Ankylosing Spondylitis: A Risk Factor for Parkinsonism-A Nationwide Population-Based Study

Association between ankylosing spondylitis and neurodegenerative diseases: Systematic review and meta-analysis

BACKGROUND

Increasing evidence indicates the mechanism of overlapping immune dysfunction and inflammation disorder shared by ankylosing spondylitis (AS) and neurodegenerative diseases (NDs). However, the exact correlation between the two is still unclear. Different studies have reported inconsistent results about how AS and NDs are related.

OBJECTIVE

This study aimed to investigate the association between AS and risk of NDs.

METHODS

We searched electronic databases including PubMed, EMBASE, Web of Science, and Cochrane Central Register of Controlled Trials to identify studies reporting relationship between NDs risk and AS published before April 10th, 2024. Pooled odds ratios (ORs) and corresponding 95% confidence intervals (95% CIs) were estimated. All analyses were conducted using Stata V.12.0 software.

RESULTS

A total of 15 comparisons out of 10 studies involving 851,936 participants were included. The results showed that the risk of NDs was higher among AS patients than those who were not (OR: 1.36, 95% CI: 1.15-1.60, P<0.001). In addition, subgroup analysis showed that AS was associated with an increased risk of Parkinson's disease (PD) development (OR: 1.55, 95% CI: 1.31-1.83, P<0.001), whereas there is no observed association with Alzheimer's disease (AD) and dementia (OR: 1.22, 95% CI: 0.96-1.55, P=0.098; OR: 1.34, 95% CI: 0.96-1.87, P=0.089, respectively).

CONCLUSION

The current meta-analysis identified AS as a risk factor for the development of NDs. Clinicians should be aware of the potential association between these diseases. Further research is necessary to confirm the causal relationship and underlying mechanisms between AS and NDs.

No causal relationship between ankylosing spondylitis and Parkinson's disease: Insights from Mendelian randomization studies

Background

Retrospective cohort and cross-sectional studies have indicated an association between ankylosing spondylitis (AS) and Parkinson's disease (PD). However, owing the multitude of limitations, a consistent conclusion has not been determined. Furthermore, whether a causal relationship exists between these two diseases remains unclear.

Methods

We conducted a two-way Mendelian randomization (MR) analysis using genome-wide association study data. For patients with PD, we utilised data from the ieu-b-7 database, whereas for patients with AS, we employed the three databases with the largest sample sizes for a combined analysis. These databases included ebi-a-GCST005529, finn-b-M13 ANKYLOSPON, and finn-b-M13 ANKYLOSPON STRICT. Our primary method of analysis was inverse variance weighting (IVW), supplemented by four other effective methods, to comprehensively infer a potential causal relationship between AS and PD. Additionally, we conduct various sensitivity analyses to assess the robustness of our estimates.

Results

Based on our IVW MR analysis, no significant causal relationship between AS and PD was observed (odds ratio [OR] = 1.01, 95 % confidence interval [CI] = 0.99-1.03, P = 0.26). Additionally, our reverse MR analysis found no evidence supporting a significant causal relationship between PD and AS (OR = 0.93, 95 % CI = 0.85-1.01, P = 0.068). These results were substantiated by comprehensive sensitivity analyses that indicated minimal bias in the causal estimates.

Conclusion

In contrast to numerous existing clinical studies, this study failed to provide evidence supporting a significant impact of AS on PD risk, or vice versa. Further investigations regarding the potential causal mechanisms linking AS and PD are warranted.

Identification of necroptosis-related genes in ankylosing spondylitis by bioinformatics and experimental validation

The pathogenesis of ankylosing spondylitis (AS) remains unclear, and while recent studies have implicated necroptosis in various autoimmune diseases, an investigation of its relationship with AS has not been reported. In this study, we utilized the Gene Expression Omnibus database to compare gene expressions between AS patients and healthy controls, identifying 18 differentially expressed necroptosis-related genes (DENRGs), with 8 upregulated and 10 downregulated. Through the application of three machine learning algorithms-least absolute shrinkage and selection operation, support vector machine-recursive feature elimination and random forest-two hub genes, FASLG and TARDBP, were pinpointed. These genes demonstrated high specificity and sensitivity for AS diagnosis, as evidenced by receiver operating characteristic curve analysis. These findings were further supported by external datasets and cellular experiments, which confirmed the downregulation of FASLG and upregulation of TARDBP in AS patients. Immune cell infiltration analysis suggested that CD4+ T cells, CD8+ T cells, NK cells and neutrophils may be associated with the development of AS. Notably, in the group with high FASLG expression, there was a significant infiltration of CD8+ T cells, memory-activated CD4+ T cells and resting NK cells, with relatively less infiltration of memory-resting CD4+ T cells and neutrophils. Conversely, in the group with high TARDBP expression, there was enhanced infiltration of naïve CD4+ T cells and M0 macrophages, with a reduced presence of memory-resting CD4+ T cells. In summary, FASLG and TARDBP may contribute to AS pathogenesis by regulating the immune microenvironment and immune-related signalling pathways. These findings offer new insights into the molecular mechanisms of AS and suggest potential new targets for therapeutic strategies.

Depressive Symptoms and the Subsequent Risk of Parkinson's Disease: A Nationwide Cohort Study

OBJECTIVE

Only a few studies have focused on depressive symptoms and Parkinson's disease (PD) risk. As a time lag exists from the onset of depressive symptoms to the diagnosis of depression, elucidating the association between depressive symptoms and PD development might be helpful for the early prediction of PD. We investigate the association between depressive symptoms and subsequent PD risk using nationwide population-based cohort database.

DESIGN AND SETTING

Cohort study using the Korean National Health Insurance Service data between 2007 and 2017, with longitudinal follow-up until 2019.

PARTICIPANTS

A total of 98,296 elderly people responded to a self-reported questionnaire from the National Health Screening Program on depressive symptoms.

MEASUREMENTS

The association between depressive symptoms such as 1) decreased activity or motivation, 2) worthlessness, and 3) hopelessness and PD risk was analyzed.

RESULTS

During median 5.06-year follow-up, 839 PD cases occurred: 230 in individuals with depressive symptoms and 609 in those without symptoms. Results showed an increased risk of PD development in those with depressive symptoms (HR = 1.47, 95% CI, 1.26-1.71), with dose-response association between the number of depressive symptoms and PD risk. Even in those already diagnosed with depression, combined depressive symptoms were linked to a higher risk compared to those without symptoms (with symptoms, HR = 2.71, 95% CI, 2.00-3.68; without symptoms, HR = 1.84, 95% CI, 1.43-2.36).

CONCLUSION

Individuals with depressive symptoms were at an increased risk of developing PD, and there was a dose-response association between the number of depressive symptoms and PD risk.

Machine learning based risk prediction for Parkinson's disease with nationwide health screening data

Although many studies have been conducted on machine learning (ML) models for Parkinson's disease (PD) prediction using neuroimaging and movement analyses, studies with large population-based datasets are limited. We aimed to propose PD prediction models using ML algorithms based on the National Health Insurance Service-Health Screening datasets. We selected individuals who participated in national health-screening programs > 5 times between 2002 and 2015. PD was defined based on the ICD-code (G20), and a matched cohort of individuals without PD was selected using a 1:1 random sampling method. Various ML algorithms were applied for PD prediction, and the performance of the prediction models was compared. Neural networks, gradient boosting machines, and random forest algorithms exhibited the best average prediction accuracy (average area under the receiver operating characteristic curve (AUC): 0.779, 0.766, and 0.731, respectively) among the algorithms validated in this study. The overall model performance metrics were higher in men than in women (AUC: 0.742 and 0.729, respectively). The most important factor for predicting PD occurrence was body mass index, followed by total cholesterol, glucose, hemoglobin, and blood pressure levels. Smoking and alcohol consumption (in men) and socioeconomic status, physical activity, and diabetes mellitus (in women) were highly correlated with the occurrence of PD. The proposed health-screening dataset-based PD prediction model using ML algorithms is readily applicable, produces validated results, and could be a useful option for PD prediction models.

Inflammatory rheumatic diseases and the risk of Parkinson's disease: A systematic review and meta-analysis

BACKGROUND

Several studies showed inconsistencies in the relationships between inflammatory rheumatic diseases (IRDs) and the risk of Parkinson's disease (PD). Therefore, we carried out a meta-analysis to investigate the associations between different IRDs and PD risk.

METHODS

A comprehensive search was undertaken on PubMed, Embase, Cochrane Library, and Web of Science databases up to June 2022. Studies reporting the relationships between IRDs and PD risk were included. Pooled relative risks (RRs) with 95% confidence intervals (CIs) were calculated by using random-effects models.

RESULTS

Twenty-two publications covering seven IRDs containing data from 833,004 patients were identified for quantitative analysis. The pooled results indicated that ankylosing spondylitis (RR = 1.55, 95% CI: 1.31-1.83, I2 = 32.1%, P < 0.001), Sjögren's syndrome (RR = 1.34, 95% CI: 1.22-1.47, I2 = 58.5%, P < 0.001), and Behcet's disease (RR = 1.93, 95% CI: 1.07-3.49, I2 = 57.6%, P = 0.030) were associated with an increased PD risk. However, no significant associations were observed between gout, rheumatoid arthritis, systemic lupus erythematosus, as well as polymyalgia rheumatica and the subsequent development of PD.

CONCLUSION

Ankylosing spondylitis, Sjögren's syndrome, and Behcet's disease may increase PD risk.

New Insights into the Regulatory Role of Ferroptosis in Ankylosing Spondylitis via Consensus Clustering of Ferroptosis-Related Genes and Weighted Gene Co-Expression Network Analysis

Background: The pathogenesis of ankylosing spondylitis (AS) remains undetermined. Ferroptosis is a newly discovered form of regulated cell death involved in multiple autoimmune diseases. Currently, there are no reports on the connection between ferroptosis and AS. Methods: AS samples from the Gene Expression Omnibus were divided into two subgroups using consensus clustering of ferroptosis-related genes (FRGs). Weighted gene co-expression network analysis (WGCNA) of the intergroup differentially expressed genes (DEGs) and protein–protein interaction (PPI) analysis of the key module were used to screen out hub genes. A multifactor regulatory network was then constructed based on hub genes. Results: The 52 AS patients in dataset GSE73754 were divided into cluster 1 (n = 24) and cluster 2 (n = 28). DEGs were mainly enriched in pathways related to mitochondria, ubiquitin, and neurodegeneration. Candidate hub genes, screened by PPI and WGCNA, were intersected. Subsequently, 12 overlapping genes were identified as definitive hub genes. A multifactor interaction network with 45 nodes and 150 edges was generated, comprising the 12 hub genes and 32 non-coding RNAs. Conclusions: AS can be divided into two subtypes according to FRG expression. Ferroptosis might play a regulatory role in AS. Tailoring treatment according to the ferroptosis status of AS patients can be a promising direction.