Objectives:To investigate the biological and clinical relevance of knee alignment in osteoarthritis by integrating population-scale imaging, genome-wide association, and functional genetic analyses. Methods:Femorotibial angle was derived from dual-energy X-ray absorptiometry scans in UK Biobank using machine-learning methods. Associations with knee and hip osteoarthritis outcomes were assessed. A genome-wide association study of mean femorotibial angle was performed, followed by fine-mapping and pathway enrichment analyses. Mendelian randomization was used to explore potential causal relationships. Results:Varus alignment was strongly and progressively associated with knee pain, knee osteoarthritis, and total knee replacement (HR 3.42 [95% CI 2.92, 4.02]), with no association observed for hip osteoarthritis. GWAS identified 20 independent loci associated with femorotibial angle, enriched for pathways related to skeletal development, cartilage biology, and endochondral ossification. Post-GWAS analyses demonstrated regulatory effects across fetal and adult joint tissues, supporting life course influences on alignment. Genetic correlation analyses showed shared architecture between femorotibial angle and knee osteoarthritis. Causal analyses suggested that genetic liability to osteoarthritis reduces femorotibial angle (β -0.11 [-0.16, - 0.06]), while evidence for an overall causal effect of femorotibial angle on osteoarthritis risk was limited (OR 0.93 [0.79, 1.10]). Conclusions:Knee alignment and susceptibility to knee osteoarthritis are partially genetically determined. At the population level, these genetic determinants support a causal effect of osteoarthritis on knee alignment, whereas evidence for a causal effect of alignment on knee osteoarthritis was limited. Furthermore, this study identifies novel genetic loci linking knee alignment with pathways involved in skeletal development and cartilage biology relevant to osteoarthritis. Key messages:What is already known on this topic?: Lower limb malalignment, particularly varus alignment, is associated with knee osteoarthritis.Previous studies have been observational, limiting their ability to determine whether malalignment is a cause or consequence of osteoarthritis.Genetic influences on knee alignment have been recognized, but its underlying genetic architecture and relationship with osteoarthritis susceptibility remain poorly understood.What this study adds?: This study identifies 20 genetic loci associated with femorotibial angle, which demonstrate enrichment in molecular pathways responsible for bone and cartilage development and maintenance across the life course.Instrumental variables analyses using genetic data provides greater evidence that osteoarthritis has a causal influence on knee alignment than alignment has on osteoarthritis risk.How might this affect research, practice or policy?: At a population level, knee alignment should be considered a marker of disease severity and progression rather than a causal target for osteoarthritis prevention.These findings support the use of alignment-modifying interventions, such as osteotomy and bracing, to slow progression in established osteoarthritis but not as a means of preventing disease onset in the general population.Novel genetic pathways associated with varus deformity and osteoarthritis could guide future patient stratification and targeted treatment.

OBJECTIVE:Patients with osteoarthritis (OA) affecting multiple joints experience greater pain than those with single-joint disease, yet most research examines isolated joints, leaving a gap in multi-joint disease. This study aimed to describe radiographic hip (rHOA) and knee OA (rKOA) within UK Biobank (UKB), exploring interrelationships across joints and associations with joint pain, body size, race and deprivation. DESIGN:This cross-sectional study applied machine learning to hip and knee dual-energy X-ray absorptiometry scans. Radiographic OA (rOA) was defined as custom grades ≥2. Joint pain was assessed through self-reported questionnaires. Logistic regression models examined bilateral and cross-joint associations, as well as associations with joint pain. Adjustments were made for age, sex, race, height, weight and deprivation. RESULTS:Among 59,475 individuals (mean age 65 years; range 45-85; SD 7.7; 52.8% female), rHOA prevalence was 4098 (6.9%, right-side) and 4841 (8.1%, left-side). The corresponding estimates for rKOA were 3750 (6.3%) and 4220 (7.1%). Overall, increasing grades of rOA and number of joints affected were more strongly associated with concurrent joint pain (four-joints: OR 4.3 [95%CI 2.6-7.2]). Regarding joint-interrelationships, bilateral associations were stronger at the knee (26.1 [24.1-28.2]) than the hip (5.6 [5.2-6.1]). Cross-joint associations (hip-knee) were weaker. BMI was more strongly associated with rKOA (1.57 [1.52-1.61]) than rHOA (1.05 [1.02-1.09]). Greater height was positively associated with rHOA but appeared protective for rKOA. CONCLUSIONS:Radiographic hip and knee OA exhibit distinct patterns of interrelationship, associations with symptoms and risk factors, suggesting heterogeneity in disease processes and the need for joint-specific treatment.

Despite advancements in fracture prediction tools and osteoporosis management, hip fractures remain a significant consequence of bone fragility, carrying a 22% 1-yr mortality rate. Hip geometric measures (GMs) have been associated with fracture risk; however, their strong correlation hinders the identification of independent influences, leaving their relative predictive value unclear. Statistical shape modeling (SSM) provides a more holistic assessment of hip shape compared to using predetermined GMs. This study aimed to evaluate whether SSM-derived hip shape from DXA scans can predict hip fracture, independently of individual GMs. Previously, we applied SSM to left hip DXA images in UK Biobank—a large prospective cohort with linked hospital records—generating 10 orthogonal hip shape modes (HSMs) that explained 86% of shape variance. Additionally, FN width (FNW), femoral head diameter (FHD), and hip axis length (HAL) were derived from these DXAs. In the current analysis, Cox proportional hazard models, adjusted for age, sex, height, weight, BMD, and GMs (FNW, HAL, and FHD), were used to examine the longitudinal associations between each HSM and first incident hospital diagnosed hip fracture. A Bonferroni adjusted p-value threshold (p < .004) was used to account for the 13 exposures. Among the 38 123 participants (mean age 63.7 yr; 52% female; mean follow-up 5 yr), 133 (0.35%) experienced subsequent hip fracture. HSM2, characterized by a narrower FNW, a higher neck shaft angle, and reduced acetabular coverage, showed a strong association with hip fracture risk (HR: 1.32, 95% CI: 1.11-1.58, p: 1.47 × 10−3), which persisted after full adjustment (1.30, 1.09-1.55, 3.27 × 10−3). There was no evidence for an association with other HSMs. These findings suggest that DXA-derived hip shape is associated with hip fracture risk independently of BMD and GMs. Incorporating global hip shape into fracture risk assessment tools could enhance prediction accuracy and inform targeted interventions.

Objectives DXA scans may offer a novel means of evaluating radiographic knee OA (rKOA) in large population studies and through opportunistic screening. We aimed to develop and apply a semi-automated method for assessing rKOA using approximate to 20 000 knee DXA images from UK Biobank (UKB) and assess its face validity by checking for expected relationships with clinical outcomes.Methods Right knee DXA scans were manually annotated for osteophytes to derive corresponding grades. Joint space narrowing (JSN) grades in the medial joint compartment were determined from automatically measured minimum joint space width. Overall rKOA grade (0-4) was determined by combining osteophyte and JSN grades. Logistic regression was employed to investigate the associations of osteophyte, JSN and rKOA grades with knee pain and hospital-diagnosed KOA. Cox proportional hazards modelling was used to examine the associations of these variables with risk of subsequent total knee replacement (TKR).Results Of the 19 595 participants included (mean age 63.7 years), 19.5% had rKOA grade >= 1 (26.1% female, 12.5% male). Grade >= 1 osteophytes and grade >= 1 JSN were associated with knee pain, hospital-diagnosed KOA and TKR. Higher rKOA grades were linked to stronger associations with these clinical outcomes, with the most pronounced effects observed for TKR. Hazard ratios for the association of rKOA grades with TKR were 3.28, 8.75 and 28.63 for grades 1, 2 and 3-4, respectively.Conclusions Our DXA-derived measure of rKOA demonstrated a progressive relationship with clinical outcomes. These findings support the use of DXA for classifying rKOA in large epidemiological studies and in future population-based screening. What does this mean for patients?Knee osteoarthritis (KOA) is a common condition causing pain and disability, often requiring knee replacement surgery. Diagnosing KOA typically involves X-rays to detect changes such as bone growths (osteophytes) and narrowing between bones (joint space narrowing). However, using X-rays in large studies is time-consuming and subjective. Dual-energy X-ray absorptiometry (DXA), a low-radiation imaging method primarily used to measure bone density, offers a promising alternative for KOA assessment. In this study, we analysed nearly 20 000 knee DXA scans from UK Biobank, using computer-aided techniques to measure osteophytes and joint space narrowing. We found that higher grades of DXA-derived radiographic KOA were strongly linked to knee pain, hospital-diagnosed KOA and total knee replacement. These findings suggest that DXA scans can effectively identify KOA and its progression, making them valuable for large studies. As DXA is already widely used for bone health checks, this method could enable early detection and better management of KOA, improving outcomes for patients.

Objective: Lower limb alignment may predispose to, or exacerbate, symptoms of knee osteoarthritis. To examine the role of this and other joint shape variation, we conducted a cross-sectional study investigating relationships between radiographic knee osteoarthritis (rKOA) and knee shape in dual-energy X-ray absorptiometry (DXA) images from UK Biobank (UKB). Methods: Associations between the first ten knee shape modes (KSMs), derived from statistical shape modelling, and rKOA grade were analysed using logistic regression, adjusting for age, sex, height, weight, and ethnicity. An additional model included adjustment for hip-knee-ankle (HKA) angle, derived from total body DXA scans, to reflect knee alignment. Composite figures illustrate knee shape characteristics associated with each rKOA grade. Results: 19,053 individuals were included (mean 63.7 years, 48 ​% males), of whom 80.7 ​%, 14.6 ​%, 3.6 ​% and 1.2 ​% had rKOA grades 0, 1, 2 and 3–4, respectively. Several KSMs were associated with rKOA in confounder-adjusted analyses, with higher grades showing stronger relationships. These associations were attenuated by adjustment for HKA. As expected, composite shape models revealed that higher rKOA grades were associated with greater varus malalignment. After HKA adjustment, composite shape models showed less varus alignment, with other shape differences, such as altered proximal tibial metaphysis and lateral patella displacement, emerging in higher-grade rKOA. Conclusions: Our cross-sectional analyses between joint shape and DXA-derived rKOA grade showed expected relationships with varus malalignment, which were attenuated after adjusting for HKA. Other shape differences, particularly in higher-grade rKOA, emerged independently of alignment, warranting further investigation.

Objective: We aimed to create an imaging biomarker for knee shape using knee dual-energy x-ray absorptiometry (DXA) scans and investigate its potential association with subsequent total knee replacement (TKR), independently of radiographic features of knee osteoarthritis and established risk factors. Methods: Using a 129-point statistical shape model, knee shape (expressed as a B-score) and minimum joint space width (mJSW) of the medial joint compartment (binarized as above or below the first quartile) were derived. Osteophytes were manually graded in a subset of images and an overall score was assigned. Cox proportional hazards models were used to examine the associations of B-score, mJSW and osteophyte score with TKR risk, adjusting for age, sex, height and weight. Results: The analysis included 37,843 individuals (mean age 63.7 years). In adjusted models, B-score was associated with TKR: each unit increase in B-score, re flecting one standard deviation from the mean healthy shape, corresponded to a hazard ratio (HR) of 2.25 (2.08, 2.43), while a lower mJSW had a HR of 2.28 (1.88, 2.77). Among the 6719 images scored for osteophytes, mJSW was replaced by osteophyte score in the most strongly predictive model for TKR. In ROC analyses, a model combining B-score, osteophyte score, and demographics outperformed a model including demographics alone (AUC 1 / 4 0.87 vs 0.73). Conclusions: Using statistical shape modelling, we derived a DXA-based imaging biomarker for knee shape that was associated with kOA progression. When combined with osteophytes and demographic data, this biomarker may help identify individuals at high risk of TKR, facilitating targeted interventions.

Objective We sought to examine associations between height gain across childhood and adolescence with hip shape in individuals aged 60–64 years from the Medical Research Council National Survey of Health and Development, a nationally representative British birth cohort.Methods Height was measured at ages 2, 4, 6, 7, 11 and 15 years, and self-reported at age 20 years. 10 modes of variation in hip shape (HM1–10), described by statistical shape models, were previously ascertained from DXA images taken at ages 60–64 years. Associations between (1) height at each age; (2) Super-Imposition by Translation And Rotation (SITAR) growth curve variables of height size, tempo and velocity; and (3) height gain during specific periods of childhood and adolescence, and HM1–10 were tested.Results Faster growth velocity was associated with a wider, flatter femoral head and neck, as described by positive scores for HM6 (regression coefficient 0.014; 95% CI 0.08 to 0.019; p<0.001) and HM7 (regression coefficient 0.07; 95% CI 0.002 to 0.013; p=0.009), and negative scores for HM10 (regression coefficient −0.006; 95% CI −0.011 to 0.00, p=0.04) and HM2 (males only, regression coefficient −0.017; 95% CI −0.026 to −0.09; p<0.001). Similar associations were observed with greater height size and later height tempo. Examination of height gains during specific periods of childhood and adolescence identified those during the adolescence period as being most consistently associated.Conclusion Our analyses suggest that individual growth patterns, particularly in the adolescent period, are associated with modest variations in hip shape at 60–64 years, which are consistent with features seen in osteoarthritis.

Objectives: Hip shape is thought to be an important causal risk factor for hip osteoarthritis and fracture. We aimed to identify genetic determinants of hip shape and use these to assess causal relationships with hip osteoarthritis. Methods: Statistical hip shape modelling was used to derive 10 hip shape modes (HSMs) from DXA images in UK Biobank and Shanghai Changfeng cohorts (n( total) =43485). Genome-wide association study meta-analyses were conducted for each HSM. Two-sample Mendelian randomisation (MR) was used to estimate causal effects between HSM and hip osteoarthritis using hip fracture as a positive control. Results: Analysis of the first 10 HSMs identified 203 independent association signals (P < 5 x 10(-9)). Hip shape SNPs were also associated (P <2.5 x 10(-4)) with hip osteoarthritis (n = 26) and hip fracture (n =4). Fine mapping implicated SMAD3 and PLEC as candidate genes that may be involved in the development of hip shape and hip osteoarthritis. MR analyses suggested there was no causal effect between any HSM and hip osteoarthritis, however there was evidence that HSM2 (more obtuse neck-shaft angle) and HSM4 (wider femoral neck) have a causal effect on hip fracture (ORIVW method 1.27 [95% CI 1.12-1.44], P =1.79 x 10(-4) and OR IVW 0.74 [0.65-0.84], P = 7.60 x 10(-6) respectively). Conclusions: We report the largest hip shape GWAS meta-analysis that identifies hundreds of novel loci, some of which are also associated with hip osteoarthritis and hip fracture. MR analyses suggest hip shape may not cause hip osteoarthritis but is implicated in hip fractures. Consequently, interventions targeting hip shape in older adults to prevent hip osteoarthritis may prove ineffective.

Objective Minimum joint space width (mJSW) from 2-dimensional images provides a proxy for cartilage thickness. This study aimed to conduct a genome-wide association study (GWAS) of mJSW to (i) identify new genetic determinants of mJSW and use them to (ii) examine causal effects of mJSW on hip osteoarthritis (HOA) risk. Methods GWAS meta-analysis of hip mJSW derived from plain X-rays (four cohorts) or DXA (one cohort) was performed, stratified by sex and adjusted for age and ancestry principal components. Mendelian randomisation (MR) and cluster analyses were used to examine causal effect of mJSW on HOA. Results 50,745 individuals were included in the meta-analysis. 42 SNPs, which mapped to 39 loci (35 novel), were identified. Mendelian randomisation (MR) revealed little evidence of a causal effect of mJSW on HOA ( β IVW -0.01 [95% CI -0.19, 0.17]). However, MR-Clust analysis suggested the null MR estimates reflected the net effect of two distinct causal mechanisms cancelling each other out, one of which was protective, whereas the other increased HOA susceptibility. For the latter mechanism, all loci were positively associated with height, suggesting mechanisms leading to greater height and mJSW increase the risk of HOA in later life. Conclusions GWAS and MR analyses suggested one group of mJSW loci reduces HOA risk via increased mJSW, suggesting possible utility as targets for chondroprotective therapies. The second group of mJSW loci increased HOA risk, despite increasing mJSW, but were also positively related to height, suggesting they contribute to mJSW and HOA risk via a growth-related mechanism. ### Competing Interest Statement TC & CL have a patent Image processing apparatus and method for fitting a deformable shape model to an image using random forest regression voting. This is licensed with royalties to Optasia Medical. NH reports consultancy fees and honoraria from UCB, Amgen, Kyowa Kirin, Thornton Ross, Consilient. ### Funding Statement MF, RE, FS are supported, and this work is funded by a Wellcome Trust collaborative award (reference number 209233). BGF is supported by a Medical Research Council (MRC) clinical research training fellowship (MR/S021280/1). ML is supported by a University of Queensland Research Training Scholarship from The University of Queensland (UQ). ML thanks the Commonwealth Scientific and Industrial Research Organisation for the support through a Postgraduate Top-Up Scholarship. CL was funded by the MRC (MR/S00405X/1) as well as a Sir Henry Dale Fellowship jointly funded by the Wellcome Trust and the Royal Society (223267/Z/21/Z). This research was funded in whole, or in part, by the Wellcome Trust [Grant numbers 080280/Z/06/Z, 20378/Z/16/Z, 223267/Z/21/Z]. For the purpose of open access, the authors have applied a CC BY public copyright licence to any Author Accepted Manuscript version arising from this submission. NCH acknowledges support from the MRC (MC\_PC\_21003; MC\_PC\_21001) and NIHR Southampton Biomedical Research Centre, University of Southampton and University Hospital Southampton. BGF, MF, AEH, GDS, JHT work in the MRC Integrative Epidemiology Unit at the University of Bristol, which is supported by the MRC (MC\_UU\_00011/1). JPK is funded by a National Health and Medical Research Council (Australia) Investigator grant (GNT1177938). DSE acknowledges funding from NIH/NIA U24AG051129. The Osteoporotic Fractures in Men (MrOS) Study is supported by National Institute of Health funding from the following institutes: the National Institute on Aging (NIA), the National Institute of Arthritis and Musculoskeletal and Skin Diseases (NIAMS), the National Center for Advancing Translational Sciences (NCATS), and NIH Roadmap for Medical Research under the following grant numbers: R01 AR052000, K24 AR048841, U01 AG027810, U01 AG042124, U01 AG042139, U01 AG042140, U01 AG042143, U01 AG042145, U01 AG042168, U01 AR066160, and UL1 TR000128. The Study of Osteoporotic Fractures (SOF) is supported by National Institutes of Health funding. The National Institute on Aging (NIA) provides support under the following grant numbers: R01 AG005407, R01 AR35582, R01 AR35583, R01 AR35584, R01 AG005394, R01 AG027574, and R01 AG027576. ### Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes The details of the IRB/oversight body that provided approval or exemption for the research described are given below: UKB study (application number 17295) is overseen by the Ethics Advisory Committee and received approval from the National Information Governance Board for Health and Social Care and Northwest Multi-Centre Research Ethics Committee (11/NW/0382), all participants provided informed consent for this study. The Rotterdam Study has been approved by the Medical Ethics Committee of the Erasmus MC (registration number MEC 02.1015) and by the Dutch Ministry of Health, Welfare and Sport (Population Screening Act WBO, license number 1071272-159521-PG). The Rotterdam Study has been entered into the Netherlands National Trial Register (NTR; [www.trialregister.nl][1]) and into the WHO International Clinical Trials Registry Platform (ICTRP; www.who.int/ictrp/network/primary/en/) under shared catalogue number NTR6831. All participants provided written informed consent to participate in the study and to have their information obtained from treating physicians. I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable. Yes The UK Biobank mJSW data from this study will be available in a forthcoming data release. Users must be registered with UK Biobank to access their resources (). [1]: http://www.trialregister.nl

OBJECTS:Joint morphology is a risk factor for hip osteoarthritis (HOA) and could explain ethnic differences in HOA prevalence. Therefore, we aimed to compare the prevalence of radiographic HOA (rHOA) and hip morphology between the predominantly White UK Biobank (UKB) and exclusively Chinese Shanghai Changfeng (SC) cohorts. METHODS:Left hip iDXA scans were used to quantify rHOA, from a combination of osteophytes (grade ≥1) and joint space narrowing (grade ≥1), and hip morphology. Using an 85-point Statistical Shape Model (SSM) we evaluated cam (alpha angle ≥60°) and pincer (lateral centre-edge angle (LCEA) ≥45°) morphology and acetabular dysplasia (LCEA <25°). Diameter of femoral head (DFH), femoral neck width (FNW), and hip axis length (HAL) were also obtained from these points. Results were adjusted for differences in age, height, and weight and stratified by sex. RESULTS:Complete data were available for 5924 SC and 39,020 White UKB participants with mean ages of 63.4 and 63.7 years old. rHOA prevalence was considerably lower in female (2.2% versus 13.1%) and male (12.0% and 25.1%) SC compared to UKB participants. Cam morphology, rarely seen in females, was less common in SC compared with UKB males (6.3% versus 16.5%). Composite SSM modes, scaled to the same overall size, revealed SC participants to have a wider femoral head compared to UKB participants. FNW and HAL were smaller in SC compared to UKB, whereas DFH/FNW ratio was higher in SC. CONCLUSIONS:rHOA prevalence is lower in Chinese compared with White individuals. Several differences in hip shape were observed, including frequency of cam morphology, FNW, and DFH/FNW ratio. These characteristics have previously been identified as risk factors for HOA and may contribute to observed ethnic differences in HOA prevalence.

Objective To examine the genetic architecture of cam morphology using alpha angle (AA) as a proxy measure and conduct an AA genome‐wide association study (GWAS) followed by Mendelian randomization (MR) to evaluate its causal relationship with hip osteoarthritis (OA). Methods Observational analyses examined associations between AA measurements derived from hip dual x‐ray absorptiometry (DXA) scans from the UK Biobank study and radiographic hip OA outcomes and subsequent total hip replacement. Following these analyses, an AA GWAS meta‐analysis was performed (N = 44,214) using AA measurements previously derived in the Rotterdam Study. Linkage disequilibrium score regression assessed the genetic correlation between AA and hip OA. Genetic associations considered significant ( P < 5 × 10 −8 ) were used as AA genetic instrument for 2‐sample MR analysis. Results DXA‐derived AA showed expected associations between AA and radiographic hip OA (adjusted odds ratio [OR] 1.63 [95% confidence interval (95% CI) 1.58, 1.67]) and between AA and total hip replacement (adjusted hazard ratio 1.45 [95% CI 1.33, 1.59]) in the UK Biobank study cohort. The heritability of AA was 10%, and AA had a moderate genetic correlation with hip OA (r g = 0.26 [95% CI 0.10, 0.43]). Eight independent genetic signals were associated with AA. Two‐sample MR provided weak evidence of causal effects of AA on hip OA risk (inverse variance weighted OR 1.84 [95% CI 1.14, 2.96], P = 0.01). In contrast, genetic predisposition for hip OA had stronger evidence of a causal effect on increased AA (inverse variance weighted β = 0.09 [95% CI 0.04, 0.13], P = 4.58 × 10 −5 ). Conclusion Expected observational associations between AA and related clinical outcomes provided face validity for the DXA‐derived AA measurements. Evidence of bidirectional associations between AA and hip OA, particularly for risk of hip OA on AA, suggests that hip shape modeling secondary to a genetic predisposition to hip OA contributes to the well‐established relationship between hip OA and cam morphology in older adults.

BACKGROUND:Hip minimum joint space width (mJSW) provides a proxy for cartilage thickness. This study aimed to conduct a genome-wide association study (GWAS) of mJSW to (i) identify new genetic determinants of mJSW and (ii) identify which mJSW loci convey hip osteoarthritis (HOA) risk and would therefore be of therapeutic interest. METHODS:GWAS meta-analysis of hip mJSW derived from plain X-rays and DXA was performed, stratified by sex and adjusted for age and ancestry principal components. Mendelian randomisation (MR) and cluster analyses were used to examine causal effect of mJSW on HOA. FINDINGS:50,745 individuals were included in the meta-analysis. 42 SNPs, which mapped to 39 loci, were identified. Mendelian randomisation (MR) revealed little evidence of a causal effect of mJSW on HOA (ORIVW 0.98 [95% CI 0.82-1.18]). However, MR-Clust analysis suggested the null MR estimates reflected the net effect of two distinct causal mechanisms cancelling each other out, one of which was protective, whereas the other increased HOA susceptibility. For the latter mechanism, all loci were positively associated with height, suggesting mechanisms leading to greater height and mJSW increase the risk of HOA in later life. INTERPRETATIONS:One group of mJSW loci reduce HOA risk via increased mJSW, suggesting possible utility as targets for chondroprotective therapies. The second group of mJSW loci increased HOA risk, despite increasing mJSW, but were also positively related to height, suggesting they contribute to HOA risk via a growth-related mechanism. FUNDING:Primarily funded by the Medical Research Council and Wellcome Trust.

ObjectivesTo examine the genetic architecture of cam morphology, using alpha angle (AA) as a proxy measure, we conducted an AA genome wide association study (GWAS), followed by Mendelian randomisation (MR) to evaluate its causal relationship with hip osteoarthritis (HOA).MethodsObservational analyses examined associations between AA derived from hip DXA scans in UK Biobank (UKB), and radiographic HOA (rHOA) and subsequent total hip replacement (THR). Afterwards, an AA GWAS meta-analysis was performed (n=44,214), using AA previously derived in the Rotterdam Study (RS). Linkage disequilibrium score regression assessed the genetic correlation between AA and HOA. Genetic associations with P<5×10−8instrumented AA for two-sample MR.ResultsDXA-derived AA showed expected associations between AA and rHOA (OR 1.63 [95% CI 1.58-1.67]) and THR (HR 1.45 [1.33-1.59]) in UKB. The heritability of AA was 10% and AA had a moderate genetic correlation with HOA (rg=0.26 [0.10-0.43]). Eight independent genetic signals were associated with AA. Two-sample MR provided weak evidence of causal effects of AA on HOA risk (inverse variance weighted (IVW): OR=1.84 [1.14-2.96], P 0.01). In contrast, genetic predisposition for HOA had stronger evidence of a causal effect on increased AA (IVW: β=0.09 [0.04-0.13], P 4.58 × 10−05).ConclusionsExpected observational associations between AA and related clinical outcomes provided face-validity for the DXA-derived AA measures. Evidence of bidirectional associations between AA and HOA, particularly in the reverse direction, suggest that hip shape remodelling secondary to a genetic predisposition to HOA contribute to the well-established relationship between HOA and cam morphology in older adults.

Objective Conventional scoring methods for radiographic hip OA (rHOA) are subjective and show inconsistent relationships with clinical outcomes. To provide a more objective rHOA scoring method, we aimed to develop a semi-automated classifier based on DXA images and confirm its relationships with clinical outcomes. Methods Hip DXAs in UK Biobank (UKB) were marked up for osteophyte area from which acetabular, superior and inferior femoral head osteophyte grades were derived. Joint space narrowing (JSN) grade was obtained automatically from minimum joint space width (mJSW) measures. Clinical outcomes related to rHOA comprised hip pain, hospital diagnosed OA (HES OA) and total hip replacement. Logistic regression and Cox proportional hazard modelling were used to examine associations between overall rHOA grade (0-4; derived from combining osteophyte and JSN grades) and the clinical outcomes. Results A toal of 40 340 individuals were included in the study (mean age 63.7), of whom 81.2% had no evidence of rHOA, while 18.8% had grade >= 1 rHOA. Grade >= 1 osteophytes at each location and JSN were associated with hip pain, HES OA and total hip replacement. Associations with all three clinical outcomes increased progressively according to rHOA grade, with grade 4 rHOA and total hip replacement showing the strongest association [57.70 (38.08-87.44)]. Conclusions Our novel semi-automated tool provides a useful means for classifying rHOA on hip DXAs, given its strong and progressive relationships with clinical outcomes. These findings suggest DXA scanning can be used to classify rHOA in large DXA-based cohort studies supporting further research, with the future potential for population-based screening.

The growing burden from osteoporosis and fragility fractures highlights a need to improve osteoporosis management across healthcare systems. Sub-optimal management of osteoporosis is an area suitable for digital health interventions. While fracture liaison services (FLSs) are proven to greatly improve care for people with osteoporosis, such services might benefit from technologies that enhance automation. The term ‘Digital Health’ covers a variety of different tools including clinical decision support systems, electronic medical record tools, patient decision aids, patient apps, education tools, and novel artificial intelligence (AI) algorithms. Within the scope of this review are AI solutions that use algorithms within health system registries to target interventions. Clinician-targeted, patient-targeted, or system-targeted digital health interventions could be used to improve management and prevent fragility fractures. This review was commissioned by The Royal Osteoporosis Society and Bone Research Academy during the production of the 2020 Research Roadmap ( https://theros.org.uk ), with the intention of identifying gaps where targeted research funding could lead to improved patient health. We explore potential uses of digital technology in the general management of osteoporosis. Evidence suggests that digital technologies can support multidisciplinary teams to provide the best possible patient care based on current evidence and to support patients in self-management. However, robust randomised controlled studies are still needed to assess the effectiveness and cost-effectiveness of these technologies.

Abstract Background/Aims Radiographic hip osteoarthritis (rHOA) is traditionally defined on hip x-rays, using subjective methods such as Kellgren-Lawrence scoring. Associations between subjective rHOA measures and symptoms are inconsistent. Applying digital tools to high-resolution dual-energy X-ray absorptiometry (DXA) scans, we aimed to develop a novel semi-automated classifier for rHOA and evaluate the face validity of the classifier based on relationships with hip pain, hospital diagnosed OA (HES OA), and risk of total hip replacement (THR). Methods Using hip DXAs in UK Biobank, osteophyte grades 0-3 were assigned based on manually measured osteophyte area. Minimum joint space width (mJSW) was automatically measured using outline points placed by a machine learning-based algorithm and then used to categorise individuals into joint space narrowing (JSN) grades 0-3. Osteophyte and JSN grades were combined, using a novel system giving greater to weight to osteophytes, to categorise individuals into rHOA grades 0-4. Logistic regression giving odds ratios (OR) was used to examine associations between rHOA grade and hip pain, and HES OA. Cox proportional hazard models giving hazard ratios (HR) were used to examine associations between rHOA grade and subsequent THR. Our adjusted model included age, sex, height and weight as covariates. Results 40,340 individuals were included in the study (mean age 63.7 [range 44-82], 19294/21046 male/female). 32758 (81.2%) had rHOA grade 0, 4565 (11.3%) grade 1, 2317 (5.7%) grade 2, 543 (1.3%) grade 3, 157 (0.4%) grade 4, with all features of rHOA being more common in males than females. rHOA grades ≥2 were associated with all three clinical outcomes in both unadjusted and adjusted models, a clear dose-response relationship was seen with each increase in grade showing a large rise in OR/HRs (Table 1). Grade 4 rHOA was strongly predictive of THR (HR 57.70 [95%CI 38.08-87.44]). Conclusion We successfully applied a novel semi-automated classifier to over 40,000 individuals from UKB. The validity of our classifier was supported by the strong and progressive relationships observed between rHOA and hip pain, and HES OA and risk of THR. We conclude that hip DXAs provide a promising means of defining rHOA, with potential screening applications in the clinic. Disclosure B.G. Faber: None. R. Ebsim: None. F.R. Saunders: None. M. Frysz: None. C. Lindner: None. J.S. Gregory: None. R.M. Aspden: None. N.C. Harvey: None. G. Davey Smith: None. T. Cootes: None. J.H. Tobias: None.

ABSTRACT The contribution of shape changes to hip osteoarthritis (HOA) remains unclear, as is the extent to which these vary according to HOA severity. In the present study, we used statistical shape modeling (SSM) to evaluate relationships between hip shape and HOA of different severities using UK Biobank DXA images. We performed a cross-sectional study in individuals with left hip dual-energy X-ray absorptiometry (DXA) scans. Statistical shape modeling (SSM) was used to quantify hip shape. Radiographic HOA (rHOA) was classified using osteophyte size and number and joint space narrowing. HOA outcomes ranged in severity from moderate (grade 2) to severe (grade ≥3) rHOA, hospital-diagnosed HOA, and subsequent total hip replacement (THR). Confounder-adjusted logistic regression between the top 10 hip shape modes (HSMs) and OA outcomes was performed. Further models adjusted for alpha angle (AA) and lateral center-edge angle (LCEA), reflecting acetabular dysplasia and cam morphology, respectively. Composite HSM figures were produced combining HSMs associated with separate OA outcomes. A total of 40,311 individuals were included (mean 63.7 years, 47.8% male), of whom 5.7% had grade 2 rHOA, 1.7% grade ≥3 rHOA, 1.3% hospital-diagnosed HOA, and 0.6% underwent THR. Composite HSM figures for grade 2 rHOA revealed femoral neck widening, increased acetabular coverage, and enlarged lesser and greater trochanters. In contrast, grade ≥3 rHOA, hospital-diagnosed HOA, and THR were suggestive of cam morphology and reduced acetabular coverage. Associations between HSMs depicting cam morphology and reduced acetabular coverage and more severe HOA were attenuated by AA and LCEA adjustment, respectively. Relationships between hip shape and HOA differed according to severity. Notably, cam morphology and acetabular dysplasia were features of severe HOA, but unrelated to moderate disease, suggesting possible prognostic utility. © 2022 The Authors. Journal of Bone and Mineral Research published by Wiley Periodicals LLC on behalf of American Society for Bone and Mineral Research (ASBMR).

Osteoporosis causes bones to become weak, porous and fracture more easily. While a vertebral fracture is the archetypal fracture of osteoporosis, it is also the most difficult to diagnose clinically. Patients often suffer further spine or other fractures, deformity, height loss and pain before diagnosis. There were an estimated 520,000 fragility fractures in the United Kingdom (UK) in 2017 (costing £4.5 billion), a figure set to increase 30% by 2030. One way to improve both vertebral fracture identification and the diagnosis of osteoporosis is to assess a patient's spine or hips during routine computed tomography (CT) scans. Patients attend routine CT for diagnosis and monitoring of various medical conditions, but the skeleton can be overlooked as radiologists concentrate on the primary reason for scanning. More than half a million CT scans done each year in the National Health Service (NHS) could potentially be screened for osteoporosis (increasing 5% annually). If CT-based screening became embedded in practice, then the technique could have a positive clinical impact in the identification of fragility fracture and/or low bone density. Several companies have developed software methods to diagnose osteoporosis/fragile bone strength and/or identify vertebral fractures in CT datasets, using various methods that include image processing, computational modelling, artificial intelligence and biomechanical engineering concepts. Technology to evaluate Hounsfield units is used to calculate bone density, but not necessarily bone strength. In this rapid evidence review, we summarise the current literature underpinning approved technologies for opportunistic screening of routine CT images to identify fractures, bone density or strength information. We highlight how other new software technologies have become embedded in NHS clinical practice (having overcome barriers to implementation) and highlight how the novel osteoporosis technologies could follow suit. We define the key unanswered questions where further research is needed to enable the adoption of these technologies for maximal patient benefit.

Objective It remains unclear how the different features of radiographic hip osteoarthritis (rHOA) contribute to hip pain. We examined the relationship between rHOA, including its individual components, and hip pain using a novel dual-energy x-ray absorptiometry (DXA)-based method. Methods Hip DXAs were obtained from UK Biobank. An automated method was developed to obtain minimum joint space width (mJSW) from points placed around the femoral head and acetabulum. Osteophyte areas at the lateral acetabulum, superior and inferior femoral head were derived manually. Semi-quantitative measures of osteophytes and joint space narrowing (JSN) were combined to provide a measure of rHOA. Logistic regression was used to examine the relationships between these variables and hip pain, obtained via questionnaires. Results 6,807 hip DXAs were examined. rHOA was present in 353 [5.2%] individuals and was associated with hip pain [OR 2.07 (95% CI 1.54-2.80)] and hospital diagnosed OA [5.73 (2.89-11.36)]. Total osteophyte area and mJSW were associated with hip pain [1.29 (1.21-1.36), 0.84 (0.77-0.92) respectively] in unadjusted models. After mutually adjusting and adding demographic covariates, total osteophyte area continued to have strong evidence of association with hip pain [1.31 (1.23-1.39)] but mJSW did not [0.95 (0.87-1.04)]. Acetabular, superior and inferior femoral osteophyte areas were all independently associated with hip pain [1.19 (1.13-1.26), 1.22 (1.15-1.29), 1.21 (1.14-1.28) respectively]. Conclusion The relationship between DXA-derived rHOA and prevalent hip pain is explained by osteophyte area rather than mJSW. Osteophytes at different locations showed important, potentially independent, associations with hip pain, possibly reflecting the contribution of distinct biomechanical pathways.