Nature Reviews Rheumatology

Mechanical stimuli have fundamental roles in articular cartilage during health and disease. Chondrocytes respond to the physical properties of the cartilage extracellular matrix (ECM) and the mechanical forces exerted on them during joint loading. In osteoarthritis (OA), catabolic processes degrade the functional ECM and the composition and viscoelastic properties of the ECM produced by chondrocytes are altered. The abnormal loading environment created by these alterations propagates cell dysfunction and inflammation. Chondrocytes sense their physical environment via an array of mechanosensitive receptors and channels that activate a complex network of downstream signalling pathways to regulate several cell processes central to OA pathology. Advances in understanding the complex roles of specific mechanosignalling mechanisms in healthy and OA cartilage have highlighted molecular processes that can be therapeutically targeted to interrupt pathological feedback loops. The potential for combining these mechanosignalling targets with the rapidly expanding field of smart mechanoresponsive biomaterials and delivery systems is an emerging paradigm in OA treatment. The continued advances in this field have the potential to enable restoration of healthy mechanical microenvironments and signalling through the development of precision therapeutics, mechanoregulated biomaterials and drug systems in the near future.

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Acute exacerbation of interstitial lung disease associated with rheumatic disease

Fabrizio Luppi, Marco Sebastiani, Carlo Salvarani, Elisabeth Bendstrup & Andreina Manfredi

doi : 10.1038/s41584-021-00721-z

Nature Reviews Rheumatology volume 18, pages85–96 (2022)

Interstitial lung disease (ILD) is a cause of morbidity and mortality in patients with rheumatic diseases, such as connective-tissue diseases, rheumatoid arthritis and systemic vasculitis. Some patients with ILD secondary to rheumatic disease (RD–ILD) experience acute exacerbations, with sudden ILD progression and high mortality during or immediately after the exacerbation, and a very low 1-year survival rate. In the ILD subtype idiopathic pulmonary fibrosis (IPF), an acute exacerbation is defined as acute worsening or development of dyspnoea associated with new bilateral ground-glass opacities and/or consolidations at high-resolution CT, superimposed on a background pattern consistent with fibrosing ILD. However, acute exacerbation in RD–ILD (AE–RD–ILD) currently has no specific definition. The aetiology and pathogenesis of AE–RD–ILD remain unclear, but distinct triggers might include infection, mechanical stress, microaspiration and DMARD treatment. At this time, no effective evidence-based therapeutic strategies for AE–RD–ILD are available. In clinical practice, AE–RD–ILD is often empirically treated with high-dose systemic steroids and antibiotics, with or without immunosuppressive drugs. In this Review, we summarize the clinical features, diagnosis, management and prognosis of AE–RD–ILD, enabling the similarities and differences with acute exacerbation in IPF to be critically assessed.

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Excess comorbidities in gout: the causal paradigm and pleiotropic approaches to care

Hyon K. Choi, Natalie McCormick & Chio Yokose

doi : 10.1038/s41584-021-00725-9

Nature Reviews Rheumatology volume 18, pages97–111 (2022)

Gout is a common hyperuricaemic metabolic condition that leads to painful inflammatory arthritis and a high comorbidity burden, especially cardiometabolic-renal (CMR) conditions, including hypertension, myocardial infarction, stroke, obesity, hyperlipidaemia, type 2 diabetes mellitus and chronic kidney disease. Substantial advances have been made in our understanding of the excess CMR burden in gout, ranging from pathogenesis underlying excess CMR comorbidities, inferring causal relationships from Mendelian randomization studies, and potentially discovering urate crystals in coronary arteries using advanced imaging, to clinical trials and observational studies. Despite many studies finding an independent association between blood urate levels and risk of incident CMR events, Mendelian randomization studies have largely found that serum urate is not causal for CMR end points or intermediate risk factors or outcomes (such as kidney function, adiposity, metabolic syndrome, glycaemic traits or blood lipid concentrations). Although limited, randomized controlled trials to date in adults without gout support this conclusion. If imaging studies suggesting that monosodium urate crystals are deposited in coronary plaques in patients with gout are confirmed, it is possible that these crystals might have a role in the inflammatory pathogenesis of increased cardiovascular risk in patients with gout; removing monosodium urate crystals or blocking the inflammatory pathway could reduce this excess risk. Accordingly, data for CMR outcomes with these urate-lowering or anti-inflammatory therapies in patients with gout are needed. In the meantime, highly pleiotropic CMR and urate-lowering benefits of sodium–glucose cotransporter 2 (SGLT2) inhibitors and key lifestyle measures could play an important role in comorbidity care, in conjunction with effective gout care based on target serum urate concentrations according to the latest guidelines.

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Studying osteoarthritis with artificial intelligence applied to magnetic resonance imaging

Francesco Calivà, Nikan K. Namiri, Maureen Dubreuil, Valentina Pedoia, Eugene Ozhinsky & Sharmila Majumdar

doi : 10.1038/s41584-021-00719-7

Nature Reviews Rheumatology volume 18, pages112–121 (2022)

The 3D nature and soft-tissue contrast of MRI makes it an invaluable tool for osteoarthritis research, by facilitating the elucidation of disease pathogenesis and progression. The recent increasing employment of MRI has certainly been stimulated by major advances that are due to considerable investment in research, particularly related to artificial intelligence (AI). These AI-related advances are revolutionizing the use of MRI in clinical research by augmenting activities ranging from image acquisition to post-processing. Automation is key to reducing the long acquisition times of MRI, conducting large-scale longitudinal studies and quantitatively defining morphometric and other important clinical features of both soft and hard tissues in various anatomical joints. Deep learning methods have been used recently for multiple applications in the musculoskeletal field to improve understanding of osteoarthritis. Compared with labour-intensive human efforts, AI-based methods have advantages and potential in all stages of imaging, as well as post-processing steps, including aiding diagnosis and prognosis. However, AI-based methods also have limitations, including the arguably limited interpretability of AI models. Given that the AI community is highly invested in uncovering uncertainties associated with model predictions and improving their interpretability, we envision future clinical translation and progressive increase in the use of AI algorithms to support clinicians in optimizing patient care.

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