Background: The use of initial clinical assessments to predict therapeutic outcomes via machine learning (ML) is a promising frontier in precision medicine. The study aims to construct ML models capable of predicting disease activity in patients with rheumatoid arthritis (RA), thereby optimizing clinical decision-making and treatment selection. Methods: This multicenter retrospective study analyzed electronic health records (EHRs) from 1,864 patients with RA across 5 tertiary hospitals in China between 2017 and 2022. The dataset from Peking University People’s Hospital (PKUPH) was employed as the training and internal validation cohort, whereas data from 4 other centers were used for external validation. Longitudinal variables, including demographics, laboratory indices, and medication regimens, at baseline, 3-month, and 6-month follow-up were integrated to capture dynamic disease patterns. Four ML models were trained to predict disease status 6 months post-treatment, with the primary outcome defined as clinical remission (disease activity score in 28 joints with erythrocyte sedimentation rate ≤ 2.6). Results: The final analysis included 1,629 patients from PKUPH and 235 from 4 other tertiary hospitals. In the internal validation phase, the optimal model achieved an accuracy of 95.3% and an area under the receiver operating characteristic curve (AUROC) of 0.971, with sensitivity, specificity, positive predictive, and negative predictive values of 98.1%, 84.2%, 96.1%, and 91.8%, respectively. The model exhibited generalizability in external validation, presenting an accuracy of 87.3% and an AUROC of 0.922. Furthermore, in the multiclass task of stratifying patients into remission, low, moderate, or high disease activity, the deep neural network model showed an accuracy of 68.6% and AUROC of 0.860. Conclusions: Longitudinal clinical data extracted from EHRs can be effectively leveraged to develop prognostic models. This study confirms that deep learning approaches trained on large-scale multicenter cohorts can accurately predict disease trajectories in RA, offering a valuable tool for personalized patient management.

ObjectiveTo explore the real-world efficacy and safety of telitacicept in patients with systemic lupus erythematosus (SLE).MethodsThis retrospective study included SLE patients treated with telitacicept for at least 3 months at Mianyang Central Hospital between January 2022 and December 2025. Baseline characteristics and clinical outcomes at 12, 24, and 52 weeks were assessed.ResultsA total of 139 patients were enrolled in this study. The SLE Disease Activity Index 2000 (SLEDAI-2K) score significantly decreased from a baseline of 11.42 ± 4.68 to 4.623 ± 3.65, 2.42 ± 2.98, and 1.48 ± 2.58 at 12, 24, and 52 weeks, respectively (P < 0.0001). The anti-dsDNA positivity rate declined from 35.25% at baseline to 7.94% by week 52, accompanied by an upward trend in complement 3 (C3) and complement 4 (C4) levels. Among the 74 patients with lupus nephritis, the baseline 24-hour urine protein was 1.59 ± 1.68 g/24h, which significantly decreased to 1.12 ± 1.41 g/24h, 0.42 ± 0.46 g/24h, and 0.39 ± 0.64 g/24h at 12 weeks, 24 weeks, and 52 weeks, respectively (P < 0.001). The serum albumin level significantly increased (P < 0.0001), while the serum estimated glomerular filtration rate (eGFR) showed significant reductions (P < 0.05). Furthermore, white blood cell and platelet count also improved significantly in patients with leukopenia and thrombocytopenia (P < 0.0001). The mean daily glucocorticoids dose (equivalent to prednisone) was successfully tapered from 39.98 ± 10.09 mg/d at baseline to 18.35 ± 7.87 mg/d at 12 weeks, 9.752 ± 4.72 mg/d at 24 weeks, and 5.69 ± 2.95 mg/d at 52 weeks (P < 0.0001). Adverse events occurred in 28 (20.14%) patients, with upper respiratory tract infections being the most common (53%), and no serious adverse events occurred.ConclusionIn this real-world study, telitacicept combined with conventional therapy was associated with improvements in disease activity and organ functions in SLE with favorable safety.

OBJECTIVES:RA-associated interstitial lung disease (RA-ILD) shortens survival and impairs quality of life, with prognosis assessment challenging. This study aimed to predict RA-ILD patients' survival via clinical features and radiomics. METHODS:Patients from two centres (n = 230) were divided into training (n = 144), internal validation (n = 37) and external validation (n = 49) sets. Univariate/multivariate Cox regression identified independent clinical predictors (age, lymphocyte count). Radiomics features (n = 1688) were selected via variance thresholding, univariate selection and LASSO-Cox regression (24 optimal features retained). Clinical, radiomics and integrated models (with nomogram) were built; performance was assessed by concordance index (C-index), calibration curves and decision curve analysis (DCA). Patients were stratified by Rad-score (threshold = 0.15) for survival analysis. RESULTS:Age (HR = 1.046, 95%CI = 1.013-1.080, P = 0.006) and lymphocyte count (HR = 1.385, 95%CI = 1.059-1.812, P = 0.018) were independent overall survival (OS) predictors. The integrated model outperformed others (C-index: training = 0.832, internal = 0.816, external = 0.812), with excellent calibration and higher DCA net benefit. High-risk patients (Rad-score ≥ 0.15) had significantly shorter OS than low-risk patients (all P < 0.01). CONCLUSION:The integrated nomogram (age, lymphocyte count, Rad-score) enables precise, user-friendly prediction of RA-ILD 5-year survival. It addresses unmet clinical needs by complementing current guidelines and supporting personalized risk-stratified management.

Short-form video platforms are increasingly used to obtain information about chronic obstructive pulmonary disease (COPD), but the quality and reliability of COPD-related content across Chinese platforms remain unclear. We evaluated 228 COPD-related videos from Douyin, Kwai, and Bilibili using the Journal of the American Medical Association (JAMA) benchmark criteria, Global Quality Scale, modified DISCERN (mDISCERN), and Patient Education Materials Assessment Tool. Video characteristics, creator identity, verification status, content theme, presentation format, and visible engagement metrics were also analyzed. Video quality differed significantly across platforms. Douyin showed the highest transparency, reliability, and overall quality scores. Kwai showed the highest visible engagement but had lower overall quality and actionability scores. Bilibili had the longest videos and the highest understandability scores. Organization verified accounts generally achieved higher quality scores than individual verified or unverified accounts, although this finding should be interpreted cautiously because of the small number of such accounts. Visible engagement metrics, including likes, comments, saves, and shares, were not significantly correlated with medical quality scores. Together, these findings suggest that visible popularity is a poor proxy for medical quality. COPD-related digital health communication may benefit from more accessible evidence-based content, clearer source identification, and closer oversight of high-risk therapeutic claims.

Objective: To investigate the potential toxicological effects of acetyl tributyl citrate (ATBC) on osteoarthritis (OA) and elucidate the underlying mechanisms using bioinformatics, machine learning, and network toxicology. Methods: ATBC targets were identified from multiple databases, and OA-associated differentially expressed genes (DEGs) were sourced from GSE51588. Intersection analysis identified common targets. Functional enrichment and protein-protein interaction (PPI) network analysis were performed. Machine learning algorithms (LASSO, Random Forest and SVM) validated core targets, with ROC curves assessing diagnostic potential. Immune infiltration differences were analyzed via Cibersort. Molecular docking confirmed ATBC binding to core targets, and an adverse outcome pathway (AOP) framework was developed to elucidate ATBC's role in exacerbating OA through key genes and pathways. Results: Intersection analysis identified 40 common targets related to both ATBC and OA. Functional enrichment analysis revealed that these targets were significantly involved in calcium signaling pathways and neuroactive ligand-receptor interactions, both of which are implicated in OA pathogenesis. The PPI network analysis identified TNF, MMP8, CXCR4, and SLC2A1 as core targets. Machine learning algorithms further validated these core targets. ROC curve analysis showed that these genes have diagnostic potential, with AUC values ranging from 0.762 to 0.970. Immune infiltration analysis using Cibersort revealed significant differences in immune cell infiltration between OA and control groups, with core targets showing distinct correlations with various immune cells. Molecular docking confirmed strong binding affinities between ATBC and the core targets, with binding energies less than -5 kcal/mol. A novel adverse outcome pathway (AOP) framework was established, suggesting that ATBC may influence the expression of TNF, CXCR4, MMP8, and SLC2A1, with the calcium signaling and neuroactive ligand-receptor interaction pathways potentially contributing to immune dysregulation and OA progression. Conclusion: The identification of key targets (TNF, MMP8, CXCR4, and SLC2A1) and molecular docking results elucidates potential mechanisms by which ATBC exposure may exacerbate OA progression. The AOP provides evidence for joint-health risk assessment of plasticizers and offers readily measurable biomarkers for regulatory toxicology and future therapeutic development.

OBJECTIVES:This phase II trial aimed to evaluate the efficacy and safety of SSGJ-613, a fully human anti-interleukin 1β monoclonal antibody, compared with compound betamethasone (CB) in Chinese patients with acute gouty arthritis. METHODS:In this multicenter, double-blind, double-dummy, active-controlled study, 90 adults with gout according to the 2015 American College of Rheumatology (ACR) criteria and who had experienced an acute gouty arthritis flare within 7 days were randomized (1:1:1) to receive a single subcutaneous injection of SSGJ-613 (200 mg or 300 mg) or intramuscular CB. The primary endpoint was the change from baseline in visual analog scale (VAS) pain scores at 72 h post-dosing. Safety was evaluated through monitoring of adverse events and laboratory parameters. RESULTS:The mean changes from baseline in VAS scores at 72 h were comparable between SSGJ-613200 mg (-45.8 mm), 300 mg (-40.4 mm), and CB (-48.2 mm) groups. However, SSGJ-613 significantly reduced the risk of new flares: 16.7% (200 mg) and 14.3% (300 mg) of patients experienced new flares versus 54.8% with CB (p = 0.0037 and p = 0.0019, respectively). The safety profile of SSGJ-613 was similar to CB, with no drug-related serious adverse events reported. CONCLUSION:A single injection of SSGJ-613 demonstrated rapid pain relief comparable to corticosteroid therapy at 72 h and provided superior protection against gout flare recurrence over 12 weeks in Chinese patients, supporting its potential as an effective treatment option for acute gouty arthritis with prolonged therapeutic benefits. CLINICAL TRIALS:NCT06169891.

BackgroundThe clinical differentiation between obstetric antiphospholipid syndrome (OAPS) and undifferentiated connective tissue disease (UCTD) presents significant diagnostic challenges. This study employs metabolomics to investigate metabolic reprogramming patterns in OAPS and UCTD, aiming to identify potential biomarkers for early diagnosis.MethodsUsing LC-MS-based metabolomics, we analyzed serum profiles from 40 OAPS patients (B1), 30 OAPS + UCTD patients (B2), 27 UCTD patients (B3), and 30 healthy controls (A1). Multivariate PLS-DA modeling, combined with KEGG pathway and Gene Set Enrichment Analysis (GSEA), was applied to identify disease-specific metabolic signatures.ResultsMetabolomic profiling detected 1,227 metabolites, including 412 in negative ion mode and 815 in positive ion mode. The two ionization modes exhibited distinct chemical profiles, with PLS-DA analysis demonstrating superior group discrimination in positive ion mode. B1 vs B2 (Negative ion mode): nine metabolites were upregulated (notably 17(S)-HpDHA, showing the largest fold-change as a potential biomarker), and one metabolite was downregulated (5-sulfosalicylic acid). B1 vs B2 (Positive ion mode): 17 metabolites were upregulated (including 4-methyl-5-thiazoleethanol, a promising biomarker), and eight were downregulated. B1 vs B3 (Negative ion mode): 14 metabolites were upregulated (highlighted by 3-hydroxybenzoic acid, the most significantly altered candidate), and four were downregulated. B1 vs B3 (Positive ion mode): 30 metabolites were upregulated (again featuring 4-methyl-5-thiazoleethanol), and 32 were downregulated. B2 vs B3 (Negative ion mode): 15 metabolites were upregulated (e.g., chlortetracycline), and 15 were downregulated (notably 6α-prostaglandin I1). B2 vs B3 (Positive ion mode): 29 metabolites were upregulated (e.g., senecionine), and 64 were downregulated (e.g., SM 9:1 2O/16:4). These metabolites represent robust candidates for group discrimination. Enrichment analysis revealed that distinct metabolic pathways were significantly associated with different groups and ionization modes, suggesting divergent underlying metabolic mechanisms.ConclusionThis study systematically characterizes the metabolic reprogramming in OAPS, UCTD, and their comorbid states, identifying potential diagnostic biomarkers. Differential metabolites and pathway analyses highlight the critical role of immunity, contributing to a theoretical framework for “metabolism-immunity-vascular” interactions.

Objectives:This study aimed to evaluate the efficacy of iguratimod (IGU) and hydroxychloroquine (HCQ) in the treatment of primary Sjögren's syndrome (pSS). Methods:This was a randomised controlled study. A total of 60 patients with pSS in Mianyang Central Hospital were recruited between December 2020 and December 2022. They were randomly divided into two groups: the IGU group and the HCQ group. Treatment in the IGU group was as follows: ≤10 mg of prednisone per day, 25 mg of IGU twice a day; treatment in the HCQ group was as follows: ≤10 mg of prednisone per day, 0.2 g of HCQ twice a day. The EULAR Sjögren's Syndrome Disease Activity Index (ESSDAI) and the European League against Desiccation Sjögren's Syndrome Patient Reported Index (ESSPRI) were used to assess disease activity. Results:After 6 months of treatment, the levels of immunoglobulin G (IgG) and ESSPRI in the IGU group were significantly lower than those in the HCQ group and the levels of CD19+CD5+CD1d+ B cells were significantly higher than those in the HCQ group (p < 0.05). Compared with baseline, the serum IgG level, erythrocyte sedimentation rate (ESR), B lymphocytes, ESSDAI, ESSPRI and Functional Assessment of Chronic Illness Therapy (FACIT) were significantly decreased and CD19+CD5+CD1d+ B cells were significantly increased in the IGU group after 6 months of treatment. In the HCQ group, C-reactive protein, ESR, ESSDAI, ESSPRI and FACIT were significantly decreased; there was no significant difference in regulatory B cells before and after treatment. Conclusion:Both IGU and HCQ can reduce the disease activity and fatigue score of patients with pSS. However, IGU was superior to HCQ in reducing IgG levels. Furthermore, IGU can affect the levels of peripheral blood B lymphocytes and CD19+CD5+CD1d+ B cells.

Here, we proposed a flexible umbrella-shaped metamaterial (USM) biosensor and conducted modeling analysis for six pesticides simultaneously. To improve the contrast of the sensor, we introduced triple-resonance sensing based on high-order modes, taking into account both the resonance depth and the Q-factor. During the model-building process, we adopted a two-stage cascade ensemble learning framework, which significantly improved the accuracy, scalability, and convenience of qualitative and quantitative sensing results. The qualitative accuracy of the six pesticides reached up to 100%. For the quantitative analysis, the root mean square error (RMSE) was only 2.21 mu g, and R-2 reached 0.9952, showing excellent sensing performance.

Purpose:IgA vasculitis nephritis (IgAVN) is one of the most common secondary glomerulonephritis in children. Although guidelines have reached a consensus about the effectiveness of cyclophosphamide (CYC) in IgAVN, the recommendations regarding the use of tacrolimus (TAC) and mycophenolate mofetil (MMF) are still inconsistent. Studies have demonstrated that TAC is safe and effective in IgAVN. However, the impact of immunosuppressive agents on the long-term outcome remains ambiguous. Therefore, the objective of this study is to compare the effectiveness and long-term outcome of TAC, CYC, and MMF in combination with glucocorticoid in pediatric IgAVN. Patients and Methods:A retrospective analysis was conducted on children with grade II-V IgAVN by renal biopsy at Tongji Hospital from November 2011 to October 2021. The collected clinical, pathological, treatment and follow-up data were analyzed. Results:A total of 422 patients were eligible. Among them, 108 patients received glucocorticoid in combination with oral TAC, 143 with intravenous CYC, and 171 with oral MMF. The complete remission rate (CR) of TAC (25.9%/44.3%) was significantly higher than that of CYC (16.1%/36.4%) at 3 and 6 months. Additionally, mean absolute decrease in urine protein at 1, 3, and 6 months were significantly higher in TAC than that in CYC and MMF groups. Compared to CYC, TAC and MMF groups had significantly lower overall incidence of adverse events (60.2%, 65.7% vs 84.4%). Moreover, TAC and MMF group had a more favorable renal prognosis (grade A and B) and a significantly lower recurrence rate (17.9%, 23.9% vs 41.8%) than CYC. Conclusion:This study reveals that TAC can rapidly and effectively reduce proteinuria and achieve renal complete remission with fewer adverse effects. Moreover, TAC and MMF are more favourable for renal prognosis.

This study aimed to analyze and compare the proportion of patients with different types of inflammatory arthritis and investigate the clinical characteristics, including symptoms and signs, medication choices, and disease activity, in the daily clinical practice of China. Patients with inflammatory arthritis were recruited from 16 Grade-A tertiary hospitals between August 2021 and April 2022. The medical profiles, encompassing sociodemographic characteristics, clinical and laboratory date, were collected. This study included 2,693 patients with arthritis, with rheumatoid arthritis (RA) accounting for the highest proportion (50.50

BACKGROUND:Retinol-binding protein 4 (RBP4) is a vitamin A transport protein synthesized in the liver and also plays a crucial role in inflammation and immune regulation. Low serum vitamin A levels have been observed in both pediatric and adult patients with ulcerative colitis (UC). The association between serum vitamin A levels and serum RBP4 levels, as well as the underlying mechanism involved inimpaired vitamin A transport during inflammation in UC patients, has yet to been investigated. METHODS:A validation cohort comprising 103 UC patients and 22 controls was analyzed. Serum RBP4 levels were measured using anenzyme-linked immunosorbent assay (ELISA), and correlations with vitamin A levels and disease severity wereassessed. A dextran sulfate sodium (DSS)-induced colitis mouse model was used to valuatehepatic RBP4 expression and inflammatory changes. Primary hepatocytes from C57BL/6 mice were exposed to inflammatory cytokines to assess the impact of these cytokines on RBP4 expression. RESULTS:Serum vitamin A (p < 0.001) and RBP4 levels (p < 0.001) were significantly lower in UC patients compared to controls, exhibiting a pronounced decreasing trend as disease severity increased (vitamin A: p < 0.001; RBP4: p < 0.001), while vitamin A levels increased after effective treatment (p < 0.001). Hepatic RBP4 expression was reduced in the DSS-induced colitis model and negatively correlated with TNF-α, IL-6, and IL-17. CONCLUSIONS:Serum RBP4 levels are decreased in UC patients and negatively correlate with disease severity, possibly due to proinflammatory cytokine-induced suppression which might contribute to inflammation-driven vitamin A transport dysfunction.

Xeligekimab is a novel immunoglobulin G4 (IgG4) monoclonal antibody targeting interleukin-17A (IL-17A). In a phase III trial in patients with plaque psoriasis, xeligekimab showed efficacy and safety consistent with other IL-17A inhibitors, supporting its potential application in the treatment of spondyloarthritis. This phase III trial aimed to investigate the efficacy and safety of xeligekimab in patients with radiographic axial spondyloarthritis (r-axSpA). This was a phase III study conducted at multiple centers in China. Eligible patients were randomly assigned (1:1:1) to receive xeligekimab 100 mg, xeligekimab 200 mg, or placebo. Randomization was stratified by medication history (biologic-experienced vs. biologic-naïve) and weight (≥ 70 kg vs. < 70 kg). The primary endpoint was the proportion of patients achieving an Assessment of SpondyloArthritis International Society 20 (ASAS20) response at week 16. A key secondary endpoint was the ASAS40 response rate at the same time point. A total of 465 patients were recruited. A significantly higher proportion of patients receiving xeligekimab 200 mg (n = 114 (74.0

Background:Advanced cardiovascular-kidney-metabolic (CKM) syndrome refers to stages 3 and 4 of CKM syndrome, which are associated with higher mortality compared to earlier stages (0-2). The albumin (ALB)-to-neutrophil/lymphocyte ratio (ANLR) is a new predictive marker that participates in immune inflammation and dietary status. However, the influence of ANLR on all-cause mortality (ACM) and cardiovascular mortality (CVM) in individuals with advanced CKM syndrome remains unclear. This investigation aims to examine the link between ANLR and both ACM and CVM in this population using data from a large-scale cross-sectional survey in the United States. Methods:Data were from the National Health and Nutrition Examination Survey (NHANES) spanning 1999 to 2018, a nationally representative cross-sectional survey with longitudinal mortality follow-up from the National Death Index. The formula of ANLR is ALB/NLR. The diagnostic criteria of CKM syndrome was based on the concept proposed by the American Heart Association and modified criteria adapted for NHANES data availability. The outcomes of interested included ACM and CVM. A 1:1 propensity score matching (PSM) approach was used to control for potential confounding variables. The threshold value of ANLR influencing survival was determined using maximally selected rank statistics, which is based on the log-rank test. This method identifies the optimal cutoff for continuous variables where the difference in survival rates is most pronounced, making it particularly well-suited for analyzing time-to-event data, such as survival outcomes. Kaplan-Meier survival analysis and multivariate Cox proportional hazards models were employed to assess the effects of ANLR on both ACM and CVM. Restricted cubic spline (RCS) analysis evaluated the linear or non-linear association between ANLR and mortality outcomes. Stratified analysis and interaction testing were carried out to estimate the influence of covariates on the ANLR-mortality correlation. Results:A total of 3,266 adults with advanced CKM syndrome (41.12% male) were included in the analysis, with median (interquartile range) age of 73 (63-80). Prior to PSM, and fully adjustment, the lowest ANLR Tertile 1 was related to significantly higher risks of ACM (hazard ratio [HR]: 1.58, 95% confidence interval [CI]: 1.39-1.78, p < 0.001) and CVM (HR: 1.65, 95% CI: 1.34-2.04, p < 0.001) compared to the highest Tertile 3. After applying PSM, and fully adjusting for confounders, an ANLR score below 1.04 was independently linked to increased risks of both CVM (HR: 2.02, 95% CI: 1.49-2.75, p < 0.001) and ACM (HR: 1.52, 95% CI: 1.27-1.81, p < 0.001). Interaction tests revealed no significant interactions for CVM across subgroups (All P interaction > 0.05). Regarding ACM, interactions were noted between ANLR and age, gender, and CKM stages (All P interaction < 0.05). RCS analysis indicated an L-shaped link between ANLR and both ACM and CVM, both before and after PSM (all P non-linearity < 0.001). The predictive value of ANLR, NLR, and ALB for CVM and ACM in individuals with advanced CKM syndrome demonstrated that ANLR and NLR exhibited comparable predictive capabilities for both ACM and CVM, outperforming ALB. Furthermore, the predictive performance of ANLR and NLR for ACM was superior to that for CVM. Conclusion:Lower ANLR values, indicative of elevated systemic inflammation and malnutrition, are independently linked to increased risks of both ACM and CVM in individuals with advanced CKM syndrome in the US. These readily accessible and low-cost blood markers could serve as valuable prognostic indicators for identifying high-risk individuals. Future research should focus on incorporating additional biomarkers, validating the indices in larger and more diverse cohorts, and employing advanced analytical methods to refine the diagnostic efficiency of ANLR and NLR for better clinical utility.

Objective:Osteoarthritis (OA) is a degenerative disorder associated with glycolysis. However, the precise mechanisms remain unclear. This study aimed to identify glycolysis-associated biomarkers and elucidate how glycolysis-related genes interact with the synovial immune microenvironment in OA progression. Methods:Normal and OA synovial gene expression profile microarrays were obtained from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) were identified using limma package. Gene Ontology (GO) and KEGG enrichment analyses were conducted to explore biological functions. Weighted Gene Co-expression Network Analysis (WGCNA) was used to identify OA-associated genes, which were intersected with glycolysis genes from The Molecular Signatures Database (MSigDB) and DEGs to obtain key genes. Lasso regression and random forest models were employed to establish a risk model, and its predictive performance was evaluated using nomogram, Receiver Operating Characteristic (ROC) analysis, and Decision Curve Analysis (DCA). Gene Set Enrichment Analysis (GSEA) and Cibersort analysis were conducted to explore pathways and immune infiltration correlations. Results:A total of 239 OA-associated genes were identified through WGCNA. Six hub genes were obtained by intersecting with glycolysis genes and DEGs. Four key glycolytic genes were selected by Lasso regression and random forest models. The nomogram showed that three genes (DDIT4, SLC16A7, SLC2A3) could predict OA risk accurately. The ROC analysis demonstrated an area under the curve (AUC) of 0.85, indicating good predictive performance. Distinct immune cell distribution patterns were observed in OA groups. Interaction networks were constructed for the key genes with related miRNAs, transcription factors (TFs), and small molecule drugs. Conclusion:This study identified three key glycolysis-related genes (DDIT4, SLC16A7, SLC2A3) in OA, revealing their potential roles in disease progression and immune infiltration. These findings may provide new insights into the pathogenesis and therapeutic targets for OA, based on the identified genes and their interactions with the immune microenvironment.

OBJECTIVE:To evaluate the efficacy and safety of firsekibart versus colchicine for prophylaxis against acute gout flares in participants initiating urate-lowering therapy (ULT). METHODS:In this randomized, multicenter, open-label, active-controlled, phase 2 trial, participants were randomized (1:1:1) to receive a single subcutaneous injection of firsekibart 100 mg or 200 mg or oral colchicine 0.5 mg/day for 12 weeks. ULT was initiated at baseline or within 1 week before screening. The primary endpoint was the mean number of acute gout flares per participant over 12 weeks. RESULTS:A total of 162 participants received firsekibart 100 mg (n = 55), 200 mg (n = 52), or colchicine (n = 55). Baseline characteristics were comparable between groups. No flares were observed in the firsekibart 200 mg group within 12 weeks. The adjusted mean number of acute gout flares per participant was 0.02 with firsekibart 100 mg and 0.34 with colchicine, with a rate ratio of 0.05 (95% confidence interval 0.01-0.43; P = 0.0060). Both treatment groups receiving firsekibart (at either dose) had a lower proportion of patients with at least one acute gout flare than the colchicine group. Firsekibart had a favorable safety profile, with no treatment-emergent adverse events (AEs) leading to treatment discontinuation/study withdrawal, no treatment-related serious AEs, and no deaths. CONCLUSION:Firsekibart provided superior prophylaxis against acute gout flares compared with colchicine in patients initiating ULT, with a favorable tolerability profile, supporting its potential as a treatment option for patients with gout initiating ULT.

The systemic immune-inflammation index (SII), a metric reflecting systemic inflammatory response and immune activation, remains underexplored concerning its correlation with mortality among rheumatoid arthritis (RA) patients. This study aimed to delineate the association between SII and both all-cause and cardiovascular mortality within the cohort of American adults diagnosed with RA, utilizing data from the National Health and Nutrition Examination Survey (NHANES) spanning 1999 to 2018. The investigation extracted data from NHANES cycles between 1999 and 2018, identifying RA patients through questionnaire responses. The SII was computed based on complete blood counts, employing the formula: (platelets × neutrophils) / lymphocytes. The optimal SII cutoff value for significant survival outcomes was determined using maximally selected rank statistics. Multivariable Cox proportional hazards models assessed the relationship between SII levels and mortality (all-cause and cardiovascular) among RA patients, with subgroup analyses examining potential modifications by clinical confounders. Additionally, restricted cubic spline (RCS) analyses were conducted to explore the linearity of the SII-mortality association. The study encompassed 2070 American adults with RA, among whom 287 exhibited a higher SII (≥ 919.75) and 1783 a lower SII (< 919.75). Over a median follow-up duration of 108 months, 602 participants died. After adjustments for demographic, socioeconomic, and lifestyle variables, a higher SII was associated with a 1.48-fold increased risk of all-cause mortality (hazard ratio [HR] = 1.48, 95% confidence interval [CI] 1.21–1.81, P < 0.001) and a 1.51-fold increased risk of cardiovascular mortality (HR = 1.51, 95% CI 1.04–2.18, P = 0.030) compared to a lower SII. Kaplan–Meier analyses corroborated significantly reduced survival rates within the higher SII cohort for both all-cause and cardiovascular mortality (Pall-cause mortality < 0.0001 and Pcardiovascular mortality = 0.0004). RCS analyses confirmed a positive nonlinear relationship between SII and mortality rates. In conclusion, the SII offers a straightforward indicator of the equilibrium between detrimental innate inflammation and beneficial adaptive immunity. Our investigation, utilizing a comprehensive and nationally representative sample, reveals that elevated SII levels independently forecast a greater risk of mortality from all causes, as well as cardiovascular-specific mortality, in individuals suffering from RA. These insights underscore the clinical relevance of the SII as an affordable and readily accessible biomarker. Its incorporation into regular clinical practice could significantly enhance the precision of risk assessment and forecasting for patients with RA, facilitating more tailored and effective management strategies. Specifically, patients with high SII levels could be identified for more stringent cardiovascular risk management, including closer monitoring, lifestyle interventions, and aggressive pharmacological treatments to mitigate their increased risk of mortality.