Socioeconomic status across the early life course predicts gene expression signatures of disease and senescence.
Details
Serval ID
serval:BIB_E61139497F8A
Type
Article: article from journal or magazin.
Collection
Publications
Institution
Title
Socioeconomic status across the early life course predicts gene expression signatures of disease and senescence.
Journal
Journal of epidemiology and community health
ISSN
1470-2738 (Electronic)
ISSN-L
0143-005X
Publication state
Published
Issued date
11/11/2024
Peer-reviewed
Oui
Volume
78
Number
12
Pages
752-758
Language
english
Notes
Publication types: Journal Article
Publication Status: epublish
Publication Status: epublish
Abstract
Socioeconomic status (SES) is associated with many chronic diseases, indicators of senescence and mortality. However, the changing salience of SES in the prediction of adult health is not well understood. Using mRNA-seq abundance data from wave V of the National Longitudinal Study of Adolescent to Adult Health (Add Health), we examine the extent to which SES across the early life course is related to gene expression-based signatures for chronic diseases, senescence and inflammation in the late 30s.
We use Bayesian methods to identify the most likely model of life course epidemiology (critical, sensitive and accumulation models) that characterises the changing importance of parental SES and SES during young (ages 27-30) and mid-adulthood (ages 36-39) in the prediction of the signatures.
For most signatures, SES is an important predictor in all periods, although parental SES or SES during young adulthood are often the most predictive. For three signatures (components of diabetes, inflammation and ageing), critical period models involving the exclusive salience of SES in young adulthood (for diabetes) or parental SES (for inflammation and ageing) are most probable. The observed associations are likely mediated by body mass index.
Models of life course patterns of SES may inform efforts to identify age-specific mechanisms by which SES is associated with health at different points in life and they also suggest an enhanced approach to prediction models that recognise the changing salience of risk factors.
We use Bayesian methods to identify the most likely model of life course epidemiology (critical, sensitive and accumulation models) that characterises the changing importance of parental SES and SES during young (ages 27-30) and mid-adulthood (ages 36-39) in the prediction of the signatures.
For most signatures, SES is an important predictor in all periods, although parental SES or SES during young adulthood are often the most predictive. For three signatures (components of diabetes, inflammation and ageing), critical period models involving the exclusive salience of SES in young adulthood (for diabetes) or parental SES (for inflammation and ageing) are most probable. The observed associations are likely mediated by body mass index.
Models of life course patterns of SES may inform efforts to identify age-specific mechanisms by which SES is associated with health at different points in life and they also suggest an enhanced approach to prediction models that recognise the changing salience of risk factors.
Keywords
Humans, Adult, Female, Social Class, Male, Longitudinal Studies, Bayes Theorem, Aging/genetics, Chronic Disease, Inflammation/genetics, Adolescent, BIOSTATISTICS, EPIDEMIOLOGY, GENETICS, Health inequalities, Life course epidemiology
Pubmed
Web of science
Open Access
Yes
Create date
09/09/2024 14:40
Last modification date
20/11/2024 7:28