Cam morphology but neither acetabular dysplasia nor pincer morphology is associated with osteophytosis throughout the hip: findings from a cross-sectional study in UK Biobank

Objectives: to examine whether acetabular dysplasia (AD), cam and/or pincer morphology are associated with radiographic hip osteoarthritis (rHOA) and hip pain in UK Biobank (UKB) and, if so, what distribution of osteophytes is observed.Design: participants from UKB with a left hip dual-energy X-ray absorptiometry (DXA) scan had alpha angle (AA), lateral centre-edge angle (LCEA) and joint space narrowing (JSN) derived automatically. Cam and pincer morphology, and AD were defined using AA and LCEA. Osteophytes were measured manually and rHOA grades were calculated from JSN and osteophyte measures. Logistic regression was used to examine the relationships between these hip morphologies and rHOA, osteophytes, JSN, and hip pain.Results: 6,807 individuals were selected (mean age: 62.7; 3382/3425 males/females). Cam morphology was more prevalent in males than females (15.4% and 1.8% respectively). In males, cam morphology was associated with rHOA [OR 3.20 (95% CI 2.41–4.25)], JSN [1.53 (1.24–1.88)], and acetabular [1.87 (1.48–2.36)], superior [1.94 (1.45–2.57)] and inferior [4.75 (3.44–6.57)] femoral osteophytes, and hip pain [1.48 (1.05–2.09)]. Broadly similar associations were seen in females, but with weaker statistical evidence. Neither pincer morphology nor AD showed any associations with rHOA or hip pain.Conclusions: cam morphology was predominantly seen in males in whom it was associated with rHOA and hip pain. In males and females, cam morphology was associated with inferior femoral head osteophytes more strongly than those at the superior femoral head and acetabulum. Further studies are justified to characterise the biomechanical disturbances associated with cam morphology, underlying the observed osteophyte distribution

Machine-learning derived acetabular dysplasia and cam morphology are features of severe hip osteoarthritis : findings from UK Biobank

Acknowledgements and disclosures The authors would like to thank Dr Martin Williams, Consultant Musculoskeletal Radiologist North Bristol NHS Trust, who provided substantial training and expertise in osteophyte assessment on DXA images. This research has been conducted using the UK Biobank Resource (application number 17295). Financial Support: RE, MF, 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). CL was funded by the MRC, UK (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). NCH acknowledges support from the MRC and NIHR Southampton Biomedical Research Centre, University of Southampton, and University Hospital Southampton. This research was funded in whole, or in part, by the Wellcome Trust [Grant number 223267/Z/21/Z]. NCH has received consultancy, lecture fees and honoraria from Alliance for Better Bone Health, AMGEN, MSD, Eli Lilly, Servier, UCB, Shire, Consilient Healthcare, Kyowa Kirin and Internis Pharma. For the purpose of open access, the author has applied a CC BY public copyright licence to any Author Accepted Manuscript version arising from this submission.Peer reviewedPublisher PD

A Genome‐Wide Association Study Meta‐Analysis of Alpha Angle Suggests Cam‐Type Morphology May Be a Specific Feature of Hip Osteoarthritis in Older Adults

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 (rg = 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

Automatic Segmentation of Hip Osteophytes in DXA Scans using U-Nets

Osteophytes are distinctive radiographic features of osteo-arthritis (OA) in the form of small bone spurs protruding from joints that contribute significantly to symptoms. Identifying the genetic determinants of osteophytes would improve the understanding of their biological pathways and contributions to OA. To date, this has not been possible due to the costs and challenges associated with manually outlining osteophytes in sufficiently large datasets. Automatic systems that can segment osteophytes would pave the way for this research and also have potential clinical applications. We propose, to the best of our knowledge, the first work on automating pixel-wise segmentation of osteophytes in hip dual-energy x-ray absorptiometry scans (DXAs). Based on U-Nets, we developed an automatic system to detect and segment osteophytes at the superior and the inferior femoral head, and the lateral acetabulum. The system achieved sensitivity, specificity, and average Dice scores (±std) of (0.98, 0.92, 0.71±0.19) for the superior femoral head [793 DXAs], (0.96, 0.85, 0.66±0.24) for the inferior femoral head [409 DXAs], and (0.94, 0.73, 0.64±0.24) for the lateral acetabulum [760 DXAs]. This work enables large-scale genetic analyses of the role of osteophytes in OA, and opens doors to using low-radiation DXAs for screening for radiographic hip OA

Automatic segmentation of hip osteophytes in DXA scans sing U-nets

Osteophytes are distinctive radiographic features of osteo-arthritis (OA) in the form of small bone spurs protruding from joints that contribute significantly to symptoms. Identifying the genetic determinants of osteophytes would improve the understanding of their biological pathways and contributions to OA. To date, this has not been possible due to the costs and challenges associated with manually outlining osteophytes in sufficiently large datasets. Automatic systems that can segment osteophytes would pave the way for this research and also have potential clinical applications. We propose, to the best of our knowledge, the first work on automating pixel-wise segmentation of osteophytes in hip dual-energy x-ray absorptiometry scans (DXAs). Based on U-Nets, we developed an automatic system to detect and segment osteophytes at the superior and the inferior femoral head, and the lateral acetabulum. The system achieved sensitivity, specificity, and average Dice scores (±std) of (0.98, 0.92, 0.71±0.19) for the superior femoral head [793 DXAs], (0.96, 0.85, 0.66±0.24) for the inferior femoral head [409 DXAs], and (0.94, 0.73, 0.64±0.24) for the lateral acetabulum [760 DXAs]. This work enables large-scale genetic analyses of the role of osteophytes in OA, and opens doors to using low-radiation DXAs for screening for radiographic hip OA

Hip geometric parameters are associated with radiographic and clinical hip osteoarthritis: findings from a cross-sectional study in UK Biobank

Objectives: to examine the extent to which geometric parameters derived from dual-energy x-ray absorptiometry (DXA) scans in the UK Biobank study are related to hip osteoarthritis (HOA) independently of sex, age and body size.Design: femoral neck width (FNW), diameter of the femoral head (DFH) and hip axis length (HAL) were derived automatically from left hip DXA scans in UK Biobank using outline points placed around the hip by a machine-learning program. Correlations were calculated between geometric parameters, age, height, and weight. Logistic regression was used to examine the relationship of geometric parameters with radiographic hip osteoarthritis (radiographic HOA), and hospital diagnosed HOA (HESOA), and Cox proportional hazards model to evaluate the relationship with total hip replacement (THR). Analyses were adjusted for sex, age, height, weight, and geometric parameters.Results: the study consisted of 40,312 participants. In age and sex-adjusted analyses, FNW, HAL and DFH were related to increased risk of radiographic HOA. In a model adjusted for age, sex, height, weight and other geometric parameters, both FNW and HAL retained independent relationships with radiographic HOA [FNW: OR 2.38 (2.18-2.59), HAL: 1.25 (1.15-1.36)], while DFH was now protective [0.55 (0.50-0.61)]. Only FNW was independently related to HESOA [2.20 (1.80-2.68)] and THR [HR 2.51 (1.89-3.32)].Conclusion: greater FNW and HAL were independently related to an increased risk of radiographic HOA, whereas greater DFH appeared to be protective. Greater FNW was independently associated with HESOA and THR. These results suggest DXA-derived geometric parameters, particularly FNW, could help determine HOA and THR risk