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The Journal of Clinical Endocrinology & Metabolism Vol. 88, No. 6 2607-2613
Copyright © 2003 by The Endocrine Society

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

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{at}med.vu.nl.


   Abstract
Top
 Abstract
Introduction
 Subjects and Methods
 Results
 Discussion
 Conclusion
 References
 
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. 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.


   Introduction
Top
 Abstract
 Introduction
Subjects and Methods
 Results
 Discussion
 Conclusion
 References
 
THE 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, 2, 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, 7, 8, 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?


   Subjects and Methods
Top
 Abstract
 Introduction
 Subjects and Methods
Results
 Discussion
 Conclusion
 References
 
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.

Body composition components

To assess the body composition components, anthropometrical measurements were performed according the guidelines of the International Biological Program (14).

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 dual-energy 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 x the body weight), 1 (GRF between 1 and 2 x the body weight), 2 (GRF between 2 and 4 x the body weight), and 3 (GRF > 4 x 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, 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
Top
 Abstract
 Introduction
 Subjects and Methods
 Results
Discussion
 Conclusion
 References
 
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 1Go. 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 (-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.


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  		Table 1. Subject characteristics (means ± SD) at the mean ages of 27, 32, and 36 yr

 
Univariate relationship

The results of the crude and adjusted univariate regression analyses performed on all anthropometrical measures included are shown in Table 2Go.


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  		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.

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.

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 3Go. Similar results were found for crude predictive models (data not shown).


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  		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.

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. 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
Top
 Abstract
 Introduction
 Subjects and Methods
 Results
 Discussion
Conclusion
 References
 
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.

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, 34, 35, 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 contractions 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 components, 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 weight-bone 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 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. 1Go) shows that the FFM values are higher than the values of lean body mass from DEXA. The variability of the within-person differences for both measures is similar over the whole range of average values.



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  		Figure 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).

 
We used FFM as a measure of lean body mass, which, however, is muscle mass and bone mass together. Therefore, 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
Top
 Abstract
 Introduction
 Subjects and Methods
 Results
 Discussion
 Conclusion
References
 
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.


   Footnotes
 
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.

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.

Received October 2, 2002.

Accepted February 28, 2003.


   References
Top
 Abstract
 Introduction
 Subjects and Methods
 Results
 Discussion
 Conclusion
 References
 

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