Associations between polygenic risk scores and accelerated brain ageing in smokers.

Ageing brain age enrichment analysis polygenic risk score sMRI smoking

Journal

Psychological medicine
ISSN: 1469-8978
Titre abrégé: Psychol Med
Pays: England
ID NLM: 1254142

Informations de publication

Date de publication:
09 Aug 2023
Historique:
medline: 9 8 2023
pubmed: 9 8 2023
entrez: 9 8 2023
Statut: aheadofprint

Résumé

Smoking contributes to a variety of neurodegenerative diseases and neurobiological abnormalities, suggesting that smoking is associated with accelerated brain aging. However, the neurobiological mechanisms affected by smoking, and whether they are genetically influenced, remain to be investigated. Using structural magnetic resonance imaging data from the UK Biobank ( The BrainAge in smokers was predicted with very high accuracy ( By using a simplified single indicator of the entire brain (BAG) in combination with the PRS, this study highlights the greater BAG in smokers and its linkage with genes and smoking behavior, providing insight into the neurobiological underpinnings and potential features of smoking-related aging.

Sections du résumé

BACKGROUND BACKGROUND
Smoking contributes to a variety of neurodegenerative diseases and neurobiological abnormalities, suggesting that smoking is associated with accelerated brain aging. However, the neurobiological mechanisms affected by smoking, and whether they are genetically influenced, remain to be investigated.
METHODS METHODS
Using structural magnetic resonance imaging data from the UK Biobank (
RESULTS RESULTS
The BrainAge in smokers was predicted with very high accuracy (
CONCLUSION CONCLUSIONS
By using a simplified single indicator of the entire brain (BAG) in combination with the PRS, this study highlights the greater BAG in smokers and its linkage with genes and smoking behavior, providing insight into the neurobiological underpinnings and potential features of smoking-related aging.

Identifiants

DOI: 10.1017/S0033291723001812 PMID: 37555321
pubmed: 37555321
doi: 10.1017/S0033291723001812
pii: S0033291723001812
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

1-10

Auteurs

Zeyu Yang (Z)

MOE-LCSM, School of Mathematics and Statistics, Hunan Normal University, Changsha 410006, P.R.China.
Key Laboratory of Applied Statistics and Data Science, Hunan Normal University, College of Hunan Province, Changsha 410006, P.R.China.
ORCID: 0000-0001-8879-0278

Wei Zhao (W)

MOE-LCSM, School of Mathematics and Statistics, Hunan Normal University, Changsha 410006, P.R.China.
Key Laboratory of Applied Statistics and Data Science, Hunan Normal University, College of Hunan Province, Changsha 410006, P.R.China.

Zeqiang Linli (Z)

School of Mathematics and Statistics, Guangdong University of Foreign Studies, Guangzhou, 510006, P.R.China.

Shuixia Guo (S)

MOE-LCSM, School of Mathematics and Statistics, Hunan Normal University, Changsha 410006, P.R.China.
Key Laboratory of Applied Statistics and Data Science, Hunan Normal University, College of Hunan Province, Changsha 410006, P.R.China.

Jianfeng Feng (J)

Centre for Computational Systems Biology, Fudan University, Shanghai 200433, P.R.China.
Department of Computer Science, University of Warwick, Coventry CV4 7AL, England.

Classifications MeSH