Summary
measures of agreement (such as Po, Kappa, 7f, and PABAK) may reflect the degree of
overall agreement of fracture definitions. Other supplementary indices (such as Pposs Pneg ,
PI, and BI) may provide additional information that cannot captured by these summary
agreement measures. Ppos is recommended as an appropriate measure of comparability
between prevalence based on different fracture definitions in this study.
2. The prevalence of vertebral fractures varied according to the location within the spine.
In agreement with other studies, a bimodal distribution of vertebral fracture, with peaks
around T12 and T8, was observed in all three study populations regardless of the fracture
definition. This can be attributed to the anatomic and biomechanical characteristics of the
spine.
95
3. Among different types of vertebral fractures, anterior wedge fracture was most
common, endplate fracture was less so, and crush fracture was least common in all three
study populations.
4. In general, the single-fracture, multiple-fracture, and overall prevalence of vertebral
fracture increased dramatically with age in all three study populations. Compared with
Japanese-American women living in Hawaii, the age-adjusted odds ratios for native
Japanese women living in Hiroshima were significantly and consistently greater than one
(range from 1.6 to 2.6, depending on fracture definition), while the age-adjusted odds
ratios for Caucasian women living in Minnesota were closer to 1.0 (range from 0.5 to
1.5, depending on fracture definition). These data indicated that the age-adjusted
prevalence of vertebral fracture among immigrant Japanese-American women was quite
different from the prevalence of the original country, but more similar to the prevalence
of the host country, suggesting non-genetic factors may have some impact on vertebral
fractures.
5. Native Japanese women differed in several aspects from their Japanese-American
counterparts of similar birth year. On the average, native Japanese women were shorter
and lighter, tended to have a later menarche, an earlier natural menopause, and thus a
shorter period between menarche and menopause compared with Japanese-American
women. The proportion of artificial menopause was much lower among native Japanese
born before 1935 than that among Japanese-Americans. When focusing on women with
96
natural menopause, regression analysis suggested that older women with lower body
weight, shorter duration between menarche and menopause, and longer period since
menopause would have lower BMD, and thus a higher risk of vertebral fracture. Weight
and duration between menarche and menopause appeared to explain much of the age
adjusted mean difference in spine BMD between native Japanese and Japanese-American
women. Both observed differences in body size, age at menarche, and age at menopause
between the two study populations and the strong cohort effects on these variables within
each population pointed toward environmental effects, such as changes in nutrition, life
styles, and radiation effects. Differences between the populations were also observed for
the lactation period, number of live birth, current smoking and alcohol use. None of
them, however, were found to have significant influence on BMD or help account for the
observed difference in BMD between populations.
6. Age and duration between menarche and menopause/age at menopause were found to
be significantly associated with prevalent vertebral fracture after adjustment for spine
BMD. This finding is in agreement with other investigators' observations that current
BMD is a major but not a sole risk factor for osteoporotic fractures. Age-related and
menopause-related mechanisms including bone loss over time and factors other than bone
loss also appear to play an important role in osteoporosis.
97
\000
APPENDIX A: TABLES
Table 2.1 Nomenclature and Definitions Used to Diagnose Prevalent Fractures
Code Definition Code Definition
PVl A < 3 SD below mean PVIA A < 4 SD below mean
PV2 A, M, or P < 3 SD below mean PV2A A, M, or P < 4 SD below mean
PV3 AlP < 3 SD below mean PV3A AlP < 4 SD below mean
PV4 AlP, M/P, or P/P j•• < 3 SD below mean PV4A AlP, M/P, or P/P j•• < 4 SD below mean
PV5 AlT4A < 3 SD below mean PV5A A/T4A < 4 SD below mean
PV6 A/T4A, M/T4M, or P/T4P < 3 SD below mean PV6A A/T4A, M/T4M, or P/T4P < 4 SD below mean
'\ - antenor vertebral height, M - medial vertebral height, P - postenor vertebral height; T4A,M,P - antenor, medial and posterior heightsvertebra T4; PH = posterior height of adjacent vertebra above.
of
Table 3.1 Agreement between Prevalent Fracture Definitions(Study Unit: Individual Woman)
pg pamp ary pe g729-805; Minnesota: N = 742-762.
HAWAII' (HaS) JAPAN' (AHS) MINNESOTN (ROS)
Po KAPPA Po KAPPA Po KAPPAPpcs 7r Ppcs 7r Ppcs 7r
PlICa PABAK PlICa PABAK PlICa PABAK
PV1 96.8% 0.837 95.7% 0.782 94.8% 0.762VS 85.4% 0.836 80.7% 0.782 79.2% 0.762PV3 98.2% 0.937 97.6% 0.913 97.0% 0.895
PV2 93.9% 0.751 87.2% 0.579 91.6% 0.705VS 78.6% 0.750 64.8% 0.570 75.6% 0.705
PV4 96.5% 0.878 92.2% 0.744 94.9% 0.832
PVI 97.6% 0.871 96.4% 0.809 94.6% 0.701VS 88.4% 0.871 82.9% 0.809 73.0% 0.700
PV5 98.7% 0.952 98.0% 0.929 97.0% 0.892
PV3 96.6% 0.821 96.3% 0.816 95.4% 0.725VS 84.0% 0.821 83.6% 0.815 75.0% 0.725
PV5 98.1% 0.931 97.9% 0.926 97.5% 0.908
PV2 97.0% 0.859 93.8% 0.724 93.9% 0.750VS 87.6% 0.859 75.9% 0.724 78.5% 0.749
PV6 98.3% 0.940 96.5% 0.877 96.5% 0.879
PV4 93.7% 0.739 87.4% 0.583 91.2% 0.644VS 77.6% 0.739 65.2% 0.574 69.5% 0.644
PV6 96.3% 0.874 92.3% 0.748 94.9% 0.825
PV1A 97.7% 0.824 97.3% 0.811 97.0% 0.777VS 83.6% 0.824 82.5% 0.811 79.3% 0.777
PV3A 98.8% 0.955 98.5% 0.945 98.4% 0.940
PV2A 97.5% 0.857 93.7% 0.688 96.1% 0.778VS 87.1% 0.857 72.1% 0.686 80.0% 0.778
PV4A 98.6% 0.950 96.4% 0.873 97.8% 0.921
PV1A 98.7% 0.899 97.7% 0.826 97.6% 0.773VS 90.6% 0.899 83.8% 0.826 78.6% 0.773
PV5A 99.3% 0.975 98.7% 0.953 98.7% 0.951
PV3A 97.8% 0.826 97.0% 0.794 98.5% 0.837VS 83.8% 0.826 81.0% 0.794 84.5% 0.837
PV5A 98.8% 0.956 98.4% 0.940 99.2% 0.970
PV2A 98.9% 0.926 96.7% 0.788 97.0% 0.784VS 93.2% 0.926 80.6% 0.788 80.0% 0.784
PV6A 99.4% 0.977 98.2% 0.934 98.4% 0.941
PV4A 97.4% 0.843 93.3% 0.668 96.4% 0.742VS 85.7% 0.843 70.3% 0.665 76.1% 0.741
PV6A 98.5% 0.947 96.2% 0.866 98.0% 0.927
a. s IJle SIZeS V de ndin on the definitions bein com ared. Hawaii; N - ~72-887;Ja an:l'l -
99
Table 3.2 Agreement between Prevalent Fracture Definitions(Study Unit: Individual Vertebra)
pp aryp g g PN = 9391-11075; Minnesota: N = 9634-10648.
HAWAlI" (HOS) JAPAN' (AHS) MINNESOTAi (ROS)
Po KAPPA Po KAPPA Po KAPPAPpos '/I" PplJI '71" Ppos '71"
PileS PABAK Prq PABAK PileS PABAK
PVl 99.4% 0.770 99.3% 0.749 99.1% 0.690VS 77.3% 0.770 75.3% 0.749 69.5% 0.690PV3 99.7% 0.989 99.6% 0.986 99.5% 0.981
PV2 99.2% 0.782 98.1% 0.614 98.4% 0.667VS 78.6% 0.782 62.4% 0.614 67.5% 0.667
PV4 99.6% 0.984 99.0% 0.962 99.2% 0.969
. PVI 99.6% 0.836 99.2% 0.745 99.3% 0.716VS 83.8% 0.836 74.9% 0.745 72.0% 0.716
PV5 99.8% 0.991 99.6% 0.985 99.6% 0.986
PV3 99.4% 0.767 99.3% 0.769 99.4% 0.696VS 77.0% 0.767 77.2% 0.769 70.0% 0.696
PV5 99.7% 0.989 99.6% 0.986 99.7% 0.987
PV2 99.6% 0.865 98.7% 0.669 99.0% 0.725VS 86.8% 0.865 67.5% 0.669 73.0% 0.725
PV6 99.8% 0.991 99.3% 0.974 99.5% 0.979
PV4 99.1 % 0.755 98.0% 0.621 98.7% 0.635VS 75.9% 0.755 63.1% 0.621 64.1% 0.635
PV6 99.5% 0.982 99.0% 0.961 99.4% 0.974
PVIA 99.7% 0.775 99.6% 0.767 99.5% 0.717VS 77.7% 0.775 76.9% 0.767 71.9% 0.717
PV3A 99.8% 0.993 99.8% 0.992 99.8% 0.991
PV2A 99.7% 0.874 99.0% 0.670 99.4% 0.749VS 87.6% 0.874 67.5% 0.670 75.2% 0.749
PV4A 99.8% 0.994 99.5% 0.980 99.7% 0.988
PVIA 99.8% 0.893 99.5% 0.733 99.7% 0.743VS 89.4% 0.893 73.5% 0.733 74.4% 0.742
PV5A 99.9% 0.996 99.8% 0.991 99.8% 0.993
PV3A 99.7% 0.784 99.6% 0.764 99.8% 0.779VS 78.6% 0.784 76.7% 0.764 78.0% 0.779
PV5A 99.8% 0.993 99.8% 0.991 99.9% 0.995
PV2A 99.8% 0.922 99.4% 0.734 99.6% 0.771VS 92.3% 0.922 73.7% 0.734 77.3% 0.771
PV6A 99.9% 0.996 99.7% 0.988 99.8% 0.992
PV4A 99.6% 0.847 98.9% 0.651 99.5% 0.724VS 84.9% 0.847 65.6% 0.650 72.6% 0.724
PV6A 99.8% 0.992 99.4% 0.978 99.8% 0.990
a. Sam ole SIZeS v de endin on the dennttions bern com ared, ~awal1:~ - :1253-12304; Ja an:
100
Table 3.3 Comparisonof Overall Agreement between Different Populations, Study Units, and Diagnosis Cutoff"
pansons 10 tnis tame renect me reianve magnitude or agreement measures ramer man absoluteuirrerences. The degree or agstudy populations based on either individual women or vertebrae were all in the range considered to be good to excellent.
Po Ppos Pneg Kappa 7r PABAK
Hawaii higher higher higher higher higher highervs
Japan & Minnesota lower lower lower lower lower lower
Individual Woman lower higher lower no no lowervs consistent consistent
Individual vertebra higher lower higher pattern pattern higher
-4 SD cutoff higher higher higher higher higher highervs
-3 SD cutoff lower lower lower lower lower lower...
o-
Table 3.4 Indices of Bias and Prevalence (Study Unit: Individual Woman)
gary uepp
HAWAII" (HOS) JAPAN" (AHS) MINNESOTA" (ROS)
BI PI BI PI BI PI
PVl VS PV3 0.0090 -0.7835 0.0211 -0.7752 -0.0184 -0.7480
PV2 VS PV4 0.0406 -0.7159 0.1130 -0.6360 0.0026 -0.6562
PVl VS PV5 0.0080 -0.7924 -0.0027 -0.7915 -0.0350 -0.8005
PV3 VS PV5 0.0000 -0.7844 -0.0206 -0.7737 -0.0189 -0.8167
PV2 VS PV6 0.0092 -0.7592 -0.0014 -0.7435 -0.0202 -0.7183
PV4 VS PV6 -0.0310 -0.7190 -0.1070 -0.6379 -0.0256 -0.7129
PV1A VS PV3A 0.0000 -0.8625 0.0124 -0.8435 -0.0223 -0.8543
PV2A VS PV4A 0.0135 -0.8083 0.0584 -0.7727 0.0000 -0.8031
PV1A VS PV5A 0.0034 -0.8658 -0.0041 -0.8560 -0.0243 -0.8868
PV3A VS PV5A 0.0034 -0.8658 -0.0192 -0.8409 -0.0067 -0.9043
PV2A VS PV6A 0.0023 -0.8303 -0.0055 -0.8299 -0.0189 -0.8518
PV4A VS PV6A -0.0126 -0.8154 -0.0617 -0.7737 -0.0229 -0.8477- --- .. on the definitions beinz comnared. See the footnote for Table 3.1
....fa
Table 3.5 Indices of Bias and Prevalence (Study Unit: Individual Vertebra)
pp
HAWAII" (HaS) JAPAN" (AHS) MINNESOTAi (ROS)
BI PI BI PI BI PI
PVl VS PV3 -0.0016 -0.9750 0.0011 -0.9707 -0.0051 -0.9692
PV2 VS PV4 0.0034 -0.9627 0.0125 -0.9493 -0.0044 -0.9524
PVl VS PV5 -0.0005 -0.9737 -0.0016 -0.9699 -0.0049 -0.9752
PV3 VS PV5 0.0011 -0.9753 -0.0023 -0.9691 -0.0011 -0.9789
PV2 VS PV6 0.0002 -0.9664 -0.0018 -0.9597 -0.0057 -0.9619
PV4 VS PV6 -0.0039 -0.9623 -0.0148 -0.9467 -0.0032 -0.9644
PVIA VS PV3A -0.0013 -0.9847 0.0012 -0.9824 -0.0039 -0.9833
PV2A VS PV4A 0.0010 -0.9751 0.0081 -0.9695 -0.0017 -0.9758
PV1A VS PV5A 0.0002 -0.9833 -0.0009 -0.9823 -0.0028 -0.9866
PV3A VS PV5A 0.0015 -0.9846 -0.0023 -0.9808 0.0002 -0.9896
PV2A VS PV6A 0.0003 -0.9759 -0.0016 -0.9769 -0.0027 -0.9817
PV4A VS PV6A -0.0010 -0.9747 -0.0103 -0.9682 -0.0019 -0.9826-- L ~
... _ .J_L": _'"' ___ L. __ - __ .oJ "' __ ..L_ 1:. _.. ..._ 1:_ 'T"'_L _ ., ""
-s
Table 3.6 Spine Fracture Prevalence (Cases per 100 Women) by Diagnosis Criterion, Age and Population
ample SiZeS may vary sng
AGE Na PVl PV2 PV3 PV4 PV5 PV6 PVIA PV2A PV3A PV4A PV5A PV6A50-54 1 0.0 0.0 0.0 0.0 0.0 0.0 HAWAII 0.0 0.0 0.0 0.0 0.0 0.055-59 15 0.0 0.0 0.0 0.0 0.0 6.7 0.0 0.0 0.0 0.0 0.0 0.060-64 102 1.0 1.0 1.0 4.9 1.0 2.9 1.0 1.0 1.0 1.0 1.0 1.065-69 313 5.1 6.1 6.7 10.9 6.2 7.6 3.8 4.8 3.8 7.0 3.9 4.970-74 290 11.7 14.8 13.8 17.9 12.6 14.4 7.2 10.0 8.3 11.7 8.1 9.875-79 96 22.9 25.0 20.8 31.3 22.3 24.5 13.5 18.8 11.5 18.8 12.8 18.180-84 19 21.1 26.3 21.1 42.1 26.3 26.3 21.1 21.1 21.1 21.1 21.1 21.185+ 4 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0TOTAL 840 9.2 11.0 10.2 15.4 9.9 11.7 6.1 8.0 6.2 9.4 6.3 7.950-54 56 1.8 5.4 7.1 17.9 1.9 3.8 JAPAN 0.0 1.8 5.4 7.1 1.9 1.955-59 147 2.7 4.1 2.7 16.3 2.3 3.8 1.4 2.7 1.4 6.1 1.5 1.560-64 224 4.0 4.9 4.9 14.3 3.8 4.8 2.2 2.2 2.2 5.4 2.9 2.965-69 159 6.9 8.2 11.3 20.1 7.5 10.3 3.8 3.8 6.3 11.3 4.1 5.570-74 109 21.1 24.8 23.9 35.8 21.3 25.5 16.5 19.3 16.5 26.6 16.0 18.175-79 76 28.9 36.8 32.9 50.0 33.8 38.5 23.7 27.6 25.0 39.5 21.5 26.280-84 28 39.3 42.9 35.7 53.6 38.5 46.2 28.6 32.1 35.7 42.9 26.9 34.685+ 4 25.0 25.0 25.0 25.0 0.0 0.0 25.0 25.0 25.0 25.0 0.0 0.0TOTAL 803 10.2 12.6 12.3 23.8 10.3 12.8 7.2 8.5 8.5 14.3 7.0 8.350-54 106 3.8 4.7 1.9 5.7 1.9 5.7 MINNESOTA 0.0 0.0 0.9 1.9 0.0 0.055-59 137 3.6 5.8 2.9 7.3 1.5 2.9 1.5 2.2 0.7 1.5 0.7 0.760-64 III 6.3 6.3 2.7 5.4 4.5 6.4 1.8 2.7 0.9 1.8 0.9 1.865-69 106 10.4 13.2 11.3 12.3 4.7 6.6 6.6 7.5 3.8 5.7 2.8 3.870-74 80 11.3 15.0 10.0 20.0 7.7 12.8 7.5 7.5 3.8 10.0 2.6 5.175-79 99 17.2 22.2 20.2 25.3 12.1 18.2 11.1 12.1 10.1 13.1 7.1 11.180-84 59 40.7 50.8 33.9 47.5 28.3 39.6 30.5 33.9 22.0 33.9 18.9 24.585+ 61 41.0 50.8 32.8 44.3 26.9 44.2 29.5 36.1 23.0 34.4 17.3 23.1TOTAL 759 13.4 17.0 11.7 17.3 8.2 13.0 8.4 9.7 6.2 9.7 4.5 6.4
- htly between fracture defimtlOns.
.....~
-~
Table 3.7 Age-adjusted Odds Ratios
JAPAN MINNESOTA
FRACTURE DEFINITION ODDS RATIO 95% CI ODDS RATIO 95% CI
PVl 1.7 1.2,2.4 1.3 1.0, 1.9
PV2 1.8 1.3, 2.4 1.5 1.1,2.1
PV3 1.8 1.3, 2.5 1.0 0.7, 1.4
PV4 2.6 2.0,3.3 1.1 0.8, 1.5
PV5 1.6 1.2,2.3 0.6 0.4,0.9
PV6 1.7 1.3, 2.4 1.0 0.8, 1.4
PVIA 1.8 1.2,2.8 1.0 0.7, 1.5
PV2A 1.6 1.1,2.4 0.9 0.6, 1.4
PV3A 2.1 1.5, 3.2 0.7 0.4, 1.0
PV4A 2.5 1.8,3.5 0.8 0.6, 1.2
PV5A 1.7 1.2,2.6 0.5 0.3,0.8
PV6A 1.7 1.1,2.4 0.6 0.4,0.9
I--'o0\
Table 3.8 Comparison of Basic Characteristics between Japanese-American and Native Japanese Women
VARIABLE JAPANESE-AMERICAN NATIVE JAPANESE
MEAN SD N MEAN SD N
AGE AT THE MOST RECENT EXAM ** 70.01 4.90 844 64.96 7.57 804
BONE MASS DENSITY (L2-L4)" ** 0.83 0.17 770 0.81 0.15 797
HEIGHT (ern) 151.64 5.21 1046 151.24 5.50 884
WEIGHT (kg) ** 53.80 8.86 1046 52.76 8.75 884
BODY MASS INDEX (kg/m') * 23.38 3.55 1046 23.05 3.51 884
AGE AT MENARCHE ** 13.42 1.59 1046 14.83 1.73 679
AGE AT NATURAL MENOPAUSEb 49.83 4.32 687 49.56 3.99 624
YEARS BETWEEN MENARCHE & MENOPAUSEb ** 36.36 4.75 687 34.59 4.36 481
TOTAL LACTATION PERIOD IN MONTHe ** 23.29 21.67 777 17.78 15.00 316
AVERAGE LACTATION PERIOD PER CHILDe 7.00 5.44 777 7.59 5.92 316
PROPORTION (%) N PROPORTION (%) N
ONE OR MORE LIVE BIRTH 94.6 1045 94.1 656
ARTIFICIAL MENOPAUSEb ** 29.0 967 14.0 745
SMOKING (CURRENT)** 8.1 1046 12.8 741
ALCOHOL USE (CURRENT) 31.8 1046 33.8 722~. A ,_,,- ... ..... .... ..p
b. Since the existence of higher proportion of premenopausal women could bias the estimates of means or proportion, subjects less than 55 years oldwere excluded from calculation.
c. Based on women with lactation experience only.* P<0.05 based on t or x2 test.** P<O.OI based on t or i test.
Table 3.9 Proportion of Women by Number of Live Births and the Women's Year of Birth
p
JAPANESE-AMERICANS(%) NATIVE JAPANESE(%)
BIRTH YEAR N 0 1 2 ~3 N 0 1 2 ~3
1900-1904 8 - - - - 3 - - - -1905-1909 54 13.0 13.0 25.9 48.2 13 - - - -1910-1914 214 7.0 10.3 19.2 63.6 54 11.1 7.4 11.1 70.4
1915-1919 407 4.2 11.8 27.0 57.0 82 8.5 12.2 22.0 57.3
1920-1924 287 3.8 8.7 30.3 57.1 120 4.2 14.2 29.2 52.5
1925-1929 68 5.9 8.8 29.4 55.9 182 6.6 14.3 44.5 34.6
1930-1934 6 - - - - 113 4.4 22.1 55.8 17.7
1935-1939 1 - - - - 36 0.0 19.4 55.6 25.0
1940-1944 0 - - - - 53 0.0 13.2 56.6 30.2
1945-1949 0 - - - - 0 - - - -,.,,,,1,...~.1 ..... ,,,,..:I ~-.._ .. J....... 1-.~_1.... ........L_ _ ...... L __
lp C::17P lssec th!lon 'lfla. Proportion was not
....s
Table 3.10 Proportion of Artificial Menopause amongJapanese-American and Native Japanese Women by Birth Year
p
BIRTH YEAR JAPANESE-AMERICAN NATIVE JAPANESE
PROPORTIOW N PROPORTIOW N(%) (%)
1900-1904 - 8 - 5
1905-1909 29.6 54 - 20
1910-1914 29.0 214 5.5 73
1915-1919 27.9 405 10.5 105
1920-1924 30.8 286 13.5 148
1925-1929 29.4 68 17.0 224
1930-1934 - 4 17.5 166
1935-1939 - 0 28.6 35
1940-1944 - 0 - 14
1945-1949 - 0 - 2
a. Pro ornon was not calculated tor the birth cohorts with sample SIZe less than 3U.
108
Table 3.11 Proportion of Current Smoking and Alcohol Useamong Japanese-American and Native Japanese Women by Birth Year
p
SMOKING ALCOHOL USE
JAPANESE-AMERICANS NATIVE JAPANESE JAPANESE-AMERICANS NATIVE JAPANESE
BIRTH PROPORTION" N PROPORTION" N PROPORTION" N PROPORTION" NYEAR (%) (%) (%) (%)
1900-1904 - 8 - 3 - 8 - 3
1905-1909 7.4 54 - 19 25.9 54 - 18
1910-1914 4.7 214 15.9 63 27.1 214 31.7 60
1915-1919 7.9 407 14.9 91 33.9 407 33.7 86
1920-1924 8.7 287 15.0 133 31.4 287 27.7 130
1925-1929 13.2 68 12.9 202 39.7 68 41.5 195
1930-1934 - 6 12.8 133 - 6 31.1 132
1935-1939 - 1 4.9 41 - 1 28.6 42
1940-1944 - 0 8.9 56 - 0 33.9 56
1945-1949 - 0 - 0 - 0 - 0
ornon was not calculated tor the birth cohorts with samnle size less than 3C .
-o10
Table 3.12 Linear Regression Analyses' of the Association betweenJAPAN, BIRTH YEAR and Continuous Variables
, uav v 't' ..,... U....... .w"". a."a .. ..,:gresslon was slgb. JAPAN is a dummy variable indexing two study populations to be compared (JAPAN = 1 if nativeJapanese, JAPAN=0 if Japanese-American).c. "Ho: coefficient for JAPAN + coefficient for JAPAN x (BIRTH YEAR) = 0" was rejected at a=0.05 level.d. Based on women aged 55 or older.e. Based on women with lactation experience only.N. Numberof subjects included in the regression analysis.+ Marginal significance 0.05 < P < 0.06* P < 0.05** P < 0.01
CHARACTERISTIC INTERCEPT JAPANb BIRTH YEAR (BIRTH YEAR)2 JAPAN X N(DEPENDENT VARIABLE) (BIRTH YEAR)
(SE) (SE) (SE) (SE) R2
HEIGHT" -373.174678 241.242713** 0.273693** --- -0.126677** 1891(cm) (72.5860) (0.0321) (0.0378) 0.06
WEIGHT" -26950 293.685647+ 27.839521 ** -0.007174** -0.153989+ 1891(kg) (152.7265) (10.4517) (0.0027) (0.0796) 0.03
BODY MASS INDEX -87.768418 154.129481** 0.057966** --- -0.080445** 1891(kg/m2) (49.4208) (0.0218) (0.0257) 0.01
AGE AT MENARCHE 118.291336 1.851802** -0.054689** --- --- 1692(0.0944) (0.0059) 0.19
AGE AT MENOPAUSEd -132.183331 -0.821308** 0.094955** --- --- 1277(0.2640) (0.0198) 0.02
YEARS BETWEENd -19173 -2.252372** 19.901329+ -0.005154+ --- 1140MENARCHE & MENOPAUSE (0.3326) (10.2403) (0.0027) 0.05
TOTAL LACTATIONc,e 1820.993126 -1002.202903* -0.937581 ** --- 0.521613* 1076DURATION (MONTHS) (407.1055) (0.1466) (0.2119) 0.06
LACTATION DURATIONc,e 471.841494 -293.633255* -0.242422** --- 0.153807* 1076PER CHILD (MONTHS) (116.0656) (0.0418) (0.0604) 0.04
"lI FI"'P "lI ""'I'\ltP c.o thp overa11 F tpc.ot nr r~t nificant at (l!"':O.OC 01 level.
--o
Table 3.13 Logistic Regression Analyses of the Associationbetween JAPAN, BIRTH YEAR and Binary Variables
p
OUTCOME JAPAN" BIRTH YEAR JAPAN x (BIRTH YEAR)VARIABLE (SE) (SE) (SE)
ARTIFICIAL -95.2 * 0.00362 0.049 *MENOPAUSE (45.2804) (0.0162) (0.0236)
SMOKING 160.8 ** 0.071 ** -0.0835 **(51.193) (0.0232) (0.0267)
ALCOHOL USE 63.1679 * 0.034 * -0.0329 *(31.5057) (0.0135) (0.0164)
a. JAPAN IS a dumm vanable mdexm two stud- o ulations to be com ared (JAi'AN"":1 n nativey gyp pJapanese, JAPAN=O if Japanese-American).
* p < 0.05** P < 0.01
111
Table 3.14 Multiple Linear Regression Analysis: Effect of Age and Body Size on Spine BMD (L2-lA)'
INTERCEPT JAPANb AGE WEIGHT (kg) HEIGHT (em) BMI (kg/m') N(SE) (SE) (SE) (SE) (SE) R2
1.142830 -0.048282 ** -0.004361 ** --- --- --- 1566(0.0087) (0.0006) 0.04
0.707556 -0.034873 ** -0.003470 ** 0.006887 ** --- --- 1566(0.0081) (0.0006) (0.0004) 0.18
0.167437 -0.037336 ** -0.003430 ** --- 0.005984 ** --- 1566(0.0087) (0.0006) (0.0007) (0.08)
0.792394 -0.044189 ** -0.004251 ** --- --- 0.014662 ** 1566(0.0083) (0.0006) (0.0011) 0.14
0.452959 -0.032514 ** -0.003255 ** 0.006484 ** 0.001718 * --- 1566(0.0082) (0.0006) (0.0005) (0.0008) 0.18
a. i-or all models. t tle overall r test for re -if"i-- ._. n(\(' 'I ___ 1gression was sig
b. JAPAN is a dummy variable indexing two study populations to be compared (JAPAN = 1 if native Japanese, JAPAN =0 if Japanese-American).N. Number of subjects included in the regression analysis.* p < 0.05, ** p < 0.01
--NTable 3.15 Effect of Cause of Menopause on Spine BMD
INTERCEPT JAPAN" AGE WEIGHT HEIGHT CAUSE OFb INTERACTIONc NMENOPAUSE
(SE) (SE) (SE) (SE) (SE) (SE) R2
0.399286 -0.018764* -0.002757** 0.006465** 0.001748* 0.049568** -0.049595** 1527(0.0090) (0.0006) (0.0005) (0.0008) (0.0115) (0.0188) 0.19
a. JAPAN IS a dummv vanable indexinz two studv nonu auons to be compared (JAPAN -lIt natrve Japanese. JAPAN -=0 it Jananese-Amertcan).b. CAUSE OF MENOPAUSE is a dummy variable indexing two categories of menopause (CAUSE OF MENOPAUSE = 1 if artificial menopause,
CAUSE OF MENOPAUSE=O if natural menopause).c. INTERACTION = JAPAN * (CAUSE OF MENOPAUSE).N. Number of subjects included in the regression analysis.* p < 0.05, ** P < 0.01
Table 3.16 Multiple Linear Regression Coefficientsfor Potential Predictors of Spine BMD (L2-LA)'
BMD PREDICTORS COEFFICIENT OF JAPANb NBMD PREDICTORS
(SE) (SE) R2
AGE AT MENARCHE -0.002190 -0.007796 1358(0.0025) (0.0109) 0.18
AGE AT MENOPAUSE 0.004784 ...... -0.015110 1174(0.0010) (0.0091) 0.18
YEARS BETWEEN MENARCHE 0.004644 ...... 0.000326 1028& MENOPAUSE (0.0010) (0.0102) 0.17
TOTAL LACTAnON PERIODc -0.000604 ... -0.015410 855IN MONTH (0.0003) (0.0133) 0.18
AVERAGE LACTATION PERIODc -0.001930 ... -0.011245 855PER CHILD (0.0009) (0.0133) 0.18
LACTATION 0.018790 -0.011201 1100(I:YES,O:NO) (0.0112) (0.0118) 0.19
NUMBER OF LIVE BIRTH -0.002088 -0.014235 1340(0.0028) (0.0098) 0.18
SMOKING (CURRENT) -0.008842 -0.016627 1417(1:YES,O:NO) (0.0128) (0.0095) 0.19
ALCOHOL USE (CURRENT) 0.001395 -0.018631 ... 1399(I:YES,O:NO) (0.0084) (0.0096) 0.19
RADIAnON EXPOSURE 0.002974 -0.019630 ... 1495(Gy) (0.0061) (0.0090) 0.18
a. Re ression models mvoivmg AUE AT ..USE and YEARS DCI wccn '1-1 .... &gMENOPAUSE was only based on women with natural menopause and was adjusted for age, weight, andheight. All other models were based on women with either natural menopause or artificial menopause,and were also adjusted for cause of menopause as well as the interaction between cause of menopauseand study population indexed by a dummy variable JAPAN (see footnote b)
b. JAPAN is a dummy variable indexing two study populations to be compared (JAPAN=1 if nativeJapanese, JAPAN =0 if Japanese-American). Its coefficient represented the adjusted average differencein BMD between native Japanese and Japanese-American women with natural menopause.
c. Based on women with lactation experience only.N. Number of subjects included in the regression analysis .... p < 0.05...* p < 0.01
113
Table 3.17 Final Linear Regression Models for Spine BMD'
y on women wun natural menopb. JAPAN is a dummy variable indexing two study populations to be compared (JAPAN = 1 if native Japanese, JAPAN=O if Japanese-American). Its
coefficient represented the adjusted average difference in BMD between native Japanese and Japanese-American women with natural menopause.N. Number of subjects included in the regression analysis.* p < 0.05** P < 0.01
INTERCEPT JAPAN b AGE WEIGHT AGE AT YEARS BETWEEN NMENOPAUSE MENARCHE & R2
(SE) (SE) (SE) (SE) MENOPAUSE(SE)
0.408452 -0.016888 -0.002529** 0.006508** 0.004776** --- 1174(0.0091) (0.0006) (0.0005) (0.0010) 0.18
0.403737 -0.000567 -0.001654* 0.006719** --- 0.004637** 1028(0.OlD1) (0.0007) (0.0005) (0.0010) 0.17
..~ , - - -- . ----- -
..........~
Table 3.18 Age-adjusted Odds Ratios Based on Fracture Definition PV2
pornt estimate ana enapb. Radiation exposure doses for all JA subjects were set equal to O.
MODEL INDEPENDENT VARIABLES OR' 95% CIa UNIT OF CHANGE N
1 JAPAN 1.8a 1.37 - 2.59 1 (l =NJ, O=JA) 1642AGE 2.m 1.77 - 2.29 5 years
2 JAPAN 1.66 1.18 - 2.34 1 (I=NJ, O=JA) 1562AGE 1.K\ 1.59 - 2.10 5 yearsBMD 1.9L~ 1.59 - 2.37 -1 SD=-0.1607 g/cm2
3 JAPAN 1.7L. 1.26 - 2.41 1 (l =NJ, O=JA) 1642AGE 1.9~: 1.68 - 2.19 5 years
HEIGHT 1.30 1.12 - 1.52 -5 em
4 JAPAN 1.82 1.32 - 2.51 1 (l =NJ, O=JA) 1642AGE 1.97 1.73 - 2.24 5 years
WEIGHT 1.14 1.04 - 1.26 -5 kg
5 JAPAN 1.88 1.36 - 2.58 1 (l=NJ, O=JA) 1642AGE 2.01 1.77 - 2.29 5 years
BODY MASS INDEX 1.03 0.99 - 1.08 -1 kg/m'
6 JAPAN 1.69 1.21 - 2.36 1 (l =NJ, O=JA) 1521AGE 1.93 1.68 - 2.21 5 years
SMOKING HISTORY 0.89 0.56 - 1.41 1 (1=yes, O=no)
7 JAPAN 1.82 1.30 - 2.55 1 (1 =NJ, O=JA) 1501AGE 2.03 1.77 - 2.34 5 years
ALCOHOL USE 0.92 0.64 - 1.32 1 (l =yes, O=no)
8 JAPAN 1.85 1.29 - 2.64 1 (l =NJ, O=JA) 1610AGE 2.03 1.78 - 2.31 5 years
RADIATION!' 1.01 0.98 - 1.03 0.1 Gy
omts ot the connnence Interval of OR are corresnondinz to the units of change soecified m this table.
--lJl
Table 3. 18(Continued) Age-adjusted Odds Ratios Based on Fracture Definition PV2
ge spgppp
MODEL INDEPENDENT VARIABLES ORa 95% CIa UNIT OF CHANGE N
9 JAPAN 1.81 1.30 - 2.53 I (I =NJ, O=JA) 1602AGE 2.06 1.80 - 2.36 5 years
CAUSE OF MENOPAUSE 0.98 0.65 - 1.47 1 (1 = artificial , O=spontaneous)
10 JAPAN 1.67 1.17 - 2.38 1 (l =NJ, O=JA) 1440AGE 2.06 1.78 - 2.39 5 years
No. OF LIVE BIRTH 1.02 0.92 - 1.14 I
11 JAPAN 1.75 1.15 - 2.67 1 (l =NJ, O=JA) 1190AGE 2.24 1.88 - 2.66 5 years
TOTAL LACTATION PERIOD 1.02 0.98 - 1.07 -5 months
12 JAPAN 1.80 1.17 - 2.76 1 (1=NJ, O=JA) 1185AGE 2.24 1.88 - 2.67 5 years
AVERAGE LACTATION PERIOD PER CHILD 1.02 0.99 - 1.05 -1 month
13 JAPAN 1.51 1.01 - 2.26 1 (l =NJ, O=JA) 1459AGE 1.98 1.71 - 2.29 5 years
AGE AT MENARCHE 1.13 0.66 - 1.92 5 years
14 JAPAN 1.80 1.24 - 2.62 1 (I =NJ, O=JA) 1233AGE 2.04 1.75 - 2.37 5 years
AGE AT MENOPAUSE 1.31 1.06 - 1.60 -5 years
15 JAPAN 1.42 0.94 - 2.15 I (l =NJ, O=JA) 1086AGE 1.98 1.68 - 2.34 5 years
DURATION BETWEEN MENARCHE & MENOPAUSE 1.31 1.07 - 1.61 -5 years• n • - ••••- •••••..1 --...1, n to the units o chan
........0\
Table 3.19 Age-adjusted Odds Ratios Based on Fracture Definition PV2A
pomt estimate ana enapb. Radiation exposure doses for all JA subjects were set equal to O.
MODEL INDEPENDENT VARIABLES ORa 95% CIa UNIT OF CHANGE N
1 JAPAN 1.68 1.16 - 2.42 1 (l=NJ, O=JA) 1641AGE 2.07 1.79 - 2.41 5 years
2 JAPAN 1.42 0.95 - 2.11 1 (l =NJ, O=JA) 1562AGE 1.85 1.57-2.18 5 yearsBMD 2.13 1.68 - 2.70 -1 SD=-0.1607 g/cm'
3 JAPAN 1.58 1.09 - 2.30 1 (1 =NJ, O=JA) 1641AGE 2.00 1.71 - 2.33 5 years
HEIGHT 1.20 1.01 - 1.44 -5 em
4 JAPAN 1.64 1.13 - 2.37 1 (l =NJ, O=JA) 1641AGE 2.04 1.75 - 2.37 5 years
WEIGHT 1.10 0.98 - 1.23 -5 kg
5 JAPAN 1.67 1.16 - 2.42 1 (l =NJ, O=JA) 1641AGE 2.07 1.78 - 2.40 5 years
BODY MASS INDEX 1.02 0.97 - 1.08 -1 kg/m2
6 JAPAN 1.48 1.00 - 2.19 1 (1 =NJ, O=JA) 1520AGE 2.03 1.73 - 2.39 5 years
SMOKING HISTORY 1.11 0.66 - 1.86 1 (1 =yes, O=no)
7 JAPAN 1.57 1.06 - 2.33 1 (1 =NJ, O=JA) 1500AGE 2.09 1.78 - 2.46 5 years
ALCOHOL USE 0.95 0.63 - 1.45 1 (l =yes, O=no)
8 JAPAN 1.68 1.11 - 2.54 1 (l =NJ, O=JA) 1609AGE 2.08 1.79 - 2.41 5 years
RADIATION" 1.01 0.98 - 1.04 0.1 Gyn • omts of the connnence mterval of UK are corresnondma to the units o change snecinec m tms table.
--'-I
Table 3. 19(Continued) Age-adjusted Odds Ratios Based on Fracture Definition PV2A
pgppp
MODEL INDEPENDENT VARIABLES ORa 95% CIa UNIT OF CHANGE N
9 JAPAN 1.67 1.14 - 2.46 1 (l =NJ, O=JA) 1601AGE 2.08 1.78 - 2.43 5 years
CAUSE OF MENOPAUSE 1.07 0.66 - 1.71 1 (l = artificial, O=spontaneous)
10 JAPAN 1.46 0.96 - 2.20 1 (l =NJ, O=JA) 1439AGE 2.08 1.75 - 2.47 5 years
No. OF LIVE BIRTH 1.00 0.89 - 1.13 1
11 JAPAN 1.83 1.15 - 2.92 1 (I=NJ, O=JA) 1189AGE 2.10 1.73 - 2.54 5 years
TOTAL LACTATION PERIOD 1.01 0.96 - 1.06 -5 months
12 JAPAN 1.84 1.15 - 2.95 1 (l =NJ, O=JA) 1184AGE 2.10 1.73 - 2.54 5 years
AVERAGE LACTATION PERIOD PER CHILD 1.01 0.97 - 1.04 -1 month
13 JAPAN 1.33 0.84 - 2.11 1 (l =NJ, O=JA) 1458AGE 2.02 1.71 - 2.39 5 years
AGE AT MENARCHE 1.24 0.67 - 2.29 5 years
14 JAPAN 1.62 1.05 - 2.49 1 (l =NJ, O=JA) 1232AGE 2.02 1.70 - 2.40 5 years
AGE AT MENOPAUSE 1.48 1.18 - 1.87 -5 years
15 JAPAN 1.21 0.75 - 1.96 I (l =NJ, O=JA) 1085AGE 1.92 1.58 - 2.32 5 years
DURATION BETWEEN MENARCHE & MENOPAUSE 1.57 1.24 - 1.98 -5 years- _.
-- • __0- _0- - ,iI -.iI . ~ to the units of change s . . - . -
.........00
Table 3.20 Age-adjusted and BMD-adjusted Odds Ratios Based on Fracture Definition PV2
p
MODEL INDEPENDENT VARIABLES ORa 95% CIa UNIT OF CHANGE N
1 JAPAN 1.56 1.10 - 2.20 1 (l =NJ, O=JA) 1562BMD 1.88 1.54 - 2.30 -1 SD=-0.1607 g/cm'AGE 1.77 1.54 - 2.04 5 years
HEIGHT 1.22 1.04 - 1.44 -5 cm
2 JAPAN 1.67 1.18 - 2.35 1 (l =NJ, O=JA) 1562BMD 1.98 1.60 - 2.45 -I SD=-0.1607 g/cm2
AGE 1.83 1.60 - 2.11 5 yearsWEIGHT 0.97 0.87 - 1.08 -5 kg
,3 JAPAN 1.65 1.17 - 2.33 1 (l =NJ, O=JA) 1562
BMD 2.06 1.66 - 2.54 -1 SD=-0.1607 g/cm2
AGE 1.82 1.59 - 2.09 5 yearsBODY MASS INDEX 0.96 0.91 - 1.01 -1 kg1m2
4 JAPAN 1.49 1.04 - 2.13 1 (l =NJ, O=JA) 1443BMD 1.90 1.55 - 2.33 -1 SD=-Q.1607 g/cm'AGE 1.76 1.52 - 2.03 5 years
SMOKING HISTORY 1.01 0.63 - 1.63 1 (1=yes, O=no)
5 JAPAN 1.59 1.11 - 2.28 1 (I=NJ,O=JA) 1423BMD 1.91 1.55 - 2.34 -1 SD=-0. 1607 g/cm2
AGE 1.85 1.59 - 2.15 5 yearsALCOHOL USE 0.87 0.59 - 1.28 1 (l =yes, O=no)
~ . omt estimate and endnoints ot the confidence interval of OR are corresnondinz to the umts o chanze specified m tnis table.
--\0
Table 3.20(Continued) Age-adjusted and BMD-adjusted Odds Ratios Based on Fracture Definition PV2
pomt estimate ana enapb. Radiation exposure doses for all JA subjects were set equal to O.
MODEL INDEPENDENT VARIABLES ORa 95% CIa UNIT OF CHANGE N
6 JAPAN 1.63 1.12 - 2.39 1 (1=NJ, O=JA) 1530BMD 1.95 1.59 - 2.37 -1 SD=-0.1607 g/cm'AGE 1 84 1.60 - 2.12 5 years
RADIATlONb 1.01 0.98 - 1.04 0.1 Gy
7 JAPAN 1.59 1.12 - 2.28 1 (l =NJ, O=JA) 1522BMD 1.96 1.60 - 2.40 -1 SD=-Q.1607 g/cm2
AGE 1.87 1.62-2.16 5 yearsCAUSE OF MENOPAUSE 1.03 0.66 - 1.61 1 (1 = artificial, O=spontaneous)
8 JAPAN 1.47 1.01 - 2.15 1 (l =NJ, O=JA) 1361BMD 1.88 1.52 - 2.32 -1 SD=-0.1607 g/cm'AGE 1.88 1.60 - 2.21 5 years
No. OF LIVE BIRTH 1.02 0.91 - 1.14 1
9 JAPAN 1.59 1.02 - 2.51 1 (l =NJ, O=JA) 1114BMD 1.88 1.50 - 2.37 -1 SD=-Q.1607 g/cm2
AGE 2.11 1.74 - 2.55 5 yearsTOTAL LACTATION PERIOD 1.03 0.98 - 1.08 -5 months
10 JAPAN 1.65 1.04 - 2.60 1 (1=NJ, O=JA) 1109BMD 1.90 1.50 - 2.39 -1 SD=-0.1607 g/cm'AGE 2.10 1.74 - 2.55 5 years
AVERAGE LACTATION PERIOD PER CHILD 1.03 0.99 - 1.07 -1 month
oomts or the confidence interval or uR are corresnondtnz to the units of change snecified in this table.
..-No
Table 3.20(Continued) Age-adjusted and BMD-adjusted Odds Ratios Based on Fracture Definition PV2
pg,pPOint estimate ana enapb. Radiation exposure doses for all JA subjects were set equal to O.
MODEL INDEPENDENT VARIABLES ORa 95% CI" UNIT OF CHANGE N
11 JAPAN 1.45 0.94 - 2.25 1 (1 =NJ, O=JA) 1380BMD 1.91 1.55 - 2.36 -1 SD=-0.1607 g/cm2
AGE 1.84 1.57 - 2.15 5 yearsAGE AT MENARCHE 0.92 0.51 - 1.66 5 years
12 JAPAN 1.68 1.13 - 2.50 1 (1=NJ, O=JA) 1170BMD 1.79 1.42 - 2.26 -1 5D=-0.1607 g/cm'AGE 1.89 1.61 - 2.22 5 years
AGE AT MENOPAUSE 1.27 1.02 - 1.58 -5 years
13 JAPAN 1.37 0.88 - 2.13 1 (1=NJ, O=JA) 1024BMD 1.76 1.37 - 2.25 -1 SD=-0.1607 g/cm2
AGE 1.88 1.57 - 2.24 5 yearsDURATION BETWEEN MENARCHE & MENOPAUSE 1.25 1.00 - 1.56 -5 years
14 JAPAN 1.33 0.81 - 2.16 1 (I=NJ, O=JA) 1000BMD 1.78 1.39 - 2.28 -1 SD=-0.1607 g/cm'AGE 1.87 1.56 - 2.24 5 years
DURATION BETWEEN MENARCHE & MENOPAUSE 1.26 1.01 - 1.58 -5 yearsRADIATION" 1.02 0.98 - 1.06 0.1 Gy
.- - ·- ....f 'h,.. --......n,..--- ._-- .- ....f .11 --- -- -- to the umts o cnanze SI. .
.-N.-
Table 3.21 Age-adjusted and BMD-adjusted Odds Ratios Based on Fracture Definition PV2A
pp
MODEL INDEPENDENT VARIABLES ORa 95% CIa UNIT OF CHANGE N
1 JAPAN 1.37 0.91 - 2.05 1 (l =NJ, O=JA) 1562BMD 2.10 1.65 - 2.66 -1 SD =-0.1607 g/cm'AGE 1.82 1.54 - 2.15 5 years
HEIGHT 1.12 0.92 - 1.36 -5 cm
2 JAPAN 1.45 0.97 - 2.16 1 (l =NJ, O=JA) 1562BMD 2.33 1.80 - 3.01 -1 SD=-0.1607 g/cm'AGE 1.88 1.59 - 2.21 5 years
WEIGHT 0.89 0.78 - 1.01 -5 kg
3 JAPAN 1.41 0.94 - 2.10 1 (l =NJ, O=JA) 1562BMD 2.37 1.84 - 3.07 -1 SD=-Q.l607 g/cm'AGE 1.85 1.57 - 2.18 5 years
BODY MASS INDEX 0.93 0.87 - 0.98 -1 kg/m'
4 JAPAN 1.25 0.82 - 1.91 1 (1 =NJ, O=JA) 1443BMD 2.09 1.63 - 2.67 -1 SD =-0.1607 g/cm'AGE 1.84 1.55 - 2.20 5 years
SMOKING HISTORY 1.33 0.78 - 2.27 1 (l =yes, O=no)
5 JAPAN 1.31 0.86 - 2.01 1 (1 =NJ, O=JA) 1423BMD 2.08 1.63 - 2.67 -1 SD=-0.1607 g/cm'AGE 1.88 1.58 - 2.25 5 years
ALCOHOL USE 0.89 0.56 - 1.41 1 (1 =yes, O=no). - - • --- --- - ,n - .n, omts of the confidence interval of OR are corresnondmz to the units o Change snecined m tnis table.
-t:3
Table 3.21(Continued) Age-adjusted and BMD adjusted Odds Ratios Based on Fracture Definition PV2A
gpoint estimate and enopb. Radiation exposure doses for all JA subjects were set equal to O.
MODEL INDEPENDENT VARIABLES ORa 95% CIa UNIT OF CHANGE N
6 JAPAN 1.42 0.91 - 2.22 1 (l =NJ, O=JA) 1530BMD 2.15 1.70 - 2.73 -1 SD=-0.1607 g/cm'AGE 1.86 1.58 - 2.19 5 years
RADIATION' 1.01 0.98 - 1.04 0.1 Gy
7 JAPAN lAO 0.93 - 2.12 1 (l =NJ, O=JA) 1522BMD 2.13 1.68 - 2.72 -1 SD=-0.1607 g/cm'AGE 1.86 1.57-2.19 5 years
CAUSE OF MENOPAUSE 1.09 0.65 - 1.83 1 (1 = artificial , O=spontaneous)
8 JAPAN 1.23 0.78 - 1.91 1 (l =NJ, O=JA) 1361BMD 2.03 1.58 - 2.62 -1 SD=-O.1607 g/cm2
AGE 1.86 1.54 - 2.24 5 yearsNo. OF LIVE BIRTH 1.02 0.90 - 1.16 1
9 JAPAN 1.63 0.98 - 2.70 1 (1 =NJ, O=JA) 1114BMD 2.11 1.61 - 2.77 -1 SD=-0.1607 g/cm'AGE 1.93 1.57 - 2.38 5 years
TOTAL LACTATION PERIOD 1.01 0.96 - 1.07 -5 months
10 JAPAN 1.64 0.98 - 2.73 1 (l =NJ, O=JA) 1109BMD 2.14 1.63 - 2.81 -1 SD=-0.1607 g/cm'AGE 1.92 1.56 - 2.38 5 years
AVERAGE LACTAnON PERIOD PER CHILD 1.02 0.98 - 1.06 -1 month~ . . ., -' ... ~ to the units of change specified m this table.
....NW
Table 3.21(Continued) Age-adjusted and BMD-adjusted Odds Ratios Based on Fracture Definition PV2A
ppoint estimate ana ennpb. Radiation exposure doses for all JA subjects were set equal to O.
MODEL INDEPENDENT VARIABLES ORa 95% CIa UNIT OF CHANGE N
11 JAPAN 1.24 0.74 - 2.07 1 (1 =NJ, O=JA) 1380BMD 2.11 1.64 - 2.72 -1 SD=-0.1607 g/cm2
AGE 1.85 1.54 - 2.23 5 yearsAGE AT MENARCHE 0.98 0.49 - 1.95 5 years
12 JAPAN 1.42 0.89 - 2.26 1 (l =NJ, O=JA) 1170BMD 1.99 1.51 - 2.63 -1 SD=-0.1607 g/cm2
AGE 1.84 1.53 - 2.22 5 yearsAGE AT MENOPAUSE 1.42 1.10 - 1.83 -5 years
13 JAPAN 1.10 0.65 - 1.85 1 (1 =NJ, O=JA) 1024BMD 2.00 1.48 - 2.70 -1 SD=-0.1607 g/cm'AGE 1.78 1.45-2.19 5 years
DURATION BETWEEN MENARCHE & MENOPAUSE 1.47 1.14 - 1.89 -5 years
14 JAPAN 1.18 0.66 - 2.10 1 (l =NJ, O=JA) 1000BMD 2.02 1.49 - 2.73 -1 SD=-0.1607 g/cm2
AGE 1.78 1.44 - 2.20 5 yearsDURATION BETWEEN MENARCHE & MENOPAUSE 1.49 1.15 - 1.94 -5 years
RADIATION" 1.00 0.95 - 1.05 0.1 Gy~ . . omts ot the confidence interval of OR are corresnondina to the umts ot change S .
...t __~ £
-~
-NVI
APPENDIX B: FIGURES
IFracture RiSkl
iI
I Bone Strength I Propensity To Trauma [
T 11\
I Bone Quality r-I Bone Mass I CoordinationI Equilibrium
I Reflex ResponseMuscle StrengthProtective MechanismEnvironmental Hazard
Architecture Ipeak Bone Massi IBone Loss RatelFatigue Damage
1\ 1\Mineralization
iIInteraction between Genetic and Environmental Factors I
FIGURE 1.1 DIAGRAM ILLUSTRATING THE DETERMINANTS OF FRACTURE RISK
D Normal Vertebral Body
Crush Fracture
Wedge Fracture
Endplate Fracture
Figure 2.1 C~SSIFICATION OF VERTEBRAL FRACTURE
126
6ii' i I I I I Iii i I I i I
5
,.-....~ 4---
. ,", ,.,.."
-.
..•..
PV1PV2PV3PV4
-- PV5PV6
»:2
3
~UZ~....:l<C:>~
~0...
.....N-...l
o I I I I I I I I I I I I I I I I
T4 T5 T6 T7 T8 T9 T10 T11 T12 L1 L2 L3 L4 L5
VERTEBRA
FIGURE 3.1 VERTEBRA-SPECIFIC PREVALENCE IN HAWAII
N00
6
5
-..~ 4'--"
r:ilUZr:il 3~
<>t::il~ 2P-.
o
PVlPV2PV3
- PV4---- PV5
.-- PV6
............
",-.." "'.
' .... _------_..~-_....... .....,
'"
,'.
"""":':.::::::.:::::::".,...<
~--
-,
T4 T5 T6 T7 T8 T9 T10 T11 T12 L1 L2 L3 L4 L5
VERTE~RAI,
FIGURE 3.2 VERTEBRA-SPECIFIC PREVALENCE IN JAPAN
..
,,,
,
.>
.,.
,.
". .,", '
..\.~:.\.\0\\'\~ .r"> .\...... '. .-~.:..... ~.:..~.
'. ',\~ ".
\\.. '\ .:... :..~ .: - -.:::~.:.~~ ..~..
/;,., ,
, ,
//.'.:::::~<.~",
- PVl..--..... PV2................ PV3- PV4. -_.-- PV5.. __ ..-.. PV6
, , ,,,
, .'
..................<.;~.,//
5
o
6
~UZ~ 3.....:l
~~~ 2o,
..........~ 4'--"
-N\0
T4 T5 T6 T7 T8 T9 T10 T11 T12 L1 L2 L3 L4 L5
VERTEBRA
FIGURE 3.3 VERTEBRA-SPECIFIC PREVALENCE IN MINNESOTA
- PV1A.-------. PV2A
PV3A- PV4A
PV5A.-.....-. PV6A
....._.._---
, ." ......, .
,v-,
...... ::~.,.,
.....~~~ .......~.~......
, ,
i, ~"
!, ... ". "i ,-
.' '; .
fj//.......i' : -,
.../~/ ...../
/~//
..:- .
_ .6tf:;·:~~- ;-o
5
6
~UZ~ 3.....:l~::>~~ 20...
...--~ 4"-'"
IoUo
T4 T5 T6 T7 T8 T9 T10 T11 T12 L1 L2 L3 L4 L5
VERTEBRA
FIGURE 3.4 VERTEBRA-SPECIFIC PREVALENCE IN HAWAII
w-
6
5
,--....
~ 4"--'"
~UZ~ 3io--::J
~~~ 2c,
o
PYlAPV2APV3A
- PV4A----- PV5A
.-------. PV6A
t·,
/ -, ../ .....
/ / '.' -----.J
,/1~;;~;~~:~:-:;'"
._~---
.-
v,
T4 T5 T6 T7 T8 T9 T10 T11 T12 L1 L2 L3 L4 L5
VERTEBRA
FIGURE 3.5 VERTEBRA-SPECIFIC PREVALENCE IN JAPAN
6
5
-----~ 4..........,
- PVIA.------- PV2A................ PV3A
- PV4A-_ ...-..- PV5A.... ----. PV6A
, ,
..-----;~,i "
/ "
./ ,'1····· -.. :.:.;.,.•...•
'.... ,'.,
--\:::::::: ::::::>.:m __
,. ~ ---~
o
J::ilUZJ::il 3~
~J::il~ 2c,
.....I.JJN
T4 T5 T6 T7 T8 T9 T10 T11 T12 L1 L2 L3 L4 L5
VERTEBRA
FIGURE 3.6 VERTEBRA-SPECIFIC PREVALENCE IN MINNESOTA
4
IZLI WEDGEHAWAII
3E;;SI ENDPLATE
~ CRUSH
2
,-...,.Q.)
0I.-
..aQ.)
.-+-'0I.-
Q.) T4 T5 T6 T7 T8 T9 no TIl TI2 L1 L2 L3 L4 L5>
4a JAPANa..--I.- 3Q.)
0... IZLI WEDGE
en2 E;;SI ENDPLATE
Q.)I.-
~ CRUSH:J
-+-'o0l.-
'+-'--'"
w00
ZT4 T5 T6 T7 T8 T9 no nl n2 LI L2 L3 L4 L5
W 4---l MINNESOTA-c>W
3IZLI WEDGE
0:::o, lS:SI ENDPLATE
ISZSiI CRUSH
2
T4 T5 T6 T7 T8 T9 no TIl r tz L1 L2 L3 L4 L5
Location in spine
FIGURE 3.7 VERTEBRA-SPECIFIC PREVALENCE
OF DIFFERENT TYPES OF FRACTURE
133
85
............
80
"
75
....................
,.
, ,
"
70
PV2
.,." ,
"
PV4
.' .
65
.........................:.~.;.;~.~ .
6055
._--- .. __ ....
- HAWAII.------ JAJ>AN............ MI~NESOTA
...........................................................
- HAWAII.------ JAJ>A~
............. MI~NESOTA
a
60
..-.... 50~.....--
40~C,)
z 30~.....:l~ 20:>~0:: 100...
0
5060
50..-....~
-- 40~uz 30~.....:l~ 20:>-~
0:: 100...
8580757065605550
60 ..---.------,-----,------r---.------.,.-----,
50".......
~
-- 40~uz 30~.....:l< 20::>~
g: 10
- HAWAII PV6------ JAJ>AN............ MI~NESOTA
o
50 55 60 65 70 75 80 85AGE
FIGURE 3.8 AGE-SPECIFIC PREVALENCE OF SPINEFRACTURE BY STUDY POPULATION
134
.- ----_._----------------
85
85
....
80
80
75
75
......................
....................
70
70
PV2A
PV4A
PV6A
65
65
60
60
55
55
---------- .._-----
- HAWAII..----- JAPAN............ MINNESOTA
- HAWAII..----- JAPAN............ MINNESOTA
- HAWAII.------ JAPAN............. MINNESOTA
~.:.:.:.7.7.:.:.:.:.:'·:·':'·':'·':'.:·~····· .._·..·..·,;:;·~·.:·:.·:·; .•........................, .
'.:.:.:.:.:.:.:.:.:.:.:.:::.=:.;.;.:-.:.:.:.:.:.:.:.:.:.:".:':'.:'.:'. - - - - - - :.:.:.:.:' .
...............................................................
o
o
50
60 ,----..,------,----,------.------,---,------,
50.--..~
-- 40I:i:loz 30I:i:l.....:l~ 20>I:i:l
g: 10
60
......... 50~'--' 40~uz 30~.....:l~ 20:>~
0:: 100...
0
5060
50.--..~
-- 40~uz 30~.....:l~ 20:>I:i:l0:: 100...
50 55 60 65 70 75 80 85AGE
FIGURE 3.9 AGE-SPECIFIC PREVALENCE OF SPINEFRACTURE BY STUDY POPULATION
135
HAWAII40
1 FRACTURE
30 2 + FRACTURES
20
10
'"' 0 - - - --c:: 50 55 60 65 70 75 80 85CD
E0 JAPAN~ 40
0 1 FRACTURE0.- 2+ FRACTURES<, 30
CDCDCD ,/
0 20 ,/
o ,/.......,/
W ./ -U 10
'7ZW
,/
./...J-c 0> 50 55 60 65 70 75 80 85WD:::a.
MINNESOTAwD::: 40:::l 1 FRACTUREt-U 2+ FRACTURES I-c 30 ID:::u, I
I20 I
10
------050 55 60 65 70 75 80 85
AGE (years)
FIGURE 3.10 AGE-SEPCIFIC PREVALENCE OF SINGLEAND MULTIPLE VERTEBRAL FRACTURE
136
----.... - ._-_._----
154 I I I I I I I i
153HAWAIIJAPAN
152
..-... I~
/u"-' 151 L
, ... - .....
E-t
/
:r::
,
0I
t--t 150- ~
/
W ::r::-....l
149 r-, ,,
148 .,
,,
,
1910 1915 1920 1925 1930 1935 1940
BIRTH YEAR
FIGURE 3.11 MEAN HEIGHT BY BIRTH YEAR
56 i I I I I I I I I
55 I- HAWAII
sJJAPAN
..- , ,
,.--...
c.J53 1
~/
"--"
.. ---_.. --------_.-_.-. ----_.. _._.- ....
E--<
, ,
::r::o 52 I- /
_ r£lw ~00
51
50
194019351930192519201915191049 I I I I I I I I I
1905
BIRTH YEAR
FIGURE 3.12 MEAN WEIGHT BY BIRTH YEAR
24.0iii Iii I I
..--....C\2
::g 23.5<,c..J~<;»
:x:I::t:l0Z 23.01--1
tr:u:
.... <~ ::g
:>-<Qo 22.5o:l
r,
- HAWAII
.------- JAPAN
,,
, ,,
'..... ,
,
,
"
, ,
1945194019351930192519201915191022.0 I , , , ! , ! , I
1905
BIRTH YEAR
FIGURE 3.13 MEAN BODY MASS INDEX BY BIRTH YEAR
16
IHAWAII
15 ~JAPAN
'-.."
~
'.~--- ..
::r::. - ---. --..... ---..
u~<r::z~ 14::;E
E-<<r::
I..-~
»>~.;:..
0 0
,
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