ABSTRACT
Currently, the relationship between household size and incident dementia, along with the underlying neurobiological mechanisms, remains unclear. This prospective cohort study was based on UK Biobank participants aged ≥ 50 years without a history of dementia. The linear and non-linear longitudinal association was assessed using Cox proportional hazards regression and restricted cubic spline models. Additionally, the potential mechanisms driven by brain structures were investigated by linear regression models. We included 275,629 participants (mean age at baseline 60.45 years [SD 5.39]). Over a mean follow-up of 9.5 years, 6031 individuals developed all-cause dementia. Multivariable analyses revealed that smaller household size was associated with an increased risk of all-cause dementia (HR, 1.06; 95% CI 1.02-1.09), vascular dementia (HR, 1.08; 95% CI 1.01-1.15), and non-Alzheimer's disease non-vascular dementia (HR, 1.09; 95% CI 1.03-1.14). No significant association was observed for Alzheimer's disease. Restricted cubic splines demonstrated a reversed J-shaped relationship between household size and all-cause and cause-specific dementia. Additionally, substantial associations existed between household size and brain structures. Our findings suggest that small household size is a risk factor for dementia. Additionally, brain structural differences related to household size support these associations. Household size may thus be a potential modifiable risk factor for dementia.
Subject(s)
Biological Specimen Banks , Dementia , Family Characteristics , Humans , Female , Male , United Kingdom/epidemiology , Dementia/epidemiology , Dementia/etiology , Middle Aged , Aged , Risk Factors , Prospective Studies , Incidence , Proportional Hazards Models , Brain/pathology , UK BiobankABSTRACT
Parkinson's disease (PD) is the second most prevalent neurodegenerative disease in ageing individuals. Current therapeutic regimen suffers from general side effects and a poor efficiency for PD symptoms. The need for development new therapeutic agents for PD is urgent. Here, we aimed to explore the metabolic mechanism of PD and identified potential novel agents for PD by a sub-pathway-based method. By using the GSE7621 microarray data from the GEO database, we first identified the 1226 differentially expressed genes (DEGs) between PD and normal samples. Then we identified 19 significant enriched metabolic sub-pathways, which may involve in development of PD. Finally, by an integrated analysis of PD-involved sub-pathways and drug-affected sub-pathways, we identified 49 novel small molecular drugs capable to target the PD-involved sub-pathways. Our method could not only identify existing drug (apomorphine) for PD, but also predict potentially novel agents (ketoconazole and astemizole), which might have therapeutic effects via targeting some key enzymes in arachidonic acid metabolism. These candidate agents identified by our approach may provide insights into a novel therapy approach for PD.
Subject(s)
Drug Discovery/methods , Molecular Targeted Therapy/methods , Parkinson Disease/drug therapy , Signal Transduction/drug effects , Case-Control Studies , Databases, Factual , Gene Expression , Humans , Parkinson Disease/genetics , Signal Transduction/geneticsABSTRACT
OBJECTIVE: Parkinson disease (PD) is a degenerative disorder of the central nervous system, and in the majority of cases, the causes of PD are unknown. Coupled with impressive advances in statistical tools for analyzing large, complex data sets, well-designed microarray experiments are poised to make a big impact in the field of diseases. So we set the study to identify distinct PD-associated candidates. METHODS: Candidate genes, with statistical significant changes of expression in PD patients' samples, were extracted from a transcriptome-wide microarray data in 105 individuals, which were downloaded from GEO, NCBI, by using statistical methods; Selected findings were confirmed by principal component analysis (PCA) and functional and pathway enrichment analysis were used to further study about the distinct candidates. RESULTS: A total of 10 distinctly differentially expressed genes were identified in PD patitents' samples. After PCA confirmation, we specifically pointed out 4 genes (PRKAG2, DLG1, DDX3Y, RPS4Y) as the high confidence distinct candidates in PD. Network and functional categories showed that they were most related to translational elongation(GO:0006414) and participated in mTOR signaling pathway(hsa04150). CONCLUSION: Among 10 distinct genes which are identified in PD patients' samples, DLG1, XIST, DDX3Y and RPS4Y1 genes can classify samples into different group clearly, and they are regarded as high confidence distinct gene biomarkers of PD. Our results provide a systematic view of the functional alterations of PD that may help to elucidate the mechanisms of PD and lead to improved treatments for PD patients.
