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

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.