{"paper_id":"d8651b31-c8da-475f-afeb-7db3df91cd71","body_text":"1 \nDXA-derived hip shape is associated with hip fracture: a longitudinal study of 38,123 1 \nUK Biobank participants  2 \nSophie Scott BSc1, Asad Hashmi BSc1, Raja Ebsim PhD2, Fiona R Saunders PhD3, Jennifer S 3 \nGregory PhD3, Richard M Aspden DSc3, Claudia Lindner PhD2, Timothy Cootes PhD2, 4 \nNicholas C Harvey PhD4,5, Jonathan H Tobias MD PhD1,6, Benjamin G Faber MBBS 5 \nPhD1,6*, Rhona A Beynon PhD1,6* 6 \n*Joint senior authors  7 \n 8 \n1. Musculoskeletal Research Unit, University of Bristol, UK 9 \n2. Division of Informatics, Imaging and Data Sciences, The University of Manchester, UK 10 \n3. Centre for Arthritis and Musculoskeletal Health, University of Aberdeen, UK 11 \n4. Medical Research Council Lifecourse Epidemiology Centre, University of Southampton, 12 \nUK  13 \n5. NIHR Southampton Biomedical Research Centre, University of Southampton and 14 \nUniversity Hospitals Southampton NHS Foundation Trust, Southampton, UK 15 \n6. Medical Research Council Integrative Epidemiology Unit, University of Bristol, UK 16 \n 17 \nCorresponding author: 18 \nBenjamin Faber, Musculoskeletal Research Unit, Learning and Research Building, 19 \nSouthmead Hospital, Bristol BS10 5FN 20 \nben.faber@bristol.ac.uk  21 \n . CC-BY 4.0 International licenseIt is made available under a \nperpetuity. \n is the author/funder, who has granted medRxiv a license to display the preprint in(which was not certified by peer review)preprint \nThe copyright holder for thisthis version posted October 2, 2025. ; https://doi.org/10.1101/2025.09.30.25336960doi: medRxiv preprint \nNOTE: This preprint reports new research that has not been certified by peer review and should not be used to guide clinical practice.\n\n 2 \nAbstract 22 \nDespite advancements in fracture prediction tools and osteoporosis management, hip fractures 23 \nremain a significant consequence of bone fragility, with a 22% one-year mortality. Hip 24 \ngeometric measures (GMs) have been associated with fracture  risk; however,  their strong 25 \ncorrelation hinders the identification of independent influences, leaving their relative predictive 26 \nvalue unclear. Statistical shape modelling (SSM) provides a more holistic assessment of hip 27 \nshape compared to  using pre-determined GMs. This study aimed to evaluate whether SSM-28 \nderived hip shape from dual -energy X-ray absorptiometry (DXA) scans can predict hip 29 \nfracture, independently of individual GMs. Previously, we applied SSM to left hip DXA images 30 \nin UK Biobank, a large prospective cohort with link ed hospital records , generating ten 31 \northogonal hip shape modes (HSMs) , that explained  86% of shape variance. Additionally, 32 \nfemoral neck width (FNW), femoral head diameter (FHD), and hip axis length (HAL) were 33 \nderived from these DXAs . In the current analysis , Cox proportional hazard models , adjusted 34 \nfor age, sex, height, weight, bone mineral density (BMD), and GMs (FNW, HAL, FHD), were 35 \nused to examine the longitudinal associations between each HSM and  first incident hospital 36 \ndiagnosed hip fracture. A Bonferroni adjusted p-value threshold (p<0.004) was used to account 37 \nfor the 13 exposures. Among the 38,123 participants (mean age 63.7 years; 52% female; mean 38 \nfollow-up 5 years), 133 (0.35%) experienced subsequent hip fracture. HSM2, characterised by 39 \na narrower FNW, a higher neck shaft angle, and reduced acetabular coverage, showed a strong 40 \nassociation with hip fracture risk (HR 1.32, 95% CI 1.11-1.58, P 1.47×10-3), which persisted 41 \nafter full adjustment (1.30, 1.09-1.55, 3.27×10 -3). There was no evidence for an association 42 \nwith other HSMs. These findings suggest that DXA-derived hip shape is associated with hip 43 \nfracture risk independently of BMD and GMs. Incorporating global hip shape into fracture risk 44 \nassessment tools could enhance prediction accuracy and inform targeted interventions. 45 \n . CC-BY 4.0 International licenseIt is made available under a \nperpetuity. \n is the author/funder, who has granted medRxiv a license to display the preprint in(which was not certified by peer review)preprint \nThe copyright holder for thisthis version posted October 2, 2025. ; https://doi.org/10.1101/2025.09.30.25336960doi: medRxiv preprint \n\n 3 \nLay summary  46 \nDespite improvements in hip fracture prevention , they remain a major problem, with 22% of 47 \npeople dying within a year of sustaining one. This study looked at medical images from 38,123 48 \nindividuals in UK Biobank to assess the shape of their hip using computer -aided statistical 49 \ntechniques. The results indicate that a hip shape variation describing a narrower femoral neck 50 \nand a larger angle linking the neck and the femoral shaft is linked to fracture. This association 51 \npersisted after accounting for other known hip shape measures related to fracture risk . 52 \nTherefore, hip shape could help improve prediction and prevention of hip fractures. 53 \nKey words: epidemiology, hip morphology, hip fracture, statistical shape modelling, DXA 54 \n55 \n . CC-BY 4.0 International licenseIt is made available under a \nperpetuity. \n is the author/funder, who has granted medRxiv a license to display the preprint in(which was not certified by peer review)preprint \nThe copyright holder for thisthis version posted October 2, 2025. ; https://doi.org/10.1101/2025.09.30.25336960doi: medRxiv preprint \n\n 4 \nIntroduction 56 \nThe annual number of hip fractures in the UK is projected to rise by 32% over the next 4 years1, 57 \nhighlighting the need for accurate prediction of hip fracture risk. These fractures represent a 58 \nsignificant consequence of osteoporosis-related bone fragility and carry a one-year mortality 59 \nrate of 22%2. However, not all individuals who sustain a hip fracture meet the diagnostic criteria 60 \nfor osteoporosis3, which is primarily based on bone mineral density (BMD) . Clinical risk 61 \nassessment tools such as FRAX®4 – widely used in over 100 international guidelines – and the 62 \nUK-specific Qfracture5, have been developed to better predict the risk of incident fractures, but 63 \nstill lack optimal sensitivity 6,7. Consequently, incorporating additional factors  not currently 64 \nconsidered in existing tools could help improve the accuracy of fracture risk prediction8. 65 \nVariation in hip shape is increasingly recognised as a contributor to hip fracture9,10, having also 66 \nbeen linked to osteoarthritis 11. Hip shape  can be assessed through measuring individual 67 \ngeometric measures (GMs), or by evaluating the overall shape. Common examples of GMs 68 \ninclude hip axis length (HAL), neck shaft angle (NSA), femoral neck width (FNW), and 69 \nfemoral head diameter (FHD), which can all be derived from DXA scans , either manually or 70 \nusing software such as Hip Structural Analysis12. Although evidence linking G Ms to fracture 71 \nrisk is inconsistent, a recent meta -analysis found that increased HAL, NSA, and FNW are 72 \nassociated with higher fracture risk, with pooled odds ratios (OR) of 1.53 , 1.47, and 2.68  73 \nrespectively10. This did not account for factors such as age and sex. Nonetheless, the 74 \nInternational Society of Clinical Densitometry recommends using only HAL for assessing hip 75 \nfracture risk in females, and advises against us ing GMs to guide treatment decisions 12. 76 \nMoreover, the high correlation between GMs such as  FNW and HAL 11, as well as the 77 \ncorrelation between GMs and body size 13, complicates the evaluation of their individual 78 \ncontributions to hip fracture risk . In contrast , assessing hip shape as a whole , rather than 79 \n . CC-BY 4.0 International licenseIt is made available under a \nperpetuity. \n is the author/funder, who has granted medRxiv a license to display the preprint in(which was not certified by peer review)preprint \nThe copyright holder for thisthis version posted October 2, 2025. ; https://doi.org/10.1101/2025.09.30.25336960doi: medRxiv preprint \n\n 5 \nfocusing on individual GMs, may provide a more comprehensive understanding of hip health 80 \nand fracture risk by accounting for overall morphology and the relationships between different 81 \nfeatures14. 82 \nStatistical shape modelling (SSM), a computer-aided technique designed to  capture the 83 \nstatistical variability of shapes within a dataset15, can be used to provide a more holistic 84 \nmeasure of hip shape. SSM uses outline points derived from hip images and employs principal 85 \ncomponent analysis (PCA) to produce orthogonal modes of shape variation, termed hip shape 86 \nmodes (HSM)16, which each capture a different aspect of hip morphology.  Although research 87 \nlinking HSMs to hip fracture risk is limited, one study that applied SSM to radiographs found 88 \nthat a HSM characterised by a longer femoral neck, smaller femoral head and a narrower FNW 89 \nwas associated with a higher fracture risk (OR 2.4 8)8. Studies comparing SSM -derived 90 \nmeasures of hip shape to GMs in the context of hip fractures have been limited to small studies9, 91 \nwhich have been unable to show that SSM -derived hip fracture risk is independent of GMs. 92 \nThis underscores the need for a comparative analysis to identify the most effective predictors 93 \nof hip fracture risk. In our recent work using UK Biobank (UKB)  we developed a machine-94 \nlearning algorithm that automatically plac es outline points o n high resolution hip DXA 95 \nimages17, facilitating the generation of hip shape measures in large numbers. 96 \nIn the present study, we aimed to establish whether SSM-derived hip shape, obtained using our 97 \nautomated point placement method,  is associated with hip fracture risk independently  of 98 \nestablished risk factors and hip GMs, while also analysing potential sex differences within these 99 \nassociations in the UKB cohort.  100 \n . CC-BY 4.0 International licenseIt is made available under a \nperpetuity. \n is the author/funder, who has granted medRxiv a license to display the preprint in(which was not certified by peer review)preprint \nThe copyright holder for thisthis version posted October 2, 2025. ; https://doi.org/10.1101/2025.09.30.25336960doi: medRxiv preprint \n\n 6 \nMethods and materials 101 \nPopulation 102 \nUKB is a prospective cohort study that recruited ~500,000 males and females, aged 40-69 103 \nyears, from 22 assessment centres across the UK between 2006 -201018. Baseline genetic and 104 \nphenotypic information was obtained  through questionnaires, physical measurements and 105 \nbiological samples.  In 2014, UKB launche d the Image Enhancement study , which aims to 106 \ngather imaging data, including hip DXA scans, from  100,000 participants 19. For this study, 107 \n~40,000 left-hip DXA images with outline points delineating the bone contour were available 108 \n(October 2023). This study is overseen by the UKB Ethics Advisory Committee, and ethical 109 \napproval was given by the National Information Governing Board for Health and Social Care 110 \nand North -West Multi -centre Research Ethic s committee (11/NW/0382). All participants 111 \nprovided informed consent for their data to be used in the study. 112 \nAcquisition of DXA-derived hip shape 113 \nHip DXA images were acquired following a standardised protocol using an iDXA scanner (GE-114 \nLunar, Madison, WI, USA), with participants ’ legs positioned at an internal rotation  of 15-115 \n25°19. A Random Forest-based machine learning algorithm20 (BoneFinder®, The University of 116 \nManchester) had been  previously used to automatically place the hip outline points 17. This 117 \nalgorithm was initially trained on a subset of ~7,000 manually marked-up images before being 118 \napplied to the remaining ~33,000 images . A total of 85 outline points were placed around the 119 \nfemoral head and acetabulum , including the greater and lesser trochanters  (Figure 1) . The 120 \nplacement of the outline points was manually verified, with only 10% requiring adjustment and 121 \nan average correction distance of 1.9 mm. 122 \n . CC-BY 4.0 International licenseIt is made available under a \nperpetuity. \n is the author/funder, who has granted medRxiv a license to display the preprint in(which was not certified by peer review)preprint \nThe copyright holder for thisthis version posted October 2, 2025. ; https://doi.org/10.1101/2025.09.30.25336960doi: medRxiv preprint \n\n 7 \nOnce outlined, principal component analysis ( PCA) was performed to generate a set of 123 \northogonal hip shape modes ( HSMs), which collectively explain 100% of the variance 17. To 124 \nminimise the burden of multiple testing, this analysis focused on the first ten HSMs, which 125 \naccounted for 86% of the shape variance within the data set. Subsequent HSM s explained 126 \nminimal additional shape variance and are unlikely to hold clinical significance. Additionally, 127 \nFNW, FHD, and HAL were previously derived from the DXA scans using an openly available 128 \ncustom Python script, as described elsewhere11,21. 129 \nAscertainment of hip fracture 130 \nHip fracture data w ere obtained through linkage to hospital episode statistics (HES), which 131 \nuses the International Classification of Diseases (ICD) 10th revision codes. Hip fractures were 132 \nidentified based on the following codes:  fractured neck of femur (S72.0), pertrochanteric 133 \nfracture (S72.1), subtrochanteric fracture (S72.2), stress fracture, not elsewhere classified 134 \n(Pelvic region and thigh) (M84.359), or pathological fracture, not elsewhere classified (Pelvic 135 \nregion and thigh) (M84.459). Recording of HES data began on the 1st April 1997. Hip fracture 136 \ndata were downloaded in August 2023, capturing information up until the end of October 2022.  137 \nStatistical analysis 138 \nDescriptive statistics, including means, standard deviations (SDs), and ranges, were used to 139 \nsummarise population characteristics and the distribution s of HSMs and G Ms. Histograms 140 \nwere plotted for each HSM to confirm normal distribution. The correlation between each HSM, 141 \nGM, BMD and demographic factors ( height, weight, age ), were assessed using Pearson 142 \ncorrelation co -efficient (r). Cox proportional hazard models were used to examine the 143 \nlongitudinal associations between each HSM and hip fracture risk, as well as between each GM 144 \n(FNW, FHD, HAL), and hip fracture risk. The follow-up period concluded at the earliest event, 145 \nwhich was either the first incident hip fracture during follow-up, withdrawal, censoring due to 146 \n . CC-BY 4.0 International licenseIt is made available under a \nperpetuity. \n is the author/funder, who has granted medRxiv a license to display the preprint in(which was not certified by peer review)preprint \nThe copyright holder for thisthis version posted October 2, 2025. ; https://doi.org/10.1101/2025.09.30.25336960doi: medRxiv preprint \n\n 8 \ndeath or until the end of the study (31/10/2022). Individuals who had a hip fracture before 147 \nattending the imaging assessment, i.e. before the DXA scan, were excluded from the analysis. 148 \nThe Cox proportional hazards assumption was tested using the Schoenfeld residuals approach. 149 \nA Bonferroni adjusted p -value threshold ( P<0.004) was used to account for the thirteen 150 \nexposures tested (ten HSMs and three G Ms). Results are shown as hazard ratios (HR), which 151 \nrepresent the relative risk of experiencing a hip fracture over time, with 95% CI and p-values. 152 \nResults are presented across four models: Model 1 is unadjusted; Model 2 adjusts for 153 \ndemographic characteristics ( age, sex, height, and weight ); Model 3 additionally adjusts for 154 \nleft hip femoral BMD; and Model 4 further adjusts for GMs (FNW, FHD, and HAL). When a 155 \nGM is the exposure, model 4 adjust s for the other two G Ms. Both combined -sex and sex -156 \nstratified analyses were conducted to account for known disparities in fracture risk 22 and hip 157 \nshape23 between males and females. All statistical analys es were performed using STATA 158 \nversion 18 (Stata Corp, College Station, TX, USA). 159 \nComposite models 160 \nTo investigate the overall at-risk hip shape for fracture a composite HSM figure was plotted by 161 \ncombining all HSMs. Briefly, to do this, unadjusted beta coefficients for the associations 162 \nbetween HSMs and fracture were first computed. Each beta was then multiplied by 10 to 163 \nenhance the visualisation of shapes, and subsequently multiplied by the HSM -specific SD to 164 \naccount for the contribution of each HSM to the overall shape variance. These adjusted values 165 \nwere combined into a single vector to assess the collective impact of hip shape on hip fracture.  166 \n . CC-BY 4.0 International licenseIt is made available under a \nperpetuity. \n is the author/funder, who has granted medRxiv a license to display the preprint in(which was not certified by peer review)preprint \nThe copyright holder for thisthis version posted October 2, 2025. ; https://doi.org/10.1101/2025.09.30.25336960doi: medRxiv preprint \n\n 9 \nResults 167 \nBaseline characteristics 168 \nThis study included 38,128 UKB individuals with complete data and a left hip DXA image  169 \navailable (Table 1). The mean age was 63.7 years, and 52% of participants were female. Mean 170 \nBMD of the left femur was 0.99 g/cm2, with females having a lower mean BMD (0.93 g/cm2) 171 \ncompared to males (1.06 g/cm2). A total of 133 participants (0.35%) had a hip fracture, with a 172 \nhigher prevalence among females (89 cases, 0.45%) compared with males (44 cases, 0.24%). 173 \nHSMs 1-10 had a mean value of 0 by design (Figure 2). Mean HSM values differed between 174 \nsexes, with the greatest difference seen in HSM1, HSM3, and HSM9. For the  GMs, the 175 \ncombined sex mean for FNW was 31.6 mm, FHD was 45.9 mm and HAL was 96.7 mm. Males 176 \nhad a greater mean FNW, FHD, and HAL than females. 177 \nGeometric measures and their inter-relationships 178 \nFHD, FNW, and HAL were all highly correlated with each other (r 0.81-0.89) and with height 179 \n(r 0.75-0.81) (Supplementary Figure 1). Weight was moderately correlated with FHD, FNW, 180 \nHAL, height, and BMD  (r 0.52-0.57). The HSMs were orthogonal by design . Similarly, no 181 \ncorrelation was observed between the HSMs and the other covariates. 182 \nGeometric measures and their association to hip fracture 183 \nFemoral Neck Width 184 \nIn the unadjusted analysis of all participants (Figure 5, Supplementary Table 2), FNW was not 185 \nassociated with hip fracture  (Model 1: 1.15, 0.97 -1.36, 0.11). However, a strong association 186 \nwas seen between a wider FNW and hip fracture following adjustment for demographic 187 \ncharacteristics and BMD  (Model 3: 1.77, 1.30 -2.43, 3.26×10 -4). In sex -stratified analysis  188 \n . CC-BY 4.0 International licenseIt is made available under a \nperpetuity. \n is the author/funder, who has granted medRxiv a license to display the preprint in(which was not certified by peer review)preprint \nThe copyright holder for thisthis version posted October 2, 2025. ; https://doi.org/10.1101/2025.09.30.25336960doi: medRxiv preprint \n\n 10 \n(Supplementary Table 2), a wider FNW showed a strong association with hip fracture in both 189 \nsexes, both in the unadjusted model and following adjustment for demographic characteristics. 190 \nIn males, the strongest association was observed in the unadjusted model (Model 1: 2.17, 1.44-191 \n3.25, 1.99×10-4). The association weakened with adjustment for BMD ( Model 3: 1.75, 1,08 -192 \n2.82, 0.02). A similar trend was noted in females, with the strongest association being in the 193 \nunadjusted model (Model 1: 2.88, 2.05-4.06, 1.40×10-9). Further adjustment for BMD resulted 194 \nin attenuation (Model 3: 1.70, 1.11 -2.59, 0.01). The effect sizes were greater in females 195 \ncompared to males in models 1 and 2, with a similar effect size seen in both sexes in model 3. 196 \nFemoral Head Diameter 197 \nIn the unadjusted analysis  of all participants , t here was little evidence for an observed 198 \nassociation between FH D and hip fracture  (Model 1: 1.12, 0.95 -1.53, 0.17)  (Figure 5, 199 \nSupplementary Table 2). However, a strong positive association was seen when adjus ting for 200 \nBMD (Model 3: 1.89, 1.39-2.57, 4.48×10-5). In the unadjusted sex-stratified analysis, a larger 201 \nFHD demonstrated a greater effect size in females compared with males (Model 1: females - 202 \n2.43, 1.73-4.30, 2.60×10 -7; males - 2.30, 1.54-3.44, 4.50×10 -5). When adjusting for BMD, a 203 \nlarger effect size was seen in males compared with females (Model 3: males - 2.01, 1.28-3.14, 204 \n2.26×10-3; females - 1.70, 1.11-2.60, 0.01). 205 \nHip Axis Length 206 \nSimilar to FNW and FH D, HAL did not show an association with hip fracture in unadjusted 207 \nanalysis of all participants (Model 1: 1.08, 0.91-1.28, 0.39) (Figure 5, Supplementary Table 2). 208 \nHowever, an increased HAL was associated with hip fracture after adjusting for BMD (Model 209 \n3: 1.61, 1.18 -2.21, 3.08×10-3). When compared with FNW and FH D, HAL exhibited the 210 \nsmallest effect size  across all models. A strong positive association was seen only in the 211 \nunadjusted sex-stratified analysis (Model 1: males - 2.07, 1.37-3.11, 4.84×10-4; females - 2.05, 212 \n . CC-BY 4.0 International licenseIt is made available under a \nperpetuity. \n is the author/funder, who has granted medRxiv a license to display the preprint in(which was not certified by peer review)preprint \nThe copyright holder for thisthis version posted October 2, 2025. ; https://doi.org/10.1101/2025.09.30.25336960doi: medRxiv preprint \n\n 11 \n1.48-2.85, 1.58×10 -5) (Supplementary Table 2), with the  associations seen diminishing after 213 \nfurther adjustment for BMD (Model 3: males – 1.84, 1.11-3.04, 0.02; females - 1.40, 0.92-2.13, 214 \n0.11). 215 \nMutual Adjustment 216 \nWhen e ach G M was mutually adjusted  for the other two G Ms, along with  demographic 217 \ncharacteristics and BMD, there was less evidence for an association with hip fracture in both 218 \ncombined and sex-stratified analysis. All results fell below the statistical significance threshold 219 \nfor multiple testing (Figure 5, Supplementary Table 2).  220 \n . CC-BY 4.0 International licenseIt is made available under a \nperpetuity. \n is the author/funder, who has granted medRxiv a license to display the preprint in(which was not certified by peer review)preprint \nThe copyright holder for thisthis version posted October 2, 2025. ; https://doi.org/10.1101/2025.09.30.25336960doi: medRxiv preprint \n\n 12 \nAssociation between HSMs and hip fracture 221 \nEach HSM was initially assessed for its association with hip fracture. In the unadjusted 222 \ncombined-sex analysis, there was evidence of a strong positive association between HSM2 and 223 \nhip fracture ( Model 1: HR 1.32, 95% CI 1.11 -1.56, P= 1.47×10-3) (Figure 4 , Table 2). This 224 \nassociation persisted upon adjustment for demographic characteristics and BMD ( Model 3: 225 \n1.31, 1.11-1.55, 1.51×10-3). HSM2 captures features of a narrower FNW, a higher NSA, and 226 \nreduced acetabular coverage (Figure 2). No other HSMs were found to be associated with hip 227 \nfracture in combined-sex analysis.  228 \nIn female sex-stratified analysis (Supplementary Table 1), HSM2 showed a positive association 229 \nwith hip fracture when adjusted for demographic characteristics (Model 2: 1.37, 1.11 -1.68, 230 \n2.79×10-3). Apart from this, sex -stratified analyses failed to show statistical evidence for an 231 \nassociation with hip fracture potentially because they were underpowered. 232 \nTo evaluate the association between each HSM and hip fracture risk, independent of the hip 233 \nshape components captured by GMs, each HSM was further adjusted for all three GMs (FNW, 234 \nFHD, HAL) (Figure 4, Table 2). Analysis of all participants showed that the associations seen 235 \nin Models 1, 2, and 3 were maintained after adjusting for demographic characteristics, BMD, 236 \nand GMs. HSM2 emerged as the only HSM to show strong evidence of an association with hip 237 \nfracture in this model  (Model 4: 1.30, 1.09 -1.55, 3.27×10 -3). In sex -stratified analysis  238 \n(Supplementary Table 1) , none of the associations met the Bonferroni -adjusted p -value 239 \nthreshold. However, HSM2 showed weak evidence of an association with hip fracture when 240 \nfully adjusted in both females and males (Model 4: females - 1.27, 1.03-1.57, 0.02; males - 241 \n1.34, 0.96-1.84, 0.06). Additionally, in males, HSM9 continued to show weak evidence of a 242 \nnegative association with hip fractures after full adjustment (Model 4: 0.66, 0.48-0.89, 0.01). 243 \nNo other HSM was associated with hip fracture when fully adjusted in either sex. 244 \n . CC-BY 4.0 International licenseIt is made available under a \nperpetuity. \n is the author/funder, who has granted medRxiv a license to display the preprint in(which was not certified by peer review)preprint \nThe copyright holder for thisthis version posted October 2, 2025. ; https://doi.org/10.1101/2025.09.30.25336960doi: medRxiv preprint \n\n 13 \nComposite model 245 \nThe composite model (Figure 3) showed that the overall at-risk shape, which is represented by 246 \nthe solid line, included a narrower FNW, reduced acetabular coverage, smaller greater 247 \ntrochanters, and a smaller FH D. This closely reflects HSM2 , which shares these shape 248 \ncharacteristics.  249 \n . CC-BY 4.0 International licenseIt is made available under a \nperpetuity. \n is the author/funder, who has granted medRxiv a license to display the preprint in(which was not certified by peer review)preprint \nThe copyright holder for thisthis version posted October 2, 2025. ; https://doi.org/10.1101/2025.09.30.25336960doi: medRxiv preprint \n\n 14 \nDiscussion 250 \nThis large, longitudinal cohort study  explored the relationship  between DXA-derived HSMs 251 \nand GMs with hip fracture risk. The findings indicate that HSM2, characterised by a narrower 252 \nFNW, higher NSA, smaller femoral head, and reduced acetabular coverage, was positively 253 \nassociated with hip fractur e risk, even after adjusting for  age, sex, height, weight and BMD. 254 \nWhile GMs (FNW, FHD, HAL) also showed associations with hip fracture when adjusted for 255 \nthe same covariates, these relationships attenuated upon mutual adjustmen t, confirming their 256 \ninter-relatedness. In contrast, HSM2 retained its association with hip fracture after accounting 257 \nfor GMs, suggesting that HSM2 captures additional information beyond these three measures 258 \nof hip geometry. 259 \nCurrently, t here are few comparative studies in the literature that have investigated the 260 \nassociation between SSM-derived hip shape and hip fractures. Furthermore, these studies have 261 \nderived their SSM from different populations , meaning it is not possible to draw direct 262 \ncomparisons between specific HSMs. For instance, Gregory et al. applied a SSM consisting of 263 \n29 points outlining the femoral head and neck to standard radiographs in a small  group of 264 \nfemales (26 cases, 24 controls) 9. They found that SSM -derived hip shape predicted fracture 265 \nrisk after adjusting for height and weight. Specifically, a HSM with a longer, narrower femoral 266 \nneck and a higher NSA was more likely to fracture, reflecting the at -risk shape identified in 267 \nthis study. However, their sample size was considerably smaller than that of our current study 268 \nand the outline points on the radiographs used to perform SSM did not include the lesser 269 \ntrochanter. Baker-LePain et al. used a similar approach in a nested case-control study involving 270 \nCaucasian females (168 cases, 231 controls)8. They employed a larger number of outline points 271 \n(n=60) than Gregory et al. (n=29), and their model included the lesser trochanter. They found 272 \nthat hips exhibiting extreme values of HSM4, characterised by a  narrower FNW, increased 273 \n . CC-BY 4.0 International licenseIt is made available under a \nperpetuity. \n is the author/funder, who has granted medRxiv a license to display the preprint in(which was not certified by peer review)preprint \nThe copyright holder for thisthis version posted October 2, 2025. ; https://doi.org/10.1101/2025.09.30.25336960doi: medRxiv preprint \n\n 15 \nfemoral neck length, and a smaller femoral head, were associated with hip fractur e. These 274 \nfeatures closely resemble the at-risk hip shape identified in this study (narrower FNW and 275 \nsmaller femoral head).  Although Baker-LePain et al. adjusted for age, body mass index , and 276 \nfemoral neck BMD , they, like Gregory et al., only included females within their analys es, 277 \nleaving it unclear whether the observed relationships are sex -specific. Goodyear et al. 278 \nperformed SSM using 72 outline points on DXA scans of females aged over 75 years (182 279 \nsubjects, 364 controls)24. The authors identified a hip shape associated with fracture that also 280 \nclosely resembles the findings of our study, including a narrower FNW, greater NSA, reduced 281 \nacetabular coverage, and smaller greater trochanters. This study offers the closest comparison 282 \nto the present analysis as it was performed on DXA scans and used similar outline points , 283 \nincluding the acetabulum and lesser trochanter . However, the sample size was  smaller, and 284 \nanalysis focused on females only. This limitation is significant because HSMs are known to be 285 \ninfluenced by sex 25, and our study found notable differences in HSMs between  the sexes. 286 \nFurthermore, none of the studies adjusted for GMs. 287 \nAlthough the at-risk hip shape (HSM2) identified in this study was characterised by a narrower 288 \nFNW, the analysis of GMs and hip fracture revealed that a wider FNW was associated with hip 289 \nfracture ( Figure 5, S upplementary Table 2 ). This finding has been reported in other 290 \nobservational studies26-28, including a recent genetic analysis29 that found that individuals with 291 \na genetic predisposition  to a greater FNW were at higher risk of fracture . The seemingly 292 \ncontradictory findings between HSM and G Ms regarding FNW and fracture risk may be 293 \nattributable to several factors. GMs objectively quantify individual aspects of hip morphology, 294 \nmeaning that bone size can impact the magnitude of the measurement. For example, larger 295 \nindividuals are likely to have a bigger femur across all dimensions; thus, a taller and heavier 296 \nperson would be expected to have a larger FNW and HAL. Moreover, FNW is highly correlated 297 \nwith height and moderately correlated with weight  (Supplementary Figure 1 ), a relationship 298 \n . CC-BY 4.0 International licenseIt is made available under a \nperpetuity. \n is the author/funder, who has granted medRxiv a license to display the preprint in(which was not certified by peer review)preprint \nThe copyright holder for thisthis version posted October 2, 2025. ; https://doi.org/10.1101/2025.09.30.25336960doi: medRxiv preprint \n\n 16 \nthat has been consistently reported in other studies11,30, highlighting the significant influence 299 \nof demographic characteristics, such as height and weight, on FNW. In contrast, SSM employs 300 \nProcrustes analysis to align and scale hip outlines based on shape, effectively capturing bone 301 \nmorphology while excluding the influence of individual size. This ability to isolate shape from 302 \nsize is important because HSM2 remained associated with hip fracture risk , independent of 303 \nFNW, FHD, and HAL . This suggests that these individual measures are not independently 304 \ndriving hip fracture risk. Instead, the interactions and combined influence of these factors, 305 \neffectively captured by SSM, likely contribute to fracture risk . Ratios of  GMs have been 306 \nsuggested as an alternative, aiming to reduce the influence of correlation by standardising 307 \nmeasures against one GM13. However, SSM still outperformed ratio values in a previous small 308 \nstudy9. 309 \nPrevious research has explored sex differences in hip shape23,25,31,32; few studies have examined 310 \nthese differences within the context of hip fractures. HSM2 showed similar effect sizes between 311 \nthe sexes, but a notable difference was seen with HSM9 (Supplementary Table 1). Studies of 312 \nindividual hip shape measures have shown that females tend to have a smaller FHD, narrower 313 \nFNW, and shorter femoral neck length compared to males32, which likely reflects that females 314 \nare typically smaller than males. Similarly, Frysz et al. found sex differences in HSMs, with 315 \nfemales exhibiting a narrower FNW and smaller lesser trochanter compared with males25. This 316 \nfinding is noteworthy, particularly given the weak evidence of a negative association with hip 317 \nfracture seen with HSM9 in males  (Supplementary Table 1). HSM9 was characterized by a 318 \nlarger lesser trochanter but a narrower femoral neck, suggesting that a larger lesser trochanter, 319 \na feature more common in male hip shapes , could offer some  protective effect against hip 320 \nfracture. Since the lesser trochanter serves as the insertion point for hip flexor muscles33 its size 321 \ncould be indicative of muscle mass.  Given that sarcopenia (loss of muscle mass and 322 \nfunction)34,35 is a known risk factor for hip fracture36-39, a larger lesser trochanter may represent 323 \n . CC-BY 4.0 International licenseIt is made available under a \nperpetuity. \n is the author/funder, who has granted medRxiv a license to display the preprint in(which was not certified by peer review)preprint \nThe copyright holder for thisthis version posted October 2, 2025. ; https://doi.org/10.1101/2025.09.30.25336960doi: medRxiv preprint \n\n 17 \na proxy for muscle strength and function , potentially reducing fracture risk in males . 324 \nAdditionally, innate female hip shape characteristics may predispose females to a higher 325 \nfracture risk, as they often exhibit features linked to fractures, such as a narrower FNW. 326 \nInterestingly, although HSM9 included a narrower FNW, similar to the fracture-prone HSM2, 327 \nthis reinforces the idea that fracture risk is influenced by multiple interacting shape constituents 328 \nrather than any single measurement.  329 \nThis study has several key strengths. Its large sample size and population-based design greatly 330 \nenhances the representativeness of the findings, thereby improving  the reliability of effect 331 \nestimates. The study also simultaneously examined the relationship between SSM-derived hip 332 \nshape and GMs with hip fracture, allowing for a direct comparison of these two methods and 333 \nan evaluation of their independent associations with fracture risk . One of the limitations of 334 \nSSM is that each study uses a different population to derive their HSMs, thus you cannot 335 \ncompare across models. This UK Biobank model could provide a reference for other 336 \npopulations. Both the SSM -derived hip shape and the G Ms were semi-automatically derived 337 \nfrom DXA scans, requiring minimal manual point correction. Given the widespread use of 338 \nDXA scans in clinical practice for assessing osteoporosis, this approach makes accommodating 339 \nSSM-derived hip shape measures through tools like FRAX a feasible option. Additionally, the 340 \ninclusion of both combined and sex-stratified analyses represents a significant strength of this 341 \nstudy. While many studies primarily examine  post-menopausal females, this study also 342 \nincluded m ale participants , providing valuable  insights into male hip shape and its role in 343 \nfracture risk. 344 \nThere are limitations to this study. As an observational study, it cannot establish causality. 345 \nFurther research to understand the factors driving the association between HSM2 and hip 346 \nfracture risk is needed, although a recent study using genetic data found evidence of a causal 347 \n . CC-BY 4.0 International licenseIt is made available under a \nperpetuity. \n is the author/funder, who has granted medRxiv a license to display the preprint in(which was not certified by peer review)preprint \nThe copyright holder for thisthis version posted October 2, 2025. ; https://doi.org/10.1101/2025.09.30.25336960doi: medRxiv preprint \n\n 18 \nassociation between HSM2 and hip fracture in the same population40. NSA could not be derived 348 \nfrom the DXA scans due to the limited view of the femoral shaft. Given that prior studies have 349 \nlinked higher NSA to hip fracture , and HSM2 represents a higher NSA, we were unable to 350 \ndetermine if the association between HSM2 and hip fracture was independent of NSA10,41. The 351 \npredominantly Caucasian study population may limit the generalisability of the findings . 352 \nNotably, differences in hip shape have been reported between the UK Biobank cohort and the 353 \nexclusively Chinese Shanghai Changfeng cohort 42. The mean age of participants  (63.7 years) 354 \nmay have reduced the study’s power, as hip fractures predominantly occur in older 355 \nindividuals43. However, as participants continue to be followed-up and additional DXA images 356 \nfrom UKB become available, analysis can be repeated with more hip fracture cases, potentially 357 \nstrengthening findings. Since the analysis focused only on left hip DXA scans, and the side of 358 \nthe body the hip fracture occurred on is unknown, it is plausible that effect estimates could be 359 \nbiased towards the null.  As a result, the true effect of hip shape on fracture risk may be 360 \nunderestimated or not fully captured in the analysis.  Furthermore, using 2-dimensional DXA 361 \nscans to assess the shape of a 3-dimentional structure may result in the loss of spatial detail ; 362 \nhowever, SSM can help mitigate these limitations by using proportional rather than absolute 363 \nvalues of hip shape as described by GMs9. 364 \nIn conclusion, this study examined SSM -derived hip shape using high -resolution DXA scans 365 \nfrom a large cohort of UKB participants, demonstrating risk of incident hip fracture is higher 366 \nwith a narrower femoral neck, a higher femoral neck angle and reduced acetabular coverage. 367 \nImportantly these associations were independent of a wide range of covariates including 368 \nestablished measures of femoral geometry. Given that DXA scans are already routinely used to 369 \nassess osteoporosis risk, it is conceivable that SSM -derived measures of hip shape could be  370 \naccommodated into existing fracture risk tools such as FRAX® to improve prediction. This 371 \napproach could facilitate targeted preventative treatments for individuals with hip shapes 372 \n . CC-BY 4.0 International licenseIt is made available under a \nperpetuity. \n is the author/funder, who has granted medRxiv a license to display the preprint in(which was not certified by peer review)preprint \nThe copyright holder for thisthis version posted October 2, 2025. ; https://doi.org/10.1101/2025.09.30.25336960doi: medRxiv preprint \n\n 19 \nresembling HSM2, thereby reducing the risk of hip fractures  and alleviating the resultant 373 \nmorbidity and mortality.  374 \n . CC-BY 4.0 International licenseIt is made available under a \nperpetuity. \n is the author/funder, who has granted medRxiv a license to display the preprint in(which was not certified by peer review)preprint \nThe copyright holder for thisthis version posted October 2, 2025. ; https://doi.org/10.1101/2025.09.30.25336960doi: medRxiv preprint \n\n 20 \nACKNOWLEDGEMENTS  \nThis study has us ed the UK Biobank resource, access application 17295.  The authors would \nlike to thank Dr Monika Frysz, who was instrumental in deriving the hip shape modes in UK \nBiobank. For the purpose of Open Access, the author has applied a Creative Commons \nAttribution (CC BY) licence to any Author Accepted Manuscript version arising from this \nsubmission. NCH is supported by the UK Medical Research Council (MRC) [MC_PC_21003; \nMC_PC_21001] and National Institute for Health Research (NIHR) Southampton Biomedical \nResearch Centre, University of Southampton, and University Hosp ital Southampton NHS \nFoundation Trust, UK. \nAuthor contributions  \nContribution to study conception and design: SS, JHT, BGF, RAB \nContribution to acquisition of data: SS, AH, RE, FRS, JSG, RMA, CL, TC, NCH, JHT, RAB, \nBGF \nContribution to analysis and interpretation of data: SS, AH, RE, FRS, JSG, RMA, CL, TC, \nNCH, JHT, RAB, BGF  \nDrafting the article: SS, BGF, RAB \nReviewing the final manuscript: SS, AH, RE, FRS, JSG, RMA, CL, TC, NCH, JHT, RAB, \nBGF \nConflict of interest statement \nThe authors have no conflicts of interest to disclose. \n  \n . CC-BY 4.0 International licenseIt is made available under a \nperpetuity. \n is the author/funder, who has granted medRxiv a license to display the preprint in(which was not certified by peer review)preprint \nThe copyright holder for thisthis version posted October 2, 2025. ; https://doi.org/10.1101/2025.09.30.25336960doi: medRxiv preprint \n\n 21 \nData access statement \nAll data variables are available from UK Biobank. The BoneFinder® search model and the \nSSM can be requested via the BoneFinder® website for independent validation: https://bone-\nfinder.com/ \nEthics statement \nThis study is overseen by the UKB Ethics Advisory Committee, and ethical approval was given \nby the National Information Governing Board for Health and Social Care and North -West \nMulti-centre Research Ethic s committee (11/NW/0382). All participants provided informed \nconsent for their data to be used in the study. \nFunding statement \nSS and AH were self-funded undergraduate students. BGF is supported by an NIHR Academic \nClinical Lectureship and an Academy of Medical Sciences Starter Grant (SGL030\\1057). RB, \nRE, FS and MJ were supported by a Wellcome Trust collaborative award (209233/Z/17/Z). CL \nis funded by a Sir Henry Dale Fellowship jointly funded by the Wellcome Trust and the Royal \nSociety (223267/Z/21/Z). For the purposes of open access, the authors have applied a CC BY \npublic copyright licence to any Author Accepted Manuscript version arising from this \nsubmission.\n . CC-BY 4.0 International licenseIt is made available under a \nperpetuity. \n is the author/funder, who has granted medRxiv a license to display the preprint in(which was not certified by peer review)preprint \nThe copyright holder for thisthis version posted October 2, 2025. ; https://doi.org/10.1101/2025.09.30.25336960doi: medRxiv preprint \n\n 22 \nReferences  \n1. Harris E, Clement N, MacLullich A, Farrow L. 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Geriatr Gerontol Int. \nApr 2013;13(2):413-20. doi:10.1111/j.1447-0594.2012.00918.x \n37. Hong W, Cheng Q, Zhu X, et al. Prevalence of Sarcopenia and Its Relationship with \nSites of Fragility Fractures in Elderly Chinese Men and Women. PLoS One. \n2015;10(9):e0138102. doi:10.1371/journal.pone.0138102 \n38. Harvey NC, Orwoll E, Kwok T, et al. Sarcopenia Definitions as Predictors of Fracture \nRisk Independent of FRAX(®) , Falls, and BMD in the Osteoporotic Fractures in Men \n(MrOS) Study: A Meta-Analysis. J Bone Miner Res. Jul 2021;36(7):1235-1244. \ndoi:10.1002/jbmr.4293 \n39. Testa G, Vescio A, Zuccalà D, et al. Diagnosis, Treatment and Prevention of \nSarcopenia in Hip Fractured Patients: Where We Are and Where We Are Going: A Systematic \nReview. J Clin Med. Sep 17 2020;9(9)doi:10.3390/jcm9092997 \n40. Faber BG, Frysz M, Zheng J, et al. The genetic architecture of hip shape and its role \nin the development of hip osteoarthritis and fracture. Human Molecular Genetics. \n2024;doi:10.1093/hmg/ddae169 \n41. Gómez Alonso C, Díaz Curiel M, Hawkins Carranza F, Pérez Cano R, Díez Pérez A. \nFemoral Bone Mineral Density, Neck-Shaft Angle and Mean Femoral Neck Width as \nPredictors of Hip Fracture in Men and Women. Osteoporos Int. Sep 2000;11(8):714-20. \ndoi:10.1007/s001980070071 \n42. Zheng J, Frysz M, Faber BG, et al. Comparison between UK Biobank and Shanghai \nChangfeng suggests distinct hip morphology may contribute to ethnic differences in the \nprevalence of hip osteoarthritis. Osteoarthritis and Cartilage. 2023/11/05/ \n2023;doi:10.1016/j.joca.2023.10.006 \n43. Johnell O, Kanis JA. An estimate of the worldwide prevalence and disability \nassociated with osteoporotic fractures. Osteoporos Int. Dec 2006;17(12):1726-33. \ndoi:10.1007/s00198-006-0172-4 \n \n  \n . CC-BY 4.0 International licenseIt is made available under a \nperpetuity. \n is the author/funder, who has granted medRxiv a license to display the preprint in(which was not certified by peer review)preprint \nThe copyright holder for thisthis version posted October 2, 2025. ; https://doi.org/10.1101/2025.09.30.25336960doi: medRxiv preprint \n\n 25 \nTABLES \nTable 1 Descriptive statistics of UK Biobank participants included in this study \n  Combined Female Male \n  N = 38,123 N = 19,820 (52%) N = 18,303 (48%) \nExposures Mean [SD, Range] Mean [SD, Range] Mean [SD, Range] \nAge (years) 63.7 [7.6, 44-82] 63.0 [7.4, 45-82] 64.3 [7.7, 44-81] \nHeight (cm) 170.2 [9.4, 135-204] 163.7 [6.4, 135-196] 177.2 [6.6, 150-204] \nWeight (kg) 75.4 [15.1, 34-171] 68.2 [12.9, 34-169] 83.2 [13.4, 47-171] \nLeft femoral bone mineral \ndensity (g/cm2) \n1.0 [0.2, 0.0-1.7] 0.9 [0.1, 0.1-1.7] 1.1 [0.1, 0.0-1.7] \nHip shape mode 1 0.0 [1.0, -4.6-3.9] 0.3 [0.9, -3.8-3.9] -0.3 [1.0, -4.6-3.6] \nHip shape mode 2 0.0 [1.0, -4.7-4.5] -0.0 [1.0, -4.7-4.2] 0.0 [1.0, -4.5-4.5] \nHip shape mode 3 0.0 [1.0, -4.1-4.3] -0.3 [0.9, -4.1-4.0] 0.3 [1.0, -3.6-4.3] \nHip shape mode 4 0.0 [1.0, -4.4-4.0] -0.1 [1.0, -4.4-4.0] 0.1 [1.0, -3.8-4.0] \nHip shape mode 5 0.0 [1.0, -4.5-3.5] 0.0 [1.1, -4.4-3.4] -0.0 [0.9, -4.5-3.5] \nHip shape mode 6 0.0 [1.0, -4.6-5.0] 0.2 [1.0, -3.4-5.0] -0.2 [1.0, -4.6-3.9] \nHip shape mode 7 0.0 [1.0, -4.9-5.0] 0.1 [1.0, -4.9-4.7] -0.1 [1.0, -4.6-5.0] \nHip shape mode 8 0.0 [1.0, -4.4-4.5] -0.1 [1.0, -4.4-4.0] 0.1 [1.0, -4.0-4.5] \nHip shape mode 9 0.0 [1.0, -4.1-5.0] -0.3 [0.9, -4.1-4.5] 0.3 [1.0, -3.7-5.0] \nHip shape mode 10 0.0 [1.0, -4.1-3.8] 0.0 [1.0, -4.1-3.8] -0.0 [1.0, -4.1-3.8] \nNarrowest neck width (mm) 31.6 [3.5, 21.4-45.8] 29.0 [2.0, 21.4-37.8] 34.5 [2.4, 22.9-45.8] \nDiameter of femoral head (mm) 45.9 [3.8, 33.4-64.4] 43.0 [2.3, 33.4-53.7] 49.0 [2.6, 34.7-64.4] \nHip axis length (mm) 96.7 [8.0, 68.1-127.1] 90.8 [4.8, 68.1-115.5] 103.1 [5.5, 76.9-127.1] \n  Number fractured [%] Number fractured [%] Number fractured [%] \nHospital diagnosed fracture 133 [0.35] 89 [0.45] 44 [0.24] \n  Mean [SD, Range] Mean [SD, Range] Mean [SD, Range] \nTime to end of study (years) 5.0 [1.5, 0.2-8.5] 5.0 [1.5, 0.1-8.5] 5.0 [1.5, 0.2-8.5] \nPopulation characteristics of the UK Biobank participants included in this study with complete \ndata for all covariates. \n . CC-BY 4.0 International licenseIt is made available under a \nperpetuity. \n is the author/funder, who has granted medRxiv a license to display the preprint in(which was not certified by peer review)preprint \nThe copyright holder for thisthis version posted October 2, 2025. ; https://doi.org/10.1101/2025.09.30.25336960doi: medRxiv preprint \n\n 26 \nTable 2: Cox proportional hazard results for the association between each hip shape mode and hip fracture in combined sex analysis \nHazard ratios (HR) with 95% confidence intervals (CI) and p -values are shown for each hip shape mode and their association to hip fracture. \nModel 1 = unadjusted; model 2 = adjusted for age, sex, height, and weight; model 3 = adjusted for model 2 plus bone mineral density; model 4 = \nadjusted for model 3 plus the geometric measures.\n  Model 1 Model 2 Model 3 Model 4 \nExposure HR [95% CI] p-value HR [95% CI] p-value HR [95% CI] p-value HR [95% CI] p-value \nHip shape mode 1 1.01 [0.85-1.20] 0.91 0.94 [0.79-1.13] 0.52 1.11 [0.92-1.33] 0.27 1.12 [0.90-1.40] 0.33 \nHip shape mode 2 1.32 [1.11-1.56] 1.47 × 10-3 1.36 [1.15-1.62] 3.3 × 10-4 1.31 [1.11-1.55] 1.51 × 10-3 1.30 [1.09-1.55] 3.27 × 10-3 \nHip shape mode 3 0.99 [0.84-1.18] 0.94 1.13 [0.94-1.35] 0.19 1.16 [0.97-1.39] 0.10 1.10 [0.91-1.31] 0.33 \nHip shape mode 4 0.87 [0.74-1.03] 0.11 0.84 [0.71-1.00] 0.05 0.88 [0.74-1.05] 0.17 0.88 [0.71-1.07] 0.20 \nHip shape mode 5 0.97 [0.82-1.15] 0.74 0.98 [0.84-1.16] 0.86 1.02 [0.87-1.21] 0.79 0.99 [0.83-1.17] 0.89 \nHip shape mode 6 1.09 [0.92-1.29] 0.31 1.00 [0.84-1.19] 1.00 0.96 [0.80-1.14] 0.63 0.96 [0.79-1.16] 0.68 \nHip shape mode 7 0.96 [0.81-1.14] 0.66 0.99 [0.84-1.17] 0.93 1.05 [0.88-1.24] 0.60 1.11 [0.93-1.31] 0.25 \nHip shape mode 8 1.00 [0.84-1.19] 0.99 1.02 [0.86-1.21] 0.82 0.95 [0.80-1.12] 0.53 1.00 [0.83-1.21] 0.98 \nHip shape mode 9 0.89 [0.75-1.06] 0.19 0.96 [0.81-1.15] 0.68 0.99 [0.82-1.18] 0.87 0.90 [0.75-1.08] 0.26 \nHip shape mode 10 0.99 [0.84-1.18] 0.93 0.95 [0.80-1.13] 0.59 0.97 [0.82-1.15] 0.74 0.91 [0.76-1.09] 0.30 \n . CC-BY 4.0 International licenseIt is made available under a \nperpetuity. \n is the author/funder, who has granted medRxiv a license to display the preprint in(which was not certified by peer review)preprint \nThe copyright holder for thisthis version posted October 2, 2025. ; https://doi.org/10.1101/2025.09.30.25336960doi: medRxiv preprint \n\n 27 \nFIGURES \n \nFigure 1: An example hip DXA scan from UKB showing the points placed around the \nhip joint. \n . CC-BY 4.0 International licenseIt is made available under a \nperpetuity. \n is the author/funder, who has granted medRxiv a license to display the preprint in(which was not certified by peer review)preprint \nThe copyright holder for thisthis version posted October 2, 2025. ; https://doi.org/10.1101/2025.09.30.25336960doi: medRxiv preprint \n\n 28 \n \n \nFigure 2: The ten hip shape modes (HSMs). The solid line shows the shape +2 standard deviations ( SD) from the mean, and the dotted line \nshows the shape -2 SDs from the mean.\n . CC-BY 4.0 International licenseIt is made available under a \nperpetuity. \n is the author/funder, who has granted medRxiv a license to display the preprint in(which was not certified by peer review)preprint \nThe copyright holder for thisthis version posted October 2, 2025. ; https://doi.org/10.1101/2025.09.30.25336960doi: medRxiv preprint \n\n 29 \n \nFigure 3: Composite image of the ten hip shape modes.  The solid line shows the shape at \nrisk of fracture, the dotted line shows the mean shape. \n \n . CC-BY 4.0 International licenseIt is made available under a \nperpetuity. \n is the author/funder, who has granted medRxiv a license to display the preprint in(which was not certified by peer review)preprint \nThe copyright holder for thisthis version posted October 2, 2025. ; https://doi.org/10.1101/2025.09.30.25336960doi: medRxiv preprint \n\n 30 \n \nFigure 4: Cox proportional hazard results for the association between each hip shape mode (HSM) and hip fracture in combined sex \nanalysis. Hazard ratios (HR) with 95% confidence intervals (CI) are plotted. Square = unadjusted (model 1); circle = adjusted for age, sex, height, \nand weight (model 2); triangle = adjusted for model 2 plus bone mineral density (model 3) ; diamond = fully adjusted for model 3 plus the three \ngeometric measures (model 4). \n . CC-BY 4.0 International licenseIt is made available under a \nperpetuity. \n is the author/funder, who has granted medRxiv a license to display the preprint in(which was not certified by peer review)preprint \nThe copyright holder for thisthis version posted October 2, 2025. ; https://doi.org/10.1101/2025.09.30.25336960doi: medRxiv preprint \n\n 31 \nFigure 5: Cox proportional hazard results for the association between each geometric measure (GM) and hip fracture in combined sex \nanalysis. Hazard ratios (HR) with 95% confidence intervals (CI) are plotted. Square = unadjusted (model 1); circle = adjusted for age, sex, height, \nand weight (model 2); triangle = adjusted for model 2 plus bone mineral density (model 3) ; diamond = fully adjusted for model 3 plus the other \ntwo geometric measures (model 4). \nFNW = femoral neck width, FHD = femoral head diameter, HAL = hip axis length \n \n . CC-BY 4.0 International licenseIt is made available under a \nperpetuity. \n is the author/funder, who has granted medRxiv a license to display the preprint in(which was not certified by peer review)preprint \nThe copyright holder for thisthis version posted October 2, 2025. ; https://doi.org/10.1101/2025.09.30.25336960doi: medRxiv preprint \n\n 32 \nSUPPLEMENTARY TABLES AND FIGURES  \nSupplementary Table 1: Cox proportional hazard results for the association between each hip shape mode and hip fracture in sex-\nstratified analysis \n  Model 1 Model 2  Model 3 Model 4 \nExposure HR [95% CI] p-value HR [95% CI] p-value HR [95% CI] p-value HR [95% CI] p-value \nMales                 \nHip shape mode 1 1.12 [0.83-1.52] 0.47 1.09 [0.80-1.47] 0.59 1.28 [0.94-1.75] 0.12 1.32 [0.89-1.97] 0.17 \nHip shape mode 2 1.43 [1.06-1.93] 0.02 1.39 [1.03-1.88] 0.03 1.38 [1.02-1.86] 0.04 1.34 [0.98-1.84] 0.06 \nHip shape mode 3 1.20 [0.88-1.62] 0.25 1.25 [0.92-1.69] 0.16 1.27 [0.94-1.73] 0.12 1.17 [0.86-1.60] 0.33 \nHip shape mode 4 0.79 [0.59-1.07] 0.13 0.77 [0.57-1.04] 0.09 0.79 [0.58-1.07] 0.13 0.72 [0.50-1.03] 0.08 \nHip shape mode 5 0.84 [0.62-1.15] 0.28 0.85 [0.63-1.16] 0.32 0.92 [0.67-1.26] 0.59 0.87 [0.63-1.20] 0.40 \nHip shape mode 6 1.00 [0.73-1.36] 1.00 0.99 [0.73-1.35] 0.97 0.96 [0.70-1.31] 0.79 0.94 [0.68-1.31] 0.73 \nHip shape mode 7 1.10 [0.82-1.47] 0.53 1.14 [0.85-1.52] 0.39 1.20 [0.89-1.61] 0.24 1.29 [0.96-1.74] 0.09 \nHip shape mode 8 0.98 [0.73-1.32] 0.89 0.98 [0.73-1.31] 0.89 0.90 [0.67-1.21] 0.48 0.96 [0.70-1.32] 0.81 \nHip shape mode 9 0.72 [0.54-0.98] 0.04 0.70 [0.52-0.95] 0.02 0.73 [0.54-0.99] 0.04 0.66 [0.48-0.89] 0.01 \nHip shape mode 10 0.95 [0.72-1.26] 0.73 0.94 [0.71-1.25] 0.68 0.97 [0.74-1.28] 0.84 0.91 [0.67-1.23] 0.53 \nFemales                 \nHip shape mode 1 0.83 [0.67-1.03] 0.09 0.87 [0.70-1.09] 0.22 1.02 [0.82-1.28] 0.83 1.04 [0.80-1.36] 0.76 \nHip shape mode 2 1.27 [1.04-1.56] 0.02 1.37 [1.11-1.68] 2.79 × 10-3 1.26 [1.03-1.54] 0.02 1.27 [1.03-1.57] 0.02 \nHip shape mode 3 1.05 [0.84-1.31] 0.68 1.07 [0.86-1.34] 0.54 1.13 [0.90-1.40] 0.29 1.08 [0.86-1.35] 0.52 \nHip shape mode 4 0.97 [0.78-1.19] 0.75 0.87 [0.71-1.08] 0.21 0.94 [0.76-1.15] 0.54 0.96 [0.75-1.23] 0.76 \nHip shape mode 5 1.01 [0.83-1.24] 0.89 1.05 [0.86-1.27] 0.65 1.05 [0.86-1.29] 0.60 1.03 [0.84-1.26] 0.78 \nHip shape mode 6 1.03 [0.84-1.27] 0.78 1.01 [0.82-1.24] 0.96 0.96 [0.78-1.18] 0.68 0.96 [0.76-1.22] 0.75 \nHip shape mode 7 0.87 [0.71-1.07] 0.18 0.94 [0.77-1.15] 0.56 0.99 [0.81-1.22] 0.96 1.04 [0.84-1.27] 0.73 \nHip shape mode 8 1.05 [0.85-1.29] 0.68 1.04 [0.85-1.28] 0.70 0.98 [0.80-1.21] 0.87 1.03 [0.82-1.29] 0.82 \n . CC-BY 4.0 International licenseIt is made available under a \nperpetuity. \n is the author/funder, who has granted medRxiv a license to display the preprint in(which was not certified by peer review)preprint \nThe copyright holder for thisthis version posted October 2, 2025. ; https://doi.org/10.1101/2025.09.30.25336960doi: medRxiv preprint \n\n 33 \nHip shape mode 9 1.14 [0.91-1.41] 0.25 1.15 [0.92-1.43] 0.22 1.15 [0.92-1.44] 0.21 1.07 [0.85-1.35] 0.56 \nHip shape mode 10 1.01 [0.81-1.26] 0.92 0.97 [0.78-1.21] 0.80 0.96 [0.77-1.19] 0.68 0.91 [0.72-1.14] 0.40 \nHazard ratios (HR) with 95% confidence intervals (CI) and p -values are shown for each hip shape mode and their association to hip fracture. \nModel 1 = unadjusted ; model 2 = adjusted for age, height, and weight ; model 3 = adjusted for model 2 plus bone mineral density ; model 4 = \nadjusted for model 3 plus the geometric measures. \n  \n . CC-BY 4.0 International licenseIt is made available under a \nperpetuity. \n is the author/funder, who has granted medRxiv a license to display the preprint in(which was not certified by peer review)preprint \nThe copyright holder for thisthis version posted October 2, 2025. ; https://doi.org/10.1101/2025.09.30.25336960doi: medRxiv preprint \n\n 34 \nSupplementary Table 2: Cox proportional hazard results for the association between each geometric measurement and hip fracture. \n  Model 1 Model 2 Model 3 Model 4 \nExposure HR [95% CI] p-value HR [95% CI] p-value HR [95% CI] p-value HR [95% CI] p-value \nCombined sex                 \nFNW (mm) 1.15 [0.97-1.36] 0.11 2.45 [1.80-3.33] 1.17 × 10-8 1.77 [1.30-2.43] 3.26 × 10-4 1.31 [0.88-1.96] 0.19 \nFHD (mm)  1.12 [0.95-1.33] 0.17 2.42 [1.76-3.32] 5.20 × 10-8 1.89 [1.39-2.57] 4.48 × 10-5 1.47 [0.96-2.25] 0.07 \nHAL (mm) 1.08 [0.91-1.28] 0.39 1.85 [1.34-2.55] 1.93 × 10-4 1.61 [1.18-2.21] 3.08 × 10-3 1.21 [0.84-1.74] 0.31 \nMales                 \nFNW (mm) 2.17 [1.44-3.25] 1.99 × 10-4 2.13 [1.33-3.43] 1.74 × 10-3 1.75 [1.08-2.82] 0.02 1.17 [0.63-2.18] 0.62 \nFHD (mm)  2.30 [1.54-3.44] 4.50 × 10-5 2.36 [1.45-3.84] 5.54 × 10-4 2.01 [1.28-3.14] 2.26 × 10-3 1.61 [0.85-3.06] 0.14 \nHAL (mm) 2.07 [1.37-3.11] 4.84 × 10-4 2.01 [1.19-3.37] 0.01 1.84 [1.11-3.04] 0.02 1.39 [0.78-2.45] 0.26 \nFemales                 \nFNW (mm) 2.88 [2.05-4.06] 1.40 × 10-9 2.71 [1.80-4.08] 1.63 × 10-6 1.70 [1.11-2.59] 0.01 1.39 [0.82-2.35] 0.22 \nFHD (mm)  2.43 [1.73-3.40] 2.60 × 10-7 2.46 [1.62-3.74] 2.36 × 10-5 1.70 [1.11-2.60] 0.01 1.35 [0.77-2.36] 0.29 \nHAL (mm) 2.05 [1.48-2.85] 1.58 × 10-5 1.73 [1.15-2.62] 0.01 1.40 [0.92-2.13] 0.11 1.09 [0.67-1.75] 0.73 \nHazard ratios (HR) with 95% confidence intervals (CI) and p -values are shown for each geometric measure and their association to hip fracture. \nModel 1 = unadjusted; model 2 = adjusted for age, sex, height, and weight  (no sex adjustment in sex -stratified analysis); model 3 = adjusted for \nmodel 2 plus bone mineral density; model 4 = adjusted for model 3 plus the remaining 2 geometric measures. \nFNW = femoral neck width, FHD = femoral head diameter, HAL = hip axis length \n . CC-BY 4.0 International licenseIt is made available under a \nperpetuity. \n is the author/funder, who has granted medRxiv a license to display the preprint in(which was not certified by peer review)preprint \nThe copyright holder for thisthis version posted October 2, 2025. ; https://doi.org/10.1101/2025.09.30.25336960doi: medRxiv preprint \n\n 35 \n \nSupplementary Figure 1: Pearson’s correlation matrix (r) showing the correlation between each \nHSM, GM (FHD, HAL, FNW), height, weight,  age and BMD within the cohort. Green shows a \nstrong correlation (r ≥0.7 -1), orange shows a moderate correlation (r ≥0.5 -<0.7), red shows a weak \ncorrelation (r <0.5). \nFHD = femoral head diameter, HAL = hip axis length, FNW = femoral neck width, BMD = bone mineral \ndensity, HSM = hip shape mode, GM = geometric measure. \n \n . CC-BY 4.0 International licenseIt is made available under a \nperpetuity. \n is the author/funder, who has granted medRxiv a license to display the preprint in(which was not certified by peer review)preprint \nThe copyright holder for thisthis version posted October 2, 2025. ; https://doi.org/10.1101/2025.09.30.25336960doi: medRxiv preprint","source_license":"CC-BY-4.0","license_restricted":false}