Steve Horvath University of California, Los Angeles
Transcript of Steve Horvath University of California, Los Angeles
![Page 1: Steve Horvath University of California, Los Angeles](https://reader031.fdocuments.us/reader031/viewer/2022012510/61880dd4bc24c62ad25adb08/html5/thumbnails/1.jpg)
Accelerated epigenetic aging in HIVSteve Horvath
University of California, Los Angeles
![Page 2: Steve Horvath University of California, Los Angeles](https://reader031.fdocuments.us/reader031/viewer/2022012510/61880dd4bc24c62ad25adb08/html5/thumbnails/2.jpg)
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.
![Page 3: Steve Horvath University of California, Los Angeles](https://reader031.fdocuments.us/reader031/viewer/2022012510/61880dd4bc24c62ad25adb08/html5/thumbnails/3.jpg)
Epigenetic clock applies to threeIlumina 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
![Page 4: Steve Horvath University of California, Los Angeles](https://reader031.fdocuments.us/reader031/viewer/2022012510/61880dd4bc24c62ad25adb08/html5/thumbnails/4.jpg)
Personal definition of biological aging clock
• Definition: an accurate molecular marker for chronological age (in years)
• Definition of “accurate”– high correlation (r>0.80) between estimated value and
chronological age in subjects aged between 0 and 100.
– validation in independent test data
• Candidate aging clocks1. based on telomere length
2. based on gene expression levels
3. based on protein expression levels
4. DNA methylation levels
![Page 5: Steve Horvath University of California, Los Angeles](https://reader031.fdocuments.us/reader031/viewer/2022012510/61880dd4bc24c62ad25adb08/html5/thumbnails/5.jpg)
Multi-tissue biomarker of agingbased on DNA methylation levels
was published less than 3 years ago
![Page 6: Steve Horvath University of California, Los Angeles](https://reader031.fdocuments.us/reader031/viewer/2022012510/61880dd4bc24c62ad25adb08/html5/thumbnails/6.jpg)
Development of the epigenetic clock
• Downloaded 82 publicly available DNA methylation data sets (over 8000 samples).
• Regressed chronological age (transformed) on CpGs using an elastic net regression model
– The regression model automatically selected 353 CpGs.
![Page 7: Steve Horvath University of California, Los Angeles](https://reader031.fdocuments.us/reader031/viewer/2022012510/61880dd4bc24c62ad25adb08/html5/thumbnails/7.jpg)
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.
![Page 8: Steve Horvath University of California, Los Angeles](https://reader031.fdocuments.us/reader031/viewer/2022012510/61880dd4bc24c62ad25adb08/html5/thumbnails/8.jpg)
Accuracy across test data
![Page 9: Steve Horvath University of California, Los Angeles](https://reader031.fdocuments.us/reader031/viewer/2022012510/61880dd4bc24c62ad25adb08/html5/thumbnails/9.jpg)
Phenotypes linked to the epigenetic clockCondition/Phenotype Source of DNA Effect Citation
Alzheimer's disease prefrontal cortex Yes but weak Levine 2015 Aging + Unpublished
Amyloid load prefrontal cortex Yes but weak Levine 2015 Aging +Unpublished
Body mass index liver+blood Yes Horvath 2014 PNAS+unpublished
Calorie restriction liver Yes Horvath 2014 PNAS
Cancer Malignant tissue Yes and opposite Horvath 2013 Genome Biology
Cell passaging various Yes Horvath 2013 Genome Biology+Lowe 2016 Oncotarget
Cellular senescence various Yes and no Horvath 2013 Genome Biology+Lowe 2016 Oncotarget
Centenarian (offspring status) blood Yes Horvath 2015 Aging
Cognitive Performance blood+brain Yes Marioni 2015 Int J Epid.
Diet blood Yes but very weak Quach 2016 unpublished
Down syndrome blood+brain Yes strong Horvath 2015 Aging Cell
Frailty blood Yes but weak Breitling 2016 Clinical Epigenetics
Gestational age brain, etc Yes but weak Spiers 2015 Genome Research+unpublished
Grip strength blood Yes unpublished
Hayflick limit various Yes Horvath 2013 Genome Biology+Lowe 2016 Oncotarget
HIV status blood+brain Yes strong Horvath 2015 Int J Infectious Diseases
Huntington disease blood+brain Yes Horvath 2016 Aging+unpublished
Lipid levels blood Yes but weak Quach 2016 unpublished
Menopause blood+saliva Yes but weak Levine 2016 (probably in PNAS)
Mortality (all cause) blood Yes but weak Marioni 2015 Genome Biol+Christiansen 2015 Aging Cell
Neuropathological variables frontal cortex Yes but weak Levine 2015 Aging + Unpublished
Obesity liver+blood Yes strong in liver Horvath 2014 PNAS + unpublished
Osteoarthritis Yes unpublished
Parkinson's disease blood Yes but weak Horvath 2015 Aging
Sex=Gender blood+brain Yes Horvath 2016+unpublished
Sleep blood Yes but weak Carroll 2016 Biological Psychiatry
Walking speed blood Yes unpublished
![Page 10: Steve Horvath University of California, Los Angeles](https://reader031.fdocuments.us/reader031/viewer/2022012510/61880dd4bc24c62ad25adb08/html5/thumbnails/10.jpg)
Comparison with telomere length
![Page 11: Steve Horvath University of California, Los Angeles](https://reader031.fdocuments.us/reader031/viewer/2022012510/61880dd4bc24c62ad25adb08/html5/thumbnails/11.jpg)
DNAm Age and telomere length on the same samples (Framingham Heart study, Brian Chen)
The Bradford Hill criteria for causation, are a group of minimal conditions necessary to provide adequate evidence of a causal relationship between an incidence and a possible consequence:
1. Strength: The larger the association, the more likely that it is causal
![Page 12: Steve Horvath University of California, Los Angeles](https://reader031.fdocuments.us/reader031/viewer/2022012510/61880dd4bc24c62ad25adb08/html5/thumbnails/12.jpg)
No relationship with telomere length in blood or adipose tissue
For adipose tissue see
![Page 13: Steve Horvath University of California, Los Angeles](https://reader031.fdocuments.us/reader031/viewer/2022012510/61880dd4bc24c62ad25adb08/html5/thumbnails/13.jpg)
Applications to HIV
![Page 14: Steve Horvath University of California, Los Angeles](https://reader031.fdocuments.us/reader031/viewer/2022012510/61880dd4bc24c62ad25adb08/html5/thumbnails/14.jpg)
![Page 15: Steve Horvath University of California, Los Angeles](https://reader031.fdocuments.us/reader031/viewer/2022012510/61880dd4bc24c62ad25adb08/html5/thumbnails/15.jpg)
![Page 16: Steve Horvath University of California, Los Angeles](https://reader031.fdocuments.us/reader031/viewer/2022012510/61880dd4bc24c62ad25adb08/html5/thumbnails/16.jpg)
![Page 17: Steve Horvath University of California, Los Angeles](https://reader031.fdocuments.us/reader031/viewer/2022012510/61880dd4bc24c62ad25adb08/html5/thumbnails/17.jpg)
Discovery brain data from HIV+ cases and HIV-controls
![Page 18: Steve Horvath University of California, Los Angeles](https://reader031.fdocuments.us/reader031/viewer/2022012510/61880dd4bc24c62ad25adb08/html5/thumbnails/18.jpg)
Validation brain data from HIV+ cases and HIV-controls
![Page 19: Steve Horvath University of California, Los Angeles](https://reader031.fdocuments.us/reader031/viewer/2022012510/61880dd4bc24c62ad25adb08/html5/thumbnails/19.jpg)
Age acceleration in blood
![Page 20: Steve Horvath University of California, Los Angeles](https://reader031.fdocuments.us/reader031/viewer/2022012510/61880dd4bc24c62ad25adb08/html5/thumbnails/20.jpg)
Age acceleration versus blood cell counts in HIV+ individuals
![Page 21: Steve Horvath University of California, Los Angeles](https://reader031.fdocuments.us/reader031/viewer/2022012510/61880dd4bc24c62ad25adb08/html5/thumbnails/21.jpg)
Models that could explain our findings• Model 1: Telomere length shortening mediates the effect
– HIV→ telomere length → epigenetic age acceleration– Not plausible
• Model 2: Changes in lymphocytes mediates the effect– HIV→ exhausted/senescent T cells → age acceleration– Our blood data support this model to some extent– But it is difficult to use this model for explaining accelerated
aging effects in brain tissue owing to the blood-brain barrier.
• Model 3: Independent model– exhausted T cells ←HIV→ age acceleration– HIV confounds the relationship between the exhausted T-cell
count and age acceleration.– This is a plausible model
![Page 22: Steve Horvath University of California, Los Angeles](https://reader031.fdocuments.us/reader031/viewer/2022012510/61880dd4bc24c62ad25adb08/html5/thumbnails/22.jpg)
![Page 23: Steve Horvath University of California, Los Angeles](https://reader031.fdocuments.us/reader031/viewer/2022012510/61880dd4bc24c62ad25adb08/html5/thumbnails/23.jpg)
HIV-associated neurocognitive disorders (HAND) is associated with increase age acceleration in the occipital cortex
![Page 24: Steve Horvath University of California, Los Angeles](https://reader031.fdocuments.us/reader031/viewer/2022012510/61880dd4bc24c62ad25adb08/html5/thumbnails/24.jpg)
![Page 25: Steve Horvath University of California, Los Angeles](https://reader031.fdocuments.us/reader031/viewer/2022012510/61880dd4bc24c62ad25adb08/html5/thumbnails/25.jpg)
Next steps: epigenetic profiling of several human tissues and organs
• NNAB team: Andrew J. Levine, Susan Morgello, Elyse Singer, Jonathan Said
• Open questions:– Can we detect accelerated aging effects due to HIV in
lung, kidney, liver, heart?– Which measures of tissue pathology correlate with
epigenetic age acceleration?– How does epigenetic age acceleration relate to anti-
retroviral therapy?– How does epigenetic age acceleration relate to HIV-
associated Non-AIDS conditions?
![Page 26: Steve Horvath University of California, Los Angeles](https://reader031.fdocuments.us/reader031/viewer/2022012510/61880dd4bc24c62ad25adb08/html5/thumbnails/26.jpg)
Does the epigenetic clock predict all-cause mortality?
![Page 27: Steve Horvath University of California, Los Angeles](https://reader031.fdocuments.us/reader031/viewer/2022012510/61880dd4bc24c62ad25adb08/html5/thumbnails/27.jpg)
![Page 28: Steve Horvath University of California, Los Angeles](https://reader031.fdocuments.us/reader031/viewer/2022012510/61880dd4bc24c62ad25adb08/html5/thumbnails/28.jpg)
Blood-based epigenetic measures of age that predict all-cause mortality: a meta-analysis
(2016) AgingBrian H. Chen, Riccardo E. Marioni , Elena Colicino, Marjolein J. Peters,
Cavin Ward-Caviness, Pei-Chien Tsai, Nicholas S. Roetker, Ellen W. Demerath, Weihua Guan, Jan Bressler, Myriam Fornage, Stephanie Studenski, Amy R. Vandiver, Ann Zenobia Moore, Toshiko Tanaka,
Douglas P. Kiel, Liming Liang, Kathryn L. Lunetta, Joanne M. Murabito, Stefania Bandinelli, Dena G. Hernandez, David Melzer, Michael Nalls,
Luke C. Pilling, Timothy R. Price, Andrew B. Singleton, Christian Gieger, Rolf Holle, Anja Kretschmer, Florian Kronenberg, Sonja Kunze, Jakob
Linseisen, Christine Meisinger, Wolfgang Rathmann, Melanie Waldenberger, Peter M. Visscher, Sonia Shah, Naomi R. Wray, Allan F. McRae, Oscar H. Franco, Albert Hofman, André G. Uitterlinden, Devin Absher, Themistocles Assimes, Morgan E. Levine, Ake T. Lu, Philip S.
Tsao, Stephen Pan, Lifang Hou, JoAnn E. Manson, Cara Carty, Andrea Z. LaCroix, Alex P. Reiner, Tim D. Spector, Andrew P. Feinberg, Daniel Levy, Andrea Baccarelli, Joyce van Meurs, Jordana T. Bell, Annette Peters, Ian
J. Deary, James S. Pankow, Luigi Ferrucci, Steve Horvath
Brian H. Chen
![Page 29: Steve Horvath University of California, Los Angeles](https://reader031.fdocuments.us/reader031/viewer/2022012510/61880dd4bc24c62ad25adb08/html5/thumbnails/29.jpg)
Largest meta analysis-13 cohorts-13k individuals
Cohort N
1. WHI (white) 995
2. WHI (black) 675
3. WHI (Hispanic) 431
4. LBC 1921 445
5. LBC 1936 919
6. NAS 647
7. ARIC (black) 2,768
8. FHS 2,614
9. KORA 1,257
10. InCHIANTI 506
11. Rotterdam 710
12.Twins UK 805
13. BLSA (white) 317
![Page 30: Steve Horvath University of California, Los Angeles](https://reader031.fdocuments.us/reader031/viewer/2022012510/61880dd4bc24c62ad25adb08/html5/thumbnails/30.jpg)
Univariate Cox regression meta-analysis of all-cause mortality
![Page 31: Steve Horvath University of California, Los Angeles](https://reader031.fdocuments.us/reader031/viewer/2022012510/61880dd4bc24c62ad25adb08/html5/thumbnails/31.jpg)
Multivariate Cox regression meta-analysis adjusted for chronological age, body mass index , education,
alcohol, smoking, prior history of diabetes, prior cancer, hypertension, recreational physical activity
![Page 32: Steve Horvath University of California, Los Angeles](https://reader031.fdocuments.us/reader031/viewer/2022012510/61880dd4bc24c62ad25adb08/html5/thumbnails/32.jpg)
Offspring of centenarians age slowly
![Page 33: Steve Horvath University of California, Los Angeles](https://reader031.fdocuments.us/reader031/viewer/2022012510/61880dd4bc24c62ad25adb08/html5/thumbnails/33.jpg)
Various applications
![Page 34: Steve Horvath University of California, Los Angeles](https://reader031.fdocuments.us/reader031/viewer/2022012510/61880dd4bc24c62ad25adb08/html5/thumbnails/34.jpg)
Morgan E Levine
![Page 35: Steve Horvath University of California, Los Angeles](https://reader031.fdocuments.us/reader031/viewer/2022012510/61880dd4bc24c62ad25adb08/html5/thumbnails/35.jpg)
Multivariate Meta-analysis of AgeAccel in blood
versus Age at Menopause
Beta Coefficient (P-value)
Outcome=AgeAccel WHI study InCHIANTI
study
PEG
Study
Meta P-Value
Age at Menopause -0.06 (0.001) -0.012 (0.8) -0.06 (0.35) P=8.32×10-4
Former Smoker -0.31 (0.23) 0.44 (0.7) -1.18 (0.24)
Current Smoker -0.19 (0.7) -0.87 (0.4) -1.37 (0.6)
Menopausal
hormone therapy
0.041 (0.9) 0.94 (0.4) 2.86 (0.02)
Age at Menarche -0.055 (0.5) 0.28 (0.18) -0.020 (0.950)
![Page 36: Steve Horvath University of California, Los Angeles](https://reader031.fdocuments.us/reader031/viewer/2022012510/61880dd4bc24c62ad25adb08/html5/thumbnails/36.jpg)
Effect of surgical menopause
![Page 37: Steve Horvath University of California, Los Angeles](https://reader031.fdocuments.us/reader031/viewer/2022012510/61880dd4bc24c62ad25adb08/html5/thumbnails/37.jpg)
Mendelian randomization argument:a SNP associated with early menopause also relate to
epigenetic age acceleration in blood
SNPs from a genome-wide association study for age at menopause,
• rs11668344 (replication P value = 2.65 × 10-18)
• rs16991615 (replication P value = 7.90 ×10-21)
• Citation: Stolk L, et al (2012) Meta-analyses identify 13 loci associated
with age at menopause and highlight DNA repair and immune pathways. Nat Genet 44(3):260–268.
![Page 38: Steve Horvath University of California, Los Angeles](https://reader031.fdocuments.us/reader031/viewer/2022012510/61880dd4bc24c62ad25adb08/html5/thumbnails/38.jpg)
Menopausal hormone therapy keeps the buccal epithelium young
![Page 39: Steve Horvath University of California, Los Angeles](https://reader031.fdocuments.us/reader031/viewer/2022012510/61880dd4bc24c62ad25adb08/html5/thumbnails/39.jpg)
CITATION
Horvath S, Gurven M,
Levine ME, Trumble BC
Kaplan H, Allayee H,
Beate R. Ritz, Brian Chen
Ake T. Lu, Tammy M. Rickabaugh
Beth D. Jamieson,
Dianjianyi Sun,
Shengxu Li, Wei Chen, Lluis
Quintana-Murci Maud Fagny
Michael S. Kobor, Philip S. Tsao,
Alexander P. Reiner, Kerstin L.
Edlefsen, Devin Absher
Themistocles L. Assimes
(2016) Genome Biol
![Page 40: Steve Horvath University of California, Los Angeles](https://reader031.fdocuments.us/reader031/viewer/2022012510/61880dd4bc24c62ad25adb08/html5/thumbnails/40.jpg)
Hispanics have a lower intrinsic age acceleration than Caucasians in blood
and saliva
![Page 41: Steve Horvath University of California, Los Angeles](https://reader031.fdocuments.us/reader031/viewer/2022012510/61880dd4bc24c62ad25adb08/html5/thumbnails/41.jpg)
Hispanic mortality paradox
• The low biological aging rate in Hispanics points might resolve a long-standing paradox known as
“Hispanic Epidemiological Paradox” – First observed in 1986 by K. Markides
– The paradox usually refers to the low mortality among Hispanics in the United States relative to non-Hispanic Whites.
– Hispanics are expected to live 3 years longer than Caucasians according to statistics from the Centers of Disease Control
![Page 42: Steve Horvath University of California, Los Angeles](https://reader031.fdocuments.us/reader031/viewer/2022012510/61880dd4bc24c62ad25adb08/html5/thumbnails/42.jpg)
Conclusions
• The epigenetic clock is an attractive molecularbiomarker of aging– highly robust measurement
– accurate measure of tissue age
– associated with many age related conditions
– prognostic of mortality
– it allows one to contrast the ages of different tissues
• Most studies that involved telomere length and other biomarkers can be revisited
• Consider other tissues beyond blood
• User friendly software can be found on webpage
![Page 43: Steve Horvath University of California, Los Angeles](https://reader031.fdocuments.us/reader031/viewer/2022012510/61880dd4bc24c62ad25adb08/html5/thumbnails/43.jpg)
Acknowledgement
• HIV: Andrew J. Levine, Elyse Singer, Susan Morgello, Tammy Rickabaugh, Beth Jamieson, Jonathan Said
• National NeuroAIDS Tissue Consortium (Morgello et al. 2001, NNTC.org)• Lab: Ake Lu, Morgan Levine, Austin Quach• NIA: Luigi Ferrucci, Brian Chen, Toshiko Tanaka• Mortality: Andrea A Baccarelli, Elena Colicino, Riccardo Marioni, Brian Chen,
Daniel Levy, Peter M Visscher, Naomi R Wray, Ian J Deary• Centenarians: H. Vinters, J. Braun, Claudio Franceschi, Paolo Garagnani, Steve
Coles• Many researchers who answered my emails and freely shared their DNA
methylation data using public repositories such as– Gene Expression Omnibus– Array Express– The Cancer Genome Atlas