Bone phenotypes in rheumatology – there is more to bone than just bone

Thudium, Christian S.; Nielsen, Signe Holm; Sardar, Samra; Mobasheri, Ali; Spil, W.E. van; Lories, Rik; Henriksen, Kim; Bay‐Jensen, Anne‐Christine; Karsdal, Morten A.

doi:10.1186/s12891-020-03804-2

Cited by 22 publications

(19 citation statements)

References 228 publications

(288 reference statements)

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“…An example of an OA endotype could be a group of patients with elevated bone biochemical markers, as compared with the remaining of the OA population. Then, based on the link with clinical data, this subgroup could be annotated as having a bone-driven disease (ie, a OA disease phenotype), and hypothetically, this group of patients should be enriched for in clinical trials testing the efficacy of a bone-modulating drug 18…”

Section: Introductionmentioning

confidence: 99%

Osteoarthritis endotype discovery via clustering of biochemical marker data

et al. 2022

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ObjectivesOsteoarthritis (OA) patient stratification is an important challenge to design tailored treatments and drive drug development. Biochemical markers reflecting joint tissue turnover were measured in the IMI-APPROACH cohort at baseline and analysed using a machine learning approach in order to study OA-dominant phenotypes driven by the endotype-related clusters and discover the driving features and their disease-context meaning.MethodData quality assessment was performed to design appropriate data preprocessing techniques. The k-means clustering algorithm was used to find dominant subgroups of patients based on the biochemical markers data. Classification models were trained to predict cluster membership, and Explainable AI techniques were used to interpret these to reveal the driving factors behind each cluster and identify phenotypes. Statistical analysis was performed to compare differences between clusters with respect to other markers in the IMI-APPROACH cohort and the longitudinal disease progression.ResultsThree dominant endotypes were found, associated with three phenotypes: C1) low tissue turnover (low repair and articular cartilage/subchondral bone turnover), C2) structural damage (high bone formation/resorption, cartilage degradation) and C3) systemic inflammation (joint tissue degradation, inflammation, cartilage degradation). The method achieved consistent results in the FNIH/OAI cohort. C1 had the highest proportion of non-progressors. C2 was mostly linked to longitudinal structural progression, and C3 was linked to sustained or progressive pain.ConclusionsThis work supports the existence of differential phenotypes in OA. The biomarker approach could potentially drive stratification for OA clinical trials and contribute to precision medicine strategies for OA progression in the future.Trial registration numberNCT03883568.

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Section: Introductionmentioning

confidence: 99%

Osteoarthritis endotype discovery via clustering of biochemical marker data

et al. 2022

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“…Several diseases such as: osteoarthritis, rheumatoid arthritis, osteoporosis, and psoriatic arthritis are characterized with severe structural changes in subchondral bone. Note that the subchondral bone changes in all these diseases are elicited mainly due to altered bone turnover but their manifestations are various from bone erosion to bone sclerosis 49 .…”

Section: Discussionmentioning

confidence: 99%

Hip osteoarthritis: A novel network analysis of subchondral trabecular bone structures

Dorraki

Muratovic

Fouladzadeh

et al. 2022

Preprint

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Hip osteoarthritis (HOA) is a degenerative joint disease that leads to the progressive destruction of subchondral bone and cartilage at the hip joint. Development of effective treatments for HOA remains an open problem, primarily due to the lack of knowledge of its pathogenesis and a typically late-stage diagnosis. We describe a novel network analysis methodology for micro-computed tomography (micro-CT) images of human trabecular bone. We explored differences between the trabecular bone microstructure of femoral heads with and without HOA. Large-scale automated extraction of the network formed by trabecular bone revealed significant network properties not previously reported for bone. Profound differences were discovered, particularly in the proximal third of the femoral head, where HOA networks demonstrated elevated numbers of edges, vertices and graph components. When further differentiating healthy joint and HOA networks, the latter showed fewer small-world network properties, due to decreased clustering coefficient and increased characteristic path length. Furthermore, we found that HOA networks had reduced length of edges, indicating the formation of compressed trabecular structures. In order to assess our network approach, we developed a deep learning model for classifying HOA and control cases, and we fed it with two separate inputs: (i) micro-CT images of the trabecular bone, and (ii) the network extracted from them. The model with plain micro-CT images achieves 74.63% overall accuracy while the trained model with extracted networks attains 96.47% accuracy. We anticipate our findings to be a starting point for a novel description of bone microstructure in HOA, by considering the phenomenon from a graph theory viewpoint.

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“…In mouse models, inhibition of TNF showed a positive effect on bone formation and decreased osteoclastogenesis [43]. Anti-TNF therapy was also associated with elevated bone formation markers (e.g., procollagen Type I N-terminal propeptide (PINP)) and a decrease in bone resorption markers (e.g., C-terminal telopeptide of Type I collagen (CTX-I) and C-terminal cross-linked telopeptide of Type I collagen (ICTP)) in serum [44,45]. The effects on bone remodeling produced decreased levels of DKK-1 and increased levels of intact PINP [46].…”

Section: Tumor Necrosis Factor Inhibitorsmentioning

confidence: 99%

Effects of Biological/Targeted Therapies on Bone Mineral Density in Inflammatory Arthritis

Chen

Chang

2022

IJMS

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Inflammatory arthritis has been reported to be associated with the development of osteoporosis. Recent research has investigated the mechanisms of bone metabolism in chronic inflammatory arthritis such as rheumatoid arthritis (RA) and spondyloarthritis (SpA). Progress in both animal and clinical studies has provided a better understanding of the osteoclastogenesis-related pathways regarding the receptor activator of nuclear factor-κB ligand (RANKL), anti-citrullinated protein antibodies (ACPAs), and Wnt signaling and Dickkopf-related protein 1 (Dkk-1). The complex interplay between inflammatory cytokines and bone destruction has been elucidated, especially that in the interleukin-17/23 (IL-17/23) axis and Janus kinase and signal transducer and activator of transcription (JAK-STAT) signaling. Moreover, advances in biological and targeted therapies have achieved essential modifications to the bone metabolism of these inflammatory arthritis types. In this narrative review, we discuss recent findings on the pathogenic effects on bone in RA and SpA. Proinflammatory cytokines, autoantibodies, and multiple signaling pathways play an essential role in bone destruction in RA and SpA patients. We also reviewed the underlying pathomechanisms of bone structure in biological and targeted therapies of RA and SpA. The clinical implications of tumor necrosis factor inhibitors, abatacept, rituximab, tocilizumab, Janus kinase inhibitors, and inhibitors of the IL-17/23 axis are discussed. Since these novel therapeutics provide new options for disease improvement and symptom control in patients with RA and SpA, further rigorous evidence is warranted to provide a clinical reference for physicians and patients.

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Bone phenotypes in rheumatology – there is more to bone than just bone

Cited by 22 publications

References 228 publications

Osteoarthritis endotype discovery via clustering of biochemical marker data

Osteoarthritis endotype discovery via clustering of biochemical marker data

Hip osteoarthritis: A novel network analysis of subchondral trabecular bone structures

Effects of Biological/Targeted Therapies on Bone Mineral Density in Inflammatory Arthritis

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