J Bone Miner Metab DOI 10.1007/s00774-014-0613-7
ORIGINAL ARTICLE
Contributions of fat mass and fat distribution to hip bone strength in healthy postmenopausal Chinese women Hong Da Shao • Guan Wu Li • Yong Liu • Yu You Qiu • Jian Hua Yao • Guang Yu Tang
Received: 14 November 2013 / Accepted: 15 June 2014 Ó The Japanese Society for Bone and Mineral Research and Springer Japan 2014
Abstract The fat and bone connection is complicated, and the effect of adipose tissue on hip bone strength remains unclear. The aim of this study was to clarify the relative contribution of body fat accumulation and fat distribution to the determination of proximal femur strength in healthy postmenopausal Chinese women. This cross-sectional study enrolled 528 healthy postmenopausal women without medication history or known diseases. Total lean mass (LM), appendicular LM (ALM), percentage of lean mass (PLM), total fat mass (FM), appendicular FM (AFM), percentage of body fat (PBF), android and gynoid fat amount, android-to-gynoid fat ratio (AOI), bone mineral density (BMD), and proximal femur geometry were measured by dual energy X-ray absorptiometry. Hip structure analysis was used to compute some variables as geometric strength-related parameters by analyzing the images of the hip generated from DXA scans. Correlation analyses among anthropometrics, variables of body composition and bone mass, and geometric indices of hip bone strength were performed with stepwise linear regression analyses as well as Pearson’s correlation analysis. In univariate analysis, there were significantly inverse correlations between age, years since menopause (YSM), hip BMD, and hip geometric parameters. Bone data were positively related to
height, body weight, LM, ALM, FM, AFM, and PBF but negatively related to AOI and amount of android fat (all P \ 0.05). AFM and AOI were significantly related to most anthropometric parameters. AFM was positively associated with height, body weight, and BMI. AFM was negatively associated with age and YSM. AOI was negatively associated with height, body weight, and BMI. AOI positively associated with age and YSM. LM, ALM, and FM had a positive relationship with anthropometric parameters (P \ 0.05 for all). PLM had a negative relationship with those parameters. The correlation between LM, ALM, FM, PLM, ALM, age, and YSM was not significant. In multivariate linear regression analysis, the hip bone strength was observed to have a consistent and unchanged positive association with AFM and a negative association with AOI, whereas its association with other variables of body composition was not significant after adjusting for age, years since menopause, height, body weight, and BMI. AFM may be a positively protective effect for hip bone strength while AOI, rather than android fat, shows a strong negative association with hip bone strength after making an adjustment for confounders (age, YSM, height, body weight, and BMI) in healthy postmenopausal Chinese women. Rational weight control and AOI reduction during menopause may have vital clinical significance in decreasing postmenopausal osteoporosis.
H. D. Shao and G. W. Li contributed equally to this work. H. D. Shao Y. Liu Y. Y. Qiu J. H. Yao G. Y. Tang (&) Department of Radiology, Shanghai Tenth People’s Hospital, Tongji University School of Medicine, Shanghai 200072, China e-mail: [email protected] G. W. Li Department of Radiology, Yueyang Hospital, Shanghai University of Traditional Chinese Medicine, Shanghai 200437, China
Keywords Bone mineral density Hip structure analysis Lean mass Fat mass Fat distribution
Introduction Obesity and osteoporosis (OP), two representative disorders of body composition, have become increasingly
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critical public health concerns throughout the world with an increasing prevalence. Body weight contributes to approximately 30 % variance of bone mineral density (BMD), which makes it one of the best determinants of BMD [1]. Large amounts epidemiological data have shown that lower body weight or body mass index (BMI) is a higher risk factor for osteoporosis and low-energy fractures in both men and women [2], but detailed mechanisms have not been revealed. In absolute terms, heavier women do have stronger femurs, but their increment in bone strength is not commensurate with their higher body weight. In fact, femur strength is reduced in obese women [3]. In postmenopausal women, increased body weight is mainly caused by increased fat mass; however, little evidence is found to support that adipose tissue directly regulates bones at the macroscopic level [4]. When talking about the relative effect of soft tissue components on bone mass, inconsistent and conflicting results exist. Some studies suggested that total fat mass (FM) rather than total lean mass (LM) had the closest positive effect on BMD in postmenopausal women [5–7], while other evidences showed a detrimental influence of FM on bone mass after adjusting for confounders [4, 8, 9]. In addition, some other studies demonstrated that both FM and LM were equally important contributors to increased bone mass and lower fracture risk [10–13]. Moreover, most studies have consistently focused on total fat accumulation, which makes it unclear whether there is a greater phenotypic characterization to account for this association. It is suggested that android fat, apart from increasing the risk of chronic diseases such as metabolic syndrome and cardiovascular disease, is deleterious to bone [14]. Therefore, body fat distribution rather than its magnitude may play a more important role in bone health, particularly in the varying impacts of appendicular skeletal fat mass (AFM) and android fat to gynoid fat ratio (AOI). Thus, assessment of body fat distribution may be crucial in the clinical evaluation of its effect on bone strength. BMD is widely used to assess bone strength and predict fracture risk. Nevertheless, BMD alone only accounts for about 50–70 % of total bone strength [15, 16]. Bone strength is related to both bone mass and bone structural geometry. Recently, hip structure analysis (HSA) has drawn considerable clinical attention and some HSA indices have been reported as an independent hip fracture risk predictors without BMD [17, 18]. The relative impact of body composition on bone was mostly restricted to bone mineral content or BMD in previous studies. The fat and bone connection is complicated, and the effect of soft tissue components on hip bone strength remains unclear. Thus, the present study aimed to investigate the relevance of body fat accumulation and its distribution in hip geometric indices in
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healthy postmenopausal Chinese women. The purpose of the present study was to gain new findings.
Materials and methods Ethics statement All research procedures were approved by the appropriate institutional research ethics committee (Shanghai Tenth People’s Hospital of Tongji University, Shanghai, China) and were conducted in accordance with the Declaration of Helsinki. Written informed consent for each study was obtained independently for all participants. Study subjects The study was a cross-sectional, retrospective study. We analyzed data of 528 healthy postmenopausal Chinese women aged from 48.7 to 83.1-years-old (average 62.1years-old). All the participants were recruited between January 1, 2011 and September 30, 2012. Every participant’s medical history was collected, especially those regarding risk factors for osteoporosis. None of the subjects had current/previous use of therapies that affect bone, lean tissue, or fat tissue metabolism (such as hormone replacement therapy or oral contraceptive pill, diuretics, glucocorticoids, anticonvulsants, immunosuppressive medications, nonsteroidal anti-inflammatory drugs, asthma medications with corticosteroid, and oral anticoagulants). No patient had been diagnosed with self-reported malignancy, diseases of cardiovascular, pulmonary, metabolic, renal, hepatic, parathyroid gland diseases, rheumatoid arthritis, malabsorption syndrome, blood diseases or orthopedic medical conditions, as well as previous pathological fractures. None of the subjects were drinkers. The participants who took supplementary vitamin D and/or calcium were excluded from the study. Coexisting diabetes or hysterectomy was also considered as an exclusion criterion. Data collection Standardized interviews and self-reported questionnaires were used to obtain the following information: age (years) and years since menopause (YSM) for postmenopausal women. Regular menstruation was defined as the 25–35day interval between menstrual onsets. Menopause was designated if there was a complete natural cessation of menses for more than 12 months [15, 19]. Drinkers were those who drank an alcoholic beverage more than once a day during the past month.
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Anthropometrical parameters, including age, body weight, and standing height, were obtained. Body weight was measured to the nearest 0.1 kg on a portable electronic beam scale, wearing light clothing and no shoes. Height was measured to the nearest 0.5 cm using a stadiometer. Body weight and height were measured twice per time point, and the average of the two measures was used. BMI was calculated as body weight (in kg) divided by height (in m) squared. Body composition Bone mineral content (BMC, g) and BMD (g/cm2) were determined for each individual with dual-energy X-ray absorptiometry (DXA; Software Version enCORE 13.40.038; Lunar Prodigy, GE Healthcare, USA) at the left proximal femur (including femoral neck and total hip) and the whole body. DXA is quantified by body tissue absorption of photons that are emitted at two energy levels to resolve body weight into BMC and soft tissue mass(LM and FM). Total FM, total LM, android and gynoid fat amount, and the percentage of body fat (PBF, calculated as FM/(FM ? LM ? BMC) 9 100 %) were derived from the whole body scan. The regions of interest (ROI) for regional body composition were defined using the software provided by the manufacturer. Appendicular LM (ALM)
and AFM were generated as the sum of LM and FM in the arms and legs and were determined by the ROI program. For the android region, the lower boundary is pelvis cut. The upper boundary is above the pelvis cut by 20 % of the distance between the pelvis and the femoral neck cuts. Lateral boundaries are the lines for the arms when in normal position for a whole body scan. The gynoid region is defined by the upper boundary positioned below the pelvis cut line by 1.5 times the height of the android region. The lower boundary is positioned so that it is equal to two times the height of the android region (Fig. 1). The lateral boundaries are the outer leg lines of demarcation. AOI was calculated using the FM within the android and gynoid fat regions of interest [14, 20, 21]. Hip structure analysis (HSA) The HSA software derives geometry of the load supporting surface by employing a projection principle first described by Martin and Burr [22] (Fig. 2). This is a computational algorithm applied to 2-dimensional projected images of the hip generated from DXA scans following conventional bone mineral analysis. The program uses the distribution of mineral mass in a line of pixels across the bone axis. The femoral neck at its narrowest region was analyzed and used in this study. The HSA program computed the following variables used in this analysis [15, 17, 18]: 1.
Fig. 1 Regional body composition measurement by dual-energy X-ray absorptiometry (T trunk ROI, A android fat distribution ROI, U umbilicus ROI, G gynoid fat distribution ROI; A = T 9 0.2, U = A 9 1.5, G = A 9 2.0)
Cross-sectional moment of inertia (CSMI, mm4): derived from the integral of the bone mass weighted
Fig. 2 The proximal femur variables used to measure hip strength indices and hip axis length
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2.
3.
4.
5.
by the square of distance from the center of mass. The CSMI is relevant to bending in the plane of the DXA image; Cross-sectional area (CSA, mm2): defined as the surface area of bone tissue in the cross-section after excluding soft tissue (marrow) spaces and is proportional to conventional bone mineral content in the corresponding cross section. In mechanical terms, CSA is an indicator of resistance to loads directed along the bone axis; Section modulus (SM, mm3): SM is an index of resistance to bending and torsion, which is calculated as CSMI/dmax, where dmax is the maximum distance from either bone edge to the centroid of the profile; Mean cortical thickness (CT, mm): CT is calculated by modeling cortices of the narrow neck cross section as concentric circles and assuming that 60 % of the measured mass is cortical and 40 % is trabecular in the narrow neck region; Femoral strength index (SI, unitless): the ratio of the estimated compressive yield strength of the femoral neck to the expected compressive stress of a fall on the greater trochanter (adjustment for the patient’s age, height and body weight). Among HSA indices, SM and CSMI are highly correlated with each other. Accordingly, CSMI was omitted from the results [17].
The scanner was calibrated daily against the standard calibration block supplied by the manufacturer to control for possible baseline drift. All DXA scanning and HSA analyses were completed by the same technician. The precision of the DXA methods for BMC, BMD, and body composition is excellent. In vivo precision for body composition measurements using DXA was proven previously [23]. In our laboratory, the coefficient of variability (CV) of DXA measurements at the femoral neck, total hip, and whole body was \1 % for BMC and BMD, \1.3 % for body composition variables, and \2.8 % for structural parameters evaluated by duplicate measurements from the study cohort as described previously [15]. Statistical analysis All analyses were performed using SPSS (version 17.0, SPSS Inc., Chicago, IL). Descriptive statistics consisted of the mean ± standard deviation (SD). To examine the consistency of data with normal distribution, the Shapiro– Wilk test was applied. Correlation analyses among anthropometrics, variables of body composition and bone mass, and geometric indices of hip bone strength were performed with Pearson’s correlation analysis, or if the analyzed data did not meet the required assumptions, Spearman rank correlation would be performed. Stepwise
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Table 1 Anthropometric participants
and
densitometric
characteristics
Parameters
Mean ± SD
Age (years)
62.1 ± 7.3
48.7–83.1
YSM (years)
11.8 ± 7.5
0.3–34.2
Height (cm)
157.1 ± 6.8
of
Range
137.0–176.0
Body weight (kg)
59.4 ± 10.2
34.0–98.1
BMI (kg/m2)
23.8 ± 3.4
15.2–37.1
Lean mass (kg)
37.35 ± 5.20
25.53–58.01
ALM (kg) PLM (%)
15.59 ± 2.39 63.7 ± 5.5
9.75–24.75 51.0–81.1
Fat mass (kg)
19.97 ± 5.95
5.13–37.31
AFM (kg)
7.70 ± 2.49
2.60–14.54
Android fat (kg)
1.91 ± 0.67
0.33–4.24
Gynoid fat (kg)
3.48 ± 0.89
1.56–5.85
AOI
0.55 ± 0.12
0.20–0.98
PBF (%)
33.1 ± 5.6
14.4–45.9
FN_BMD (g/cm2)
0.648 ± 0.121
0.359–1.053
TH_BMD (g/cm2)
0.830 ± 0.134
0.468–1.172
CT (mm)
3.8 ± 1.6 2
2.1–7.8
CSA (mm )
110.2 ± 18.8
61.5–176.9
SM (mm3)
430.5 ± 101.5
167.2–942.1
SI
1.28 ± 0.30
0.72–2.35
YSM years since menopause, BMI body mass index, ALM appendicular skeletal lean mass, PLM percentage of lean mass, AFM appendicular fat mass, AOI android to gynoid fat ratio, PBF percentage of body fat, FN_BMD femoral neck BMD, TH_BMD total hip BMD, CT mean cortical thickness, CSA cross-sectional area, SM section modulus, SI femoral strength index
linear regression analyses were performed to evaluate the strength of the relationship between soft tissue components (treated as independent variables) and hip BMD, with hip bone strength indices (treated as outcome variables) to explore what are the important variables influencing the outcome. The variable would not be put into the stepwise linear regression analysis if it was not significantly related to hip bone strength indices in Pearson’s correlation analysis. Multivariable adjusted associations were assessed with inclusion of age, YSM, height, body weight, and BMI as fixed effects. If a variable is not in normal distribution, log transformation would be applied before entering the linear regression model. We investigated the associations of total LM, ALM, FM, AFM, AOI, and android with hip bone strength indices. The results of multivariate regressions are expressed as standardized regression coefficients. To assess multicollinearity of the regression model, we checked the tolerance and variance inflation factor (VIF). A tolerance value \0.3 and a VIF value [2 were regarded as indicating multicollinearity [21, 23]. A value of P \ 0.05 was considered statistically significant.
J Bone Miner Metab Table 2 Correlations between anthropometric parameters, soft tissue components, and bone mass with geometric indices of hip bone strength FN_BMD
TH_BMD
CT
Age (years)
–0.197a
–0.310b
–0.231a
–0.265b
YSM(years)
a
b
b
–0.285
b c
–0.204
c
–0.298
–0.311
a
CSA
c
SM
SI –0.349c
–0.151 a
–0.306b
c
0.554
0.306b
–0.189
Height (cm)
0.289
0.243
0.378
0.502
Body weight (kg)
0.441c
0.315b
0.482c
0.491c
0.517c
0.370c
c
a
c
0.274
b
b
0.257b
0.460
c
c
0.297b
0.448
c
c
0.301b
–0.249
c
a
–0.259b
2
BMI (kg/m )
0.354
Lean mass (kg)
c
0.351
b
ALM (kg)
0.324
PLM (%)
–0.346
Fat mass (kg) AFM (kg)
c
0.233
0.357
b
c
0.261
0.382
a
b
0.238 –0.224
0.340
a
–0.349
c
0.271 0.502 0.515 –0.237
0.409c
0.270b
0.469c
0.393c
0.408c
0.353b
c
c
c
0.494
c
c
0.416c
–0.237 0.046
–0.303 –0.120
c
c
–0.286c –0.022
0.432
c
0.448
a
0.415
Android fat (kg) Gynoid fat (kg)
–0.352 –0.180
AOI
–0.568c
–0.561c
–0.417c
–0.493c
–0.375c
–0.412c
b
a
b
a
a
0.237a
PBF (%)
0.298
–0.203 –0.193
0.421
c
0.164
0.331
0.200
–0.292 –0.032
0.204
YSM years since menopause, FN_BMD femoral neck BMD, TH_BMD total hip BMD, CT mean cortical thickness, CSA cross-sectional area, SM section modulus, SI femoral strength index, BMI body mass index, ALM appendicular skeletal lean mass, PLM percentage of lean mass, AFM appendicular fat mass, AOI android to gynoid fat ratio, PBF percentage of body fat a
P \ 0.05;
b
P \ 0.01; c P \ 0.001
Results The demographic, clinical characteristics of anthropometry, body composition parameters, bone mass (FN_BMD and TH_BMD) and the average values for hip geometric parameters (CT, CSA, SM, and SI) are presented in Table 1. The average age of participants was 62.1 years ranged from 48.7 to 83.1 years. All women were postmenopausal, with a mean year of 11.8 since menopause (range 0.3–34.2). The average FM in the entire sample was 19.94 kg, which is 33.1 % of body weight. Associations between anthropometry, body composition, and proximal femur strength In univariate analysis, greater LM or FM was associated with greater BMD (FN_BMD, TH_BMD) and hip geometric parameters (CT, CSA, SM, and SI) (P \ 0.001 for all). As can be noted, geometric indices of hip bone were positively related to absolute LM and ALM but negatively related to percentage of lean mass (PLM) because percentage LM and FM are the converse of one another. As shown in Table 2, there was a significantly negative correlation between advancing age, YSM, PLM, android fat, AOI and FN_BMD, TH_BMD, and hip structural geometric properties (CT, CSA, SM, and SI) (P \ 0.05 for all). Height, body weight, and BMI had a positive relationship with FN_BMD, TH_BMD, and hip geometric parameters (P \ 0.05 for all). Correlation of soft tissue components with anthropometric parameters are recorded
Table 3 Correlation of soft tissue components with anthropometric parameters
Lean mass (kg)
Age (years)
YSM (years)
Height (cm)
Body weight (kg)
0.052
0.063
0.616c
0.893c
0.704c
c
c
BMI
ALM (kg)
0.002
0.012
0.626
0.854
0.650c
PLM (%)
–0.031
0.006
–0.090
–0.552c
–0.619c
c
0.870c
b
0.231a
b
–0.290b
Fat mass (kg) AFM (kg)
0.047 –0.275
b b
AOI
0.311
PBF (%)
0.077
0.033 –0.285
b b
0.305
0.041
c
0.372 0.193
a
–0.208 0.068
0.908
0.298 –0.340
0.569
c
0.652c
YSM years since menopause, BMI body mass index, ALM appendicular skeletal lean mass, PLM percentage of lean mass, AFM appendicular fat mass, AOI android to gynoid fat ratio, PBF percentage of body fat a
P \ 0.05;
b
P \ 0.01; c P \ 0.001
in Table 3 in detail. As is shown, AFM and AOI were significantly related with most anthropometric parameters. AFM were positively associated with body weight, BMI, and negatively associated with age and YSM. AOI was negatively associated with height, body weight, and BMI and positively associated with age and YSM. LM, ALM, and FM had a positive relationship with height, body weight, and BMI (P \ 0.05 for all). PLM had a negative relationship with body weight, and BMI. The correlation between LM, ALM, FM, PLM, ALM, age, and YSM was
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not significant. LM was significantly associated with FM (r = 0.623; P \ 0.001) and AFM (r = 0.286; P = 0.004).
total FM were replaced by PBF, similar associations were also observed.
Multivariate analysis Discussion The variable gynoid fat has not been put into the stepwise linear regression analysis because it was not significantly correlated with hip strength parameters in Pearson’s correlation analysis in order to avoid statistical error. The results of the regression analyses are shown in Table 4. No multicollinearity effect was identified by a variance inflation factor between 1.0 and 1.435 except for total FM and PBF. Now only AFM was positively associated with FN_BMD, TH_BMD, and geometric indices of hip bone strength (CT, CSA, SM, and SI), whereas AOI was negatively associated with those geometric indices after controlling for age, YSM, height, body weight, BMI, LM, ALM, and FM. Android fat were not significantly associated with the hip bone strength indices in the regression model. Increasing age was associated with decreases in FN_BMD (Sb -0.218, P = 0.017), TH_BMD (Sb -0.324, P = 0.001), and SI (Sb -0.367, P \ 0.001). YSM was related to decreases in CT (Sb -0.327, P \ 0.001) and CSA (Sb -0.181, P = 0.047). Due to high collinearity between PBF and total FM (the variance inflation factor was 6.297), the effects of total FM and PBF on the parameters of proximal femur strength were examined separately in the regression models. When
Recent studies have shown that obesity and OP share several common genetic and environmental factors. Similarities between these two complex diseases have been identified. This suggests some type of pathophysiologic linkage. Although both obesity and OP generally become more prevalent with advancing age, complex relationships exist between these two conditions. The effect of fat mass on bone mass remains controversial. It may have beneficial effects on bone strength. Contrasting studies, however, suggest that excessive fat mass may not protect against OP fracture [4, 8, 24]. Differences in experimental design, sample structure, and even the selection of covariates may account for some of these inconsistent or contradictory results. Since there are few studies reporting the effect of body fat accumulation and its distribution on bone health and their relationship with hip geometry parameters, it is worthwhile to identify the specific roles that fat mass plays in hip bone strength regulation. In this present study, we evaluated the relationships between anthropometric parameters, body fat accumulation, fat distribution, and hip geometric strength indices assessed by HSA, using several comprehensive statistical
Table 4 Determinants of bone mass and hip bone strength indices: results of multiple linear regression analysis in postmenopausal women FN_BMD (g/cm2) Sb LM (kg)
t
TH_BMD (g/cm2) P values
Sb
t
CT (mm) P values
Sb
t
P values
0.68
–0.397
0.692
0.087
1.028
0.307
–0.2
–1.085
0.281
ALM (kg)
–0.028
–0.248
0.805
–0.037
–0.294
0.769
–0.038
–0.464
–0.644
FM (kg) AFM (kg)
–0.049 0.224
–0.267 2.634
0.79 0.010
0.055 0.264
0.633 3.023
0.528 0.003
0.174 0.236
0.874 2.588
0.384 0.011
AOI
–0.371
–4.284
\0.001
–0.454
–5.197
\0.001
–0.206
–2.229
0.028
Android fat (kg)
–0.052
–0.279
0.692
–0.047
–2.034
0.307
–0.164
–0.759
0.281
2
3
CSA (mm ) Sb LM (kg) ALM (kg) FM (kg)
SM (mm ) t
P values
Sb
SI t
P values
Sb
t
P values 0.545
0.117
1.231
0.221
0.095
0.879
0.382
–0.114
–0.607
–0.003
–0.688
0.493
0.034
–0.265
0.792
–0.005
–0.685
0.485
0.098
1.185
0.239
0.086
0.979
0.33
0.114
0.562
0.575
4.048
\0.001
0.303
3.774
\0.001
0.316
2.141
0.035
AOI
–0.291
–3.616
\0.001
–0.243
–3.19
0.002
–0.16
–1.644
0.044
Android fat (kg)
–0.072
–1.021
0.221
–0.066
–0.758
0.382
–0.103
–0.548
0.545
AFM (kg)
0.203
In this model, age, YSM, height, body weight, FM, AFM, LM, ALM, android and gynoid fat, and AOI were selected as independent variables LM lean mass, ALM appendicular skeletal lean mass, FM fat mass, AFM appendicular fat mass, AOI android to gynoid fat ratio, FN_BMD femoral neck BMD, TH_BMD total hip BMD, CT mean cortical thickness, CSA cross-sectional area, SM section modulus, SI femoral strength index, Sb standardized coefficients b
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methods. First, Pearson’s correlation analysis was used to pick out the key factors in all of the variables that correlated to the hip strength parameters. Then, stepwise linear regression analyses were performed to explore what are the important variables influencing the outcome. Finally, multivariable adjusted associations were conducted to evaluate the effects of the important variables after control for confounded parameters in Pearson’s correlation analyses. It may be more efficient than those in the other studies. As expected, we found, firstly, anthropometric parameters were strongly related to hip BMD, CT, CSA, SM, and SI in Pearson’s correlation analyses. The parameters such as advancing age, YSM, PLM, android fat, and AOI are negatively related with those hip bone strength indices. Conversely, height, body weight, and BMI are positively related with those indices. However, these potentially effects became complicated when the correlation of soft tissue components with anthropometric parameters was further analyzed. Some soft tissue components were positively associated with height, body weight, BMI or age, and YSM. Some were negatively associated or were even not associated with those anthropometric parameters. Thus, it can be inferred that anthropometric parameters, body fat accumulation and fat distribution may interact with each other in ways that may be helpful or harmful to hip geometric strength. It suggested that it is necessary to adjust anthropometric parameters in order to explore which soft tissue component exerts a stronger effect on HSA indices. Therefore, we carried out multivariable linear regression analyses with soft tissue components as independent variables, whereas hip BMD and hip bone strength indices were outcome variables. The anthropometric parameters such as age, YSM, height, body weight, and BMI were included as fixed effects to assess multivariable adjusted associations. Using this approach, we demonstrated that only AFM and AOI were more closely associated with proximal femur strength, but not the overall LM, ALM, FM, or android fat. This finding challenges the view that FM played an important detrimental role on BMD [4, 8, 9], as well as the view that LM positively affected bone strength [25–27]. These differences may contribute to some of the above studies not making an adjustment for confounders (age, YSM and height, body weight, etc.) or body weight was not a included covariate in the analysis [12, 21, 27]. It has been demonstrated that, in absolute terms, femur bone mass and geometric strength are greater in postmenopausal overweight women [3].The higher mechanical loading can stimulate osteocytes and dendritic resident cells, then trigger anabolic responses [8]. Although the bearing of weight may partly explain the effect of FM on bone, FM approximately accounts for only 16–25 % of total body weight in men and women of normal weight
[28]. Therefore, gravitational forces associated with weight may be insufficient to explain the impact of FM on bone strength. The influence of AFM on bone strength is likely to be a result of the effect of not bearing weight. The metabolically heterogenicity between subcutaneous fat and visceral fat can be interpreted as the differences in both adipokines production and steroid hormone metabolism regulation. Our data combined with previous evidence [23] suggested that subcutaneous fat tissue (i.e., AFM) was beneficial to bone structure and strength, while visceral adiposity (android fat deposition) was deleterious. Several mechanisms may be helpful to explain the association between the effect of not bearing weight of AFM and the bone strength. First, it has been suggested that subcutaneous preadipocytes have higher aromatase activity in comparison to visceral adiposity [29]. In postmenopausal women, adipocytes are the major sources of estrogen, and estrogen inhibits bone resorption by inducing osteoclasts apoptosis [30]. Furthermore, being overweight or obesity is associated with insulin resistance, which may contribute to androgen and estrogen overproduction and reduce sex hormone binding proteins. As a result, the elevated sex hormone leads to increased bone mass and the activity of osteoblast increased due to reduced osteoclast activity. Secondly, regional fat depot can release adipocytokines (such as leptin, adiponectin and visfatin), which appear to influence bone metabolism, through alternative mechanisms such as adipocyte-dependent hormonal factors [24, 31, 32]. Thus, subcutaneous adipose tissue is more beneficial than visceral fat in bone health after menopause. Marques et al. [21] demonstrated that AFM had a strong and independent association with femoral neck BMD [21]. In our regression model, we used both AOI and android fat as the central adiposity indicator rather than the waistto-hip ratio or the waist-to-thigh ratio to assess their associations with hip strength indices. We found, although hip geometric indices increased with total FM, the relation between them is not significant after adjusting for some confounders. On the contrary, hip bone strength was negatively associated with increased AOI rather than andriod fat amount. Namely, an inverse association between body fat distribution and bone strength was explained by higher AOI but not by higher android fat accumulation independent of body weight. Our results are consistent with most of the available data supporting that DXA-based AOI is deleterious for bone health [12, 14]. There is no clear answer for this, but several explanations can be postulated. First, there is an increasing trend in android fat but a relative reduction in gynoid fat in postmenopausal women. Android fat distribution was reported to be partly in relation to advancing age and partly due to estrogen deficiency [33]. Sumino et al. [34] also found that high AOI can be counteracted at least in part by oral hormonal replacement
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therapy (HRT). So it can be indicated that unnatural women who look like men (high AOI) may be subject to hip fracture possibly due to more severe lack of estrogen. Second, AOI, which is a stronger predictor of metabolic risk factors, is significantly greater in postmenopausal women than in premenopausal women [35]. It was also found that in men and postmenopausal women that there was no significant correlation between fat and bone parameters after adjusting for age and body weight [36]. The possible reasons for those different conclusions from these kinds of studies are as follows: the methods for measuring bone density (the expression of bone mass as BMC or BMD), the sample size, the population structure, and environmental and genetic factors in all these studies are not all the same, and, most importantly, the statistical methods are different. Additionally, it is not easy to interpret those different results from different studies, considering the gender-specific and menopause statusspecific study population, the incomplete adjustment for confounders (especially body weight). Our data inferred the importance of regular physical activity, which can not only decrease the central obesity and slow down sarcopenia with aging, but also increase mechanical loading of the skeleton. Nevertheless, several limitations of this study must be acknowledged. First, the study design was cross-sectional in nature, and it is not possible to establish any cause-andeffect inference on the relationship between body composition and hip geometric indices. Second, several lines of evidence have suggested that the effects of fat mass on bone mass may be mediated by hormonal factors with the principal candidates being serum estrogen, insulin, and leptin levels [37, 38]. We did not evaluate the role of the candidate hormones in mediating the relationship between fat mass and bone strength. Despite careful adjustment for potential confounders, the impact of estrogen and gonadotropin on bone mass and bone geometry indices cannot be removed because of the range wide of YSM in the subjects. Third, we did not evaluate the potential effect of physical activity on bone mass and HSA indices as it was a retrospective study, which should be overcome in future studies. In summary, current study has demonstrated that body fat distribution is associated with proximal femur strength after making an adjustment for confounders (age, YSM, height, body weight, and BMI) in healthy postmenopausal Chinese women, that is, AFM (subcutaneous fat) appears to be a positively protective effect on hip BMD and hip geometric strength while AOI shows a strong negative association with them. These data highlight that the pattern of fat distribution is more important for proximal femur strength than the overall FM or the LM alone. Therefore, besides body weight, the fat distribution should be taken
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into account when evaluating the effect of factors on bone strength. Rational weight controlling and reducing AOI during menopause may have vital clinical significance in decreasing postmenopausal OP. Acknowledgments This study was supported by grants from the National Natural Science Foundation of China (81071134, 81371517) and from the 5810 Foundation of Shanghai Tenth People’s Hospital (11RD104). Conflict of Interest Hong-Da Shao, MD, Guan-Wu Li, MD, Yong Liu, MD, Yu-You Qiu, MD, Guang-Yu Tang declared that they have no conflicts of interest.
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J Bone Miner Metab 15. Li GW, Chang SX, Xu Z et al (2013) Prediction of hip osteoporotic fractures from composite indices of femoral neck strength. Skeletal Radiol 42:195–201 16. Li GW, Tang GY, Liu Y et al (2012) MR spectroscopy and micro-CT in evaluation of osteoporosis model in rabbits: comparison with histopathology. Eur Radiol 22:923–929 17. Dongmei N, Iki M, Tamaki J et al (2012) Association between weight changes and changes in hip geometric indices in the Japanese female population during 10-year follow-up: Japanese Population-based Osteoporosis (JPOS) Cohort Study. Osteoporos Int 23:1581–1591 18. Deere K, Sayers A, Rittweger J et al (2012) Habitual levels of high, but not moderate or low, impact activity are positively related to hip BMD and geometry: results from a populationbased study of adolescents. J Bone Miner Metab 27:1887–1895 19. Khalil N, Sutton-Tyrrell K, Strotmeyer ES et al (2011) Menopausal bone changes and incident fractures in diabetic women: a cohort study. Osteoporos Int 22:1367–1376 20. Kang SM, Yoon JW, Ahn HY et al (2011) Android fat depot is more closely associated with metabolic syndrome than abdominal visceral fat in elderly people. PLoS ONE 6:e27694 21. Marques EA, Moreira P, Wanderley F et al (2012) Appendicular fat mass is positively associated with femoral neck bone mineral density in older women. Menopause 19:311–318 22. Faulkner KG, Wacker WK, Barden HS et al (2006) Femur strength index predicts hip fracture independent of bone density and hip axis length. Osteoporos Int 17:593–599 23. Choi HS, Kim KJ, Kim KM et al (2010) Relationship between visceral adiposity and bone mineral density in Korean adults. Calcif Tissue Int 87:218–225 24. Reid IR (2010) Fat and bone. Arch Biochem Biophys 503:20–27 25. Sahin G, Polat G, Baethis¸ S et al (2003) Body composition, bone mineral density, and circulating leptin levels in postmenopausal Turkish women. Rheumatol Int 23:87–91 26. Genaro PS, Pereira GA, Pinheiro MM et al (2010) Influence of body composition on bone mass in postmenopausal osteoporotic women. Arch Gerontol Geriatr 51:295–298 27. Park JH, Song YM, Sung J et al (2012) The association between fat and lean mass and bone mineral density: the healthy twin study. Bone 50:1006–1011
28. Abernathy RP, Black DR (1996) Healthy body weights: an alternative perspective. Am J Clin Nutr 63:448S–451S 29. McTernan PG, Anderson LA, Anwar AJ et al (2002) Glucocorticoid regulation of p450 aromatase activity in human adipose tissue: gender and site differences. J Clin Endocrinol Metab 87:1327–1336 30. Faloni AP, Sasso-Cerri E, Rocha FR et al (2012) Structural and functional changes in the alveolar bone osteoclasts of estrogentreated rats. J Anat 220:77–85 31. Williams GA, Wang Y, Callon KE et al (2009) In vitro and in vivo effects of adiponectin on bone. Endocrinology 150:3603–3610 32. Tu Q, Zhang J, Dong LQ et al (2011) Adiponectin inhibits osteoclastogenesis and bone resorption via APPL1-mediated suppression of Akt1. J Biol Chem 286:12542–12553 33. Gaspard UJ, Gottal JM, van den Bruˆle FA (1995) Postmenopausal changes of lipid and glucose metabolism: a review of their main aspects. Maturitas 21:171–178 34. Sumino H, Ichikawa S, Yoshida A et al (2003) Effects of hormone replacement therapy on weight, abdominal fat distribution, and lipid levels in Japanese postmenopausal women. Int J Obes 27:1044–1051 35. Lee K, Lee S, Kim YJ et al (2008) Waist circumference, dualenergy X-ray absortiometrically measured abdominal adiposity, and computed tomographically derived intra-abdominal fat area on detecting metabolic risk factors in obese women. Nutrition 24:625–631 36. Yoo HJ, Park MS, Yang SJ et al (2012) The differential relationship between fat mass and bone mineral density by gender and menopausal status. J Bone Miner Metab 30:47–53 37. Thomas T, Burguera B, Melton LJ 3rd et al (2001) Role of serum leptin, insulin, and estrogen levels as potential mediators of the relationship between fat mass and bone mineral density in men versus women. Bone 29:114–120 38. Van Pelt RE, Gozansky WS, Wolfe P et al (2014) Estrogen or raloxifene during postmenopausal weight loss: adiposity and cardiometabolic outcomes. Obesity 22:1024–1031
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ORIGINAL ARTICLE
Contributions of fat mass and fat distribution to hip bone strength in healthy postmenopausal Chinese women Hong Da Shao • Guan Wu Li • Yong Liu • Yu You Qiu • Jian Hua Yao • Guang Yu Tang
Received: 14 November 2013 / Accepted: 15 June 2014 Ó The Japanese Society for Bone and Mineral Research and Springer Japan 2014
Abstract The fat and bone connection is complicated, and the effect of adipose tissue on hip bone strength remains unclear. The aim of this study was to clarify the relative contribution of body fat accumulation and fat distribution to the determination of proximal femur strength in healthy postmenopausal Chinese women. This cross-sectional study enrolled 528 healthy postmenopausal women without medication history or known diseases. Total lean mass (LM), appendicular LM (ALM), percentage of lean mass (PLM), total fat mass (FM), appendicular FM (AFM), percentage of body fat (PBF), android and gynoid fat amount, android-to-gynoid fat ratio (AOI), bone mineral density (BMD), and proximal femur geometry were measured by dual energy X-ray absorptiometry. Hip structure analysis was used to compute some variables as geometric strength-related parameters by analyzing the images of the hip generated from DXA scans. Correlation analyses among anthropometrics, variables of body composition and bone mass, and geometric indices of hip bone strength were performed with stepwise linear regression analyses as well as Pearson’s correlation analysis. In univariate analysis, there were significantly inverse correlations between age, years since menopause (YSM), hip BMD, and hip geometric parameters. Bone data were positively related to
height, body weight, LM, ALM, FM, AFM, and PBF but negatively related to AOI and amount of android fat (all P \ 0.05). AFM and AOI were significantly related to most anthropometric parameters. AFM was positively associated with height, body weight, and BMI. AFM was negatively associated with age and YSM. AOI was negatively associated with height, body weight, and BMI. AOI positively associated with age and YSM. LM, ALM, and FM had a positive relationship with anthropometric parameters (P \ 0.05 for all). PLM had a negative relationship with those parameters. The correlation between LM, ALM, FM, PLM, ALM, age, and YSM was not significant. In multivariate linear regression analysis, the hip bone strength was observed to have a consistent and unchanged positive association with AFM and a negative association with AOI, whereas its association with other variables of body composition was not significant after adjusting for age, years since menopause, height, body weight, and BMI. AFM may be a positively protective effect for hip bone strength while AOI, rather than android fat, shows a strong negative association with hip bone strength after making an adjustment for confounders (age, YSM, height, body weight, and BMI) in healthy postmenopausal Chinese women. Rational weight control and AOI reduction during menopause may have vital clinical significance in decreasing postmenopausal osteoporosis.
H. D. Shao and G. W. Li contributed equally to this work. H. D. Shao Y. Liu Y. Y. Qiu J. H. Yao G. Y. Tang (&) Department of Radiology, Shanghai Tenth People’s Hospital, Tongji University School of Medicine, Shanghai 200072, China e-mail: [email protected] G. W. Li Department of Radiology, Yueyang Hospital, Shanghai University of Traditional Chinese Medicine, Shanghai 200437, China
Keywords Bone mineral density Hip structure analysis Lean mass Fat mass Fat distribution
Introduction Obesity and osteoporosis (OP), two representative disorders of body composition, have become increasingly
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critical public health concerns throughout the world with an increasing prevalence. Body weight contributes to approximately 30 % variance of bone mineral density (BMD), which makes it one of the best determinants of BMD [1]. Large amounts epidemiological data have shown that lower body weight or body mass index (BMI) is a higher risk factor for osteoporosis and low-energy fractures in both men and women [2], but detailed mechanisms have not been revealed. In absolute terms, heavier women do have stronger femurs, but their increment in bone strength is not commensurate with their higher body weight. In fact, femur strength is reduced in obese women [3]. In postmenopausal women, increased body weight is mainly caused by increased fat mass; however, little evidence is found to support that adipose tissue directly regulates bones at the macroscopic level [4]. When talking about the relative effect of soft tissue components on bone mass, inconsistent and conflicting results exist. Some studies suggested that total fat mass (FM) rather than total lean mass (LM) had the closest positive effect on BMD in postmenopausal women [5–7], while other evidences showed a detrimental influence of FM on bone mass after adjusting for confounders [4, 8, 9]. In addition, some other studies demonstrated that both FM and LM were equally important contributors to increased bone mass and lower fracture risk [10–13]. Moreover, most studies have consistently focused on total fat accumulation, which makes it unclear whether there is a greater phenotypic characterization to account for this association. It is suggested that android fat, apart from increasing the risk of chronic diseases such as metabolic syndrome and cardiovascular disease, is deleterious to bone [14]. Therefore, body fat distribution rather than its magnitude may play a more important role in bone health, particularly in the varying impacts of appendicular skeletal fat mass (AFM) and android fat to gynoid fat ratio (AOI). Thus, assessment of body fat distribution may be crucial in the clinical evaluation of its effect on bone strength. BMD is widely used to assess bone strength and predict fracture risk. Nevertheless, BMD alone only accounts for about 50–70 % of total bone strength [15, 16]. Bone strength is related to both bone mass and bone structural geometry. Recently, hip structure analysis (HSA) has drawn considerable clinical attention and some HSA indices have been reported as an independent hip fracture risk predictors without BMD [17, 18]. The relative impact of body composition on bone was mostly restricted to bone mineral content or BMD in previous studies. The fat and bone connection is complicated, and the effect of soft tissue components on hip bone strength remains unclear. Thus, the present study aimed to investigate the relevance of body fat accumulation and its distribution in hip geometric indices in
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healthy postmenopausal Chinese women. The purpose of the present study was to gain new findings.
Materials and methods Ethics statement All research procedures were approved by the appropriate institutional research ethics committee (Shanghai Tenth People’s Hospital of Tongji University, Shanghai, China) and were conducted in accordance with the Declaration of Helsinki. Written informed consent for each study was obtained independently for all participants. Study subjects The study was a cross-sectional, retrospective study. We analyzed data of 528 healthy postmenopausal Chinese women aged from 48.7 to 83.1-years-old (average 62.1years-old). All the participants were recruited between January 1, 2011 and September 30, 2012. Every participant’s medical history was collected, especially those regarding risk factors for osteoporosis. None of the subjects had current/previous use of therapies that affect bone, lean tissue, or fat tissue metabolism (such as hormone replacement therapy or oral contraceptive pill, diuretics, glucocorticoids, anticonvulsants, immunosuppressive medications, nonsteroidal anti-inflammatory drugs, asthma medications with corticosteroid, and oral anticoagulants). No patient had been diagnosed with self-reported malignancy, diseases of cardiovascular, pulmonary, metabolic, renal, hepatic, parathyroid gland diseases, rheumatoid arthritis, malabsorption syndrome, blood diseases or orthopedic medical conditions, as well as previous pathological fractures. None of the subjects were drinkers. The participants who took supplementary vitamin D and/or calcium were excluded from the study. Coexisting diabetes or hysterectomy was also considered as an exclusion criterion. Data collection Standardized interviews and self-reported questionnaires were used to obtain the following information: age (years) and years since menopause (YSM) for postmenopausal women. Regular menstruation was defined as the 25–35day interval between menstrual onsets. Menopause was designated if there was a complete natural cessation of menses for more than 12 months [15, 19]. Drinkers were those who drank an alcoholic beverage more than once a day during the past month.
J Bone Miner Metab
Anthropometrical parameters, including age, body weight, and standing height, were obtained. Body weight was measured to the nearest 0.1 kg on a portable electronic beam scale, wearing light clothing and no shoes. Height was measured to the nearest 0.5 cm using a stadiometer. Body weight and height were measured twice per time point, and the average of the two measures was used. BMI was calculated as body weight (in kg) divided by height (in m) squared. Body composition Bone mineral content (BMC, g) and BMD (g/cm2) were determined for each individual with dual-energy X-ray absorptiometry (DXA; Software Version enCORE 13.40.038; Lunar Prodigy, GE Healthcare, USA) at the left proximal femur (including femoral neck and total hip) and the whole body. DXA is quantified by body tissue absorption of photons that are emitted at two energy levels to resolve body weight into BMC and soft tissue mass(LM and FM). Total FM, total LM, android and gynoid fat amount, and the percentage of body fat (PBF, calculated as FM/(FM ? LM ? BMC) 9 100 %) were derived from the whole body scan. The regions of interest (ROI) for regional body composition were defined using the software provided by the manufacturer. Appendicular LM (ALM)
and AFM were generated as the sum of LM and FM in the arms and legs and were determined by the ROI program. For the android region, the lower boundary is pelvis cut. The upper boundary is above the pelvis cut by 20 % of the distance between the pelvis and the femoral neck cuts. Lateral boundaries are the lines for the arms when in normal position for a whole body scan. The gynoid region is defined by the upper boundary positioned below the pelvis cut line by 1.5 times the height of the android region. The lower boundary is positioned so that it is equal to two times the height of the android region (Fig. 1). The lateral boundaries are the outer leg lines of demarcation. AOI was calculated using the FM within the android and gynoid fat regions of interest [14, 20, 21]. Hip structure analysis (HSA) The HSA software derives geometry of the load supporting surface by employing a projection principle first described by Martin and Burr [22] (Fig. 2). This is a computational algorithm applied to 2-dimensional projected images of the hip generated from DXA scans following conventional bone mineral analysis. The program uses the distribution of mineral mass in a line of pixels across the bone axis. The femoral neck at its narrowest region was analyzed and used in this study. The HSA program computed the following variables used in this analysis [15, 17, 18]: 1.
Fig. 1 Regional body composition measurement by dual-energy X-ray absorptiometry (T trunk ROI, A android fat distribution ROI, U umbilicus ROI, G gynoid fat distribution ROI; A = T 9 0.2, U = A 9 1.5, G = A 9 2.0)
Cross-sectional moment of inertia (CSMI, mm4): derived from the integral of the bone mass weighted
Fig. 2 The proximal femur variables used to measure hip strength indices and hip axis length
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2.
3.
4.
5.
by the square of distance from the center of mass. The CSMI is relevant to bending in the plane of the DXA image; Cross-sectional area (CSA, mm2): defined as the surface area of bone tissue in the cross-section after excluding soft tissue (marrow) spaces and is proportional to conventional bone mineral content in the corresponding cross section. In mechanical terms, CSA is an indicator of resistance to loads directed along the bone axis; Section modulus (SM, mm3): SM is an index of resistance to bending and torsion, which is calculated as CSMI/dmax, where dmax is the maximum distance from either bone edge to the centroid of the profile; Mean cortical thickness (CT, mm): CT is calculated by modeling cortices of the narrow neck cross section as concentric circles and assuming that 60 % of the measured mass is cortical and 40 % is trabecular in the narrow neck region; Femoral strength index (SI, unitless): the ratio of the estimated compressive yield strength of the femoral neck to the expected compressive stress of a fall on the greater trochanter (adjustment for the patient’s age, height and body weight). Among HSA indices, SM and CSMI are highly correlated with each other. Accordingly, CSMI was omitted from the results [17].
The scanner was calibrated daily against the standard calibration block supplied by the manufacturer to control for possible baseline drift. All DXA scanning and HSA analyses were completed by the same technician. The precision of the DXA methods for BMC, BMD, and body composition is excellent. In vivo precision for body composition measurements using DXA was proven previously [23]. In our laboratory, the coefficient of variability (CV) of DXA measurements at the femoral neck, total hip, and whole body was \1 % for BMC and BMD, \1.3 % for body composition variables, and \2.8 % for structural parameters evaluated by duplicate measurements from the study cohort as described previously [15]. Statistical analysis All analyses were performed using SPSS (version 17.0, SPSS Inc., Chicago, IL). Descriptive statistics consisted of the mean ± standard deviation (SD). To examine the consistency of data with normal distribution, the Shapiro– Wilk test was applied. Correlation analyses among anthropometrics, variables of body composition and bone mass, and geometric indices of hip bone strength were performed with Pearson’s correlation analysis, or if the analyzed data did not meet the required assumptions, Spearman rank correlation would be performed. Stepwise
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Table 1 Anthropometric participants
and
densitometric
characteristics
Parameters
Mean ± SD
Age (years)
62.1 ± 7.3
48.7–83.1
YSM (years)
11.8 ± 7.5
0.3–34.2
Height (cm)
157.1 ± 6.8
of
Range
137.0–176.0
Body weight (kg)
59.4 ± 10.2
34.0–98.1
BMI (kg/m2)
23.8 ± 3.4
15.2–37.1
Lean mass (kg)
37.35 ± 5.20
25.53–58.01
ALM (kg) PLM (%)
15.59 ± 2.39 63.7 ± 5.5
9.75–24.75 51.0–81.1
Fat mass (kg)
19.97 ± 5.95
5.13–37.31
AFM (kg)
7.70 ± 2.49
2.60–14.54
Android fat (kg)
1.91 ± 0.67
0.33–4.24
Gynoid fat (kg)
3.48 ± 0.89
1.56–5.85
AOI
0.55 ± 0.12
0.20–0.98
PBF (%)
33.1 ± 5.6
14.4–45.9
FN_BMD (g/cm2)
0.648 ± 0.121
0.359–1.053
TH_BMD (g/cm2)
0.830 ± 0.134
0.468–1.172
CT (mm)
3.8 ± 1.6 2
2.1–7.8
CSA (mm )
110.2 ± 18.8
61.5–176.9
SM (mm3)
430.5 ± 101.5
167.2–942.1
SI
1.28 ± 0.30
0.72–2.35
YSM years since menopause, BMI body mass index, ALM appendicular skeletal lean mass, PLM percentage of lean mass, AFM appendicular fat mass, AOI android to gynoid fat ratio, PBF percentage of body fat, FN_BMD femoral neck BMD, TH_BMD total hip BMD, CT mean cortical thickness, CSA cross-sectional area, SM section modulus, SI femoral strength index
linear regression analyses were performed to evaluate the strength of the relationship between soft tissue components (treated as independent variables) and hip BMD, with hip bone strength indices (treated as outcome variables) to explore what are the important variables influencing the outcome. The variable would not be put into the stepwise linear regression analysis if it was not significantly related to hip bone strength indices in Pearson’s correlation analysis. Multivariable adjusted associations were assessed with inclusion of age, YSM, height, body weight, and BMI as fixed effects. If a variable is not in normal distribution, log transformation would be applied before entering the linear regression model. We investigated the associations of total LM, ALM, FM, AFM, AOI, and android with hip bone strength indices. The results of multivariate regressions are expressed as standardized regression coefficients. To assess multicollinearity of the regression model, we checked the tolerance and variance inflation factor (VIF). A tolerance value \0.3 and a VIF value [2 were regarded as indicating multicollinearity [21, 23]. A value of P \ 0.05 was considered statistically significant.
J Bone Miner Metab Table 2 Correlations between anthropometric parameters, soft tissue components, and bone mass with geometric indices of hip bone strength FN_BMD
TH_BMD
CT
Age (years)
–0.197a
–0.310b
–0.231a
–0.265b
YSM(years)
a
b
b
–0.285
b c
–0.204
c
–0.298
–0.311
a
CSA
c
SM
SI –0.349c
–0.151 a
–0.306b
c
0.554
0.306b
–0.189
Height (cm)
0.289
0.243
0.378
0.502
Body weight (kg)
0.441c
0.315b
0.482c
0.491c
0.517c
0.370c
c
a
c
0.274
b
b
0.257b
0.460
c
c
0.297b
0.448
c
c
0.301b
–0.249
c
a
–0.259b
2
BMI (kg/m )
0.354
Lean mass (kg)
c
0.351
b
ALM (kg)
0.324
PLM (%)
–0.346
Fat mass (kg) AFM (kg)
c
0.233
0.357
b
c
0.261
0.382
a
b
0.238 –0.224
0.340
a
–0.349
c
0.271 0.502 0.515 –0.237
0.409c
0.270b
0.469c
0.393c
0.408c
0.353b
c
c
c
0.494
c
c
0.416c
–0.237 0.046
–0.303 –0.120
c
c
–0.286c –0.022
0.432
c
0.448
a
0.415
Android fat (kg) Gynoid fat (kg)
–0.352 –0.180
AOI
–0.568c
–0.561c
–0.417c
–0.493c
–0.375c
–0.412c
b
a
b
a
a
0.237a
PBF (%)
0.298
–0.203 –0.193
0.421
c
0.164
0.331
0.200
–0.292 –0.032
0.204
YSM years since menopause, FN_BMD femoral neck BMD, TH_BMD total hip BMD, CT mean cortical thickness, CSA cross-sectional area, SM section modulus, SI femoral strength index, BMI body mass index, ALM appendicular skeletal lean mass, PLM percentage of lean mass, AFM appendicular fat mass, AOI android to gynoid fat ratio, PBF percentage of body fat a
P \ 0.05;
b
P \ 0.01; c P \ 0.001
Results The demographic, clinical characteristics of anthropometry, body composition parameters, bone mass (FN_BMD and TH_BMD) and the average values for hip geometric parameters (CT, CSA, SM, and SI) are presented in Table 1. The average age of participants was 62.1 years ranged from 48.7 to 83.1 years. All women were postmenopausal, with a mean year of 11.8 since menopause (range 0.3–34.2). The average FM in the entire sample was 19.94 kg, which is 33.1 % of body weight. Associations between anthropometry, body composition, and proximal femur strength In univariate analysis, greater LM or FM was associated with greater BMD (FN_BMD, TH_BMD) and hip geometric parameters (CT, CSA, SM, and SI) (P \ 0.001 for all). As can be noted, geometric indices of hip bone were positively related to absolute LM and ALM but negatively related to percentage of lean mass (PLM) because percentage LM and FM are the converse of one another. As shown in Table 2, there was a significantly negative correlation between advancing age, YSM, PLM, android fat, AOI and FN_BMD, TH_BMD, and hip structural geometric properties (CT, CSA, SM, and SI) (P \ 0.05 for all). Height, body weight, and BMI had a positive relationship with FN_BMD, TH_BMD, and hip geometric parameters (P \ 0.05 for all). Correlation of soft tissue components with anthropometric parameters are recorded
Table 3 Correlation of soft tissue components with anthropometric parameters
Lean mass (kg)
Age (years)
YSM (years)
Height (cm)
Body weight (kg)
0.052
0.063
0.616c
0.893c
0.704c
c
c
BMI
ALM (kg)
0.002
0.012
0.626
0.854
0.650c
PLM (%)
–0.031
0.006
–0.090
–0.552c
–0.619c
c
0.870c
b
0.231a
b
–0.290b
Fat mass (kg) AFM (kg)
0.047 –0.275
b b
AOI
0.311
PBF (%)
0.077
0.033 –0.285
b b
0.305
0.041
c
0.372 0.193
a
–0.208 0.068
0.908
0.298 –0.340
0.569
c
0.652c
YSM years since menopause, BMI body mass index, ALM appendicular skeletal lean mass, PLM percentage of lean mass, AFM appendicular fat mass, AOI android to gynoid fat ratio, PBF percentage of body fat a
P \ 0.05;
b
P \ 0.01; c P \ 0.001
in Table 3 in detail. As is shown, AFM and AOI were significantly related with most anthropometric parameters. AFM were positively associated with body weight, BMI, and negatively associated with age and YSM. AOI was negatively associated with height, body weight, and BMI and positively associated with age and YSM. LM, ALM, and FM had a positive relationship with height, body weight, and BMI (P \ 0.05 for all). PLM had a negative relationship with body weight, and BMI. The correlation between LM, ALM, FM, PLM, ALM, age, and YSM was
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not significant. LM was significantly associated with FM (r = 0.623; P \ 0.001) and AFM (r = 0.286; P = 0.004).
total FM were replaced by PBF, similar associations were also observed.
Multivariate analysis Discussion The variable gynoid fat has not been put into the stepwise linear regression analysis because it was not significantly correlated with hip strength parameters in Pearson’s correlation analysis in order to avoid statistical error. The results of the regression analyses are shown in Table 4. No multicollinearity effect was identified by a variance inflation factor between 1.0 and 1.435 except for total FM and PBF. Now only AFM was positively associated with FN_BMD, TH_BMD, and geometric indices of hip bone strength (CT, CSA, SM, and SI), whereas AOI was negatively associated with those geometric indices after controlling for age, YSM, height, body weight, BMI, LM, ALM, and FM. Android fat were not significantly associated with the hip bone strength indices in the regression model. Increasing age was associated with decreases in FN_BMD (Sb -0.218, P = 0.017), TH_BMD (Sb -0.324, P = 0.001), and SI (Sb -0.367, P \ 0.001). YSM was related to decreases in CT (Sb -0.327, P \ 0.001) and CSA (Sb -0.181, P = 0.047). Due to high collinearity between PBF and total FM (the variance inflation factor was 6.297), the effects of total FM and PBF on the parameters of proximal femur strength were examined separately in the regression models. When
Recent studies have shown that obesity and OP share several common genetic and environmental factors. Similarities between these two complex diseases have been identified. This suggests some type of pathophysiologic linkage. Although both obesity and OP generally become more prevalent with advancing age, complex relationships exist between these two conditions. The effect of fat mass on bone mass remains controversial. It may have beneficial effects on bone strength. Contrasting studies, however, suggest that excessive fat mass may not protect against OP fracture [4, 8, 24]. Differences in experimental design, sample structure, and even the selection of covariates may account for some of these inconsistent or contradictory results. Since there are few studies reporting the effect of body fat accumulation and its distribution on bone health and their relationship with hip geometry parameters, it is worthwhile to identify the specific roles that fat mass plays in hip bone strength regulation. In this present study, we evaluated the relationships between anthropometric parameters, body fat accumulation, fat distribution, and hip geometric strength indices assessed by HSA, using several comprehensive statistical
Table 4 Determinants of bone mass and hip bone strength indices: results of multiple linear regression analysis in postmenopausal women FN_BMD (g/cm2) Sb LM (kg)
t
TH_BMD (g/cm2) P values
Sb
t
CT (mm) P values
Sb
t
P values
0.68
–0.397
0.692
0.087
1.028
0.307
–0.2
–1.085
0.281
ALM (kg)
–0.028
–0.248
0.805
–0.037
–0.294
0.769
–0.038
–0.464
–0.644
FM (kg) AFM (kg)
–0.049 0.224
–0.267 2.634
0.79 0.010
0.055 0.264
0.633 3.023
0.528 0.003
0.174 0.236
0.874 2.588
0.384 0.011
AOI
–0.371
–4.284
\0.001
–0.454
–5.197
\0.001
–0.206
–2.229
0.028
Android fat (kg)
–0.052
–0.279
0.692
–0.047
–2.034
0.307
–0.164
–0.759
0.281
2
3
CSA (mm ) Sb LM (kg) ALM (kg) FM (kg)
SM (mm ) t
P values
Sb
SI t
P values
Sb
t
P values 0.545
0.117
1.231
0.221
0.095
0.879
0.382
–0.114
–0.607
–0.003
–0.688
0.493
0.034
–0.265
0.792
–0.005
–0.685
0.485
0.098
1.185
0.239
0.086
0.979
0.33
0.114
0.562
0.575
4.048
\0.001
0.303
3.774
\0.001
0.316
2.141
0.035
AOI
–0.291
–3.616
\0.001
–0.243
–3.19
0.002
–0.16
–1.644
0.044
Android fat (kg)
–0.072
–1.021
0.221
–0.066
–0.758
0.382
–0.103
–0.548
0.545
AFM (kg)
0.203
In this model, age, YSM, height, body weight, FM, AFM, LM, ALM, android and gynoid fat, and AOI were selected as independent variables LM lean mass, ALM appendicular skeletal lean mass, FM fat mass, AFM appendicular fat mass, AOI android to gynoid fat ratio, FN_BMD femoral neck BMD, TH_BMD total hip BMD, CT mean cortical thickness, CSA cross-sectional area, SM section modulus, SI femoral strength index, Sb standardized coefficients b
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methods. First, Pearson’s correlation analysis was used to pick out the key factors in all of the variables that correlated to the hip strength parameters. Then, stepwise linear regression analyses were performed to explore what are the important variables influencing the outcome. Finally, multivariable adjusted associations were conducted to evaluate the effects of the important variables after control for confounded parameters in Pearson’s correlation analyses. It may be more efficient than those in the other studies. As expected, we found, firstly, anthropometric parameters were strongly related to hip BMD, CT, CSA, SM, and SI in Pearson’s correlation analyses. The parameters such as advancing age, YSM, PLM, android fat, and AOI are negatively related with those hip bone strength indices. Conversely, height, body weight, and BMI are positively related with those indices. However, these potentially effects became complicated when the correlation of soft tissue components with anthropometric parameters was further analyzed. Some soft tissue components were positively associated with height, body weight, BMI or age, and YSM. Some were negatively associated or were even not associated with those anthropometric parameters. Thus, it can be inferred that anthropometric parameters, body fat accumulation and fat distribution may interact with each other in ways that may be helpful or harmful to hip geometric strength. It suggested that it is necessary to adjust anthropometric parameters in order to explore which soft tissue component exerts a stronger effect on HSA indices. Therefore, we carried out multivariable linear regression analyses with soft tissue components as independent variables, whereas hip BMD and hip bone strength indices were outcome variables. The anthropometric parameters such as age, YSM, height, body weight, and BMI were included as fixed effects to assess multivariable adjusted associations. Using this approach, we demonstrated that only AFM and AOI were more closely associated with proximal femur strength, but not the overall LM, ALM, FM, or android fat. This finding challenges the view that FM played an important detrimental role on BMD [4, 8, 9], as well as the view that LM positively affected bone strength [25–27]. These differences may contribute to some of the above studies not making an adjustment for confounders (age, YSM and height, body weight, etc.) or body weight was not a included covariate in the analysis [12, 21, 27]. It has been demonstrated that, in absolute terms, femur bone mass and geometric strength are greater in postmenopausal overweight women [3].The higher mechanical loading can stimulate osteocytes and dendritic resident cells, then trigger anabolic responses [8]. Although the bearing of weight may partly explain the effect of FM on bone, FM approximately accounts for only 16–25 % of total body weight in men and women of normal weight
[28]. Therefore, gravitational forces associated with weight may be insufficient to explain the impact of FM on bone strength. The influence of AFM on bone strength is likely to be a result of the effect of not bearing weight. The metabolically heterogenicity between subcutaneous fat and visceral fat can be interpreted as the differences in both adipokines production and steroid hormone metabolism regulation. Our data combined with previous evidence [23] suggested that subcutaneous fat tissue (i.e., AFM) was beneficial to bone structure and strength, while visceral adiposity (android fat deposition) was deleterious. Several mechanisms may be helpful to explain the association between the effect of not bearing weight of AFM and the bone strength. First, it has been suggested that subcutaneous preadipocytes have higher aromatase activity in comparison to visceral adiposity [29]. In postmenopausal women, adipocytes are the major sources of estrogen, and estrogen inhibits bone resorption by inducing osteoclasts apoptosis [30]. Furthermore, being overweight or obesity is associated with insulin resistance, which may contribute to androgen and estrogen overproduction and reduce sex hormone binding proteins. As a result, the elevated sex hormone leads to increased bone mass and the activity of osteoblast increased due to reduced osteoclast activity. Secondly, regional fat depot can release adipocytokines (such as leptin, adiponectin and visfatin), which appear to influence bone metabolism, through alternative mechanisms such as adipocyte-dependent hormonal factors [24, 31, 32]. Thus, subcutaneous adipose tissue is more beneficial than visceral fat in bone health after menopause. Marques et al. [21] demonstrated that AFM had a strong and independent association with femoral neck BMD [21]. In our regression model, we used both AOI and android fat as the central adiposity indicator rather than the waistto-hip ratio or the waist-to-thigh ratio to assess their associations with hip strength indices. We found, although hip geometric indices increased with total FM, the relation between them is not significant after adjusting for some confounders. On the contrary, hip bone strength was negatively associated with increased AOI rather than andriod fat amount. Namely, an inverse association between body fat distribution and bone strength was explained by higher AOI but not by higher android fat accumulation independent of body weight. Our results are consistent with most of the available data supporting that DXA-based AOI is deleterious for bone health [12, 14]. There is no clear answer for this, but several explanations can be postulated. First, there is an increasing trend in android fat but a relative reduction in gynoid fat in postmenopausal women. Android fat distribution was reported to be partly in relation to advancing age and partly due to estrogen deficiency [33]. Sumino et al. [34] also found that high AOI can be counteracted at least in part by oral hormonal replacement
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therapy (HRT). So it can be indicated that unnatural women who look like men (high AOI) may be subject to hip fracture possibly due to more severe lack of estrogen. Second, AOI, which is a stronger predictor of metabolic risk factors, is significantly greater in postmenopausal women than in premenopausal women [35]. It was also found that in men and postmenopausal women that there was no significant correlation between fat and bone parameters after adjusting for age and body weight [36]. The possible reasons for those different conclusions from these kinds of studies are as follows: the methods for measuring bone density (the expression of bone mass as BMC or BMD), the sample size, the population structure, and environmental and genetic factors in all these studies are not all the same, and, most importantly, the statistical methods are different. Additionally, it is not easy to interpret those different results from different studies, considering the gender-specific and menopause statusspecific study population, the incomplete adjustment for confounders (especially body weight). Our data inferred the importance of regular physical activity, which can not only decrease the central obesity and slow down sarcopenia with aging, but also increase mechanical loading of the skeleton. Nevertheless, several limitations of this study must be acknowledged. First, the study design was cross-sectional in nature, and it is not possible to establish any cause-andeffect inference on the relationship between body composition and hip geometric indices. Second, several lines of evidence have suggested that the effects of fat mass on bone mass may be mediated by hormonal factors with the principal candidates being serum estrogen, insulin, and leptin levels [37, 38]. We did not evaluate the role of the candidate hormones in mediating the relationship between fat mass and bone strength. Despite careful adjustment for potential confounders, the impact of estrogen and gonadotropin on bone mass and bone geometry indices cannot be removed because of the range wide of YSM in the subjects. Third, we did not evaluate the potential effect of physical activity on bone mass and HSA indices as it was a retrospective study, which should be overcome in future studies. In summary, current study has demonstrated that body fat distribution is associated with proximal femur strength after making an adjustment for confounders (age, YSM, height, body weight, and BMI) in healthy postmenopausal Chinese women, that is, AFM (subcutaneous fat) appears to be a positively protective effect on hip BMD and hip geometric strength while AOI shows a strong negative association with them. These data highlight that the pattern of fat distribution is more important for proximal femur strength than the overall FM or the LM alone. Therefore, besides body weight, the fat distribution should be taken
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into account when evaluating the effect of factors on bone strength. Rational weight controlling and reducing AOI during menopause may have vital clinical significance in decreasing postmenopausal OP. Acknowledgments This study was supported by grants from the National Natural Science Foundation of China (81071134, 81371517) and from the 5810 Foundation of Shanghai Tenth People’s Hospital (11RD104). Conflict of Interest Hong-Da Shao, MD, Guan-Wu Li, MD, Yong Liu, MD, Yu-You Qiu, MD, Guang-Yu Tang declared that they have no conflicts of interest.
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