Explore the vast range of titles available.

Emerging evidence has suggested a close correlation between COVID-19 and neurodegenerative disorders. However, whether there exists a causal association and the effect direction remains unknown. To examine the causative role of COVID-19 in the risk of neurodegenerative disorders, we estimated their genetic correlation, and then conducted a two-sample Mendelian randomization analysis using summary statistics from genome-wide association studies of susceptibility, hospitalization, and severity of COVID-19, as well as six major neurodegenerative disorders including Alzheimer’s disease (AD), amyotrophic lateral sclerosis, frontotemporal dementia, Lewy body dementia, multiple sclerosis, and Parkinson’s disease. We identified a significant and positive genetic correlation between hospitalization of COVID-19 and AD (genetic correlation: 0.23, P  = 8.36E–07). Meanwhile, hospitalization of COVID-19 was significantly associated with a higher risk of AD (OR: 1.02, 95% CI: 1.01–1.03, P : 1.19E–03). Consistently, susceptibility (OR: 1.05, 95% CI: 1.01–1.09, P : 9.30E–03) and severity (OR: 1.01, 95% CI: 1.00–1.02, P : 0.012) of COVID-19 were nominally associated with higher risk of AD. The results were robust under all sensitivity analyses. These results demonstrated that COVID-19 could increase the risk of AD. Future development of preventive or therapeutic interventions could attach importance to this to alleviate the complications of COVID-19.

Multiple system atrophy is a rare, debilitating, adult-onset neurodegenerative disorder that manifests clinically as a diverse combination of parkinsonism, cerebellar ataxia, and autonomic dysfunction. It is pathologically characterized by oligodendroglial cytoplasmic inclusions containing abnormally aggregated α-synuclein. According to the updated Movement Disorder Society diagnostic criteria for multiple system atrophy, the diagnosis of clinically established multiple system atrophy requires the manifestation of autonomic dysfunction in combination with poorly levo-dopa responsive parkinsonism and/or cerebellar syndrome. Although symptomatic management of multiple system atrophy can substantially improve quality of life, therapeutic benefits are often limited, ephemeral, and they fail to modify the disease progression and eradicate underlying causes. Consequently, effective breakthrough treatments that target the causes of disease are needed. Numerous preclinical and clinical studies are currently focusing on a set of hallmarks of neurodegenerative diseases to slow or halt the progression of multiple system atrophy: pathological protein aggregation, synaptic dysfunction, aberrant proteostasis, neuronal inflammation, and neuronal cell death. Meanwhile, specific biomarkers and measurements with higher specificity and sensitivity are being developed for the diagnosis of multiple system atrophy, particularly for early detection of the disease. More intriguingly, a growing number of new disease-modifying candidates, which can be used to design multi-targeted, personalized treatment in patients, are being investigated, notwithstanding the failure of most previous attempts.

Cognitive impairment (CI) ranging from mild cognitive impairment (MCI) to dementia is a common and disturbing complication in patients with Parkinson’s disease (PD). Numerous studies have focused on neuropathological mechanisms underlying CI in PD, along with the identification of specific biomarkers for CI. Magnetic resonance imaging (MRI), a promising method, has been adopted to examine the changes in the brain and identify the candidate biomarkers associated with CI. In this review, we have summarized the potential biomarkers for CI in PD which have been identified through multi-modal MRI studies. Structural MRI technology is widely used in biomarker research. Specific patterns of grey matter atrophy are promising predictors of the evolution of CI in patients with PD. Moreover, other MRI techniques, such as MRI related to small-vessel disease, neuromelanin-sensitive MRI, quantitative susceptibility mapping, MR diffusion imaging, MRI related to cerebrovascular abnormality, resting-state functional MRI, and proton magnetic resonance spectroscopy, can provide imaging features with a good degree of prediction for CI. In the future, novel combined biomarkers should be developed using the recognized analysis tools and predictive algorithms in both cross-sectional and longitudinal studies.

Epidemiological and clinical studies have suggested comorbidity between rheumatoid arthritis and Parkinson’s disease (PD), but whether there exists a causal association and the effect direction of rheumatoid arthritis on PD is controversial and elusive. To evaluate the causal relationship, we first estimated the genetic correlation between rheumatoid arthritis and PD, and then performed a two-sample Mendelian randomization analysis based on summary statistics from large genome-wide association studies of rheumatoid arthritis (N = 47,580) and PD (N = 482,703). We identified negative and significant correlation between rheumatoid arthritis and PD (genetic correlation: −0.10, P = 0.0033). Meanwhile, one standard deviation increase in rheumatoid arthritis risk was associated with a lower risk of PD (OR: 0.904, 95% CI: 0.866–0.943, P: 2.95E–06). The result was robust under all sensitivity analyses. Our results provide evidence supporting a protective role of rheumatoid arthritis on PD. A deeper understanding of the inflammation and immune response is likely to elucidate the potential pathogenesis of PD and identify therapeutic targets for PD.

Despite the discovery of numerous molecules and pathologies, the pathophysiology of various neurodegenerative diseases remains unknown. Genetics participates in the pathogenesis of neurodegeneration. Neural dysfunction, which is thought to be a cell-autonomous mechanism, is insufficient to explain the development of neurodegenerative disease, implying that other cells surrounding or related to neurons, such as glial cells, are involved in the pathogenesis. As the primary component of glial cells, astrocytes play a variety of roles in the maintenance of physiological functions in neurons and other glial cells. The pathophysiology of neurodegeneration is also influenced by reactive astrogliosis in response to central nervous system injuries. Furthermore, those risk gene variants identified in neurodegenerations involve in astrocyte activation and senescence. In this review, we summarized the relationships between gene variants and astrocytes in four neurodegenerative diseases including Alzheimer’s disease (AD), amyotrophic lateral sclerosis (ALS), frontotemporal dementia (FTD), and Parkinson’s disease (PD), and provided insights into the implications of astrocytes in the neurodegenerations.

BackgroundNeuroimaging studies have reported gray matter changes in patients with idiopathic dystonia but with considerable variations. Here, we aimed to investigate the convergence of dystonia-related gray matter changes across studies.MethodsThe whole brain voxel-based morphometry studies comparing idiopathic dystonia and healthy controls were systematically searched in the PubMed, Web of Science and Embase. Meta-analysis of gray matter changes was performed using the anisotropic effect size-based signed differential mapping.ResultsTwenty-eight studies comparing 701 idiopathic dystonia patients and 712 healthy controls were included in the meta-analysis. Compared to healthy controls, idiopathic dystonia patients showed increased gray matter in bilateral precentral and postcentral gyri, bilateral putamen and pallidum, right insula, and left supramarginal gyrus, while decreased gray matter in bilateral temporal poles, bilateral supplementary motor areas, right angular gyrus, inferior parietal gyrus and precuneus, left insula and inferior frontal gyrus. These findings remained robust in the jackknife sensitivity analysis, and no significant heterogeneity was detected. Subgroup analyses of different phenotypes of dystonia were performed to further confirm the above findings.ConclusionThe meta-analysis showed that consistent widespread gray matter abnormalities were shared in different subtypes of idiopathic dystonia and were not restricted to the corticostriatal circuits.

Parkinson disease (PD) is a progressive neurodegenerative disorder with increasing global prevalence, necessitating innovative management. Digital health interventions (DHIs) offer potential advantages for PD care; yet, a comprehensive systematic review and synthesis across all DHI types and core outcomes is still lacking. This review aimed to assess the effectiveness of DHIs for improving motor symptoms, nonmotor symptoms, and quality of life in patients with PD and to summarize the reach, uptake, and feasibility. We searched PubMed, Ovid Embase, Web of Science, CINAHL, Cochrane Central Register of Controlled Trials, and APA PsycINFO up to November 2025. Pooled standardized mean differences (SMDs) were calculated using random-effects models. We calculated 95% prediction intervals (PIs) to estimate the true effects. The revised Cochrane Risk of Bias 2 tool was used to assess risk of bias. Heterogeneity was assessed using I , τ , and 95% PI. Subgroup analyses, meta-regression, and sensitivity analyses were conducted to address heterogeneity and potential bias. The quality of evidence was assessed using GRADE (Grading of Recommendations Assessment, Development, and Evaluation). The review included 112 randomized controlled trials involving 5594 participants. Significant postintervention improvements were identified in motor symptoms (SMD=-0.39, 95% CI -0.60 to -0.18, 95% PI -1.75 to 0.99; I =80.3%) and overall nonmotor symptoms (SMD=-0.26, 95% CI -0.49 to -0.03, 95% PI -0.56 to 0.03; I =13.8%), including cognitive function (SMD=0.47, 95% CI 0.22 to 0.72, 95% PI -0.41 to 1.35; I =63.5%) and psychiatric symptoms (SMD=-0.42, 95% CI -0.74 to -0.09, 95% PI -1.82 to -0.99; I =85.4%); however, there was no significant enhancement in quality of life (SMD=-0.19, 95% CI -0.47 to 0.09, 95% PI -1.50 to 1.12; I =81.2%). The certainty of evidence was very low for quality of life, motor, and psychiatric symptoms and low for cognitive function and overall nonmotor symptoms. Improvements in motor symptoms and cognitive function remained stable at follow-up. Meta-regression analysis indicated that age, percentage of female participants, and supervision mode were possible sources of heterogeneity. Overall, 94 studies reported reach (median 37.5%), 38 reported fidelity (95.7%), and 105 reported dropout rates (9.1%). In contrast to previous reviews focused on single technologies or outcomes, this review provided the first comprehensive synthesis across all DHI types on multiple outcomes and indicated their potential as nonpharmacological interventions for PD management. However, current evidence is of low to very low certainty, and wide 95% PIs, together with high risk of bias and substantial heterogeneity, indicate considerable uncertainty regarding the true effect in future implementations. Therefore, findings should be interpreted with caution. These findings provide integrated evidence to guide the design and prioritization of future research. The results have important real-world implications, supporting cautious implementation while underscoring the need for more robust trials, particularly in resource-limited settings. PROSPERO CRD42023492123; https://www.crd.york.ac.uk/PROSPERO/view/CRD42023492123.

Rehabilitation is a high-potential approach to improving physical and cognitive functions in Parkinson’s disease (PD). Dual-task training innovatively combines motor and cognitive rehabilitation in a comprehensive module. Patients perform motor and cognitive tasks at the same time in dual-task training. The previous studies of dual-task training in PD had high heterogeneity and achieved controversial results. In the current review, we aim to summarize the current evidence of the effect of dual-task training on motor and cognitive functions in PD patients to support the clinical practice of dual-task training. In addition, we also discuss the current opinions regarding the mechanism underlying the interaction between motor and cognitive training. In conclusion, dual-task training is suitable for PD patients with varied disease duration to improve their motor function. Dual-task training can improve motor symptoms, single-task gait speed, single-task steep length, balance, and objective experience of freezing of gait in PD. The improvement in cognitive function after dual-task training is mild.

Parkinson disease (PD) is a common neurodegenerative disease characterized by both motor and nonmotor symptoms. Cognitive impairment often occurs early in the disease and can persist throughout its progression, severely impacting patients' quality of life. The utilization of machine learning (ML) has recently shown promise in identifying cognitive impairment in patients with PD. This study aims to summarize different ML models applied to cognitive impairment in patients with PD and to identify determinants for improving diagnosis and predictive power for early detection of cognitive impairment. PubMed, Cochrane, Embase, and Web of Science were searched for relevant articles on March 2, 2024. The risk of bias was assessed using the Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2). Bivariate meta-analysis was used to estimate pooled sensitivity and specificity results, presented as odds ratio (OR) and 95% CI. A summary receiver operator characteristic (SROC) curve was used. A total of 38 articles met the criteria, involving 8564 patients with PD and 1134 healthy controls. Overall, 120 models reported sensitivity and specificity, with mean values of 71.07% (SD 13.72%) and 77.01% (SD 14.31%), respectively. Predictors commonly used in ML models included clinical features, neuroimaging features, and other variables. No significant heterogeneity was observed in the bivariate meta-analysis, which included 12 studies. Using sensitivity as the metric, the combined sensitivity and specificity were 0.76 (95% CI 0.67-0.83) and 0.83 (95% CI 0.76-0.88), respectively. When specificity was used, the combined values were 0.77 (95% CI 0.65-0.86) and 0.76 (95% CI 0.63-0.85), respectively. The area under the curves of the SROC were 0.87 (95% CI 0.83-0.89) and 0.83 (95% CI 0.80-0.86) respectively. Our findings provide a comprehensive summary of various ML models and demonstrate the effectiveness of ML as a tool for diagnosing and predicting cognitive impairment in patients with PD. PROSPERO CRD42023480196; https://www.crd.york.ac.uk/PROSPERO/view/CRD42023480196.

Multiple sclerosis (MS) is a chronic inflammatory disease affecting the central nervous system. While previous studies have indicated that albumin, the primary protein in human plasma, may exert influence on the inflammatory process and confer beneficial effects in neurodegenerative disorders, its role in the context of MS has been underexplored. Here, we aimed to explore the link between albumin and the risk of MS. Employing data from the UK Biobank, we investigated the association between baseline levels of serum and urine albumin and the risk of MS using Cox proportional hazards regression analysis. A higher baseline level of serum albumin was associated with a lower risk of incident MS (HR=0.94, 95% CI: 0.91-0.98, P=7.66E-04). Subgroup analysis revealed a more pronounced effect in females, as well as participants with younger ages, less smoking and deficient levels of vitamin D. Conversely, no association was identified between baseline microalbuminuria level and risk of incident MS. Higher serum albumin level at baseline is linked to a reduced risk of MS. These results contribute to an enhanced understanding of albumin's role in MS, propose the potential use of albumin as a biomarker for MS, and have implications for the design of therapeutic interventions targeting albumin in clinical trials.