0013-7227/03/$15.00/0
Printed in U.S.A.
The Journal of Clinical Endocrinology & Metabolism 88(6):2607–2613
Copyright © 2003 by The Endocrine Society
doi: 10.1210/jc.2002-021538
Fat-Free Body Mass Is the Most Important Body
Composition Determinant of 10-yr Longitudinal
Development of Lumbar Bone in Adult Men and Women
INGRID BAKKER, JOS W. R. TWISK, WILLEM VAN MECHELEN,
AND
HAN C. G. KEMPER
Institute for Research in Extramural Medicine (I.B., J.W.R.T., W.V.M., H.C.G.K.) and Department of Social Medicine and
Research Centre ‘Body@Work’ TNO VU (W.V.M.), VU University Medical Center, 1081 BT Amsterdam, The Netherlands
The purpose of this study was to analyze the longitudinal
relationship between body composition and lumbar bone mineral density (LBMD) and lumbar bone mineral content
(LBMC) in (young) adults over a 10-yr period. The data are
from the Amsterdam Growth and Health Longitudinal Study.
Two hundred twenty-five men and 241 women were measured
at 27, 32, and/or 36 yr of age. Nine body composition components were explored: total body weight, standing height, body
mass index, waist circumference, hip circumference, waist to
hip ratio, sum of four skinfolds, fat mass, and fat-free mass
(FFM). Stratified analyses were performed by gender and adjustment was made for physical activity and calcium intake.
T
Univariate multilevel analyses indicated that FFM was significantly positively related to the 10-yr development of both
LBMD and LBMC in both sexes. Total body weight, standing
height, and body mass index also showed a significant positive
univariate relationships with LBMD and LBMC in both sexes,
fat mass only with female LBMD. All best predictive multiple
regression models included FFM, explaining 4 –27% of the
variation in bone mineral over this 10-yr period. Because FFM
can be interpreted as a proxy for skeletal muscle mass, these
results indicate the importance of muscle contractions
on bone to increase bone strength in (young) adults. (J Clin
Endocrinol Metab 88: 2607–2613, 2003)
HE INFLUENCE OF several body composition components on the human skeleton are widely explored and
investigated because they are thought to be important determinants of bone mineral accrual and maintenance (1–3).
Because total body weight, known to be related to bone
mineral (4, 5), is composed of fat mass (FM) and fat-free mass
(FFM), several studies have been performed to identify
which of these two components mainly contributes to this
relationship (6 –9). Obesity, mostly accompanied with adverse health effects, is assumed to protect against osteoporosis because of the observed decreased risk for osteoporotic
fractures and increased bone mineral density (10, 11).
A great deal of research has focused on the influence of
body composition on bone mineral during the growth period
(i.e. during childhood and adolescence) and the period of
common bone deterioration because of aging (e.g. after
menopause). Therefore, little is known about this relationship among the general population during the third and
fourth decade of life. There is uncertainty in the literature
about whether there is a relationship between body composition and bone mineral after growth and before aging, and
if so, which component of the body composition is the best
predictor of bone mineral during this period (12). Finally,
most of what we know of (young) adult skeletal development
has been determined from cross-sectional studies. Consequently, the long-term effects of body composition on bone
mineral during (young) adulthood are not fully understood
and should be evaluated (7).
This article reports the results of a 10-yr longitudinal study
that evaluates the longitudinal relationships between body
composition components and lumbar bone mineral in a
group of Dutch men and women passing through the ages
of 27 to 36 yr. The following two questions are addressed for
men and women separately: 1) What is the univariate relationship between the development of body composition
components and the development of lumbar bone mineral
density and content during (young) adulthood; and 2) what
combination of body composition components predicts best
the development of lumbar bone mineral density and content
during (young) adulthood?
Abbreviations: AGAHLS, Amsterdam Growth and Health Longitudinal Study; BMI, body mass index; DEXA, dual-energy x-ray absorptiometry; FFM, fat-free mass; FM, fat mass; GRF, ground reaction force;
LBMC, lumbar bone mineral content; LBMD, lumbar bone mineral
density.
Body composition components
Subjects and Methods
Study design and subjects
The study population included 241 women and 225 men from the
Amsterdam Growth and Health Longitudinal Study (AGAHLS). This
cohort study started in 1977 in a group of Dutch men and women from
a general healthy population, with a mean age of 13 yr to investigate the
natural development of health, fitness, and lifestyle (13). The present
study deals with the measurements at the mean ages of 27, 32, and/or
36 yr, when lumbar bone mineral measurements were taken. The Medical Ethical Committee of the VU University Medical Center approved
the aim and design of the study, and all subjects gave their written
informed consent.
To assess the body composition components, anthropometrical measurements were performed according the guidelines of the International
Biological Program (14).
2607
2608
J Clin Endocrinol Metab, June 2003, 88(6):2607–2613
Total body weight. Participants dressed in underwear had body weights
measured on a spring balance scale (van Vucht, Amsterdam, The Netherlands). Weights were recorded to the nearest 0.1 kg.
Standing height. Height was measured without shoes, with a Harpenden
digital readout, wall-mounted, or portable stadiometer (Holtain UK, van
Rietschoten & Houwens, Rotterdam, The Netherlands) and recorded to
the nearest 0.1 cm.
Body mass index (BMI). This index was calculated by dividing total body
weight (kilograms) to the squared standing height (square meter).
Circumferences of waist and hip. Circumferences of the waist and hip were
measured with a flexible steel tape (Martin circumeter, Franken & Itallie,
Amsterdam, The Netherlands) and recorded to the nearest 1 mm.
Waist to hip ratio. This ratio was calculated by dividing the waist circumference (millimeters) to the hip circumference (millimeters).
Sum of four skinfolds. Thickness of four skinfolds (i.e. biceps, triceps,
subscapular, and suprailiac skinfold) were measured at the right side of
the body with a Harpenden skinfold caliper (Holtain UK, van Rietschoten & Houwens) (15). Skinfold thickness was measured at standardized
anatomic locations and recorded to the nearest 0.1 mm.
FM. From the equations, developed by Durnin and Womersley, fat mass
was calculated from the sum of the four skinfolds, gender, and age (16).
FFM. As an alternative measure of muscle mass, although also including
bone mass, FFM was calculated as total body weight minus FM.
Lumbar bone mineral
Bone mineral measurements were performed by means of the dualenergy x-ray absorptiometry (DEXA) at the lumbar spine (L2–L4). An
estimation of lumbar bone mineral density (LBMD) and lumbar bone
mineral content (LBMC) was made on each lumbar vertebral body
L2–L4, from which the average LBMD and LBMC was calculated. For
measurements at the mean age of 27 yr, the Norland XR 26 (Norland
Corp., Fort Atkinson, WI) was used. Because of replacement of the
Norland XR 26 by the Hologic QDR-2000 (S/N 2513; Hologic, Inc.,
Waltham, MA) during measurements at the mean age of 32 yr, some of
the subjects (n ⫽ 296) were measured by the Norland XR 26 and others
(n ⫽ 111) by the Hologic QDR-2000. For all bone measurements at the
mean age of 36 yr, the Hologic QDR-2000 was used. The DEXA machine
was calibrated daily. The coefficient of variation of the Norland apparatus was 1.3% for the short-term reproducibility (24 h) and 2.3% for the
long-term reproducibility (2– 6 months). For the Hologic, the coefficient
of variation for the L1–L4 region was less than 2% (17). The correlation
between the Norland and Hologic was 0.988 for the lumbar spine (18).
Although the correlations were very high, differences in absolute values
could be present between the measurements on both machines. Therefore, standardized values (z-scores) against the mean LBMD of all measured subjects were used for each measurement. For the bone measurements at the age of 32 yr, the subjects measured on the same machine
were grouped together as z-scores were calculated.
Covariates
Physical activity. Physical activity was measured by means of a structured
detailed interview based on the questionnaire developed by Verschuur
(13, 19, 20). All activities (at school, during courses, at work, at home,
during leisure time, organized and unorganized sports, stair climbing,
and used transportation), with a duration of at least 5 min nonstop and
exceeding the level of intensity of 4 times the basal metabolic rate, were
taken into account (21).
Physical activity was expressed in a score for its biomechanical
ground reaction forces (GRFs), as described by Groothausen et al. (22).
Based on these GRFs, all physical activities were classified into four
categories, according to which a score was assigned: 0 (GRF ⬍ 1 ⫻ the
body weight), 1 (GRF between 1 and 2 ⫻ the body weight), 2 (GRF
between 2 and 4 ⫻ the body weight), and 3 (GRF ⬎ 4 ⫻ the body weight).
The total GRF score was calculated as the sum of all GRF scores and used
in the analyses. This measure is irrespective of the duration, intensity,
Bakker et al. • Body Composition and Adult Bone Development
and frequency of the activity. Detailed information is provided elsewhere (23, 24).
Dietary calcium intake. The habitual food intake was measured by a
detailed cross-check dietary history face-to-face interview method,
based on the method developed by Beal (25) and Marr (26) and adapted
to the AGAHLS (21, 27, 28). This method provides information about the
habitual dietary intake, including calcium intake, of the subjects, using
the preceding 4 wk as a reference period. The interview comprises the
entire range of foods and drinks. Only items that were consumed at least
twice a month were recorded. From this, mean daily calcium intake was
calculated by use of the 1996 database from the Dutch Food and Nutrition Table (29).
Data analysis
Multilevel analysis (MLwiN, version 1.10.0007; Centre for Multilevel
Modeling, Institute of Education, London, UK) was used to analyze the
longitudinal relationship between body composition components and
LBMD and LBMC development during the period across the ages of 27,
32, and 36 yr (30). Multilevel analysis was chosen because it combines
a within-subject relationship with a between-subjects relationship, resulting in one single regression coefficient. This has the following implications for the interpretation of the regression coefficients: suppose
that for a particular subject the value of the outcome variable LBMD is
relatively high at each of the repeated measurements and that this value
does not change much over time. Suppose further that for that particular
subject the value of the analyzed body composition component is also
relatively high at each of the repeated measurements and also does not
change much over time. This indicates a longitudinal between-subjects
relationship between LBMD and the body composition component.
Suppose that for another subject the value of LBMD increases rapidly
along the longitudinal period, and suppose that for the same subject this
pattern is also found for the body composition component. This indicates within-subject relationship between LBMD and the body composition component. Both relationships are part of the overall longitudinal
relationship between LBMD and the body composition component, so
both should be taken into account in the analysis of the longitudinal
relationship. The regression coefficient estimated with multilevel analysis combines the two possible relationships into one regression coefficient. Furthermore, it should be mentioned that multilevel analysis (or
random coefficient analysis, which is the same) is considered state of the
art in the analysis of longitudinal data (31).
Univariate relationship. Univariate multilevel analyses are performed on
all included body composition components separately to investigate the
crude and adjusted longitudinal relationship between each explored
component and LBMD and LBMC.
Best predictive model. Multiple multilevel analysis was used to evaluate
the best combination of body composition components for prediction of
the development of (young) adult LBMD and LBMC. Models were built
by backward regression, including all components with a significant
univariate relationship. Thereafter, the model was extended one by one
by the other body composition variables as a final check for possible
significant contributors. This is resulting in a model containing body
composition components that each contributes significantly (P ⬍ 0.05).
Both the univariate relationships and the best predictive model were
analyzed with and without adjusting for physical activity and calcium
intake. All analyzes were preformed separately for men and women.
Results
Subject characteristics
The characteristics of the 225 male and 241 female subjects,
measured at the mean ages of 27, 32, and/or 36 yr, are shown
in Table 1. A significant increase over the 10-yr period was
found for all body composition components in both sexes,
except for FFM in men (P ⫽ 0.08) and standing height in both
men (P ⫽ 0.61) and women (P ⫽ 0.55). Between the ages of
27 and 32 yr, paired LBMD measures obtained with the
Norland apparatus showed a significant decrease in men
Bakker et al. • Body Composition and Adult Bone Development
TABLE 1. Subject characteristics (means ⫾
Variable
Age (yr)
Total body weight (kg)
Standing height (cm)
BMI (kg/m2)
Waist circumference (cm)
Hip circumference (cm)
Waist/hip ratio
Sum of four skinfolds (mm)
FM (kg)
FFM (kg)
LBMD (L2–L4) (g/cm2)
Norland XR 26
LBMD (L2–L4) (g/cm2)
Hologic QDR-2000
LBMC (L2–L4) (g)
Norland XR 26
LBMC (L2–L4) (g)
Hologic QDR-2000
SD)
J Clin Endocrinol Metab, June 2003, 88(6):2607–2613 2609
at the mean ages of 27, 32, and 36 yr
Mean age 27
Mean age 32
Mean age 36
Men (n ⫽ 84)
Women (n ⫽ 94)
Men (n ⫽ 195)
Women (n ⫽ 207)
27.1 ⫾ 0.8
75.5 ⫾ 8.4
183.0 ⫾ 6.6
22.5 ⫾ 2.2
78.4 ⫾ 6.0
86.9 ⫾ 5.5
0.90 ⫾ 0.06
36.3 ⫾ 13.5
11.0 ⫾ 4.0
64.5 ⫾ 6.1
1.170 ⫾ 0.158
27.1 ⫾ 0.7
63.2 ⫾ 7.9
170.2 ⫾ 6.2
21.8 ⫾ 2.5
67.7 ⫾ 5.6
85.8 ⫾ 7.5
0.79 ⫾ 0.07
45.8 ⫾ 16.5
15.9 ⫾ 4.6
47.3 ⫾ 4.9
1.144 ⫾ 0.038
32.3 ⫾ 0.9
81.1 ⫾ 10.1
183.7 ⫾ 6.2
24.0 ⫾ 2.6
82.8 ⫾ 7.5
89.8 ⫾ 6.5
0.92 ⫾ 0.05
42.0 ⫾ 16.9
15.6 ⫾ 4.9
65.4 ⫾ 6.3
1.158 ⫾ 0.180
(n ⫽ 146)
1.118 ⫾ 0.146
(n ⫽ 52)
62.92 ⫾ 12.17
(n ⫽ 146)
65.65 ⫾ 11.53
(n ⫽ 52)
32.3 ⫾ 0.9
65.3 ⫾ 8.6
169.5 ⫾ 6.5
22.7 ⫾ 2.7
70.3 ⫾ 6.4
89.0 ⫾ 7.8
0.79 ⫾ 0.04
51.9 ⫾ 18.4
18.5 ⫾ 4.9
46.7 ⫾ 4.8
1.127 ⫾ 0.139
(n ⫽ 150
1.095 ⫾ 0.111
(n ⫽ 59)
52.19 ⫾ 8.52
(n ⫽ 150)
54.85 ⫾ 8.29
(n ⫽ 59)
61.66 ⫾ 10.41
52.55 ⫾ 8.23
(⫺0.017 g/cm2; P ⫽ 0.02; n ⫽ 48) and a nonsignificant decrease in women (⫺0.009 g/cm2; P ⫽ 0.19; n ⫽ 52). Paired
measures obtained with the Hologic apparatus showed no
significant LBMD change in both men (0.003 g/cm2; P ⫽ 0.69;
n ⫽ 41) and women (⫺0.007 g/cm2; P ⫽ 0.18; n ⫽ 50) between
the ages of 32 and 36 yr. Concerning LBMC, neither the
paired measures obtained with the Norland (men: ⫺0.320 g,
P ⫽ 0.47, n ⫽ 48; women: ⫺0.411 g, P ⫽ 0.25, n ⫽ 52) nor those
obtained with the Hologic (men: 0.126 g, P ⫽ 0.81, n ⫽ 41;
women: ⫺0.561 g, P ⫽ 0.13, n ⫽ 50) showed significant
changes over time.
Univariate relationship
The results of the crude and adjusted univariate regression
analyses performed on all anthropometrical measures included are shown in Table 2.
LBMD. The anthropometrical measures total body weight,
BMI, and FFM were significantly correlated to LBMD in the
crude and adjusted univariate models for both sexes and FM
only for women.
LBMC. Total body weight, standing height, and FFM were
significantly correlated to LBMC in the crude and adjusted
univariate model in both sexes.
Men (n ⫽ 170)
Women (n ⫽ 181)
36.0 ⫾ 0.7
83.6 ⫾ 10.7
183.8 ⫾ 6.5
24.7 ⫾ 2.7
85.1 ⫾ 8.0
89.2 ⫾ 7.2
0.95 ⫾ 0.05
46.8 ⫾ 15.5
17.2 ⫾ 4.7
66.4 ⫾ 6.9
36.1 ⫾ 0.7
68.0 ⫾ 10.3
170.1 ⫾ 6.4
23.5 ⫾ 3.3
73.2 ⫾ 8.4
89.4 ⫾ 8.6
0.82 ⫾ 0.07
55.7 ⫾ 19.5
20.0 ⫾ 5.7
48.0 ⫾ 5.4
1.111 ⫾ 0.160
1.095 ⫾ 0.120
64.86 ⫾ 13.03
54.81 ⫾ 8.69
The best predictive adjusted model for female LBMC included total body weight, standing height, sum of four skinfolds, and FFM.
The overall highest explained variances in bone mineral
development by body composition components were found
within the analysis of LBMC in women (i.e. 28% by total body
weight, 27% by FFM, 9% by standing height, and 4% by sum
of four skinfolds). In male LBMC, the explained variance was
6% for FFM, 5% for standing height, and 1% for waist
circumference.
Best predictor
FFM was a significant contributor in all adjusted models
and considered the best predictor of bone mineral development during (young) adulthood. Overall, FFM explained
most of the variance in bone mineral: 27% of LBMC in
women, 6% of LBMC in men, and 4% of LBMD in both men
and women.
Discussion
To our knowledge, this is the first observational study
evaluating the longitudinal relationship between body composition components and lumbar bone mineral in healthy
Caucasian men and women during the (young) adult period.
Best predictive model
A multiple regression analysis was used to evaluate the
best combination of the body composition components. For
both sexes, the values of the body composition components
significantly contributing to the best predictive adjusted
models are shown in Table 3. Similar results were found for
crude predictive models (data not shown).
LBMD. The adjusted model was best predictive for the 10-yr
LBMD development for both sexes with only FFM included.
The explained variance in LBMD development by FFM was
4% in both men and women.
LBMC. For men, the best predictive adjusted LBMC model
included standing height, waist circumference, and FFM.
Previous studies
From cross-sectional studies among women, it is generally
concluded that during skeletal growth there is a strong positive relationship between FFM and bone mineral measures
(32). Among studies in postmenopausal women, there are
some contradictory results, although most of these studies
report a positive relationship between FM and bone mineral
(8, 33–36). They support the hypothesis that the endocrine
role of adipose tissue is more important than mechanical
stresses in the postmenopausal period. However, some other
studies on postmenopausal women reported FFM to be the
main determinant of bone mineral (1, 34, 37), supporting the
main importance of the mechanical impact by muscle con-
2610
J Clin Endocrinol Metab, June 2003, 88(6):2607–2613
Bakker et al. • Body Composition and Adult Bone Development
TABLE 2. Univariate crude and adjusted regression coefficients (SE) and their P values for the linear relationship between body
composition components and standardized (L2–L4) LBMD and LBMC
LBMD
Crude
Total body weight (10 kg)
Men
Women
Standing height (10 cm)
Men
Women
BMI (kg/m2)
Men
Women
Waist circumference (10 cm)
Men
Women
Hip circumference (10 cm)
Men
Women
Waist/hip ratio
Men
Women
Sum of 4 skinfolds (cm)
Men
Women
FM (10 kg)
Men
Women
FFM (10 kg)
Men
Women
a
LBMC
Adjusteda
Crude
Adjusteda
0.151 (0.046)
P ⫽ 0.001
0.141 (0.037)
P ⫽ 0.0001
0.160 (0.045)
P ⬍ 0.001
0.142 (0.037)
P ⫽ 0.0001
0.128 (0.043)
P ⫽ 0.003
0.079 (0.031)
P ⬍ 0.001
0.140 (0.042)
P ⬍ 0.001
0.080 (0.031)
P ⫽ 0.009
0.158 (0.104)
P ⫽ 0.13
0.116 (0.082)
P ⫽ 0.16
0.175 (0.102)
P ⫽ 0.09
0.120 (0.082)
P ⫽ 0.14
0.539 (0.091)
P ⬍ 0.0001
0.502 (0.061)
P ⬍ 0.0001
0.556 (0.089)
P ⬍ 0.0001
0.502 (0.061)
P ⬍ 0.0001
0.046 (0.016)
P ⫽ 0.005
0.036 (0.011)
P ⬍ 0.001
0.048 (0.016)
P ⫽ 0.002
0.036 (0.011)
P ⬍ 0.001
0.013 (0.015)
P ⫽ 0.40
⫺0.003 (0.009)
P ⫽ 0.78
0.016 (0.015)
P ⫽ 0.29
⫺0.002 (0.009)
P ⫽ 0.81
0.005 (0.045)
P ⫽ 0.92
0.056 (0.037)
P ⫽ 0.13
0.036 (0.045)
P ⫽ 0.42
0.057 (0.037)
P ⫽ 0.12
⫺0.049 (0.042)
P ⫽ 0.24
⫺0.010 (0.030)
P ⫽ 0.73
⫺0.025 (0.042)
P ⫽ 0.55
⫺0.008 (0.030)
P ⫽ 0.79
0.031 (0.047)
P ⫽ 0.51
0.047 (0.029)
P ⫽ 0.10
0.056 (0.046)
P ⫽ 0.23
0.048 (0.029)
P ⫽ 0.09
0.027 (0.043)
P ⫽ 0.54
⫺0.029 (0.023)
P ⫽ 0.22
0.048 (0.043)
P ⫽ 0.26
⫺0.028 (0.023)
P ⫽ 0.23
⫺0.249 (0.381)
P ⫽ 0.51
⫺0.022 (0.276)
P ⫽ 0.94
⫺0.165 (0.373)
P ⫽ 0.66
⫺0.023 (0.275)
P ⫽ 0.94
⫺0.593 (0.351)
P ⫽ 0.09
0.187 (0.224)
P ⫽ 0.40
⫺0.528 (0.347)
P ⫽ 0.13
0.193 (0.224)
P ⫽ 0.39
⫺0.015 (0.023)
P ⫽ 0.51
0.028 (0.015)
P ⫽ 0.06
⫺0.009 (0.023)
P ⫽ 0.70
0.028 (0.015)
P ⫽ 0.06
⫺0.028 (0.021)
P ⫽ 0.19
⫺0.014 (0.012)
P ⫽ 0.27
⫺0.021 (0.021)
P ⫽ 0.32
⫺0.013 (0.012)
P ⫽ 0.28
0.064 (0.079)
P ⫽ 0.42
0.149 (0.056)
P ⫽ 0.008
0.095 (0.078)
P ⫽ 0.22
0.148 (0.056)
P ⫽ 0.009
⫺0.014 (0.073)
P ⫽ 0.85
⫺0.006 (0.046)
P ⫽ 0.89
0.016 (0.073)
P ⫽ 0.81
⫺0.005 (0.046)
P ⫽ 0.91
0.341 (0.073)
P ⬍ 0.0001
0.314 (0.074)
P ⬍ 0.0001
0.337 (0.072)
P ⬍ 0.0001
0.316 (0.074)
P ⬍ 0.0001
0.376 (0.068)
P ⬍ 0.0001
0.355 (0.060)
P ⬍ 0.0001
0.377 (0.067)
P ⬍ 0.0001
0.356 (0.060)
P ⬍ 0.0001
Adjusted for physical activity and calcium intake.
tractions on bone and/or by the gravitational effect. Baumgartner et al. (6) found that in postmenopausal women taking
estrogen, neither FM nor FFM was significantly related to
bone mineral content, indicating an important role of estrogens in the relationship between body composition and bone
mineral in postmenopausal women.
Cross-sectional studies evaluating the relationship among
adult premenopausal women, as in our study, show contradictory results. In some of these studies, FM emerged as the
most powerful body composition determinant of bone
change (32). In others, it was concluded that FFM was an
important determinant of premenopausal bone mineral (7,
34). It was suggested that a higher FM is protective only when
associated with substantial FFM (7) and that especially in
nonobese premenopausal women, FM is likely to play a less
significant role (34).
In general, studies investigating the relationship in men
report a stronger relationship with FFM than FM (1, 6, 33).
When comparing men and women, it is generally concluded that there is a stronger relationship between FM
and bone mineral in women as compared with men (9).
They are, therefore, also supporting the postulated importance of estrogen on bone mineral in women, particularly in elderly women (1).
Univariate relationships
The significant univariate relationship between total body
weight and the bone mineral measures can be explained
partly by its gravitational effect on skeletal loading. This
mechanism is not likely to be the principal mechanism for the
total body weight-bone relationship because both its com-
Bakker et al. • Body Composition and Adult Bone Development
J Clin Endocrinol Metab, June 2003, 88(6):2607–2613 2611
TABLE 3. Adjusted regression coefficients (SE), their P values, and the explained variances (R2) of the significantly contributing body
composition components from the best predictive model for the multiple linear relationship with standardized (L2–L4) LBMD and LBMC
LBMD
Adjusteda
LBMC
R2
Total body weight (10 kg)
Men
Women
Standing height (10 cm)
Men
Women
BMI (kg/m2)
Men
Women
Waist circumference (10 cm)
Men
Women
Hip circumference (10 cm)
Men
Women
Waist/hip ratio
Men
Women
Sum of 4 skinfolds (cm)
Men
Women
FM (10 kg)
Men
Women
FFM (10 kg)
Men
Women
a
0.337 (0.072)
P ⬍ 0.0001
0.316 (0.074)
P ⬍ 0.0001
0.039
0.035
Adjusteda
R2
⫺0.487 (0.194)
P ⫽ 0.012
0.276
0.370 (0.094)
P ⬍ 0.0001
0.405 (0.070)
P ⬍ 0.0001
0.045
0.090
⫺0.169 (0.050)
P ⬍ 0.001
0.014
0.095 (0.046)
P ⫽ 0.041
0.042
0.424 (0.085)
P ⬍ 0.0001
0.871 (0.275)
P ⫽ 0.001
0.061
0.265
Adjusted for physical activity and calcium intake.
ponents, FFM and FM, would then be expected to be independently related to bone mineral in both men and women
(9), which is not true for FM in men. The total body weightbone relationship might, therefore, for a greater part be explained by only its major component, FFM, indicating a
relationship concerning the force of muscle contractions on
bone (4).
The relationship between FFM and lumbar bone mineral
suggests the importance of physical activity as a determinant
of bone strength. And indeed there is a relationship between
physical activity expressed in metabolic equivalents and
FFM. However, in an earlier study (23), it is shown that the
mechanical component of physical activity, not the metabolic
component of physical activity, was more important in the
relation to lumbar bone mineral. This suggests that FFM is
not likely to be a strong mediator in the relationship between
physical activity and lumbar bone mineral.
The finding of a significant longitudinal relationship between standing height and LBMC in both sexes should not
surprise us. Taller people have taller bones in all three dimensions and, therefore, a higher bone mineral content. With
LBMD, this effect of standing height is for a great deal filtered
out, although not completely, because of the included bone
area instead of the bone volume into the measure. Standing
height was not related to LBMD.
Despite that both total body weight and standing height
are significantly related to bone mineral development, this
can only partly explain the significant BMI-LBMD relationship. Because BMI is a measure of body mass density, the
proportion of both its components is apparently also important in its relationship with LBMD development.
FM was significantly related only to LBMD in women. This
fat-bone relationship can be explained by a number of mechanisms. The gravitational effect of soft tissue on skeletal
loading might play a role, but the association of FM with the
secretion of bone active hormones from the pancreas and the
secretion of bone active hormones like estrogens and leptin
from the adipose tissue might also be important (38). Because
no relationship was found between FM and bone mineral in
men, this might indicate an important role for the estrogens
in women, but it might equally indicate an important role for
testosterone in men.
The relationship between FFM and bone mineral can be
explained by mechanical stresses mediated through gravitational action and muscle contractions on bone. However, it
is also postulated that the positive relationship could be due
to the fact that the aromization to form estrogen not only
takes place in adipose tissue but also muscle tissue (39).
Plasma estrogen levels may, therefore, be higher in those
with large muscle mass as well as in those with large adipose
2612
J Clin Endocrinol Metab, June 2003, 88(6):2607–2613
Bakker et al. • Body Composition and Adult Bone Development
tissue mass (1). FFM being the major determinant of bone
mineral is also demonstrated in other studies but not longitudinally in a group of (young) adult men and women. As
postulated by others, FFM in both men and women is the
most important determinant and at this stage in life is not yet
overruled by factors accompanying FM in women (32).
Best predictive models
All constructed best predictive models included FFM,
which can be interpreted as a proxy for skeletal muscle mass.
FFM explained from only 4% up to 27% of the variation in
LBMD and/or LBMC development over this 10-yr period.
This finding is consistent with the hypothesis that the increase in bone mineral and, therefore, bone strength is for a
greater part caused by the force of muscle contractions on
bone (4).
In both men and women, standing height was (as expected) a positive predictor of LBMC.
Our results show that in both men and women, FFM is the
most important predictor of LBMC and even the only predictor of LBMD. The results further indicate that for LBMC
development during (young) adulthood, waist circumference is a negative determinant in men. Waist circumference
can be interpreted as a proxy for central fat mass. In women,
total body weight was a negative determinant and sum of
four skinfolds a positive determinant. No underlying mechanism can be proposed for this.
Comparable with our results on LBMD, but from a study
on older women, Bevier et al. (40) found that although both
FFM and FM were associated with lumbar bone mineral
density, stepwise multiple regression indicated that only the
FFM contributed significantly to the prediction of LBMD.
Study limitations
Originally, the AGAHLS consisted of two cohorts, of
which only one cohort was to follow longitudinally. This
cohort has been measured nine times in total. From the subjects’ mean age of 32 yr onward, it was decided to invite the
subjects from the other cohort as well. Therefore, the number
of subjects at the measurements at mean ages 32 and 36 yr
is higher than at the mean age of 27 yr. Because there were
no important differences between the cohorts, this is not a
problem in the longitudinal analysis (13).
Lean body mass as measured by DEXA is one of the golden
standards for its measure. Because total body DEXA measures are available only from the mean age of 36 yr, and not
from the mean ages of 27 and 32 yr, these DEXA lean body
mass measures could not be used in the longitudinal analysis. To check the accuracy of the used calculated FFM measure, a comparison was made between these values and the
values of lean body mass from total body DEXA scans, both
obtained at the mean age of 36 yr. The correlation between
both measures was high: 0.96. The Bland-Altman plot (Fig.
1) shows that the FFM values are higher than the values of
lean body mass from DEXA. The variability of the withinperson differences for both measures is similar over the
whole range of average values.
We used FFM as a measure of lean body mass, which,
however, is muscle mass and bone mass together. Therefore,
FIG. 1. Bland-Altman plot: average of FFM at the mean age of 36 yr,
calculated from the sum of four skinfolds and lean body mass from
total body scan measured by DEXA at the mean age of 36 yr, against
the difference between both measures, including 95% limits of agreement (mean ⫾ 1.96 SD).
the relationships found with the bone parameters, LBMD
and LBMC, could be overestimated. However, bone mass
accounts for only 4 – 8% of FFM (41). Therefore, the possible
overestimation will be relatively small.
The use of longitudinal measurements of area instead of
volumetric LBMD is not considered a problem because our
subjects were nongrowing adults. There is, however, a possibility of periosteal expansion during adult life, which could
have an impact on the measurements over the 10-yr period
(42).
Because of the necessity to use z-scores of LBMD, results
of the analysis can be interpreted only as negative or positive
influences and cannot be translated into a decrease or increase in LBMD. A negative relationship can mean a decrease
but also a smaller increase and a positive relationship can
mean an increase or a smaller decrease.
Conclusion
From the nine investigated body composition components, FFM appeared to be the most important predictor of
the 10-yr development of bone mineral in (young) adult men
and women. Because FFM can be interpreted as a proxy for
skeletal muscle mass, it is assumed that bone mineral is
largely affected by mechanical stresses mediated through the
force of muscle contractions on bone. Furthermore, because
FFM represents the major component of total body weight,
the relationship between FFM and bone mineral might also
partly be explained by a gravitational effect. For FM, only a
univariate relationship with female LBMD was found, indicating that the endocrine role of FM in (young) adult women
does not (yet) have a major influence of bone mineral.
Acknowledgments
We thank all participants of the AGAHLS.
Received October 2, 2002. Accepted February 28, 2003.
Bakker et al. • Body Composition and Adult Bone Development
Address all correspondence and requests for reprints to: Dr. Han C. G.
Kemper, Professor, Institute for Research in Extramural Medicine
(EMGO), VU University Medical Center, Van der Boechorststraat 7,
1081 BT Amsterdam, The Netherlands. E-mail: hcg.kemper.emgo@
med.vu.nl.
This work was supported by grants from the Dairy Foundation on
Nutrition and Health, the Dutch Heart Foundation (Grant 76051-79051),
the Dutch Prevention Fund (Grants 28-189a, 28-1106, and 28-1106-1), the
Dutch Ministry of Well Being and Public Health (Grant 90-170), the
Dutch Olympic Committee/Netherlands Sports Federation, Heineken
Inc., and the Scientific Board of Smoking and Health.
References
1. Edelstein SL, Barrett-Connor E 1993 Relation between body size and bone
mineral density in elderly men and women. Am J Epidemiol 138:160 –169
2. Dawson-Hughes B, Shipp C, Sadowski L, Dallal G 1987 Bone density of the
radius, spine, and hip in relation to percent of ideal body weight in postmenopausal women. Calcif Tissue Int 40:310 –314
3. Glauber HS, Vollmer WM, Nevitt MC, Ensrud KE, Orwoll ES 1995 Body
weight versus body fat distribution, adiposity and frame size as predictors of
bone density. J Clin Endocrinol Metab 80:1118 –1123
4. Kroke A, Klipstein-Grobusch K, Bergmann MM, Weber K, Boeing H 2000
Influence of body composition on quantitative ultrasound parameters of the
os calcis in a population-based sample of pre- and postmenopausal women.
Calcif Tissue Int 66:5–10
5. Harris SS, Dawson-Hughes B 1996 Weight, body composition, and bone
density in postmenopausal women. Calcif Tissue Int 59:428 – 432
6. Baumgartner RN, Stauber PM, Koehler KM, Romero L, Garry PJ 1996 Associations of fat and muscle masses with bone mineral in elderly men and
women. Am J Clin Nutr 63:365–372
7. Sowers MF, Kshirsagar A, Crutchfield MM, Updike S 1992 Influence of fat
and lean body composition compartments on femoral bone mineral density in
premenopausal women. Am J Epidemiol 136:257–265
8. Reid IR, Ames R, Evans MC, Sharpe S, Gamble G, France JT, Lim TMT,
Cundy TF 1992 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
9. Reid IR, Plank LC, Evans MC 1992 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
10. Kiel DP 1987 Hip fracture and the use of estrogens in post-menopausal
women. The Framingham Study. N Engl J Med 317:1169 –1174
11. Wickham CA, Walsh K, Cooper C, Barker DJ, Margetts BM, Morris J, Bruce
SA 1989 Dietary calcium, physical activity and risk of hip fracture: a prospective study. Br Med J 299:889 – 892
12. Pietrobelli A, Faith MS, Wang J, Brambilla P, Chiumello G, Heymsfield SB
2002 Association of lean tissue and fat mass with bone mineral content in
children and adolescents. Obes Res 10:56 – 60
13. Kemper HCG 1985 Growth, health and fitness of teenagers: longitudinal
research in international perspective. In: Kemper HCG, ed. Medicine and sport
science series, no. 20. Basel and New York: Karger
14. Weiner JS, Lourie JA 1968 Human biology: a guide to field methods. Oxford:
Blackwell
15. Durnin JVGA, Rahaman MM 1967 The assessment of the amount of fat in the
human body from measurements of skinfold thickness. Br J Nutr 21:681– 689
16. Durnin JVGA, Womersley J 1974 Body fat assessed from total body density
and its estimation from skinfold thickness: measurements on 481 men and
women aged form 16 to 72 years. Br J Nutr 32:77–97
17. Welten DC, Kemper HCG, Post GB, van Mechelen W, Twisk J, Lips P, Teule
GJ 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
18. Genant HK, Grampp S, Glüer CC, Faulkner KG, Jergas M, Engelke K,
Hagiwara S, van Kuijk C 1994 Universal standardization for dual x-ray ab-
J Clin Endocrinol Metab, June 2003, 88(6):2607–2613 2613
19.
20.
21.
22.
23.
24.
25.
26.
27.
28.
29.
30.
31.
32.
33.
34.
35.
36.
37.
38.
39.
40.
41.
42.
sorptiometry: patient and phantom cross-calibration results. J Bone Miner Res
9:1503–1514
Verschuur R 1987 Daily Physical Activity and Health. Longitudinal Changes
during the Teenage Period, PhD thesis, Universiteit van Amsterdam, De
Vrieseborch, Haarlem, The Netherlands
Montoye HJ, Kemper HCG, Saris WHM, Washburn RA 1996 Appendix P. In:
Montoye HJ, Kemper HCG, Saris WHM, Washburn RA, eds. Measuring physical activity and energy expenditure. Champaign, IL: Human Kinetics; 182–183
Kemper HCG, van Mechelen W 1995 Methods and measurements used in the
longitudinal study. In: Kemper HCG, ed. The Amsterdam Growth Study: a
longitudinal analysis of health, fitness and lifestyle. HKP Sport Science Monograph Series, vol 6. Champaign, IL: Human Kinetics; 40 – 44
Groothausen J, Siemer H, Kemper HCG, Twisk JWR, Welten DC 1997
Influence of peak strain on lumbar bone mineral density: an analysis of 15-year
physical activity in young men and women. Pediatr Exerc Sci 9:159 –173
Bakker I, Twisk JWR, van Mechelen W, Roos JC, Kemper HCG 2003 10-year
longitudinal relationship between physical activity and lumbar bone mass in
(young) adults. J Bone Miner Res 18:325–332
Kemper HCG, Bakker I, Twisk JWR, van Mechelen W 2002 Validation of a
physical activity questionnaire to measure the effect of mechanical strain on
bone mass. Bone 30:799 – 804
Beal VA 1967 The nutritional history in longitudinal research. J Am Diet Assoc
51:426 – 432
Marr JW 1971 Individual dietary surveys: purpose and methods. World Rev
Nutr Diet 13:105–164
Post GB 1989 Nutrition in Adolescence: A Longitudinal Study in Dietary
Pattern from Teenager to Adult, PhD thesis, Agricultural University Wageningen, De Vrieseborch, Haarlem, The Netherlands
Bakker I, Twisk JWR, van Mechelen W, Mensink GBM, Kemper HCG 2003
Computerization of a dietary history interview in a running cohort; evaluation
within the Amsterdam Growth and Health Longitudinal Study. Eur J Clin Nutr
57:394 – 404
Stichting Nederlandse Voedingsstoffen bestand 1996 Nevo Tabel 1996
(Dutch Food Composition Table 1996). Den Haag, The Netherlands: Voorlichtingsbureau voor de voeding
Goldstein H 1995 Multilevel statistical models. London: Edward Arnold; New
York: Wiley
Twisk JWR 2003 Applied longitudinal data analysis for epidemiology; a
practical guide. Cambridge, UK: University Press
Young D, Hopper JL, Macinnis RJ, Nowson CA, Hoang NH, Wark JD 2001
Changes in body composition as determinants of longitudinal changes in bone
mineral measures in 8- to 26-year-old female twins. Osteoporos Int 12:506 –515
Coin A, Sergi G, Benincà P, Lupoli L, Cinti G, Ferrara L, Benedetti G, Tomasi
G, Pisent C, Enzi G 2000 Bone mineral density and body composition in
underweight and normal elderly subjects. Osteoporos Int 11:1043–1050
Aloia JF, Vaswani A, Ruimei MA, Flaster E 1995 To what extent is bone mass
determined by fat-free or fat mass? Am J Clin Nutr 6:1110 –1114
Reid IR, Evans MC, Ames RW 1994 Volumetric bone density of the lumbar
spine is related to fat mass but not lean mass in normal postmenopausal
women. Osteoporos Int 4:362–367
Lindsay R, Cosman F, Herrington BS, Himmelstein S 1992 Bone mass and
body composition in normal women. J Bone Miner Res 7(Suppl 1):55– 63
Compston JE, Bhambhani M, Laskey MA, Murphy S, Khaw KT 1992 Body
composition and bone mass in post-menopausal women. Clin Endocrinol
37:426 – 431
Reid IR 2002 Relationships among body mass, its components, and bone.
Bone 31:547–555
Matsumine H, Hirato K, Yanaihara T, Tamada T, Yoshida M 1986 Aromization by skeletal muscle. J Clin Endocrinol Metab 63:717–720
Bevier WC, Wiswell RA, Pyka G, Kozak KC, Newhall KM, 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
Mazess RB, Peppler WW, Gibbons M 1984 Total body composition by dualphoton (153Gd) absorptiometry. Am J Clin Nutr 40:834 – 839
Khan K, McKay H, Kannus P, Bailey C, Wark J, Bennell K, Forwood M 2001
Physiology. In: Khan K, McKay H, Kannus P, Bailey C, Wark J, Bennell K, eds.
Physical activity and bone health. Champaign, IL: Human Kinetics; 11–21