ObjectiveLower limb malalignment accelerates the progression of knee osteoarthritis (KOA). Knee realignment osteotomy is a well-established treatment for unicompartmental KOA with malalignment. Traditional planning in KOA patients corrects deformities with an osteotomy at the metaphysis but overlooks Paley's approach, which targets the center of rotation angulation (CORA). Osteotomy at the metaphysis may induce secondary translational deformities, which remain unstudied in KOA patients. This study aims to identify the CORA in KOA patients with tibial malalignment.MethodsThirty tibiae (10 varus, 10 neutral, 10 valgus) from the IMI-APPROACH cohort were analyzed using computed tomography (CT) scans. The CORA, defined as the intersection of the proximal and distal mechanical axes, was identified. Translational deformity was calculated by multiplying the CORA-to-osteotomy distance by the tangent of the correction angle.ResultsAmong the varus tibiae, 9 out of 10 CORAs were located in the diaphysis, while 8 out of 10 valgus tibiae had their CORA in the diaphysis. When osteotomies were performed in the proximal metaphysis instead of the CORA location, secondary translational deformities of up to 3 cm were induced.ConclusionIn KOA patients with tibial malalignment, the CORA is predominantly located in the diaphysis rather than in the proximal metaphysis, where osteotomies are typically performed. This discrepancy leads to iatrogenic translational deformities. Future research should investigate the clinical impact of these deformities to optimize osteotomy planning and potentially improve long-term surgical outcomes.

OBJECTIVE:To develop a pragmatic model to predict total knee replacement (TKR) in knee osteoarthritis using non-imaging clinical, genetic and lifestyle data with machine learning (ML)-guided feature selection. METHODS:We analysed 3790 Osteoarthritis Initiative participants. Nested ML feature selection on the training set identified 15 informative variables. Classifiers were benchmarked, then a multivariable logistic regression was fit on the full cohort. Performance was summarised by discrimination (area under the curve (AUC) with 95% CI) and calibration (Brier score). To assess the incremental value of genetics, we refit an otherwise identical clinical model excluding the Polygenic Risk Score (PRS) and compared specificity at fixed sensitivities using Bonferroni-adjusted McNemar tests. A prespecified analysis examined performance by baseline Kellgren-Lawrence (KL) grade (KL 0-1 vs KL ≥2). RESULTS:On the test set, classifier AUCs ranged 0.716-0.748, with Elastic Net and XGBoost performing best. The final logistic model fit on the full cohort achieved AUC 0.765 (95% CI 0.736 to 0.793) with acceptable calibration (Brier 0.097). Performance remained robust by disease stage, with higher discrimination in pre-radiographic knees (KL 0-1: AUC 0.827) and moderate discrimination in KL ≥2 (AUC 0.720); decile plots indicated broadly aligned observed versus predicted risks. PRS added modest, statistically significant gains in specificity at several fixed sensitivities without materially changing AUC. CONCLUSIONS:We present a pragmatic, non-imaging, ML-informed model that predicts TKR with clinically acceptable discrimination and calibration using routinely collected data. This framework provides a practical basis for individualised risk stratification and decision support without reliance on imaging.

ABSTRACT Background Rheumatoid arthritis (RA) is a chronic immune-mediated inflammatory disease characterized by a heterogeneous clinical course with periods of remission and flare. Although biologic DMARDs (bDMARDs) have revolutionized RA treatment by enabling sustained disease control, their long-term use is associated with adverse effects and high costs, making dose tapering an attractive but clinically challenging strategy. The lack of reliable biomarkers to predict flare risk limits safe implementation of treatment de-escalation. This study aimed to identify novel circulating protein biomarkers associated with flare risk in RA patients undergoing bDMARDs tapering, useful to enable biomarker-guided treatment optimization strategies. Methods A discovery proteomic analysis using mass spectrometry was performed on baseline serum samples from a subset of the OPTIBIO clinical trial (n=44), followed by validation in the full cohort (n=194) using ELISA. Functional pathway analysis explored biological processes associated with candidate biomarkers. In parallel, anti-cytokine autoantibodies were profiled using multiplex immunoassays. Logistic and Cox regression models were used to assess associations with flare risk. Predictive models integrating biomarkers and clinical variables were evaluated using receiver operating characteristic (ROC) analysis, sensitivity and specificity metrics, and decision curve analysis to assess clinical utility. Results Mass spectrometry identified 806 proteins, of which 87 were differentially expressed at baseline between patients who flared and those who maintained remission during follow-up within the intervention (tapering) arm. Functional enrichment analysis highlighted immune-regulatory and innate immune pathways. Among the candidates, V-set immunoglobulin-domain-containing 4 (VSIG4) was validated as a biomarker associated with increased flare risk. Anti-interferon-γ (anti-IFNγ) autoantibodies were also associated with flare. A combined model including VSIG4, anti-IFNγ, and the clinical variable DAS28-CRP improved predictive performance compared with clinical variables alone (AUC 0.76 vs 0.66), achieving significantly higher sensitivity. Decision curve analysis demonstrated higher net benefit of the combined model, indicating improved clinical decision-making. In a secondary analysis focused on patients with prolonged remission, representing the most suitable candidates for safe treatment tapering, the model performance further improved (AUC 0.84). Conclusion Integration of novel serum proteomic and autoantibody biomarkers with clinical parameters improves prediction of flare during biologic tapering in RA and provides clinically relevant benefit for patient stratification. These findings support further development of biomarker-driven approaches for personalized treatment optimization strategies.

Current therapies for osteoarthritis (OA) focus on symptom management, rather than halting disease progression. Vasoactive intestinal peptide (VIP) has shown promising effects in musculoskeletal diseases, preserving joint integrity and modulating inflammation. This study investigates the potential of VIP to promote chondrogenic differentiation of human bone marrow mesenchymal stem cells (BM-hMSC) and to modulate inflammatory and cartilage extracellular matrix (ECM)-degrading mediators in human osteoarthritis articular chondrocytes (OA-hAC). BM-hMSC from healthy donors were cultured in 3D pellet sytems under chondrogenic conditions, with or without VIP, for up to 21 days. Chondrogenesis was evaluated through the expression of key markers (SOX9, COL2A1, and ACAN), hypertrophic markers (RUNX2, COL10A1, and MMP13), and glycosaminoglycans (GAG). VIP accelerated chondrogenic differentiation by inducing earlier mRNA and protein expression of chondrogenic markers and enhancing GAG production. In parallel, OA-hAC were cultured in 3D alginate microbeads and stimulated with fibronectin fragments (Fn-fs) in the presence and absence of VIP. We analysed the effects of VIP on cell proliferation, GAG production, and the modulation of complement components (C1R and C3) and matrix metalloproteinases (MMP1, MMP3, MMP9, and MMP13). VIP increased cell proliferation and GAG deposition while significantly reducing the production of complement component C1R and matrix metalloproteinases MMP1 and MMP13. Overall, these findings demonstrate that VIP advances chondrogenesis and exerts anti-inflammatory and anti-catabolic effects in 3D culture models. This study highlights the potential of VIP as a therapeutic agent and supports the combination of MSC-based approaches with VIP as a promising strategy to enhance cartilage regeneration and slow OA progression.

ABSTRACT Objective To develop an interpretable multimodal machine-learning model for risk stratification of the rapid pain progression phenotype in knee osteoarthritis and to evaluate its performance in the independent PROCOAC cohort. Methods An elastic-net logistic regression model was trained using Osteoarthritis Initiative (OAI) data. Rapid pain progression was defined over overlapping 24-month windows using normalized WOMAC pain. Harmonized clinical, genetic and proteomic candidates were evaluated, with feature selection by permutation importance. The frozen algorithm was tested in an OAI hold-out set and externally evaluated in PROCOAC. Logistic recalibration corrected prevalence shifts. Clinical utility was assessed by decision curve analysis. Results OAI comprised 2,934 individuals and 14,488 instances. Feature pruning reduced 159 candidates to a 19-variable clinical-genetic signature driven by Kellgren-Lawrence grade, localized knee pain, BMI and two genetic variants (rs73631790, rs9912678); no proteomic variable was retained. External testing in PROCOAC (582 individuals, 1609 instances) showed ROC-AUC 0.744 (95% CI 0.714 to 0.772) and PR-AUC 0.519. Following recalibration, the sensitive screening threshold yielded NPV 0.875 (95% CI 0.849 to 0.898) and sensitivity 0.804 (95% CI 0.760 to 0.844), whereas the high-specificity threshold achieved PPV 0.610 (95% CI 0.523 to 0.692) and specificity 0.941 (95% CI 0.924 to 0.954). Decision curve analysis showed positive net benefit at both thresholds, supporting a three-tier risk stratification framework. Conclusions This externally evaluated model identified patients at risk of rapid pain progression using an MRI-free clinical-genetic signature. Recalibrated thresholds may support risk-adapted monitoring, advanced imaging prioritization and trial enrichment.

Rapid pain progression in knee osteoarthritis (OA) is heterogeneous and may reflect redox-related mechanisms. We performed an exploratory analysis in Osteoarthritis Initiative (OAI) participants, combining nuclear genome-wide association, mitochondrial DNA (mtDNA) haplogroups, and leukocyte telomere length. Rapid pain progression was defined using the rescaled Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) for pain (0-100) within 24-month windows. An additive genome-wide association study (GWAS) in 2946 participants tested 7,762,204 imputed variants, adjusting for age, sex, body mass index (BMI) and three principal components. Haplogroups were analysed in 3357 participants, and telomere length (telomere-to-single-copy gene, T/S, ratio) was analysed in 301 participants. No variant reached genome-wide significance (p < 5 × 10-8), but six loci were suggestive (p < 5 × 10-6), with minimal inflation (λ = 0.995). mtDNA haplogroup H was nominally associated with rapid pain progression (odds ratio, OR = 1.179, p = 0.023). Rapid pain progressors had shorter baseline telomeres (0.825 ± 0.268 vs. 0.985 ± 0.375; p < 0.001), and telomere length was inversely associated with progression (OR per 1-unit T/S = 0.260, p = 0.007). These preliminary, hypothesis-generating findings are compatible with a redox-related interpretation of rapid pain progression and require external validation in independent cohorts, while providing candidates for future mechanistic studies.

A substantial proportion of individuals with palindromic rheumatism develop rheumatoid arthritis (RA). This randomized, open-label, multicenter trial aimed to assess whether 2 years of treatment with abatacept (n = 34; 125 mg subcutaneous injections weekly during the first year and every 2 weeks during the second year) compared with oral hydroxychloroquine (n = 36; 5 mg kg-1 per day) could reduce the frequency of RA development in individuals with palindromic rheumatism positive for rheumatoid factor and/or anticitrullinated protein antibody. The primary outcome was the development of persistent arthritis that fulfilled the 2010 RA classification criteria of the American College of Rheumatology and the European Alliance of Associations for Rheumatology, as evaluated by the participant clinicians during the 24 months of follow-up. Secondary outcomes included the frequency, intensity and duration of joint attacks, the proportion of patients in remission and the frequency of adverse events. In the primary analysis, in the modified full analysis set with failure imputation, 7 (20.6%) of the 34 participants treated with abatacept and 18 (50.0%) of the 36 participants treated with hydroxychloroquine developed RA during the 24 months of follow-up (P = 0.010; risk difference 29.4%, 95% confidence interval 8.2 to 50.7), meeting the primary endpoint. Using the available-data-only approach, the corresponding figures were 3 (10.0%) of 30 individuals and 10 (35.7%) of 28 individuals, respectively (P = 0.019). Compared with participants treated with hydroxychloroquine, participants treated with abatacept had a significantly longer time to progression to RA (hazard ratio 0.27, 95% confidence interval 0.07 to 0.96; log-rank test P = 0.0299). Abatacept was also associated with a reduced intensity of joint attacks and a higher frequency of symptom remission; however, there were no differences in the frequency of attacks between the two study drugs. No relevant differences in the evolution of antimodified peptide and/or protein antibody titers were observed between the two treatment arms. Both drugs were well tolerated. In patients with seropositive palindromic rheumatism, compared with hydroxychloroquine, abatacept given for 2 years reduced the risk of progression to RA and improved symptoms. ClinicalTrials.gov identifier NCT03669367 and EudraCT no. 2017-004543-20.

OBJECTIVES:The aim of this study was to identify robust predictive markers which may help personalize tapering protocols, minimizing flare risk while optimizing long-term disease management in RA patients. METHODS:The OPTIBIO trial (EudraCT 2012-004482-40) was a phase IV, randomized, open-label, non-inferiority study conducted in five hospitals in Spain. RA patients in sustained remission on stable bDMARD therapy were randomized 1:1 to standard care or dose reduction. The primary outcome was to compare the proportion of joint flare between baseline and 12 months by a non-inferiority analysis analysed by the intention-to-treat principle and to identify predictors for flare and sustained remission. RESULTS:A total of 195 patients were randomized: 99 to the control group and 96 to the optimization group. Thirty-nine flares occurred (optimization: 22.7%, control: 17.2%), with a risk difference of -5.5% (95% CI: -16.8% to 5.7%; P = 0.33). Two predictive models were developed: one for flares (AUC: 0.84) including 3v-DAS28-CRP, VAS pain, erosions, systolic blood pressure and haemoglobin, and another for sustained remission (AUC: 0.77) including 3v-DAS28-CRP, age and rheumatoid factor. Adding molecular biomarkers improved AUCs to 0.91 and 0.88, respectively. No significant differences in adverse events were observed. CONCLUSION:bDMARD dose optimization was not non-inferior to standard therapy on the flare rate but demonstrated similar safety. Predictive models for remission and flares were developed, which may help select patients to ensure safe implementation of this strategy, highlighting the need for personalized treatment. TRIAL REGISTRATION:www.clinicaltrialsregister.eu; EudraCT 2012-004482-40.