Musculoskeletal (MSK) disorders are highly heterogeneous and have diverse cellular and molecular pathobiological mechanisms, termed “endotypes”. This systematic review aimed to summarize current knowledge of molecular endotypes in MSK disorders. This review was conducted and reported in accordance with the Preferred Reporting Items for Systematic Review and Meta-Analyses (PRISMA). Five databases including PubMed, Web of Science, Scopus, Ovid Medline, and Embase were searched for publications on molecular endotypes of MSK disorders classified by the International Classification of Diseases 11th Revision (ICD-11) that were published prior to September 1st, 2024. Articles were included if they were peer-reviewed original research articles on adult-onset human MSK disorder molecular endotypes/subtypes that used directly collected human specimen, in English language, and had full text available. Citations of the included studies were also screened manually using the same inclusion criteria. Our search identified 44,660 unique records and 34 eligible studies were included. The majority of these studies focused on osteoarthritis (OA, n = 14) and rheumatoid arthritis (RA, n = 12), with a small number of studies focused on other MSK conditions. In OA, high- and low-inflammation clusters were consistently identified using different types of samples, and high- and low-pain clusters were observed in several other studies. In RA, endotypes with different levels of inflammation markers or treatment responses were reported in several studies, and one study reported that RA patients displayed two different dysbiotic microbiota patterns which corresponded to different responses to treatments. MSK endotype research is still in its early stage. More endotype studies on MSK disorders are needed to assist in designing clinical trials and developing personalized therapy strategies.

OBJECTIVES:To investigate gut microbial alteration and their functional consequences in obesity (OB)-related knee osteoarthritis (OA) by integrating microbiome with metabolomic, proteomic, and dietary data. METHODS:Fecal and fasting plasma samples were collected from 91 knee OA patients and 12 OA-free controls, classified into four subgroups based on OB and OA status: 66 OB+OA+, 25 OB-OA+, 5 OB+OA-, and 7 OB-OA-. 16S rRNA gene sequencing was performed to profile gut microbiota. MaAsLin2 modelling was applied, and dietary intake was incorporated into the models. Plasma metabolomics (n=630 metabolites) and proteomics (n=5,416 proteins) were integrated with microbial signatures to assess functional associations. RESULTS:OB+OA+ patients exhibited significantly lower a- and β-diversity than OB-OA+ (p<0.05). Seventeen microbial taxa were identified to be significantly associated with OB+OA+ (all p<7.65×10-5 after correcting tests for 654 ASVs), and 16 of them remained significant after adjustment for age, sex, antibiotic use, and dietary intake. PICRUSt2-based predictive analysis on these taxa suggested that bile acid biosynthesis was upregulated in OB+OA+ group. These taxa were correlated with 376 metabolites (p<0.05) with enrichment in fatty acid biosynthesis, linoleic/arachidonic acid metabolism, and propanoate metabolism pathways. They were also associated with 146 proteins (p<0.001) with enrichment in PI3K-Akt signalling, ECM-receptor interaction, and lipid/atherosclerosis pathways. CONCLUSIONS:OB+OA+ patients exhibited significant gut microbial dysbiosis associated with systemic metabolic and proteomic alterations relevant to OA pathophysiology. The microbiome-metabolome-proteome axis may provide mechanistic insights into worsened OA outcomes in OB individuals and could inform microbiome-targeted interventions.

PURPOSE:This study aimed to identify metabolic biomarkers and pathways that might be associated with total joint arthroplasty (TJA) early revision for heterogeneous failure modes using an individual data meta-analysis of metabolomics. METHODS:Two independent osteoarthritis (OA) cohorts were included. Revision records of patients with primary knee and hip OA were extracted at an average of 11.1 and 7.8 years after primary TJA, respectively. Preoperative fasting plasma was metabolomically profiled. Concentrations of metabolites/metabolism indicators were natural log-transformed, and their associations with early revision for all reasons in each individual cohort were assessed using logistic regression; the summary statistics from each cohort were then subjected to random-effects meta-analysis modelling. RESULTS:Five hundred seventy-two patients with primary OA in The Newfoundland Osteoarthritis Study and 368 in the Longitudinal Evaluation in the Arthritis Program: Osteoarthritis Study were included. The revision rates were 4% and 6%, and mean times to revision were 1.7 and 2.1 years, respectively. No metabolite reached the prespecified significance threshold for multiple testing correction. However, 119 metabolites including choline, tryptophan betaine, indole, ornithine, three acylcarnitines, four cholesteryl esters, two lysophosphatidylcholines, five long-chain diglycerides, and 101 unsaturated (very) long-chain triglycerides were nominally significant with P < 0.05, suggesting potential links between these metabolites and early revision. Among these, indole and one acylcarnitine were positively associated with revision (odds ratio ≥1.73), while all others were negatively associated (odds ratio ≤0.73). CONCLUSION:Overactivation of the tryptophan-indole metabolic pathway may be associated with early revision after primary TJA. However, the findings were suggestive and represented a composite signal from heterogeneous failure modes.

Abstract Background/Aims While randomised controlled trials (RCTs) in psoriatic arthritis (PsA) show early and durable efficacy of IL-23 inhibitors (i) and IL-17i, real-world data are limited. Previous interim analysis of the PsABIOnd study showed similar 6-month guselkumab (GUS) and IL-17i persistence and effectiveness across PsA domains. This analysis aimed to assess 12-month treatment persistence and effectiveness. Methods PsABIOnd (NCT05049798) is an ongoing global observational study in participants with PsA starting GUS or IL17i as 1st-to-4th line of biologic therapy per standard of care. The primary outcome is treatment persistence at 36 months. In this interim analysis, PsABIOnd participants who had a baseline and ≥1 follow-up assessment up until the 12-month visit (±3 months) were analysed according to their initial treatment, regardless of later switches. Treatment persistence (i.e., no stop/switch) for the overall population and by subgroups of interest (prior biologic experience, biological sex) was assessed over 12 months via Kaplan-Meier estimator function. Propensity score (PS) analysis was used to evaluate hazard ratio (HR) of stopping/switching GUS vs IL-17i prior to the 12-month visit, adjusting for baseline variable imbalances across cohorts. Rates of achievement of clinical Disease Activity Index for PsA (cDAPSA) based minimal clinically important improvement (MCII; improvement by ≥ 5.7), low disease activity (LDA)/remission (REM; ≤13), and REM (≤4), minimal disease activity (MDA), psoriasis body surface area (BSA)<3%, and Dermatology Life Quality Index (DLQI; ≥4) at the 12-month visit were assessed. Results A total of 511 and 504 participants received GUS or IL-17i, respectively, as their initial treatment. Mean age (53.0/53.7 years) and prior targeted therapy use (not GUS/IL-17i; 62.6%/62.9%) were comparable across GUS/IL17i cohorts at baseline. Treatment persistence up to the 12-month visit was high in both cohorts, with 407/511 (79.6%) GUS and 417/504 (82.7%) IL-17i cohort participants remaining on their initial treatment line (PS-adjusted HR GUS vs IL-17i stop/switch [95% confidence interval]: 1.11 [0.85-1.44]). Reasons for initial treatment line discontinuation were generally consistent across groups. Persistence on GUS and IL-17i remained comparable across prior biologic experience and biological sex subgroups. Improvements in joint, skin, and overall disease activity at 12 months were also similar with GUS and IL-17i. Conclusion Participants with PsA had similar 12-month treatment persistence and rates of effectiveness across key PsA domains with GUS or IL-17i, overall and across subgroups of interest. These results add to real-world evidence of the long-term effectiveness of GUS and IL-17i, supporting efficacy data from RTCs. Disclosure S. Siebert: Consultancies; Abbvie, Amgen, Astrazeneca, Johnson & Johnson, Syncona, Teijin Pharma, UCB. Honoraria; AbbVie, Amgen, AstraZeneca, Johnson & Johnson, Syncona, Teijin Pharma, UCB. Member of speakers’ bureau; AbbVie, Amgen, Johnson & Johnson, Novartis, Pfizer, UCB. Grants/research support; Eli Lilly, GSK, Johnson & Johnson, Pfizer, UCB. M. Sharaf: Corporate appointments; Johnson & Johnson. Shareholder/stock ownership; Johnson & Johnson. F. Behrens: Consultancies; Abbvie, Boehringer Ingelheim, Bristol Myers Squibb, Celgene, Chugai, Eli Lilly, Galapagos, Genzyme, Gilead, Johnson & Johnson, MSD, Novartis, Pfizer, Roche, Sanofi, UCB. Honoraria; Abbvie, Boehringer Ingelheim, Bristol Myers Squibb, Celgene, Chugai, Eli Lilly, Galapagos, Genzyme, Gilead, Johnson & Johnson, MSD, Novartis, Pfizer, Roche, Sanofi, UCB. Grants/research support; Celgene, Chugai, Johnson & Johnson, Pfizer, Roche. P. Rahman: Consultancies; Abbvie, Amgen, Bristol Myers Squibb, Celgene, Eli Lilly, Johnson & Johnson, Merck, Novartis, Pfizer, UCB. Grants/research support; Johnson & Johnson, Novartis. Other; Meeting attendance/travel support: Johnson & Johnson. M. Kishimoto: Consultancies; AbbVie, Amgen, Asahi-Kasei Pharma, Astellas, Ayumi, Bristol Myers Squibb, Chugai, Daiichi-Sankyo, Eisai, Eli Lilly, Gilead, Johnson & Johnson, Novartis, Pfizer, Tanabe-Mitsubishi, UCB. Honoraria; AbbVie, Amgen, Asahi-Kasei Pharma, Astellas, Ayumi, Bristol Myers Squibb, Chugai, Daiichi-Sankyo, Eisai, Eli Lilly, Gilead, Johnson & Johnson, Novartis, Pfizer, Tanabe-Mitsubishi, UCB. E. Soriano: Consultancies; AbbVie, Johnson & Johnson, Novartis, Roche. Member of speakers’ bureau; AbbVie, Amgen, Bristol Myers Squibb, Eli Lilly, Johnson & Johnson, Novartis, Pfizer, Roche, UCB. Grants/research support; AbbVie, Johnson & Johnson, Novartis, Pfizer, Roche, UCB. E. Rampakakis: Corporate appointments; JSS Medical Research. Consultancies; Johnson & Johnson. L. Köleséri: Corporate appointments; IQVIA. Consultancies; Johnson & Johnson. K. Lozenski: Corporate appointments; Johnson & Johnson. Shareholder/stock ownership; Johnson & Johnson, Bristol Myers Squibb. M. Koivunen: Corporate appointments; Former employee of Johnson & Johnson. Shareholder/stock ownership; Johnson & Johnson. R. Queiro: Consultancies; AbbVie, Amgen, Celgene, Johnson & Johnson, Eli Lilly, MSD, Novartis, Pfizer. Honoraria; AbbVie, Amgen, Celgene, Johnson & Johnson, Eli Lilly, MSD, Novartis, Pfizer. Grants/research support; AbbVie, Johnson & Johnson, Novartis. E. Lubrano: Honoraria; AbbVie, Amgen, Eli Lilly, GSK, Johnson & Johnson, Novartis, UCB. D. Aletaha: Consultancies; Abbvie, Gilead, Galapagos, Eli Lilly, Johnson & Johnson, Merck, Novartis. Honoraria; Abbvie, Gilead, Galapagos, Eli Lilly, Johnson & Johnson, Merck, Novartis. Grants/research support; Abbvie, Gilead, Galapagos, Eli Lilly, Johnson & Johnson, Merck, Novartis. L. Gossec: Consultancies; AbbVie, AlfaSigma, Amgen, Bristol Myers Squibb, Celltrion, Johnson & Johnson, Eli Lilly, MSD, Novartis, Pfizer, Stada, UCB. Honoraria; AbbVie, AlfaSigma, Amgen, Bristol Myers Squibb, Celltrion, Johnson & Johnson, Eli Lilly, MSD, Novartis, Pfizer, Stada, UCB. Grants/research support; AbbVie, Biogen, Eli Lilly, Novartis, UCB.

Objectives In immune-mediated inflammatory diseases (IMIDs), multiple advanced therapy (targeted synthetic/biologic disease-modifying antirheumatic drugs ts/bDMARDs) concurrent exposures may occur. We assessed the duration, and infectious risks of combination advanced therapy (CAT) exposure in IMID in the United States. Methods We created a cohort of initiators of any advanced IMID therapy using MarketScan administrative health data (2016-2023). Treatment episodes began at initiation and ended after a dispensation/infusion gap of ≥5 half-lives. Episodes were classified as CAT with any 30+ day overlap between advanced therapy classes. Patients were followed from start of the first advanced therapy (time zero) until disenrollment, death, or study end (December 31, 2024). We described baseline characteristics of those exposed to CAT and estimated multivariate hazard ratios (HR) for persistence of the first CAT episode, defined as time until discontinuation of at least one of the combined advanced therapies. We adjusted for baseline age, sex, IMID, comorbidities, conventional synthetic DMARDs (csDMARDs), and glucocorticoids. We also assessed HRs for first serious infection (defined as requiring hospitalization or intravenous antibiotics) comparing person-time on combined vs single advanced therapy, adjusting for the same covariates. Results There were 270,198 individuals initiating 693,375 episodes of advanced therapy. Of these, 38,456 individuals (14.2%) initiated 52,212 episodes of CAT. Mean age at CAT initiation was 49 (standard deviation 13.5) years; 62.5% were female. Among 38,456 CAT users, the most common IMIDs were psoriasis, PsO (36.6%), inflammatory bowel disease, IBD (30.0%), rheumatoid arthritis, RA (28.0%) and psoriatic arthritis, PsA (18.9%). Baseline glucocorticoid use was common (44.0%). Among CAT episodes, 51.2% involved TNFα inhibitor exposure (overlapping most commonly with IL17 inhibitors, IL12/23 inhibitors, anti-adhesion molecules, JAK inhibitors or CTLA-4 agonists). Median duration of first CAT exposure was 42 days. Factors associated with lower duration of CAT exposure included RA (HR 1.27, 95% CI 1.24–1.31) and concomitant csDMARDs. Lower HRs were seen with PsO (HR 0.82, 95% CI 0.79–0.84), IBD (HR 0.85, 95% CI 0.83–0.88), and baseline Charlson Comorbidity Index ≥3 (HR 0.77, HR 0.67–0.89). During CAT exposure there were 4.8 serious infections per 100 person-years (95% CI 4.3–5.3). In multivariate analysis, CAT exposure was associated with an increased HR for serious infection (HR 1.20, 95% CI 1.08–1.33) (Table) vs single advanced therapy. Table. Risk of serious infection associated with combination advanced therapy exposures Conclusion In individuals starting advanced IMID therapy, 14.2% had CAT exposures, lasting a median of 42 days. CAT exposure was associated with serious infection risk.

Objectives To assess bimekizumab (BKZ) efficacy in subgroups of patients with psoriatic arthritis (PsA) with varying baseline (BL) joint involvement over up to 2 years. Methods Post hoc analysis assessed subcutaneous BKZ 160mg every 4 weeks (wks; Q4W) in patients with PsA and varying BL joint involvement. Patients were grouped by BL swollen joint count (SJC) based on quartiles: SJC ≤5, 6-≤7, 8-≤12, >12. Due to few patients with BL SJC 3/4, patients with SJC 5 were included in first quartile; quartile sizes vary. Patients were pooled from BE OPTIMAL ( NCT03895203 ; biologic DMARD [bDMARD]-naïve) and BE COMPLETE ( NCT03896581 ; TNF inhibitor inadequate response/intolerance [TNFi-IR]). Unequal trial sizes (BE OPTIMAL 431 BKZ, 281 PBO; BE COMPLETE 267 BKZ, 133 PBO) yielded unequal quartile proportions. Both trials required ≥3 tender and swollen joints and had 16-wk double-blind, placebo (PBO)-controlled periods. Completers of BE OPTIMAL Wk52 or BE COMPLETE Wk16 could enter BE VITAL ( NCT04009499 ; open-label extension), with all patients receiving BKZ. BE OPTIMAL included a reference arm (adalimumab 40mg Q2W); data not reported due to small patient numbers in SJC quartiles. Outcomes reported: ACR ≥50% improvement, Psoriasis Area and Severity Index 100% improvement, minimal disease activity, SJC=0, Pain visual analog scale ≥50/70% improvement from BL, Health Assessment Questionnaire - Disability Index minimal clinically important difference (≥0.35 decrease from BL in patients with BL score ≥0.35). Data were pooled across trials and reported by randomization group at Wk16, Year 1 (Wk52), and Year 2 (Wk104/100 from BE OPTIMAL/BE COMPLETE). Results At BL, SJC distributions were: ≤5 (n=370), 6-≤7 (n=215), 8-≤12 (n=275), >12 (n=252). At Wk16, BKZ-treated patients demonstrated numerically greater improvements in joint, skin, composite, and patient-reported outcomes vs PBO, across patients with varying BL joint involvement. For all domains assessed, improvements were sustained to 2 years in BKZ-randomized patients, with robust efficacy across patients with varying BL joint involvement (Figure 1); (patient-reported outcome data not shown). For patients who switched from PBO to BKZ at Wk16, improvements in efficacy similar to BKZ-randomized patients with varying BL joint involvement were reported to Year 1 and sustained to Year 2. Figure 1. (A) ACR50, (B) PASI100, (C) MDA, and (D) SJC=0 responders at Week 16, Year 1, and Year 2, reported by baseline joint involvement (NRI, OC) Conclusion In patients with PsA and varying BL joint involvement, BKZ treatment demonstrated greater improvements vs PBO across disease domains at Wk16, and improvements were sustained to 2 years. Efficacy was robust across all groups of patients with varying BL joint involvement, reflecting the broader range of patients seen in clinical practice.

Our study assesses the Horizon model, a novel CNV classification tool developed in line with American College of Medical Genetics (ACMG) guidelines, to enhance the classification of pathogenicity in CNVs. Horizon utilizes a ranking-based algorithm, incorporating multiple proprietary databases and variant inheritance models as per ACMG standards. The model’s effectiveness was verified through Area Under the Curve (AUC) analyses on three datasets comprising 635 pathogenic inherited or de novo variants, as classified by clinical geneticists and several established tools. Horizon achieved an AUC of 0.96 (accuracy: 0.9611) in the discovery cohort, demonstrating high accuracy in CNV interpretation and proficiency in predicting pathogenicity. We observed an AUC of 0.96 (accuracy: 0.8776) in the de novo variant cohort and an overall AUC of 0.93 across all cohorts, surpassing tools like ClassifyCNV (AUC: 0.81),AnnotSV (AUC: 0.85), and ISV-CNV (AUC: 0.84). It showed particular effectiveness in interpreting duplication CNVs and the highest performance for CNVs sized 3–5 Mb. The Horizon model offers robust and accurate CNV interpretation, outperforming existing tools and aligning closely with clinical evaluations. Its comprehensive approach, integrating a range of genomic features and following ACMG guidelines, makes it a crucial tool in the genomic interpretation landscape, facilitating the rapid and accurate diagnosis of genetic disorders.

Psoriatic arthritis (PsA) is a chronic inflammatory disease with a heterogeneous presentation including peripheral joint arthritis, axial inflammation, enthesitis, dactylitis, and psoriatic skin and nail changes. A substantial proportion of patients develop structural joint damage that can be monitored using standard radiographs. In patients with PsA, structural damage progression has been associated with significant impairment of physical function, health-related quality of life, and work productivity. Tumor necrosis factor inhibitors were the first biologic therapies approved for patients with PsA and have demonstrated efficacy in reducing the rate of structural damage progression in these patients. More recently, biologics targeting the interleukin (IL)-23p19 subunit (guselkumab and risankizumab) and IL-17 (secukinumab, ixekizumab, and bimekizumab) have been approved to treat patients with active PsA and are the subject of this review, with a focus on guselkumab. In separate phase 3, randomized, controlled studies, participants with active PsA treated with guselkumab, secukinumab, ixekizumab, and bimekizumab exhibited less structural damage progression in comparison with placebo. Both guselkumab and risankizumab inhibit the IL-23p19 subunit; however, to date, only guselkumab has demonstrated statistically significant efficacy in inhibiting structural damage progression in this patient population.

Objectives To identify and validate molecular markers for obesity-related knee osteoarthritis (OB + OA + ) by integrating proteomic and genetic data. Methods Plasma samples from 171 primary knee OA patients, including 86 OB+OA+ and 85 OB-OA+ patients, with age, sex, and comorbidities matched between the 2 groups, were analyzed using the Olink Explore HT platform, quantifying 5,416 proteins. Logistic regression models were utilized to identify significant proteins. Genetic variants located within the corresponding genes of the identified proteins were retrieved from the available genome-wide genotype data, and their associations with the identified protein expressions were examined. Results Proteomic analysis revealed leptin (LEP) and fatty acid-binding protein 4 (FABP4) as significantly associated with the OB+OA+ group (p < 9.23×10^-6) after adjusting for age, sex, hypertension, hyperlipidemia, other cardiovascular diseases, and diabetes, and controlling for multiple testing across 5,416 proteins (Figure 1A). Genetic analysis identified SNPs rs141614112 (G>A) in LEP (p < 0.04) and rs33998908 (delT) in FABP4 (p < 0.02) as variants associated with their respective protein concentrations (Figure 1B and C). However, neither variant demonstrated a significant association with OB+OA+ phenotype. Validation of the findings in 4 groups including OB+OA+, OB-OA+, OB+OA−, and OB-OA- with a large sample size is underway. Figure 1: (A) Volcano plot for proteome-wide association analysis results for obesity-related knee OA. Logistic regression modeling was used with adjustment for age, sex, BMI, and comorbidities. Dash line is proteome-wide significance for controlling multiple testing for 5,416 proteins. (B). Box plot for association between rs141614112 and LEP levels. (C). Box plot for association between rs33998908 and FABP4. Conclusion Our data demonstrated LEP and FABP4 as key protein biomarkers for OB+OA+. While genetic variants influenced circulating protein levels, their lack of direct association with the clinical phenotype suggests that environmental and metabolic factors may play a greater role in the manifestation of obesity-related knee OA.

Objectives The economic burden of immune-mediated diseases (IMDs) is a significant concern in healthcare, particularly in patients who present with multiple conditions. This study aims to evaluate the admission rates and hospitalization costs associated with patients with psoriatic disease (PsD) alone, inflammatory bowel disease (IBD) alone, and those with both conditions. Methods A retrospective analysis was conducted using data from the Newfoundland and Labrador Centre for Health Information (NLCHI) spanning from 2009 to 2019. Patients diagnosed with PsD were identified using the ICD-9 code 696 and matched with a control group of approximately 75,500 individuals who did not have PsD. From this cohort of around 100,000 patients, those with IBD were identified using ICD-9 codes 555 for Crohn’s disease (CD) and 556 for ulcerative colitis (UC). Data on the number of hospital admissions and total hospitalization costs were collected from the NLCHI database. Results A total of 15,100 patients were identified with PsD, and 2,800 patients had IBD. Among these cohorts, 14,368 had PsD alone, 2,068 had IBD alone, and 732 had both conditions. Thus, 4.8% of PsD patients were also diagnosed with IBD, comprising 525 patients (3.4%) with CD and 207 patient (1.4%) with UC. The mean number of admissions for patients with both IMIDs was significantly greater at 6.08 over a ten-year period, compared to 3.09 for those with PsD alone (p < 0.0001) and 5.04 for those with IBD alone (p < 0.001). The total hospitalization costs over 10 years for patients with both conditions amounted to $21,814, significantly higher than the $10,742 for PsD alone (p < 0.001), and numerically, though not statistically, higher than the $17,767 for IBD alone (p = 0.09). Similar numerical trends were observed in hospitalizations primarily due to cardiovascular-related events, with costs for both diseases at $2,476 compared to $1,622 for PsD alone and $1,987 for IBD alone. For mental health-related hospitalizations, the cost was $3,931 for both conditions compared to $1,886 for PsD alone and $3,560 for IBD alone. Conclusion These findings highlight the necessity for targeted healthcare strategies that address the complexities of managing multiple IMDs. By focusing on integrated care approaches and efficient resource allocation, healthcare systems can better support patients with these overlapping conditions, ultimately reducing the economic burden and improving patient outcomes.

OBJECTIVE:Emerging evidence suggests that distinct gut microbial profiles might differentially contribute to the development of knee osteoarthritis (OA) and hip OA. The aim of this study was to identify gut microbial alteration and their potential functional consequences in primary knee OA and hip OA. METHODS:Fecal and fasting plasma samples were collected from 24 participants with knee OA, 24 participants with hip OA, and 12 age-, sex-, and BMI-matched OA-free controls. Gut microbiota were profiled by 16S ribosomal ribonucleic acid gene sequencing, and plasma metabolomic profiling was performed. Microbiome Multivariable Association with Linear Models 2 (MaAsLin2) with a zero-inflated negative binomial model was applied to identify significantly differentially abundant taxa, which were then integrated with plasma metabolomic profiles to assess functional associations. RESULTS:Patients with hip OA showed significantly lower α-diversity compared with controls (P < 0.05), whereas β-diversity did not differ among the groups. MaAsLin2 identified 4 microbial taxa that differed between knee OA and controls, 6 between hip OA and controls, and 11 between knee OA and hip OA (P < 7.48 × 10-5). These taxa were correlated with 117, 247, and 189 metabolites, respectively (P < 0.05), and were enriched in arginine biosynthesis, sphingolipid metabolism, and one-carbon pool by folate pathway. Sparse partial least squares discriminant analysis showed that these metabolites moderately distinguished patients with OA from controls. CONCLUSION:Gut microbiome and metabolome signatures in knee OA and hip OA exhibited both shared and joint-specific features, suggesting distinct microbiome-driven mechanisms in OA pathogenesis. These signatures were linked to inflammatory, amino acid, lipid, and vitamin metabolic pathways, underscoring the potential for personalized, joint-specific approaches in microbiome-based interventions.

The recent availability of large-scale genomic datasets in psoriatic disease, combined with advances in molecular tools, next-generation genomic technologies, and informatics, has led to a better understanding of the genomic basis of psoriatic arthritis (PsA). Although no current genetic tests exist for the management of PsA, the potential for early diagnosis and treatment orientation through genomic studies remains a source of continued optimism. Ongoing studies aim to advance the stratification, prognosis, and pharmacogenomics of PsA. This review highlights recent advances in the genomics of PsA, focusing on genomic variants that may become clinically actionable. We will discuss the importance of elucidating family history, highlight potential clinically significant psoriatic genes, emphasize genetic variants that may identify PsA among patients with psoriasis, and explore the emerging roles of transcript profiling, single-cell sequencing, and spatial omics in PsA.

Objectives To identify molecular pathways and key proteins associated with postoperative joint pain following total joint arthroplasty (TJA) in osteoarthritis (OA) patients using a plasma proteomics approach. Methods Primary OA patients who underwent total knee or hip arthroplasty were assessed for their postoperative pain at least 1-year after surgery using the WOMAC Likert 3.0 pain subscale. Three pain phenotypes were defined: sustained pain (pain on all 5 questions), pain while active (pain while walking and taking stairs), and pain at rest (pain while sitting/lying and at night while in bed). Patients reporting no pain were classified as controls. Plasma proteomic profiling was performed using the Olink® Explore HT platform. Associations between postoperative joint pain and protein expression were assessed using logistic regression adjusted for age, sex, and body mass index. Functional enrichment analysis was conducted using KEGG and GO databases. Protein-protein interaction network was constructed using the STRING database and visualized in Cytoscape 3.10.4 to identify hub proteins. Bonferroni correction was applied to control for multiple testing across 5416 proteins and 3 pain phenotypes (α=3.08×10-6). Results A total of 149 patients were included. The prevalence of sustained pain, pain while active, and pain at rest 4 years after TJA was 5, 13, and 7%, respectively; 81% reported no pain, thereby served as controls (Figure 1A). No individual protein remained significant after multiple testing correction. However, 246, 332, and 260 proteins were nominally associated (p<0.05) with sustained pain, pain while active, and pain at rest, respectively. For sustained pain and pain at rest, the associated proteins were enriched in the MAPK signaling pathway, extracellular matrix, and growth factor activity. Hub proteins included CCL2, ERBB4, SHC1, NCAM1, MMP9, HRAS, FGF3, and NTRK2 for sustained pain, CD86, NCAM1, and ANXA5 for pain at rest (Figure 1B, 1D). Proteins associated with pain while active were enriched in the interleukin 17 signaling pathway, myeloid leukocyte mediated immunity, and cytokine activity. Hub proteins included ITGB2, CASP8, and CCL2 (Figure 1C). Figure 1. Conclusion Our data showed distinct molecular signatures for different postoperative pain phenotypes following TJA. While sustained and rest pain shared enrichment in MAPK-related and extracellular matrix pathways, pain while active appeared to involve immune and inflammatory mechanisms. These findings highlight potential protein biomarkers and pathways contributing to heterogeneous postoperative pain experience in OA patients.

Objectives To identify potential causal biomarkers for obesity-related knee osteoarthritis (OB+OA+) by metabolome-wide association analysis (MWA) and Mendelian randomization (MR). Methods Four cohorts were included: the Newfoundland Osteoarthritis Study (NFOAS) as discovery, the Tasmanian Older Adult Cohort Study (TASOAC) and Longitudinal Evaluation in the Arthritis Program: Osteoarthritis Study (LEAP OA) as replications, and the Multicenter Osteoarthritis Study (MOST) for assessing longitudinal prediction. OB+OA+ was defined as either end-stage or radiographic knee OA with BMI≥30 kg/m 2 . Plasma metabolomic profiling and genome-wide genotyping were performed. Regression models were used to identify biomarkers for OB+OA+ and MR for assessing causal relationships. Results Metabolome-wide association analysis of 310 OB+OA+ and 99 OB-OA+ patients from the NFOAS identified that acyl-alkyl-phosphatidylcholine C40:6 (PC ae C40:6) was associated with OB+OA+ at metabolome-wide significance (P=1.80×10^-6), which was replicated in the TASOAC including 102 OB+OA+ and 254 OB-OA+ and the LEAP OA including 118 OB+OA+ and 114 OB-OA+ (P≤7.78×10^-3) (Figure 1A). MR analyses showed causal relationships of PC ae C40:6 with knee OA and obesity (Figure 1B). Our longitudinal data showed that the baseline PC ae C40:6 predicted overweight status and BMI at 10-year follow-up in the TASOAC (n=159; P<0.02) and incidence radiographic and symptomatic knee OA at 5-year follow-up in the MOST (n=337; P<0.03) in subjects with baseline normal weight (Figure 1C,D). Furthermore, structural equation modeling analysis in the NFOAS (n=526) revealed that PC ae C40:6 had a significant indirect via obesity and a direct effect on knee OA (all P<0.001). Conclusion Our data suggested a causal relationship between PC ae C40:6 and OB+OA+. PC ae C40:6 could be a promising biomarker for monitoring OB+OA+ disease progression and a novel target for developing new therapies.

Objectives Epidemiological studies show that psoriatic arthritis (PsA) develops after psoriasis in 70% of patients, appears simultaneously in 15%, and precedes psoriasis in the remaining 15%. This study aimed to investigate whether genetic factors influence the time between the onset of psoriasis and PsA, and to identify any genetic differences among these 3 subgroups. Methods A total of 703 patients from the Gladman Krembil PsA program were analyzed, with ages at psoriasis and PsA onset recorded. All samples underwent a genome-wide association scan that included over one million single-nucleotide polymorphisms (SNPs). Quality control excluded SNPs with more than 1% missing data and those that did not meet Hardy-Weinberg equilibrium criteria. We treated the age difference between PsA and psoriasis onset as a quantitative trait by subtracting the age at PsA onset from the age at psoriasis onset for each patient. We then performed a quantitative trait locus (QTL) analysis. PsA patients were categorized into 4 groups and compared for genetic differences: (A) PsA occurring at least one year before psoriasis; (B) psoriasis and PsA occurring within one year of each other; (C) PsA occurring one to 10 years after psoriasis; and (D) PsA starting 10 or more years after psoriasis onset. Results The QTL analysis identified over 50 SNPs significantly affected the onset of inflammatory arthritis (p < 1 x 10^-5). Most identified loci were associated with a delay in PsA onset in individuals with the mutant allele compared with those with the wild-type allele. Genes associated with delayed PsA included PSORS1C1, CDSN, TXB5, and OSBLV. Conversely, loci on chromosome 15 (in linkage disequilibrium with MYO1E) and chromosome 17 (in LD with CCDC43 and MEIOC) were associated with an earlier onset of PsA. Comparisons among the PsA onset subsets noted above revealed notable differences, particularly between groups A and D (59 SNPs) and between groups A and C (30 SNPs) at a significance level of p < 1 × 10^-5. Development of polygenic risk scores to identify early- and late-onset PsA is ongoing. Conclusion This study underscores the significant role of genetic mutations in influencing the variability in the onset of PsA among patients with psoriasis. By identifying over 50 SNPs that significantly impact the timing of PsA onset, our findings highlight the complex genetic landscape that contributes to progression from psoriasis to PsA. Best Abstract On Basic Science Research By A Trainee Award

Objectives To identify molecular pathways and key proteins associated with early revision after total joint arthroplasty (TJA) in osteoarthritis (OA) patients using a multi-omics approach integrating plasma proteomics and metabolomics. Methods Primary OA patients who underwent total knee or hip arthroplasty were included. Plasma proteomic profiling was performed using the Olink® Explore HT platform, and plasma metabolomic profiling was conducted using the Biocrates MxP Quant 500 kit. Associations between early revision after TJA and protein expression were evaluated using logistic regression adjusted for age, sex, and body mass index. Functional enrichment analysis was conducted using KEGG and GO databases. Protein-protein interaction network was constructed via the STRING database, visualized in Cytoscape 3.10.4, and hub proteins were identified using the CytoHubba plug-in. Metabolites associated with hub proteins were identified using Spearman correlation analysis. Bonferroni correction was applied for multiple testing (α=9.23×10^-6 for 5,416 proteins; α=2.70×^10-5 for 622 metabolites and 3 hub proteins). Results A total of 168 patients were included, with revision data extracted an average of 10.5 years after primary TJA. The early revision rate was 3% (Figure 1A), with a mean time to revision of 2.6 years. No individual protein reached significance after multiple testing correction. However, 337 proteins were nominally associated with early revision (p<0.05). These proteins were enriched in complement and coagulation cascades, hematopoietic cell lineage, and regulation of angiogenesis and vasculature development pathways (Figure 1B,C). FLT3, IL10, and NRAS were identified as hub proteins (Figure 1D), among which FLT3 and NRAS were negatively associated with early revision TJA, while IL10 was positively associated. Although no metabolite reached multiple-testing corrected significance, 28, 17, and 18 metabolites were nominally correlated (p<0.05) with FLT3, IL10, and NRAS, respectively, predominantly long-chain diglycerides, triglycerides, and phosphatidylcholines. Figure 1 Conclusion Multi-omics integration of plasma proteomics and metabolomic data revealed that dysregulation of angiogenesis and lipid metabolic pathways may contribute to the risk of early revision TJA in patients with primary OA. These pathways and their key molecular mediators warrant further validation as potential predictive biomarkers or therapeutic targets.

Objectives Sex differences can substantially influence clinical features, disease progression, and treatment responses across various medical conditions, including psoriatic arthritis (PsA). Understanding these differences at a genetic level is crucial for developing personalized treatment approaches and improving disease management. This study utilizes data from the UK Biobank to identify sex-specific genetic variants that may contribute to the susceptibility to PsA, thereby enhancing our understanding of the disease’s underlying mechanisms. Methods Data were extracted from the UK Biobank, including 459 female and 497 male PsA patients. To minimize population stratification, only Caucasian participants were included, resulting in 444 female and 423 male patients, along with 226,198 female and 191,928 male Caucasian controls. A genome-wide association study (GWAS) was conducted, comparing 97,013,422 SNPs between males and females among PsA patients and controls, focusing exclusively on autosomes. Only SNPs with allele frequencies greater than 0.005 in affected cases and controls were included, and associations were considered significant at P < 1 × 10^-6. Results In males, 2,596 SNPs were identified across 104 genes, and in females, 4,542 SNPs were associated with 108 genes. Among these genes, 72 were shared between sexes, including PSORS1C1, the strongest genetic associated locus for psoriatic disease. Additionally, 32 unique genes were identified in males, including ERAP-1 and TRAF3IP2, whereas 35 were identified in females, including HLA-DRB1 and HLA-DQA1. Notably, sex-specific genes have been previously documented to exhibit sex-specific differences in immune responses. Ongoing studies are focusing on pathway enrichment analysis of sex-specific genes for both genders. Conclusion The identification of unique genes in each sex, alongside shared genetic markers, highlights the complexity of PsA’s genetic landscape. These findings suggest that sex-specific genetic factors may play a role in the manifestation and progression of the disease. Further research is essential to validate these results and explore their clinical and molecular implications, which could ultimately lead to more tailored therapeutic strategies for PsA patients.

Objectives While numerous proteins have been linked to ankylosing spondylitis (AS), the causal nature of these associations remains unconfirmed. This study employed a Mendelian randomization (MR) approach to evaluate whether circulating plasma protein levels, derived from large-scale international dataset, are causally associated with AS risk. Methods The study analyzed genetic summary data from 1,462 AS patients and 164,682 controls, all of whom had undergone genome-wide association scans, as identified from the FinnGen consortium. Large-scale plasma protein quantitative trait locus (pQTL) data were sourced from the UK Biobank Proteomics Project (UKB-PPP; 2,923 unique proteins), the INTERVAL study (2,995 unique proteins), and the Icelandic study (4,719 proteins).[1] Potential protein candidates were identified based on SNP associations with proteins (p < 5×10^-8), followed by linkage disequilibrium (LD) clumping to identify independent pQTLs for each protein (r^2 < 0.001), and defining SNPs when the leading SNP was located within 500kb of the transcription site of the protein-coding gene. A 2-sample MR analysis was then conducted, using the Wald ratio for genes with 1 SNP and inverse variance weighting (IVW) for those genes with multiple SNPs. Sensitivity analyses, including tests for pleiotropy and heterogeneity, were performed to ensure robustness of the causal estimates. Results A total of 21 unique proteins were identified as being associated with the risk of AS. In the UKB-PPP dataset, 3 proteins were linked to an increased risk of AS (DXO, LTA, AGER), while 6 proteins associated with a decreased risk (TRIM40, LTB, AIF1, HLA-E, MICB_MICA, CFB). The INTERVAL dataset identified 5 proteins correlated with an elevated risk of AS (IL-23R, TNXB, CFB, MICB, ERAP1) and AGER protein was linked to a reduced risk. From a 2021 dataset,[1] MR identified 8 proteins significantly associated with AS (HLA-DQA2, ERAP1, MICB, BTN3A3, TAPBP, C2, CFB, TNXB and 7 proteins with decreased risk NCR3, HSPA1L, MICA, VARS, APOM, AIF1, AGER). The top 6 Reactome pathways identified from pathway enrichment analysis included Immune system, cytokine signaling, adaptive immune system, adaptive immune system, innate immune system and signaling by interleukins (1 ×10^-6). Conclusion This proteome-wide MR study identified 21 unique proteins associated with AS risk, offering novel insights into the disease’s pathogenesis. These prioritized proteins also present potential druggable targets, warranting further investigation to explore novel therapeutic opportunities. References [1.] Ferkingstad E. Nat Genet 2021;53:1712-21.