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Associations of Fat Mass and Fat Distribution With Bone Mineral Density in Chinese Obese Population
Journal of Clinical Densitometry: Assessment & Management of Musculoskeletal Health, vol. -, no. -, 1e6, 2014 Ó Copyright 2014 by The International Society for Clinical Densitometry 1094-6950/-:1e6/$36.00 http://dx.doi.org/10.1016/j.jocd.2014.03.001
Original Article
Associations of Fat Mass and Fat Distribution With Bone Mineral Density in Chinese Obese Population Jun Zhang,1 Yongming Jin,1 Shaonan Xu,1 Jiayin Zheng,2 Qi Zhang,3 Jinping Chen,1 Yazeng Huang,1 Haiyu Shao,1 Di Yang,1 and Qifeng Ying4 1
Department of Orthopedics, Zhejiang Provincial People’s Hospital, Hangzhou, Zhejiang, China; 2Department of Probability and Statistics, School of Mathematical Sciences, Peking University, Beijing, China; 3Department of Surgery, Zhejiang Provincial People’s Hospital, Hangzhou, Zhejiang, China; and 4Department of Dual Energy X-ray Absorptiometry (DEXA), Zhejiang Provincial People’s Hospital, Hangzhou, Zhejiang, China
Abstract The purpose of the study was to investigate the associations of fat mass (FM) and fat distribution with bone mineral density (BMD) in Chinese obese population. Three hundred and forty-seven Chinese obese females and 339 males aged 20e39 years were analyzed. Lean mass (LM), FM, percent body fat (%BF), android FM, gynoid FM, and total and regional BMD were measured using dual-energy X-ray absorptiometry. Fat distribution was assessed by android-to-gynoid FM ratio (AOI). As a result, increased central body fat had an inverse association with total and leg BMD in females but not in males. Increased FM and %BF were positively associated with arm, trunk, and pelvic BMD in Chinese obese females. Increased FM was positively associated with total, rib, and trunk BMD in Chinese obese males. The results remained almost unchanged after adjusting for LM, and LM was significantly positively associated with spine BMD in female group. FM was positively associated with trunk BMD in male group after adjusting for LM. AOI was inversely associated with total and leg BMD, and %BF was positively associated with arm, trunk, and pelvic BMD when replacing FM with %BF in female group. The results remained almost unchanged after adjusting for LM. There is no significant association in male group when replacing FM with %BF. In conclusion, our findings demonstrate that there are different associations of FM and fat distribution with BMD, and AOI has a negative association with BMD. Key Words: Bone mineral density; fat distribution; fat mass; obesity.
disease, has been demonstrated to be closely related with osteoporosis (2e4). Bone mineral density (BMD) has been widely accepted as a surrogate measure for the diagnosis of osteopenia and osteoporosis (5,6). BMD is highly related to body weight, such that individuals with higher body weight have increased bone size and bone strength (7,8). However, the relative contribution of lean mass (LM) and fat mass (FM) to the variation in BMD has been highly contentious. Some studies have indicated that LM, not FM, is associated with BMD (9e16); other studies by Reid et al (17e20) have demonstrated that FM, not LM, is an important determinant of BMD. Moreover, other studies have found that both FM and LM were significant predictors of BMD (21e24). Although the associations between FM and BMD in different
Introduction Osteoporosis is a major public health problem with growing prevalence. Approximately 9 million adults in the United States have osteoporosis, and more than 48 million have low bone mass, placing them at increased risk for osteoporosis and broken bones (1). Obesity, another common Received 01/28/14; Accepted 03/26/14. JZ and YJ authors contributed equally to this article. *Address correspondence to: Qifeng Ying, MD, Department of Dual Energy X-ray Absorptiometry (DEXA), Zhejiang Provincial People’s Hospital, Shangtang Rd 158, Hangzhou, Zhejiang 310014, China. E-mail: [email protected]
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2 ethnicities are inconsistent, most previous studies were based on Caucasian population, and the results cannot necessarily be extrapolated to Asian population. Chinese study has found that increased central body fat had an inverse association with BMD (25). Another Chinese study also demonstrated recently that FM is inversely associated with BMD beyond its weightbearing effect, whereas abdominal fat in women and limb fat in men seems to have the greatest effect on BMD (26). Moreover, Korean studies demonstrated that abdominal obesity was significantly associated with bone mineral content independent of total FM (27). Indian studies investigated in nonobese adults indicated that total percent body fat (%BF) and regional fat have positive association with BMD at all sites in men and women (28). These conflicting clinical and epidemiologic studies suggest a complex influence of FM and fat distribution on BMD. To the best of our knowledge, there are still few studies engaged directly in obese population to investigate the association between FM and its distribution and BMD. The above facts prompted us to evaluate the different associations of FM and central fat distribution with BMD directly in Chinese obese adults.
Subjects and Methods Subjects A total of 686 Chinese obese adults, whose body mass index (BMI) 28, aged from 20 to 39 yr were included in Zhejiang Provincial People’s Hospital from January 2009 to August 2013. Those with known metabolic bone diseases or those under any medications likely to influence BMD were excluded from the study. Twenty-three female participants were excluded because of hysterectomy. In the end, 347 females and 339 males were included in the analysis. Written informed consent was obtained, and the study was approved by the Ethics Committee of the Zhejiang Provincial People’s Hospital.
Demographic, Anthropometry, and Body Composition Measurement All subjects completed a demographic questionnaire. Participants who smoked at least 1 cigarette per day or drank alcohol once a week for at least 6 mo were defined as smokers or drinkers. None of the subjects were heavy drinkers. All the females were premenopausal. Physical measurements were obtained based on standardized protocol. Height was measured without shoes to the nearest 0.1 cm, weight with only light clothing to the nearest 0.1 kg (Detecto, Webb City, MO). All values were recorded as the mean of the 3 measures. BMI was calculated as body weight (kg) divided by height (m2). Dual-energy X-ray absorptiometry (DXA, software version 13.60.033, GE-lunar iDXA; GE Healthcare Lunar, Madison, WI) was used to measure LM, FM, %BF, android FM, gynoid FM, and total and regional BMD through whole-body scans. For the android region, the lower boundary is at pelvis cut. The upper boundary is above pelvis cut by 20% of the distance
Zhang et al. Table 1 Characteristics of the Subjects by Gender Variables
Female (n 5 347)
Age (y) 31.7 6.7 Height (cm) 163.8 5.1 Weight (kg) 94.3 15.5 BMI (kg/m2) 35.1 5.4 Body composition measures FM (kg) 40.8 13.4 Android FM (kg) 3.9 1.4 Gynoid FM (kg) 6.7 2.3 LM (kg) 48.1 6.9 %BF 44.5 8.8 AOI 0.6 0.1 Body mineral density measures (g/cm2) Total body 1.26 0.08 Head 2.41 0.28 Rib 0.77 0.24 Arm 0.97 0.10 Spine 1.14 0.14 Trunk 1.00 0.07 Pelvic 1.20 0.09 Leg 1.31 0.08 Drinkers (%) 21 Smokers (%) 4
Male (n 5 339) 30.5 172.5 111.4 37.3
7.2 6.1 22.5 6.2
40.6 5.1 6.0 68.1 36.9 0.9
12.2 2.1 2.1 12.8 5.2 0.2
1.33 2.10 0.74 1.10 1.06 0.99 1.24 1.45 27 42
0.10 0.22 0.05 0.13 0.14 0.08 0.13 0.10
Abbr: AOI, android-to-gynoid FM ratio; %BF, percent body fat; BMI, body mass index; FM, fat mass; LM, lean mass.
between pelvis and femoral neck cuts. Lateral boundaries are the arm cuts. The gynoid region is defined by the upper boundary below the pelvis cut line by 1.5 times the height of the android region. The height of the gynoid region is equal to 2 times the height of the android region. Lateral boundaries are the outer leg cuts. Body fat distribution was assessed by android-to-gynoid FM ratio (AOI). Regional BMD refers to the mean BMD in the regions of head, rib, arm, spine, trunk, hip, and leg. DXA was calibrated daily using a standard phantom provided by the manufacturer.
Statistical Analyses According to our existing knowledge, we specified the partial correlation between the tested predictors and the response, adjusting for any other predictors in the model, to be 0.2. Thus, we calculated that 325 patients per group would suffice to achieve the power of 0.95 with a 5 0.05. Basic characteristics of subjects were presented as mean standard deviation. Male and female obese population were analyzed separately to evaluate the associations of BMD with AOI, FM, and LM in multiple regression models. In model 1, we first explored the associations of FM and AOI with total body and regional BMD. We then added LM into model 1 to investigate the associations of LM with total body and regional BMD with the presence of FM and AOI in the model (model 2). In addition, the regression was rerun
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Table 2 b Coefficients of FM, AOI, and LM for Total Body and Regional BMD From Multiple Regression Analysis in Female and Male Obese Population Variables
Total
Head
Model 1 Female FM 1.07 0.69 AOI 3.18** 1.14 Male FM 2.82* 1.01 AOI 0.47 0.32 Model 2 (additional adjusted for LM) Female FM 1.21 0.32 AOI 2.83* 1.91 LM 0.61 1.57 Male FM 2.01 1.14 AOI 0.11 0.01 LM 1.07 0.57
Rib
Arm
Spine
0.11 0.05
2.24* 1.35
0.02 0.37
2.34* 1.57
0.3 0.89
0.57 0.48 0.84 2.1 1.24 0.2
Trunk
Pelvic
Leg
2.13* 0.48
3.56** 1.19
0.93 3.22**
1 2.54*
2.7* 2.29*
0.82 0.42
0.04 0.03
3.15** 2.4* 2.01
1.92 1.71 3.38**
2.47* 1.13 1.22
3.23** 0.52 0.65
0.64 2.35* 0.17
0.53 0.02 1.74
1.28 1.78 0.86
2.42* 1.83 0.22
0.76 0.3 0.12
0.5 0.58 1.14
Abbr: AOI, android-to-gynoid FM ratio; BMD, bone mineral density; FM, fat mass; LM, lean mass. Note: Covariates included in the regression model were age, height, smoking status, and drinking status. *p ! 0.05; **p ! 0.01.
replacing FM with %BF. Covariates, such as age, height, smoking, and drinking, were included in the regression models. SPSS (version 16.0 for Windows; SPSS Inc., Chicago, IL) was used for analysis. All statistical tests were 2 tailed, and p ! 0.05 was considered significant.
Results Descriptive Statistics The basic characteristics of the subjects are listed in Table 1. The mean age of the participants was 31.7 6.7 yr for females and 30.5 7.2 yr for males. The height was 163.8 5.1 cm for females and 172.5 6.1 cm for males. The weight was 94.3 15.5 kg for females and 111.4 22.5 kg for males. The percent of drinkers and smokers were 21 and 4 for females and 27 and 42 for males, respectively. And the BMI was 35.1 5.4 kg/m2 for females and 37.3 6.2 kg/m2 for males. The BMD and body composition at all the studied sites were shown in detail in Table 1.
Multiple Regression Analysis The results of multiple linear regression analysis are shown in Table 2. In model 1, FM of females was in a significantly positive association with BMD in arm, trunk, and pelvis ( p ! 0.05) and FM of males was in a significant positive association with BMD in total body, rib, and trunk ( p ! 0.05), whereas AOI of females was significantly inversely associated with BMD in total body and leg ( p ! 0.01). When additionally adjusted for LM (model 2), in females, the significant associations between both FM and AOI and
BMD were almost identical as in model 1 (all p ! 0.05) although LM had a significantly inverse association with BMD in spine ( p ! 0.01). In male obese population, FM had a significantly positive association with BMD in trunk ( p ! 0.05), whereas AOI and LM had no significant association with BMD. We further replaced FM with %BF and reran the regression models (Table 3). The results were almost identical. The %BF was significantly associated with BMD in arm, trunk, and pelvis in females ( p ! 0.01) but not in male obese population. AOI was significantly inversely associated with BMD in total body and leg in females ( p ! 0.01) but not in males. When additionally adjusting for LM, the associations between %BF and AOI and BMD were almost identical as model 1. LM was inversely associated with BMD in spine in female population ( p ! 0.01). In males, FM, AOI, and LM had no significant association with BMD. Covariates such as smoking and drinking had no significant associations with BMD in regression models. Covariates such as age and height were also considered to be taken into regression models. For females, age and height had no significant associations with BMD in all regression models. For males, age had no significant associations with BMD in all regression models except the model for BMD of the spine, in which age showed a positive association with BMD. Height had no significant associations with BMD in all regression models except 2 models for the total BMD and BMD of the arm. Height had a positive association with total BMD, but such an association was eliminated when there was additionally adjusted for LM. Height also showed a strong positive
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Zhang et al. Table 3 b Coefficients of %BF, AOI, and LM for Total Body and Regional BMD From Multiple Regression Analysis in Female and Male Obese Population
Variables
Total
Head
Model 1 Female %BF 1.47 0.36 AOI 3.54** 0.71 Male %BF 1.14 0.93 AOI 1.23 0.05 Model 2 (additional adjusted for LM) Female %BF 1.43 0.62 AOI 2.54* 1.78 LM 0.09 1.78 Male %BF 1.53 0.9 AOI 0.24 0.07 LM 2.32* 0.05
Rib
Arm
Spine
Trunk
Pelvic
Leg
0.57 0.21
3.04** 1.77
1.58 1.04
3.53** 0.62
4.4*** 0.95
0.56 3.67**
1.53 0.89
1.03 1.23
0.86 2.4*
1.66 1.44
0.63 0.2
0.95 0.1
0.65 0.34 0.71
3.04** 1.7 0.65
2.41* 1.29 3.23**
3.41** 0.56 0.19
4.24*** 1.45 1.09
0.47 2.19* 0.58
1.63 1.32 1.02
0.9 0.09 1.61
0.81 1.76 0.2
1.79 1.85 1.15
0.65 0.35 0.33
0.84 0.48 0.94
Abbr: AOI, android-to-gynoid FM ratio; BMD, bone mineral density, %BF, percent body fat; LM, lean mass. Note: Covariates included in the regression model were age, height, smoking status, and drinking status. *p ! 0.05; **p ! 0.01; ***p ! 0.001.
association with BMD of the arm (with the corresponding P values close to 0.001; data not shown).
Discussion The relative contribution of FM and fat distribution to BMD remain a contentious issue. Although a majority of studies have found a positive association between LM and BMD (9e16), few studies have shown that FM is an important determinant of BMD (17e20). The present study explored the associations of total body FM and fat distribution with BMD in Chinese obese population. Our study found that FM, LM, and central fat distribution have different associations with total and some regional BMD in Chinese obese population. The inverse association between central fat distribution and BMD of total body and leg in females yields information that positive control central adiposity deposition has harmful influence on osteoporosis. The observed positive association between FM and BMD of pelvis in females was partly in accordance with previous studies (17,25,29). In our present study, FM had a significant positive association with BMD of arm, trunk, and pelvis in female group and trunk in male group. Although the precise mechanism of the relationship between FM and BMD is not been fully understood, several potential theories have been proposed. It was partly in accordance with previous studies (25,30e32). Partly agreeing with previous studies, in this present study, there was a positive relationship between FM and BMD in arm, trunk, and pelvis. Although the precise mechanism of the relationship between FM and BMD in obese population is
not clear, several potential theories have been proposed. Fat tissue affects the skeleton not only through a weight-bearing effect but also through other noneweight-bearing effects, including the hormonal metabolism of adipocytes (33e35). Adipocytes are an important source of estrogen production, and estrogen is known to protect against bone mineral loss by inducing osteoclast apoptosis (17,36e38). There are still some studies that demonstrated that adipose tissue can release more than 20 adipocytokines into circulation, which indicates that adiposity influences BMD through alternative mechanisms such as adipocytes-dependent hormonal factors (39,40). Further possible link between FM and BMD is the common stromal cell origin of both osteoblasts and adipocytes (19). Although regional BMD increased with total body FM in the present study, it is noteworthy that BMD in total body and leg was negatively associated with central adiposity accumulation, indicated as AOI in female obese population. A recent study using computerized tomography to measure abdominal fat found a negative effect of visceral fat on femoral bone phenotypes (41). Another study indicated that abdominal obesity is negatively related to BMD in sedentary obese children and adolescents (32). Studies involving anthropometric (waist-to-hip ratio) (31) and DXA variables (abdominal fatness in kilograms) (30) identified similar relationship patterns in children and adults, respectively. The difference between fat having beneficial effects and visceral fat having adverse effects may be mediated by the presence of lower levels of leptin and higher levels of adiponectin and proinflammatory cytokines in visceral fat (41,42). In addition, increased visceral fat is associated with insulin resistance,
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Associations of Fat Mass and Fat Distribution With BMD which also may exert adverse effects on bone (43,44). Furthermore, vitamin D status is inversely related to BMI and to insulin resistance providing another mechanism by which visceral FM might contribute to bone loss (45,46). Finally, the higher serum parathyroid hormone levels reported in obese individuals could have adverse effects on cortical bone (47,48). The precise underlying mechanism should be addressed by further studies. The finding of this study confirmed previous studies partly that LM has a positive effect on BMD in spine (12,25,49). The relationship between FM and BMD remains almost identical in females, whereas this relationship was eliminated in males when additionally adjusting for LM. This indicates that bone strength is primarily determined by the dynamic loads from muscle force only in males. And it is determined by both dynamic and static loads, such as FM, in females. The major strength of our study is that all the measurements were obtained by DXA by 1 technician using 1 densitometer. Additionally, no participant had taken a drug known to interfere with the normal bone, fat tissue, or lean tissue metabolism. Finally, all the participants were Chinese obese population living in the same region. Limitations include the cross-sectional nature of the study, which is not possible to make any cause-and-effect inference on the relationship between FM, AOI, LM, and BMD. Additionally, we did not analyze the lifestyle of participants, such as physical exercise, calcium supplementation, or vitamin D levels, which might also have influenced results. Finally, all the participants were Chinese obese population, and our results may not be generalized to other ethnicities. In our present study, we calculated the correlation coefficients between FM and body weight and between LM and body weight to be 0.94 and 0.91, respectively. We have not included body weight as a controlling variable because to avoid its multicollinearity with FM and LM in the regression models. Although previous study indicated that FM was positively associated with BMD without weight adjustment but turned to be negatively associated with it after weight adjustment (4), adjustment for body weight was still not done in our present study to avoid its multicollinearity with FM and LM. Our methods and results were consistent with some studies published recently (25,50). In conclusion, the finding that there are different associations of FM and fat distribution with BMD has major public health implications. AOI had a negative association with BMD. Our finding reinforces the concept that the prevention of centralized fat deposition may have significant implications in decreasing osteoporosis in females.
Acknowledgments This study was support by grants from Application Research of Public Technology of Science and Technology Department of Zhejiang Province (2012C33069), Natural Scientific Research Foundation of Zhejiang Medical College (2011XZB01), and the Zhejiang Provincial Natural Science Foundation of China (Y14H060025).
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Journal of Clinical Densitometry: Assessment & Management of Musculoskeletal Health
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Original Article
Associations of Fat Mass and Fat Distribution With Bone Mineral Density in Chinese Obese Population Jun Zhang,1 Yongming Jin,1 Shaonan Xu,1 Jiayin Zheng,2 Qi Zhang,3 Jinping Chen,1 Yazeng Huang,1 Haiyu Shao,1 Di Yang,1 and Qifeng Ying4 1
Department of Orthopedics, Zhejiang Provincial People’s Hospital, Hangzhou, Zhejiang, China; 2Department of Probability and Statistics, School of Mathematical Sciences, Peking University, Beijing, China; 3Department of Surgery, Zhejiang Provincial People’s Hospital, Hangzhou, Zhejiang, China; and 4Department of Dual Energy X-ray Absorptiometry (DEXA), Zhejiang Provincial People’s Hospital, Hangzhou, Zhejiang, China
Abstract The purpose of the study was to investigate the associations of fat mass (FM) and fat distribution with bone mineral density (BMD) in Chinese obese population. Three hundred and forty-seven Chinese obese females and 339 males aged 20e39 years were analyzed. Lean mass (LM), FM, percent body fat (%BF), android FM, gynoid FM, and total and regional BMD were measured using dual-energy X-ray absorptiometry. Fat distribution was assessed by android-to-gynoid FM ratio (AOI). As a result, increased central body fat had an inverse association with total and leg BMD in females but not in males. Increased FM and %BF were positively associated with arm, trunk, and pelvic BMD in Chinese obese females. Increased FM was positively associated with total, rib, and trunk BMD in Chinese obese males. The results remained almost unchanged after adjusting for LM, and LM was significantly positively associated with spine BMD in female group. FM was positively associated with trunk BMD in male group after adjusting for LM. AOI was inversely associated with total and leg BMD, and %BF was positively associated with arm, trunk, and pelvic BMD when replacing FM with %BF in female group. The results remained almost unchanged after adjusting for LM. There is no significant association in male group when replacing FM with %BF. In conclusion, our findings demonstrate that there are different associations of FM and fat distribution with BMD, and AOI has a negative association with BMD. Key Words: Bone mineral density; fat distribution; fat mass; obesity.
disease, has been demonstrated to be closely related with osteoporosis (2e4). Bone mineral density (BMD) has been widely accepted as a surrogate measure for the diagnosis of osteopenia and osteoporosis (5,6). BMD is highly related to body weight, such that individuals with higher body weight have increased bone size and bone strength (7,8). However, the relative contribution of lean mass (LM) and fat mass (FM) to the variation in BMD has been highly contentious. Some studies have indicated that LM, not FM, is associated with BMD (9e16); other studies by Reid et al (17e20) have demonstrated that FM, not LM, is an important determinant of BMD. Moreover, other studies have found that both FM and LM were significant predictors of BMD (21e24). Although the associations between FM and BMD in different
Introduction Osteoporosis is a major public health problem with growing prevalence. Approximately 9 million adults in the United States have osteoporosis, and more than 48 million have low bone mass, placing them at increased risk for osteoporosis and broken bones (1). Obesity, another common Received 01/28/14; Accepted 03/26/14. JZ and YJ authors contributed equally to this article. *Address correspondence to: Qifeng Ying, MD, Department of Dual Energy X-ray Absorptiometry (DEXA), Zhejiang Provincial People’s Hospital, Shangtang Rd 158, Hangzhou, Zhejiang 310014, China. E-mail: [email protected]
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2 ethnicities are inconsistent, most previous studies were based on Caucasian population, and the results cannot necessarily be extrapolated to Asian population. Chinese study has found that increased central body fat had an inverse association with BMD (25). Another Chinese study also demonstrated recently that FM is inversely associated with BMD beyond its weightbearing effect, whereas abdominal fat in women and limb fat in men seems to have the greatest effect on BMD (26). Moreover, Korean studies demonstrated that abdominal obesity was significantly associated with bone mineral content independent of total FM (27). Indian studies investigated in nonobese adults indicated that total percent body fat (%BF) and regional fat have positive association with BMD at all sites in men and women (28). These conflicting clinical and epidemiologic studies suggest a complex influence of FM and fat distribution on BMD. To the best of our knowledge, there are still few studies engaged directly in obese population to investigate the association between FM and its distribution and BMD. The above facts prompted us to evaluate the different associations of FM and central fat distribution with BMD directly in Chinese obese adults.
Subjects and Methods Subjects A total of 686 Chinese obese adults, whose body mass index (BMI) 28, aged from 20 to 39 yr were included in Zhejiang Provincial People’s Hospital from January 2009 to August 2013. Those with known metabolic bone diseases or those under any medications likely to influence BMD were excluded from the study. Twenty-three female participants were excluded because of hysterectomy. In the end, 347 females and 339 males were included in the analysis. Written informed consent was obtained, and the study was approved by the Ethics Committee of the Zhejiang Provincial People’s Hospital.
Demographic, Anthropometry, and Body Composition Measurement All subjects completed a demographic questionnaire. Participants who smoked at least 1 cigarette per day or drank alcohol once a week for at least 6 mo were defined as smokers or drinkers. None of the subjects were heavy drinkers. All the females were premenopausal. Physical measurements were obtained based on standardized protocol. Height was measured without shoes to the nearest 0.1 cm, weight with only light clothing to the nearest 0.1 kg (Detecto, Webb City, MO). All values were recorded as the mean of the 3 measures. BMI was calculated as body weight (kg) divided by height (m2). Dual-energy X-ray absorptiometry (DXA, software version 13.60.033, GE-lunar iDXA; GE Healthcare Lunar, Madison, WI) was used to measure LM, FM, %BF, android FM, gynoid FM, and total and regional BMD through whole-body scans. For the android region, the lower boundary is at pelvis cut. The upper boundary is above pelvis cut by 20% of the distance
Zhang et al. Table 1 Characteristics of the Subjects by Gender Variables
Female (n 5 347)
Age (y) 31.7 6.7 Height (cm) 163.8 5.1 Weight (kg) 94.3 15.5 BMI (kg/m2) 35.1 5.4 Body composition measures FM (kg) 40.8 13.4 Android FM (kg) 3.9 1.4 Gynoid FM (kg) 6.7 2.3 LM (kg) 48.1 6.9 %BF 44.5 8.8 AOI 0.6 0.1 Body mineral density measures (g/cm2) Total body 1.26 0.08 Head 2.41 0.28 Rib 0.77 0.24 Arm 0.97 0.10 Spine 1.14 0.14 Trunk 1.00 0.07 Pelvic 1.20 0.09 Leg 1.31 0.08 Drinkers (%) 21 Smokers (%) 4
Male (n 5 339) 30.5 172.5 111.4 37.3
7.2 6.1 22.5 6.2
40.6 5.1 6.0 68.1 36.9 0.9
12.2 2.1 2.1 12.8 5.2 0.2
1.33 2.10 0.74 1.10 1.06 0.99 1.24 1.45 27 42
0.10 0.22 0.05 0.13 0.14 0.08 0.13 0.10
Abbr: AOI, android-to-gynoid FM ratio; %BF, percent body fat; BMI, body mass index; FM, fat mass; LM, lean mass.
between pelvis and femoral neck cuts. Lateral boundaries are the arm cuts. The gynoid region is defined by the upper boundary below the pelvis cut line by 1.5 times the height of the android region. The height of the gynoid region is equal to 2 times the height of the android region. Lateral boundaries are the outer leg cuts. Body fat distribution was assessed by android-to-gynoid FM ratio (AOI). Regional BMD refers to the mean BMD in the regions of head, rib, arm, spine, trunk, hip, and leg. DXA was calibrated daily using a standard phantom provided by the manufacturer.
Statistical Analyses According to our existing knowledge, we specified the partial correlation between the tested predictors and the response, adjusting for any other predictors in the model, to be 0.2. Thus, we calculated that 325 patients per group would suffice to achieve the power of 0.95 with a 5 0.05. Basic characteristics of subjects were presented as mean standard deviation. Male and female obese population were analyzed separately to evaluate the associations of BMD with AOI, FM, and LM in multiple regression models. In model 1, we first explored the associations of FM and AOI with total body and regional BMD. We then added LM into model 1 to investigate the associations of LM with total body and regional BMD with the presence of FM and AOI in the model (model 2). In addition, the regression was rerun
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Table 2 b Coefficients of FM, AOI, and LM for Total Body and Regional BMD From Multiple Regression Analysis in Female and Male Obese Population Variables
Total
Head
Model 1 Female FM 1.07 0.69 AOI 3.18** 1.14 Male FM 2.82* 1.01 AOI 0.47 0.32 Model 2 (additional adjusted for LM) Female FM 1.21 0.32 AOI 2.83* 1.91 LM 0.61 1.57 Male FM 2.01 1.14 AOI 0.11 0.01 LM 1.07 0.57
Rib
Arm
Spine
0.11 0.05
2.24* 1.35
0.02 0.37
2.34* 1.57
0.3 0.89
0.57 0.48 0.84 2.1 1.24 0.2
Trunk
Pelvic
Leg
2.13* 0.48
3.56** 1.19
0.93 3.22**
1 2.54*
2.7* 2.29*
0.82 0.42
0.04 0.03
3.15** 2.4* 2.01
1.92 1.71 3.38**
2.47* 1.13 1.22
3.23** 0.52 0.65
0.64 2.35* 0.17
0.53 0.02 1.74
1.28 1.78 0.86
2.42* 1.83 0.22
0.76 0.3 0.12
0.5 0.58 1.14
Abbr: AOI, android-to-gynoid FM ratio; BMD, bone mineral density; FM, fat mass; LM, lean mass. Note: Covariates included in the regression model were age, height, smoking status, and drinking status. *p ! 0.05; **p ! 0.01.
replacing FM with %BF. Covariates, such as age, height, smoking, and drinking, were included in the regression models. SPSS (version 16.0 for Windows; SPSS Inc., Chicago, IL) was used for analysis. All statistical tests were 2 tailed, and p ! 0.05 was considered significant.
Results Descriptive Statistics The basic characteristics of the subjects are listed in Table 1. The mean age of the participants was 31.7 6.7 yr for females and 30.5 7.2 yr for males. The height was 163.8 5.1 cm for females and 172.5 6.1 cm for males. The weight was 94.3 15.5 kg for females and 111.4 22.5 kg for males. The percent of drinkers and smokers were 21 and 4 for females and 27 and 42 for males, respectively. And the BMI was 35.1 5.4 kg/m2 for females and 37.3 6.2 kg/m2 for males. The BMD and body composition at all the studied sites were shown in detail in Table 1.
Multiple Regression Analysis The results of multiple linear regression analysis are shown in Table 2. In model 1, FM of females was in a significantly positive association with BMD in arm, trunk, and pelvis ( p ! 0.05) and FM of males was in a significant positive association with BMD in total body, rib, and trunk ( p ! 0.05), whereas AOI of females was significantly inversely associated with BMD in total body and leg ( p ! 0.01). When additionally adjusted for LM (model 2), in females, the significant associations between both FM and AOI and
BMD were almost identical as in model 1 (all p ! 0.05) although LM had a significantly inverse association with BMD in spine ( p ! 0.01). In male obese population, FM had a significantly positive association with BMD in trunk ( p ! 0.05), whereas AOI and LM had no significant association with BMD. We further replaced FM with %BF and reran the regression models (Table 3). The results were almost identical. The %BF was significantly associated with BMD in arm, trunk, and pelvis in females ( p ! 0.01) but not in male obese population. AOI was significantly inversely associated with BMD in total body and leg in females ( p ! 0.01) but not in males. When additionally adjusting for LM, the associations between %BF and AOI and BMD were almost identical as model 1. LM was inversely associated with BMD in spine in female population ( p ! 0.01). In males, FM, AOI, and LM had no significant association with BMD. Covariates such as smoking and drinking had no significant associations with BMD in regression models. Covariates such as age and height were also considered to be taken into regression models. For females, age and height had no significant associations with BMD in all regression models. For males, age had no significant associations with BMD in all regression models except the model for BMD of the spine, in which age showed a positive association with BMD. Height had no significant associations with BMD in all regression models except 2 models for the total BMD and BMD of the arm. Height had a positive association with total BMD, but such an association was eliminated when there was additionally adjusted for LM. Height also showed a strong positive
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Zhang et al. Table 3 b Coefficients of %BF, AOI, and LM for Total Body and Regional BMD From Multiple Regression Analysis in Female and Male Obese Population
Variables
Total
Head
Model 1 Female %BF 1.47 0.36 AOI 3.54** 0.71 Male %BF 1.14 0.93 AOI 1.23 0.05 Model 2 (additional adjusted for LM) Female %BF 1.43 0.62 AOI 2.54* 1.78 LM 0.09 1.78 Male %BF 1.53 0.9 AOI 0.24 0.07 LM 2.32* 0.05
Rib
Arm
Spine
Trunk
Pelvic
Leg
0.57 0.21
3.04** 1.77
1.58 1.04
3.53** 0.62
4.4*** 0.95
0.56 3.67**
1.53 0.89
1.03 1.23
0.86 2.4*
1.66 1.44
0.63 0.2
0.95 0.1
0.65 0.34 0.71
3.04** 1.7 0.65
2.41* 1.29 3.23**
3.41** 0.56 0.19
4.24*** 1.45 1.09
0.47 2.19* 0.58
1.63 1.32 1.02
0.9 0.09 1.61
0.81 1.76 0.2
1.79 1.85 1.15
0.65 0.35 0.33
0.84 0.48 0.94
Abbr: AOI, android-to-gynoid FM ratio; BMD, bone mineral density, %BF, percent body fat; LM, lean mass. Note: Covariates included in the regression model were age, height, smoking status, and drinking status. *p ! 0.05; **p ! 0.01; ***p ! 0.001.
association with BMD of the arm (with the corresponding P values close to 0.001; data not shown).
Discussion The relative contribution of FM and fat distribution to BMD remain a contentious issue. Although a majority of studies have found a positive association between LM and BMD (9e16), few studies have shown that FM is an important determinant of BMD (17e20). The present study explored the associations of total body FM and fat distribution with BMD in Chinese obese population. Our study found that FM, LM, and central fat distribution have different associations with total and some regional BMD in Chinese obese population. The inverse association between central fat distribution and BMD of total body and leg in females yields information that positive control central adiposity deposition has harmful influence on osteoporosis. The observed positive association between FM and BMD of pelvis in females was partly in accordance with previous studies (17,25,29). In our present study, FM had a significant positive association with BMD of arm, trunk, and pelvis in female group and trunk in male group. Although the precise mechanism of the relationship between FM and BMD is not been fully understood, several potential theories have been proposed. It was partly in accordance with previous studies (25,30e32). Partly agreeing with previous studies, in this present study, there was a positive relationship between FM and BMD in arm, trunk, and pelvis. Although the precise mechanism of the relationship between FM and BMD in obese population is
not clear, several potential theories have been proposed. Fat tissue affects the skeleton not only through a weight-bearing effect but also through other noneweight-bearing effects, including the hormonal metabolism of adipocytes (33e35). Adipocytes are an important source of estrogen production, and estrogen is known to protect against bone mineral loss by inducing osteoclast apoptosis (17,36e38). There are still some studies that demonstrated that adipose tissue can release more than 20 adipocytokines into circulation, which indicates that adiposity influences BMD through alternative mechanisms such as adipocytes-dependent hormonal factors (39,40). Further possible link between FM and BMD is the common stromal cell origin of both osteoblasts and adipocytes (19). Although regional BMD increased with total body FM in the present study, it is noteworthy that BMD in total body and leg was negatively associated with central adiposity accumulation, indicated as AOI in female obese population. A recent study using computerized tomography to measure abdominal fat found a negative effect of visceral fat on femoral bone phenotypes (41). Another study indicated that abdominal obesity is negatively related to BMD in sedentary obese children and adolescents (32). Studies involving anthropometric (waist-to-hip ratio) (31) and DXA variables (abdominal fatness in kilograms) (30) identified similar relationship patterns in children and adults, respectively. The difference between fat having beneficial effects and visceral fat having adverse effects may be mediated by the presence of lower levels of leptin and higher levels of adiponectin and proinflammatory cytokines in visceral fat (41,42). In addition, increased visceral fat is associated with insulin resistance,
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Associations of Fat Mass and Fat Distribution With BMD which also may exert adverse effects on bone (43,44). Furthermore, vitamin D status is inversely related to BMI and to insulin resistance providing another mechanism by which visceral FM might contribute to bone loss (45,46). Finally, the higher serum parathyroid hormone levels reported in obese individuals could have adverse effects on cortical bone (47,48). The precise underlying mechanism should be addressed by further studies. The finding of this study confirmed previous studies partly that LM has a positive effect on BMD in spine (12,25,49). The relationship between FM and BMD remains almost identical in females, whereas this relationship was eliminated in males when additionally adjusting for LM. This indicates that bone strength is primarily determined by the dynamic loads from muscle force only in males. And it is determined by both dynamic and static loads, such as FM, in females. The major strength of our study is that all the measurements were obtained by DXA by 1 technician using 1 densitometer. Additionally, no participant had taken a drug known to interfere with the normal bone, fat tissue, or lean tissue metabolism. Finally, all the participants were Chinese obese population living in the same region. Limitations include the cross-sectional nature of the study, which is not possible to make any cause-and-effect inference on the relationship between FM, AOI, LM, and BMD. Additionally, we did not analyze the lifestyle of participants, such as physical exercise, calcium supplementation, or vitamin D levels, which might also have influenced results. Finally, all the participants were Chinese obese population, and our results may not be generalized to other ethnicities. In our present study, we calculated the correlation coefficients between FM and body weight and between LM and body weight to be 0.94 and 0.91, respectively. We have not included body weight as a controlling variable because to avoid its multicollinearity with FM and LM in the regression models. Although previous study indicated that FM was positively associated with BMD without weight adjustment but turned to be negatively associated with it after weight adjustment (4), adjustment for body weight was still not done in our present study to avoid its multicollinearity with FM and LM. Our methods and results were consistent with some studies published recently (25,50). In conclusion, the finding that there are different associations of FM and fat distribution with BMD has major public health implications. AOI had a negative association with BMD. Our finding reinforces the concept that the prevention of centralized fat deposition may have significant implications in decreasing osteoporosis in females.
Acknowledgments This study was support by grants from Application Research of Public Technology of Science and Technology Department of Zhejiang Province (2012C33069), Natural Scientific Research Foundation of Zhejiang Medical College (2011XZB01), and the Zhejiang Provincial Natural Science Foundation of China (Y14H060025).
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