Brain age predicts mortality
By James H. Cole, Stuart J. Ritchie, M. Bastin, M. Hernández, S. M. Maniega, N. Royle, J. Corley, A. Pattie, S. Harris, Qian Zhang and 8 more. Molecular Psychiatry, 2017
752 citations recorded by the source corpus.
Age-associated disease and disability are placing a growing burden on society. However, ageing does not affect people uniformly. Hence, markers of the underlying biological ageing process are needed to help identify people at increased risk of age-associated physical and cognitive impairments and ultimately, death. Here, we present such a biomarker, ‘brain-predicted age’, derived using structural neuroimaging. Brain-predicted age was calculated using machine-learning analysis, trained on neuroimaging data from a large healthy reference sample (N=2001), then tested in the Lothian Birth Cohort 1936 (N=669), to determine relationships with age-associated functional measures and mortality. Having a brain-predicted age indicative of an older-appearing brain was associated with: weaker grip strength, poorer lung function, slower walking speed, lower fluid intelligence, higher allostatic load and increased mortality risk. Furthermore, while combining brain-predicted age with grey matter and cerebrospinal fluid volumes (themselves strong predictors) not did improve mortality risk prediction, the combination of brain-predicted age and DNA-methylation-predicted age did. This indicates that neuroimaging and epigenetics measures of ageing can provide complementary data regarding health outcomes. Our study introduces a clinically-relevant neuroimaging ageing biomarker and demonstrates that combining distinct measurements of biological ageing further helps to determine risk of age-related deterioration and death.
Subfields: Health, Toxicology and Mutagenesis, Molecular Biology, Psychiatry and Mental health
View Paper: https://www.semanticscholar.org/paper/3f047527ede8c26ab0ee82f4282b7b9dd0d07cd8
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Citations in the corpus
- Brain age prediction using deep learning uncovers associated sequence variants (2019, methodology): “This procedure entails repeating these three main steps four times: (1) Train four CNNs on the Icelandic dataset on the four previously mentioned image types.”
- Tensor Regression Networks (2017, background): “, 2018), and published studies (Gaser et al., 2013; Kolenic et al., 2018; Cole et al., 2018) on other brain MRI datasets.”
- Predicting Age Using Neuroimaging: Innovative Brain Ageing Biomarkers. (2017, neutral): “Brain age appears tomeet these criteria, as a recent large-scale study in 73-year-olds found a significant relationship between brain age and mortality risk, ascertainedup to7 yearsafter scanning [20].”