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Congruent, low-friction relative movement between the articulating elements of a synovial joint is an essential pre-requisite for sustained, efficient, function. Where disorders of joint formation or maintenance exist, mechanical overloading and osteoarthritis (OA) follow. The heritable component of OA accounts for ~ 50% of susceptible risk. Although almost 100 genetic risk loci for OA have now been identified, and the epidemiological relationship between joint development, joint shape and osteoarthritis is well established, we still have only a limited understanding of the contribution that genetic variation makes to joint shape and how this modulates OA risk. In this article, a brief overview of synovial joint development and its genetic regulation is followed by a review of current knowledge on the genetic epidemiology of established joint shape disorders and common shape variation. A summary of current genetic epidemiology of OA is also given, together with current evidence on the genetic overlap between shape variation and OA. Finally, the established genetic risk loci for both joint shape and osteoarthritis are discussed.

Osteoarthritis causes pain and functional disability for over 500 million people worldwide. To develop disease-stratifying tools and modifying therapies, we need a better understanding of the molecular basis of the disease in relevant tissue and cell types. Here, we study primary cartilage and synovium from 115 patients with osteoarthritis to construct a deep molecular signature map of the disease. By integrating genetics with transcriptomics and proteomics, we discover molecular trait loci in each tissue type and omics level, identify likely effector genes for osteoarthritis-associated genetic signals and highlight high-value targets for drug development and repurposing. These findings provide insights into disease aetiopathology, and offer translational opportunities in response to the global clinical challenge of osteoarthritis. Understanding the molecular effects of disease variants in relevant tissues is essential to understanding and treating disease. Here, the authors discover expression and protein quantitative trait loci in cartilage and synovium from 115 osteoarthritis patients to pinpoint genes of action and potential drug treatments.

Background Investigation of cartilage and chondrocytes has revealed that the osteoarthritis risk marked by the independent DNA variants rs11583641 and rs1046934 mediate  their effects by decreasing the methylation status of CpG dinucleotides in enhancers and increasing the expression of shared target gene COLGALT2 . We set out to investigate if these functional effects operate in a non-cartilaginous joint tissue. Methods Nucleic acids were extracted from the synovium of osteoarthritis patients. Samples were genotyped, and DNA methylation was quantified by pyrosequencing at CpGs within the COLGALT2 enhancers. CpGs were tested for enhancer effects using a synovial cell line and a reporter gene assay. DNA methylation was altered using epigenetic editing, with the impact on gene expression determined using quantitative polymerase chain reaction. In silico analysis complemented laboratory experiments. Results The rs1046934 genotype did not associate with DNA methylation or COLGALT2 expression in the synovium, whereas the rs11583641 genotype did. Surprisingly, the effects for rs11583641 were opposite to those previously observed in cartilage. Epigenetic editing in synovial cells revealed that enhancer methylation is causally linked to COLGALT2 expression. Conclusions This is the first direct demonstration for osteoarthritis genetic risk of a functional link between DNA methylation and gene expression operating in opposite directions between articular joint tissues. It highlights pleiotropy in the action of osteoarthritis risk and provides a cautionary note in the application of future genetically based osteoarthritis therapies: an intervention that decreases the detrimental effect of a risk allele in one joint tissue may inadvertently increase its detrimental effect in another joint tissue.

To assess the impact of local commissioners' policies for body mass index on access to knee replacement surgery in England. A Natural Experimental Study using interrupted time series and difference-in-differences analysis. We used National Joint Registry for England data linked to the 2015 Index of Multiple Deprivation for 481,555 patients who had primary knee replacement surgery in England between January 2009 and December 2019. Clinical Commissioning Group policies introduced before June 2018 to alter access to knee replacement for patients who were overweight or obese were considered the intervention. The main outcome measures were rate per 100,000 of primary knee replacement surgery and patient demographics (body mass index, Index of Multiple Deprivation, independently-funded surgery) over time. Rates of surgery had a sustained fall after the introduction of a policy (trend change of -0.98 operations per 100,000 population aged 40+, 95% confidence interval -1.22 to -0.74, P<0.001), whereas rates increased in localities with no policy introduction. At three years after introduction, there were 10.5 per 100,000 population fewer operations per quarter aged 40+ compared to the counterfactual, representing a fall of 14.1% from the rate expected had there been no change in trend. There was no dose response effect with policy severity. Rates of surgery fell in all patient groups, including non-obese patients following policy introduction. The proportion of independently-funded operations increased after policy introduction, as did the measure of socioeconomic deprivation of patients. Body mass index policy introduction was associated with decreases in the rates of knee replacement surgery across localities that introduced policies. This affected all patient groups, not just obese patients at whom the policies were targeted. Changes in patient demographics seen after policy introduction suggest these policies may increase health inequalities and further qualitative research is needed to understand their implementation and impact.

The risk of mortality following elective total hip (THR) and knee replacements (KR) may be influenced by patients' pre-existing comorbidities. There are a variety of scores derived from individual comorbidities that can be used in an attempt to quantify this. The aims of this study were to a) identify which comorbidity score best predicts risk of mortality within 90 days or b) determine which comorbidity score best predicts risk of mortality at other relevant timepoints (30, 45, 120 and 365 days). We linked data from the National Joint Registry (NJR) on primary elective hip and knee replacements performed between 2011-2015 with pre-existing conditions recorded in the Hospital Episodes Statistics. We derived comorbidity scores (Charlson Comorbidity Index-CCI, Elixhauser, Hospital Frailty Risk Score-HFRS). We used binary logistic regression models of all-cause mortality within 90-days and within 30, 45, 120 and 365-days of the primary operation using, adjusted for age and gender. We compared the performance of these models in predicting all-cause mortality using the area under the Receiver-operator characteristics curve (AUROC) and the Index of Prediction Accuracy (IPA). We included 276,594 elective primary THRs and 338,287 elective primary KRs for any indication. Mortality within 90-days was 0.34% (N = 939) after THR and 0.26% (N = 865) after KR. The AUROC for the CCI and Elixhauser scores in models of mortality ranged from 0.78-0.81 after THR and KR, which slightly outperformed models with ASA grade (AUROC = 0.77-0.78). HFRS performed similarly to ASA grade (AUROC = 0.76-0.78). The inclusion of comorbidities prior to the primary operation offers no improvement beyond models with comorbidities at the time of the primary. The discriminative ability of all prediction models was best for mortality within 30 days and worst for mortality within 365 days. Comorbidity scores add little improvement beyond simpler models with age, gender and ASA grade for predicting mortality within one year after elective hip or knee replacement. The additional patient-specific information required to construct comorbidity scores must be balanced against their prediction gain when considering their utility.

Osteoarthritis causes debilitating pain and disability, resulting in a considerable socioeconomic burden, yet no drugs are available that prevent disease onset or progression. Here, we develop, validate and use rapid-throughput imaging techniques to identify abnormal joint phenotypes in randomly selected mutant mice generated by the International Knockout Mouse Consortium. We identify 14 genes with functional involvement in osteoarthritis pathogenesis, including the homeobox gene Pitx1 , and functionally characterize 6 candidate human osteoarthritis genes in mouse models. We demonstrate sensitivity of the methods by identifying age-related degenerative joint damage in wild-type mice. Finally, we phenotype previously generated mutant mice with an osteoarthritis-associated polymorphism in the Dio2 gene by CRISPR/Cas9 genome editing and demonstrate a protective role in disease onset with public health implications. We hope this expanding resource of mutant mice will accelerate functional gene discovery in osteoarthritis and offer drug discovery opportunities for this common, incapacitating chronic disease. Osteoarthritis is a chronic, heritable disease with no available treatment. Here, the authors show that a validated, rapid-throughput joint phenotyping pipeline detects osteoarthritis in the mouse knee following surgical provocation, in aging and after single gene deletion or point mutation.

Background Late-stage breast cancer preferentially metastasises to bone; despite advances in targeted therapies, this condition remains incurable. The lack of clinically relevant models for studying breast cancer metastasis to a human bone microenvironment has stunted the development of effective treatments for this condition. To address this problem, we have developed humanised mouse models in which breast cancer patient-derived xenografts (PDXs) metastasise to human bone implants with low variability and high frequency. Methods To model the human bone environment, bone discs from femoral heads of patients undergoing hip replacement surgery were implanted subcutaneously into NOD/SCID mice. For metastasis studies, 7 patient-derived xenograft tumours (PDX: BB3RC32, ER+ PR+ HER2−; BB2RC08, ER+ PR+ ER2−; BB6RC37, ER− PR− HER2− and BB6RC39, ER+ PR+ HER2+), MDA-MB-231-luc2, T47D-luc2 or MCF7-Luc2 cells were injected into the 4th mammary ducts and metastases monitored by luciferase imaging and confirmed on histological sections. Bone integrity, viability and vascularisation were assessed by uCT, calcein uptake and histomorphometry. Expression profiling of genes/proteins during different stages of metastasis were assessed by whole genome Affymetrix array, real-time PCR and immunohistochemistry. Importance of IL-1 was confirmed following anakinra treatment. Results Implantation of femoral bone provided a metabolically active, human-specific site for tumour cells to metastasise to. After 4 weeks, bone implants were re-vascularised and demonstrated active bone remodelling (as evidenced by the presence of osteoclasts, osteoblasts and calcein uptake). Restricting bone implants to the use of subchondral bone and introduction of cancer cells via intraductal injection maximised metastasis to human bone implants. MDA-MB-231 cells specifically metastasised to human bone (70% metastases) whereas T47D, MCF7, BB3RC32, BB2RC08, and BB6RC37 cells metastasised to both human bone and mouse bones. Importantly, human bone was the preferred metastatic site especially from ER+ PDX (100% metastasis human bone compared with 20–75% to mouse bone), whereas ER-ve PDX developed metastases in 20% of human and 20% of mouse bone. Breast cancer cells underwent a series of molecular changes as they progressed from primary tumours to bone metastasis including altered expression of IL-1B, IL-1R1, S100A4, CTSK , SPP1 and RANK. Inhibiting IL-1B signalling significantly reduced bone metastasis. Conclusions Our reliable and clinically relevant humanised mouse models provide significant advancements in modelling of breast cancer bone metastasis.

Background Despite their widespread use, the impact of commissioners’ policies for body mass index (BMI) for access to elective surgery is not clear. Policy use varies by locality, and there are concerns that these policies may worsen health inequalities. The aim of this study was to assess the impact of policies for BMI on access to hip replacement surgery in England. Methods A natural experimental study using interrupted time series and difference-in-differences analysis. We used National Joint Registry data for 480,364 patients who had primary hip replacement surgery in England between January 2009 and December 2019. Clinical commissioning group policies introduced before June 2018 to alter access to hip replacement for patients with overweight or obesity were considered the intervention. The main outcome measures were rate of surgery and patient demographics (BMI, index of multiple deprivation, independently funded surgery) over time. Results Commissioning localities which introduced a policy had higher surgery rates at baseline than those which did not. Rates of surgery fell after policy introduction, whereas rates rose in localities with no policy. ‘Strict’ policies mandating a BMI threshold for access to surgery were associated with the sharpest fall in rates (trend change of − 1.39 operations per 100,000 population aged 40 + per quarter-year, 95% confidence interval − 1.81 to − 0.97, P  < 0.001). Localities with BMI policies have higher proportions of independently funded surgery and more affluent patients receiving surgery, indicating increasing health inequalities. Policies enforcing extra waiting time before surgery were associated with worsening mean pre-operative symptom scores and rising obesity. Conclusions Commissioners and policymakers should be aware of the counterproductive effects of BMI policies on patient outcomes and inequalities. We recommend that BMI policies involving extra waiting time or mandatory BMI thresholds are no longer used to reduce access to hip replacement surgery.

Osteoarthritis (OA) is a chronic disease, affecting approximately one third of people over the age of 45. Whilst the etiology and pathogenesis of the disease are still not well understood, mechanics play an important role in both the initiation and progression of osteoarthritis. In this study, we demonstrate the application of stepwise compression, combined with microCT imaging and digital volume correlation (DVC) to measure and evaluate full-field strain distributions within osteoarthritic femoral heads under uniaxial compression. A comprehensive analysis showed that the microstructural features inherent in OA bone did not affect the level of uncertainties associated with the applied methods. The results illustrate the localization of strains at the loading surface as well as in areas of low bone volume fraction and subchondral cysts. Trabecular thickness and connectivity density were identified as the only microstructural parameters with any association to the magnitude of local strain measured at apparent yield strain or the volume of bone exceeding yield strain. This work demonstrates a novel approach to evaluating the mechanical properties of the whole human femoral head in case of severe OA.

Osteoarthritis (OA) is a common disease characterized by cartilage degeneration and joint remodeling. The underlying molecular changes underpinning disease progression are incompletely understood. We investigated genes and pathways that mark OA progression in isolated primary chondrocytes taken from paired intact versus degraded articular cartilage samples across 38 patients undergoing joint replacement surgery (discovery cohort: 12 knee OA, replication cohorts: 17 knee OA, 9 hip OA patients). We combined genome-wide DNA methylation, RNA sequencing, and quantitative proteomics data. We identified 49 genes differentially regulated between intact and degraded cartilage in at least two –omics levels, 16 of which have not previously been implicated in OA progression. Integrated pathway analysis implicated the involvement of extracellular matrix degradation, collagen catabolism and angiogenesis in disease progression. Using independent replication datasets, we showed that the direction of change is consistent for over 90% of differentially expressed genes and differentially methylated CpG probes. AQP1 , COL1A1 and CLEC3B were significantly differentially regulated across all three –omics levels, confirming their differential expression in human disease. Through integration of genome-wide methylation, gene and protein expression data in human primary chondrocytes, we identified consistent molecular players in OA progression that replicated across independent datasets and that have translational potential.