BACKGROUND

Rheumatoid arthritis (RA) is a systemic autoimmune disease characterized by inflammatory arthritis and extra-articular involvement. Comorbidities are highly prevalent in patients with RA, in particular cardiovascular disease (CVD), which is responsible for over 50% of premature deaths. This study aimed to describe cardiovascular diseases and their risk factors among patients with rheumatoid arthritis in the Philippine General Hospital (PGH).

To describe cardiovascular (CV) diseases and their risk factors among patients with rheumatoid arthritis.

A retrospective descriptive cross-sectional study was done in the University of the Philippines – Philippine General Hospital (UP-PGH) inpatient and outpatient services. The study included patients 18 years old and above diagnosed with RA and fulfills the 1987 American College of Rheumatology or 2010 American College of Rheumatology-European League Against Rheumatism (ACR/EULAR) classification criteria with no overlap features with other autoimmune connective tissue diseases and with complete records of the information required for the study from January 2019-December 2022. The primary outcomes of interest were the prevalence of CV diseases and CV risk factors. Descriptive statistics were used to summarize the data.

There were 123 patients in the study, 93.4% outpatients, and 95.1% females, with a mean age and disease duration of 51.3 and 9.8 years, respectively. Disease activity was moderate in 35% and high in 9.7%, based on disease activity score (DAS 28) or clinical disease activity index (CDAI) scores. Methotrexate (54%) was the most commonly used conventional synthetic disease-modifying antirheumatic drug (csDMARD). Glucocorticoid use was observed in 51.2%. None of the patients were receiving a biologic DMARD. There were 24 (19.5%) patients with CV diseases, namely myocardial infarction, heart failure, and stroke. There were 87 (70%) patients with at least one CV risk factor and 62 (50.4%) with multiple risk factors. The risk factors identified were: dyslipidemia (43.1%), hypertension (40.7%), elevated body mass index (35.7%), and diabetes mellitus (15.4%). There were f ive deaths in the hospitalized patients (4%), one due to a myocardial infarction.

The majority (70%) in our cohort had at least one CV risk factor, 19.5% had an identified CV disease, and one died from a myocardial infarction. Dyslipidemia was the most common CV risk factor. The high proportion of patients with CV disease and CV risk factors highlights the need to add the screening and management of CV diseases and risk factors as a priority among patients with rheumatoid arthritis.

Introduction: X-linked hypophosphatemia (XLH) is a rare genetic disorder caused by PHEX mutations, characterized by chronic hypophosphatemia and renal phosphate wasting, resulting in skeletal, dental, and extra-skeletal complications. To date, no data from Malaysia are currently available on clinical characteristics or health-related quality of life (HRQoL) in paediatric patients. Methodology: This cross-sectional study evaluated HRQoL among children and adolescents with XLH, using validated PROMIS (PatientReported Outcomes Measurement Information System) instruments, sociodemographic and clinical factors associated with HRQoL. This includes children and adolescents with confirmed XLH followed up at Universiti Malaya Medical Centre (UMMC) between November 2024 and March 2025. Results: Seventeen patients were analyzed (76.5% female and 23.5% male). The mean age at symptom onset was 2.82 ± 2.42 years, with a mean age at diagnosis of 5.21 ± 3.29 years. All patients continued to have musculoskeletal complications, including short stature (76.5%), bowing of legs (76.5%), bone/joint pain (47.1%), muscle pain (47.1%), and dental complications (52.9%). Serum alkaline phosphatase improved significantly (p = 0.011); persistent hypophosphatemia and ongoing musculoskeletal manifestations indicated suboptimal disease control. Elevated parathyroid hormone levels and an increase in urine calciumto-creatinine ratio (p = 0.020) lead to secondary hyperparathyroidism and nephrocalcinosis. Adherence to conventional therapy was poor. Non-adherence was associated with worse pain outcomes (higher pain interference [p = 0.037] and greater pain intensity [p = 0.025]). PROMIS scores revealed severely impaired mobility (mean T-score 31.31 ± 12.17), increased fatigue (mean T-score 54.22 ± 7.60), and high pain interference (mean T-score 63.11 ± 9.50). Larger household size was also strongly associated with higher pain intensity (p = 0.004). Conclusion Malaysian children and adolescents with XLH continue to have significantly poor HRQoL, particularly in mobility, fatigue, and pain. These findings highlight the need for access to targeted therapies, such as burosumab, to improve long-term outcomes and QOL in XLH patients.
Adolescent ; Child ; Familial Hypophosphatemic Rickets ; Malaysia ; Quality of Life

OBJECTIVE: To explore, identify, and develop novel blood-based indicators using machine learning algorithms for accurate preoperative assessment and effective prediction of postoperative complication risks in patients with rheumatoid arthritis (RA) undergoing total knee arthroplasty (TKA). METHODS: A retrospective cohort study was conducted including RA patients who underwent unilateral TKA between January 2019 and December 2024. Inpatient and 30-day postoperative outpatient follow-up data were collected. Six machine learning algorithms, including decision tree, random forest, logistic regression, support vector machine, extreme gradient boosting, and light gradient boosting machine, were used to construct predictive models. Model performance was evaluated using the area under the receiver operating characteristic curve (AUC), F1-score, accuracy, precision, and recall. SHapley Additive exPlanations (SHAP) values were employed to interpret and rank the importance of individual variables. RESULTS: According to the inclusion criteria, a total of 1 548 patients were enrolled. Ultimately, 18 preoperative indicators were identified as effective predictive features, and 8 postoperative complications were defined as prediction labels for inclusion in the study. Within 30 days after surgery, 453 patients (29.2%) developed one or more complications. Considering overall accuracy, precision, recall, and F1-score, the random forest model [AUC=0.930, 95% CI (0.910, 0.950)] and the extreme gradient boosting model [AUC=0.909, 95% CI (0.880, 0.938)] demonstrated the best predictive performance. SHAP analysis revealed that anti-cyclic citrullinated peptide antibody, C-reactive protein, rheumatoid factor, interleukin-6, body mass index, age, and smoking status made significant contributions to the overall prediction of postoperative complications. CONCLUSION Machine learning-based models enable accurate prediction of postoperative complication risks among RA patients undergoing TKA. Inflammatory and immune-related blood biomarkers, such as anti-cyclic citrullinated peptide antibody, C-reactive protein, and rheumatoid factor, interleukin-6, play key predictive roles, highlighting their potential value in perioperative risk stratification and individualized management.
Humans ; Arthroplasty, Replacement, Knee/adverse effects* ; Arthritis, Rheumatoid/blood* ; Machine Learning ; Postoperative Complications/blood* ; Female ; Male ; Retrospective Studies ; Middle Aged ; Aged ; Risk Factors ; Preoperative Period ; C-Reactive Protein/analysis* ; Risk Assessment

OBJECTIVE: To explore the co-morbid influencing factors of postmenopausal osteoporosis(PMOP) and dyslipidemia, and to provide evidence-based basis for clinical co-morbidity management. METHODS: Based on the 2017 to 2018 Beijing community cross-sectional survey data, PMOP patients were included and divided into the dyslipidemia group and the uncomplicated dyslipidemia group according to whether they were comorbid with dyslipidemia. Demographic characteristics, living habits and disease history were collected through questionnaires, and bone mineral density and bone metabolism biomarkers (osteocalcin, blood calcium, serum typeⅠprocollagen N-terminal prepeptide, etc.) were detected on site. Co-morbidity risk factors were analyzed using binary logistic regression. RESULTS: Three hundred and twenty patients with PMOP were included, including the comorbid group (75 patients) and the uncomplicated group (245 patients). The results showed that history of cardiovascular disease [OR=1.801, 95%CI(1.003, 3.236), P=0.049], history of cerebrovascular disease [OR=2.923, 95%CI(1.460, 5.854), P=0.002], frying and cooking methods[OR=5.388, 95%CI(1.632, 17.793), P=0.006], OST results[OR=0.910, 95%CI(0.843, 0.983), P=0.016], and blood Ca results [OR=60.249, 95%CI(1.862, 1 949.926), P=0.021] were the influencing factors of PMOP complicated with dyslipidemia. CONCLUSION Focus should be placed on the influencing factors of PMOP and dyslipidemia co-morbidities, with emphasis on multidimensional assessment, combining lifestyle interventions with bone metabolism marker monitoring to optimize co-morbidity management.
Humans ; Dyslipidemias/epidemiology* ; Female ; Middle Aged ; Osteoporosis, Postmenopausal/metabolism* ; Aged ; Cross-Sectional Studies ; Risk Factors ; Bone Density

OBJECTIVE: To explore the functional rehabilitation of patients with rheumatoid arthritis (RA) after total knee arthroplasty (TKA). METHODS: A retrospective analysis was conducted on 101 patients who needed TKA due to rheumatoid arthritis (RA) involving both knees from January 2017 to December 2020, including 16 males and 85 females, aged from 41 to 65 years old with an average of (58.13±5.53) years old;body mass index (BMI) ranged from 16.88 to 33.33 kg·m^-2 with an average of (23.16±3.49) kg·m^-2;63 patients with grade 1, 29 patients with grade 2, and 9 patients with grade 3 according to classification of American Society of Anesthesiologists (ASA). According to the latest follow-up results at 12 months after operation, 82 patients returned to work and 19 patients did not return to work. Visual analogue scale(VAS) was used to evaluate the degree of pain relief before operation and 12 months after operation, and work, osteoarthritis and joint replacement questionnaire (WORQ) was used to evaluate knee joint activity status of all patients before and after operation, and the working ability index was used to evaluate working ability of all patients before operation and 12 months after operation. For the 82 patients who returned to work, the labor time stopped before operation and within 12 months after operation was compared, and the changes in labor grades, types of work and labor hours of patients before and after operation were recorded. For the 19 patients who did not return to work, the specific reasons for their non-return to work was analyzed;the postoperative satisfaction of patients was evaluated by using Likert satisfaction scale. All patients were followed up for at least 12 months. VAS was decreased from (6.49±0.59) before operation to (1.10±0.43) at 12 months after operation (P<0.05);for WORQ questionnaire survey, scores of walking, sitting posture, standing and stair climbing were increased from (1.07±0.35), (1.05±0.29), (1.06±0.34) and (1.14±0.42) before operation to (3.00±0.00), (2.87±0.33), (2.95±0.21) and (2.95±0.21) after operation, respectively, had statistically significant (P<0.05);the labor work index of all patients increased from 1.11±0.46 before operation to 2.99±0.10 at 12 months after operation, and the difference was statistically significant (P<0.05). Among the 82 patients who returned to work after operation, regarding the time of stopping labor, 81 patients stopped working within 3 months before operation, 1 patient stopped working for 4 to 6 months after operation, and the number of patients who stopped working was 81, 1, and 0 respectively. Forty patients returned to work within 3 months after operation, 4 to 6 months after operation for 29 patients, and 12 months after operation for 13 patients. 95.1% (78/82) of patients engaged in light labor before operation, and 85.4% (70/82) of patients engaged in moderate labor after operation. At 12 months after operation, the types of jobs and working hours available to all patients increased compared with those before operation. Among 19 patients who did not return to work after TKA, 7 patients had poor control of rheumatoid arthritis, 5 patients still felt pain, swelling and numbness on knee joint, 2 patients had retired, and 5 patients had other reasons. Eighty-six patients (85%) expressed great satisfaction with the postoperative working ability, 8 patients (8%) expressed satisfaction with the postoperative working ability, 6 patients (6%) expressed acceptance of postoperative working ability, and 1 patient (1%) expressed dissatisfaction with postoperative working ability. CONCLUSION TKA is an effective treatment option for patients with RA. After undergoing TKA, patients could significantly improve pain and functional activities of knee joint, and effectively enhance the quality of life and working ability. For patients whose rehabilitation labor capacity is not fully met, postoperative management and personalized rehabilitation treatment need to be strengthened to achieve the best rehabilitation effect.
Humans ; Female ; Male ; Arthroplasty, Replacement, Knee/rehabilitation* ; Arthritis, Rheumatoid/physiopathology* ; Middle Aged ; Aged ; Retrospective Studies ; Adult

OBJECTIVE: To explore the potential causal relationship between intervertebral disc degeneration and certain autoimmune diseases. METHODS: Genome-wide association study (GWAS) data of 5 autoimmune diseases were obtained from large-scale GWAS databases. Data on internal vertebral disc degeneration (IVDD) were derived from the FinnGen consortium, which included 294, 770 controls and 41, 669 cases. A two-sample bidirectional Mendelian randomization (MR) analysis was performed to investigate the potential causal relationship between the 5 autoimmune diseases and IVDD. Multiple analytical methods were adopted, including MR methods such as inverse variance weighting(IVW), MR-Egger, weighted median, weighted mode, and simple mode. Cochran's Q test, leave-one-out analysis, and MR-Egger intercept test were conducted to assess heterogeneity, robustness, and pleiotropy. For the robustness of the results, MR-PRESSO was used to detect outliers, and MR analysis was re-conducted after removing the outliers. RESULTS: The MR analysis results showed that there might be a bidirectional causal relationship between ankylosing spondylitis(AS) and IVDD:AS on IVDD, OR=1.038, 95%CI (1.024, 1.053), P=0.000;and IVDD on AS, OR=2.117, 95%CI(1.065, 4.207), P=0.032. There might be a positive correlation between IVDD and rheumatoid arthritis(RA) as well as systemic lupus erythematosus(SLE):IVDD on RA, OR=1.184, 95%CI(1.071, 1.309), P=0.001;and IVDD on SLE, OR=1.678, 95%CI(1.187, 2.372), P=0.003. There was no significant correlation between ulcerative colitis(UC), autoimmune thyroiditis(ATD) and IVDD. After removing outliers by MR-PRESSO and re-conducting MR analysis, the results did not change qualitatively. Sensitivity analysis indicated that the results were robust to potential sources of bias. CONCLUSION AS and IVDD may be risk factors for each other, and IVDD may be a potential risk factor for RA and SLE. These findings provide a basis for guiding the prevention and combined diagnosis and treatment of IVDD, AS, RA, and SLE, while the specific underlying mechanisms still require further experimental basic research.
Humans ; Intervertebral Disc Degeneration/etiology* ; Mendelian Randomization Analysis ; Autoimmune Diseases/complications* ; Genome-Wide Association Study ; Spondylitis, Ankylosing/genetics* ; Arthritis, Rheumatoid/genetics*

Objective Based on 25 indicators including immune factors, cell count classification, and smear results of the knee joint fluid, machine learning models were established to distinguish between osteoarthritis (OA) and rheumatoid arthritis (RA). Methods 100 OA and 40 RA patients scheduled for total knee arthroplasty were enrolled respectively. Each patient's knee joint fluid was collected preoperatively. Nucleated cells were counted and classified. The expression levels of immune factors, including tumor necrosis factor alpha (TNF-α), interleukin-1 beta (IL-1β), IL-6, IL-8, IL-15, matrix metalloproteinase 3 (MMP3), MMP9, MMP13, rheumatoid factor (RF), serum amyloid A (SAA), C-reactive protein (CRP), and others were measured. Smears and microscopic classification of all the immune factors were performed. Independent influencing factors for OA or RA were identified using univariate binary logistic regression, Lasso regression, and multivariate binary logistic regression. Based on the independent influencing factors, three machine learning models were constructed which are logistic regression, random forest, and support vector machine. Receiver operating characteristic curve (ROC), calibration curve and decision curve analysis (DCA) were used to evaluate and compare the models. Results A total of 5 indicators in the knee joint fluid were screened out to distinguish OA and RA, which were IL-1β(odds ratio(OR)=10.512, 95× confidence interval (95×CI) was 1.048-105.42, P=0.045), IL-6 (OR=1.007, 95×CI was 1.001-1.014, P=0.022), MMP9 (OR=3.202, 95×CI was 1.235-8.305, P=0.017), MMP13 (OR=1.002, 95× CI was 1-1.004, P=0.049), and RF (OR=1.091, 95×CI was 1.01-1.179, P=0.026). According to the results of ROC, calibration curve and DCA, the accuracy (0.979), sensitivity (0.98) and area under the curve (AUC, 0.996, 95×CI was 0.991-1) of the random forest model were the highest. It has good validity and feasibility, and its distinguishing ability is better than the other two models. Conclusion The machine learning model based on immune factors in the knee joint fluid holds significant value in distinguishing OA and RA. It provides an important reference for the clinical early differential diagnosis, prevention and treatment of OA and RA.
Humans ; Arthritis, Rheumatoid/metabolism* ; Machine Learning ; Male ; Female ; Middle Aged ; Aged ; Synovial Fluid/immunology* ; Osteoarthritis, Knee/metabolism* ; Knee Joint/metabolism* ; ROC Curve ; Diagnosis, Differential

Objective To investigate the effects of hyaluronic acid and proteoglycan-linked protein 1 (HAPLN1) secreted by synovial fibroblasts (FLS) on the polarization of macrophages (Mϕ) in rheumatoid arthritis (RA). Methods Human monocytic leukemia cells (THP-1) were differentiated into Mϕ, which were subsequently exposed to recombinant HAPLN1 (rHAPLN1). RA-FLS were transfected separately with HAPLN1 overexpression plasmid (HAPLN1^OE) or small interfering RNA targeting HAPLN1 (si-HAPLN1), and then co-cultured with Mϕ to establish a co-culture model. The viability of Mϕ was assessed using the CCK-8 assay, and the proportions of pro-inflammatory M1-type and anti-inflammatory M2-type Mϕ were analyzed by flow cytometry. Additionally, the expression levels of inflammatory markers, including interleukin 1β (IL-1β), tumor necrosis factor α (TNF-α), and inducible nitric oxide synthase (iNOS), were quantified using quantitative real-time PCR and Western blot analysis. Results The viability of Mϕ was increased in the rHAPLN1 group compared to the control group. Furthermore, both the M1/Mϕ ratio and inflammatory factor levels were elevated in the rHAPLN1 and HAPLN1^OE groups. In contrast, the si-HAPLN1 group exhibited a decrease in the M1/Mϕ ratio and inflammatory factor expression. Notably, the introduction of rHAPLN1 in rescue experiments further promoted Mϕ polarization towards the M1 phenotype. Conclusion HAPLN1, secreted by RA fibroblast-like synoviocytes (RA-FLS), enhances Mϕ polarization towards the M1 phenotype.
Humans ; Arthritis, Rheumatoid/genetics* ; Macrophages/immunology* ; Fibroblasts/metabolism* ; Phenotype ; Extracellular Matrix Proteins/genetics* ; Proteoglycans/genetics* ; Synovial Membrane/cytology* ; Tumor Necrosis Factor-alpha/genetics* ; Interleukin-1beta/genetics* ; Nitric Oxide Synthase Type II/genetics* ; Cell Differentiation ; Coculture Techniques ; THP-1 Cells

Rheumatoid arthritis (RA) is an autoimmune disease characterized by complex pathogenesis, with its development closely linked to immune dysregulation. Regulatory T cells (Tregs), as specialized immunomodulatory cells, play a pivotal role in negatively regulating immune responses and maintaining autoimmune tolerance. In recent years, extensive research has focused on the relationship between Tregs and RA pathogenesis, with the functional role of CD8⁺ Tregs as a critical area of investigation. This review summarizes the alterations in CD8⁺ Tregs during RA progression and their potential mechanisms of action. By elucidating the diversity of CD8⁺ Treg subsets and their intricate roles in modulating immune responses, this analysis provides novel insights and therapeutic strategies for RA diagnosis and treatment.
Arthritis, Rheumatoid/pathology* ; Humans ; T-Lymphocytes, Regulatory/immunology* ; CD8-Positive T-Lymphocytes/immunology* ; Animals

Objective To explore the expression of coagulation indexes in rheumatoid arthritis (RA) and dry syndrome (pSS) and their relationships with inflammation and immune function. Methods A total of 61 patients with RA who were hospitalized in the Department of Rheumatology of Anhui Provincial Hospital of Traditional Chinese Medicine from March 12 to September 9, 2024 were selected as the RA group. And 61 patients with pSS who were hospitalized in the Department of Rheumatology of the same hospital September 4, 2023, to August 17, 2024, were selected as the pSS group. 61 healthy individuals who underwent routine medical checkups at the Physical Examination Center of Anhui Provincial Hospital of Traditional Chinese Medicine during the same period were included as the control group. Baseline clinical indexes before treatment were collected from patients in each group, including prothrombin time(PT), international normalized ratio(INR), thrombia time(TT), fibrinogen(FBG), activated partial thromboplastin time(APTT), fibrin (ogen) degradation products(FDP) and D-Dimer(D-D). Results The expression levels of PT, FBG, TT, FDP, and D-D in the RA group, the pSS group, and the normal group were significantly different. The expression levels of PT, FBG, FDP, and D-D in the RA group were all higher than those in the pSS group and the control group, respectively. And the expression level of TT in the pSS group was lower than that in control group. ROC curve analysis showed that the AUC of PT was 0.638, the AUC of FBG was 0.899, the AUC of FDP was 0.866, and the AUC of D-D was 0.919 in the RA group compared with the normal group. And the AUC of coagulation indexes for joint diagnosis of RA was higher than that of the indexes detected individually. pSS group had an AUC of PT of 0.618 compared with that of the normal group. The AUC of TT was 0.645, and the AUC of coagulation indexes for the joint diagnosis of pSS was higher than the AUC of each index detected separately. Association rule analysis showed that elevated D-D in RA patients had a significant correlation with elevated hs-CRP, CCP and RF, and elevated FBG had a significant correlation with elevated hs-CRP, ESR, RF and CCP. Elevated D-D in pSS patients had a correlation with elevated hs-CRP and anti-SSA, and elevated INR has correlation with elevated hs-CRP, anti-SSA and anti-SSB. Correlation analysis showed that PT, INR, FBG, FDP, and D-D were positively correlated with CRP and ESR, and TT was negatively correlated with CRP and ESR in the RA group. FBG, FDP, and D-D were positively correlated with CRP and ESR in the pSS group. Moreover, coagulation indexes were positively correlated with immune indexes in RA group and pSS group which were all significant. The results of multiple linear regression analysis showed that FBG was a positive correlate of hs-CRP and ESR in RA patients. For PSS patients, FBG and FDP were positive correlates of hs-CRP. APTT and FBG were positive correlates of ESR. Conclusion Compared with pSS, coagulation indexes (especially PT, FBG, FDP and D-D) are more informative for the early diagnosis of RA and the judgment of the degree of the disease, and can be used as an important predictor for the confirmation of the diagnosis of RA.
Humans ; Female ; Male ; Arthritis, Rheumatoid/diagnosis* ; Middle Aged ; Fibrin Fibrinogen Degradation Products/analysis* ; Blood Coagulation ; Adult ; Fibrinogen/metabolism* ; Partial Thromboplastin Time ; Prothrombin Time ; Aged ; Inflammation/immunology* ; ROC Curve