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Epigenetic Clock Analysis of Cognitive Aging Steve Horvath Conceptual Framework: the Biological Age of the Brain Is a Latent Variable

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Epigenetic Clock Analysis of Cognitive Aging Steve Horvath Conceptual Framework: the Biological Age of the Brain Is a Latent Variable Epigenetic clock analysis of cognitive aging Steve Horvath Conceptual framework: The biological age of the brain is a latent variable Epigenetic biomarker R=0.5? Neuropathology R=0.9? R=0.2? Age Biological Age Cognitive assessments Expected correlation between epigenetic clock and phenotype =0.5*0.2=0.10 2 DNA methylation: epigenetic modification of DNA Illustration of a DNA molecule that is methylated at the two center cytosines. DNA methylation plays an important role for epigenetic gene regulation in development and disease. Epigenetic clock applies to three Ilumina platforms 1. EPIC chip: measures over 850k locations on the DNA. 2. Infinium 450K: 486k CpGs. 3. Infinium 27K: 27k CpGs. Each CpG specifies the amount of methylation that is present at this location. – Number between 0 and 1 Epigenetic clock method • Step 1: Measure the DNA methylation levels of 353 CpGs. • Step 2: Form a weighted average • Step 3: Transform the average so it is in units of “years” Result: age estimate (a number) that is known as “epigenetic age” or “DNA methylation age” Comment: same definition for every tissue and cell type. 2013 The human multi-tissue DNAm age estimator is the most accurate molecular biomarker of age to date. Hence the name “epigenetic clock”. Accuracy in brain tissue Application to Alzheimer’s disease, cognition, and neuropathology Morgan E. Levine DATA • 700 subjects from the Religious Order Study (ROS) and the Rush Memory and Aging Project (MAP). • The majority of subjects are 75–80 years old at baseline, with no known dementia. • Inclusion in the studies requires participants to consent to undergoing annual clinical evaluations as well as postmortem organ donation. Epigenetic Age Acceleration: The residual resulting from regressing DNAm age on chronological age and sex. Neuropathological variables: Neuritic plaques, diffuse plaques, NFT, amyloid load Cognitive functioning assessed annually: Episodic memory, Working memory, Semantic Memory, Perceptual Orientation, Perceptual Speed, Global Cognitive Functioning Levine et al. Aging. (2016) Epigenetic age acceleration correlates with neuropathology Alzheimer’s Disease Neuropathology (Post-Mortem) Beta (P-Value) Amyloid Load 0.117 (0.002) Neuritic Plaques 0.468 (0.0002) Diffuse Plaques 0.329 (0.046) Neurofibrillary Tangles 0. 452 (0.009) Results are from independent multivariate models that adjust for age at death, study, and sex Normal vs. AD (P=0.009) Neuropathology Mediates the Association Between DNAm Age & Cognitive Functioning Beta (P-Value) Global Cognitive Functioning -0.457 (0.004) Episodic Memory -0.385 (0.001) Working Memory -0.227 (0.160) Semantic Memory -0.302 (0.028) Perceptual Orientation -0.169 (0.303) Perceptual Speed -0.199 (0.182) Model: Our genetic analysis Genetic indicates that pleiotropic genetic loci Signature affect epigenetic age acceleration, neuropathological variables, and cognitive traits. Biological DNAm Age Age APOE4 Neuro- inflammation pathology proteostasis Cognitive Decline Age acceleration is associated with Parkinson Disease status but not with medication Risk factors for PD status 1) Organo phosphate exposure 2) Age 3) Epigenetic age accel 4) Granulocyte count Protective factors 1) Smoking !? 2) Number of years in school Accelerated aging diseases Down syndrome Adult Progeria: Werner syndrome But what about Hutchinson Gilford progeria? 2015 Epigenetic clocks predict time to death even after adjusting for other risk factors Epigenetic clocks: continuous read- out linking development, tissue maintenance, and decline. Multi-tissue DNAm age estimator (353 CpGs) applies to all tissues/cell types across the entire life course Epigenetic clock applies to in vitro models of neuronal development iPS vs differentiation state fetal retina 3-D cortical spheroids Bill Lowry UCLA Thomas Reh Dan Geschwind Epigenetic clock: a continuous read-out that links development, maintenance, and aging Epigenetic clock links purposeful molecular processes to un-intended adverse consequences later in life (antagonistic pleiotropy). Novel epigenetic clock, DNAm PhenoAge, strongly predicts healthspan/morbidity. • Developed mainly for blood methylation data but it also applies to other tissues • 513 CpGs Prediction of life span with DNAm PhenoAge Morbidity Validation for DNAm PhenoAge • Higher DNAm PhenoAge is associated with – Incident coronary heard disease (P-value=2.43E-10) – a decrease in likelihood of being disease-free (P=1.06E-7), – a person’s number of coexisting morbidities (P=4.6E-15), – an increase in physical functioning problems (P=2.1E-13). DNAm PhenoAge of blood is associated with FTD and AD dementia • Using blood methylation data from living individuals with clinically diagnosed dementia. • Alzheimer’s disease (n=154) and/or frontotemporal dementia ( n=116) have significantly higher DNAm PhenoAge compared to non- demented (n=334) individuals (P=2.2E-2) Epigenetic clocks are useful for identifying and validating anti-aging targets • Reality check: epigenetic clocks do not stand out in terms of lifespan prediction. – Many alternatives: blood pressure, smoking, frailty indices, lipid levels, glucose levels • Advantage of epigenetic clocks – Clocks relate to at least one root cause of aging – proximal to an innate aging process – they can be applied to cells in a dish (in vitro studies) – DNAm age can be used as phenotype in genetic studies Results for various anti-aging treatments Allogeneic hematopoietic stem cell transplantation (HSCT) resets the epigenetic age of blood to that of the donor Friedrich Stoelzel, M. Brosch, M. Bornhaeuser 2017 Haematologica Menopausal hormone therapy keeps buccal cells young but not blood cells. Message: collect buccal cells as well Blood methylation data from • 4,173 postmenopausal female participants from the Women's Health Initiative • 402 participants from the Italian cohort study, Invecchiare nel Chianti Marginal correlations with epigenetic age acceleration in the WHI. Correlations between select variables and the two measures of epigenetic age acceleration are colored according to their magnitude with positive correlations in red Evaluation of metformin in the WHI Fasting glucose IEAA EEAA 1) Never users 2) Current users 3) Future users Yamanaka factors completely resets the epigenetic age to prenatal stage. Genome Biol 2013 How to develop a cell model of old neurons? Answer: Direct conversion also known as transdifferentiation Christine Huh, Andrew Yoo • Using 1,796 brain samples from 1,163 individuals, we carried out a GWAS of three DNA methylation based biomarkers of brain age: 1. epigenetic age acceleration=AgeAccelerationResidual 2. age acceleration adjusted for the proportion of neurons 3. proportion of neurons based on CETS algorithm (Guintivano - 2013) • Locus 17q11.2 is significantly associated (P=4.5x10-9) with the aging rate across five brain regions and harbors a cis-expression quantitative trait locus for EFCAB5 (P=3.4x10-20). • Locus 1p36.12 is significantly associated (P=2.2x10-8) with epigenetic aging of the prefrontal cortex, independent of the proportion of neurons. • Our GWAS of the proportion of neurons identified two genome-wide significant loci (10q26 and 12p13.31) Cis-expression QTL study between with expression levels of EFCAB5 Elevated expression levels of EFCAB5 are associated with delayed brain aging • Using individual level data, we find a striking negative correlation between EFCAB5 expression levels and epigenetic age acceleration in the cerebellum (Meta P= 1.7x10-10), frontal cortex (Meta P=7.8x10-6), pre-frontal cortex (P=9.2x10-3), and temporal cortex (P=2.9x10-4). • Overall, we find a highly significant association between EFCAB5 expression and epigenetic age acceleration in brain across all studies (P=1.2x10-16, Table 3). • We cannot rule out that the genome-wide significant SNPs directly affect epigenetic aging rates, which subsequently alter gene transcript levels. Epigenetic clock relates to age related conditions Condition (alphabetical order) Source of DNA Estimator Gestational week brain, blood multiT Glucose blood multiT, Hann, PhenoAge Condition (alphabetical order) Source of DNA Estimator Grip strength blood multiT, PhenoAge Alzheimer's disease prefrontal cortex multiT, PhenoAge Amyloid load+neuropathology prefrontal cortex multiT Hayflick limit various multiT Blood pressure (systolic) blood Hann Heart disease blood PhenoAge Body mass index liver+blood multiT Cancer blood Hann, multiT, PhenoAge HIV status blood+brain multiT, Hann, PhenoAge Huntington disease blood+brain multiT Cancer Malignant tissue multiT Income blood Hann, PhenoAge Cardiovascular disease blood PhenoAge Insulin levels blood multiT, Hann Cellular senescence various multiT Lipid levels blood Hann Centenarian (offspring status) blood multiT, PhenoAge Menopause blood+saliva multiT, Hann, PhenoAge Mortality (all cause) blood multiT, Hann Cholesterol HDL (not LDL) blood Hann, PhenoAge Neuropathological variables blood+brain multiT, PhenoAge Cognitive Performance blood+brain multiT, PhenoAge Obesity liver+blood multiT, Hann, PhenoAge Osteoarthritis cartilage multiT C-reactive protein blood Hann, multiT Parkinson's disease blood multiT, PhenoAge Diet blood Hann, PhenoAge Pubertal development blood multiT Sex,Gender blood+brain multiT, Hann Dementia blood PhenoAge Sleep blood Hann Smoking blood PhenoAge Down syndrome blood+brain multiT, PhenoAge Telomerase reverse transcriptase blood, fibroblasts Education blood Hann, PhenoAge Triglyceridesgene blood Hann, multiT Exercise (recreational) blood Hann, PhenoAge
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