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BackgroundPrevious epidemiological studies have reported an association between Sjögren’s syndrome (SS) and Parkinson’s disease (PD); however, the causality and direction of this relationship remain unclear. In this study, we aimed to investigate the causal relationship between genetically determined SS and the risk of PD using bidirectional Mendelian randomization (MR).MethodsSummary statistics for Sjögren’s syndrome used as exposure were obtained from the FinnGen database, comprising 1,290 cases and 213,145 controls. The outcome dataset for PD was derived from the United Kingdom Biobank database, including 6,998 cases and 415,466 controls. Various MR methods, such as inverse variance weighted (IVW), Mendelian randomization Egger regression (MR-Egger), weighted median (WM), simple mode, weighted mode, MR-pleiotropy residual sum and outlier (MR-PRESSO), and robust adjusted profile score (RAPS), were employed to investigate the causal effects of SS on PD. Instrumental variable strength evaluation and sensitivity analyses were conducted to ensure the reliability of the results. In addition, reverse MR analysis was performed to examine the causal effects of PD on SS.ResultsThe WM, IVW, RAPS and MR-PRESSO methods demonstrated a significant association between genetically predicted SS and reduced risk of PD (odds ratio ORWM = 0.9988, ORIVW = 0.9987, ORRAPS = 0.9987, ORMR-PRESSO = 0.9987, respectively, P < 0.05). None of the MR analyses showed evidence of horizontal pleiotropy (P > 0.05) based on the MR-Egger and MR-PRESSO tests, and there was no statistical heterogeneity in the test results of the MR-Egger and IVW methods. The leave-one-out sensitivity analysis confirmed the robustness of the causal relationship between SS and PD. Furthermore, reverse MR analysis did not support any causal effects of PD on SS.ConclusionOur MR study supports a potential causal association between SS and a reduced risk of PD. Further extensive clinical investigations and comprehensive fundamental research are warranted to elucidate the underlying mechanisms linking SS and PD.

IntroductionParkinson’s disease (PD) is the second most common neurodegenerative disease and affects millions of people. Accurate diagnosis and subsequent treatment in the early stages can slow down disease progression. However, making an accurate diagnosis of PD at an early stage is challenging. Previous studies have revealed that even for movement disorder specialists, it was difficult to differentiate patients with PD from healthy individuals until the average modified Hoehn-Yahr staging (mH&Y) reached 1.8. Recent researches have shown that dysarthria provides good indicators for computer-assisted diagnosis of patients with PD. However, few studies have focused on diagnosing patients with PD in the early stages, specifically those with mH&Y ≤ 1.5.MethodWe used a machine learning algorithm to analyze voice features and developed diagnostic models for differentiating between healthy controls (HCs) and patients with PD, and for differentiating between HCs and patients with mild PD (mH&Y ≤ 1.5). The models were independently validated using separate datasets.ResultsOur results demonstrate that, a remarkable diagnostic performance of the model in identifying patients with mild PD (mH&Y ≤ 1.5) and HCs, with area under the ROC curve 0.93 (95% CI: 0.851.00), accuracy 0.85, sensitivity 0.95, and specificity 0.75.ConclusionThe results of our study are helpful for screening PD in the early stages in the community and primary medical institutions where there is a lack of movement disorder specialists and special equipment.

BackgroundCognitive impairment is a prevalent condition that substantially elevates mortality rates among the elderly. The impact of hypertension on mortality in older adults with cognitive impairment is a subject of contention. This study aims to examine the influence of hypertension on both all-cause and CVD-specific mortality in elderly individuals experiencing cognitive impairment within a prospective cohort.MethodsThis study encompassed 2,925 participants (weighted 53,086,905) aged 60 years or older from National Health and Nutrition Examination Survey (NHANES) spanning 2011–2014. Incidence of all-cause and CVD-specific mortality was ascertained through linkage with National Death Index records until 31 December 2019. Survival was performed employing the Kaplan–Meier method. Hazard ratios (HRs) were calculated via Cox proportional hazards regression models.ResultsOver the follow-up period of up to 9.17 years [with a median (IQR) time to death of 6.58 years], equivalent to 18,731.56 (weighted 3.46 × 108) person-years, there were a total of 576 recorded deaths. Participants with CI exhibited a 1.96-fold higher risk of all-cause mortality (95% CI: 1.55–2.49; p