Introduction: It is well-documented that systemic lupus erythematosus (SLE) is associated with dementia. However, the genetic causality of this association remains unclear. Mendelian randomization (MR) was used to investigate the potential causal relationship between SLE and dementia risk in the current study.Methods: We selected 45 single nucleotide polymorphisms (SNPs) associated with SLE from publicly available genome-wide association studies (GWAS). Summary level statistics were obtained from the dementia GWAS database. MR estimates were performed using the inverse variance weighted (IVW) method, MR-Egger method and weighted median (WM) method. Cochran’s Q test, the intercept of MR-Egger, MR-Pleiotropy Residual Sum and Outlier method, leave-one-out analysis and funnel plot were applied for sensitivity analyses.Results: No significant causal association was found between SLE and any type of dementia, including Alzheimer’s disease, vascular dementia, frontotemporal dementia, and dementia with Lewy bodies. These findings were robust across several sensitivity analyses.Conclusion: Overall, our findings do not support a causal association between SLE and dementia risk.

Objectives: A growing body of studies related to antiphospholipid syndrome (APS) have been published in recent years. Nevertheless, there is a lack of visualized and systematic analysis in the literature on APS. Hence, this study sought to conduct a bibliometric analysis to identify research status and discover frontiers in the field.Methods: Articles and reviews concerning APS were acquired from the Web of Science Core Collection (WoSCC) database. CiteSpace, VOSviewer and a bibliometric online analysis platform were employed to conduct a visualization and knowledge-map analysis.Results: A total of 1,390 publications regarding APS were identified. Globally, Italy contributed the most publications. The University of Padua was the most productive institution. Lupus ranked first in both the most published and most co-cited journals. Savino Sciascia and Spiros Miyakis were the most prolific and most co-cited authors, respectively. “Vitamin K antagonists (VKA)” and “immunoglobulin A (IgA)” were current research foci. Burst analysis of keywords suggested that “neutrophil extracellular trap (NET),” “direct oral anticoagulant (DOAC),” “open label,” “outcome,” “hydroxychloroquine (HCQ),” and “arterial thrombosis (AT)” were significant future research frontiers.Conclusion: The scientific literature on APS has increased steadily in the past 10 years. The clinical studies on the treatment and mechanism research of APS are recognized as promising research hotspots in the domain of APS. The research status and trends of APS publications from the bibliometric perspective can provide a practical guide and important reference for subsequent studies by researchers and physicians in the domain.

To provide objective diagnostic markers for fibromyalgia symptoms (FMS) diagnosis, we have created interpretable extreme gradient boosting (XGBoost) models using radiomics to aid in the diagnosis of chronic pain (CP) and to develop nomogram models for diagnosing subgroups of FMS. A group of 54 patients with CP and 71 healthy controls was randomly separated into training and validation groups, using a 7:3 ratio. Radiomics features were extracted from grey-matter and white-matter in the filtered mwp0* image. The Mann-Whitney U test, Spearman’s rank correlation test, and least absolute shrinkage and selection operator (LASSO) were utilized to select features. An XGBoost model was created based on these features, and Shapley Additive exPlanations (SHAP) was used for personalization and visual interpretation. A nomogram was developed for the diagnosis of FMS subgroups, utilizing radiomics scores and clinical predictors. The efficacy of the nomogram was evaluated using the area under the receiver operating characteristic curve, while decision curve analysis was employed to evaluate its clinical efficacy. The XGBoost model displays stability in the training validation group, indicating lower overfitting of CP model. The nomogram model combined with the rad-score has a greater ability to distinguish between typical and sub-clinical than the clinical factor model alone. We developed and validated a CP diagnosis model by XGBoost and realized model visualization through SHAP. The rad-score obtained by machine learning was used to build a nomogram model that combines clinical scales to distinguish patients with typical and sub-clinical fibromyalgia.

Background: The extra-articular lesions of rheumatoid arthritis (RA) are reported to involve multiple organs and systems throughout the body, including the heart, kidneys, liver, and lungs. This study assessed the potential causal relationship between RA and the risk of chronic kidney diseases (CKDs) using the Mendelian randomization (MR) analysis.Method: Independent genetic instruments related to RA and CKD or CKD subtypes at the genome-wide significant level were chosen from the publicly shared summary-level data of genome-wide association studies (GWAS). Then, we obtained some single-nucleotide polymorphisms (SNPs) as instrumental variables (IVs), which are associated with RA in individuals of European origin, and had genome-wide statistical significance (p5 × 10⁻⁸). The inverse-variance weighted (IVW) method was the main analysis method in MR analysis. The other methods, such as weighted median, MR–Egger, simple mode, and weighted mode were used as supplementary sensitivity analyses. Furthermore, the levels of pleiotropy and heterogeneity were assessed using Cochran’s Q test and leave-one-out analysis. Furthermore, the relevant datasets were obtained from the Open GWAS database.Results: Using the IVW method, the main method in MR analysis, the results showed that genetically determined RA was associated with higher risks of CKD [odds ratio (OR): 1.22, 95% confidence interval (CI) 1.13–1.31; p < 0.001], glomerulonephritis (OR: 1.23, 95% CI 1.15–1.31; p < 0.000), amyloidosis (OR = 1.43, 95% CI 1.10–1.88, p < 0.001), and renal failure (OR = 1.18, 95% CI 1.00–1.38, p < 0.001). Then, using multiple MR methods, it was confirmed that the associations persisted in sensitivity analyses, and no pleiotropy was detected.Conclusion: The findings revealed a causal relationship between RA and CKD, including glomerulonephritis, amyloidosis, and renal failure. Therefore, RA patients should pay more attention to monitoring their kidney function, thus providing the opportunity for earlier intervention and lower the risk of progression to CKDs.