Clonal hematopoiesis associated with epigenetic aging and clinical outcomes

Daniel Nachun¹, Ake T Lu², Alexander G Bick³, Pradeep Natarajan^{4

5}, Joshua Weinstock⁶, Mindy D Szeto⁷, Sekar Kathiresan^{4

5}, Goncalo Abecasis⁶, Kent D Taylor⁸, Xiuqing Guo⁸, Russ Tracy⁹, Peter Durda⁹, Yongmei Liu¹⁰, Craig Johnson¹¹, Stephen S Rich¹², David Van Den Berg¹³, Cecilia Laurie¹¹, Tom Blackwell⁶, George J Papanicolaou¹⁴, Adolfo Correa¹⁵, Laura M Raffield¹⁶, Andrew D Johnson¹⁷, Joanne Murabito¹⁸, JoAnn E Manson¹⁹, Pinkal Desai²⁰, Charles Kooperberg²¹, Themistocles L Assimes²², Daniel Levy¹⁷, Jerome I Rotter⁸, Alex P Reiner²³, Eric A Whitsel^{24

25}, James G Wilson^{26

27}, Steve Horvath², Siddhartha Jaiswal^{1

28}; NHLBI Trans-Omics for Precision Medicine (TOPMed) Consortium

Affiliations

¹ Department of Pathology, Stanford University School of Medicine, Stanford, CA, USA.
² Department of Human Genetics, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA.
³ Department of Medicine, Vanderbilt University School of Medicine, Nashville, TN, USA.
⁴ Department of Medicine, Massachusetts General Hospital, Boston, MA, USA.
⁵ Broad Institute, Cambridge, MA, USA.
⁶ Department of Biostatistics, University of Michigan School of Public Health, Ann Arbor, MI, USA.
⁷ Division of Biomedical Informatics and Personalized Medicine, Department of Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO, USA.
⁸ Department of Pediatrics, The Institute for Translational Genomics and Population Sciences, The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA, USA.
⁹ Department of Pathology and Laboratory Medicine, Larner College of Medicine, University of Vermont, Burlington, VT, USA.
¹⁰ Department of Medicine, Duke University Medical Center, Durham, NC, USA.
¹¹ Department of Biostatistics, School of Public Health, University of Washington, Seattle, WA, USA.
¹² Center for Public Health Genomics, University of Virginia, Charlottesville, VA, USA.
¹³ Department of Clinical Preventative Medicine, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA.
¹⁴ Division of Cardiovascular Sciences, National Heart, Lung, and Blood Institute, Bethesda, MD, USA.
¹⁵ Department of Medicine, University of Mississippi Medical Center, Jackson, MS, USA.
¹⁶ Department of Genetics, University of North Carolina School of Medicine, Chapel Hill, NC, USA.
¹⁷ National Heart Lung and Blood Institute, Framingham, MA, USA.
¹⁸ Department of Medicine, Boston University School of Medicine, Boston, MA, USA.
¹⁹ Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.
²⁰ Department of Hematology/Oncology, Joan & Sanford I. Weill Medical College of Cornell University, New York, NY, USA.
²¹ Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, WA, USA.
²² Department of Medicine, Stanford University School of Medicine, Stanford, CA, USA.
²³ Department of Epidemiology, School of Public Health, University of Washington, Seattle, WA, USA.
²⁴ Department of Medicine, University of North Carolina, Chapel Hill, NC, USA.
²⁵ Department of Epidemiology, Gillings School of Global Public Health, Chapel Hill, NC, USA.
²⁶ Department of Cardiology, Beth Israel Deaconess Medical Center, Boston, MA, USA.
²⁷ Department of Physiology and Biophysics, University of Mississippi Medical Center, Jackson, MS, USA.
²⁸ Institute for Stem Cell Biology and Regenerative Medicine, Stanford University School of Medicine, Stanford, CA, USA.

PMID: 34050697
PMCID: PMC8208788
DOI: 10.1111/acel.13366

Clonal hematopoiesis associated with epigenetic aging and clinical outcomes

Daniel Nachun et al. Aging Cell. 2021 Jun.

. 2021 Jun;20(6):e13366.

doi: 10.1111/acel.13366. Epub 2021 May 29.

Authors

Affiliations

¹ Department of Pathology, Stanford University School of Medicine, Stanford, CA, USA.
² Department of Human Genetics, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA.
³ Department of Medicine, Vanderbilt University School of Medicine, Nashville, TN, USA.
⁴ Department of Medicine, Massachusetts General Hospital, Boston, MA, USA.
⁵ Broad Institute, Cambridge, MA, USA.
⁶ Department of Biostatistics, University of Michigan School of Public Health, Ann Arbor, MI, USA.
⁷ Division of Biomedical Informatics and Personalized Medicine, Department of Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO, USA.
⁸ Department of Pediatrics, The Institute for Translational Genomics and Population Sciences, The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA, USA.
⁹ Department of Pathology and Laboratory Medicine, Larner College of Medicine, University of Vermont, Burlington, VT, USA.
¹⁰ Department of Medicine, Duke University Medical Center, Durham, NC, USA.
¹¹ Department of Biostatistics, School of Public Health, University of Washington, Seattle, WA, USA.
¹² Center for Public Health Genomics, University of Virginia, Charlottesville, VA, USA.
¹³ Department of Clinical Preventative Medicine, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA.
¹⁴ Division of Cardiovascular Sciences, National Heart, Lung, and Blood Institute, Bethesda, MD, USA.
¹⁵ Department of Medicine, University of Mississippi Medical Center, Jackson, MS, USA.
¹⁶ Department of Genetics, University of North Carolina School of Medicine, Chapel Hill, NC, USA.
¹⁷ National Heart Lung and Blood Institute, Framingham, MA, USA.
¹⁸ Department of Medicine, Boston University School of Medicine, Boston, MA, USA.
¹⁹ Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.
²⁰ Department of Hematology/Oncology, Joan & Sanford I. Weill Medical College of Cornell University, New York, NY, USA.
²¹ Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, WA, USA.
²² Department of Medicine, Stanford University School of Medicine, Stanford, CA, USA.
²³ Department of Epidemiology, School of Public Health, University of Washington, Seattle, WA, USA.
²⁴ Department of Medicine, University of North Carolina, Chapel Hill, NC, USA.
²⁵ Department of Epidemiology, Gillings School of Global Public Health, Chapel Hill, NC, USA.
²⁶ Department of Cardiology, Beth Israel Deaconess Medical Center, Boston, MA, USA.
²⁷ Department of Physiology and Biophysics, University of Mississippi Medical Center, Jackson, MS, USA.
²⁸ Institute for Stem Cell Biology and Regenerative Medicine, Stanford University School of Medicine, Stanford, CA, USA.

PMID: 34050697
PMCID: PMC8208788
DOI: 10.1111/acel.13366

Abstract

Clonal hematopoiesis of indeterminate potential (CHIP) is a common precursor state for blood cancers that most frequently occurs due to mutations in the DNA-methylation modifying enzymes DNMT3A or TET2. We used DNA-methylation array and whole-genome sequencing data from four cohorts together comprising 5522 persons to study the association between CHIP, epigenetic clocks, and health outcomes. CHIP was strongly associated with epigenetic age acceleration, defined as the residual after regressing epigenetic clock age on chronological age, in several clocks, ranging from 1.31 years (GrimAge, p < 8.6 × 10^-7 ) to 3.08 years (EEAA, p < 3.7 × 10^-18 ). Mutations in most CHIP genes except DNA-damage response genes were associated with increases in several measures of age acceleration. CHIP carriers with mutations in multiple genes had the largest increases in age acceleration and decrease in estimated telomere length. Finally, we found that ~40% of CHIP carriers had acceleration >0 in both Hannum and GrimAge (referred to as AgeAccelHG+). This group was at high risk of all-cause mortality (hazard ratio 2.90, p < 4.1 × 10^-8 ) and coronary heart disease (CHD) (hazard ratio 3.24, p < 9.3 × 10^-6 ) compared to those who were CHIP-/AgeAccelHG-. In contrast, the other ~60% of CHIP carriers who were AgeAccelHG- were not at increased risk of these outcomes. In summary, CHIP is strongly linked to age acceleration in multiple clocks, and the combination of CHIP and epigenetic aging may be used to identify a population at high risk for adverse outcomes and who may be a target for clinical interventions.

Keywords: clonal hematopoiesis; epigenomics; heart disease.

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Conflict of interest statement

S. Jaiswal is a scientific advisor to Novartis, Roche Genentech, and Foresite Labs. UC Regents (the employer of S. Horvath and A. T. Lu) has filed patents surrounding several epigenetic biomarkers of aging (including GrimAge) which list S. Horvath and A. T. Lu as inventors. P. Natarajan reports grants support from Amgen, Apple, and Boston Scientific, and is a scientific advisor to Apple. S. Kathiresan is an employee of Verve Therapeutics and holds equity in Verve Therapeutics, Maze Therapeutics, Catabasis, and San Therapeutics. He is a member of the scientific advisory boards for Regeneron Genetics Center and Corvidia Therapeutics; he has served as a consultant for Acceleron, Eli Lilly, Novartis, Merck, Novo Nordisk, Novo Ventures, Ionis, Alnylam, Aegerion, Haug Partners, Noble Insights, Leerink Partners, Bayer Healthcare, Illumina, Color Genomics, MedGenome, Quest, and Medscape. G. Abecasis is an employee of Regeneron Pharmaceuticals and owns stock and stock options for Regeneron Pharmaceuticals. S. Jaiswal and S. Kathiresan have jointly filed patents relating to clonal hematopoiesis and atherosclerotic cardiovascular disease.

Figures

**FIGURE 1**
CHIP is associated with increased age acceleration. Forest plot of the effect sizes and confidence intervals for the effect of CHIP on age acceleration estimate from seven methylation clocks

**FIGURE 2**
CHIP and epigenetic age acceleration identify persons at high risk of all‐cause mortality and development of coronary heart disease (CHD). (a) Scatterplot of correlation between AgeAccelGrim and AgeAccelHannum in all cohorts. (b, c) Forest plots showing hazard ratios, confidence intervals, and p‐values for Cox proportional hazard models of all‐cause mortality (b) and development of CHD (c) in persons from FHS, JHS, and WHI. All models included chronological age, race, low‐density lipoprotein cholesterol, high‐density lipoprotein cholesterol, triglycerides, systolic blood pressure, type 2 diabetes status and smoking status as covariates. Top two sections show the overall effect size of CHIP and age acceleration and bottom section shows effect sizes based on dividing persons into four groups based upon presence of CHIP and age acceleration. The results in c are a meta‐analysis of events in FHS, JHS, WHI EMPC (unselected for CHD), and WHI BA23 (case‐control study for CHD). (d, e) Cumulative incidence plots of death (d) and CHD (e) in persons divided into groups by the presence of CHIP (CHIP+/CHIP−) and age acceleration (AgeAccelHG+/AgeAccelHG−). The numbers in parentheses indicate the number of persons in each group for these analyses. Only persons over 65 and free of CHD at baseline were used in d and e, while all persons were used for b and c. (f) Cumulative incidence plot of death in persons with incident CHD after age 70. Individuals who died less than 30 days after CHD were excluded

See this image and copyright information in PMC

References

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Clonal hematopoiesis associated with epigenetic aging and clinical outcomes

Affiliations

Clonal hematopoiesis associated with epigenetic aging and clinical outcomes

Authors

Affiliations

Abstract

Conflict of interest statement

Figures

References

Publication types

MeSH terms

Grants and funding

LinkOut - more resources

Full Text Sources

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