Association of relative brain age with tobacco smoking, alcohol consumption, and genetic variants

Abstract

Brain age is a metric that quantifies the degree of aging of a brain based on whole-brain anatomical characteristics. While associations between individual human brain regions and environmental or genetic factors have been investigated, how brain age is associated with those factors remains unclear. We investigated these associations using UK Biobank data. We first trained a statistical model for obtaining relative brain age (RBA), a metric describing a subject's brain age relative to peers, based on whole-brain anatomical measurements, from training set subjects (n = 5,193). We then applied this model to evaluation set subjects (n = 12,115) and tested the association of RBA with tobacco smoking, alcohol consumption, and genetic variants. We found that daily or almost daily consumption of tobacco and alcohol were both significantly associated with increased RBA (P < 0.001). We also found SNPs significantly associated with RBA (p-value < 5E-8). The SNP most significantly associated with RBA is located in MApt gene. our results suggest that both environmental and genetic factors are associated with structural brain aging.

Improving brain age estimates with deep learning leads to identification of novel genetic factors associated with brain aging

“…1). This negative association was similar to the pattern found between PBA-CA and chronological age where PBA was developed using a linear regression model (5,6). This negative association is likely a result of regression dilution and has been discussed previously by Smith et al (6) Deriving PBA using a CNN does not eliminate this issue, since the last layer of the CNN model was fundamentally a linear regression model.…”

“…2). Multiple SNPs significantly associated with RBA are located in a chromosome 17 locus that has been previously linked to brain aging (4,5). This locus includes genes such as NSF, MAPT, and WNT3 (Figure3).…”

“…By comparing PBA and chronological age, researchers can quantify whether a subject's brain appears older or younger than expected, which is reflective of accelerated or decelerated aging of the brain. This has allowed identification of genetic factors, lifestyle factors, and diseases that are associated with accelerated brain aging (4)(5)(6)(7).…”

“…Two recent studies investigated the association between single nucleotide polymorphisms (SNPs) and brain aging using UK Biobank data. Jonsson et al used a CNN model for predicting brain age where the predictor was the whole 3D MRI scan (4), while Ning et al used a linear regression model for predicting brain age, where predictors were brain morphometric measurements derived from MRI scans (5). Both studies reported mean absolute error (MAE) between PBA and the true chronological age around 3.5 years and found an association between a chromosome 17 locus and brain aging.…”

“…In our study, we used a much larger sample of brain imaging data of 16,998 UK Biobank subjects for training a CNN to obtain PBA. Following this, we calculated relative brain age (RBA), a metric describing a subject's PBA relative to peers (5). We then conducted a genome-wide association study on this CNN-derived RBA to identify SNPs significantly associated with RBA.…”