DNA methylation aging clocks: challenges and recommendations

Bell, Christopher; Lowe, Robert; Adams, Peter D.; Baccarelli, Andrea A.; Beck, Stephan; Bell, Jordana T.; Christensen, Brock C.; Gladyshev, Vadim N.; Heijmans, Bastiaan T.; Horvath, Steve; Ideker, Trey; Issa, Jean Pierre J.; Kelsey, Karl T.; Marioni, Riccardo E.; Reik, Wolf; Relton, Caroline L; Schalkwyk, Leonard C.; Teschendorff, Andrew E.; Wagner, Wolfgang; Zhang, Kang; Rakyan, Vardhman K.

doi:10.1186/s13059-019-1824-y

Cited by 412 publications

(393 citation statements)

References 214 publications

(393 reference statements)

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“…However, a limitation is that individuals born in different years have grown up under different historical conditions (Schaie, 1967). For example, people born 70 years ago experienced more exposure to childhood diseases, tobacco smoke, airborne lead, and less exposure to antibiotics and other medications, and lower quality nutrition, all of which leave signatures on DNA methylation (Bell et al, 2019). As a result, the clocks confound methylation patterns arising from early-life exposures to methylation-altering factors with methylation patterns related to biological aging during adulthood.…”

Section: Introductionmentioning

confidence: 99%

Quantification of the pace of biological aging in humans through a blood test: The DunedinPoAm DNA methylation algorithm

Belsky

Caspi

Arseneault

et al. 2020

Preprint

143

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ABSTRACTBiological aging is the gradual, progressive decline in system integrity that occurs with advancing chronological age, causing morbidity and disability. Measurements of the pace of aging are needed to serve as surrogate endpoints in trials of therapies designed to prevent disease by slowing biological aging. We report a blood DNA-methylation measure that is sensitive to variation in the pace of biological aging among individuals born the same year. We first modeled longitudinal change in 18 biomarkers tracking organ-system integrity across 12 years of follow-up in the Dunedin birth cohort. Rates of change in each biomarker were composited to form a measure of aging-related decline, termed Pace of Aging. Elastic-net regression was used to develop a DNA-methylation predictor of Pace of Aging, called DunedinPoAm for Dunedin (P)ace (o)f (A)ging (m)ethylation. Validation analyses showed DunedinPoAm was associated with functional decline in the Dunedin Study and more advanced biological age in the Understanding Society Study, predicted chronic disease and mortality in the Normative Aging Study, was accelerated by early-life adversity in the E-risk Study, and DunedinPoAm prediction was disrupted by caloric restriction in the CALERIE trial. DunedinPoAm generally outperformed epigenetic clocks. Findings provide proof-of-principle for DunedinPoAm as a single-time-point measure of a person’s pace of biological aging.

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“…Briefly, we examine DNA methylation-based measures of (a) accelerated chronological age, (b) accelerated phenotypic health age, (c) accelerated mortality risk age, and (d) telomere length. We do not examine the full range of possible DNA methylation-based aging measures which have continued to expand (see Bell et al, 2019 for a review) [34], but rather examine the most widely used exemplars. Likewise, we do not examine the full set of DNA methylation-based indices of smoking, which have also expanded substantially, choosing instead to focus on an early simple, single locus index (cg05575921) and a more recent complex index that is widely available (PACKYRS).…”

Section: Introductionmentioning

confidence: 99%

The Effect of Tobacco Smoking Differs across Indices of DNA Methylation-Based Aging in an African American Sample: DNA Methylation-Based Indices of Smoking Capture These Effects

Simons

Gibbons

Philibert

et al. 2020

Genes

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Smoking is one of the leading preventable causes of morbidity and mortality worldwide, prompting interest in its association with DNA methylation-based measures of biological aging. Considerable progress has been made in developing DNA methylation-based measures that correspond to self-reported smoking status. In addition, assessment of DNA methylation-based aging has been expanded to better capture individual differences in risk for morbidity and mortality. Untested to date, however, is whether smoking is similarly related to older and newer indices of DNA methylation-based aging, and whether DNA methylation-based indices of smoking can be used in lieu of self-reported smoking to examine effects on DNA methylation-based aging measures. In the current investigation we examine mediation of the impact of self-reported cigarette consumption on accelerated, intrinsic DNA methylation-based aging using indices designed to predict chronological aging, phenotypic aging, and mortality risk, as well as a newly developed DNA methylation-based measure of telomere length. Using a sample of 500 African American middle aged smokers and non-smokers, we found that a) self-reported cigarette consumption was associated with accelerated intrinsic DNA methylation-based aging on some but not all DNA methylation-based aging indices, b) for those aging outcomes associated with self-reported cigarette consumption, DNA methylation-based indicators of smoking typically accounted for greater variance than did self-reported cigarette consumption, and c) self-reported cigarette consumption effects on DNA methylation-based aging indices typically were fully mediated by DNA methylation-based indicators of smoking (e.g., PACKYRS from GrimAge; or cg05575921 CpG site). Results suggest that when DNA methylation-based indices of smoking are substituted for self-reported assessments of smoking, they will typically fully reflect the varied impact of cigarette smoking on intrinsic, accelerated DNA methylation-based aging.

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“…16 The heterogeneity in these findings reflect the current uncertainty in the field of epigenetic ageing overall. 22 Studies that followed a candidate gene approach focused primarily on SERT (n=13), followed by OXT/R (n=8), FKBP5 (n=7), NR3C1 (n=4), and BDNF (n=4). Findings related to the three most commonly investigated genes (SERT, FKBP5, OXTR) were generally inconsistent.…”

Section: Approachmentioning

confidence: 99%

A systematic review of neuroimaging epigenetic research: Calling for an increased focus on development

Walton¹,

Calhoun²,

Heijmans³

et al. 2020

Preprint

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Epigenetic mechanisms, such as DNA methylation (DNAm), have gained increasing attention in the field of neuroimaging as a potential biomarker of -or mechanism mediating -genetic and environmental influences on the brain. Yet, the extent to which DNAm associates with individual differences in the brain -the most relevant organ for the study of psychiatric disorders -is currently unclear.We systematically reviewed research combining structural or functional neuroimaging measures with DNAm to provide an overview of the current state-of-the-art in this new field of research and discuss current challenges.We identified 78 articles, published between 2011 -2019. Most studies investigated DNAm-brain associations in the context of psychiatric and behavioural outcomes (76%), often based on adult (77%) or clinical samples (46%). Only a few studies focussed on risk exposures (21%), developmental periods other than adulthood (23%) or analyzed repeated measures of DNAm or neuroimaging (5%). Studies were highly heterogeneous in design (longitudinal versus cross-sectional), sample characteristics and methods used (candidate-driven versus genome-wide) with relatively few shared practices and common standards. Sample sizes were generally low to moderate (median n=99).On the basis of the strength and weaknesses of existing studies, we recommend how best to address current challenges, including the need for collaborative science to increase comparability across studies. We also advocate for the use of large, prospective, paediatric cohorts with repeated measures of methylation and imaging to draw conclusions about the directionality of associations between these measures.

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“…Such data are used to build regression models termed epigenetic clocks, which enable biological age to be predicted from DNA methylation status. Standard approaches employ elastic net regression, which performs well but typically results in only ~100 CpG sites with non-zero effect, limiting the potential for their genome-wide annotation and interpretation (Bell et al, 2019). We split the data set into training ( = 75) and validation ( = 66) data, where all age classes appeared almost equally in both sets, and applied the sparse-group lasso variant of seagull 1.0.5.…”

Section: Case Studymentioning

confidence: 99%

seagull: lasso, group lasso and sparse-group lasso regularisation for linear regression models via proximal gradient descent

Klosa

Simon

Westermark

et al. 2020

Preprint

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Statistical analyses of biological problems in life sciences often lead to high-dimensional linear models. To solve the corresponding system of equations, penalisation approaches are often the methods of choice. They are especially useful in case of multicollinearity which appears if the number of explanatory variables exceeds the number of observations or for some biological reason. Then, the model goodness of fit is penalised by some suitable function of interest. Prominent examples are the lasso, group lasso and sparse-group lasso. Here, we offer a fast and numerically cheap implementation of these operators via proximal gradient descent. The grid search for the penalty parameter is realised by warm starts. The step size between consecutive iterations is determined with backtracking line search. Finally, the package produces complete regularisation paths. Availability and implementation: seagull is an R package that is freely available on the Comprehensive R Archive Network (CRAN; https://CRAN.R-project.org/package=seagull; vignette included). The source code is available on https://github.com/jklosa/seagull.

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“…Modifications of the normal DNA methylation patterns have been commonly demonstrated in cancer, for example as hypermethylation of tumor suppressor gene promoters [11]. More recently, methylated CpGs that correlate with chronological age and with risk of mortality or developing multi-factorial diseases have been demonstrated [12][13][14]. Specific DNA methylation patterns have been more rarely reported for monogenic diseases [7,9].…”

Section: Introductionmentioning

confidence: 99%

DNA Methylation in the Diagnosis of Monogenic Diseases

Cerrato

Sparago

Ariani

et al. 2020

Genes

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DNA methylation in the human genome is largely programmed and shaped by transcription factor binding and interaction between DNA methyltransferases and histone marks during gamete and embryo development. Normal methylation profiles can be modified at single or multiple loci, more frequently as consequences of genetic variants acting in cis or in trans, or in some cases stochastically or through interaction with environmental factors. For many developmental disorders, specific methylation patterns or signatures can be detected in blood DNA. The recent use of high-throughput assays investigating the whole genome has largely increased the number of diseases for which DNA methylation analysis provides information for their diagnosis. Here, we review the methylation abnormalities that have been associated with mono/oligogenic diseases, their relationship with genotype and phenotype and relevance for diagnosis, as well as the limitations in their use and interpretation of results.

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DNA methylation aging clocks: challenges and recommendations

Cited by 412 publications

References 214 publications

Quantification of the pace of biological aging in humans through a blood test: The DunedinPoAm DNA methylation algorithm

The Effect of Tobacco Smoking Differs across Indices of DNA Methylation-Based Aging in an African American Sample: DNA Methylation-Based Indices of Smoking Capture These Effects

A systematic review of neuroimaging epigenetic research: Calling for an increased focus on development

seagull: lasso, group lasso and sparse-group lasso regularisation for linear regression models via proximal gradient descent

DNA Methylation in the Diagnosis of Monogenic Diseases

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