Are you sure you want to leave this community? Leaving the community will revoke any permissions you have been granted in this community.
In this study, we explored the association between the personality traits, neuroticism and introversion, and risk of Parkinson disease (PD). A population-based cohort study was conducted using questionnaire data from the Swedish Twin Registry for twins born 1926-1958 (n > 29,000). Personality traits were assessed in 1973 by a short form of Eysenck's Personality Inventory. The cohort was followed from 1974 to 2012 through Swedish patient and cause of death registers for PD ascertainment. Cox proportional hazards regression was used to estimate subsequent risk of PD, adjusting for attained age, sex and smoking. A mediation analysis was performed to further explore the role of smoking in the relationship between personality trait and PD. Confounding by familial factors was explored using a within-pair analysis. During a mean follow-up time of 36.8 years, 197 incident PD cases were identified. Both neuroticism and introversion were associated with an increased risk of PD after adjustment. Smoking was a significant mediator in the relationship between personality traits and PD that partly accounted for the effect of introversion, whereas it acted as a suppressor for the effect of neuroticism on PD risk. In the within-pair analyses, associations for neuroticism and introversion were attenuated. In conclusion, our study provides evidence that neuroticism is associated with an increased risk of PD that is in part suppressed by smoking. There was a weak association between introversion and PD and this effect was at least partly mediated through smoking. The observed effects may partly be explained by familial factors shared by twins.
Pubmed ID: 26130127
Publication data is provided by the National Library of Medicine ® and PubMed ®. Data is retrieved from PubMed ® on a weekly schedule. For terms and conditions see the National Library of Medicine Terms and Conditions.
Software that can accurately and sensitivity classify short reads of next-generation sequencing (NGS) into protein domain families. It is based on profile HMM and a supervised graph contribution algorithm. Compared to existing tools, it has high sensitivity and specificity in classifying short reads into their native domain families.
View all literature mentions