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

Ning, Kaida; Zhao, Lu; Matloff, William; Sun, Fengrui; Toga, Arthur W.

doi:10.1101/469924

Cited by 6 publications

(9 citation statements)

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“…The di erent regional brain-age delta estimates showed varying associations with disease. However, as with our all-in-one predictions and also [Ning et al, 2018], direct GWAS of the delta estimates showed virtually no significant assocation, even with these high subject numbers.…”

Section: Discussionsupporting

confidence: 85%

“…This was done with the methods described in . These two all-in-one brain-age delta estimations showed no genetic assocations that were significant and replicated, consistent with previous GWAS of all-in-one brain-aging modelling [Ning et al, 2018]. This suggests that biological specificity driving the mode/mode-cluster results has been lost (diluted) when generating a single brain-age delta.…”

Section: Discovery Validationsupporting

confidence: 88%

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Brain aging comprises many modes of structural and functional change with distinct genetic and biophysical associations

Smith

Elliott

Alfaro-Almagro

et al. 2020

eLife

146

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Brain imaging can be used to study how individuals’ brains are aging, compared against population norms. This can inform on aspects of brain health; for example, smoking and blood pressure can be seen to accelerate brain aging. Typically, a single ‘brain age’ is estimated per subject, whereas here we identified 62 modes of subject variability, from 21,407 subjects’ multimodal brain imaging data in UK Biobank. The modes represent different aspects of brain aging, showing distinct patterns of functional and structural brain change, and distinct patterns of association with genetics, lifestyle, cognition, physical measures and disease. While conventional brain-age modelling found no genetic associations, 34 modes had genetic associations. We suggest that it is important not to treat brain aging as a single homogeneous process, and that modelling of distinct patterns of structural and functional change will reveal more biologically meaningful markers of brain aging in health and disease.

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Section: Discussionsupporting

confidence: 85%

Section: Discovery Validationsupporting

confidence: 88%

Brain aging comprises many modes of structural and functional change with distinct genetic and biophysical associations

Smith

Elliott

Alfaro-Almagro

et al. 2020

eLife

146

View full text Add to dashboard Cite

show abstract

“…This was done with the methods described in [Smith et al, 2019]. These two all-in-one brain-age delta estimations showed no genetic assocations that were significant and replicated, consistent with previous GWAS of all-in-one brain-aging modelling [Ning et al, 2018]. This suggests that biological specificity driving the mode/mode-cluster results has been lost (diluted) when generating a single brain-age delta.…”

Section: Genome-wide Associations Studies Of All Brain-aging Modessupporting

confidence: 80%

“…all-in-one prediction and also [Ning et al, 2018], direct GWAS of the delta estimates showed virtually no significant assocation, even with these high subject numbers.…”

Section: Discussionmentioning

confidence: 84%

Brain aging comprises many modes of structural and functional change with distinct genetic and biophysical associations

Smith

Elliott

Alfaro-Almagro

et al. 2019

Preprint

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Brain imaging can be used to study how individuals' brains are aging, compared against population norms. This can inform on aspects of brain health; for example, smoking and blood pressure can be seen to accelerate brain aging. Typically, a single "brain age" is estimated per subject, whereas here we we identified 62 modes of subject variability, from 21,407 subjects' multimodal brain imaging data in UK Biobank. The modes represent different aspects of brain aging, showing distinct patterns of functional and structural brain change, and distinct patterns of association with genetics, lifestyle, cognition, physical measures and disease. While conventional brain-age modelling found no genetic associations, 34 modes had genetic associations. We suggest that it is important not to treat brain aging as a single homogeneous process, and that modelling of distinct patterns of structural and functional change will reveal more biologically meaningful markers of brain aging in health and disease. KeywordsBrain aging, brain imaging, UK Biobank.Corresponding author: Stephen Smith, steve@fmrib.ox.ac.uk IntroductionBrain imaging can be used to predict "brain age" -the apparent age of individuals' brains -by comparing their imaging data against a normative population dataset. The difference between brain age and actual chronological age (the "delta", or "brain age gap") is often then computed, providing a measure of whether a subject's brain appears to have aged more (or less) than the average age-matched population data. For example, looking at structural magnetic resonance imaging (MRI) data, a high degree of atrophy would cause a subject's brain to appear older than a normal age-matched brain. Estimation of brain age and the delta is of value in studying both normal aging and disease, with some diseases, such as Alzheimer's disease, showing similar patterns of change to that of accelerated healthy aging [Franke et al., 2010, Cole and Franke, 2017.The typical approach uses one or more imaging modalities, most commonly using just a single structural image from each subject. The data is then preprocessed, and features identified, for use in the brain age prediction. For example, the structural images may be warped into a standard space, and grey matter segmentation carried out; the voxelwise segmentation values themselves can then be the features. Alternatively, a smaller number of more highly-condensed features may be derived, such as volumes of grey and white matter within multiple brain regions. The resulting dataset, of multiple subjects' feature sets, along with their true ages, is then passed into a supervised-learning algorithm (e.g., regression, support vector machine or deep learning). The algorithm then learns to predict the subjects' ages from their brain imaging features. Finally, the true age is typically subtracted from the estimated brain age, to create a delta, potentially with corrections for biases such as systematic mis-estimation of brain age [Le et al., 2018.The imaging feature set can be derived from more ...

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“…Brain morphometric measurements were obtained by processing MRI data with FreeSurfer 6.0 23 . More details including imaging hardware, acquisition protocols, and quality control are described elsewhere 23,24 .…”

Section: Obtaining Relative Brain Age (Rba) Based On Magnetic Resonanmentioning

confidence: 99%

Parity is associated with cognitive function and brain age in both females and males

Ning

Zhao

Franklin

et al. 2020

Sci Rep

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previous studies of the association between parity and long-term cognitive changes have primarily focused on women and have shown conflicting results. We investigated this association by analyzing data collected on 303,196 subjects from the UK Biobank. We found that in both females and males, having offspring was associated with a faster response time and fewer mistakes made in the visual memory task. Subjects with two or three children had the largest differences relative to those who were childless, with greater effects observed in men. We further analyzed the association between parity and relative brain age (n = 13,584), a brain image-based biomarker indicating how old one's brain structure appears relative to peers. We found that in both sexes, subjects with two or three offspring had significantly reduced brain age compared to those without offspring, corroborating our cognitive function results. Our findings suggest that lifestyle factors accompanying having offspring, rather than the physical process of pregnancy experienced only by females, contribute to these associations and underscore the importance of studying such factors, particularly in the context of sex. Pregnancy involves dramatic hormonal and physiological changes. In part due to these large changes, the effect of pregnancy on cognitive and cardiovascular health has been studied 1,2. Declined cognitive function was observed during pregnancy 1,2. Researchers have also hypothesized that hormonal changes, which occur both during and after pregnancy, drive the association between parity (number of offspring) and cognitive function in later life. Multiple studies have investigated this association in females, though different conclusions have been found. Some studies found that parity was associated with better episodic memory and had a protective effect against Alzheimer's disease (AD) 3,4. Contrarily, parity has been associated with poor word recall score, Mini Mental State Exam score, and AD neural pathology 5,6. A recent study of approximately 10,000 male and female subjects found an association between the number of offspring and cognitive function in later life in both sexes, including memory and executive function, and suggested that socioeconomic status largely accounted for the association 7. In addition to the association between cognitive function and parity, the association between brain structural change and parity has been studied previously. Hoekzema et al. reported that the volume of certain gray matter regions was reduced during pregnancy and the reductions did not recover for at least 2 years post partum 2 , while others reported that the gray matter restoration process was evident within the first few months postpartum 8,9. Most studies on the association between brain structure and parity had a relatively small sample size (n < 100) and less than three years of postpartum follow-up 2,8,9. To date, it is still unclear if there are any long-term effects of parity on brain structure in the mid-to-old age population. We therefore so...

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

Cited by 6 publications

References 50 publications

Brain aging comprises many modes of structural and functional change with distinct genetic and biophysical associations

Brain aging comprises many modes of structural and functional change with distinct genetic and biophysical associations

Brain aging comprises many modes of structural and functional change with distinct genetic and biophysical associations

Parity is associated with cognitive function and brain age in both females and males

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