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Obstetrics & Gynecology 2000;95:722-725
© 2000 by The American College of Obstetricians and Gynecologists
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ORIGINAL RESEARCH

Relationship Between Body Fat Distribution and Bone Mineral Density in Premenopausal Japanese Women

TSUTOMU DOUCHI, MD, SHINAKO YAMAMOTO, MD, TOSHIMICHI OKI, MD, KUNINORI MARUTA, MD, RIKI KUWAHATA, MD and YUKIHIRO NAGATA, MD

From the Department of Obstetrics and Gynecology, Faculty of Medicine, Kagoshima University, Kagoshima, Japan.

Address reprint requests to: Tsutomu Douchi, MD Department of Obstetrics and Gynecology Kagoshima University 8-35-1 Sakuragaoka Kagoshima, 890-8520 Japan


   Abstract
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Abstract
Materials and Methods
 Results
 Discussion
 References
 
Objective: To investigate the relationship between body fat distribution and bone mineral density (BMD).

Methods: Subjects were 282 premenopausal women (mean age ± standard deviation [SD], 38.8 ± 8.5 years; range, 20–51 years) with regular menstrual cycles. Baseline characteristics included age, age at menarche, height, weight, body mass index ([BMI], weight/height2), and parity. Anthropometric characteristics including the ratio of trunk fat mass to leg fat mass (trunk–leg fat ratio), percentage of body fat, and total body lean mass were measured by whole-body scanning with dual-energy x-ray absorptiometry. Lumbar spine BMD (L2–4) was also measured by dual-energy x-ray absorptiometry. Correlations of BMD to baseline and anthropometric characteristics were investigated using univariate and multivariate analysis.

Results: Although height, trunk–leg fat ratio, and total body lean mass were positively correlated with lumbar spine BMD (r = .18, P < .01; r = .17, P < .01; and r = .25, P < .001; respectively), age at menarche was inversely correlated with BMD (r = -.19, P < .01). On multivariable analysis, trunk–leg fat ratio, height, age at menarche, and total body lean mass were still independently correlated with lumbar spine BMD (P < .05). However, total fat mass was not correlated with BMD.

Conclusion: Upper body fat distribution rather than overall adiposity is associated with lumbar spine BMD in premenopausal women. Humoral factors associated with body fat mass appear to influence lumbar spine BMD.

When androgen levels are elevated, as in polycystic ovary syndrome, the development of male physical characteristics and muscle mass, structure and function, as well as android (male-type) body fat distribution and function is seen.1 It is well known that androgens directly and indirectly influence bone mineral density (BMD). These relationships suggest that android body fat distribution is associated with higher BMD. However, data on this issue are limited. The major reason for this may be the difficulty in precisely assessing body fat distribution. Recent technological advances in dual-energy x-ray absorptiometry enable more precise measurement of bone mass, lean mass, and fat mass separately.2

The purpose of the present study was to investigate the relationship of body fat distribution to lumbar spine BMD in premenopausal women.


   Materials and Methods
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Abstract
 Materials and Methods
Results
 Discussion
 References
 
A total of 322 premenopausal Japanese women who had requested screening for uterine cancer, ovarian tumor, or hyperlipidemia were included. All subjects were recruited between January 1995 and May 1999 at the Department of Obstetrics and Gynecology, Kagoshima University Hospital. Forty subjects with chronic or acute diseases (n = 12), ovarian tumor (n = 9), uterine cancer (n = 3), previous or current usage of oral contraceptives (OCs) (n = 3), excessive alcohol consumption (n = 8), cigarette smoking (n = 11), and endurance physical training (n = 10) were excluded (some cases had multiple exclusion factors). The remaining subjects, including 282 premenopausal women (mean age ± standard deviation [SD], 38.8 ± 8.5 years; range 20–51 years), were enrolled in the study. All premenopausal women had regular menstrual cycles at the time of investigation. Baseline characteristics included age, height, weight, and body mass index (BMI). Body mass index was calculated as weight (kg) divided by height squared (m2). The ratios of trunk fat mass to leg fat mass (trunk–leg ratio) and total body lean mass were assessed by whole-body scanning with dual-energy x-ray absorptiometry. The precision of trunk and leg fat mass measurements was determined by repeated measurements of six volunteers over 8 weeks. Precision of regional fat mass measurements in term of coefficients of variation were all less than 4%. Bone mineral density of the lumbar spine (L2–4) was also measured by dual-energy x-ray absorptiometry.

Dual-energy x-ray absorptiometry measurements were performed between 9:00 AM and 12:00 PM with a total body scanner (QDR 2,000/W; Hologic, Waltham, MA) and results were evaluated by the same examiner. This equipment uses switched pulsed stable dual-energy radiation with kilovoltages of 70 and 140. The machine performs serial transverse scans from head to toe at 1.2-cm intervals, providing a pixel size of 1.9 x 1.2 mm. The radiation dose is 0.05–0.15 µGy. Default software readings divided body measurements into areas corresponding to arm, trunk, and leg. The trunk region was delineated by an upper horizontal border below the chin, vertical borders lateral to the ribs, and a lower border formed by the oblique lines passing through the hip joints. The leg region was defined as tissue below the oblique line passing through the hip joint.2 All recordings were performed by the same experienced investigator. The examiner was masked to the study status.

Institutionally approved informed written consent was obtained for all subjects, and this study was conducted in accordance with the Helsinki Declaration.

All variables were distributed normally. Correlations between lumbar spine BMD and variables were investigated using Pearson’s correlation coefficient and partial correlation coefficient. Weight, BMI, percentage of body fat, and total fat mass are very similar variables indicating overall adiposity. Thus, on partial correlation analysis only total fat mass was included as an indicator of overall adiposity. Correlation between trunk–leg fat ratio and total body lean mass was also investigated using Pearson’s correlation coefficient. Confidence intervals (CIs) for the correlations and prediction intervals were calculated to evaluate the accuracy of the regressions. P < .05 was considered significant.


   Results
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Abstract
 Materials and Methods
 Results
Discussion
 References
 
Table 1Go presents subject characteristics. Table 2Go presents the correlation coefficient between lumbar spine BMD and variables. Height, trunk–leg fat ratio, and total body lean mass were significantly correlated with lumbar spine BMD (r = .18, P < .01; r = .17, P < .01; and r = .25, P < .001, respectively). Age at menarche was inversely correlated with lumbar spine BMD (r =- .19, P < .01). However, total fat mass, age, and parity were not correlated with BMD. Trunk–leg fat ratio was significantly correlated with total body lean mass (r = .26, P < .001).


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  		Table 1. Subject Characteristics
 

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  		Table 2. Correlation Coefficient Between Lumbar Spine Bone Mineral Density and Variables
 
Table 3Go presents the partial correlation coefficient between lumbar spine BMD and variables. Trunk–leg fat ratio, height, age at menarche, and total body lean mass were independently correlated with lumbar spine BMD (P < .05). These relationships were also independent of age, parity, and total fat mass.


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  		Table 3. Partial Correlation Coefficient Between Lumbar Spine Bone Mineral Density and Variables
 

   Discussion
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Abstract
 Materials and Methods
 Results
 Discussion
References
 
Subjects with an android (or upper) body fat distribution have greater BMD than subjects with a gynoid body fat distribution.3 We also previously demonstrated that upper body fat distribution is associated with higher lumbar spine BMD in women with endometrial cancer.4 However, there is a contrary report indicating that osteoporotic women have significantly higher waist-to-hip ratios compared with controls.5 All of these reports studied postmenopausal women, but significant disagreements persist with regard to the relationship between body fat distribution and BMD. Possible reasons for these discrepancies include the heterogeneity of the enrolled subjects, the number of studied subjects, and the differences in subjects’ age distribution. Levels of physical activity of the enrolled subjects may also play a part. Increased physical activity reduces upper body adiposity, whereas reduced physical activity with advancing age induces upper body fat distribution.6 Thus, some postmenopausal women with lower body fat distribution might have higher BMD owing to increased physical activity, whereas some women with upper body fat distribution might also have higher BMD owing to an excessive amount of body fat.7 In postmenopausal women, it may be difficult to elucidate the relationship of body fat distribution to BMD because precise assessment of physical activity is difficult and significant variation in physical activity exists among postmenopausal women. Thus, in the present study we investigated this relationship between body fat distribution and BMD in premenopausal women, in whom variation in physical activity is minimal.

We found that trunk–leg fat ratio was significantly positively correlated with lumbar spine BMD. Reid et al8 found that total fat mass was the most important predictor of BMD; however, body fat distribution was not considered in their regression analysis. It is important to clarify why upper body fat distribution is positively associated with lumbar spine BMD. Many factors influence body fat distribution. These may include obesity, aging, changes in energy intake, decreased muscle strength and physical activity, changes in sex hormones, or other hormonal factors.3,9–12 We found that the strength of correlation of BMD to trunk–leg fat ratio was greater than the strength of correlation between BMD and percentage of body fat or total fat mass on both univariate and multivariate analyses. Body fat distribution rather than overall adiposity is an important predictor of BMD in premenopausal women. Thus, it is likely that the effect of fat mass on BMD is mediated not only by its weight-bearing effect, but also by related humoral factors. Unfortunately, we did not measure serum androgen levels, so we cannot directly address the relationship between androgenic activity and body fat distribution. However, androgenic activity is reported to be inversely associated with fat percentage in the legs and positively associated with fat percentage in the abdominal regions.13 Testosterone administration increases visceral fat in women.14 Testosterone and dehydroepiandrosterone sulfate are independently associated with the waist-to-hip circumference ratio.15 Many other reports indicate that androgenic activity is higher in women with upper body fat distribution than in those with lower body fat distribution.3,16–19 Furthermore, android body fat distribution was associated with lower sex-hormone binding globulin levels, resulting in higher free estrogen and testosterone levels, the biologically active forms of these hormones, thus promoting bone formation. It has been shown that increased androgen levels in postmenopausal women can protect them against accelerated bone loss compared with age-matched controls.20 Taking these findings into consideration, it is plausible that android body fat distribution, with a more androgenic hormone profile, had a higher BMD.

Little attention has been paid to the relationship between body fat distribution and muscle size. We found that android body fat distribution is also associated with greater lean mass, which includes muscle mass. Our observation supports the report by Bjorntorp,1 who proposed that elevated androgen levels are followed by the development of male physical characteristics and muscle mass, structure, and function, as well as android body fat distribution and function. Muscle size is associated with BMD.21 Thus, higher BMD in android women may be attributable in part to greater muscle size.


   Footnotes
 
PII S0029-7844(99)00663-8

Received August 9, 1999. Received in revised form October 27, 1999. Accepted November 4, 1999.


   References
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Abstract
 Materials and Methods
 Results
 Discussion
 References
 
1. Bjorntorp P. The android women—a risky condition. J Intern Med 1996;239:105–10.[Medline]

2. Ley CJ, Lees B, Stevenson JC. Sex- and menopause-associated changes in body-fat distribution. Am J Clin Nutr 1992;55:950–4.[Abstract/Free Full Text]

3. Heiss CJ, Sanborn CF, Nichols DL, Bonnick SL, Alford BB. Association of body fat distribution, circulating sex hormones, and bone density in postmenopausal women. J Clin Endocrinol Metab 1995;80:1591–6.[Abstract/Free Full Text]

4. Douchi T, Ijuin H, Nakamura S, Oki T, Maruta K, Nagata Y. Correlation of body fat distribution with grade of endometrial cancer. Gynecol Oncol 1997;65:138–42.[Medline]

5. Blaauw R, Arbertse EC, Hough S. Body fat distribution as a risk factor for osteoporosis. S Afr Med J 1996;86:1081–4.[Medline]

6. Aloia JF. Exercise and skeletal health. J Am Geriatr Soc 1981;29: 104–7.[Medline]

7. Shiraki M, Ito H, Fujimaki H, Higuchi T. Relation between body size and bone mineral density with special reference to sex hormones and calcium regulating hormone in elderly women. Endocrinol Jpn 1991;38:343–9.[Medline]

8. Reid IR, Ames R, Evans MC, Sharpe S, Gamble G, France JT, et al. Determinants of total body and regional bone mineral density in normal postmenopausal women—a key role for fat mass. J Clin Endocrinol Metab 1992;75:45–51.[Abstract]

9. Svendsen OL, Hassager C. Body composition and fat distribution measured by dual-energy X-ray absorptiometry in premenopausal and postmenopausal insulin-dependent and non-insulin-dependent diabetes mellitus patients. Metabolism 1998;47:212–6.[Medline]

10. Kaye SA, Folsom AR, Prineas RJ, Potter JD, Gapstur SM. The association of body fat distribution with life style and reproductive factors in a population study of postmenopausal women. Int J Obes 1990;14:583–91.[Medline]

11. Pasquali R, Casimirri F, Labate AM, Tortelli O, Pascal G, Ancontetani B, et al. Body weight, fat distribution and the menopausal status in women. Int J Obes 1994;18:614–21.

12. Reubinoff R, Rall LC. Humoral mediation of changing body composition during aging and chronic inflammation. Nutr Rev 1993;51:1–11.[Medline]

13. Hetland ML, Haarbo J, Christiansen C. Regional body composition determined by dual-energy X-ray absorptiometry. Relation to training, sex hormones, and serum lipids in male long-distance runners. Scand J Med Sci Sports 1998;8:102–8.[Medline]

14. Elbers JM, Asscheman H, Seidell JC, Megens JA, Gooren LJ. Long-term testosterone administration increases visceral fat in female to male transsexuals. J Clin Endocrinol Metab 1997;82: 2044–7.[Abstract/Free Full Text]

15. Manzoros CS, Georgiadis EI, Evangelopoulou K, Katsilambros N. Dehydroepiandrosterone sulfate and testosterone are independently associated with body fat distribution in premenopausal women. Epidemiology 1996;7:513–6.[Medline]

16. Kirschner MA, Samojlik E, Drejka M, Szmal E, Schneider G, Ertel N. Androgen-estrogen metabolism in women with upper body versus lower body obesity. J Clin Endocrinol Metab 1990;70:473–9.[Abstract]

17. Douchi T, Yamamoto S, Nakamura S, Ijuin T, Oki T, Maruta K, et al. The effect of menopause on regional and total body lean mass. Maturitas 1998;29:247–52.[Medline]

18. Guzick D. Polycystic ovary syndrome: Symptomatology, pathophysiology, and epidemiology. Am J Obstet Gynecol 1998;179:89–93.

19. Wabitsch M, Hauner H, Heinze E, Bockmann A, Benz R, Mayer H, et al. Body fat distribution and steroid hormone concentrations in obese adolescent girls before and after weight reduction. J Clin Endocrinol Metab 1995;80:3469–75.[Abstract]

20. Longcope C, Baker RS, Hui SL, Johnston CC Jr. Androgen and estrogen levels of serum estradiol, bone density, and fractures among elderly women: The study of osteoporotic fractures. J Clin Endocrinol Metab 1998;83:2239–43.[Abstract/Free Full Text]

21. Douchi T, Yamamoto S, Nakamura S, Oki T, Maruta K, Nakae M, et al. Lean mass as a significant determinant of regional and total body bone mineral density in premenopausal women. J Bone Miner Metab 1998;16:17–20.




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