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The influence of body weight and composition, pubertal status, tobacco use and exposure, physical activity and muscle strength on bone mass of Chinese adolescents

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The influence of body weight and composition, pubertal status, tobacco use and exposure, physical activity and muscle strength on bone mass of Chinese adolescents

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Content THE INFLUENCE OF BODY WEIGHT AND COMPOSITION, PUBERTAL
STATUS, TOBACCO USE AND EXPOSURE, PHYSICAL ACTIVITY AND
MUSCLE STRENGTH ON BONE MASS
OF CHINESE ADOLESCENTS
by
Afrooz Afghani
A Dissertation Presented to the
FACULTY OF THE GRADUATE SCHOOL
UNIVERSITY OF SOUTHERN CALIFORNIA
In Partial Fulfillment of the
Requirements for the Degree
DOCTOR OF PHILOSOPHY
(Biokinesiology)
May 2001
Copyright 2001 Afrooz Afghani
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UNIVERSITY OF SOUTHERN CALIFORNIA
The Graduate School
University Park
LOS ANGELES, CALIFORNIA 900894695
This dissertation, wr i t t e n b y
Afrooz Afghani_ __
U nder th e direction o f h .§ .D issertation
Com m ittee, and approved b y a l l its members,
has been p resen ted to an d accepted b y The
Graduate School, in p a rtia l fulfillm ent o f
requirem ents fo r th e degree o f
DOCTOR OF PHILOSOPHY
Dean of Graduate Studies
D ate M ay 11, 2001
DISSERD COMMITTEE
" '" ‘I
'~ ~ /7
-son
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Dedication
This work is dedicated to my parents for their endless support, kindness, and
compassion throughout my long educational career. They have encouraged me to set
goals, to pursue my ambitions, to strive for excellence, and to persevere through hard
work. Their most important wish in life, for me to excel academically, has now
come true.
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Acknowledgements
There are a number of individuals who have been a valuable part of the
success of this dissertation, and whom I wish to thank. I would like to thank the co
chairs of my dissertation: C. Anderson Johnson and Robert A. Wiswell, whose
support, guidance, patience, and friendship throughout this process was essential.
I would also like to thank the members of my dissertation committee: Chih-
Ping Chou, Michael Goran, and Sandra Howell for their careful attention to and
critique of my dissertation proposal. Their feedback throughout this process has
been helpful and has broadened my knowledge about the subject matter.
Subject recruitment, data collection, and communication with the children o f
Wuhan would not have been possible without the support and leadership of Li Yan
and the hard work of Gong Jie.
I would like to thank those few friends who have listened to my stories of
China and the details of my osteoporosis research.
Finally, I would like to thank my brother for his unlimited concern and my
parents for their encouragement and support at all times and at every difficult stage
of my graduate training and throughout my long journey towards a Ph.D.
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Table of Contents
Dedication ii
Acknowledgements iii
List of Tables v
List of Fi gures vi
Abstract vii
Chapter I. Introduction 1
Chapter II. Literature Review 3
Chapter III. Hypotheses & Model 21
Chapter IV. Research Design & Methods 24
Chapter V. Results 43
Chapter VI. Discussion 56
Literature Cited 72
Appendix 87
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List of Tables
1. Mechanisms underlying the effect of cigarette smoking on estrogen-related
phenomena
2. Sample breakdown based on gender and smoking status
3. Participant breakdown based on gender and smoking status
4. Power analysis
5. Trabecular bone content at various skeletal sites
6. BMI cut off points
7. Variable means and standard deviations for girls
8. Variable means and standard deviations for girls: interactions
9. Variable means and standard deviations for boys
10. Variable means and standard deviations for boys: interactions
11. Variable correlations with BMD and BMC of girls at forearm and heel
12. Variable correlations with BMD and BMC of boys at forearm and heel
13. Correlations among independent variables in girls and boys
14. Multiple linear regression model for forearm BMD
15. Multiple linear regression model for forearm BMC
16. Multiple linear regression model for heel BMD
17. Multiple linear regression model for heel BMC
18. Multiple linear regression model for forearm BMC: lean and fat mass
19. Multiple linear regression model for heel BMC: lean and fat mass
V
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List of Figures
1. Conceptual model
The following figures are in the Appendix:
2. Scatter Plot of forearm bone mineral content and weight in girls
3. Scatter Plot of forearm bone mineral content and strength in girls
4. Scatter Plot of forearm bone mineral content and age in girls
5. Scatter Plot of forearm bone mineral content and height in girls
6. Scatter Plot of heel bone mineral content and weight in girls
7. Scatter Plot of heel bone mineral content and height in girls
8. Scatter Plot of forearm bone mineral content and strength in boys
9. Scatter Plot of forearm bone mineral content and weight in boys
10. Scatter Plot of forearm bone mineral content and height in boys
11. Scatter Plot of forearm bone mineral content and age in boys
12. Scatter Plot of heel bone mineral content and weight in boys
13. Scatter Plot of heel bone mineral content and height in boys
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Abstract
Although much has been learned about osteoporosis occurring later in life,
large sample population studies are needed to assess potentially critical relationships
between body weight, pubertal status, tobacco use and exposure, physical activity,
and strength on bone mass in early life. Understanding these relationships during the
growing years is critical, since optimizing peak bone mass is the most powerful
preventive strategy for osteoporosis.
To address these issues and to shed light on ways to optimize peak bone mass
during growth in order to prevent osteoporosis in the later years, we measured heel
and forearm bone mass of a group of Chinese girls and boys ranging in age between
10 and 16 years.
We measured bone mineral density (BMD) and content (BMC) of the
forearm and the heel of 466 (166 girls and 300 boys) adolescents using dual-energy
x-ray absorptiometry. We estimated percent fat, fat mass and lean mass by
bioelectrical impedance analysis. We measured grip strength by isometric
dynamometry. We determined pubertal status, active and passive smoking, and
physical activity using questionnaire data.
We found that body weight was moderately correlated (r=0.57, p<0.0001) to
forearm BMC in girls and strongly correlated (r=0.67, p<0.0001) to heel BMC in
boys. Lean mass was correlated more strongly (r=0.58-0.60 in girls; r=0.53-0.71 in
boys) than fat mass (r=0.26-0.41 in girls; r=0.25-0.40 in boys) to bone mass at the
vii
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forearm and the heel. Grip strength also was significantly correlated (r=0.44 and
0.60, p<0.0001) with forearm BMC in girls and boys, respectively. Passive smoking
was only correlated to forearm BMD (r=Q.14, p<0.05) and BMC (r=0.19, p<0.05) in
girls. Physical activity and active smoking were not significantly correlated to
forearm or heel bone mineral density or content in either girls or boys.
By performing multiple linear regression models, we determined that 32% of
the variance in forearm BMC of the girls was attributed to weight, 6% to strength,
and 4% to pubertal status for a combined variance of 42%. Passive smoking, active
smoking, and physical activity did not make significant contributions to forearm
BMD or BMC in girls. In boys, 36% of the variance in forearm BMC was attributed
to strength, and 6% to body weight, for a combined variance of 42%. Pubertal
status, passive and active smoking, and physical activity did not make significant
contributions to forearm BMD or BMC in boys.
Heel bone mineral content was best predicted by weight (21% in girls; 46%
in boys) and strength (3% in girls; 1% in boys) accounting for 24% of the variability
in girls and 47% in boys. The addition of pubertal status, passive smoking, active
smoking, and physical activity into the models did not make significant contributions
to the variance of heel BMD or BMC in either girls or boys.
We failed to find significant inverse relationships between smoking and bone
mass in this cross-sectional study. The reasons may have been due to the low levels
and duration of tobacco use and exposure among the adolescents we studied.
Selecting an older sample of adolescents who smoke more frequently and for a
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longer duration may have uncovered a different relationship. Nevertheless, we
believe that this is the first study that investigated the role of smoking on bone mass
during adolescence. We are also not aware of any other study in any age-group or
gender that has assessed the role of second-hand-smoke on bone mass.
Our findings support the influence of body weight, pubertal status, and
muscle strength on bone acquisition during childhood and early adolescent period.
These findings are in agreement with the conclusions of Krall and Dawson-Hughes
(1993) who found that nearly half the variance in bone mineral density is attributable
to nonhereditary factors. Although the factors found to be important in determining
bone mass in our study are partially genetically controlled, the environment also
plays a role in the determination of body size, puberty, and strength. Our findings
support the hypothesis that the processes that determine fracture risk in adulthood
and increase the likelihood of osteoporosis in the elderly, begin during childhood and
adolescence.
IX
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Afrooz Afghani C. Anderson Johnson
ABSTRACT
THE INFLUENCE OF BODY WEIGHT AND COMPOSITION, PUBERTAL
STATUS, TOBACCO USE AND EXPOSURE, PHYSICAL ACTIVITY AND
MUSCLE STRENGTH ON BONE MASS OF CHINESE ADOLESCENTS
Although much has been learned about osteoporosis occurring later in life, large
sample population studies are needed to assess potentially critical relationships between
body weight, pubertal status, tobacco use and exposure, physical activity, and strength on
bone mass in early life. Understanding these relationships during the growing years is
critical, since optimizing peak bone mass is the most powerful preventive strategy for
osteoporosis.
To address these issues, we measured heel and forearm bone mass of 466 Chinese
adolescents ranging in age between 10 and 16 years. We measured bone mineral density
(BMD) and content (BMC) using dual-energy x-ray absorptiometry and grip strength by
isometric dynamometry. We estimated percent fat, fat mass and lean mass by
bioelectrical impedance analysis and determined pubertal status, active and passive
smoking, and physical activity using questionnaire data.
By performing multiple linear regression models, we determined that 32% of the
variance in forearm BMC of the girls was attributed to weight, 6% to strength, and 4% to
pubertal status for a combined variance of 42%. In boys, 36% of the variance in forearm
BMC was attributed to strength, and 6% to body weight, for a combined variance of 42%.
Heel BMC was best predicted by weight (21% in girls; 46% in boys) and strength (3% in
girls; 1% in boys) accounting for 24% of the variability in girls and 47% in boys.
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We failed to find significant inverse relationships between smoking and bone
mass in this cross-sectional study. The reasons may have been due to the low levels and
duration of tobacco use and exposure among the adolescents we studied. Nevertheless,
we believe that this is the first study that investigated the role of smoking on bone mass
during adolescence.
Our findings support the influence of body weight, pubertal status, and muscle
strength on bone acquisition during adolescence. We conclude that the processes that
determine fracture risk in adulthood and increase the likelihood of osteoporosis, begin
during childhood and adolescence.
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2
Chapter I. Introduction
Research addressing the role of environmental/lifestyle factors on bone
development during times of peak bone acquisition in adolescence is limited. It is
undoubtedly clear that peak bone mass is a key determinant of skeletal health
throughout life. Therefore, investigating the factors that influence bone accretion
during the developmental years is critically important.
Although the relationship between cigarette smoking and bone loss during
adulthood is well established, the effect of smoking and ETS (environmental tobacco
smoke) exposure on bone mass in children is unknown. Understanding the adverse
role of tobacco in achieving peak bone mass in children is important since obtaining
a high bone mass in childhood and adolescence is critical in preventing osteoporosis
later in life.
Numerous studies indicate that over 90% of peak bone mass is achieved by
about 15 to 18 years of age (Slemenda et al., 1994; Theintz et al., 1992; Bonjour et
al., 1991; Katzman et al., 1991). In the United States and other Western countries
(Chen & Unger, 1999; Najem et al., 1997; Swan et al., 1990) as well as in China
(Yang et al., 1999; Li et al., 1996), the first upswing in tobacco use occurs in the late
pre-adolescent and early adolescent years (ages 10-13). Body weight, pubertal
status, physical activity, and muscle strength are believed to influence bone mass in
youth. However, the relationship of these variables to tobacco exposure and their
contributions to bone mineral development are not well understood.
1
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To address these questions, we measured bone m ineral density (BMD) and
bone mineral content (BMC) of the heel and the forearm using peripheral DXA in a
group of Chinese girls and boys ranging in age from 10 to 16 years. The specific
aims of this study were the following:
1. To correlate age, weight, height, other body composition variables (BMI, fat
mass, lean mass), pubertal status, time since menarche/puberty, passive
smoking, active smoking, physical activity, and strength to bone mineral
density and bone mineral content.
2. To test the relationship of each of the critical factors (body weight, pubertal
status, tobacco use and exposure, physical activity, strength) on BMD/BMC of
the forearm and the heel, while controlling for the other factors.
3. To test the relationship of lean body mass and fat mass on BMC of the forearm
and the heel, while controlling for the other.
The hypotheses tested in this study were the following:
1. Greater body weight is associated with higher levels of bone mass.
2. Pubertal status is related positively to peripheral bone mass.
3. Environmental tobacco smoke exposure is related negatively to bone mass.
4. Cigarette smoking is associated with lower levels of peripheral bone mass.
5. Frequency and intensity of physical activity is related positively to bone mass.
6. Strength is associated positively with bone mass.
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Chapter II. Literature Review
Scope o f the Problem:
It is estimated that 16.8 million (54%) of postmenopausal white women in the
United States have low bone mass or "osteopenia" (bone mineral density: 1-2.5
standard deviations below the young adult mean) and another 9.4 million (30%) have
"osteoporosis" (bone mineral density: more than 2.5 standard deviations below the
young adult mean). Osteoporosis is a disease characterized by low bone mass and
microarchitectural deterioration of bone tissue leading to enhanced bone fragility and
a consequent increase in fracture risk. The subset of women with osteoporosis who
also have a history of one or more fractures are identified as having "established"
osteoporosis. About 4.8 million women (51% of the osteoporotic women and 16%
of all white women age 50 years or above) are estimated to have established
osteoporosis (Melton, 1995). The condition is manifested by a decrease in bone
substance and strength. This very often leads to fractures of the spine, femur, and
forearm.
Each year 1.2 million fractures in the United States are attributed to
osteoporosis (Riggs et al., 1986), resulting in health expenditures of $13.8 billion
annually (Ray et al., 1997). The estimated lifetime risk of a hip fracture in the
United States is 17.5% in white women and 6.0% in white men. The lifetime risk of
vertebral fractures is 15.6% and 5.0% for women and men, respectively, and the
lifetime risk of distal forearm fractures is 16% for women and 2.5% for men. The
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lifetime risk of any of the three fractures is 39.7% for women and 13.1% for men
(Melton et al., 1992).
Life expectancy is rising around the world and the number of elderly
individuals is increasing in nearly every geographic location. There are an estimated
323 million individuals age 65 years or over at present, and this number is expected
to reach 1555 million by the year 2050 (Cooper et al., 1992). Demographic changes
alone can be expected to cause the number of hip fractures occurring among people
age 35 years and over throughout the world to increase from 1.66 million in 1990 to
6.26 million in 2050. It has been calculated that around half of all hip fractures
among elderly people in 1990 took place in Europe and North America. By 2050,
however, the rapid aging of the Asian and Latin American populations will result in
the European and North American contribution falling to only 25%, with over half of
all hip fractures occurring in Asia (Cooper et al., 1992). Lau (1988) reported that
over the last 20 years the incidence of osteoporotic fractures in Hong Kong has
increased 3-fold in the elderly to reach a rate of about 10 in 1000 in people at 70
years and above.
Gender Differences and Bone Mass:
Fracture risks vary between men and women (Looker et al., 1997; Melton et
al., 1997). Drawing conclusions and reaching a consensus on gender variations in
bone mass during growth is difficult, due to the lack of adequate statistical power
and/or inappropriate statistical analyses. Although some studies (Lu et al., 1994;
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Kroger et al., 1992) report greater values of BMC or BMD in females during
childhood or adolescence, others (Zanchetta et al., 1995; Rubin et al., 1993; Rico et
al., 1993; Glastre et al., 1990) report greater values in females only until ages 15-18
years; the values in males surpass those of females after ages 15-18 years. Others
(Horlick et al., 2000; Specker et al., 1987; Tanner et al., 1981) report greater values
in pre-pubertal males, while still others (Grimston et al., 1992; Southard et al., 1991)
report small or no gender differences. Since the rate of bone mineral accrual, the age
of attainment of peak bone mineral content, and fracture rates vary according to
gender, establishing the impact of factors on bone mass based on gender becomes an
important strategy in data interpretation and analyses.
The Influence o f Genetics on Bone Mass:
Genetic factors are major determinants of peak bone mass. Seeman and
colleagues (1989) have shown that premenopausal daughters of postmenopausal
women with osteoporosis have lower bone mass than other women of the same age.
BMD has been found to be more highly correlated in monozygotic than in dizygotic
twins (Pocock et al., 1987). Still, nearly half the variance in bone mineral density of
U.S. adult men and women is attributable to non-hereditary factors (Krall and
Dawson-Hughes, 1993). It is possible that the proportion of the variance in bone
mass attributable to non-hereditary or environmental factors may be even greater
among Chinese men and women because of the unique environmental factors they
are exposed to. For example, Ling and colleagues (2000) assessed risk factors for
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vertebral fractures in a random sample of 402 women age 50 years or older living in
Beijing, China and concluded that women with a history of heavy physical labor had
a lower risk of vertebral fractures.
Peak bone mass can also be determined by environmental (exogenous)
factors such as body weight and pubertal status (genetically/environmentally
determined), physical activity, strength, cigarette smoking, and environmental
tobacco exposure. Although much has been learned about osteoporosis occurring
later in life, large sample population studies are needed to assess potentially critical
relationships between these environmental factors and bone mass in early life.
Understanding these relationships especially during late childhood and early
adolescence is critical, since optimizing peak bone mass during the years of maximal
growth can be the most powerful preventive strategy for osteoporosis.
Body Weight and Composition as Predictors o f Bone Mass:
Many researchers (Slemenda et al., 1994; Welten et al., 1994; Rice et al.,
1993; Katzman et al., 1991) have found body weight to be a strong predictor of bone
in adolescents (r values as high as 0.9). It is unclear, however, which component of
weight (lean or fat) is more important to bone mass during the growing years.
Whether or not the "heavy-lean" adolescent would be more protected from
osteoporosis than the obese adolescent is an important issue with tremendous health
implications.
6
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In pre- and post-menopausal women, Reid and co-investigators (1992a,
1992b) have concluded that total body fat is the most significant predictor of bone
mineral density and have reported correlation coefficients between fat mass and total
body BMD (r=0.55; p<0.0001) which are approximately equal to that between body
weight and total body BMD (r=0.54; pO.OOOl); the correlation between lean mass
and total body BMD was much weaker (r=0.18; p<0.05). Similarly, Hassager and
Christiansen (1989) have concluded that fat mass was lower in osteoporotic women
compared to normal women; lean body mass did not differ in the two groups. These
studies (Reid et al., 1992a, 1992b; Hassager & Christiansen, 1989) have used dual
energy x-ray absorptiometry to measure total body fat mass, lean mass, and bone
mass.
In contrast, Bevier et al. (1989) found correlations between spine BMD and
fat mass (r=0.31; p<0.05) as well as spine BMD and lean mass (r=0.45; p<0.005) in
adult women using bioelectrical impedance analysis. Using dual energy x-ray
absorptiometry, other investigators have concluded that lean body mass and not fat
mass, independently predicts whole body (Wegner et al., 1993) and spine (Shaw et
al., 1993) bone mineral density in women. In men, Reid et al. (1992b) have shown
that lean mass and bone mass as measured by dual energy x-ray absorptiometry, are
moderately correlated (r=0.51; p<0.001); fat mass and bone mass are unrelated
(r=0.26; non-significant).
Reid and colleagues (1995) have pointed out that discrepancies in the
literature pertaining to the contributions of fat mass and lean mass to bone mass
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include differences in subject characteristics, in body composition measurement
techniques, in sites used for bone mass measurements, as well as in statistical
methodology. The fact that lean body mass as estimated by bioelectrical impedance
analysis is actually fat-free mass because it also includes skeletal mass, is a
limitation of this body composition technique because it strengthens its relationship
with BMD. The methodology of lean and fat mass measurement by DXA
overcomes this problem, though it does make assumptions regarding the ratio of fat
and lean tissues when scanning the skeleton (Mazess, et al., 1990).
It is important to recognize the complexity of the impact of fat mass versus
lean body mass on bone mass and data must be analyzed and interpreted with
caution; bivariate correlations may not be the most accurate way of drawing
conclusions regarding the matter. To understand the role of fat mass and lean body
mass on bone mass, multiple regression models are necessary to address the
independent influence of each on bone mass when controlling for total body weight.
Mechanisms Underlvins the Influence o f Weight on Bone Mass:
A number of studies in vivo and in vitro have demonstrated that androgens
are aromatized by adipose tissue (Longcope et al., 1978; Nimrod et al., 1975;
Schindler et al., 1972). In women, obesity has been found to be associated with
enhanced conversion of androgens to estrogens (MacDonald et al., 1978; Edman &
MacDonald, 1978). In obese men, low testosterone, high estrogen levels and
enhanced conversion of testosterone to estradiol have been reported (Kley et al.,
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1979; Glass et al., 1977). Furthermore, Kley and colleagues (1980) measured
plasma concentrations of testosterone, androstenedione, estrone, and estradiol in
normal (90 to 120% of ideal body weight) and obese (>160% of ideal body weight)
young males. In the obese subjects, ideal body weight (IBW) was inversely
correlated with plasma concentrations of androstenedione (r=-0.81) and testosterone
(r=-0.87), while correlations between IBW and estrone (r=0.92) as well as IBW and
estradiol (r=0.95) were positive (pO.OOl). They concluded that the conversion of
androstenedione to estrone and of androstenedione to estradiol are enhanced in obese
subjects, suggesting that the enhanced aromatization of androstenedione due to an
increased adipose tissue mass, is accounting for the high plasma estrogen levels
observed in obese men.
The importance of the role of hormones (i.e. estrogen) on bone mass is well
established and is discussed below.
The Role o f Pubertal Status on Bone Mass:
Puberty is an important predictor of bone in adolescents. Rubin and
colleagues (1993) found pubertal stage (Tanner) to be the strongest single predictor
(R2=0.74) of bone mineral density in children aged 6 to 18 years, when they
regressed BMD with age, height, weight, body mass index, pubertal status, dietary
calcium, and physical activity. They found steady increases in BMD before puberty,
followed by accelerated increases during puberty, beginning at 10 years of age in
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girls and 13 years of age in boys. De Schepper et al. (1990) have also shown linear
increases in bone mass before puberty, with exponential increments during puberty.
Theintz et al. (1992) in a longitudinal study found that in boys, bone mass
continued to increase substantially between 15 and 18 years of age, whereas in girls,
the accretion of bone slowed remarkably by 15 to 16 years of age, suggesting an
earlier attainment of peak bone mass in girls than in boys due to an earlier onset of
puberty in girls. Theintz and colleagues (1992) also concluded that menarche is an
important marker of bone mass. Their results indicate a drastic decline in both spinal
and femoral BMD gains observed 2 years after menarche.
The importance of hormones in the attainment and maintenance of bone mass
can best be summarized by the findings of Bachrach and colleagues (1991) who
found the persistence of spinal osteopenia in young adult women who had recovered
from ovarian disturbance associated with anorexia nervosa during puberty. A later
onset of menarche and a reduced total estrogen exposure in young women was
associated with lower peak vertebral bone mass in a study by Armamento-Villereal
and colleagues (1992). In men, Finkelstein et al. (1992) found spinal and radial
osteopenia in men with a history of delayed puberty caused by decreases in
testosterone associated with intense exercise or caloric restriction. These findings
suggest that the endocrine system plays an important role in the attainment and
maintenance of bone mass during the growing years.
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The Role o f Smoking on Bone Mass:
The relationship between smoking and bone mass has not been previously
investigated in children or adolescents. In adults, smoking has been found to be
associated with an increased risk for bone fractures and osteoporosis. Smoking has
been shown to increase the risk for hip fractures in women by about 1.2 to 1.5
(Williams et al., 1982; Kiel et al., 1992; Forsen et al., 1994), and vertebral fractures
in men by about 2.3 (Seeman et al., 1983). Although extensive research has not been
conducted to investigate the adverse role of smoking on bone mass and its
subsequent increased risk for fracture and/or disease, for the most part studies
suggest that tobacco increases the risk for fractures. This risk is more apparent in the
older population and may reduce the protective effects of estrogen replacement
therapy and obesity on maintaining bone mass.
Krall and Dawson-Hughes (1991) examined the influence of smoking on
bone mineral density and rates of bone loss in a group of 320 healthy
postmenopausal women. They found that bone mineral density of the radius was
inversely related to pack-years of tobacco exposure when controlled for body mass
index and years since menopause (r=-0.18, p=0.05, n=125). The (mean ± SD) rates
of bone change at the radius were significantly different between smokers (-0.914 ±
2.6 percent/year, n=34) and nonsmokers (0.004 ± 2.6 percent/year, n=278). The
same trend was seen at the femoral neck, the os calcis and the spine. But the
difference in rates at these sites did not reach statistical significance.
11
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Hopper and Seeman (1994) studied the bone density of female twins (ages 27
to 73) discordant for tobacco use. Twin studies are important because they control
for genetic composition, as well as age and sex, all of which are major determinants
of bone mass. They found that for every 10 pack-years of smoking, the bone density
of the twin who smoked more heavily was 2.0% lower at the lumbar spine (p=0.01),
0.9% lower at the femoral neck (p=0.25), and 1.4% lower at the femoral shaft
(p=0.04). These investigators conclude that women who smoke one pack of
cigarettes each day throughout adulthood will, by the time of menopause, have an
average deficit of 5 to 10 percent in bone density, which is adequate to increase the
risk for fractures.
In men, Valimaki et al. (1994) showed inverse correlations between bone
mineral density of the lumbar spine and self-reported smoking (r range of -0.18 to -
0.46) when they controlled for weight. Inverse relationships also emerged between
bone mineral density of the hip and smoking prevalence (r range of -0.10 to -0.63).
Mechanisms Underlying the Influence o f Tobacco on Bone Mass:
The mechanisms by which tobacco effects bone mass are not well
elucidated. However, epidemiological data suggest that women who smoke
cigarettes behave physiologically as though they are relatively estrogen-deficient.
MacMahon and colleagues (1982) reported that premenopausal women who smoked
had lower luteal phase urinary excretion of estrone, estradiol, and estriol than never-
smokers, reflecting a lower estrogen production in smokers. Ex-smokers did not
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show this pattern, nor were there differences during the follicular phase. Michnovicz
et al. (1986) however, found lower levels of estrogen in both the follicular and the
luteal phases of the women smokers they studied. Furthermore, postmenopausal
women who took oral estradiol and who smoked appeared to have lower estradiol
and estrone levels than similar non-smoking women (Jensen & Christiansen, 1988;
Jensen et al., 1985). All these results suggest that cigarette smoking effects the
absorption, distribution, or metabolism of estrogen.
Studies by Barbieri and colleagues (1986) suggest that nicotine, cotinine, and
anabasine inhibit the conversion of androstenedione to estrogen in a dose-dependent
fashion. When supraphysiological doses of androstenedione were applied to human
choriocarcinoma cells and placental microsomes, the inhibition of aromatase was
blocked by these tobacco products. These investigators conclude that the
mechanisms underlying the anti-estogenic role of tobacco probably involve
inhibition of the aromatase enzyme system.
Polycyclic aromatic hydrocarbons in tobacco smoke also may induce
microsomal mixed function oxidase systems that metabolize sex hormones (Lu et al.,
1972). In vitro, these compounds can effect the metabolism of testosterone and
androstenedione and can enhance the formation of catechol metabolites of estradiol
(Chao et al., 1981; Lu et al., 1972). On the other hand, carbon monoxide can inhibit
some of these enzymes (McMurtry et al., 1972).
There are other possible pathways for the anti-estrogenic effect of smoking.
Mattison (1982) has concluded that cigarette smoke extracts cause ovarian atresia in
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rodents. He believes that this could explain the early menopause and other effects
seen in women smokers. Cigarette smoking also might disturb hypothalamic
regulation of ovarian secretion. Nicotine crosses the blood-brain barrier and is
readily taken up by brain tissue (Schmiterlow et al., 1967; Sershen et al., 1979),
where nicotinic cholinergic receptors are widespread. Cigarette smoking has also
been shown to effect neurotransmitter turnover, especially in adrenergic systems
(Balfour, 1982). These central disturbances could explain both menstrual
irregularity in premenopausal smokers and the early cessation of menses, but do not
explain postmenopausal anti-estrogenic effects.
The following table summarizes the possible mechanisms underlying the
effect of cigarette smoking on estrogen-related phenomena, as discussed above.
Table 1. Mechanisms underlying the effect of cigarette smoking on estrogen-related
phenomena
Mechanism Cigarette Constituent involved
1. Decreased production of estrogens
Disturbed gonadotropic release
Ovarian atresia
Aromatase or other enzyme inhibition
Nicotine
Polycyclic aromatic hydrocarbons
Nicotine, Carbon monoxide
2. Altered metabolism of estrogens
Shift to catechol estrogens
Other disturbances of metabolism
Polycyclic aromatic hydrocarbons
3. Increased circulating androgens
Adrenal cortical stimulation
Adrenal enzyme inhibition
Nicotine
Nicotine
Reprinted from Baron et al., 1990
In men who smoke, alterations in hormone levels have also been reported
(Seeman, 1996). A number of studies have found that smokers have higher levels of
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testosterone and adrenal androgens and possibly higher levels of estradiol compared
to non-smokers (Dai et al., 1988; Barrett-Connor et al., 1987; Klaiber et al., 1988 and
1984). In contrast, Briggs (1973) reported changes in plasma testosterone after 7
days of abstinence in men who were smoking 30 cigarettes per day. The smokers
showed a testosterone rise of 1.65 + 0.5 ng/ml after 7 days of abstinence. The
mechanisms underlying these effects are not established but may include changes in
adrenal secretion and alterations in the production and metabolism of testosterone.
The one thing that we do know for sure is that the data in men reinforces the fact that
constituents of cigarette smoke can effect the production and metabolism of steroid
hormones which subsequently influences bone mass attainment during adolescence
or bone loss during adulthood.
The Role o f Physical Activity on Bone Mass:
Mechanical stress associated with exercise enhances bone mass measured in
adults (Robinson et al., 1995; Lariviere et al., 1995; Bassey & Ramsdale, 1994).
However, the mechanisms remain unclear. In children, Slemenda et al. (1991) have
shown consistent, positive associations (r range of 0.1 to 0.4 depending on the
activity) between bone mineral density in the radius, spine, and hip and weight
bearing physical activity in a group of 118 children between the ages of 5 and 14.
Similarly, Grimston et al. (1993) in a cross-sectional study, concluded that children
who participated in impact activities (running, gymnastics, tumbling, and dance) had
higher femoral neck BMD than children who participated in sports where loads to
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the skeleton were produced primarily from muscular contraction (swimming).
Kroger and colleagues (1992) found significantly higher femoral neck bone mineral
densities in those subjects who were physically active when they studied the spine
and femur of 84 children and adolescents aged 6 to 19 years of age. Nickols-
Richardson et al. (2000) have concluded that premenarcheal gymnasts have higher
femoral neck and lumbar spine BMD than age-, height-, and weight-matched
controls. Finally, Rubin and colleagues (1993) found a positive effect of activity on
the lumbar spine of 299 children aged 6 to 18 years but were unable to detect a
similar effect on the radius.
Muscle Strength and Bone Mass:
The relationship between muscle and bone has become an important research
topic over the past few years as more investigators realize the logical interaction
between the two systems. Aloia and colleagues (1978) examined total body calcium
(index of bone mass) and total body potassium (index of lean mass) in marathon
runners and sedentary controls. Their results indicated a positive relationship
between bone mass and muscle mass. However, it is important to keep in mind that
the relationship between bone and muscle is more complex than a simple
consideration of muscle attachments to bone. According to Madsen and colleagues
(1998), it remains unclear whether bone adapts specifically to increased local
muscular forces because of increased strength, or if increased strength is more
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indicative of an increased weight (fat mass and lean mass) and therefore, an
increased bone mass.
Evaluation of the muscle strength influence on bone has been conducted
mostly in adult populations. In pre- and post-menopausal women, a site-specific
association between muscle strength of the hip abductors and femoral neck bone
mineral density has been reported (Snow-Harter et al., 1993). Others have found that
grip strength best predicted bone density at the lumbar spine (Bevier et al., 1989) and
at the spine and radius (Snow-Harter et al., 1990). Hyakutake et al. (1994) have
reported an independent relationship between femoral BMD and quadriceps torque
in Japanese women from the fourth to the seventh decades and in Japanese men in
the fourth decade. These findings in adults suggest an important connection between
the muscular and the skeletal systems.
In children, Witzke and Snow (1999) evaluated the influence of strength on
bone when they measured knee extensor strength by isokinetic dynamometry. They
found significant correlations (r=0.41-0.53; p<0.001) between knee strength and
bone mineral density of whole body, femoral neck, greater trochanter, and mid-
femoral shaft of 54 adolescent girls. Schonau et al. (1996) showed that as much as
76% of the variation in the distal radius is explained by grip strength alone. It is
apparent however, that literature on the strength and bone relationship in children is
limited. We examined this relationship and the possible mechanisms involved by
measuring grip strength and relating it to forearm and heel bone mass.
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Mechanisms Underlying the Influence o f Physical Activity and Strength on Bone' .
The association between weight-bearing physical activity, muscle strength
and bone mass is clearly established in adults (Snow-Harter et al., 1990, 1993;
Pocock et al., 1989; Stock et al., 1987). Frost (1997) proposed that "voluntary
muscle forces.. .dominate a bone's postnatal structural adaptations to mechanical
usage, modified...by body weight and one's voluntary physical activity." In other
words, regulation of bone strength is a function of the mechanical forces or loads to
which the respective bones of the skeleton are exposed. Furthermore, Frost's theory
of the "Mechanostat" requires that some minimum effective strain (MES) be
exceeded in order to initiate the bone modeling process and thus potentiate an
increase in bone mass (Frost, 1992 and 1991). Therefore, the specific response to
any bone strain is dependent upon the relationship of that strain to the strain
thresholds for that bone. If the strains fall between these threshold values, no net
change in bone mass will result and if strains fall below the minimum effective strain
for remodeling, there will be a net decrease in bone mass. Therefore, based on these
theories, if a particular physical activity/sport is to have greater potential for
maximizing bone mass than another, it should require movements that produce
strains on bones that exceed the MES at any given time.
Preliminary Work related to the Proposed Study:
The Wuhan Youth Health Study (WYHS) is a collaborative effort between
the city of Wuhan and its Public Health Bureau and Public Health and Anti-epidemic
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Institute, and the University of Southern California Keck School of Medicine and its
Institute for Health Promotion and Disease Prevention Research. The study
administration is organized into teams involving researchers, physicians, and
educators in the United States and in China. The Principal Investigator is C.
Anderson Johnson at USC; the Co-Principal Investigator is Li Yan at Wuhan.
Our research team has extensive experience in the assessment of body
composition using weight, height, BMI, and bioelectric impedance analysis (BIA).
Findings (Afghani et al., unpublished data) obtained of 96 girls and 95 boys in
Wuhan suggest that body mass index (BMI) is correlated to fat mass (r=0.8 and 0.9
in boys and girls, respectively) and to lean body mass (r=0.9 in both boys and girls)
obtained by BIA.
Forty six percent of the girls in our Wuhan sample of 2155 had not started
menstruating at the time of the completion of a questionnaire. Three percent
reported an age of menarche of ten; 15 percent by age 11; 42 percent by age 12; 52
percent by age 13, and 54 percent by age 14. In boys, 56% of a total sample of 2482
reported not experiencing secondary sexual characteristics such as appearance of
facial hair, occurrence of voice breaking, and protubation of the thyroid cartilage.
Three percent reported secondary sexual characteristics by age 10; 9 percent by age
11; 26 percent by age 12; 39 percent by age 13; 42 percent by ages 14; 43 percent by
age 15, and 44 percent by age 16.
With respect to environmental tobacco smoke, 31 percent of the Wuhan
youth reported being in the same room with a smoker in the last 7 days. Twenty nine
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percent of the Wuhan youth reported being in the same vehicle with a smoker in the
last 7 days (unpublished findings).
Lifetime smoking prevalence was 47% among Wuhan boys and 18% among
Wuhan girls (Unger et al., in press). The 30-day smoking prevalence was 16 percent
among boys and 4 percent among girls (Unger et al., in press). Established smoking
prevalence (assessed with the question: "have you smoked at least 100 cigarettes in
your life?") was 2 percent among boys and 0 percent among girls (Unger et al., in
press). The prevalence of susceptibility to smoking (absence of a firm commitment
not to smoke) was 31% among boys and 10% among girls (Unger et al., in press).
These findings suggest that exposure to tobacco smoke whether through
direct nicotine inhalation or via second-hand smoke, is a problem in the youth of
Wuhan, China and the health implications arising from it must be investigated
thoroughly and prevented quickly.
Thirty percent of the Wuhan youth reported not participating in any type of
aerobic exercises that resulted in breathlessness and sweating for at least 20 minutes
during a typical week. However, 46 percent indicated that they participate at least
once a week in an activity that results in breathlessness and sweating, respectively.
Twenty four percent of the Wuhan youth reported not knowing if they participated in
aerobic activities in a typical week (unpublished findings).
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Chapter III. Hypotheses & Model
HYPOTHESES
It is hypothesized that:
1. Greater body weight is associated with higher levels of bone mass.
2. Pubertal status is related positively to peripheral bone mass.
3. Environmental tobacco smoke exposure is associated with lower levels of bone
mass.
4. Cigarette smoking is associated with lower levels of peripheral bone mass.
5. Frequency and intensity of physical activity is related positively to peripheral
bone mass.
6. Strength is associated positively with bone mass.
Conceptual Model:
The hypotheses listed above were designed to address the following model of
osteoporosis. Bone mass is the outcome variable. Weight, pubertal status, passive
smoking, active smoking, physical activity, and strength are the independent
variables used in this study to address their influence on bone mass.
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Figure 1. Conceptual model
Weight
Pubertal
Status
/ I ,
Passive
Smoking
\ \
)
Bone
V
Mass
V Active / /
i \
Smoking
+ / /
Physical
Activity
Strength
Osteoporosis is a geriatric disease, which has pediatric roots and is caused by
multiple factors. With that in mind, bone mass at any age can be estimated by the
following equation:
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y = I - (aiti + a2 t2 + a3 t3 + a4 t4 + a5 ts + a6 t < $ + a7t7 )
Where y is bone mass at any age, I is the y intercept and equivalent to peak bone
mass, ai through a7 represent the rates of all endogenous (genetic factors) and
exogenous (body weight, pubertal status, passive smoking, active smoking, physical
activity, strength) factors that effect bone mass, while ti through t7 are the durations
of their effects. The terms in the equation, and the factors influencing them, are
additive, cumulative, and interactive.
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Chapter IV. Research Design & Methods
Study Cohort and Sampling Procedure:
The Wuhan metropolitan area is a city of 7.25-8.25 million people consisting
of 3 geographic regions: Hankou (a commercial region), Hanyang (an industrial
region), and Wuchang (a cultural region), as well as 4 rural counties. The urban
regions are divided further into 7 administrative districts. Each urban district and
rural county serves as an independent administrative unit directly under the city
administration and is responsible for schools within its administrative boundary
(Unger et al., in press).
One school was randomly selected from each of the 7 urban districts and 4
rural counties in the Wuhan area. An additional school in each district/county was
randomly selected among all schools in the same district/county that were similar to
the first selected school in terms of school size, teacher/student ratio, type of school
(best-rated, intermediate, low-rated), and functional zone. A total of 22 schools were
randomly selected. Four classes in each of the selected schools were randomly
chosen for survey data collection (Unger et al., in press).
Subject Description:
In December of 1998, a total o f 4703 students in the 7th grade completed a
paper-and-pencil questionnaire in their classrooms during a single class period (45-
50 minutes). These students became the cohort for a multi-year longitudinal
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smoking and ETS prevention trial. Based on the prevalence rates of smoking in this
cohort, 15 schools were identified and chosen for weight, height, body composition,
strength, and bone mass measurements in the Spring of 2000.
Twenty percent (958 students) indicated that they were "not smoking" and
were "not exposed to environmental tobacco smoke" at grade 7. A total of 234
subjects (84 girls and 150 boys) were randomly selected from this list. Thirty eight
percent (1798 students) indicated that they were "exposed to environmental tobacco
smoke". Environmental tobacco smoke exposure was determined by whether
student had spent time in the same room or vehicle with a smoker in the last 7 days,
or whether student had been exposed to smokers for at least 15 minutes per day. A
total of 233 subjects (80 girls and 153 boys) were randomly selected from this list.
Seven percent (330 students) indicated that they "had smoked". Smoking was
determined by whether they had smoked in the last 30 days and/or smoked a total of
100 cigarettes in their lifetime. We randomly selected 229 subjects (76 girls and 153
boys) from this list of students. Table 2 shows the breakdown of the sample based
on gender and smoking status.
Table 2. Sample breakdown based on gender and smoking status
Non
Smokers
Passive
Smokers
Active
Smokers
Total
Girls 84 80 76 240
Boys 150 153 153 456
Total 234 233 229 696
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All adolescents were between 10 and 16 years of age. Participation required
signed parental consent as well as signed child assent. Consent/assent forms (see
appendix) and research protocols were approved by the USC and Wuhan IRBs. In
March of 2000, Chinese-translated informed consent forms were distributed to the
sample of 696 students (240 girls and 456 boys) by the teachers, taken home by the
children, completed by parents and children, returned to school the following day,
and collected by the teachers.
A total of 206 subjects (30%) refused to participate in this study. Another 16
were lost due to electrical failure on the day of measurement, five had transferred to
another school, and three were absent on the day of data collection. We tested a total
of 466 subjects (166 girls and 300 boys). Table 3 shows the breakdown of
participants based on gender and smoking status.
Table 3. Participant breakdown based on gender and smoking status
Non
Smokers
Passive
Smokers
Active
Smokers
Total
Girls 59 57 50 166
Boys 87 108 105 300
Total 146 165 155 466
Power Analysis:
To determine whether we would have adequate power to detect important
effects with the number of adolescents who agreed to participate (166 girls and 300
boys), we performed a power analysis. We used a power analysis software program
(nQuery Advisor Version 3: Los Angeles, CA) with the following analytic
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assumptions: n=166 for girls, n=300 for boys, a<0.05, testing unidirectional
hypotheses (1 sided), with an alternative correlation of 0.20 for girls and 0.15 for
boys. Results of the power analysis are presented in table 4.
Table 4. Power analysis
Significance
Level
0.050 0.050
1 or 2 Sided 1 1
Null
Hypothesis
Correlation
0.000 0.000
Alternative
Correlation
0.20 0.15
Power (%) 80 80
N 166 300
We concluded that with a sample size of 166, we would have l-(3 >80 (80%
probability) to detect a correlation coefficient as small as 0.20 in girls. With a
sample size of 300, we would have 1-P >80 (80% probability) to detect a correlation
coefficient as small as 0.15 in boys.
Measures:
The following measures were collected from each participant for analysis:
Bone Mass:
Heel and forearm scans (PIXI, Lunar Corporation: Madison, WI) were
performed to provide heel and forearm bone mineral density (units: grams/cm2 ),
defined as the integral mass of bone mineral per unit projected area, and bone
mineral content (units: grams). It is believed that in growing children bone mineral
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content (BMC) is a better measure than bone mineral density (BMD) because of it's
accuracy (Witzke & Snow, 2000). Bone mineral content is a good measure of the
structural properties of bone, capturing both the material and geometric properties
(Witzke & Snow, 2000).
The non-dominant arm was used for forearm measures. Scans of the distal
radius and ulna were taken at a point 2/3 the distance from the olecranon to the
styloid process. The left heel (os calcis) was used for heel measurements. Quality
control was performed every day by measuring a forearm and a heel phantom. The
precision error (coefficient of variation for repeated measurements) for this
technique is 1.5% for the forearm and 2.0% for the heel.
Dual energy x-ray absorptiometry (DXA) is the most accurate method for
measuring bone mass. DXA has high precision, short scanning time, low radiation
dose and stable calibration (Orwoll et al., 1991; Lewis et al., 1994; Sorenson et al.,
1989). The alternative technique of quantitative computed tomography (QCT)
measures true physical density (units: g/cm3 ) rather than the areal density of DXA
(units: g/cm ). However, DXA remains the more widely used technique due to lower
cost, higher precision, and lower radiation dose (Kalender, 1992; Pacifici et al.,
1990).
Different skeletal areas contain different ratios of trabecular and cortical
bone, which often differ in their rate of bone gain/loss (Levis & Altman, 1998). The
spine is composed predominantly of trabecular bone (refer to table 5 from Einhom,
1992; Jones et al., 1987) and believed to be the ideal site of measurement. The heel
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(os calcis) is a weight-bearing site with metabolically active trabecular bone closely
related to the spine (Levis & Altman, 1998; Blake & Fogelman, 1996; Vogel et al.,
1988). A correlation coefficient of 0.77 has been reported by Vogel (1987) for bone
mineral content of the spine (L1-L4) and BMC of the heel. Others (Fordham et al.,
1999; Frediani et al., 1998; Laval-Jeantet et al., 1995) have found BMD of the heel
to be correlated with BMD of the spine (r= 0.61 to 0.69). Several studies have
confirmed that heel BMD predicts fracture risk (Bauer et al., 1995; Cummings et al.,
1993; Wasnich, 1987); age-related bone loss in the heel and the spine have also been
shown to be similar (Vogel, 1987). The forearm is composed predominantly of
cortical bone (refer to table 5) and also a useful site for bone measurements. Studies
(Cummings et al., 1993, 1990; Gardsell et al., 1989; Hui et al., 1989) have shown
that women with low bone mineral density in the forearm are at increased risk of hip
fracture. Because the measurement of the heel and forearm using this device can be
performed relatively quickly and easily in the schools, these sites were selected as
the peripheral sites of bone mass in our large population of 466. Although Einhom
or others have not studied the trabecular/cortical content of the heel bone, the heel is
considered the most sensitive peripheral site and therefore, the best trabecular site for
our study. The forearm is considered the best cortical site for our study.
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Table 5. Trabecular bone content at various skeletal sites
Vertebrae 66-90%
Hip (intertrochanteric) 50% " ..... "
Hip (femoral neck) 25%
Distal radius 25% “ ".."......
Mid-radius 1%
Femoral shaft 5% .............
Reprinted from T.A. Einhom (1992)
Because bone composition varies by skeletal area, the selection of the site
should be based on the expected effects of factors influencing bone (Levis &
Altman, 1998). Studies have shown that trabecular bone has high turnover and
responds to factors such as body composition (Slemenda et al., 1994; Stevenson et
al., 1989; Liel et al., 1988), hormones (Slemenda et al., 1994; Rubin et al., 1993;
Baron et al., 1990; Cann et al., 1988), and smoking (Hopper and Seeman, 1994;
Stevenson et al., 1989) to a greater extent than to exercise. Cortical bone has a
slower turnover rate and is influenced by mechanical strain (possibly via strain from
direct muscle attachment) to a greater extent than trabecular bone as demonstrated by
studies (Kroger et al., 1992; Slemenda et al., 1991) which have shown positive
associations between bone mineral density in the radius and the hip and most types
of physical activity in children. Hence, in our study we expected heel bone mass to
relate more closely to body weight, pubertal status, and smoking. We expected
forearm bone mass to relate more closely to physical activity and strength.
To assess future spinal fracture risk, the vertebrae would have been the ideal
site for our study purposes. However, DXA spine scans of 466 children introduces
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time, cost, radiation, and other feasibility issues that made it impossible to conduct
such measurements in China.
Body Composition:
1. Weight: Subjects were weighed in light clothing without shoes, and
weight was measured using a standard calibrated scale in pounds and was
subsequently converted to kilograms. Weight is the most commonly recorded
anthropometric variable, and generally it is measured with sufficient accuracy.
Attention was paid to detail of the measurement technique to maximize accuracy.
2. Height: A stadiometer was used to directly read-off the height of the
subjects. The subject stood barefoot or in socks on a flat surface positioned at a right
angle to the vertical board of the stadiometer. The weight of the subject was
distributed evenly on both feet, and the head was positioned horizontally. The arms
hung freely by the sides of the trunk, with the palms facing the thighs. The
measurement was recorded to the nearest 0.1 centimeters. Height is a major
indicator of general body size and of bone length.
3. Body Mass Index (BMI): BMI was computed as a measure of body mass
relative to height. BMI is the ratio of body weight to height squared (kg/m2 ).
Although BMI in childhood changes substantially with age, it is considered an
internationally acceptable method for identifying overweight and obese children
(Cole et al., 2000; Wang et al., 2000). Cole and colleagues (2000) obtained data on
body mass index for children from six (Brazil, Great Britain, Hong Kong, the
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Netherlands, Singapore, United States) large nationally representative cross-sectional
surveys on growth. They defined international cut off points for BMI for overweight
and obesity by gender between 2 and 18 years, defined to pass through body mass
index of 25 and 30 kg/m at age 18, obtained by averaging data from the six
countries. Table 6 shows these cut off points by gender in children aged 10 to 16
years.
Table 6. BMI cut off points
BMI 25 kg/m2 (age 18) BMI 30 kg/m2 (age 18)
Age (years) Girls Boys Girls Boys
10
19.9 19.8 24.1 24.0
11
20.7 20.5 25.4 25.1
12 21.7 21.2 26.7 26.0
13
22.6 21.9 27.8 26.8
14
23.3 22.6 28.6 27.6
15 23.9 23.3 29.1 28.3
16
24.4 23.9 29.4 28.9
Reprinted from Cole et al. (2000)
4. Bioelectric Impedance Analysis (BIA): BIA was used to estimate percent
body fat, fat mass, and lean mass. Measurement of impedance is believed to be
precise, consistent, easy to obtain, portable, and relatively inexpensive. For these
reasons, BIA was chosen over the more technologically advanced equipment (DXA)
for the measurement of body composition variables. The BIA technique is based on
measurement of electrical resistance in the body to a tiny imperceptible current. The
electrical resistance is a function of body tissues, which provides an estimate of total
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body water (TBW), used to obtain fat mass and lean body mass (National Institutes
of Health, 1995).
Several researchers have proposed equations for converting TBW to fat mass
and lean body mass. Kushner and colleagues (1992) have used the following
equation:
TBW = (0.59 * height2 /resistance) + (0.065 * body weight) + 0.04
LBM = TBW/0.732
These investigators have concluded that the relationship between total body water
and bioelectrical resistance is consistent across a wide age range (infants to the
elderly). Goran and colleagues (1993) have cross-validated the equation by Kushner
et al. in young children and have supported the concept of one equation for all ages.
However, Deurenberg and co-investigators (1989) have concluded that the
relationship between total body water and bioelectrical resistance appears to be
altered during adolescence. The Kushner equation fails to account for gender
differences and it was developed in a small sample (n=l 16).
Lukaski and colleagues (1986) also had a small sample (114 men and
women) ranging from 4% to 41% in body fat when they proposed a single equation
that includes heights/resistance as a unique independent variable:
LBM = (0.838 * heights/resistance) + 4.179
Their equation fails to include gender, age, and body weight. In Pima Indian men
and women with a wide range of body fat (11% to 52%), Rising and colleagues
(1991) incorporated height, weight, resistance, age, and gender to propose the
following equation:
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LBM = (0.34 * heights/resistance) + (0.33 * body weight) - (0.14 * age) +
(6.18 * gender) + 13.74
Although the incorporation of all the 5 variables into the equation is an advantage of
the Rising equation, the fact that it is specific to Pima Indians is a limitation. Segal
and colleagues (1988) have perhaps conducted the strongest study and have proposed
the following four gender and fatness-specific equations:
Non-obese men: LBM = (0.000664 * height2 ) - (0.0212 * resistance) +
(0.629 * weight) - (0.124 * age) + 9.33
Obese men: LBM = (0.000886 * height2 ) - (0.03 * resistance) + (0.427 *
weight) - (0.07 * age) + 14.52
Non-obese women: LBM = (0.000646 * height2 ) - (0.014 * resistance) +
(0.421 * weight) + 10.435
Obese women: LBM + (0.000912 * height2 ) - (0.0147 * resistance) + (0.3
* weight) - (0.07 * age) + 9.38
However, Goran & Khaled (1995) have pointed out that the multiplicity of the Segal
equations make it inconvenient; uncertainty about the conditions required for
appropriate application of the obesity-specific equations also exists.
In our study, algorithmic equations developed by Bio Analogies (Beaverton,
OR) were used for converting TBW to fat mass and lean body mass. These
equations are population specific (include height, weight, age, gender, resistance)
and validated on a wide range of subjects. The current validation studies contain
more than 1000 subjects with an error factor of ± 3.3% and a correlation coefficient
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of 0.88 compared to the criterion of hydrostatic weighing (Girandola & Contarsy,
1988).
Electrodes were carefully applied to the skin to ensure proper electrical
conduction. Two electrodes (current-introducing) were placed on the dorsal surfaces
of the hand and foot proximal to the metacarpal phalangeal and metatarsal
phalangeal joints. Another two electrodes (voltage-sensing) were applied at the
pisiform prominence of the wrist and between the medial and lateral malleoli of the
ankle. Impedance readings were subsequently converted to percent body fat, fat
mass, and lean mass by a body composition software (Bioanalogics: Beaverton, OR).
The fact that lean body mass as estimated by BIA is actually fat-free mass
because it also includes skeletal mass is a limitation of this body composition
technique because it strengthens its relationship with BMD (Reid et al., 1992a).
Pubertal Status:
A questionnaire (see appendix) was administered to all subjects in December
of 1998 and again in December of 1999. Pubertal status was determined based on
questionnaire data of December 1999. To assess pubertal status in girls, we asked
the question: "at what age did you menarche?" In boys, we asked: "at what age did
you find that your Adam's apple became protuberant, your voice became hoarse, or
your beard appeared?" Pubertal status was treated as a dichotomous variable in data
analysis. Tanner staging is believed to be the most optimal method of pubertal self-
35
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assessment, however, due to its embarrassing nature, it was judged to be
inappropriate in this setting.
Tobacco Exposure:
Questions pertaining to environmental tobacco smoke included the "number
of days in the last 7 days in which the subject was in the same room with someone
who was smoking cigarettes", "in the same vehicle with someone who was smoking
cigarettes", and whether or not the subject "was around smokers for at least 15
minutes per day". Those individuals who answered positively to these questions
(December 1998 questionnaire) were categorized as "passive" smokers. In this
study, "passive smoking", "second-hand-smoke", and "environmental tobacco
smoke" are synonymous terms.
Tobacco Use:
December 1998 questionnaire items assessed whether or not the subjects
"have smoked at least 100 cigarettes in their life" and whether or not they "have
smoked in the last 30 days".
Physical Activity:
Each subject was asked 2 questions (December 1998 questionnaire)
pertaining to the frequency and intensity of physical activity. These questions asked
the "number of times the subjects ran, jogged, rode a bicycle or did some other
36
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exercises at such intensities and duration that caused them to breathe very hard and
fast" or "sweat heavily" for at least 20 minutes. Since these 2 questionnaire items
were highly correlated (r=0.7 in girls; r=0.8 in boys), the answers to the 2 questions
were added and a physical activity index was obtained.
Strength:
Strength is usually defined as the maximum force generated by a muscle (or
muscle groups). It can be measured using one of four methods: tensiometry,
dynamometry, one-repetition maximum (1-RM), or computer-assisted force and
power output determinations. A handgrip dynamometer (Preston: Jackson, MI) was
used in our study for strength measurements. This device operates on the principle
of compression. An external force applied to the dynamometer compresses a steel
spring and moves a pointer. Knowing the force required to move the pointer a
particular distance, one can determine exactly how much external force has been
applied to the dynamometer.
In our study, the subject was standing, with arms straight by his or her side.
Subject then gripped the dynamometer as hard as possible for 3 seconds without
pressing the instrument against his or her body or bending at the elbow. Because
forearm bone mass was determined for the non-dominant forearm, we therefore also
used the non-dominant grip score. Values were recorded in pounds of force and the
highest of three trials was then converted to kilograms of force.
37
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Data Analysis:
The statistical package SPSS 9.0 software (SPSS Inc., Chicago, IL) was used
for all data analysis. The following variables were used to obtain descriptive
statistics and bivariate correlations:
Dependent Variables:
- Forearm Bone Mineral Density (grams/cm2 )
- Forearm Bone Mineral Content (grams)
- Heel Bone Mineral Density (grams/cm2 )
- Heel Bone Mineral Content (grams)
Independent Variables:
- Age (years)
- Weight (kilograms)
- Height (centimeters)
- BMI (kg/m2 )
- Fat Mass (kilograms)
- Lean Mass (kilograms)
- Pubertal Status (pre/post menarche/puberty)
- Time Since Menarche/Pub erty (years)
- Passive Smoking (exposed/not exposed to second-hand-smoke)
- Active Smoking (consumed/not consumed cigarettes)
- Physical Activity (times/week)
- Strength (kilograms)
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The following procedures were performed:
One-way Analysis o f Variance (ANOVA):
To determine and compare variable means and standard deviations, one-way
analysis of variance (ANOVA) with post-hoc (Tukey) test for multiple comparisons
were performed in each group by gender, and smoking status. Tables 7 and 9 in the
"Results" section were obtained using such procedures. The same procedures were
performed in each group by gender, pubertal status, and smoking status. Tables 8
and 10 in the "Results" section were obtained based on such groupings.
Bivariate Correlation:
To determine correlations between all independent variables and the
dependent variables ("Results": tables 11 and 12), as well as correlations among
independent variables ("Results": table 13) used in multiple regression models
("Results": tables 14-17), bivariate correlations were performed.
The following major covariates were used in multiple regression models to
examine for main effects on bone mineral density and content:
Independent Variables:
- Body Weight
- Pubertal Status
- Passive Smoking
- Active Smoking
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- Physical Activity
- Strength
Multiple Regression Models:
To assess the independent relationship between each independent variable
(body weight, pubertal status, passive smoking, active smoking, physical activity,
and strength) and the dependent variable (forearm and heel bone mineral density and
content), stepwise multiple regression analyses were performed. Regression models
examined for main effects of each independent variable on each dependent variable
while controlling for the other independent variables. Collinearity diagnostics tested
for multicollinearity among the independent variables. The findings reported in
tables 14 through 17 of the "Results" section were obtained using such procedures.
Timeline:
Self-reported measures of passive smoking, active smoking, and physical
activity were collected in December of 1998. Self-reported measures of pubertal
status were collected in December of 1999. Measures of weight, height, body
composition, strength, bone mineral density and content were collected in April of
2000. Data were analyzed between April and December of 2000.
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Gender and Minority Inclusion:
Smoking rates were lower in the Wuhan girls than the Wuhan boys (Unger et
al., in press). Therefore, our sample consisted of more boys (n=300) than girls
(n=T66).
Participation o f Children:
The subjects of this study were children and adolescents ages 10-16 years
recruited from public schools in Wuhan, China. Our research team had previously
conducted school-based studies involving adolescents and had experience with
informed consent formalities, data collection procedures, and in dealing with
potential risks to subjects in these settings and protocols.
Human Subjects:
The University of Southern California and the Wuhan Institutional Review
Boards approved all facets of this study.
Loss of class time, inconvenience, and embarrassment were potential risks to
the adolescents in this study. However, students could have declined participation in
the study at any time. To minimize the risk of loss of class time, we worked
efficiently and limited each student's participation time to half an hour. To minimize
the risk of inconvenience, we conducted our data collection in the schools during the
school day. To minimize embarrassment, we assured the subjects that their
responses to our questions and the results of their tests would be kept confidential.
41
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They were not forced to answer any questions if they felt uncomfortable, and they
were allowed to discontinue participation at any time.
It is our conclusion that the potential benefits of this research to future
generations will outweigh these minimal risks to research participants. Osteoporosis
is a debilitating disease of the elderly and its prevention should begin during
childhood and the adolescent period. Understanding the role of different
environmental factors on the accumulation of bone mass during the growing years
was the objective of this study and may be essential to preventing the disease.
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Chapter V. Results
A total of 466 (166 girls and 300 boys) students were tested. Tables 7 and 9
summarize the means and standard deviations for age, weight, height, BMI, percent
fat, fat mass, lean body mass, percent of pubertal subjects, menarcheal age and time
since menarche (girls), pubertal age and time since puberty (boys), physical activity
(PA), strength, bone mineral density and content of the forearm (BMDA and
BMCA), and of the heel (BMDH and BMCH) based on gender and smoking status.
Tables 8 and 10 summarize the means and standard deviations for BMDA, BMCA,
BMDH, and BMCH based on gender, pubertal status, and smoking status.
Table 7. Variable means and standard deviations for girls
Girls
Non
Smokers
Passive
Smokers
Active
Smokers
N 59 57 50
Age (years)
14.1 ± 0 .3 7 14.3 ± 0 .3 7 14.3 ± 0 .6 2
Weight (kg)
46.5 ± 7.00 49.5 ± 7.36* 48.9 ± 5 .9 6
Height (cm)
156.8 ± 5 .6 6 158.2 ± 5 .4 2 156.2 ± 4 .8 5
BMI (kg/m2 )
18.9 ± 2 .6 5 19.7 ±2.41 20.0 ± 2.24
Percent Fat (%)
19.8 ± 4 .8 5 20.5 ±4.31 20.2 ± 4 .6 3
Fat Mass (kg)
9.4 ±3.71 10.4 ±3.41 10.0 ± 3.00
Lean Mass (kg)
37.0 ± 4 .0 7 39.0 ± 4.57* 38.8 ± 4 .3 3
Pubertal (%)
83 89 98*
Menarcheal Age
(years)
12.3 ± 0 .8 5 12.2 ± 0 .8 6 12.3 ± 0 .7 8
Time
Since Menarche
(years)
1.9 ± 0 .7 7 2.1 ± 0 .9 2 2.0 ± 0 .8 7
PA (times/week)
3.2 ± 3 .6 6 4.5 ± 4.25 3.8 ± 4 .3 2
Strength (kg)
22.6 ± 3 .8 2 24.1 ±3.81 23.8 ± 4 .1 5
BMDA (g/cm2 )
0.33 ± 0 .0 5 0.35 ± 0.05* 0.34 ± 0.04
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BMCA (grams)
2.77 ± 0.46 3.03 ±0.57* 3.03 ± 0.43*
BMDH (g/cm2 )
0.47 ± 0.06 0.47 ± 0.06 0.48 ± 0.06
BMCH (grams)
1.63 ± 0 .3 0 1.69 ± 0 .3 2 1.76 ± 0 .3 5
*p<0.05
Table 8. Variable means and standard deviations for girls: interactions
Girls
Non
Smokers/
Pre-pubertal
Non Smokers/
Post-pubertal
Smokers/
Pre-pubertal
Smokers/
Post-pubertal
N ,10 49 7 100
BMDA (g/cm2 )
0.30 ±0.03 0.33 ±0.06 0.31 ±0.06 0.35 ±0.05
BMCA (grams)
2.47 ± 0.23 2.83 ±0.46 2.66 ±0.54 3.05 ±0.49*
BMDH (g/cm2 )
0.45 ± 0.04 0.47 ± 0.06 0.45 ± 0.06 0.47 ± 0.06
BMCH (grams)
1.56 ±0.23 1.65 ±0.32 1.81 ±0.35 1.70 ±0.32
*p<0.05
Table 8 shows that there were no significant differences in BMD or BMC of
the forearm or the heel between pre-pubertal smokers and non-smokers. Post-
pubertal smokers had significantly higher BMCA; their BMDA, BMDH and BMCH
did not differ significantly from the post-pubertal non-smokers.
Table 9. Variable means and standard deviations for boys
Boys
Non
Smokers
Passive
Smokers
Active
Smokers
N 87 108 105
Age (years)
14.5 ±0.57 14.3 ± 0.42* 14.5 ±0.55
Weight (kg)
53.1 ±11.62 53.3 ±10.18 52.0 ± 10.10
Height (cm)
163.5 ±8.45 164.0 ±8.39 163.6 ±8.08
BMI (kg/m2 )
19.7 ±3.27 19.7 ±2.70 19.3 ±2.69
Percent Fat (%)
9.6 ±3.24 9.7 ±3.21 8.8 ±2.76
Fat Mass (kg)
5.3 ±2.94 5.3 ±2.63 4.7 ±2.30
Lean Mass (kg)
47.7 ± 9.24 47.9 ±8.30 47.2 ± 8.45
Pubertal (%) 67 74 80
Pubertal Age
(years)
13.0 ±0.81 12.8 ±0.95 12.8 ±1.15
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Time Since
Puberty (years)
1.5 ±0.81 1.5 ± 0 .9 6 1.8 ± 1.13
PA (times/week)
3.8 ± 4 .6 8 5.9 ±5.75* 5.6 ± 5 .3 9
Strength (kg)
31.8 ± 8 .2 6 32.1 ± 7 .1 5 32.2 ± 7.25
BMDA (g/cm2 )
0.34 ± 0 .0 6 0.34 ± 0.04 0.35 ± 0 .0 6
BMCA (grams)
3.2 ± 0 .6 7 3.13 ± 0 .5 6 3.25 ±0.71
BMDH (g/cnri)
0.53 ± 0 .0 9 0.53 ± 0 .0 7 0.53 ± 0 .0 9
BMCH (grams)
2.4 ± 0.63 2.4 ±0.55 2.41 ± 0.65
*p<0.05
Table 10. Variable means and standard deviations for boys: interactions
B o y s
Non
Smokers/
Pre-pubertal
Non Smokers/
Post-pubertal
Smokers/
Pre-pubertal
Smokers/
Post-pubertal
N 28 55 48 159
BMDA (g/cm2 )
0.34 ± 0 .0 4 0.34 ± 0 .0 5 0.33 ± 0 .0 5 0.35 ± 0 .0 5
BMCA (grams)
3.05 ± 0 .5 3 3.25 ±0.61 2.96 ± 0 .6 9 3.26 ± 0 .5 9
BMDH (g/cm2 )
0.51 ± 0 .0 8 0.54 ± 0 .1 0 0.52 ± 0 .0 9 0.53 ± 0 .0 8
BMCH (grams)
2.28 ± 0 .5 6 2.40 ± 0.67 2.21 ± 0 .6 8 2.45 ± 0.56
*p<0.05
Table 10 shows that there were no significant differences in BMDA, BMCA,
BMDH, or BMCH between pre-pubertal smokers and non-smokers. There were also
no significant differences in BMDA, BMCA, BMDH, or BMCH between post-
pubertal smokers and non-smokers.
Bivariate Correlations:
Table 11 presents correlations between the independent variables and
measures of bone mineral density and content in girls. These were not controlled for
associations among themselves.
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Table 11. Variable correlations with BMD and BMC of girls at forearm and heel
Girls Forearm BMD Forearm BMC Heel BMD Heel BMC
Age 0.22* 0.28* 0.11 -0.01
Weight 0.49* 0.57* 0.52* 0.52*
Height 0.08 0.25* 0.26* 0.47*
BM I 0.49* 0.50* 0.44* 0.32*
Fat Mass 0.42* 0.41* 0.33* 0.26*
Lean Mass 0.44* 0.58* 0.57* 0.60*
Pubertal
Status
0.24* 0.26* 0.11 0.02
Time
Since
Menarche
0.42* 0.42* 0.22* 0.18*
Passive
Smoking
0.14* 0.19* -0.01 0.04
Active
Smoking
0.05 0.12 0.07 0.09
Physical
Activity
0.17 0.05 0.02 -0.03
Strength 0.35* 0.44* 0.33* 0.35*
*p<0.05
In girls, the following variables were significantly correlated (r range of 0.19-
0.58) with forearm bone mineral content: lean mass, weight, BMI, strength, time
since menarche, fat mass, age, pubertal status, height, and passive smoking. Active
smoking and physical activity were not significantly correlated with forearm bone
mineral density or bone mineral content in girls.
Significant correlations (r range of 0.18-0.60) for heel measurements in girls
were found between bone mineral content and the following variables: lean mass,
weight, height, strength, BMI, fat mass, and time since menarche. Passive smoking,
active smoking, and physical activity were not significantly correlated with heel
bone mineral density or bone mineral content in girls.
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Most variables (except height and lean mass) tended to correlate better with
forearm (cortical bone) than with heel (trabecular bone) in girls.
Figures 2 to 7 (see appendix) present scatter plots for critical bivariate
distributions in girls.
Table 12 presents correlations between the independent variables and
measures of bone mineral density and content in boys. These were not controlled for
associations among themselves.
Table 12. Variable correlations with BMD and BMC of boys at forearm and heel
Boys Forearm BMD Forearm BMC Heel BMD Heel BMC
Age 0.24* 0.27* -0.04 0.03
Weight 0.47* 0.50* 0.62* 0.67*
Height 0.29* 0.36* 0.40* 0.64*
BM I 0.44* 0.43* 0.56* 0.46*
Fat Mass 0.27* 0.25* 0.46* 0.40*
Lean Mass 0.49* 0.53* 0.62* 0.71*
Pubertal
Status
0.14* 0.19* 0.09 0.15*
Time
Since Puberty
0.22* 0.13* 0.01 0.07
Passive
Smoking
0.00 -0.06 0.00 0.02
Active
Smoking
0.02 0.06 0.02 0.05
Physical
Activity
0.04 0.07 0.01 0.005
Strength 0.50* 0.60* 0.35* 0.49*
*p<0.05
In boys, the following variables were significantly correlated (r range of
0.13-0.60) with forearm bone mineral content: strength, lean mass, weight, BMI,
height, age, fat mass, pubertal status, and time since puberty. Passive smoking,
47
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active smoking, and physical activity were not significantly correlated with forearm
bone mineral density or bone mineral content in boys.
Significant correlations (r range of 0.15-0.71) for heel measurements in boys
were found between bone mineral content and the following variables: lean mass,
weight, height, strength, BMI, fat mass, and pubertal status. Time since puberty,
passive smoking, active smoking, and physical activity were not significantly
correlated with heel bone mineral density or bone mineral content in boys.
Variables that tended to correlate better with forearm (cortical bone) than
with heel (trabecular bone) in boys were age, pubertal status, time since puberty, and
strength. All variables pertaining to body composition (weight, height, BMI, fat
mass, lean mass) were correlated better with the heel.
Figures 8 to 13 (see appendix) present scatter plots for critical bivariate
distributions in boys.
Intercepts and slopes obtained from the bivariate distributions in girls and
boys were compared to determine whether they were statistically different. The
distribution of forearm BMC and weight in boys had a significantly different
intercept than in girls; the slopes did not differ significantly (Figures 2 and 9). The
distribution of forearm BMC and strength in boys and girls did not have statistically
different intercepts, the slopes however were statistically different from each other
(Figures 3 and 8). The distribution of forearm BMC and age in boys and girls did
not have significantly different intercepts or slopes (Figures 4 and 11). The
distribution of forearm BMC and height in boys and girls did not have significantly
48
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different intercepts, the slopes however were statistically different (Figures 5 and
10). The distribution of heel BMC and weight in boys and girls did not have
significantly different intercepts, the slopes however were statistically different
(Figures 6 and 12). Finally, the distribution of heel BMC and height in boys and
girls had significantly different intercepts and slopes (Figures 7 and 13).
Table 13 presents correlations among the independent variables (weight,
pubertal status, passive smoking, active smoking, physical activity, strength) used in
multiple regression models.
Table 13. Correlations among independent variables in girls and boys
Body
Weight
Pubertal
Status
Passive
Smoking
Active
Smoking
Physical
Activity
Grip
Strength
Body
Weight
0.19** 0.13 0.03 0.04 0.31*
Pubertal
Status
0.20** 0.18** 0.18** 0.01 0.03
Passive
Smoking
0.01 0.09 0.36* 0.12 0.09
Active
Smoking
-0.03 0.10 0.18** 0.01 0.06
Physical
Activity
0.02 0.10 0.09 0.05 0.15
Grip
Strength
0.58* 0.26* 0.05 0.02 0.03
*p<0.0001
**p<0.05
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In girls, the following variables were significantly correlated with each other:
weight and pubertal status (r=0.19), weight and grip strength (r=0.31), passive
smoking and pubertal status (^=0.18), active smoking and pubertal status (r=0.18),
and active and passive smoking (r=0.36).
In boys, the following variables were significantly correlated with each other:
weight and pubertal status (r=0.20), weight and grip strength (r=0.58), pubertal
status and strength (r=0.26), and active and passive smoking (r=0.18).
Multivle Linear Regression Models:
Stepwise multiple linear regression analyses were performed to examine the
best regression model for bone mineral density/bone mineral content and the various
factors that influence bone. Collinearity diagnostics tested for multicollinearity
among the independent variables. Multiple regression coefficients, standardized
multiple regression coefficients, and model fit indexes ( R ) according to site
(forearm and heel) for girls and boys are presented in tables 14 through 17.
The relationship of the different variables (weight, pubertal status, passive
smoking, active smoking, physical activity and strength) and forearm BMD for girls
and boys is shown in table 14. All independent variables controlled for each other.
Table 14. Multiple linear regression model for forearm BMD
Forearm BMD Girls Boys
P
ps
P
ps
Intercept 0.11 0.18
Weight 0.003* 0.37* 0.002* 0.39*
Pubertal Status 0.04** 0.25** NS NS
Passive Smoking NS NS NS NS
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Active Smoking NS NS NS NS
Physical Activity NS NS NS NS
Strength 0.003*** 0.21*** 0.002* 0.26*
Model Fit Index (R2 ) 0.33 0.34
p: multiple regression coefficient
Ps: standardized multiple regression coefficient
*significant at the p<0.0001
**significant at the p<0.001
***significant at the p<0.05
NS: non-significant
We determined that 33% and 34% of the variability of forearm bone mineral
density in girls and boys respectively, is explained by weight, pubertal status, and
strength in girls, and by weight and strength in boys. Twenty four percent of the
variance in girls was attributed to weight, 6 percent to pubertal status, and 3 percent
to strength. In boys, 29 percent was attributed to weight and 5 percent to strength.
Active smoking, passive smoking, or physical activity did not make significant
contributions in girls. Pubertal status, active smoking, passive smoking, or physical
activity did not make significant contributions in boys.
The relationship of the different variables (weight, pubertal status, passive
smoking, active smoking, physical activity and strength) and forearm BMC for girls
and boys is shown in table 15. All independent variables controlled for each other.
Table 15. Multiple linear regression model for forearm BMC
Forearm BMC Girls Boys
P
P s
P
ps
Intercept 0.26 1.07
Weight 0.03* 0.43* 0.02* 0.29*
Pubertal Status 0.35** 0.22** NS NS
Passive Smoking NS NS NS NS
Active Smoking NS NS NS NS
Physical Activity NS NS NS NS
Strength 0.04* 0.28* 0.04* 0.43*
5 1
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Model Fit Index (R2 ) 0.42 0.42
(3 : multiple regression coefficient
Ps: standardized multiple regression coefficient
*significant at the p<0.0001
**significant at the p<0.05
NS: non-significant
In girls, a total of 42% of the variance in forearm BMC was attributed to
weight (32%), strength (6%), and pubertal status (4%). Passive smoking, active
smoking, and physical activity did not make significant contributions to forearm
BMC in girls. In boys, a total of 42% of the variance in forearm BMC was attributed
to strength (36%), and to weight (6%). Pubertal status, passive smoking, active
smoking, and physical activity did not make significant contributions in boys.
The relationship of the different variables (weight, pubertal status, passive
smoking, active smoking, physical activity and strength) and heel BMD for girls and
boys is shown in table 16. All independent variables controlled for each other.
Table 16. Multiple linear regression model for heel BMD
Heel BMD Girls Boys
P
Ps
P
Ps
Intercept 0.22 0.28
Weight 0.004* 0.44* 0.005* 0.60*
Pubertal Status NS NS NS NS
Passive Smoking NS NS NS NS
Active Smoking NS NS NS NS
Physical Activity NS NS NS NS
Strength 0.003** 0.17** NS NS
Model Fit Index (Rz ) 0.26 0.35
P: multiple regression coefficient
Ps: standardized multiple regression coefficient
* significant at the p<0.0001
** significant at the p<0.01
NS: non-significant
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Heel bone mineral density was best predicted by weight (24%) and strength
(2%) in girls, accounting for 26% of the variability when both variables were
combined. In boys, only weight was a significant predictor of heel bone mineral
density, accounting for 35% of the variability. The addition of pubertal status,
passive smoking, active smoking, and physical activity in the models did not make
significant contributions to the variance of heel BMD.
The relationship of the different variables (weight, pubertal status, passive
smoking, active smoking, physical activity and strength) and heel BMC for girls and
boys is shown in table 17. All independent variables controlled for each other.
Table 17. Multiple linear regression model for heel BMC
Heel BMC Girls Boys
S 3
ps
P
Ps
Intercept 0.44 0.18
Weight 0.02* 0.39* 0.03* 0.58*
Pubertal Status NS NS NS NS
Passive Smoking NS NS NS NS
Active Smoking NS NS NS NS
Physical Activity NS NS NS NS
Strength 0.02** 0.22** 0.01** 0.16**
Model Fit Index (Rz ) 0.24 0.47
p: multiple regression coefficient
Ps: standardized multiple regression coefficient
* significant at the p<0.0001
**significant at the p<0.05
NS: non-significant
In girls, heel bone mineral content was best predicted by weight (21%) and
strength (3%) accounting for 24% of the variability. In boys, heel BMC was best
predicted by weight (46%) and strength (1%) accounting for 47% of the variability.
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The addition of pubertal status, passive smoking, active smoking, and physical
activity into the models did not make significant contributions to the variance of heel
BMC in either girls or boys.
Stepwise multiple linear regression analyses were also performed to examine
the effects of lean and fat mass on bone mass. Multiple regression coefficients,
standardized multiple regression coefficients, and model fit indexes (R2 ) according to
site (forearm and heel) for girls and boys are presented in the tables 18 through 19.
The relationship of lean body mass and fat mass and forearm BMC for girls
and boys is shown in table 18. The two independent variables controlled for each
other.
Table 18. Multiple linear regression model for forearm BMC: lean and fat mass
Forearm BMC Girls Boys
P
ps
P
Ps
Intercept 0.40 1.29
Lean Mass 0.07* 0.58* 0.09* 1.25*
Fat Mass NS NS -0.04** -0.73**
Model Fit Index (R2 ) 0.33 0.30
P : multiple regression coefficient
Ps: standardized multiple regression coefficient
*significant at the p<0.0001
** significant at the p<0.05
NS: non-significant
In girls, 33% of the variance in forearm BMC was attributed to lean body
mass alone with no significant contribution by fat mass. In boys, a total of 30% of
the variance in forearm BMC was attributed to lean body mass (28%), and to fat
mass (2%).
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The relationship of lean body mass and fat mass and heel BMC for girls and
boys is shown in table 19. The two independent variables controlled for each other.
Table 19. Multiple linear regression model for heel BMC: lean and fat mass
Heel BMC Girls Boys
P P s P P s
Intercept -0.01 -0.16
Lean Mass 0.04* 0.60* 0.09* 1.25*
Fat Mass NS NS -0.03** -0.55**
Model Fit Index (R2 ) 0.36 0.51
P: multiple regression coefficient
Ps: standardized multiple regression coefficient
* significant at the p<0.0001
**significant at the p<0.05
NS: non-significant
In girls, heel bone mineral content was best predicted by lean body mass
(36%) with no significant contribution by fat mass. In boys, heel BMC was best
predicted by lean body mass (50%) and fat mass (1%) accounting for 51% of the
variability.
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Chapter VI. Discussion
These findings support the hypothesis that the processes that determ ine
fracture risk in adulthood and increase the likelihood of osteoporosis in the elderly,
begin during childhood and adolescence. It further suggests that interventions to
enhance bone mass should start early in life.
Krall and Dawson-Hughes (1993) concluded that nearly half the variance in
bone mineral density is attributable to nonhereditary factors. These factors include
body weight, pubertal status, tobacco use and exposure, physical activity, and
strength. Our findings support the role of environmental factors during the
childhood and early adolescent periods of peak bone acquisition.
Body Weight:
Our findings related to the influence of body weight on bone mass during
adolescence were consistent with the findings of numerous other investigators
(Slemenda et al., 1994; Welten et al., 1994; Rice et al., 1993; Katzman et al., 1991).
Findings revealed that body weight is moderately correlated (r=0.57, pO.OOOl) to
forearm BMC in girls and strongly correlated (r=0.67, pO.OOOl) to heel BMC in
boys. Multiple linear regression models determined that 32% and 29% of the
variance in forearm BMC and BMD of girls and boys, respectively, is attributable to
weight alone. Body weight emerged as the strongest single predictor of heel bone
mineral density (R2 =0.26 in girls; R2 = 0.35 in boys) and content (R2 =0.24 in girls;
R2 =0.47 in boys) after controlling for pubertal status, passive smoking, active
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smoking, physical activity, and strength. Collinearity diagnostics revealed that the
influence of weight on bone mass was independent of the other variables. Tolerance
was 0.81 in girls and 0.65 in boys; tolerance close to 1.00 is reflective of a lack of
multicollinearity (Kleinbaum et al., 1998).
The fact that body weight and other body composition variables in girls
(height, BMI, fat mass) and in boys (height, BMI, fat mass, lean mass) impact the
heel (trabecular bone) more than the forearm (cortical bone) is consistent with the
findings of other investigators (Slemenda et al., 1994; Stevenson et al., 1989; Liel et
al., 1988) who have shown that body composition influences trabecular bone more
than cortical bone. When evaluating the influence of body weight and composition
on bone mass, the spine or the heel which are both predominantly made of trabecular
bone, are the most optimal sites. These findings suggest that adolescents with low
body weight during this critical period of bone acquisition will be at an increased
risk of vertebral fractures in late adulthood.
Pubertal Status:
Our findings suggest that the role of hormones in determining peak bone
mass is important, even when controlling for potential mediators (weight, physical
activity, strength). In our study, pubertal status independently influenced girls only.
The site impacted independently by pubertal status was the forearm, composed
predominantly of cortical bone. The fact that cortical bone is influenced by
hormones suggests that amenorrheic adolescents or those with a late of onset of
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menarche/puberty due to excessive exercise or low body weight will be at an
increased risk of hip fractures later in life.
Our conclusions are similar and consistent with the findings of other
investigators who have found that the gains in bone mass as measured by dual
energy x-ray absorptiometry during adolescence, are more a function of pubertal
stage than of age (Rico et al., 1993; Kroger et al., 1993; Grimston et al., 1992;
Southard et al., 1991). Furthermore, studies (Bailey et al., 1996; Kroger et al., 1993;
Theintz et al., 1992; Gilsanz et al., 1988) have shown earlier peaks of bone mass in
girls compared to boys, due to an earlier onset of sexual maturation in girls.
Cheng and colleagues (1999) in a 3-year longitudinal study of a group of 179
healthy Chinese adolescents (92 boys and 87 girls) found that bone mass was greater
in boys who were already in the advanced pubertal Tanner stages than in those who
were in the earlier stages.. The percentage change in bone mineral content of the
forearm and in bone mineral density of the spine was greater than 25% in the
advanced pubertal group compared to 20% in the less mature group. For girls, the
reverse was true. Changes in bone mass in the girls at the earlier pubertal stages was
significantly greater than in the girls at the later pubertal stages. The fact that the
relationship between pubertal status and bone mass was stronger in our late pre
adolescent and early adolescent girls than in boys of our cross-sectional study, is
consistent with the longitudinal findings of Cheng et al.
Although our measures of puberty may not have been optimal (see discussion
in the "Measures" section of Chapter IV), the correlations between our self-reported
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measure of pubertal status and bone mass was significant in both girls and boys.
Tests for multicollinearity in both girls and boys revealed that the influence of
pubertal status on forearm bone mass is independent of the other variables in the
models. Tolerance was 0.86 in girls and 0.81 in boys.
Tobacco Use and Exposure:
Although tests for multicollinearity in both girls and boys revealed that active
and passive smoking were independent from the other variables in the models
(tolerance was 0.90 and 0.89 in girls and 0.96 and 0.95 in boys for active and passive
smoking, respectively), we failed to find a statistically significant independent
relationship between smoking and bone mass.
The frequency and duration of tobacco use are critical determinants of its
physiological influences on bone (Daniell, 1976). We speculate that the reasons we
did not find significant inverse relationships between smoking and bone mass may
have been due to the low levels of tobacco smoke exposure among the adolescents
we studied.
Past-3 0-day and 100-lifetime-cigarettes were the measures that determined
active smoking status. Smoking levels among those classified as smokers were quite
low. Thirty eight percent of the girls inconsistently answered questions regarding
smoking frequency and duration. Forty eight percent of the girls classified as
smokers indicated that they had smoked less than one cigarette in the last 30 days.
Ten percent had smoked one cigarette in the past 30 days and 4 percent had smoked
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between 2-5 cigarettes in the last 30 days. Fourteen percent of the boys
inconsistently answered questions regarding smoking frequency and duration. Fifty
one percent of the boys indicated that they had smoked less than one cigarette in the
last 30 days; 19% had smoked one a day; 14% had smoked between 2-5; 1% had
smoked between 6-10; 1% had smoked more than 20. These rates were quite low
compared to the frequencies and durations reported in tobacco-using adult men and
women.
Failure to find a relationship between tobacco and bone mass may be due to
the relative low dose and short duration of smoking at the ages we studied. A more
optimal age range to study the smoking and bone mass relationship might be during
late adolescence or young adulthood when the frequency and duration of smoking is
higher. Nevertheless, we believe that this was the first study that investigated the
role of smoking on bone mass during adolescence.
We are also not aware of any other study in any age-group or gender that has
assessed the role of second-hand-smoke on bone mass. Although the public health
impact of smoking in China is undoubtedly great, its deleterious outcome on bone
mass of the Wuhan youth was not evident in this study.
Physical Activity:
Assessing the relationship between physical activity and bone mass was
another aim of this study. According to Slemenda and colleagues (1991), accurate,
valid, and reliable measurement of physical activity in children and adolescents is
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extremely difficult. Assessing exercise, with its diverse and ever-changing range of
activities during childhood contributes to the problem. Obtaining quantitative
information from children is difficult because their recall of the specifics of
particular activities is inconsistent and somewhat unrealistic. We were confronted
with these problems when we evaluated our physical activity measures and found no
significant relationship between frequency and intensity of physical activity and
bone mineral content and density of the forearm and heel in either girls or boys.
Although tolerance was 0.97 in girls and 0.95 in boys, indicating a lack of
multicollinearity between physical activity and the other variables entered in the
models, physical activity failed to contribute to the variance of the dependent
variables.
It is important to recognize that previous investigations in children have
evaluated the role of weight-bearing physical activity on bone acquisition. Most
measures of physical activity were considered "vigorous" exercise since most were
evaluating athletic team participation. For example, Kroger et al. (1992) found that
children who had participated in sports regularly for over 5 hours a week in athletic
clubs (ice-hockey, soccer, basketball, volleyball, jogging, track-and-field) had higher
bone mineral densities and contents compared to those children who participated in
sports for 3 hours a week or those who participated in little or no physical activity
outside school. Slemenda and colleagues (1991) found that total hours of weight
bearing activity was significantly related to bone mineral density of the radius and
the hip in 118 children. They also reported that activities (swimming, biking) that do
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not involve weight bearing were not associated with higher levels of bone m ineral
density.
An important observation by Slemenda et al. (1991) with implications for our
study was that children more frequently reported swimming and biking than all other
activities. Over-reporting of these activities may not reflect constant swimming or
biking, but rather time at the pool or with the bicycle. The use of the bicycle for
transportation and/or recreational physical activity in Wuhan is common. There was
no evidence for the influence of these types of activities on bone acquisition in our
cross-sectional study.
Although multiple linear regressions failed to detect an independent
relationship between physical activity and bone mass, correlations between forearm
(cortical) and physical activity were stronger than that of heel (trabecular) and
physical activity. Previous investigations (Kroger et al., 1992; Slemenda et al.,
1991) have shown positive associations between bone mineral density in the radius
(cortical) and the hip (cortical) and most types of physical activity in children. Our
comparison of the impact of physical activity to cortical and trabecular bone were
consistent with previous studies.
Longitudinal studies evaluating the influence of physical activity on bone
mass as these children in the early adolescent years become older and more mature
might prove beneficial in understanding the role of exercise on peak bone
acquisition. Failure to find significant correlations between physical activity and
pubertal status in our subjects perhaps suggests that to optimally increase bone mass,
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regular physical activity should be implemented before the onset of puberty rather
than at or after it. Once puberty starts, exercise interventions may have no or limited
effect on bone mass acquisition.
Strength:
Previous investigations have shown that body weight and strength are
significantly related; the relationship between strength and bone mass controlled by
body weight had failed to reach statistical significance (Madsen et al., 1998; Duppe
et al., 1997; Going & Lohman, 1991).
Strength has also been shown to be related to puberty (Ramos et al., 1998;
Katzmarzyk et al., 1997; Pratt, 1989). Ramos et al. (1998) studied 72 adolescent
boys and girls and found a positive correlation (r=0.64 in boys; r=0.46 in girls)
between hormone levels and muscle strength of the knee extensors controlled for
body weight. Katzmarzyk et al. (1997) have shown a relationship between pubertal
status as assessed by Tanner stages and grip and shoulder strength in 740 American
adolescents. Pratt (1989) assessed the relationship between knee extensor and flexor
strength and Tanner stages in 84 male high school students and found that puberty
was a greater predictor of strength than age. All these findings suggest that the
relationship between muscle strength and bone mass is complex and can be mediated
by other factors (weight, puberty, physical activity); to investigate the independent
relationship of muscle strength on bone mass, controlling for mediators is required.
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This study is among the very few that has evaluated the independent
influence of strength on bone mass of adolescents. Multiple regression analyses
revealed that strength is a significant independent predictor of forearm bone mineral
density and content in girls and boys when controlling for weight, pubertal status,
passive and active smoking, and physical activity. Collinearity diagnostics revealed
that the influence of strength on bone mass was independent of the other variables.
Tolerance was 0.85 in girls and 0.61 in boys.
Marcus (1995) has previously pointed out that correlations between muscle
strength/mass at one site and bone strength/mass at another unrelated site suggest
that the relationship may be related to other confounding factors. In our study, heel
bone mineral density of the boys was no longer predicted by strength after
controlling for weight, pubertal status, passive and active smoking, and physical
activity; only one percent of the heel bone mineral content variance was attributable
to strength. In girls, after controlling for weight, pubertal status, passive and active
smoking, and physical activity, heel bone mineral density and content (2 and 3
percent, respectively) were related with grip strength.
Whether or not the independent contribution of strength to bone mass or the
proven protective effects of weight on bone are due to lean body mass or fat mass are
important questions that were also addressed and answered in this study. Although
numerous researchers have concluded that body weight is strongly correlated to bone
mass in adolescents, they have not addressed which component of weight (lean or
fat) has a stronger correlation to bone.
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Our findings revealed that lean body mass is more strongly correlated with
bone mass (r=0.58 in girls; r=0.53 in boys) than is fat mass (r=0.41 in girls; r=0.25
in boys). We found that lean mass when controlled for fat mass or total body weight,
attributed 33% and 28% to the variance of forearm BMC in girls and boys,
respectively; fat mass when controlled for lean mass or total body weight attributed
0% and 2% to the variance in girls and boys, respectively. In the heel, similar results
emerged. Lean mass when controlled for fat mass or total body weight, attributed
36% and 50% to the variance in heel BMC in girls and boys, respectively; fat mass
when controlled for lean mass or total body weight explained 0% and 1% of the
variance in girls and boys, respectively.
These findings suggest that lean body mass is a stronger predictor of bone
mass than fat mass in adolescents. The fact that the "heavy-lean" adolescent would
be more protected from osteoporosis than the obese adolescent is a critically
important finding of this study and should be brought to the attention of
pediatricians, nutritionists, orthopedics, exercise specialists and other health care
professionals.
Limitations:
The fact that this study was conducted in China made it unique in many
ways. However, it also introduced a few limitations. The researchers of this study
recognize that measures of outcome were perhaps not optimal. Validation of the
forearm and the heel BMD and BMC against whole body or spine BMD and BMC
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would have perhaps strengthened our conclusions about the influence of the various
factors on the whole skeleton or on sites known to be at greater risk for fractures
during adulthood.
The use of bioelectrical impedance analysis (BIA) as opposed to the more
technologically advanced DXA for the purposes of estimating fat mass and lean
body mass, was another limitation in our methodology. However, it is important to
realize that conducting epidemiological studies introduces time, cost, and feasibility
issues, even if conducted in the United States. With the large sample size of 466,
performing DXA scans would have been difficult anywhere.
We recognize that Tanner staging would have been the most optimal way to
assess pubertal status in our sample. Tanner staging was considered but judged
inappropriate at this setting. Although studies (Theintz et al., 1992; Armamento-
Villereal et al., 1992) have used menarche as a puberty marker for bone mass in
girls, we are unaware of the validity and reliability of using secondary sexual
characteristics as puberty markers for bone mass in boys.
The fact that our physical activity questions emphasized sweating and
breathlessness for the assessment of aerobic activity as opposed to focusing on
weight-bearing and impact, is a limitation of our physical activity evaluation and
analysis. Although assessment of physical activity is extremely difficult, especially
in children, perhaps the most optimal way to evaluate its influence on bone would
have been to determine total hours spent in weight-bearing activity, in non weight
bearing activity, and lack of participation in any activity.
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Significance o f Findings:
Research has provided much support for the influence of body weight and
composition, hormones, cigarette smoking, physical activity, and muscle strength on
bone mass in adults. However, studies addressing the role of these factors on bone
development during times of peak bone acquisition in adolescence is limited.
Peak bone mass is a key determinant of skeletal health throughout life.
Despite the fact that Bachrach and colleagues (1999) studied an American-born
Asian cohort, they found that Asian girls and boys tend to reach a plateau in bone
mineral density earlier than other ethnic groups. Therefore, the fact that we
investigated the role of factors that influence optimal bone accretion in Asian girls
and boys made our study unique and valuable to pediatric bone research.
Future Research:
Although this study showed that tobacco use and exposure do not adversely
effect the bone health of the youth of Wuhan, China in their early adolescent years, a
few questions remain unanswered. Since peak bone mass is not achieved until the
later adolescent years and smoking increases with age, determining the role of
tobacco use and exposure on bone mass during late adolescence might prove
beneficial in drawing conclusions regarding the matter. Does tobacco adversely
effect the bones of the more mature smokers? If so, does tobacco exert a greater
effect on cortical bone or on trabecular bone? If cortical bone is more affected, are
adolescent smokers then at a greater risk of hip/forearm fractures during adulthood?
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Is a greater risk for vertebral fractures associated with lower trabecular bone mass
observed in adolescent smokers?
If the risk for bone fractures in adults rises with greater cigarette consumption
(Comuz et al., 1999), is the number of cigarettes consumed by adolescents also a
determining factor for bone mass? Which is a more critical factor with regards to
cigarette smoking and bone mass: frequency or duration? Grainge and colleagues
(1998) have found that bone mineral density is more strongly related to the number
of months spent smoking than to pack-years of smoking. On the other hand, Vogel
et al. (1997) have concluded that the magnitude of the smoking effect on bone
mineral density of 1303 Japanese-American men participating in the Hawaii
Osteoporosis Study was linked to the duration of smoking and also to the number of
cigarettes smoked.
The assessment of the influence of diet on bone mass during adolescence is
critical for understanding the role of various environmental factors on peak bone
acquisition. There is evidence suggesting that calcium (Sandler et al., 1985; Chan et
al., 1984) and vitamin D (Fehily et al., 1992) intakes influence children's bones and
might even decrease the risk for accidental fractures. Therefore, it is beneficial to
determine whether calcium and vitamin D are significant independent predictors of
bone mass in these adolescents.
Furthermore, there is evidence that calcium intake and absorption efficiency
are independently and inversely associated with caffeine intake (Barger-Lux &
Heaney, 1995; Heaney & Recker, 1982). The replacement of milk with soft drinks is
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undoubtedly prevalent in the diet of American adolescents as well as the Chinese,
who try to imitate Western cultures. Is caffeine intake an independent predictor of
adolescent bone mass?
Asian youth consume less calcium compared to Caucasians (Bhudhikanok et
al., 1996; Lau & Cooper, 1996). How does low calcium intake influence peak bone
mass attained by Asian adolescents compared to that attained by Caucasian, African-
American, or Hispanic youth? In contrast, tea drinking and soybean consumption
have been identified as protective factors against osteoporosis because they contain
flavonoids (Hegarty et al., 2000; Kao and Peng, 1995); the daily habits of Asians to
drink tea and consume soybeans and vegetables are apparent. How do these various
dietary habits harm or protect the bones of the Asian youth?
Previous investigations have shown interactions between smoking and serum
calcium and vitamin D levels. Krall & Dawson-Hughes (1999, 1991) have reported
that the adverse effect of smoking on bone may be related to decreased intestinal
absorption of calcium. Brot and co-investigators (1999) have shown depressed
levels of vitamin D among 510 Danish perimenopausal smokers. Understanding the
influence of cigarette smoking on calcium and vitamin D levels may help scientists
define other probable mechanisms responsible for the link between tobacco and
osteopenia.
Future research questions regarding the influence of physical activity and
bone mass should address the role of weightbearing versus non-weightbearing
activity on bone mineral acquisition. Does time spent in weightbearing activity
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(running, jogging, soccer, basketball, etc.) correlate positively to bone mass of
adolescents? Does time spent in non-weightbearing activity such as bicycling or
swimming correlate inversely with bone mass, or is the relationship non-significant?
The relationship between puberty and weightbearing activity should also be
explored further. Does the postmenarcheal skeletal system benefit from these types
of activities or are the osteogenic effects of exercise only apparent prior to
menarche? Are physically active adolescents protected against hip/forearm fractures
because of the positive relationship of activity and cortical bone? If physical activity
does not influence trabecular bone, then physically active adolescents do not
necessarily protect themselves from vertebral fractures in late adulthood.
Moreover, the importance of conducting longitudinal studies for the
understanding of peak bone acquisition during adolescence cannot be over
emphasized. As the adolescents in this study become older and more mature, they
will experience numerous physiological and behavioral changes. Comparing
changes in bone mass in those who remain smokers over the years to those who
don't, will perhaps address the dose-response relationship seen in adult smokers.
When peak bone mass is attained in these young adults, does smoking ultimately
make a difference? Does physical activity ultimately make a difference?
Which factor is the most important determinant of bone mass acquisition?
Should teenagers increase their calcium/vitamin D consumption? Should they avoid
smoking? Should they exercise more? What types of exercises are more effective?
Or when bone health is concerned, do these factors (diet, smoking, exercise) even
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make a difference? Is it ultimately just important to avoid a low body weight? Can
identification of those adolescents participating in excessive physical activity and/or
suffering from eating disorders (anorexia nervosa and/or bulimia) become the most
important task for pediatricians and health-care professionals concerned with the
strength of their patients' bones? Grinspoon and colleagues (1997) have reported
that osteoporosis is present in over half of all patients with anorexia nervosa. Does
the treatment of eating disorders along with improvements in nutrition and
resumption of menses in amenorrheic young women result in complete recovery to
normal bone mineral density and restoring bone health? Although some studies
suggest restoration of normal bone mass with recovery from anorexia nervosa (Valla
et al., 2000), others (Ward et al., 1997; Klibanski et al., 1995; Bachrach et al., 1991)
suggest that the improvement may only be partial. If lean body mass is more
important than fat mass, how do we then draw the line between excessive exercise
and that which promotes moderate muscle gain?
To answer the numerous unanswered questions about the roles of body size,
the endocrine system and menstrual dysfunction, cigarette smoking, recreational or
competitive physical activity, diet and eating disorders on peak bone mass, further
investigations in the realm of pediatric/adolescent bone research is required.
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Literature Cited
Aloia, J., Cohn, S., and Babu, T. (1978) Skeletal mass and body composition
in marathon runners. Metabolism 27: 1793-1796.
Armamento-Villereal, R., Villereal, D.T., Avioli, L., and Civitelli, R. (1992)
Estrogen status and heredity are major determinants of pre-menopausal bone mass.
J. Clin. Invest. 90: 2464-2471.
Bachrach, L.K., Katzman, D.K., Litt, IF ., Guido, D., and Marcus, R. (1991)
Recovery from osteopenia in adolescent girls with anorexia nervosa. J. Clin.
Endocrinol. Metab. 72: 602-606.
Bachrach, L.K., Hastie, T., Wang, M.C., Narasimhan, B., and Marcus, R.
(1999) Bone mineral acquisition in healthy Asian, Hispanic, Black, and Caucasian
youth: a longitudinal study. J. Clin. Endocrinol. Metab. 84: 4702-4712.
Bailey, D.A., Faulkner, R.A., and McKay, H.A. (1996) Growth, physical
activity, and bone mineral acquisition. Exercise & Sport Sci Reviews. 24: 233-266.
Balfour, D.J.K. (1982) The effects of nicotine on brain neurotransmitter
systems. Pharmacol. Ther. 16: 269-282.
Barbieri, R.L., Gochberg, J., and Ryan, K.J. (1986) Nicotine, cotinine, and
anabasine inhibit aromatase in human trophoblast in vitro. J. Clin. Invest. 77: 1727-
1733.
Barger-Lux, M.J., and Heaney, R.P. (1995) Caffeine and the calcium
economy revisited. Osteoporos. Int. 5(2): 97-102.
Baron, J.A., La Vecchia, C., and Levi, F. (1990) The antiestrogenic effect of
cigarette smoking in women. Am. J. Obstet. Gynecol. 162: 502-514.
Barrett-Connor, E., and Khaw, K-T. (1987) Cigarette smoking and increased
endogenous estrogen levels in men. Am. J. Epidemiol. 126: 187-192.
Bassey, E.J., and Ramsdale, S.J. (1994) Increase in femoral bone density in
young women following high-impact exercise. Osteopor. Int. 4: 72-75.
Bauer, D.C., Gluer, C.C., Genant, H.K., and Stone, K. (1995) Quantitative
ultrasound and vertebral fracture in postmenopausal women. J. Bone Miner. Res. 10:
353-358.
72
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.
Bevier, W.C., Wiswell, R.A., Pyka, G., Kozak, K.C., Newhall, K.M., and
Marcus, R. (1989) Relationship of body composition, muscle strength, and aerobic
capacity to bone mineral density in older men and women. J Bone Miner. Res. 4:
421-432.
Blake, G.M., and Fogelman, I. (1996) Principles of Bone Densitometry. In:
Bilezikian, J., Raisz, L.G., and Rodan, G.A. (eds.), Principles of Bone Biology (pp.
1313-1332). San Diego, CA: Academic Press.
Bonjour, J.P., Theintz, G., and Buckes, B. (1991) Critical years and stages of
puberty for spinal and femoral bone mass accumulation during adolescence. J. Clin.
Endocrinol. Metab. 73: 555-563.
Bonjour, J.P., Theintz, G., Law, F., Slosman, D., and Rizzoli, R. (1994) Peak
bone mass. Osteopor. Int. 4(Suppl. 1): S7-S13.
Briggs, M.H. (1973) Letter to the editor. Med. J. Aust.
Brot, C., Jorgensen, N.R., and Sorensen, O.H. (1999) The influence of
smoking on vitamin D status and calcium metabolism. Eur. J. Clin. Nutr. 53(12):
920-926.
Bhudhikanok, G.S., Wang, M.C., Eckert, K., Matkin, C., Marcus, R., and
Bachrach, L.K. (1996) Differences in bone mineral in young Asian and Caucasian
Americans may reflect differences in bone size. J. Bone Miner. Res. 11(10): 1545-
1556.
Cann, C.E., Cavanaugh, D.J., Scnurpfiel, K., and Martin, M.C. (1988)
Menstrual history is the primary determinant of trabecular bone density in women.
Med. Sci. Sports Exerc. 20 (Suppl. 2): S59.
Chan, G.M., Hess, M., Hollis, J., and Book, L.S. (1984) Bone mineral status
in childhood accidental fractures. AJDC. 138: 569-570.
Chau, S.T., Omiecinski, C.J., Namkung, M.J., et al. (1981) Catechol
estrogen formation in placental and fetal tissue of humans, macaques, rats and
rabbits. Dev. Pharmacol. Ther. 2: 1-16.
Chen, X., and Unger, J.B. (1999) Hazards of smoking initiation among
Asian American and Non-Asian adolescents in California: a survival model
analysis. Preventive Medicine 28: 589-599.
73
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.
Cheng, J.C., Maffulli, N., Leung, S.S., Lee, W.T., Lau, J.T., and Chan, K.M.
(1999) Axial and peripheral bone mineral acquisition: a 3-year longitudinal study in
Chinese adolescents. Euro. J. Ped. 158(6): 506-512.
Cole, T.J., Bellizzi, M.C., Flegal, K.M., and Dietz, W.H. (2000) Establishing
a standard definition for child overweight and obesity worldwide: international
survey. BMJ 320: 1240-1243.
Cooper, C., Campion, G., and Melton, L.J. (1992) Hip fractures in the
elderly: a worldwide projection. Osteoporosis Int. 2: 285-289.
Comuz, J., Feskanich, D., Willett, W.C., and Colditz, G.A. (1999) Smoking,
smoking cessation, and risk of hip fracture in women. Am. J. Med. 106(3): 311-314.
Cummings, S.R., Black, D.M., Nevitt, M.C., Browner, W.S., Cauley, J.A.,
Genant, H.K., Mascioli, S.R., Scott, J.C., Seeley, D.G., Steiger, P. et al. (1990)
Appendicular bone density and age predict hip fracture in women. JAMA 263: 665-
668 .
Cummings, S.R., Black, D.M., Nevitt, M.C., Browner, W., Cauley, J.,
Ensrud, K., Genant, H.K., Palermo, L., Scott, J., and Vogt, T.M. (1993) Bone
density at various sites for prediction of hip fractures. Lancet 341: 72-75.
Dai, W.S., Gutai, J.P., Kuller, L.H., and Cauley, J.A. (1988) Cigarette
smoking and serum sex hormones in men. Am. J. Epidemiol. 128: 796-805.
Daniell, H.W. (1976) Osteoporosis of the slender smoker: vertebral
compression fractures and loss of metacarpal cortex in relation to postmenopausal
cigarette smoking and lack of obesity. Arch. Intern. Med. 136: 298-304.
De Schepper, J., Derde, M.P., Van den Broeck, M., Peipsz, A., and Jonkheer,
M.H. (1990) Normative data for lumbar spine bone mineral content in children:
influence of age, height, weight, and pubertal stage. J. Nucl. Med. 32: 216-220.
Deurenberg, P., van der Kooy, K., Paling, A., and Withagen, P. (1989)
Assessment of body composition in 8-11 year old children by bioelectrical
impedance. Eur. J. Clin. Nutr. 43: 623-629.
Duppe, H., Gardsell, P., Johnell, O., Nilsson, B.E., Ringsberg, K. (1997)
Bone mineral density, muscle strength and physical activity: a population-based
study of 332 subjects aged 15-42 years. Acta. Orthop. Scand. 68(2): 97-103.
Edman, C.D., and MacDonald, P.C. (1978) Effect of obesity on conversion
of plasma androstenedione to estrone in ovulatory and anovulatory young women.
Am. J. Obstet. Gynecol. 130: 456.
74
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.
Einhom, T.A. (1992) Bone strength: the bottom line. Calcif. Tissue Int. 51:
333-339.
Fehily, A.M., Coles, R.J., Evans, W.D., and Elwood, P.C. (1992) Factors
affecting bone density in young adults. Am. J. Clin. Nutr. 56: 579-586.
Finkelstein, I., Neer, R.M., Biller, B.M.K., Crawford, J.D., and Klibanski, A.
(1992) Osteopenia in men with a history of delayed puberty. N. Engl. J. Med. 326:
600-604.
Fordham, N.J., Chinn, D., and Kumar, N. (1999) Sensitivity and specificity
of /?DXA at os calcis compared with lumbar spine and femoral neck BMD. J. Bone
Miner. Res. 14: S200.
Forsen, L., Bjomdal, A., Bjartveit, K., Edna, T., Holmen, J., lessen, V., and
Westberg, G. (1994) Interaction between current smoking, leanness, and physical
inactivity in the prediction of hip fracture. J. Bone Miner. Res. 9: 1671-1678.
Frediani, B., Allegri, A., Storm, L., Falsetti, P., Baldi, P., Bisogne, S.,
Marcolongo, R. (1998) Comparison of os calcis ultrasonometry with regional and
total body x-ray absorptiometry. Presented at ASBMR-IBMS Second Joint Meeting,
San Francisco, CA.
Frost, H.M. (1991) A new direction for osteoporosis research: a review and
proposal. Bone 12: 429-437.
Frost, H.M. (1992) The role of changes in mechanical usage set points in the
pathogenesis of osteoporosis. J. Bone Miner. Res. 7: 253-261.
Frost, H.M. (1997) Perspective: on our age-related bone loss: insights from a
new paradigm. J. Bone Miner. Res. 12: 1539-1546.
Gardsell, P., Johnell, O., and Nilsson, B.E. (1989) Predicting fractures in
women by using forearm bone densitometry. Calcif. Tissue Int. 44: 235-242.
Gilsanz, V., Gibbens, D., Carlson, M., Boechat, M., Cann, C., and Schulz, E.
(1988) Peak trabecular vertebral density: a comparison of adolescent and adult
females. Calcif Tissue Int. 43: 260-262.
Girandola, R.N., and Contarsy, S.A. (1988) The validity of bioelectrical
impedance (ELG) to predict body composition. Presented at Olympic Scientific
Congress, Seoul, Korea. Published by: Olympic Scientific Congress, New Horizons
of Human Movement, page 9.
75
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.
Glass, A.R., Swerdloff, R.S., Bray, G.A., Dahms, W.T., Atkinson, R.L.
(1977) Low serum testosterone and sex-hormone-binding globulin in massively
obese men. J. Clin. Endocrinol. Metab. 45: 1211.
Glastre, C., Braillon, P., David, L., Cochat, P., Meunier, P.J., and Delmas,
P.D. (1990) Measurement of BMC of the lumbar spine by dual energy x-ray
absorptiometry in normal children: correlations with growth parameters. J. Clin.
Endocrinol. Metab. 70: 1330-1333.
Going, S., and Lohman, T. (1991) Letters to the editor-in-chief. Med. Sci.
Sports Exerc. 27: 882-883.
Goran, M.I., Kaskoun, M.C., Carpenter, W.H., Poehlman, E.T., Ravussin, E.,
and Fontvieille, A.M. (1993) Estimating body composition in young children using
bioelectrical resistance. J. Appl. Physiol. 75: 1776-1780.
Goran, M.I., and Khaled, M.A. (1995) Cross-validation of fat-free mass
estimated from body density against bioelectrical resistance: effects of obesity and
gender. Obes. Res. 3(6): 531-539.
Grainge, M.J., Coupland, C.A., Cliffe, S.J., Chilvers, C.E., and Hosking, D.J.
(1998) Cigarette smoking, alcohol and caffeine consumption, and bone mineral
density in postmenopausal women. The Nottingham EPIC Study Group.
Osteoporos. Int. 8(4): 355-363.
Grimston, S.K., Morrison, K., Harder, J.A., and Hanley, D.A. (1992) Bone
mineral density during puberty in Western Canadian children. Bone Miner. 19: 85-
96.
Grimston, S.K., Willows, N.D., and Hanley, D.A. (1993) Mechanical
loading regime and its relationship to BMD in children. Med. Sci. Sports Exerc. 25:
1203-1210.
Grinspoon, S., Herzog, D., and Klibanski, A. (1997) Mechanisms and
treatment options for bone loss in anorexia nervosa. Psychopharm. Bullet. 33(3):
399-404.
Hassager, C., and Christiansen, C. (1989) Influence of soft tissue body
composition on bone mass and metabolism. Bone 10: 415-419.
Heaney, R.P., and Recker, R. R. (1982) Effects of nitrogen, phosphorus, and
caffeine on calcium balance in women. J. Lab. Clin. Med. 99: 46-55.
76
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.
Hegarty, V.M., May, H.M., and Khaw, K.T. (2000) Tea drinking and bone
mineral density in older women. Am. J. Clin. Nutr. 71(4): 1003-1007.
Hopper, J.L., and Seeman, E. (1994) The bone density of female twins
discordant for tobacco use. N. Engl. J. Med. 330: 387-392.
Horlick, M., Thornton, J., Wang, J., Levine, L.S., Fedun, B., and Pierson,
R.N. (2000) Bone mineral in prepubertal children: gender and ethnicity. J. Bone
Miner. Res. 15: 1393-1397.
Hui, S.L., Slemenda, C.W., and Johnston, C.C. (1989) Baseline
measurement of bone mass predicts fracture in white women. Ann. Intern. Med.
Ill: 355-361.
Hyakutake, S., Goto, S., Yamagata, H., and Moriya, H. (1997) Relationship
between bone mineral density of the proximal femur and lumbar spine and
quadriceps and hamstrings torque in healthy Japanese subjects. Calcif Tissue Int.
55: 223-229.
Jensen, J., and Christiansen, C. (1988) Effects of smoking on serum
lipoproteins and bone mineral content during postmenopausal hormone replacement
therapy. Am. J. Obstet. Gynecol. 159: 820-825.
Jensen, J., Christiansen, C., and Rodbro, P. (1985) Cigarette smoking, serum
estrogens, and bone loss during hormone-replacement therapy early after menopause.
N. Engl. J. Med. 313: 973-975.
Jones, C.D., Laval-Jeantet, A.M., Laval-Jeantet, M.H., and Genant, H.K.
(1987) Importance of measurement of spongious vertebral bone mineral density in
the assessment of osteoporosis. Bone 8: 201-206.
Kalender, W.A. (1992) Effective dose values in bone mineral measurements
by photon absorptiometry and computed tomography. Osteoporosis Int. 2: 82-87.
Kao, P.C., and Peng, F.K. (1995) How to reduce the risk factors of
osteoporosis in Asia. Hua IHsueh Tsa Chih (Taipei) 55(3): 209-213.
Katzman, D.K., Bachrach, L.K., Carter, D.R., and Marcus, R. (1991)
Clinical and anthropometric correlates of bone mineral acquisition in healthy
adolescent girls. J. Clin. Endocrinol. Metab. 73: 1332-1339.
Katzmarzyk, P.T., Malina, R.M., and Beunen, G.P. (1997) The contribution
of biological maturation to the strength and motor fitness of children. Annals o f
Human Biology 24(6): 493-505.
77
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.
Khosla, S., Atkinson, E.J., Riggs, B.L., and Melton HI, L.J. (1996)
Relationship between body composition and bone mass in women. J. Bone Miner.
Res. 11: 857-863.
Kiel, D.P., Baron, J.A., Anderson, J.J., Hannan, M.T., and Felson, D.T.
(1992) Smoking eliminates the protective effect of oral estrogens on the risk for hip
fracture among women. Ann. Int. Med. 116: 716-721.
Klaiher, EX., and Broverman, D.M. (1988) Dynamics of estradiol and
testosterone and seminal fluid indexes in smokers and non-smokers. Fertil. Steril.
50: 630-634.
Klaiber, EX., Broverman, D.M., and Dalen, I.E. (1984) Serum estradiol
levels in male cigarette smokers. Am. J. Med. 77: 858-862.
Kleinbaum, D.G., Kupper, L.L., Muller, K.E., and Nizam, A. (1998) Applied
Regression Analysis and Other Multivariable Methods (3rd ed.). Pacific Grove, CA:
Brooks/Cole Publishing Company.
Kley, H.K., Solbach, H.G., McKinnan, J.C., and Kruskemper, H.L. (1979)
Testosterone decrease and estrogen increase in male patients with obesity. Acta.
Endocrinol. (Copenh) 91: 553.
Kley, H.K., Deselaers, T., Peerenboom, H., and Kruskemper, H.L. (1980)
Enhanced conversion of androstenedione to estrogens in obese males. J. Clin.
Endocrinol. Metab. 51: 1128-1132.
Klibanski, A., Biller, B.M., Schoenfeld, D.A., Herzog, D.B., and Saxe, V.C.
(1995) The effects of estrogen administration on trabecular bone loss in young
women with anorexia nervosa. J. Clin. Endocrinol. Metab. 80(3): 898-904.
Krall, E.A., and Dawson-Hughes, B. (1991) Smoking and bone loss among
postmenopausal women. J. Bone Miner. Res. 6: 331-337.
Krall, E.A., and Dawson-Hughes, B. (1993) Heritable and lifestyle
determinants of bone mineral density. J.Bone Miner. Res. 8: 1-9.
Krall, E.A., and Dawson-Hughes, B. (1999) Smoking increases bone loss
and decreases intestinal calcium absorption. J. Bone Miner. Res. 14(2): 215-220.
Kroger, H., Kotaniemi, A., Vainio, P., and Alhava, E. (1992) Bone
densitometry of the spine and femur in children by dual-energy x-ray
absorptiometry. Bone Miner. 17: 75-85.
78
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.
Kroger, H., Kotaniemi, A., Kroger, L., and Alhava, E. (1993) Development
of bone mass and bone density of the spine and femoral neck— a prospective study of
65 children and adolescents. Bone Miner. 23: 171-182.
Kushner, R.F., Schoeller, D.A., Fjeld, C.R., and Danford, L. (1992) Is the
impedance index (ht2 /R) significant in predicting total body water? Am. J. Clin.
Nutr. 56: 835-839.
Lariviere, J., Snow, C.M., and Robinson, T.L. (1995) Bone mass changes in
female competitive gymnasts over two seasons. Med. Sci. Sports Exerc. 28: S131.
Lau, E.M. (1988) Osteoporosis in elderly Chinese. British Med. J. 296:
1263.
Lau, E.M., and Cooper, C. (1996) The epidemiology of osteoporosis: the
Oriental perspective in a world context. Clin. Orthop. 323: 65-74.
Laval-Jeantet, A.M., Bergot, C., Williams, M., Davidson, K., and Laval-
Jeantet, M. (1995) Dual-energy x-ray absorptiometry of the calcaneouS: comparison
with vertebral dual-energy x-ray absorptiometry and quantitative computed
tomography. Calcif. Tissue Int. 56: 14-18.
Levis, S., and Altman, R. (1998) Bone densitometry: clinical considerations.
Arthrit. & Rheum. 41(4): 577-587.
Lewis, M.K., Blake, G.M., and Fogelman, I. (1994) Patient dose in
morphometric x-ray absorptiometry. Osteoporosis Int. 4: 11-15.
Li, X., Fang, X., and Stanton, B. (1996) Cigarette smoking among Chinese
adolescents and its association with demographic characteristics, social activities,
and problem behaviors. Substance Use & Misuse 31: 545-563.
Liel, Y., Edwards, J., Shary, J., Spicer, K.M., Gordon, L., and Bell, N.H.
(1988) The effects of race and body habitus on bone mineral density of the radius,
hip, and spine in premenopausal women. J. Clin. Endocrin. Metab. 66: 1247-1250.
Ling, X., Cummings, S.R., Mingwei, Q., Xihe, Z., Xioashu, C., Nevitt, M.,
and Stone, K. (2000) Vertebral fractures in Beijing, China: The Beijing
Osteoporosis Project. J. Bone Miner. Res. 15: 2019-2025.
Longcope, C., Pratt, J.H., Schneider, S.H., and Finberg, S.E. (1978)
Aromatization of androgens in muscle and adipose tissue in vivo. J. Clin.
Endocrinol. Metab. 46: 146.
79
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.
Looker, A.C., Orwoll, E.S., Johnston, C.C., Lindsay, R.L., Wahner, H.W.,
Dunn, W.L., Calvo, M.S., Harris, T.B., and Heyse, S.P. (1997) Prevalence of low
femoral density in older U.S. adults from NHANES III. J Bone Miner. Res. 12:
1761-1768.
Loomis, W.F. (1967) Skin-pigment regulation of vitamin-D biosynthesis in
man. Science 157: 501-506.
Lu, A.Y.H., Levin, W., West, S.B., Jacobson, M., Ryan, D., Kuntzman, R.,
and Conney, A.H. (1972) Reconstituted liver microsomal enzyme system that
hydroxylates drugs, other foreign compounds, and endogenous substrates. J. Biol.
Chem. 247: 1727-1734.
Lu, P.W., Briody, J.N., Ogle, G.D., Morley, K., Humphries, I.R., Allen, J.,
Howman-Giles, R., Sillence, D., and Cowell, C.T. (1994) Bone mineral density of
total body, spine, femoral neck in children and young adults: a cross-sectional and
longitudinal study. J. Bone Miner. Res. 9: 1451-1458.
Lukaski, H.C., Bolonchuk, W.W., Hall, C.B., and Siders, W.A. (1986)
Validation of tetrapolar bioelectrical impedance method to assess human body
composition. J. Appl. Physiol. 60: 1327-1332.
MacDonald, P.C., Edman, C.D., Hemsell, D.L., Poerter, J.C., and Siiteri, P.K.
(1978) Effect of obesity on conversion of plasma androstenedione to estrone in
postmenopausal women with and without endometrial cancer. Am. J. Obstet.
Gynecol. 130: 448.
MacMahon, B., Trichopoulos, D., Cole, P., and Brown, J. (1982) Cigarette
smoking and urinary estrogens. N Engl J Med. 307: 1062-1065.
Madsen, K.L., Adams, W.C., and Van Loan, M.D. (1998) Effects of physical
activity, body weight and composition, and muscular strength on bone density in
young women. Med. Sci. Sports Exerc. 30(1): 114-120.
Marcus, R. (1995) Relationship of age-related decreases in muscle mass and
strength to skeletal status. J. Gerontol. (Series A) 50A: 86-87.
Mattison, D.R. (1982) The effects of smoking on fertility from
gametogenesis to implantation. Environ. Res. 28: 410-433.
Mazess, R.B., Barden, H.S., Biske, J.P., and Hanson, J. (1990) Dual energy
x-ray absorptiometry for total-body and regional bone mineral and soft-tissue
composition. Am. J. Clin. Nutr. 51: 1106-1112.
80
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.
McMurtry, R.J., and Hagerman, D.D. (1972) Carbon monoxide inhibition of
progesterone hydroxylation and side-chain cleavage catalyzed by rat testis
microsomes. Steroids Lipids Res. 3: 8-13.
Melton, L.J. (1995) How many women have osteoporosis now? J. Bone
Miner. Res. 10: 175-177.
Melton, L.J., Chrischilles, E.A., Cooper, C., Lane, A.W., and Riggs, B.L.
(1992) How many women have osteoporosis? J. Bone Miner. Res. 1: 1005-1010.
Melton, L.J., Thamer, M., Ray, N.F., Chan, J.K., Chestnut, D.H., Einhom,
T.A., Johnston, C.C., Raisz, L.G., Silverman, S.L., and Siris, E.S. (1997) Fractures
attributable to osteoporosis: report from the National Osteoporosis Foundation. J.
Bone Miner. Res. 12: 16-23.
Michnovicz, J.J., Hershcopf, R.J., Naganuma, H., Bradlow, H.L., and
Fishman, J. (1986) Increased 2-hydoxylation of estradiol as a possible mechanism
for the anti-estrogenic effect of cigarette smoking. N. Engl. J. Med. 315: 1305-
1309.
Najem, G.R., Batuman, F., Smith, A.M., and Feuerman, M. (1997) Patterns
of smoking among inner-city teenagers: smoking has a pediatric age of onset. J.
Adolesc. Health 20(3): 226-231.
National Institutes of Health. (1995) Bioelectrical impedance analysis in
body composition measurement. National Institutes of Health Technology
Assessment Conference Statement.
Nickols-Richardson, S.M., Modelsky, C.M., O'Connor, P.J., and Lewis, R.D.
(2000) Premenarcheal gymnasts possess higher bone mineral density than controls.
Med. Sci. Sports Exerc. 32: 63-69.
Nimrod, A., and Ryan, K.J. (1975) Aromatization of androgens by human
abdominal and breast fat tissue. J. Clin. Endocrinol. Metab. 40: 367.
Orwoll, E.S., Oviatt, S.K., and the Nafarelin Bone study group. (1991)
Longitudinal precision of dual-energy x-ray absorptiometry in a multicenter study.
J. Bone Miner. Res. 6: 191-197.
Pacifici, R., Rupich, R., Griffin, M., Chines, A., Susman, N., and Avioli, L.V.
(1990) Dual energy radiography versus quantitative computer tomography for the
diagnosis of osteoporosis. J. Clin. Endocrinol. Metab. 70: 705-710.
81
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.
Pocock, N.A., Eisman, J.A., Yeates, M.G., Sambrook, P.N., and Eberl, S.
(1986) Physical fitness is a major determinant of femoral neck and lumbar spine
bone mineral density. J. Clin. Invest. 78: 618-621.
Pocock, N.A., Eisman, J.A., Hopper, J.L., Yeates, M.G., Sambrook, P.N., and
Eben, S. (1987) Genetic determinants of bone mass in adults. J. Clin. Invest. 80:
706-710.
Pocock, N.A., Eisman, J.A., Gwinn, T., Sambrook, P.N., Kelly, P., Freund,
J., and Yeates, M.G. (1989) Muscle strength, physical fitness and weight but not
age, predict femoral neck bone mass. J. Bone Miner. Res. 4: 441-446.
Pratt, M. (1989) Strength, flexibility, and maturity in adolescent athletes.
Amer. J. Diseases Children 143(5): 560-563.
Protiva, K.W., Snow-Harter, C., Gillis, D.E., Connolly, D., and Robinson,
T.L. (1994) Aerobic capacity correlates with femoral neck, but not spine or whole
body mineral density. Med. Sci. Sports Exerc. 26 (Suppl.): S165.
Ramos, E., Frontera, W.R., Llopart, A., and Feliciano, D. (1998) Muscle
strength and hormonal levels in adolescents: gender related differences. Int. J.
Sports Med. 19(8): 526-531.
Ray, N.F., Chan, J.K., Thamer, M., and Melton, L.J. (1997) Medical
expenditures for the treatment of osteoporotic fractures in the United States in 1995:
report from the National Osteoporosis Foundation. J. Bone Miner. Res. 12: 24-35.
Reid, I.R., Ames, R., Evans, M.C., Sharpe, S., Gamble, G., France, J.T., Lim,
T.M.T., and Cundy, T.F. (1992a) Determinants of total body and regional bone
mineral density in normal postmenopausal women— a key role for fat mass. J. Clin.
Endocrinol. Metab. 75: 45-51.
Reid, I.R., Plank, L.D., and Evans, M.C. (1992b) Fat mass is an important
determinant of whole body bone density in premenopausal women but not in men. J.
Clin. Endocrinol. Metab. 75: 779-782.
Reid, I.R., Legge, M., Stapleton, J.P., Evans, M.C., and Grey, A.B. (1995)
Regular exercise dissociates fat mass and bone density in premenopausal women. J.
Clin. Endocrinol. Metab. 80: 1764-1768.
Rice, S., Blimkie, C.J., Webber, C.E., Levy, D., Martin, J., Parker, D. and
Gordon, C.L. (1993) Correlates and determinants of BMC and density in healthy
adolescent girls. Can. J. Physiol. Pharmacol. 71: 923-930.
82
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.
Rico, H., Revilla, M., Villa, L.F., Hernandez, E.R., Alvarez de Buergo, M.,
and Villa, V. (1993) Body composition in children and Tanner's stages: a study with
dual-energy x-ray absorptiometry. Metabolism 42: 967-970.
Riggs, B.L., and Melton, L.J. (1986) Involutional osteoporosis. N. Engl. J.
Med. 314: 1676-1686.
Rising, R., Swinbum, B., Larson, K., and Ravussin, E. (1991) Body
composition in Pima Indians: validation of bioelectrical resistance. Am. J. Clin.
Nutr. 53: 594-598.
Robinson, T.L., Snow-Harter, C., Taaffe, D.R., Gillis, D., Shaw, J., and
Marcus, R. (1995) Gymnasts exhibit higher bone mass than runners despite similar
prevalence of amenorrhea and oligomenorrhea. J. Bone Miner. Res. 10: 26-35.
Rubin, K., Schirduan, V., Gendreau, P., Sarfarazi, M., Mendola, R., and
Dalsky, G. (1993) Predictors of axial and peripheral bone mineral density in healthy
children and adolescents. J. Pediatr. 123: 863-870.
Sandler, R.B., Slemenda, C.W., LaPorte, R.E., Cauley, J.A., Schramm, M.M.,
Barresi, MX., and Kriska, A.M. (1985) Postmenopausal bone density and milk
consumption in childhood and adolescence. Am. J. Clin. Nutr. 42: 270-274.
Schindler, A.E., Ebert, A., and Friedrich, E. (1972) Conversion of
androstenedione to estrone in human fat tissue. J. Clin. Endocrinol. Metab. 35: 627.
Schmiterlow, C.G., Hansson, E., Andersson, G., et al. (1967) Distribution of
nicotine in the central nervous system. Ann. NY. Acad. Sci. 142: 2-14.
Schonau, E., Werhahn, E., Schiedermaier, U., Mokow, E., Schiessl, H.,
Scheidhauer, K., and Michalk, D. (1996) Influence of muscle strength on bone
strength during childhood and adolescence. Norm. Res. 45(Suppl 1): 63-66.
Seeman, E. (1996) The Effects of Tobacco and Alcohol Use on Bone. In:
Marcus, R., Feldman, D., and Kelsey, J. (eds.), Osteoporosis (pp. 577-597). San
Diego, CA: Academic Press.
Seeman, E., Melton, L.J., O’Fallon, T., and Riggs, B.L. (1983) Risk factors
for spinal osteoporosis in men. Am. J. Med. 75: 977-983.
Seeman, E., Hopper, J.L., and Bach, L.A. (1989) Reduced bone mass in
daughters of women with osteoporosis. N. Engl. J. Med. 320: 554-558.
83
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.
Segal, K.R., Val Loan, M., Fitzgerald, P.I., Hodgdon, J.A., and Van Itallie,
T.B. (1988) Lean body mass estimation by bioelectrical impedance analysis: a four-
site cross-validation study. Am. J. Clin. Nutr. 47: 7-14.
Sershen, H., and Lajtha, A. (1979) Cerebral uptake of nicotine and of amino
acids. J Neurosci. Res. 4: 85-91.
Shaw, J.M., Snow-Harter, C.M., Robinson, T., Wegner, M., and Shelley, A.
(1993) Lean body mass and lumbar spine bone mineral density in pre-menopausal
women. Med. Sci. Sport Exerc. 25(5): S855.
Shaw, J.M., Snow-Harter, C.M., and Protiva, K.W. (1995) Effects of lower
body resistance training on lean mass and power in older women. Med. Sci. Sports
Exerc. 27: S233.
Slemenda, C.W., Miller, J.Z., Hui, S.L., Reister, T.K., and Johnston, C.C. Jr.
(1991) Role of physical activity in the development of skeletal mass in children. J.
Bone Miner. Res. 6: 1227-1233.
Slemenda, C.W., Reister, T.K., Hui, S.L., Miller, J.Z., Christian, J.C., and
Johnston, C.C. Jr. (1994) Influences on skeletal mineralization in children and
adolescents: evidence for varying effects of sexual maturation and physical activity.
J. Pediatr. 125(2): 201-207.
Snow-Harter, C., Bouxsein, M., Lewis, B., Charette, S., Weinstein, P., and
Marcus, R. (1990) Muscle strength as a predictor of bone mineral density in young
women. J. Bone Miner. Res. 5: 589-595.
Snow-Harter, C., Robinson, T., Shaw, J., Wegner, M., and Shelley, A. (1993)
Determinants of femoral neck mineral density in pre- and postmenopausal women.
Med. Sci. Sports Exerc. 25: S153.
Sorenson, J.A., Duke, P.R., and Smith, S.W. (1989) Simulation studies of
dual-energy x-ray absorptiometry. Med. Phys. 16: 75-80.
Southard, R.N., Morris, J.D., Mahan, J.D., Hayes, J.R., Torch, M.A.,
Sommer, A., and Zipf, W.B. (1991) Bone mass in healthy children: measurement
with quantitative DXA. Radiology 179: 735-738.
Specker, B.L., Brazerol, W., Tsang, R.C., Levin, B., Searcy, J., and Steichen,
J. (1987) Bone mineral content in children 1 to 6 years of age. Am. J. Dis. Child.
141:343-344.
84
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.
Stevenson, J.C., Lees, B., Devenport, M., Cust, M.P., and Ganer, K.F. (1989)
Determinants of bone density in normal women: Risk factors for future osteoporosis?
Br. Med. J. 298:924-928.
Stock, J.L. et al. (1987) Dynamic muscle strength is decreased in
postmenopausal women with low bone density. J Bone Miner. Res. 2: 338.
Swan, A.V., Creeser, R., and Murray, M. (1990) When and why children
first start to smoke. Int. J. Epidemiol. 19(2): 323-330.
Tanner, J.M., Hughes, P.C.R., and Whitehouse, R.H. (1981)
Radiographically determined widths of bone, muscle, and fat in the upper arm and
calf from age 3-18 years. Ann. Hum. Biol. 8: 495-517.
Theintz, G., Buchs, B., Rizzoli, R., Slosman, D., Clavien, H., Sizonenko,
P C., and Bonjour, J.P.H. (1992) Longitudinal monitoring of bone mass
accumulation in healthy adolescents: evidence for a marked reduction after 16 years
of age at the levels of lumbar spine and femoral neck in female subjects. J. Clin.
Endocrinol. Metab. 75: 1060-1065.
Unger, J.B., Li, Y., Chen, X., Xia, J., Azen, S., Guo, Q., Tan, S., Gong, J.,
Sun, P., Liu, C., Chou, C., Zheng, H., and Johnson, C.A. (in press) Adolescent
smoking in Wuhan, China: baseline data from the Wuhan smoking prevention trial.
Int. J. Epi.
Valimaki, M. J., Karkkainen, M., Lamberg-Allardt, C., Laitinen, K., Alhava,
E., Heikkinen, J., Impivaara, O., Makela, P., Palmgren, J., Seppanen, R., Vouri, I.,
and the Cardiovascular Risk in Young Finns Study Group. (1994) Exercise,
smoking, and calcium intake during adolescence and early adulthood as determinants
of peak bone mass. BMJ 309: 230-235.
Valla, A., Groenning, I.L., Syversen, U., and Hoeiseth, A. (2000) Anorexia
nervosa: slow regain of bone mass. Osteop. Int. 11(2): 141-145.
Vogel, J.M. (1987) Application principles and technical considerations in
SPA. In: Genant, H.K. (ed.), Osteoporosis Update (pp. 219-231). Berkeley, CA:
University Press.
Vogel, J.M., Wasnich, R.D., and Ross, P.D. (1988) The clinical relevance of
calcaneous bone mineral measurements: a review. Bone Miner. 5: 35-58.
Vogel, J.M., Davis, J.W., Nomura, A., Wasnich, R.D., and Ross, P.D. (1997)
The effects of smoking on bone mass and the rates of bone loss among elderly
Japanese-American men. J. Bone Miner. Res. 12(9): 1495-1501.
85
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.
Wang, Y., Ge, K., and Popkin, B.M. (2000) Tracking of body mass index
from childhood to adolescence: a 6-y follow-up study in China. Am. J. Clin. Nutr.
72(4): 1018-1024.
Ward, A., Brown, N., and Treasure, J. (1997) Persistent osteopenia after
recovery from anorexia nervosa. Int. J. Eat. Disord. 22(1): 71-75.
Wasnich, R.D. (1987) Fracture prediction with bone mass measurements.
In: Genant, H.K. (ed.L Osteoporosis Update (pp. 95-101). Berkeley, CA: University
Press.
Wegner, M., Snow-Harter, C., Robinson, T., Shaw, J., and Shelley, A. (1993)
Lean body mass, not fat mass, independently predicts whole body bone mineral
density in postmenopausal women. Med. Sci. Sport Exerc. 25(5): S854.
Welten, D.C., Kemper, H.C., Post, G.B., Van Mechelen, W., Twisk, J., Lips,
P., and Teule, G.J. (1994) Weight-bearing activity during youth is a more important
factor for peak bone mass than calcium intake. J. Bone Miner. Res. 9: 1089-1096.
Williams, A.R., Weiss, N.S., Ure, C.L., Ballard, J., and Daling, J.R. (1982)
Effect of weight, smoking, and estrogen use on the risk of hip and forearm fractures
in postmenopausal women. Obstet. Gynaecol. 60: 695-699.
Witzke, K.A., and Snow, C.M. (1999) Lean body mass and leg power best
predict bone mineral density in adolescent girls. Med. Sci. Sports Exerc. 31: 1558-
1563.
Witzke, K.A., and Snow, C.M. (2000) Effects of plyometric jump training on
bone mass in adolescent girls. Med. Sci. Sports Exerc. 32(6): 1051-1057.
Yang, G., Fan, L., Tan, J., Qi, G., Zhang, Y., Samet, J.M., Taylor, C.E.,
Becker, K., and Xu, J. (1999) Smoking in China: findings of the 1996 National
Prevalence Survey. JAMA 282: 1247-1253.
Zanchetta, J.R., Plotkin, H., and Alvarez Filguiera, M.L. (1995) Bone mass
in children: normative values for the 2-20 year old population. Bone 16: 393S-399S.
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.
APPENDIX
Figures 2 to 13
Parent Permission Form
Child Assent Form
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.
Figure 2. Scatter plot of forearm bone mineral content and weight in girls
Forearm BMC vs. Weight in Girls
4.S
y = 0.0414x + 0.9547
R2 = 0.3189
4
3.S
let 3
2.5
2
1.5
30 35 40 45 50 55 60 65 70 75 60
W e ig h t (k ilo g ram s)
Figure 3. Scatter plot of forearm bone mineral content and strength in girls
Forearm BMC vs. Strength in G irls
4 .5
y = 0.0571x+1 . 6 0 6 1
R 2 = 0 . 1 9 6 7
3 . 5
g 2 :5
10 1 5 20 2 5 3 0 3 5
Strength (kilograms)
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.
Figure 4. Scatter plot o f forearm bone mineral content and age in girls
Forearm BM C vs. Age in Girls
4 .5
3 .5
m 2 . 5
1 . 5
1 3 1 3 .2 1 3 .4 1 3 .6 1 3 . 8 1 4 1 4 . 2 1 4 . 4 1 4 .6 1 4 .8 1 5 1 5 . 2 1 5 .4 1 5 .6 1 5 . 8 1 6 1 6 . 2
A ge (yea rs)
Figure 5. Scatter plot of forearm bone mineral content and height in girls
Forearm BMC vs. Height in Girls
4 .5
4
y= 0. 022 x- 0 . 5 1 9 7
R 2 = 0 . 0 5 6 1
3 . 5
3
2 . 5
2
1 . 5
1 3 0 1 4 0 1 5 0 1 6 0 1 7 0 1 8 0 1 9 0
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.
Figure 6. Scatter plot of heel bone mineral content and weight in girls
H e el B M C vs. Weight in Girls
2 .5
< o
E
2
1
o
2
2
S
«
e
e
m
05
5 5 6 0 6 5 7 0 7 5 8 0 3 0 3 5 4 0 4 5 5 0
V^ght (k ilogra ms)
Figure 7. Scatter plot of heel bone mineral content and height in girls
Heel BMC vs. Height in Giris
3
2 . 5
2
&
c
O
2
s
s
s
1 . 5
1
0.5
130 1 4 0 150 160 1 7 0 180 190
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.
Figure 8. Scatter plot of forearm bone mineral content and strength in boys
Forearm BMC vs. Strength in Boys
5 .5
y = 0 .0 5 1 4 k + 1 . 5 3 9 9
R 2 = 0 .3 6 7 8
i
4.5
2
a
e
|
o
o
2 3.5
t o
c
o
f f i
2 .5
1 . 5
4 5 20 2 5 3 0 3 5 4 0 5 0 55 6 0 1 5
Strength (kilograms)
Figure 9. Scatter plot of forearm bone mineral content and weight in boys
Forearm BM C vs. Weight in Boys
5 .5
y=0.0305x + 1 . 5 7 9 1
R 2« 0 .2 5 3 4
5 2 4 .5
3.5
2 . 5
5 5 6 5 70 7 5 8 0 8 5 90 9 5 1 0 0 1 0 5 1 1 0 30 3 5 40 4 5 50 60
W fe ig h t (kilograms}
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.
Figure 10. Scatter plot of forearm bone mineral content and height in boys
Forearm BMC vs. Height in Boys
1 4 '5
120.0 125.0 130.0 135.0 140.0 145.0 150.0 155.0 100.0 165,0 170.0 175.0 180.0 105.0 190.0
Height (centimeters)
Figure 11. Scatter plot of forearm bone mineral content and age in boys
Forearm BMC vs. Age in Boys
6
5.5
5
4.5
c 4
e
u
I 3 5
s
1 1 , ■■ n
i
ffl 3
2.5
2
1.5
13.2 13.4 13.6 13.8 14 14.2 14.4 14.6 14.8 15 15.2 15.4 15.6 15.8 16 16.2 16.4 16.6
Age (years)
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.
UMI Number: 3027690
UMI
UMI Microform 3027690
Copyright 2001 by Bell & Howell Information and Learning Company.
All rights reserved. This microform edition is protected against
unauthorized copying under Title 17, United States Code.
Bell & Howell Information and Learning Company
300 North Zeeb Road
P.O. Box 1346
Ann Arbor, Ml 48106-1346
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.
Figure 12. Scatter plot of heel bone mineral content and weight in boys
Heel BMC vs. Height in Boys
4.5
4
3.5
3
y = 0 .0481x-5.4835
2.5
2
1.5
1 4__
120.0 130.0 140.0 150.0 160.0 170.0 180.0 190.0
Height (centimeters)
Figure 13. Scatter plot of heel bone mineral content and height in boys
Heel BMC vs. Weight in Boys
4.5
y = 0. 0386x + 0.348 4
R 2 = 0 .4 5 1 1
3 .5
»
E
o
o
S 2.5
S
®
e
o
a
3 0 3 5 40 4 5 50 5 5 60 6 5 70 7 5 80 85 90 9 5 100 1 0 5 110
Weight {kilograms)
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.
Revised Date: March 1,2000
TITLE OF PROJECT: The Influence of Body Composition. Nutrition, Physical
Activity, and Smoking on Bone Mineral Development in Chinese Youth
PRINCIPAL INVESTIGATOR: C. Anderson Johnson, Ph.D.
DEPARTMENT: Preventive Medicine
24-HOUR TELEPHONE #: (323)442-2628
PARENT PERMISSION FORM
STUDY PURPOSE: Your child is invited to participate in a research study that is being
performed to learn more about factors that may affect the health of Chinese youth in Wuhan,
China. Bone mineral density measurements will be performed. Your child was invited as a
possible participant in this study because he/she is Chinese and between the age of 10-16.
PROCEDURE: If you decide to allow your child to participate, he/she will provide
information regarding his/her date of birth and family history for osteoporosis and other
possible bone diseases. He/she will have his/her weight and height measured by a scale and
the bone mineral density of his/her heel and forearm measured. The complete bone mass
assessment by this device takes less than five minutes. Your child will lose a total of 10
minutes of class time due to testing. This procedure will be performed again no later than
the Spring of 2001.
RISKS: The heel and forearm of your child will receive a very small amount of radiation
which is much less than what he/she will get at the dentist office when he/she gets a tooth x-
ray and so it is not dangerous to his/her health.
BENEFITS: There are no direct benefits to you, except to learn about the health of your
child. Results of the x-ray will be given to you per your request. Society will also benefit by
improving our understanding of the causes of bone diseases/fractures and their prevention.
ALTERNATIVES: You may choose not to allow your child to participate in this study.
CONFIDENTIALITY: The confidentiality of your child’s records will be maintained by
the investigators of this study. All information you and your child provide will be kept
confidential, in files identified only by a number. Any information that is obtained in
connection with this study and that can be identified with your child will not be released or
disclosed without your written consent except as specifically required by law.
OFFER TO ANSWER QUESTIONS: Please ask the field staff any questions you may
have. The investigators will be happy to discuss your child’s results with you. You may
contact the Principal Investigator of this study, Dr. Andy Johnson in the United States at
001 -(323)442-2628 with any questions or concerns regarding the study or study related
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.
Revised Date: March 1,2000
injuries. In Wuhan, you may contact Dr. Li Yan at 85805111. If you have any questions
regarding your child’s rights as a study subject, you may contact the USC Institutional
Review Board Office at 001-(323)223-2340. You will be given a copy of this form to keep.
COMPENSATION STATEMENT: You will receive information about your child’s bone
mineral density if you request it.
COERCION AND WITHDRAWAL STATEMENTS: If you decide to allow your child
to participate in this study, you are free to withdraw your consent and to discontinue his/her
participation at any time. This will not affect his/her education or future healthcare benefits.
INJURY STATEMENT: If your child requires medical treatment as a result of injuiy
arising from his/her participation in this study, the financial responsibility for such care will
be yours.
NEW INFORMATION: Any new information that is developed during the course of this
research which may be related to your willingness to allow your child to continue or
discontinue participation in this study will be provided to you.
AGREEMENT: YOUR SIGNATURE INDICATES THAT YOU HAVE DECIDED TO
ALLOW YOUR CHILD TO PARTICIPATE HAVING READ THE INFORMATION
PROVIDED ABOVE.
Signature of Parent or Guardian Date
Signature of Principal Investigator
or Person Obtaining Consent
Signature of Witness
Date
Date
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.
Revised Date: March 1,2000
TITLE OF PROJECT: The Influence of Body Composition, Nutrition. Physical
Activity, and Smoking on Bone Mineral Development in Chinese Youth
PRINCIPAL INVESTIGATOR: C. Anderson Johnson, Ph.D.
DEPARTMENT: Preventive Medicine
24-HOUR TELEPHONE #: (323)442-2628
CHILD ASSENT FORM
STUDY PURPOSE: You are invited to participate in a research study that is being
performed to learn more about factors that may affect the health of Chinese youth in Wuhan,
China. Bone mineral density measurements will be performed in this study. Bone mineral
density is a measure of the amount and the strength of your bones. It is important because
people with strong bones are less likely to get diseases such as osteoporosis when they get
older, and they are less likely to break their bones when they fall down. The researchers
from the University of Southern California in the United States want to measure the strength
of your bones for their study on the health of youth in China. You were invited as a possible
participant in this study because you are Chinese and between the age of 10-16.
PROCEDURE: If you decide to participate, you have to give your date of birth and answer
a question about your family history of diseases that would maybe effect your bones. You
will have your weight and height measured and the researchers will also measure the bone
mineral density of your heel and forearm bone. The complete bone mass assessment by this
device takes less than five minutes. You will lose a total of 10 minutes of class time due to
testing. This procedure will also be performed again no later than the Spring of 2001.
RISKS: Your heel and forearm will receive a very small amount of radiation which is much
less than what you get at the dentist office when you get a tooth x-ray and so it is not
dangerous to your health.
BENEFITS: There are no direct benefits to you, except to learn about your health. Results
of the x-ray will be given to you per your request. Society will also benefit by improving
our understanding of the causes of bone diseases/fractures and their prevention.
ALTERNATIVES: You may choose not to participate in this study.
CONFIDENTIALITY: The investigators of this study are responsible for keeping all of
your information confidential (secret and private). All information you provide will be
kept confidential, in files identified only by a number. The only people who can look at your
records are the researchers from USC. Any information that is obtained in connection with
this study and that can be identified with you will not be released or
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.
Revised Date: March 1, 2000
disclosed without your written consent except as specifically required by law. This means
that if the researchers ever did want to show your records to someone else, they would have
to get permission from you and your parents first.
OFFER TO ANSWER QUESTIONS: Please ask the field staff any questions you may
have. The investigators will be happy to discuss your results with you. You may contact the
Principal Investigator of this study, Dr. Andy Johnson in the United States at 001-(323)442-
2628 with any questions or concerns regarding the study or study related injuries. In Wuhan,
you may contact Dr. Li Yan at 85805111. If you have any questions regarding your rights as
a study subject, you may contact the USC Institutional Review Board Office at 001-
(323)223-2340. You will be given a copy of this form to keep.
COMPENSATION STATEMENT: You will receive information about your bone
mineral density if you request it.
COERCION AND WITHDRAWAL STATEMENTS: If you decide to participate in this
study, you are free to change your mind and stop participating at any time. This will not
affect your education or future healthcare benefits.
INJURY STATEMENT: If you are injured while participating in this study and need
medical treatment, you and your parents would have to pay for this medical treatment.
NEW INFORMATION: During this study, if the researchers discover any new
information that might cause you to change your mind about participating, the researchers
will share this new information with you.
AGREEMENT: YOUR SIGNATURE INDICATES THAT YOU HAVE DECIDED TO
PARTICIPATE HAVING READ THE INFORMATION PROVIDED ABOVE.
Are you able to understand what will be expected of you? YES NO
If yes, please indicate your willingness to participate by signing here:
Signature of Child Date
Signature of Principal Investigator Date
or Person Obtaining Consent
Signature of Witness Date
97
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.

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Asset Metadata

Creator Afghani, Afrooz (author)

Core Title The influence of body weight and composition, pubertal status, tobacco use and exposure, physical activity and muscle strength on bone mass of Chinese adolescents

Degree Doctor of Philosophy

Degree Program Biokinesiology

Degree Conferral Date 2001-05

Publisher University of Southern California (original), University of Southern California. Libraries (digital)

Tag biology, anatomy,biology, animal physiology,health sciences, human development,health sciences, public health,OAI-PMH Harvest

Language English

Contributor Digitized by ProQuest (provenance)

Permanent Link (DOI) https://doi.org/10.25549/usctheses-c16-92027

Unique identifier UC11338803

Identifier 3027690.pdf (filename),usctheses-c16-92027 (legacy record id)

Legacy Identifier 3027690.pdf

Dmrecord 92027

Document Type Dissertation

Rights Afghani, Afrooz

Type texts

Source University of Southern California (contributing entity), University of Southern California Dissertations and Theses (collection)

Access Conditions The author retains rights to his/her dissertation, thesis or other graduate work according to U.S. copyright law. Electronic access is being provided by the USC Libraries in agreement with the au...

Repository Name University of Southern California Digital Library

Repository Location USC Digital Library, University of Southern California, University Park Campus, Los Angeles, California 90089, USA