Investigating the relationship between DNA methylation age acceleration and risk factors for Alzheimer's disease

McCartney, Daniel L.; Stevenson, Anna J.; Martin, Richard M; Gibson, Jude; Morris, Stewart W.; Campbell, Archie; Murray, Alison; Whalley, Heather C.; Porteous, David J.; McIntosh, Andrew M.; Evans, Kathryn; Deary, Ian J.; Marioni, Riccardo E.

doi:10.1016/j.dadm.2018.05.006

Cited by 103 publications

(114 citation statements)

References 62 publications

Supporting

Mentioning

107

Contrasting

Order By: Relevance

“…A variety of cognitive, physical, and health data were collected at the study baseline along with blood or saliva samples for DNA genotyping. Blood-based DNAm data were obtained on a subset of 5,200 participants using the Illumina EPIC array [9]. Quality control details have been reported previously [9].…”

Section: Generation Scotland: Scottish Family Health Studymentioning

confidence: 99%

An epigenome-wide association study of sex-specific chronological ageing

McCartney

Hillary

Zhang

et al. 2019

Preprint

Self Cite

View full text Add to dashboard Cite

IntroductionAdvanced age is associated with cognitive and physical decline, and is a major risk factor for a multitude of disorders including neurodegenerative diseases such as Alzheimer’s disease. There is also a gap in life-expectancy between males and females. DNA methylation differences have been shown to be associated with both age and sex. Here, we investigate age-by-sex differences in DNA methylation in an unrelated cohort of 2,586 individuals between the ages of 18 and 87 years.MethodsGenome-wide DNA methylation was measured on the Illumina HumanMethylationEPIC beadchip in a subset of unrelated individuals from the Generation Scotland cohort. Mixed linear model-based analyses were performed to investigate the relationship between DNA methylation and an interaction term between age and sex, as well as chronological age.ResultsAt a genome-wide significance level of P < 3.6 × 10−8, 14 loci were associated with the age-by-sex interaction term, the majority of which were X-linked (n = 12). Seven of these loci were annotated to genes. The site with the greatest difference mapped to GAGE10, an X-linked gene. Here, DNA methylation levels remained stable across the male adult age range (DNA methylation x age r = 0.02), but decreased across female adult age range (DNA methylation x age r = −0.61). The seven age-by-sex-associated genes were enriched among differentially-expressed genes in lung, liver, testis and blood.ConclusionThe majority of differences in age-associated DNA methylation trajectories between sexes are present on the X-chromosome. Several of these differences occur within genes which have implicated in multiple cancers, schizophrenia and systemic lupus erythematosus.

show abstract

Section: Generation Scotland: Scottish Family Health Studymentioning

confidence: 99%

An epigenome-wide association study of sex-specific chronological ageing

McCartney

Hillary

Zhang

et al. 2019

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…Given that changes in DNAm are known to index exposure to certain environmental risk factors for diseases of old age (for example, tobacco smoking) (Elliott et al, 2014;Sugden et al, 2019), and 4 variable DNAm is robustly associated with a number of age-associated disorders (Chouliaras et al, 2018;Chuang et al, 2017;Smith et al, 2016), there has been interest in the hypothesis that DNAm clocks might robustly quantify variation in biological age. Horvath's DNAm age clock, for example, has been widely applied to identify accelerated epigenetic ageing -where DNAm age predictions deviate from chronological age such that individuals appear older than they really are -in the context of numerous health and disease outcomes (Horvath and Ritz, 2015;Levine et al, 2015;Marioni et al, 2015;McCartney et al, 2018). Since age is a major risk factor for dementia and other neurodegenerative brain disorders, there is particular interest in the application of epigenetic clock algorithms to these phenotypes, especially as differential DNAm has been robustly associated with diseases including Alzheimer's disease and Parkinson's disease Smith et al, 2016;Yu et al, 2015).…”

Section: Introductionmentioning

confidence: 99%

Recalibrating the Epigenetic Clock: Implications for Assessing Biological Age in the Human Cortex

Shireby

Davies

Francis

et al. 2020

Preprint

View full text Add to dashboard Cite

Human DNA-methylation data have been used to develop biomarkers of ageingreferred to 'epigenetic clocks' -that have been widely used to identify differences between chronological age and biological age in health and disease including neurodegeneration, dementia and other brain phenotypes. Existing DNA methylation clocks are highly accurate in blood but are less precise when used in older samples or on brain tissue. We aimed to develop a novel epigenetic clock that performs optimally in human cortex tissue and has the potential to identify phenotypes associated with biological ageing in the brain. We generated an extensive dataset of human cortex DNA methylation data spanning the life-course (n = 1,397, ages = 1 to 104 years). This dataset was split into 'training' and 'testing' samples (training: n = 1,047; testing: n = 350). DNA methylation age estimators were derived using a transformed version of chronological age on DNA methylation at specific sites using elastic net regression, a supervised machine learning method. The cortical clock was subsequently validated in a novel human cortex dataset (n = 1,221, ages = 41 to 104 years) and tested for specificity in a large whole blood dataset (n = 1,175, ages = 28 to 98 years). We identified a set of 347 DNA methylation sites that, in combination optimally predict age in the human cortex. The sum of DNA methylation levels at these sites weighted by their regression coefficients provide the cortical DNA methylation clock age estimate. The novel clock dramatically out-performed previously reported clocks in additional cortical datasets. Our findings suggest that previous associations between predicted DNA methylation age and neurodegenerative phenotypes might represent false positives resulting from clocks not robustly calibrated to the tissue being tested and for phenotypes that become manifest in older ages. The age distribution and tissue type of samples included in training datasets need to be considered when building and applying epigenetic clock algorithms to human epidemiological or disease cohorts. mortem 6 samples were split into a "training" dataset (used to determine the DNAm sites included in the model and their weighted coefficients) and a "testing" dataset (used to profile the performance of the proposed model). To reduce the effects of experimental batch in our model, we maximised the number of different datasets included in the training data by combining the ten cohorts and randomly assigning individuals within them to either the training or testing dataset in a 3:1 ratio ( Table 1). In total, our training dataset (age range = 1-108 years, median = 57 years; see Supplementary Fig. S1) comprised DNAm data from 1,047 cortex samples (derived from 832 donors) and our testing dataset (age range = 1-108 years, median = 56 years; see Supplementary Fig. S1) comprised DNAm data from 350 cortex samples (derived from 323 donors). Individuals with a diagnosis of Alzheimer's disease and other major neurological phenotypes were excluded from our analysis given the ...

show abstract

“…DNAm is a commonly-studied epigenetic mechanism typically characterised by the addition of a methyl group to a cytosine-phosphate-guanine (CpG) nucleotide base pairing, thereby permitting regulation of gene activity [7]. Crucially, these biological age predictors, also referred to as 'epigenetic clocks', correlate strongly with chronological age; furthermore, for a given chronological age, an advanced epigenetic age is associated with increased mortality risk and many age-related morbidities [8][9][10][11][12].…”

Section: Introductionmentioning

confidence: 99%

An epigenetic predictor of death captures multi-modal measures of brain health

Hillary

Stevenson

Cox

et al. 2019

Preprint

Self Cite

View full text Add to dashboard Cite

Individuals of the same chronological age exhibit disparate rates of biological ageing.Consequently, a number of methodologies have been proposed to determine biological age and primarily exploit variation at the level of DNA methylation (DNAm) -a commonly studied epigenetic mechanism. A novel epigenetic clock, termed 'DNAm GrimAge' has outperformed its predecessors in predicting the risk of mortality as well as a number of age-related morbidities. However, the association between DNAm GrimAge and cognitive or neuroimaging phenotypes remains unknown. We explore these associations in the Lothian Birth Cohort 1936 (n=709, mean age 73 years). Higher DNAm GrimAge was strongly associated with all-cause mortality over twelve years of follow-up (Hazard Ratio per standard deviation increase in GrimAge: 1.81, P < 2.0 x 10 -16 ). Higher DNAm GrimAge was associated with lower age 11 IQ (β=-0.11), lower age 73 general cognitive ability (β=-0.18), decreased brain volume (β=-0.25) and increased brain white matter hyperintensities (β=0.17). Sixtyeight of 137 health-and brain-related phenotypes tested were significantly associated with DNAm GrimAge. Adjusting all models for childhood cognitive ability attenuated to nonsignificance a small number of associations (12/68 associations; 6 of which were cognitive traits), but not the association with general cognitive ability (33.9% attenuation). Higher DNAm GrimAge cross-sectionally associates with lower cognitive ability and brain vascular lesions in older age, independently of early life cognitive ability. Thus, this epigenetic predictor of mortality is also associated with multiple different measures of brain health and may aid in the prediction of age-related cognitive decline.

show abstract

Investigating the relationship between DNA methylation age acceleration and risk factors for Alzheimer's disease

Cited by 103 publications

References 62 publications

An epigenome-wide association study of sex-specific chronological ageing

An epigenome-wide association study of sex-specific chronological ageing

Recalibrating the Epigenetic Clock: Implications for Assessing Biological Age in the Human Cortex

An epigenetic predictor of death captures multi-modal measures of brain health

Contact Info

Product

Resources

About