2009.does obesity really make the femur stronger (1)

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JOURNAL OF BONE AND MINERAL RESEARCHVolume 24, Number 8, 2009Published online on March 16, 2009; doi: 10.1359/JBMR.090307 2009 American Society for Bone and Mineral Research

Does Obesity Really Make the Femur Stronger? BMD, Geometry,

and Fracture Incidence in the Womens HealthInitiative-Observational Study

Thomas J. Beck,1 Moira A. Petit,2 Guanglin Wu,3 Meryl S. LeBoff,4 Jane A. Cauley,5 and Zhao Chen3

ABSTRACT: Heavier individuals have higher hip BMD and more robust femur geometry, but it is unclearwhether values vary in proportion with body weight in obesity. We studied the variation of hip BMD andgeometry across categories of body mass index (BMI) in a subset of postmenopausal non-Hispanic whites(NHWs) from the Womens Health Initiative Observational Cohort (WHI-OS). The implications on fractureincidence were studied among NHWs in the entire WHI-OS. Baseline DXA scans of hip and total body from4642 NHW women were divided into BMI (kg/m2) categories: underweight (40). Femur BMD

and indices of bone axial (cross-sectional area [CSA]) and bending strength (section modulus [SM]) wereextracted from DXA scans using the hip structure analysis (HSA) method and compared among BMIcategories after adjustment for height, age, hormone use, diabetes, activity level, femur neck-shaft angle, andneck length. The association between BMI and incident fracture was studied in 78,013 NHWs from the entireWHI-OS over 8.5 2.6 (SD) yr of follow-up. Fracture incidence (cases/1000 person-years) was comparedamong BMI categories for hip alone, central body (hip, pelvis, spine, ribs, and shoulder girdle), upperextremity (humerus and distal), and lower extremity (femur shaft and distal but not hip). Femur BMD, CSA,and SM were larger in women with higher BMI, but values scaled in proportion to lean and not to fat or totalbody mass. Women with highest BMI reported more falls in the 12 mo before enrollment, more prevalentfractures, and had lower measures of physical activity and function. Incidence of hip fractures and all centralbody fractures declined with BMI. Lower extremity fractures distal to the hip trended upward, and upperextremity incidence was independent of BMI. BMD, CSA, and SM vary in proportion to total body leanmass, supporting the view that bones adapt to prevalent muscle loads. Because lean mass is a progressivelysmaller fraction of total mass in obesity, femur BMD, CSA, and SM decline relative to body weight in higher

BMI categories. Traumatic forces increase with body weight, but fracture rates at the hip and central bodywere less frequent with increasing BMI, possibly because of greater soft tissue padding. There was no evidentprotective effect in fracture rates at less padded distal extremity sites. Upper extremity fractures showed novariation with BMI, and lower extremity fracture rates were higher only in the overweight (BMI = 2529.9 kg/m2).J Bone Miner Res 2009;24:13691379. Published online on March 16, 2009; doi: 10.1359/JBMR.090307

Key words: obesity, hip structural geometry, femur strength, fractures, postmenopausal women, Womens

Health Initiative

Address correspondence to: Thomas J. Beck, ScD, The Johns Hopkins Outpatient Center, 601 North Caroline Street,Baltimore, MD 21287 USA, E-mail: [email protected]

INTRODUCTION

THROUGHOUT MUCH OF the developed and developingworld, obesity and osteoporosis are important publichealth concerns. For example, >50% of U.S. adults areoverweight or obese according to recent surveys.(1) Theassociations between obesity and osteoporosis have beenstudied in a recent review(2) and in a meta-analysis.(3) Al-though details remain unclear, the general consensus is that

obesity seems to moderate the effects of osteoporosis byincreasing BMD(4) and by making fractures less likely.DeLaet et al.,(3) in a meta-analysis of 12 multinationalcohorts comprising nearly 60,000 adult subjects, showedthat independent of sex, those with body mass indices(BMIs) >25 kg/m2 had significantly lower rates of hip, os-teoporotic, and all fractures. However, increasing bodyweight is mainly caused by greater fat mass, and whereasbiological mechanisms have been postulated to the con-trary, there is little evidence to suggest that fat directlyinfluences the skeleton in ways that are evident at the

1Johns Hopkins University School of Medicine, Baltimore, Maryland, USA; 2University of Minnesota School of Kinesiology, Min-neapolis, Minnesota, USA; 3Mel and Enid Zuckerman College of Public Health, University of Arizona, Tucson, Arizona, USA;4Brigham and Womens Hospital, Harvard Medical School, Boston, Massachusetts, USA; 5University of Pittsburgh Graduate School ofPublic Health, Pittsburgh, Pennsylvania, USA.

The authors state that they have no conflicts of interest.

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macroscopic (whole bone) level.(2) Moreover, from a bio-mechanical perspective, the fracture categories used by DeLaet et al. may not tell the whole story of weight andfracture risk because of the differing effects of soft tissue

padding over skeletal sites. Whereas BMD does increasewith body weight, it is not clear that either BMD or bonestrength remains in proportion to weight in the obese.Evidence in children suggests that femur geometricstrength is reduced relative to body weight in the over-weight,(57) but whether this is the case in elderly post-menopausal women has not been explored.

The probability of fracture depends not only on thestrength of the bone but on trauma severity and the like-lihood that it will occur. Without question, bones adaptto prevalent loading conditions,(8) and skeletal loads atweight-bearing sites stimulate adaptive increases in bonedensity(4) and geometry.(7) However, heavier patients are

generally less active and may have fewer trauma oppor-tunities; when traumas do occur, their greater soft tissuepadding may moderate impacts.(9,10) On the other hand,the magnitudes of traumatic impact forces on the skinsurface (if not the bone) are a direct function of bodyweight (e.g., all else equal, a doubling of body weightdoubles the impact force in a fall).

Bones fracture when external forces (loads) cause in-ternal stresses to exceed the strength of the tissue. Al-though we are unaware that the issue has been studied,there is no obvious reason why bone tissue strength inotherwise normal individuals would be influenced by bodyweight. It is therefore likely that weight effects are mainlygeometric (i.e., in the dimensional characteristics that de-

termine stress magnitudes under a given load). Becausefemur stresses generated from physical activities andcommon traumas are mainly in axial compression andbending, we concentrated on measurements of bone cross-sectional area (CSA) and section modulus (SM) that governthese stresses, respectively. CSA and SM can be computedfrom DXA scans of the hip with special software. In thisstudy, we evaluated how femur BMD and geometry vary inproportion to body weight and body composition acrosscategories of BMI (kg/m2) among non-Hispanic white(NHW) women 50 yr of age in the bone density cohortof the Womens Health Initiative Observational Study(WHI-OS). Because geometric strength should influencefracture rates, we sought to evaluate fracture incidence by

BMI category, but because few fractures occurred in thosewith DXA scans, the entire WHI-OS cohort of NHWwomen was used. To separate out effects of soft tissuepadding, fracture incidence versus BMI was evaluated atboth well-padded central body sites and at less paddedextremity sites.

MATERIALS AND METHODS

Overview of the WHI

The WHI is one of the largest prospective health studiesamong postmenopausal women. Participants between 50and 79 yr of age at baseline and lacking a condition likely tocause death within 3 mo were recruited from 40 clinical

centers across the United States. There were two compo-nents in the WHI: an observational study (OS) and a set ofthree clinical trials. This study included a cross-sectionalanalysis of BMD and other variables at baseline and used

incident fracture determined on prospective follow-up ofthe participants in the WHI-OS.

The bone density cohort

At WHI enrollment, subjects were scanned at the totalbody and hip using Hologic QDR2000 (Hologic, Bedford,MA, USA) machines at 3 of the 40 WHI clinic centers inthe United States: Tucson and Phoenix, Arizona; Pitts-burgh, Pennsylvania; and Birmingham, Alabama. The hipstructure analysis (HSA) program was subsequently usedon available hip DXA scans acquired at baseline on 6032WHI-OS participants. The complex effects of race andobesity on femur geometry are the subject of a separate

analysis; hence, this study was restricted to 4642 womenself-identified as NHW. For comparison purposes, womenwere divided into six standard BMI (kg/m2) categories in-cluding underweight (40).

The fracture cohort

Fractures were assessed among NHW women enrolledin the entire WHI-OS cohort (including the bone densitysubset). These included 78,013 subjects who were followedan average of 8.5 2.6 (SD) yr for a total of 5180.4 person-years. Within the WHI-OS, fracture assessment for all

fracture types was by annual self-report, with the exceptionthat hip fractures were adjudicated by review of medicalrecords. The overall validity of self-reported fractures inWHI was evaluated and, although there was some varia-tion by skeletal site, the investigators concluded that inci-dence rates were acceptably accurate.(11)

Data collection for other covariates

Questionnaires were used at baseline to collect infor-mation on age, race/ethnicity, recreational physical activi-ties, energy intake, and factors such as hormone use andthe presence of chronic disease. Physical activity wasassessed by questions on the frequency and duration of a

range of different types of activities using the modifiedcoding scheme of Ainsworth et al.(12) This scheme attemptsto assign intensity in terms of metabolic equivalent task(MET) scores (defined as the ratio of work metabolic rateto a standard resting metabolic rate). Scores are definedrelative to 1 MET roughly equivalent to resting metabo-lism while sitting quietly as the product of days per week,minutes per day, and MET value for each activity.(13)

Physical function was measured using the 10-item MedicalOutcomes Study Scale,(14) where a higher score indicatesbetter physical function. Weight was measured to thenearest 0.1 kg on a balance beam scale with the participantdressed in indoor clothing without shoes. Height wasmeasured to the nearest 0.1 cm using a wall-mounted sta-diometer. Body composition was measured by DXA in the

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form of total body lean and total body fat mass in kilo-grams, which by definition exclude bone mineral mass.

HSA

The HSA method uses the principle that a line of pixelstraversing the bone axis in a bone mineral mass image isa projection of the mineral in the corresponding cross-section.(15) Analysis sites include the following: narrowneck (NN) across the femur neck at its narrowest point; theshaft across the diaphysis at 1.5 times minimum neck widthdistal to the intersection of the neck and shaft axes, andintertrochanter (IT) along the bisector of the angle be-tween neck and shaft axes. As shown in Fig. 1, five parallel

profiles are generated one pixel (;1 mm) apart at eachregion, and measurements are averaged over the five pro-files. Average pixel value in the profile is reported as BMD,and outer diameter (OD) is measured from outer marginscorrected for image blur. To determine the bone surface inthe cross-section, pixel values are divided by the averagemineral density of normal adult cortical bone (1.053g/cm3), yielding a linear thickness; the profile integral isthus bone CSA (cm2). The center of mass (COM) of theprofile is determined, and the cross-sectional moment ofinertia (CSMI) is measured as the integral weighted by thesquare of distance of each pixel from the COM. Maximumbending stress in a cross-section is a function of its SM,computed as the CSMI divided by the maximum distanceof COM to the medial or lateral profile margin (dmax).

Research has suggest that homeostatic mechanisms tend topreserve the SM in aging bone cross-sections,(16,17) but theSM can overestimate strength of tubular objects if wallsbecome so thin that they buckle (fold) under compressiveloads. This complex phenomenon cannot be fully charac-terized by the limited information in a DXA scan, but ahint that a cross-section may be susceptible can be obtainedfrom the buckling ratio (BR). BR is used in engineeringdesigns incorporating hollow tubes where the ratio of outerradius to wall thickness should be kept below ;10 to avoidlocal buckling.(18) BR is estimated in HSA by modelingthe cross-section as a hollow circular (NN) or elliptical (IT)annulus with a fixed proportion (60%, 100%, and 70%,

respectively) of the CSA in the cortical shell forthe narrow-neck, intertrochanter, and shaft regions, respectively.

Cross-calibration of DXA scanners for HSA was ach-ieved by circulating a specifically designed calibrationphantom across all scanners in the project. Precision for theHSA method was not specifically evaluated in the WHIstudy, although it should be comparable to results onHologic scanners as reported by Khoo et al.(19)

Statistics

For the purposes of general exploration, a stepwisemultiple linear regression was used to identify independentvariables with a significant influence on conventionalfemoral neck DXA variables, as well as those from thethree HSA regions. Independent variables included age,

FIG. 1. A typical DXA scan of the adult hipshowing locations of the five mineral massprofiles that are averaged in each regionfor geometry measurements with the HSAsoftware.

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height, lean and fat masses, neck-shaft angle and necklength, hormone therapy use, and a diagnosis of diabetes.Differences in descriptive characteristics between BMIgroups were tested using ANOVA or the x2 test for pro-portions as appropriate. Differences in bone geometrybetween BMI groups were evaluated by analysis of co-variance (ANCOVA) using height, age, weekly energyexpended in physical activity, use of hormone therapy,

diabetes, neck-shaft angle, and femur neck length as co-variates. The latter two variables influence the length of thebending moment arm in the proximal femur and may havean independent effect on stress magnitudes. To evaluatehow BMD and geometry vary in proportion to body mass,they were evaluated again with the same covariates afternormalizing parameters to total body mass and then to leanbody mass. A significance level of p = 0.05 was used in alltests. Fracture incidence by BMI group was calculatedas numbers of incident fractures divided by the amountof at-risk experience and expressed as fractures/1000 per-son-years of risk. Differences in fracture incidence ratesbetween BMI groups were tested using a proportionalhazards model and adjusted for group differences in age,

hormone use, and a diagnosis of diabetes.

RESULTS

Exploration of body composition and other factors

on BMD and geometry

Standardizedb coefficients for significant contributors tothe stepwise multiple linear regression of age, height, totalbody lean and fat mass, neck-shaft angle and neck length,use of hormone therapy, and a diagnosis of diabetes onconventional femoral neck DXA and HSA geometry pa-rameters are listed in Table 1. A complete exposition isbeyond the scope of this paper, but note that lean bodymass produced the largest contribution to all models with

greatest effects on SM. Fat mass was a significant contrib-utor to all models except shaft OD, and contributions toSM were generally small. Fat mass had larger b coefficientson BMD than on BMC or its HSA counterpart CSA. It alsohad a weak negative effect on FN region area and on OD atthe NN and IT regions. Height effects reflect general sizescaling, and the effects of NSA and NL are mainly causedby the influence of height on the size of bending moments

at the proximal femur in ambulation. Negative coefficientson age are evident for BMD, BMC, and CSA at all regions,but age effects on region area and OD are all positive. Ageeffects on SM were weakly negative at the NN but weaklypositive at the IT and shaft regions. Hormone therapy usehad generally positive effects on BMD, BMC, CSA, andSM and a weak negative effect on shaft OD. A diagnosis ofdiabetes had negative effects on region area and OD, somenegative effects on BMD and CSA, and a small positiveeffect on SM at the IT and shaft regions.

Descriptive characteristics

General characteristics of NHW participants by BMIcategory at baseline are listed in Table 2. There is a trend

toward younger age with increasing BMI, and heaviestgroups are slightly shorter in stature (p < 0.05 for bothversus healthy weight). In absolute terms, total body leanand fat masses increase with BMI, and the proportion ofbody fat increases from 28% in the underweight to 53% inthe extremely obese. There was a significant trend towardlower physical activity and reduced physical function withincreasing BMI; fewer women walked at all for exercise,and of those that did, few walked >150 min/wk. Heavierwomen expended less energy in exercise, and fewer hadphysical function scores >90 in the 10-item Medical Out-comes Study Scale.(14) Women with BMIs greater thanhealthy weight were significantly more likely to be diag-nosed with diabetes. Healthy weight women were signifi-cantly more likely to be current users of hormone therapy.

TABLE 1. Standardizedb Coefficients From Stepwise Multiple Linear Regressions of Conventional and HSA- Derived DXA Parameterson Independent Variables Including Age, Height, Total Body Lean and Fat Masses, Femoral Neck-Shaft Angle and Neck Length,

Use of Hormone Therapy, and a Diagnosis of Diabetes in NHW Subjects From the BMD Cohort of WHI-OS ( N= 4642)

Age

TB lean

mass

TB fat

mass Height

Neck-shaft

angle

Neck

length

HRT

use Diabetes

Conventional femoral neck BMD 20.207 0.275 0.244 20.149 20.033 0.144

BMC 20.114 0.357 0.188 0.139 20.132 0.137

Area 0.205 0.216 20.098 0.346 0.026 0.121 20.028

HSA narrow-neck BMD 20.239 0.275 0.177 20.188 0.110

CSA 20.147 0.371 0.159 0.103 20.198 0.117

Outer diameter 0.248 0.211 20.054 0.313 20.028 20.028

SM 20.025 0.409 0.156 0.157 20.223 0.092

HSA intertrochanter BMD 20.173 0.256 0.206 20.089 20.241 0.214 0.101 20.173

CSA 20.067 0.400 0.179 20.246 0.189 0.102 20.067

Outer diameter 0.295 0.389 20.077 0.284 20.07 0.295

SM 0.062 0.466 0.100 0.127 20.177 0.167 0.091 0.062

HSA shaft BMD 20.153 0.338 0.190 20.114 20.156 0.134 0.104 20.153

CSA 20.06 0.472 0.184 20.164 0.164 0.079 20.060

Outer diameter 0.227 0.318 0.280 0.0722

0.055 0.227SM 0.097 0.542 0.124 0.136 20.132 0.136 0.033 0.097

, nonsignificant contribution to the regression (p > 0.05).

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Variation of BMD and bone geometry with BMI

Mean (SE) valuesof both conventional andHSA BMDarelisted by BMI category together with the HSA geometry atthe three femur regions in Table 3. With the exception of ODamong the underweight, all parameters in all regions differsignificantly from healthy weight. To show the proportionalvariation of BMD and geometric properties with BMI cat-egory, figures were plotted after dividing by the mean of thehealthy weight BMI category, thus setting healthy weightvalues to 1.0 and lower and higher values to 1,respectively. Most differences from healthy weight were

highly significant, and except for Figs. 3C and 5, only non-significant (p > 0.05) comparisons were marked on Figs. 24.

Figure 2 shows normalized, adjusted mean BMD valuesby BMI category for the three HSA and two conventionalfemur regions, illustrating similar scaling with BMI despitelarge differences in absolute value. All covariates weresignificant contributors to most adjustment models, butsome variables did not reach significance (p < 0.1) in somemodels. Bone CSA and SM showed similar variation withBMI at the three HSA regions; hence, we show only theNN region in Fig. 3. As in Fig. 2, the adjusted BMD andgeometric strength indices are larger with greater BMI inabsolute terms (Fig. 3A), but when divided by body weight(Fig. 3B), the trend with BMI is reversed for all three pa-

rameters. Relative to their body weight, heavier individuals

have lower BMD, CSA, and SM. However, when plottedrelative to total body lean mass (Fig. 3C), variation withBMI is much smaller and is nearly eliminated on SM.Despite significant differences in underweight (p < 0.002)and overweight (p < 0.04), SM normalized to lean mass waswithin 1.4% of healthy weight in all heavier categories andwas only 3% lower in the underweight. Although BMIdifferences were greatly reduced, BMD and CSA per unitlean mass remained significantly different from healthweight with larger differences at BMI extremes. BMD andCSA values in extremely obese were 10% and 8%, re-spectively, below health weight, and like SM, were 3%lower in the underweight.

Femur OD and estimated BR

Underlying the association of weight with BMD and SMis a subtle but significantly greater OD with higher BMIthat differed across HSA regions (Fig. 4A). OD trendedmore steeply upward with BMI at the IT and shaft than atthe NN region. Whereas OD in all three regions was sig-nificantly wider than healthy weight, no differences wereapparent in the underweight. Larger OD produced a pos-itive effect on SM, but in a thin-cortex bone, the strengthconferred by a given SM may be offset by greater suscep-tibility to local buckling,(20) as estimated by a higher BR. InFig. 4B the relative pattern of BMI on BR (higher values

TABLE 2. Average Characteristics of Subjects by BMI Category

UnderweightHealthyweight Overweight

Mildobesity

Moderateobesity

Extremeobesity

Category BMI limits (kg/m2

) 18.5 18.524.9 25.029.9 3034.9 35.039.9 40Subjects in category (%) 205 (4.4%) 1744 (37.6%) 1601 (34.5%) 688 (14.8%) 243 (5.2%) 161 (3.5%)

Age (yr) 65.4 6.9 64.3 7.3 64.2 7.3 64.48 7.2 62.3 7.1* 61.2 7.1*

Height (cm) 162.6 6.6 162.4 6.2 161.7 6.1* 161.1 6.0* 161.39 6.73 159.7 9.3*

Weight (kg) 46.3 4.7 59.5 6.0 71.4 6.2* 83.4 7.2* 97.2 9.1* 111.1 11.7*

Total body lean mass (kg) 30.4 3.6 31.7 3.7 33.6 3.9* 36.5 4.2* 39.7 4.5* 42.9 5.9

Total body fat mass (kg) 10.6 2.7 21.9 4.7 31.4 4.5* 40.2 5.4* 50.4 6.7* 59.4 9.3*

Percent lean mass 72.0 7.2 58.7 6.0 51.2 4.4* 47.2 4.2* 43.7 3.7* 42.5 4.9*

Percent fat mass 25.3 7.1 38.7 6.1 46.5 4.5* 50.71 4.2* 54.27 3.80* 55.6 5.0*

Minutes/week spent

walking for exercise

* * * * *

None (%) 31% 34% 43% 51% 66% 76 %

0150 min (%) 43% 46% 44% 39% 27% 20 %

150 min (%) 26% 20% 13% 10% 7% 4%

Weekly energy expenditure

from physical activity (METS)

19.8 18.3 15.0* 13.1* 9.9* 9.0*

Physical function score >90 (%) 52% 44% 32%* 19%* 13%* 6%*

One or more falls in

last 12 mo (%)

30% 32% 33% 34% 34% 41%

Diagnosis of diabetes 3% 2% 5%* 9% 8% 17%

Current user of hormone therapy 43% 49% 44% 35% 36% 25%

History of prior fracture

of any bone (%)

47% 39% 42% 48% 44% 54%

History of prior fracture

on/after age 55 (%)

16% 21% 21% 19% 18% 17%

Incident fracture (any bone) (%) 15% 15% 16% 14% 13% 17%

Incident hip fracture

(incl. pathological) (%)

2.9% 2.1% 1.9% 1.6% 0.8% 1.2%

* p < 0.01, p < 0.05, and p < 0.0001 vs. healthy weight.



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are bad) is similar in all three HSA regions, although thelow absolute values in the shaft would make buckling ir-relevant at that site. Significantly higher BRs in under-weight women would suggest that strength of their femursare more likely to be compromised by local buckling,whereas lower BRs in heavier women would suggest thatlocal buckling would be less likely than in healthy weight.

Fracture incidence

Using data from NHW women in the entire WHI-OS,

fracture incidence is plotted by BMI category in Fig. 5.

Compared with healthy weight women, fracture incidence atcentral body sites including the hip was 54% more likely inunderweight women; rates of these fractures decline signifi-cantly with BMI and were 40% less likely in the extremelyobese. Underweight women suffered hip fractures at twice therate of healthy weight, but the rate in the extremely obese wasless than one half of healthy weight. Upper extremity fracturerates did not vary with BMI. There was trend of increasing oflower extremity (distal to hip) with increasing BMI, but dif-ferences in fracture rates were significantly greater than

healthy weight only in overweight (BMI = 2529 kg/m2).

FIG. 2. The relative scaling of femur BMDwith BMI for the HSA subset of NHWwomen from WHI-OS after adjustment for

significant covariates and normalization tomean of healthy weight (18.524.9 kg/m2).All means are significant vs. healthy weight.

TABLE 3. Adjusted Baseline Mean (SE) Measurements of BMD and Proximal Femur Geometry by BMI Category

UnderweightHealthyweight Overweight

Mildobesity

Moderateobesity

Extremeobesity

Category BMI limits (kg/m2

) 18.5 18.524.9 25.029.9 3034.9 35.039.9 40Conventional DXA

Femoral neck BMD (g/cm2) 0.615 (0.008)* 0.660 (0.002) 0.706 (0.002)* 0.749 (0.004)* 0.794 (0.008)* 0.823 (0.010)*

Total hip BMD (g/cm2) 0.720 (0.007)* 0.782 (0.002) 0.841 (0.002)* 0.882 (0.004)* 0.938 (0.007)* 0.954 (0.009)*

HSA

Narrow neck BMD (g/cm2) 0.616 (0.008)* 0.663 (0.002) 0.708 (0.002)* 0.739 (0.004)* 0.786 (0.008)* 0.821 (0.010)*

CSA (cm2) 1.74 (0.021)* 1.89 (0.007) 2.03 (0.007)* 2.14 (0.012)* 2.26 (0.021)* 2.39 (0.027)*

Outer diameter 2.98 (0.014) 3.00 (0.004) 3.02 (0.005) 3.04 (0.008)* 3.03 (0.014) 3.07 (0.018)*

SM 0.768 (0.011)* 0.838 (0.003) 0.908 (0.004)* 0.964 (0.006)* 1.04 (0.011)* 1.15 (0.014)*

Buckling ratio

(dimensionless)

14.5 (0.20)* 13.5 (0.06) 12.6 (0.07)* 12.1 (0.11)* 11.2 (0.19)* 10.9 (0.25)*

Intertrochanter BMD (g/cm2) 0.611 (0.008)* 0.663 (0.002) 0.711 (0.003)* 0.748 (0.004)* 0.794 (0.008)* 0.824 (0.010)*

CSA (cm2) 2.91 (0.038)* 3.17 (0.013) 3.45 (0.014)* 3.68 (0.022)* 3.95 (0.038)* 4.11 (0.049)*

Outer diameter

(cm)

5.02 (0.022) 5.04 (0.007) 5.10 (0.008)* 5.18 (0.013)* 5.23 (0.022)* 5.27 (0.028)*

SM (cm3

) 2.42 (0.036)* 2.65 (0.012) 2.88 (0.013)* 3.09 (0.021)* 3.31 (0.036)* 3.50 (0.046)*Buckling ratio

(dimensionless)

12.2 (0.16)* 11.1 (0.05) 10.4 (0.06)* 9.99 (0.09)* 9.52 (0.16)* 9.23 (0.21)*

Shaft BMD (g/cm2) 0.982 (0.012)* 1.07 (0.004) 1.15 (0.004)* 1.20 (0.007)* 1.28 (0.012)* 1.33 (0.015)*

CSA (cm2) 2.61 (0.030)* 2.87 (0.010) 3.10 (0.011)* 3.29 (0.017)* 3.54 (0.029)* 3.72 (0.037)*

Outer diameter

(cm)

2.80 (0.012) 2.81 (0.004) 2.84 (0.004)* 2.88 (0.007)* 2.92 (0.012)* 2.95 (0.015)*

SM (cm3) 1.35 (0.017)* 1.49 (0.005) 1.62 (0.006)* 1.73 (0.009)* 1.87 (0.016)* 2.01 (0.021)*

Buckling ratio

(dimensionless)

4.44 (0.06)* 3.95 (0.02) 3.66 (0.02)* 3.51 (0.03)* 3.30 (0.06)* 3.20 (0.08)*

* p < 0.0001, p < 0.01, and p < 0.05 vs. healthy weight.

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The general pattern of fracture incidence in the muchsmaller bone density cohort mirrored that of the wholeWHI, but most differences between BMI groups did notreach significance (data not shown). Compared with NHWin the entire WHI-OS, the bone density cohort was slightlyolder (64.2 versus 63.9 yr, p = 0.02), more likely to havesuffered a fracture after age 50 (20% versus 18.2%, p =0.007), more likely to suffer an incident fracture of anybone (2.2% versus 2.0%, p < 0.0001), and fewer hadphysical function scores >90 in the 10-item Medical

Outcomes Study Scale (33.7% versus 40.0%, p < 0.0001).There were no differences in BMI, history of osteoporosis,prior fracture, incident hip fractures, and number of re-ported falls in the year before enrollment (data not shown).

DISCUSSION

In this cross-sectional analysis of baseline data from 4642NHW women between 50 and 79 yr of age from the bonedensity subset of the WHI-OS, we examined how femur

FIG. 4. The relative scaling of (A) femur OD (note expended vertical scale for clarity) and (B) estimated BR at the narrow-neck,intertrochanter, and shaft regions of the proximal femur. BMI category mean values are normalized to mean value of healthy weight andadjusted for significant covariates in the HSA subset of NHW women from WHI-OS. Except for OD in underweight, all parameters aresignificant vs. healthy weight.

FIG. 3. The relative scaling of narrow-neck BMD, bone CSA, and SM after adjustment for significant covariates and normalization tomean of healthy weight in an HSA subset of NHW women from WHI-OS: (A) adjusted means, (B) adjusted means expressed relative tobody weight, and (C) adjusted means expressed relative to total body bonefree lean mass (note expended vertical scale for clarity). Allparameters are significantly different from healthy weight in A and B. In C, *p < 0.05, **p < 0.01, and ***p < 0.001 vs. healthy weight.



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BMD and geometry varied with BMI. Our purpose was toevaluate whether obesity strengthens the geometry of theproximal femur as commonly believed. We found that, inabsolute terms, heavier women do have stronger femurs,but the increment in strength is not commensurate withtheir higher weight. This would suggest that heavierwomen should be more likely to suffer hip fractures, butrates of incident hip fractures among 78,013 NHW womenin the entire WHI-OS during 8 2.6 (SD) yr of follow-uptended to decline with higher BMI.

Scaling of femur BMD and geometry with BMI

Femur BMD, CSA, and SM do get larger with greater

BMI; the effect is proportionate to total body lean massand not to total fat or to total body mass. For example,extremely obese women were 88% heavier on average thanhealthy weight women. Their bodies contained 38% morelean mass but 167% more fat. Their difference in NN re-gion BMD, CSA, and SM of 25%, 26%, and 37% higher,respectively was more in line with the lean mass difference,especially for SM. Overall, the average ratio of SM to leanmass varied


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proportions of women scoring high in physical functionsteadily declined with increasing BMI, and they may be lesssteady on their feet. Interestingly extremely obese womenreported significantly more falls in the year before study

entry and were more likely to report a prevalent fracture ofany bone. Even if fractures are not more common, theanalysis of U.S. Medical Expenditure Panel Surveys byFinkelstein et al.(26) showed 15% higher musculoskeletalinjuries requiring medical intervention in overweight and48% higher in extremely obese individuals.

Significant covariates

Our statistical models of statistical models of BMI as-sociations with femur BMD, geometry, and fracture inci-dence included reported physical activity score, use ofhormone therapy, and a diagnosis of diabetes. Hormonetherapy has shown to reduce fragility fractures and has

known effects on BMD and geometry,(27) and heavierwomen were less likely to have used hormones. Obesity is aknown risk factor for diabetes; heavier women in this studywere two to eight times more likely to be diagnosed withthe disease, which carries a risk of increased fractures(28)

and has effects on BMD.(29) Before adjusting fracture in-cidence for covariates, lower limb fracture rates were sig-nificantly higher in heavier than healthy weight womenexcept in moderate obesity (data not shown). We suspectthat this may be associated with diabetes because otherexplanations were less likely; activity effects were small,and lower hormone use in heavier women should increaserather than decrease fracture incidence. Effects of dia-betes on femur geometry in the WHI are being studied

separately.

Femur BMD, geometry, and hip fracture rates

in the underweight

Our primary emphasis was on the effects of obesity, butobservations in the underweight are worthy of comment.Soft tissue padding should have little moderating effects onfall impact in underweight women, but this does not seemto explain why they suffered hip fractures at twice the rateof healthy weight women. In absolute terms, adjustedBMD, CSA, and SM are lower in the underweight but onlyby 78% (Fig. 2A). These parameters were also within 3%of healthy weight when scaled to lean mass, suggesting no

major deficit in adaption to muscle force. Whereas othernonbone factors may be important, it would still seem thatfracture risk is inadequately explained by BMD, CSA, andSM. The observation that BR in the underweight was 8%and 10% higher (more susceptible) at the NN and IT re-gions than healthy weight may provide an explanation.Although BR can only be crudely estimated from DXAdata, studies showelevated values in hip fracture cases.(3033)

Effectively, high BR values suggest that strength is over-estimated by SM, because bending may cause local foldingof thin cortices on surfaces subjected to high compressivestresses. Unfortunately, there is insufficient informationin a DXA scan to compute a strength estimate incorpo-rating local buckling. These data can only suggest thatgeometry is compromised by buckling instability in the

underweight and that this is probably not the case in theoverweight.

Method limitations

The HSA method has been used in a number of largeepidemiologic studies, and its limitations are reasonablywell understood. Our work shows that there are importantgeometric differences in the femur associated with obesity,but the method measures dimensions from a 2D projectedimage; thus, the main source of imprecision and systematicerror is in how the femur is positioned for imaging. BMD ismore tolerant of small inconsistencies in femur rotationalposition than geometry; exceptional care is needed to po-sition the femur consistently in dissimilar patients.(19)

There are systematic uncertainties in DXA data that areinfluenced by tissue composition as shown by Bolotinet al.(34); these may influence geometry measurements in

obese subjects, although this has not been studied. DXAscan image quality tends to worsen with increasing bodythickness so that geometry precision degrades on heavierpatients. Future technical developments may improveprecision of DXA derived geometry to levels adequate forclinical use on individual patients, but the current HSAmethod is best suited for the research setting where im-precision can be overcome with large numbers of subjects.The cross-sectional sample used in this study limits theconclusions one might draw from changes in body weight,although in an earlier longitudinal study, we previouslyshowed that femur SM in postmenopausal women changedin an appropriate direction with increasing, decreasing, andstatic body weight, even though BMD declined in allgroups.(7)

Comment on generalizability of results

The WHI-OS is one of the largest observational studiesof postmenopausal women in the world, although partici-pants are believed to be healthier than the U.S. populationas a whole; for example, hip fracture rates were one half theexpected rate of similar age groups.(35) This study was re-stricted to women self-reported as NHW because of ethnicdifferences in both BMI and in fracture rates, especiallyamong women of African heritage, which we are studyingseparately. The subset of NHW women in the bone densitycohort was on average slightly older, fewer had physicalfunction scores >90, and more had prevalent fractures thanthose in the whole WHI population. However, they did notdiffer in BMI distribution or in any other remarkable re-spect. We expect that the conclusions on patterns of obesityeffects on femur geometry should be consistent with theWHI-OS.

Summary and conclusions

We conclude that, in absolute terms, femur BMD andgeometric strength are greater with overweight in post-menopausal women, but they vary in proportion to lean(mostly muscle) mass and not to body weight or fat mass. Ineffect, femur strength is reduced relative to body weightin the obese but, although traumatic forces increase in



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proportion to body weight and that the most obese womenreported more falls, they had fewer fractures at hip andother central body sites. The most logical explanation isthat greater soft tissue padding at those sites more than

compensates for the greater impact forces resulting fromfalls in the obese. Their thicker cortices should also reducesusceptibility to local buckling compared with lower weightwomen. The obesity advantage did not extend to fracturesat extremity skeletal sites where padding is less important;fracture rates were not significantly affected by BMI.

ACKNOWLEDGMENTS

This work is the result of WHI Ancillary Study 153,which was supported by NIH Grant NIAMS/NIH: R01AR049411. The WHI program is funded by the NationalHeart, Lung, and Blood Institute, National Institutes of

Health, U.S. Department of Health and Human Servicesthrough Contracts N01WH22110, 24152, 32100-2, 32105-6,32108-9, 32111-13, 32115, 32118-32119, 32122, 42107-26,42129-32, and 44221.

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Received in original form August22, 2008; revised form December10, 2008; accepted March 11, 2009.

APPENDIX 1: WHI INVESTIGATORS

Program Office (National Heart, Lung, and Blood Institute,Bethesda, MD, USA): Elizabeth Nabel, Jacques Rossouw, ShariLudlam, Joan McGowan, Leslie Ford, and Nancy Geller.

Clinical Coordinating Center: (Fred Hutchinson Cancer Re-search Center, Seattle, WA, USA) Ross Prentice, GarnetAnderson, Andrea LaCroix, Charles L. Kooperberg, Ruth E.Patterson, Anne McTiernan; (Medical Research Labs, HighlandHeights, KY, USA) Evan Stein; (University of California at SanFrancisco, San Francisco, CA, USA) Steven Cummings.

Clinical Centers: (Albert Einstein College of Medicine, Bronx,NY, USA) Sylvia Wassertheil-Smoller; (Baylor College of Medi-cine, Houston, TX, USA) Aleksandar Rajkovic; (Brigham and

Womens Hospital, Harvard Medical School, Boston, MA, USA)JoAnn E. Manson; (Brown University, Providence, RI, USA)Charles B. Eaton; (Emory University, Atlanta, GA, USA) Law-rence Phillips; (Fred Hutchinson Cancer Research Center, Seattle,

WA, USA) Shirley Beresford; (George Washington UniversityMedical Center, Washington, DC, USA) Lisa Martin; (LosAngeles Biomedical Research Institute at HarborUCLA MedicalCenter, Torrance, CA, USA) Rowan Chlebowski; (Kaiser Per-manente Center for Health Research, Portland, OR, USA)Yvonne Michael; (Kaiser Permanente Division of Research,Oakland, CA, USA) Bette Caan; (Medical College of Wisconsin,Milwaukee, WI, USA) Jane Morley Kotchen; (MedStar ResearchInstitute/Howard University, Washington, DC, USA) Barbara V.Howard; (Northwestern University, Chicago/Evanston, IL, USA)Linda Van Horn; (Rush Medical Center, Chicago, IL, USA)Henry Black; (Stanford Prevention Research Center, Stanford,CA, USA) Marcia L. Stefanick; (State University of New York atStony Brook, Stony Brook, NY, USA) Dorothy Lane; (The OhioState University, Columbus, OH, USA) Rebecca Jackson; (Uni-versity of Alabama at Birmingham, Birmingham, AL, USA) CoraE. Lewis; (University of Arizona, Tucson/Phoenix, AZ, USA)Cynthia A Thomson; (University at Buffalo, Buffalo, NY, USA)Jean Wactawski-Wende; (University of California at Davis, Sac-ramento, CA, USA) John Robbins; (University of California atIrvine, Irvine, CA, USA) F. Allan Hubbell; (University of Cal-

ifornia at Los Angeles, Los Angeles, CA, USA) Lauren Nathan;(University of California at San Diego, LaJolla/Chula Vista, CA,USA) Robert D. Langer; (University of Cincinnati, Cincinnati,OH, USA) Margery Gass; (University of Florida, Gainesville/Jacksonville, FL, USA) Marian Limacher; (University of Hawaii,Honolulu, HI, USA) J. David Curb; (University of Iowa, IowaCity/Davenport, IA, USA) Robert Wallace; (University of Mas-sachusetts/Fallon Clinic, Worcester, MA, USA) Judith Ockene;(University of Medicine and Dentistry of New Jersey, Newark, NJ,USA) Norman Lasser; (University of Miami, Miami, FL, USA)Mary Jo OSullivan; (University of Minnesota, Minneapolis, MN,USA) Karen Margolis; (University of Nevada, Reno, NV, USA)Robert Brunner; (University of North Carolina, Chapel Hill, NC,USA) Gerardo Heiss; (University of Pittsburgh, Pittsburgh, PA,USA) Lewis Kuller; (University of Tennessee Health ScienceCenter, Memphis, TN, USA) Karen C. Johnson; (University ofTexas Health Science Center, San Antonio, TX, USA) Robert

Brzyski; (University of Wisconsin, Madison, WI, USA) Gloria E.Sarto; (Wake Forest University School of Medicine, Winston-Salem, NC, USA) Mara Vitolins; (Wayne State University Schoolof Medicine/Hutzel Hospital, Detroit, MI, USA) Michael Simon.


2009.does obesity really make the femur stronger (1)

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