Systematic underestimation of the epigenetic clock and age acceleration in older subjects

Khoury, Louis El; Gorrie-Stone, T.J.; Smart, Melissa; Hughes, Amanda; Bao, Yanchun; Andrayas, Alexandria; Burrage, Joe; Hannon, Eilís; Kumari, Meena; Mill, Jonathan; Schalkwyk, Leonard C

doi:10.1186/s13059-019-1810-4

Cited by 99 publications

(121 citation statements)

References 51 publications

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“…Applying predictors to datasets that differ in terms of these characteristics may lead to biases when estimating DNAm age, and confound phenotypic analyses using these variables (El Khoury et al, 2019). We found that existing DNAm clocks (i.e.…”

Section: Existing Dnam Clock Algorithms Work Sub-optimally In the Hummentioning

confidence: 93%

“…Briefly, to predict DNAm age using the DNAmClockMulti we applied the agep() function in wateRmelon (Pidsley et al, 2013). Although this function does not contain the custom normalisation method applied at the DNAm age calculator website (https://DNAmClock.genetics.ucla.edu/), both methods work similarly in brain and blood studies, providing the data have been pre-processed adequately (El Khoury et al, 2019).To predict age using the DNAmClockPheno (Levine et al, 2018), we also applied the agep() function, inputting a vector of the coefficients and the intercept using the data provided in the supplementary material of Levine et al's manuscript. To predict DNAm age with the DNAmClockblood, we used the authors' published code (available on GitHub https://github.com/qzhang314/DNAm-based-age-predictor) (Zhang et al, 2019).…”

Section: Deriving a Novel Cortical Dnam Age Classifiermentioning

confidence: 99%

“…To date, there has been limited research comparing the prediction accuracy and potential bias of existing clock algorithms across different tissues and ages. Recent analyses have highlighted potential biases when using Horvath's clock in older samples (>~60 years) and in samples derived from certain tissues, especially the central nervous system (El Khoury et al, 2019). This is important for the interpretation of studies of possible relationships between accelerated epigenetic age and age-related diseases affecting the human brain (e.g.…”

Section: Introductionmentioning

confidence: 99%

“…Recently, a number of tissue-specific DNA methylation clocks have been described, including clocks designed for whole blood (Hannum et al, 2013;Zhang et al, 2019), muscle (Voisin et al, 2019), bone (Gopalan et al, 2019) and paediatric buccal cells (McEwen et al, 2019). Importantly, although these DNAm age estimators have increased predictive accuracy within the specific tissues in which they were built, they lose this precision when applied to other tissues (El Khoury et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

“…The relationship between age and DNAmClock plateaus in old age By definition, DNAm age is correlated with chronological age, meaning age is a potential confounder for analyses of Δ age; not adequately controlling for age increases the likelihood that false positive associations will be identified (El Khoury et al, 2019).…”

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confidence: 99%

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

Shireby

Davies

Francis

et al. 2020

Preprint

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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 ...

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Section: Existing Dnam Clock Algorithms Work Sub-optimally In the Hummentioning

confidence: 93%

Section: Deriving a Novel Cortical Dnam Age Classifiermentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

mentioning

confidence: 99%

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

Shireby

Davies

Francis

et al. 2020

Preprint

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Epigenetic Age in Peripheral Blood Among Children, Adolescent, and Adult Survivors of Childhood Cancer

et al. 2023

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ImportanceCertain cancer therapies are risk factors for epigenetic age acceleration (EAA) among survivors of childhood cancer, and EAA is associated with chronic health conditions (CHCs). However, small numbers of younger survivors (aged &lt;20 years) previously evaluated have limited the ability to calculate EAA among this age group.ObjectiveTo evaluate the change rate of epigenetic age (EA) and EAA in younger compared with older survivors and the possible association of EAA with early-onset obesity (aged &lt;20 years), severity/burden of CHCs, and late mortality (&gt;5 years from cancer diagnosis).Design, Setting, and ParticipantsStudy participants were from the St Jude Lifetime Cohort, initiated in 2007 with ongoing follow-up. The present study was conducted from April 17, 2022, to March 23, 2023. Survivors in this cohort of European ancestry with DNA methylation data were included. Cross-sectional annual changes in EA and EAA were compared across 5 different chronologic age groups: age 0 to 9 (children), 10 to 19 (adolescents), 20 to 34 (younger adults), 35 to 49 (middle-aged adults), and greater than or equal to 50 (older adults) years. Logistic regression evaluated the association between EAA and early-onset obesity or severity/burden of CHCs. Cox proportional hazards regression assessed the association between EAA and late mortality.Main Outcomes and MeasuresEarly-onset obesity, severity/burden of CHCs (graded using the Common Terminology Criteria for Adverse Events (grade 1, mild; 2, moderate; 3, severe/disabling; 4, life-threatening) and were combined into high vs low severity/burden based on frequency and grade), and late mortality were the outcomes based on follow-up until April 2020. Expanded DNA methylation profiling increased the number of survivors younger than 20 years (n = 690). Epigenetic age was calculated primarily using the Levine clock, and EAA was derived from least squares regression of EA against chronologic age and was standardized to a z score (Levine EEA).ResultsAmong 2846 participants (median age, 30.3 [IQR, 9.3-41.5] years; 53% males), the cross-sectional annual change in EA_Levine was higher in children (1.63 years) and adolescents (1.14 years), and the adjusted least-squares mean of Levine EEA was lower in children (−0.22 years) and older adults (−1.70 years). Each 1-SD increase in Levine EEA was associated with increased risk of developing early-onset obesity (odds ratio [OR], 1.46; 95% CI, 1.19-1.78), high severity/burden of CHCs (OR, 1.13; 95% CI, 1.03-1.24), and late mortality (hazard ratio, 1.75; 95% CI, 1.35-2.26).Conclusions and RelevanceThe findings of this study suggest that EAA measured in children and adolescent survivors of childhood cancer is associated with early-onset obesity, severity/burden of all CHCs, and late mortality. Evaluating EAA may help identify survivors of childhood cancer at increased risk for early-onset obesity, morbidity in general, and mortality.

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Impact of age, antiretroviral therapy, and cancer on epigenetic aging in people living with HIV

Qing

Chan

et al. 2023

Cancer Medicine

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The introduction of highly active antiretroviral therapy (HAART or ART) in the mid-1990s, has reduced the number of deaths from AIDS, and increased overall survival. People living with HIV (PLWH) are living much longer and the number of older individuals living with HIV is increasing. In 2019, an estimated 45% of Americans living with HIV were aged 55 and older, 27% were aged 60 and older, and 6% were aged 70 and older. 1 Though ART lowered the risks of developing AIDS-defining cancers, including Kaposi's sarcoma and non-Hodgkin lymphoma (NHL), the incidence of non-AIDS-defining cancers (NADCs) rose by more than threefold. 2,3 Clinical epidemiologic research also suggests a higher prevalence of other age-associated comorbidities in PLWH. These include earlier onset cardiovascular disease, bone fractures, osteoporosis, kidney and liver disease, cognitive decline, and frailty. [4][5][6] ART therapy targets several aspects of HIV replication. As these therapies have evolved, ART treatment can be

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Systematic underestimation of the epigenetic clock and age acceleration in older subjects

Cited by 99 publications

References 51 publications

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

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

Epigenetic Age in Peripheral Blood Among Children, Adolescent, and Adult Survivors of Childhood Cancer

Impact of age, antiretroviral therapy, and cancer on epigenetic aging in people living with HIV

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