New insights into the impact of neuro-inflammation in rheumatoid arthritis.

Rheumatoid arthritis (RA) is considered to be, in many respects, an archetypal autoimmune disease that causes activation of pro-inflammatory pathways resulting in joint and systemic inflammation. RA remains a major clinical problem with the development of several new therapies targeted at cytokine inhibition in recent years. In RA, biologic therapies targeted at inhibition of tumor necrosis factor alpha (TNFα) have been shown to reduce joint inflammation, limit erosive change, reduce disability and improve quality of life. The cytokine TNFα has a central role in systemic RA inflammation and has also been shown to have pro-inflammatory effects in the brain. Emerging data suggests there is an important bidirectional communication between the brain and immune system in inflammatory conditions like RA. Recent work has shown how TNF inhibitor therapy in people with RA is protective for Alzheimer's disease. Functional MRI studies to measure brain activation in people with RA to stimulus by finger joint compression, have also shown that those who responded to TNF inhibition showed a significantly greater activation volume in thalamic, limbic, and associative areas of the brain than non-responders. Infections are the main risk of therapies with biologic drugs and infections have been shown to be related to disease flares in RA. Recent basic science data has also emerged suggesting that bacterial components including lipopolysaccharide induce pain by directly activating sensory neurons that modulate inflammation, a previously unsuspected role for the nervous system in host-pathogen interactions. In this review, we discuss the current evidence for neuro-inflammation as an important factor that impacts on disease persistence and pain in RA

A Need to Meet Patient Expectations

Funding Information: Open access funding provided by Università degli Studi di Palermo within the Nicola Veronese reports personal fees from IBSA, Mylan, and Fidia outside of the submitted work. Cyrus Cooper reports personal fees from Alliance for Better Bone Health, Amgen, Eli Lilly, GSK, Medtronic, Merck, Novartis, Pfizer, Roche, Servier, Takeda, and UCB outside of the submitted work. Jean-Yves Reginster reports CRUI-CARE Agreement. Funding Information:grants from IBSA-Genevrier, Mylan, CNIEL, and Radius Health (through his institution); consulting fees from IBSA-Genevrier, Mylan, CNIEL, Radius Health, and Pierre Fabre; fees for participation in review activities from IBSA-Genevrier, Mylan, CNIEL, Radius Health, and Teva; and payment for lectures from Ag-Novos, CERIN, CNIEL, Dairy Research Council (DRC), Echolight, IBSA-Genevrier, Mylan, Pfizer Consumer Health, Teva, and Theramex outside of the submitted work. Olivier Bruyère reports grants or lecture fees from Amgen, Aptissen, Biophytis, IBSA, MEDA, Mylan, Novartis, Sanofi, Servier, SMB, TRB Chemedica, UCB, and Viatris outside of the submitted work. Ali Mobasheri declares personal fees from Abbott, Abbvie, Achē Laboratórios Farmacêuticos, Galapagos, GSK Consumer Healthcare, Kolon TissueGene, Laboratoires Expansciences, Merck, Pacira Biosciences, Pfizer, Sanofi, and Servier. François Rannou reports grants or lecture fees from Pierre Fabre, Mylan, MSD, Thuasne, IBSA, Pfizer, Genévrier, Expanscience, Scarcell, Skindermic, and Peptinov. Ida K. Haugen reports grants from Pfizer and is a consultant for Novartis outside of the submitted work. Elaine M. Dennison declares grants/fees from Pfizer, Lilly, UCB, and Viatris. Philip G. Conaghan is supported in part by the National Institute for Health and Care Research (NIHR) Leeds Biomedical Research Centre (the views expressed are those of the authors and not necessarily those of the NHS, the NIHR, or the Department of Health), and reports consultancies or lecture fees from AbbVie, Amgen, AstraZeneca, Eli Lilly, Galapagos, GSK, Grunenthal, Pfizer, Novartis, and UCB. Nasser M. Al-Daaghri, Antonella Fioravanti, Sara Cheleschi, Jean-Pierre Pelletier, Maarten de Wit, Etienne Cavalier, Radmila Matijevic, Germain Honvo, Régis Pierre Radermecker, René Rizzoli, Jaime Branco, Andrea Laslop, María Concepción Prieto Yerro, Alberto Migliore, Gabriel Herrero-Beaumont, and Nicholas R. Fuggle declare that they have no conflicts of interest. Publisher Copyright: © 2022, The Author(s).Knee osteoarthritis (OA) is one of the most common and disabling medical conditions. In the case of moderate to severe pain, a single intervention may not be sufficient to allay symptoms and improve quality of life. Examples include first-line, background therapy with symptomatic slow-acting drugs for OA (SYSADOAs) or non-steroidal anti-inflammatory drugs (NSAIDs). Therefore, the European Society for Clinical and Economic Aspects of Osteoporosis, Osteoarthritis and Musculoskeletal Diseases (ESCEO) performed a review of a multimodal/multicomponent approach for knee OA therapy. This strategy is a particularly appropriate solution for the management of patients affected by knee OA, including those with pain and dysfunction reaching various thresholds at the different joints. The multimodal/multicomponent approach should be based, firstly, on different combinations of non-pharmacological and pharmacological interventions. Potential pharmacological combinations include SYSADOAs and NSAIDs, NSAIDs and weak opioids, and intra-articular treatments with SYSADOAs/NSAIDs. Based on the available evidence, most combined treatments provide benefit beyond single agents for the improvement of pain and other symptoms typical of knee OA, although further high-quality studies are required. In this work, we have therefore provided new, patient-centered perspectives for the management of knee OA, based on the concept that a multimodal, multicomponent, multidisciplinary approach, applied not only to non-pharmacological treatments but also to a combination of the currently available pharmacological options, will better meet the needs and expectations of patients with knee OA, who may present with various phenotypes and trajectories.publishersversionpublishe

Will social media banish the bleep? An analysis of hospital pager activity and instant messaging patterns.

Pagers or ‘bleeps’ have been used for intra-hospital communication for over 30 years but are time inefficient and antiquated. We compared pager activity with instant messaging patterns over a 6-month period in a large, UK, teaching hospital. We found that instant messaging was widely used for clinical communication, yet the introduction of an intra-hospital instant messaging platform only led to a modest reduction in pager activity, suggesting that phasing-out of pagers will require a managed transition to alternative messaging technologies. Social network analysis from instant messaging logs also provided insight into patterns of communication that could be used to optimise clinical care

Fracture prediction, imaging and screening in osteoporosis

Osteoporosis is associated with increased fragility of bone and a subsequent increased risk of fracture. The diagnosis of osteoporosis is intimately linked with the imaging and quantification of bone and BMD. Scanning modalities, such as dual-energy X-ray absorptiometry or quantitative CT, have been developed and honed over the past half century to provide measures of BMD and bone microarchitecture for the purposes of clinical practice and research. Combined with fracture prediction tools such as Fracture Risk Assessment Tool (FRAX) (which use a combination of clinical risk factors for fracture to provide a measure of risk), these elements have led to a paradigm shift in the ability to diagnose osteoporosis and predict individuals who are at risk of fragility fracture. Despite these developments, a treatment gap exists between individuals who are at risk of osteoporotic fracture and those who are receiving therapy. In this Review, we summarize the epidemiology of osteoporosis, the history of scanning modalities, fracture prediction tools and future directions, including the most recent developments in prediction of fractures.</p

What impact does osteoarthritis have on ability to self-care and receipt of care in older adults? Findings from the Hertfordshire Cohort Study

Objectives: living independently remains the aim of older adults, but musculoskeletal conditions and frailty may hamper this. We examined relationships between osteoarthritis with ability to self-care and access to formal/informal care among community-dwelling older adults, comparing results to relationships between other musculoskeletal conditions of ageing (frailty, sarcopenia, osteoporosis) and these outcomes. Design: data from the Hertfordshire Cohort Study were used. Osteoarthritis (hand, hip or knee) was defined by clinical examination. Osteoporosis was assessed using dual-energy X-ray absorptiometry and medication use. Sarcopenia was assessed using EWSGOP2 criteria, frailty using Fried criteria. Ability to self-care and access to formal/informal care were self-reported.Results: 443 men and women aged approximately 75 years participated. Osteoarthritis was reported by 26.8% participants; 11.8% had low grip strength; 21.4% had osteoporosis; 8.6% had sarcopenia; 7.6% were identified as frail. Most participants (90.7%) reported no problems with self-care, but more than one-fifth (21.4%) reported having received formal or informal care at home in the previous year. Odds of reporting difficulties with self-care were significantly greater (p ​&lt; ​0.05) for participants with osteoarthritis and for those with frailty, but not for those with osteoporosis or sarcopenia. Odds of receiving care at home in the past year were significantly greater among participants with osteoarthritis and among those with frailty, but not among those with osteoporosis or sarcopenia. Conclusions: frailty and osteoarthritis were associated with both difficulties with self-care and receipt of care; osteoporosis and sarcopenia were not. These results highlight the contribution of clinical osteoarthritis to ability to live independently in later life, and the need to actively manage the condition in older adults

Concordance between clinical and radiographic evaluations of knee osteoarthritis

Background: Significant correlation has been previously demonstrated between radiographic and clinical diagnoses of knee osteoarthritis (OA); however, the specific findings on clinical examination that relate best to a radiographic diagnosis have not been fully elicited. Aims: We aimed to explore the relationship between clinical symptoms and physical findings with radiographic diagnoses of tibiofemoral and patellofemoral OA. Methods: This study was based on 409 individuals from the Hertfordshire Cohort Study, born between 1931 and 1939. Antero-posterior and lateral radiographs were taken of both knees. The presence of tibiofemoral and patellofemoral OA was defined according to the Kellgren and Lawrence score. Clinical symptoms, assessed using WOMAC, and physical findings were ascertained by examination. Relationships were assessed using multilevel univariate logistic regression. Results: In the 775 knees studied, the prevalence of physical findings was crepitus (25%), tibiofemoral tenderness (15%), bony swelling (12%), and pain on flexion (10%). Thirty-one percent (n = 238) knees demonstrated tibiofemoral OA, 28% (n = 220) showed patellofemoral OA, and 16% demonstrated OA in both locations. A global clinical symptom score was associated with increased risk of tibiofemoral OA (OR 12.5, 95% CI 5.4–29.0) and patellofemoral OA (OR 5.1, 95% CI 2.3–13.1). On clinical examination, the presence of crepitus, tibiofemoral tenderness, bony swelling, and pain on flexion was associated with increased risk of tibiofemoral OA; however, only tenderness was found to be associated with patellofemoral OA. Conclusion: Global clinical symptom score was associated with radiographic tibiofemoral and patellofemoral OA. However, individual clinical signs were more strongly associated with tibiofemoral than patellofemoral OA

Machine learning applied to HR-pQCT images improves fracture discrimination provided by DXA and clinical risk factors

BACKGROUND: Traditional analysis of High Resolution peripheral Quantitative Computed Tomography (HR-pQCT) images results in a multitude of cortical and trabecular parameters which would be potentially cumbersome to interpret for clinicians compared to user-friendly tools utilising clinical parameters. A computer vision approach (by which the entire scan is 'read' by a computer algorithm) to ascertain fracture risk, would be far simpler. We therefore investigated whether a computer vision and machine learning technique could improve upon selected clinical parameters in assessing fracture risk.METHODS: Participants of the Hertfordshire Cohort Study (HCS) attended research visits at which height and weight were measured; fracture history was determined via self-report and vertebral fracture assessment. Bone microarchitecture was assessed via HR-pQCT scans of the non-dominant distal tibia (Scanco XtremeCT), and bone mineral density measurement and lateral vertebral assessment were performed using dual-energy X-ray absorptiometry (DXA) (Lunar Prodigy Advanced). Images were cropped, pre-processed and texture analysis was performed using a three-dimensional local binary pattern method. These image data, together with age, sex, height, weight, BMI, dietary calcium and femoral neck BMD were used in a random-forest classification algorithm. Receiver operating characteristic (ROC) analysis was used to compare fracture risk identification methods.RESULTS: Overall, 180 males and 165 females were included in this study with a mean age of approximately 76 years and 97 (28 %) participants had sustained a previous fracture. Using clinical risk factors alone resulted in an area under the curve (AUC) of 0.70 (95 % CI: 0.56-0.84), which improved to 0.71 (0.57-0.85) with the addition of DXA-measured BMD. The addition of HR-pQCT image data to the machine learning classifier with clinical risk factors and DXA-measured BMD as inputs led to an improved AUC of 0.90 (0.83-0.96) with a sensitivity of 0.83 and specificity of 0.74.CONCLUSION: These results suggest that using a three-dimensional computer vision method to HR-pQCT scanning may enhance the identification of those at risk of fracture beyond that afforded by clinical risk factors and DXA-measured BMD. This approach has the potential to make the information offered by HR-pQCT more accessible (and therefore) applicable to healthcare professionals in the clinic if the technology becomes more widely available.</p

Corrigendum to “Machine learning applied to HR-pQCT images improves fracture discrimination provided by DXA and clinical risk factors” [Bone. 2023 Mar:168:116653] (Bone (2023) 168, (S8756328222003301), (10.1016/j.bone.2022.116653))

The authors regret the omission of the following italicised text within the methods section of the above paper: A sample of 22 consecutive slices was selected for each HR-pQCT image. There were fewer participants with previous fractures compared to those without. Therefore, an oversampling strategy was used for individuals with previous fractures [33], such that multiple samples were taken from the scans of those with previous fractures. This was performed assuming intra-scan homogeneity and to provide balance to the machine learning dataset. The authors would like to apologise for any inconvenience caused.</p