Association of Diet Quality With Prevalence of Clonal Hematopoiesis and Adverse Cardiovascular Events | Cardiology | JAMA Cardiology | JAMA Network
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Figure 1.  Study Flow Diagram
Study Flow Diagram

CAD indicates coronary artery disease; WES, whole-exome sequencing.

Figure 2.  Association Between Diet Quality and Prevalence of CHIP
Association Between Diet Quality and Prevalence of CHIP

CHIP indicates clonal hematopoiesis of indeterminate potential.

Figure 3.  Survival Plot of Incident Cardiovascular Events and Deaths by CHIP Status and Diet Quality
Survival Plot of Incident Cardiovascular Events and Deaths by CHIP Status and Diet Quality

Time-to-event analysis of adverse cardiovascular events or death using a multivariate Cox proportional hazard model adjusted for presence of CHIP, diet quality, age, sex, smoking status, diabetes status, and first 10 principal components of genetic ancestry. CHIP indicates clonal hematopoiesis of indeterminate potential; CVD, cardiovascular disease.

Table 1.  Participant Characteristics
Participant Characteristics
Table 2.  Incident Cardiovascular Events and Deatha
Incident Cardiovascular Events and Deatha
Supplement.

eMethods. Diet Quality Categorization, Diet Quality Treated as a Continuous Variable, Dietary Survey Response Scores, and Definition of Cardiovascular Events

eTable 1. Distribution of Dietary Elements

eTable 2. Frequency of CHIP Driver Genes

eTable 3. Association Between CHIP Prevalence and Diet Quality

eTable 4. Sensitivity Analysis Including Additional Covariates to Account for Potential Confounding in the Association Between CHIP Prevalence and Diet Quality

eTable 5. Sensitivity Analysis Showing Odds Ratios for the Presence of CHIP Predicted by Diet Quality After Excluding Patients With Prevalent Ischemic Stroke and Heart Failure From the Study Cohort

eTable 6. Odds Ratios for Presence of CHIP Predicted by a Continuous Dietary Score and Adjusted for Key Covariates

eTable 7. Frequency of CHIP Driver Gene Variations by Diet Quality Category

eTable 8. Incidence of Adverse Events by Diet Quality Category

eTable 9. Sensitivity Analysis of Incidence of Adverse Events by CHIP Presence and Diet Quality Category After Exclusion of Individuals With Cardiac MRI Data Available

eFigure 1. Distribution of Dietary Elements by Diet Quality Category and Overall

eFigure 2. Correlation Matrix of Dietary Element Intake Values

eFigure 3. CHIP Prevalence by Age Group

eFigure 4. Frequency of Individuals Harboring 1 or More CHIP Variation

eFigure 5. Prevalence of CHIP (VAF>0.02) and Large CHIP Clones (VAF>0.10) With Improved Diet Quality

eFigure 6. Odds Ratios for CHIP Presence Across Diet Quality Categories

eFigure 7. CHIP Prevalence by Diet Quality Treated as a Continuous Variable and Trichotomized

eFigure 8. CHIP Driver Variations in Diet Quality Categories

eFigure 9. Diet Quality Categories and Risk of Adverse Events With Simple Survival Analysis

eFigure 10. Forest Plot of Hazard Ratio of Incident Cardiovascular Events and Death Predicted by CHIP Status and Diet Quality Category

1.
Hindy  G, Aragam  KG, Ng  K,  et al.  Genome-wide polygenic score, clinical risk factors, and long-term trajectories of coronary artery disease.   Arterioscler Thromb Vasc Biol. 2020;40(11):2738-2746. doi:10.1161/ATVBAHA.120.314856 PubMedGoogle ScholarCrossref
2.
GBD 2019 Diseases and Injuries Collaborators.  Global burden of 369 diseases and injuries in 204 countries and territories, 1990-2019: a systematic analysis for the Global Burden of Disease Study 2019.   Lancet. 2020;396(10258):1204-1222. doi:10.1016/S0140-6736(20)30925-9 PubMedGoogle ScholarCrossref
3.
Virani  SS, Alonso  A, Benjamin  EJ,  et al; American Heart Association Council on Epidemiology and Prevention Statistics Committee and Stroke Statistics Subcommittee.  Heart disease and stroke statistics—2020 update: a report from the American Heart Association.   Circulation. 2020;141(9):e139-e596. doi:10.1161/CIR.0000000000000757 PubMedGoogle ScholarCrossref
4.
Libby  P, Ridker  PM, Maseri  A.  Inflammation and atherosclerosis.   Circulation. 2002;105(9):1135-1143. doi:10.1161/hc0902.104353 PubMedGoogle ScholarCrossref
5.
North  BJ, Sinclair  DA.  The intersection between aging and cardiovascular disease.   Circ Res. 2012;110(8):1097-1108. doi:10.1161/CIRCRESAHA.111.246876 PubMedGoogle ScholarCrossref
6.
Genovese  G, Kahler  AK, Handsaker  RE,  et al.  Clonal hematopoiesis and blood-cancer risk inferred from blood DNA sequence.   N Engl J Med. 2014;371(26):2477-2487. doi:10.1056/NEJMoa1409405 PubMedGoogle ScholarCrossref
7.
Jaiswal  S, Fontanillas  P, Flannick  J,  et al.  Age-related clonal hematopoiesis associated with adverse outcomes.   N Engl J Med. 2014;371(26):2488-2498. doi:10.1056/NEJMoa1408617 PubMedGoogle ScholarCrossref
8.
Xie  M, Lu  C, Wang  J,  et al.  Age-related mutations associated with clonal hematopoietic expansion and malignancies.   Nat Med. 2014;20(12):1472-1478. doi:10.1038/nm.3733 PubMedGoogle ScholarCrossref
9.
Khetarpal  SA, Qamar  A, Bick  AG,  et al.  Clonal hematopoiesis of indeterminate potential reshapes age-related CVD: JACC review topic of the week.   J Am Coll Cardiol. 2019;74(4):578-586. doi:10.1016/j.jacc.2019.05.045 PubMedGoogle ScholarCrossref
10.
Abplanalp  WT, Mas-Peiro  S, Cremer  S, John  D, Dimmeler  S, Zeiher  AM.  Association of clonal hematopoiesis of indeterminate potential with inflammatory gene expression in patients with severe degenerative aortic valve stenosis or chronic postischemic heart failure.   JAMA Cardiol. 2020;5(10):1-6. doi:10.1001/jamacardio.2020.2468 PubMedGoogle ScholarCrossref
11.
Bick  AG, Pirruccello  JP, Griffin  GK,  et al.  Genetic interleukin 6 signaling deficiency attenuates cardiovascular risk in clonal hematopoiesis.   Circulation. 2020;141(2):124-131. doi:10.1161/CIRCULATIONAHA.119.044362 PubMedGoogle ScholarCrossref
12.
Bick  AG, Weinstock  JS, Nandakumar  SK,  et al; NHLBI Trans-Omics for Precision Medicine Consortium.  Inherited causes of clonal haematopoiesis in 97,691 whole genomes.   Nature. 2020;586(7831):763-768. doi:10.1038/s41586-020-2819-2 PubMedGoogle ScholarCrossref
13.
Fuster  JJ, MacLauchlan  S, Zuriaga  MA,  et al.  Clonal hematopoiesis associated with TET2 deficiency accelerates atherosclerosis development in mice.   Science. 2017;355(6327):842-847. doi:10.1126/science.aag1381 PubMedGoogle ScholarCrossref
14.
Jaiswal  S, Natarajan  P, Silver  AJ,  et al.  Clonal hematopoiesis and risk of atherosclerotic cardiovascular disease.   N Engl J Med. 2017;377(2):111-121. doi:10.1056/NEJMoa1701719 PubMedGoogle ScholarCrossref
15.
Natarajan  P, Jaiswal  S, Kathiresan  S.  Clonal hematopoiesis: somatic mutations in blood cells and atherosclerosis.   Circ Genom Precis Med. 2018;11(7):e001926. doi:10.1161/CIRCGEN.118.001926 PubMedGoogle Scholar
16.
King  KY, Huang  Y, Nakada  D, Goodell  MA.  Environmental influences on clonal hematopoiesis.   Exp Hematol. 2020;83:66-73. doi:10.1016/j.exphem.2019.12.005PubMedGoogle Scholar
17.
Jaiswal  S, Ebert  BL.  Clonal hematopoiesis in human aging and disease.   Science. 2019;366(6465):eaan4673. doi:10.1126/science.aan4673 PubMedGoogle Scholar
18.
Honigberg  MC, Zekavat  SM, Niroula  A,  et al.  Premature menopause, clonal hematopoiesis, and coronary artery disease in postmenopausal women.   Circulation. 2021;143(5):410-423. doi:10.1161/CIRCULATIONAHA.120.051775 PubMedGoogle Scholar
19.
Bick  AG, Popadin  K, Thorball  CW,  et al.  Increased CHIP prevalence amongst people living with HIV.   medRxiv. Preprint posted online November 7, 2020. doi:10.1101/2020.11.06.20225607 Google Scholar
20.
Bolton  KL, Ptashkin  RN, Gao  T,  et al.  Cancer therapy shapes the fitness landscape of clonal hematopoiesis.   Nat Genet. 2020;52(11):1219-1226. doi:10.1038/s41588-020-00710-0 PubMedGoogle ScholarCrossref
21.
Coombs  CC, Zehir  A, Devlin  SM,  et al.  Therapy-related clonal hematopoiesis in patients with non-hematologic cancers is common and associated with adverse clinical outcomes.   Cell Stem Cell. 2017;21(3):374-382. doi:10.1016/j.stem.2017.07.010 PubMedGoogle ScholarCrossref
22.
Zink  F, Stacey  SN, Norddahl  GL,  et al.  Clonal hematopoiesis, with and without candidate driver mutations, is common in the elderly.   Blood. 2017;130(6):742-752. doi:10.1182/blood-2017-02-769869 PubMedGoogle ScholarCrossref
23.
Folsom  AR, Yatsuya  H, Nettleton  JA, Lutsey  PL, Cushman  M, Rosamond  WD; ARIC Study Investigators.  Community prevalence of ideal cardiovascular health, by the American Heart Association definition, and relationship with cardiovascular disease incidence.   J Am Coll Cardiol. 2011;57(16):1690-1696. doi:10.1016/j.jacc.2010.11.041 PubMedGoogle ScholarCrossref
24.
Hosseini  B, Berthon  BS, Saedisomeolia  A,  et al.  Effects of fruit and vegetable consumption on inflammatory biomarkers and immune cell populations: a systematic literature review and meta-analysis.   Am J Clin Nutr. 2018;108(1):136-155. doi:10.1093/ajcn/nqy082 PubMedGoogle ScholarCrossref
25.
Sotos-Prieto  M, Bhupathiraju  SN, Mattei  J,  et al.  Association of changes in diet quality with total and cause-specific mortality.   N Engl J Med. 2017;377(2):143-153. doi:10.1056/NEJMoa1613502 PubMedGoogle ScholarCrossref
26.
Khera  AV, Emdin  CA, Drake  I,  et al.  Genetic risk, adherence to a healthy lifestyle, and coronary disease.   N Engl J Med. 2016;375(24):2349-2358. doi:10.1056/NEJMoa1605086 PubMedGoogle ScholarCrossref
27.
Dias  JA, Wirfalt  E, Drake  I,  et al.  A high quality diet is associated with reduced systemic inflammation in middle-aged individuals.   Atherosclerosis. 2015;238(1):38-44. doi:10.1016/j.atherosclerosis.2014.11.006 PubMedGoogle ScholarCrossref
28.
Giugliano  D, Ceriello  A, Esposito  K.  The effects of diet on inflammation: emphasis on the metabolic syndrome.   J Am Coll Cardiol. 2006;48(4):677-685. doi:10.1016/j.jacc.2006.03.052 PubMedGoogle ScholarCrossref
29.
Li  J, Lee  DH, Hu  J,  et al.  Dietary inflammatory potential and risk of cardiovascular disease among men and women in the U.S.   J Am Coll Cardiol. 2020;76(19):2181-2193. doi:10.1016/j.jacc.2020.09.535 PubMedGoogle ScholarCrossref
30.
Reedy  J, Krebs-Smith  SM, Miller  PE,  et al.  Higher diet quality is associated with decreased risk of all-cause, cardiovascular disease, and cancer mortality among older adults.   J Nutr. 2014;144(6):881-889. doi:10.3945/jn.113.189407 PubMedGoogle ScholarCrossref
31.
Penuelas  J, Krisztin  T, Obersteiner  M,  et al.  Country-level relationships of the human intake of N and P, animal and vegetable food, and alcoholic beverages with cancer and life expectancy.   Int J Environ Res Public Health. 2020;17(19):7240. doi:10.3390/ijerph17197240 PubMedGoogle ScholarCrossref
32.
Keaver  L, Ruan  M, Chen  F,  et al.  Plant- and animal-based diet quality and mortality among US adults: a cohort study.   Br J Nutr. 2020;1-11. doi:10.1017/S0007114520003670 PubMedGoogle Scholar
33.
Diallo  A, Deschasaux  M, Latino-Martel  P,  et al.  Red and processed meat intake and cancer risk: results from the prospective NutriNet-Santé cohort study.   Int J Cancer. 2018;142(2):230-237. doi:10.1002/ijc.31046 PubMedGoogle ScholarCrossref
34.
Fung  TT, Hu  FB, McCullough  ML, Newby  PK, Willett  WC, Holmes  MD.  Diet quality is associated with the risk of estrogen receptor–negative breast cancer in postmenopausal women.   J Nutr. 2006;136(2):466-472. doi:10.1093/jn/136.2.466 PubMedGoogle ScholarCrossref
35.
Norat  T, Lukanova  A, Ferrari  P, Riboli  E.  Meat consumption and colorectal cancer risk: dose-response meta-analysis of epidemiological studies.   Int J Cancer. 2002;98(2):241-256. doi:10.1002/ijc.10126 PubMedGoogle ScholarCrossref
36.
Tavani  A, La Vecchia  C, Gallus  S,  et al.  Red meat intake and cancer risk: a study in Italy.   Int J Cancer. 2000;86(3):425-428. doi:10.1002/(SICI)1097-0215(20000501)86:3<425::AID-IJC19>3.0.CO;2-S PubMedGoogle ScholarCrossref
37.
Herault  A, Binnewies  M, Leong  S,  et al.  Myeloid progenitor cluster formation drives emergency and leukaemic myelopoiesis.   Nature. 2017;544(7648):53-58. doi:10.1038/nature21693 PubMedGoogle ScholarCrossref
38.
Meisel  M, Hinterleitner  R, Pacis  A,  et al.  Microbial signals drive pre-leukaemic myeloproliferation in a Tet2-deficient host.   Nature. 2018;557(7706):580-584. doi:10.1038/s41586-018-0125-z PubMedGoogle ScholarCrossref
39.
Bycroft  C, Freeman  C, Petkova  D,  et al.  The UK Biobank resource with deep phenotyping and genomic data.   Nature. 2018;562(7726):203-209. doi:10.1038/s41586-018-0579-z PubMedGoogle ScholarCrossref
40.
Van Hout  CV, Tachmazidou  I, Backman  JD,  et al; Geisinger-Regeneron DiscovEHR Collaboration; Regeneron Genetics Center.  Exome sequencing and characterization of 49,960 individuals in the UK Biobank.   Nature. 2020;586(7831):749-756. doi:10.1038/s41586-020-2853-0 PubMedGoogle ScholarCrossref
41.
US Department of Health and Human Services; US Department of Agriculture. 2015-2020 Dietary Guidelines for Americans. 8th ed. US Department of Health and Human Services and US Department of Agriculture; December 2015. Accessed March 15, 2020. https://health.gov/sites/default/files/2019-09/2015-2020_Dietary_Guidelines.pdf
42.
Liu  B, Young  H, Crowe  FL,  et al.  Development and evaluation of the Oxford WebQ, a low-cost, web-based method for assessment of previous 24 h dietary intakes in large-scale prospective studies.   Public Health Nutr. 2011;14(11):1998-2005. doi:10.1017/S1368980011000942 PubMedGoogle ScholarCrossref
43.
Greenwood  DC, Hardie  LJ, Frost  GS,  et al.  Validation of the Oxford WebQ online 24-hour dietary questionnaire using biomarkers.   Am J Epidemiol. 2019;188(10):1858-1867. doi:10.1093/aje/kwz165 PubMedGoogle ScholarCrossref
44.
Toft  U, Kristoffersen  LH, Lau  C, Borch-Johnsen  K, Jorgensen  T.  The Dietary Quality Score: validation and association with cardiovascular risk factors: the Inter99 study.   Eur J Clin Nutr. 2007;61(2):270-278. doi:10.1038/sj.ejcn.1602503 PubMedGoogle ScholarCrossref
45.
Young  AL, Challen  GA, Birmann  BM, Druley  TE.  Clonal haematopoiesis harbouring AML-associated mutations is ubiquitous in healthy adults.   Nat Commun. 2016;7:12484. doi:10.1038/ncomms12484 PubMedGoogle ScholarCrossref
46.
Watson  CJ, Papula  AL, Poon  GYP,  et al.  The evolutionary dynamics and fitness landscape of clonal hematopoiesis.   Science. 2020;367(6485):1449-1454. doi:10.1126/science.aay9333 PubMedGoogle ScholarCrossref
47.
Bujko  K, Cymer  M, Adamiak  M, Ratajczak  MZ.  An overview of novel unconventional mechanisms of hematopoietic development and regulators of hematopoiesis—a roadmap for future investigations.   Stem Cell Rev Rep. 2019;15(6):785-794. doi:10.1007/s12015-019-09920-4 PubMedGoogle ScholarCrossref
48.
Nahrendorf  M, Swirski  FK.  Lifestyle effects on hematopoiesis and atherosclerosis.   Circ Res. 2015;116(5):884-894. doi:10.1161/CIRCRESAHA.116.303550 PubMedGoogle ScholarCrossref
49.
Cabezas-Wallscheid  N, Buettner  F, Sommerkamp  P,  et al.  Vitamin A–retinoic acid signaling regulates hematopoietic stem cell dormancy.   Cell. 2017;169(5):807-823. doi:10.1016/j.cell.2017.04.018 PubMedGoogle ScholarCrossref
50.
Cortes  M, Chen  MJ, Stachura  DL,  et al.  Developmental vitamin D availability impacts hematopoietic stem cell production.   Cell Rep. 2016;17(2):458-468. doi:10.1016/j.celrep.2016.09.012 PubMedGoogle ScholarCrossref
51.
Cimmino  L, Dolgalev  I, Wang  Y,  et al.  Restoration of TET2 function blocks aberrant self-renewal and leukemia progression.   Cell. 2017;170(6):1079-1095. doi:10.1016/j.cell.2017.07.032 PubMedGoogle ScholarCrossref
52.
Burns  SS, Kapur  R.  Putative mechanisms underlying cardiovascular disease associated with clonal hematopoiesis of indeterminate potential.   Stem Cell Reports. 2020;15(2):292-306. doi:10.1016/j.stemcr.2020.06.021 PubMedGoogle ScholarCrossref
53.
Pietras  EM, Mirantes-Barbeito  C, Fong  S,  et al.  Chronic interleukin-1 exposure drives haematopoietic stem cells towards precocious myeloid differentiation at the expense of self-renewal.   Nat Cell Biol. 2016;18(6):607-618. doi:10.1038/ncb3346 PubMedGoogle ScholarCrossref
54.
Singh  RK, Chang  HW, Yan  D,  et al.  Influence of diet on the gut microbiome and implications for human health.   J Transl Med. 2017;15(1):73. doi:10.1186/s12967-017-1175-y PubMedGoogle ScholarCrossref
55.
Baena Ruiz  R, Salinas Hernandez  P.  Diet and cancer: risk factors and epidemiological evidence.   Maturitas. 2014;77(3):202-208. doi:10.1016/j.maturitas.2013.11.010 PubMedGoogle ScholarCrossref
56.
English  DR, MacInnis  RJ, Hodge  AM, Hopper  JL, Haydon  AM, Giles  GG.  Red meat, chicken, and fish consumption and risk of colorectal cancer.   Cancer Epidemiol Biomarkers Prev. 2004;13(9):1509-1514.PubMedGoogle Scholar
57.
Hu  FB, Willett  WC.  Optimal diets for prevention of coronary heart disease.   JAMA. 2002;288(20):2569-2578. doi:10.1001/jama.288.20.2569 PubMedGoogle ScholarCrossref
58.
Koene  RJ, Prizment  AE, Blaes  A, Konety  SH.  Shared risk factors in cardiovascular disease and cancer.   Circulation. 2016;133(11):1104-1114. doi:10.1161/CIRCULATIONAHA.115.020406 PubMedGoogle ScholarCrossref
59.
Libby  P.  Inflammation and cardiovascular disease mechanisms.   Am J Clin Nutr. 2006;83(2):456S-460S. doi:10.1093/ajcn/83.2.456S PubMedGoogle ScholarCrossref
60.
Swerdlow  DI, Holmes  MV, Kuchenbaecker  KB,  et al; Interleukin-6 Receptor Mendelian Randomisation Analysis (IL6R MR) Consortium.  The interleukin-6 receptor as a target for prevention of coronary heart disease: a mendelian randomisation analysis.   Lancet. 2012;379(9822):1214-1224. doi:10.1016/S0140-6736(12)60110-X PubMedGoogle Scholar
61.
Wood  AD, Strachan  AA, Thies  F,  et al.  Patterns of dietary intake and serum carotenoid and tocopherol status are associated with biomarkers of chronic low-grade systemic inflammation and cardiovascular risk.   Br J Nutr. 2014;112(8):1341-1352. doi:10.1017/S0007114514001962 PubMedGoogle ScholarCrossref
62.
Abegunde  SO, Buckstein  R, Wells  RA, Rauh  MJ.  An inflammatory environment containing TNFα favors Tet2-mutant clonal hematopoiesis.   Exp Hematol. 2018;59:60-65. doi:10.1016/j.exphem.2017.11.002 PubMedGoogle ScholarCrossref
63.
Ferrucci  L, Fabbri  E.  Inflammageing: chronic inflammation in ageing, cardiovascular disease, and frailty.   Nat Rev Cardiol. 2018;15(9):505-522. doi:10.1038/s41569-018-0064-2 PubMedGoogle ScholarCrossref
64.
Haring  B, Reiner  AP, Liu  J,  et al.  Healthy lifestyle and clonal hematopoiesis of indeterminate potential: results from the Women’s Health Initiative.   J Am Heart Assoc. 2021;10(5):e018789. doi:10.1161/JAHA.120.018789 PubMedGoogle Scholar
65.
Hematdar  Z, Ghasemifard  N, Phishdad  G, Faghih  S.  Substitution of red meat with soybean but not non-soy legumes improves inflammation in patients with type 2 diabetes; a randomized clinical trial.   J Diabetes Metab Disord. 2018;17(2):111-116. doi:10.1007/s40200-018-0346-6 PubMedGoogle ScholarCrossref
66.
Shah  B, Newman  JD, Woolf  K,  et al.  Anti-inflammatory effects of a vegan diet versus the American Heart Association–recommended diet in coronary artery disease trial.   J Am Heart Assoc. 2018;7(23):e011367. doi:10.1161/JAHA.118.011367 PubMedGoogle Scholar
67.
Sidlow  R, Lin  AE, Gupta  D,  et al.  The clinical challenge of clonal hematopoiesis, a newly recognized cardiovascular risk factor.   JAMA Cardiol. 2020;5(8):958-961. doi:10.1001/jamacardio.2020.1271 PubMedGoogle Scholar
68.
Navar  AM, Wang  TY, Mi  X,  et al.  Influence of cardiovascular risk communication tools and presentation formats on patient perceptions and preferences.   JAMA Cardiol. 2018;3(12):1192-1199. doi:10.1001/jamacardio.2018.3680 PubMedGoogle ScholarCrossref
69.
Dharan  NJ, Yeh  P, Bloch  M,  et al; ARCHIVE Study Group.  Age-related clonal haematopoiesis is more prevalent in older adults with HIV: the ARCHIVE study.   medRxiv. Preprint posted online November 22, 2020. doi:10.1101/2020.11.19.20235069Google Scholar
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    Original Investigation
    June 9, 2021

    Association of Diet Quality With Prevalence of Clonal Hematopoiesis and Adverse Cardiovascular Events

    Author Affiliations
    • 1Cardiovascular Research Center, Massachusetts General Hospital, Boston
    • 2Program in Medical and Population Genetics and the Cardiovascular Disease Initiative, Broad Institute of Harvard and MIT, Cambridge, Massachusetts
    • 3Department of Medicine, Harvard Medical School, Boston, Massachusetts
    • 4Cardiovascular Research Center, Massachusetts General Hospital, Boston
    • 5Yale School of Medicine, New Haven, Connecticut
    • 6Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, Massachusetts
    • 7Department of Pathology, Brigham and Women’s Hospital, Boston, Massachusetts
    • 8Epigenomics Program, Broad Institute of Harvard and MIT, Cambridge, Massachusetts
    • 9Department of Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts
    • 10Division of Genetic Medicine, Department of Medicine, Vanderbilt University Medical Center, Nashville, Tennessee
    JAMA Cardiol. 2021;6(9):1069-1077. doi:10.1001/jamacardio.2021.1678
    Key Points

    Question  Is an unhealthy diet associated with clonal hematopoiesis of indeterminate potential, and are they together associated with an increased risk of adverse cardiovascular events?

    Findings  In this cohort study of 44 111 adult participants in the UK Biobank, an unhealthy diet was independently associated with a 25% increased likelihood of the presence of clonal hematopoiesis of indeterminate potential and increases in adverse cardiovascular events and death.

    Meaning  The results of this study suggest that unhealthy eating habits may be associated with the development of clonal hematopoiesis of indeterminate potential as well as substantially increased risk of adverse cardiovascular events and death.

    Abstract

    Importance  Clonal hematopoiesis of indeterminate potential (CHIP), the expansion of somatic leukemogenic variations in hematopoietic stem cells, has been associated with atherosclerotic cardiovascular disease. Because the inherited risk of developing CHIP is low, lifestyle elements such as dietary factors may be associated with the development and outcomes of CHIP.

    Objective  To examine whether there is an association between diet quality and the prevalence of CHIP.

    Design, Setting, and Participants  This retrospective cohort study used data from participants in the UK Biobank, an ongoing population-based study in the United Kingdom that examines whole-exome sequencing data and survey-based information on health-associated behaviors. Individuals from the UK Biobank were recruited between 2006 and 2010 and followed up prospectively with linkage to health data records through May 2020. The present study included 44 111 participants in the UK Biobank who were age 40 to 70 years, had data available from whole-exome sequencing of blood DNA, and were free of coronary artery disease (CAD) or hematologic cancer at baseline.

    Exposures  Diet quality was categorized as unhealthy if the intake of healthy elements (fruits and vegetables) was lower than the median of all survey responses, and the intake of unhealthy elements (red meat, processed food, and added salt) was higher than the median. Diets were classified as healthy if the intake of healthy elements was higher than the median, and the intake of unhealthy elements was lower than the median. The presence of CHIP was detected by data from whole-exome sequencing of blood DNA.

    Main Outcomes and Measures  The primary outcome was CHIP prevalence. Multivariable logistic regression analysis was used to examine the association between diet quality and the presence of CHIP. Multivariable Cox proportional hazards models were used to assess the association of incident events (acute coronary syndromes, coronary revascularization, or death) in each diet quality category stratified by the presence of CHIP.

    Results  Among 44 111 participants (mean [SD] age at time of blood sample collection, 56.3 [8.0] years; 24 507 women [55.6%]), 2271 individuals (5.1%) had an unhealthy diet, 38 552 individuals (87.4%) had an intermediate diet, and 3288 individuals (7.5%) had a healthy diet. A total of 2507 individuals (5.7%) had CHIP, and the prevalence of CHIP decreased as diet quality improved from unhealthy (162 of 2271 participants [7.1%]) to intermediate (2177 of 38 552 participants [5.7%]) to healthy (168 of 3288 participants [5.1%]; P = .003 for trend). Compared with individuals without CHIP who had an intermediate diet, the rates of incident cardiovascular events progressively decreased among those with CHIP who had an unhealthy diet (hazard ratio [HR], 1.52; 95% CI, 1.04-2.22) and those with CHIP who had a healthy diet (HR, 0.99; 95% CI, 0.62-1.58) over a median of 10.0 years (interquartile range, 9.6-10.4 years) of follow-up.

    Conclusions and Relevance  This cohort study suggests that an unhealthy diet quality may be associated with a higher prevalence of CHIP and higher rates of adverse cardiovascular events and death independent of CHIP status.

    Introduction

    Despite scientific advances in our understanding of coronary artery disease (CAD), lipid metabolism, sex-specific risk factors, germline genetic risk, and behavioral risk, age continues to be the dominant risk factor associated with CAD.1-3 The aging hematopoietic system has substantial implications for vascular homeostasis and age-associated dysfunction.4,5 However, the specific mechanisms through which the aging immune system is associated with atherosclerotic cardiovascular disease (CVD) are incompletely understood.

    Clonal hematopoiesis of indeterminate potential (CHIP) is an age-associated clonal expansion in leukemogenic genes without any overt diagnosis of cancer and is detectable from next-generation sequencing of blood DNA.6-8 Clonal hematopoiesis of indeterminate potential is a common age-associated phenomenon found in up to 10% of individuals older than 70 years.9 While CHIP is a substantial risk factor for the development of hematologic cancer, it is associated with a larger absolute risk increase for CAD.10-15 Furthermore, a greater clonal fraction of CHIP variations in the blood is associated with a greater burden of coronary atherosclerosis and CAD risk, partly through inflammatory mediators.11,14 A whole-genome sequencing analysis of CHIP recently estimated its heritability at only approximately 3.6%.12 This observation raises questions regarding which exposures and behavioral factors may be associated with the development of CHIP. Smoking, a history of chemotherapy receipt or premature menopause, type 2 diabetes, and HIV infection have been associated with the presence of CHIP.6,16-22

    Healthy diet is an important factor in the maintenance of optimal cardiovascular (CV) health.23 A health-promoting diet has long been associated with improvements in CV risk.24-28 Diets high in fruits and vegetables have been associated with reduced inflammatory burden and lower CV events, but the mechanisms that mediate these associations are incompletely understood.29,30 Some studies have suggested that diets high in fruits and vegetables and low in animal protein are associated with reductions in incident cancer diagnoses and recurrences among patients receiving cancer treatment.31-36 Diet, inflammation, and precancerous states such as CHIP are further associated through myeloid lineage propagation and clonal phenomena.37 For instance, in tet methylcytosine dioxygenase 2 (Tet2) knockout mice, bacterial translocation from the intestine and resultant activation of cytokine toll-like receptor 2 and interleukin 6 signaling were associated with clonal expansion and preleukemic myeloproliferation.38 We hypothesized that healthy dietary patterns are associated with a lower prevalence of CHIP and a reduction in incident CV events regardless of CHIP status. We also assessed whether a healthy dietary pattern was associated with incident CV events differentially by CHIP status.

    We used data from the UK Biobank, an ongoing population-based cohort study in the United Kingdom, to investigate whether diet quality is associated with the presence of CHIP and whether a healthy diet is associated with mitigated risk of incident CV events and death in the context of CHIP.

    Methods

    The study was approved by the institutional review board of Massachusetts General Hospital. Participants provided informed consent for the use of their data at the time of enrollment in the UK Biobank. The institutional review board therefore determined that no additional informed consent was required for secondary use of data because the present study posed minimal risk to participants. This study followed the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) reporting guideline for cohort studies.

    Study Population

    Data were from the UK Biobank, a population-based cohort of more than 500 000 adults in the UK between ages 40 to 70 years at recruitment who were followed up prospectively.39 Individuals from the UK Biobank were recruited between 2006 and 2010 and followed up prospectively with linkage to health data records through May 2020. At the time of the baseline study visit, participants used a touch screen to provide detailed information about medical history, medication receipt, smoking history, and dietary habits among other lifestyle factors. Whole-exome sequencing of blood DNA was performed among a subset of individuals, and the first 50 000 of these sequences had been released at the time of the present study.40 Among those 50 000 individuals with available whole-exome sequencing data, approximately 25% were recruited at a study visit to receive magnetic resonance imaging, which introduced the potential for immortal time bias that is addressed in the analysis.

    A total of 46 310 individuals in the UK Biobank had whole-exome sequencing data available. Of those, 1992 individuals were excluded because they had CAD, and 200 individuals were excluded because they had hematologic cancer at the time of the blood sample collection. Seven additional individuals were excluded based on incomplete responses on the food frequency survey, leaving a final cohort of 44 111 participants (Figure 1). All data were collected from study recruitment until August 2016.

    Exposures

    The primary exposure was diet quality, which was rated using an ordinal categorical scale comprising unhealthy, intermediate, or healthy diet. To define the quality of a participant’s diet, we compared available questionnaire response fields with the 2015 Healthy Eating Index from the US Department of Health and Human Services (eMethods in the Supplement). Alternate validated standardized diet scores were considered, but missing elements precluded their use. The Healthy Eating Index recommends that a healthy diet be enhanced with healthy foods, including a variety of vegetables, whole fruits, whole grains, and lean meats or vegetarian protein.41

    At enrollment, participants in the UK Biobank completed surveys regarding their frequency of intake of a range of common food and drink items using the Oxford WebQ survey.42 This self-administered online survey has been validated using biomarkers for protein, potassium, and sugar intake. It performs comparably well with longer and more administratively burdensome interviewer-based recall surveys.43 Diet quality was assessed based on food frequency questionnaire responses. Diet was categorized as unhealthy if the intake of healthy elements (fruits and vegetables) was lower than the median of all survey responses, and the intake of unhealthy elements (red meat, processed food, and added salt) was higher than the median. Diets were classified as healthy if the intake of healthy elements was higher than the median, and the intake of unhealthy elements was lower than the median. The remainder of individuals were categorized as having intermediate diet quality.

    A dietary score adapted from the Diet Quality Score44 was also calculated and used to replicate the primary analysis (eMethods in the Supplement). To generate this score, diet quality was treated as a continuous variable by assigning point values to each of 3 dietary elements: fruits and vegetables, red and processed meats, and added salt. Individual intake was ranked as a percentile for the fruits and vegetables category (with highest quantile of intake assigned 3 points, lowest quantile assigned 1 point, and intermediate quantiles assigned 2 points) and the red and processed meat category (with lowest quantile of intake assigned 3 points, highest quantile assigned 1 point, and intermediate quantiles assigned 2 points). For the added salt category, usually or always adding salt to food was assigned 1 point, sometimes adding salt was assigned 2 points, and never adding salt was assigned 3 points. The total dietary quality score ranged from 3 to 9 for each individual.

    Sequencing and CHIP Detection

    Exomes from participants in the UK Biobank were sequenced from blood-derived DNA at the Regeneron Sequencing Center (Tarrytown, New York), as previously described.40 Genome Analysis Toolkit MuTect2 software, version 4 (Broad Institute), was used to analyze exomes for the detection of somatic variations according to a predefined list of curated leukemogenic CHIP-associated variations in the UK Biobank, as previously described.11,12 While a previous analysis of data from the UK Biobank was restricted to DNA methyltransferase 3A (DNMT3A; OMIM 602769) and tet methylcytosine dioxygenase 2 (TET2; OMIM 612839) gene variations,11 the present study, which used realigned UK Biobank exomes, was not restricted to DNMT3A and TET2 variations but used the similarly described protocol.11

    Outcomes

    The primary outcome was the presence of all CHIP, which was defined as the presence of somatic variations in leukemogenic CHIP drive genes with a variant allele fraction of more than 2%. A secondary end point was the presence of CHIP with large clones (variant allele fraction >10%), as CHIP clones higher than this threshold have been previously associated with adverse clinical outcomes.7,11,12,14

    Additional models were used to test for an association between CHIP and incident adverse CV events (CAD or death, as previously described11) and to evaluate whether this association varied by diet quality category. Cardiovascular disease was defined as a history of myocardial infarction or revascularization with coronary artery bypass grafting or coronary angioplasty, with or without stenting. Incident events were ascertained through electronic health records linked to the UK Biobank (eMethods in the Supplement).

    Statistical Analysis

    Participant characteristics were compared between diet quality categories using an analysis of variance or the Kruskal-Wallis test, as appropriate, and a Pearson χ2 or Fisher exact test was used for categorical variables. The primary analysis evaluated the association of diet quality with CHIP using multivariable logistic regression models adjusted for age, sex, current tobacco smoking, the presence of type 2 diabetes, Townsend deprivation index score (with positive values indicating areas with high material deprivation, negative values indicating areas with low material deprivation, and 0 indicating areas with overall mean values), and the first 10 principal components of genetic ancestry. The first 10 principal components were derived through an analysis of associated genome-wide common genotypes.

    To reduce the risk of confounding across diet quality categories owing to differing health status, we performed sensitivity analyses that were also adjusted for prevalent hypertension or hyperlipidemia and systolic blood pressure, total cholesterol, triglyceride, and blood glucose levels. Additional sensitivity analyses also included models adjusted for body mass index (calculated as weight in kilograms divided by height in meters squared), alcohol use, interaction of sex and diet, interaction of type 2 diabetes and diet, and interaction of age and diet as well as models excluding individuals with stroke or heart failure. In additional exploratory analyses, we tested the association of individual dietary elements with CHIP using multivariable logistic regression analysis, and we examined the association between diet quality and individual CHIP genes using χ2 tests.

    We next assessed the association of diet quality categories with incident CAD or death according to CHIP status using Cox proportional hazards models adjusted for age, sex, current tobacco smoking, and the presence of type 2 diabetes. The Cox proportional hazards assumption was tested with Schoenfeld residuals and was met. Follow-up began at blood sample collection; those who had CAD before the blood sample collection were excluded from the analysis. To assess for potential immortal time bias, we performed an additional sensitivity analysis excluding the subset of individuals selected for exome sequencing because of the availability of cardiac magnetic resonance imaging data. We examined whether observed CAD consequences differed by CHIP status using interaction testing.

    Given the possibility of a type I error owing to multiple comparisons, findings from the secondary analyses were considered exploratory. Analyses were conducted using R software, version 4.0.2 (R Foundation for Statistical Computing), with 2-sided P < .05 considered statistically significant.

    Results
    Study Cohort

    Among 44 111 total participants, the mean (SD) age at the time of blood sample collection was 56.3 (8.0) years, and 24 507 participants (55.6%) were female (Table 1). A total of 24 731 participants (56.1%) never smoked tobacco, 3970 participants (9.0%) currently smoked tobacco, and 15 277 participants (34.6%) formerly smoked tobacco. The mean (SD) body mass index was 27.3 (4.8). Type 2 diabetes was present in 1013 participants (2.3%), heart failure in 95 participants (0.2%), and chronic kidney disease in 136 participants (0.3%).

    Dietary Elements and Quality Categories

    Healthy dietary elements, including cooked and raw fruits and vegetables, had median daily values of approximately 2 tbsp (IQR, 0-4 tbsp; range, 0-50 tbsp) of raw vegetables, 2 tbsp (IQR, 1-3 tbsp; range, 0-50 tbsp) of cooked vegetables, 2 pieces (IQR, 0-4 pieces; range, 0-50 pieces) of fresh fruit, and 0 pieces (IQR, 0-1 piece; range, 0-100 pieces) of dried fruit. In the UK Biobank intake surveys, meat and unhealthy dietary elements were scored as weekly intake frequency, in which the median scores for unhealthy dietary elements were 2 points (IQR, 0-4 points; range, 0-5 points) for processed meat, 1 point (IQR, 0-2 points; range, 0-5 points) for beef, 1 point (IQR, 0-2 points; range, 0-5 points) for pork, and 1 point (IQR, 1-1 point; range, 0-5 points) for lamb (eMethods in the Supplement).

    Among all participants, 3288 individuals (7.5%) had a healthy diet, 38 552 (87.4%) had an intermediate diet, and 2271 (5.1%) had an unhealthy diet. Compared with participants with healthy and intermediate diets, those with unhealthy diets had lower scores for healthy dietary elements (eg, raw salad: mean [SD] score, 3.71 [3.23] in the healthy group vs 2.26 [2.16] in intermediate group vs 1.30 [1.14] in the unhealthy group) and higher scores for unhealthy dietary elements (eg, added salt: mean [SD] score, 1.25 [0.43] in the healthy group vs 1.55 [0.77] in the intermediate group vs 3.30 [0.45] in the unhealthy group) based on the study definitions (eTable 1 and eFigure 1 in the Supplement).

    Healthy dietary elements were correlated with each other (r = 0.12-0.35), as were unhealthy dietary elements (r = 0.01-0.44). Healthy and unhealthy dietary elements were generally inversely correlated (r = −0.15 to 0). Few of these correlations met the criteria for statistical significance (eFigure 2 in the Supplement).

    Prevalence of CHIP

    Among participants in the study cohort, 2507 individuals (5.7%) had CHIP, and large clones were present in 1035 individuals (2.3%). Age was positively associated with the prevalence of CHIP (eFigure 3 in the Supplement). Overall, 49 different CHIP genes were identified, and the most commonly altered CHIP-associated genes were DNMT3A in 1600 participants (63.8%), TET2 in 391 participants (15.6%), ASXL transcriptional regulator 1 (ASXL1; OMIM 612990) in 156 participants (6.2%), tumor protein p53 (TP53; OMIM 191170) in 41 participants (1.6%), and protein phosphatase magnesium/manganese-dependent 1D (PPM1D; OMIM 605100) in 39 participants (1.6%), which was consistent with previous studies (eTable 2 in the Supplement). Most individuals with CHIP had only 1 gene variation (eFigure 4 in the Supplement).

    Diet Quality and CHIP

    Clonal hematopoiesis of indeterminate potential was present in 162 of 2271 participants (7.1%) with an unhealthy diet, 2177 of 38 552 participants (5.7%) with an intermediate diet, and 168 of 3288 participants (5.1%) with a healthy diet. The unadjusted CHIP prevalence progressively decreased with healthier diet quality (7.1% among those with an unhealthy diet, 5.7% among those with an intermediate diet, and 5.1% among those with an unhealthy diet; P = .003 for trend) (Figure 2). This trend was similar for participants with large CHIP clones (77 of 2194 participants [3.4%] with an unhealthy diet, 897 of 37 655 participants [2.3%] with an intermediate diet, and 61 of 3227 participants [1.9%] with a healthy diet; P < .001 for trend) (eTable 3 and eFigure 5 in the Supplement).

    Multivariable analysis adjusted for age, sex, the presence of type 2 diabetes, smoking status, Townsend deprivation index score, and the first 10 principal components of genetic ancestry indicated an association between unhealthy diet quality and CHIP prevalence, with an odds ratio (OR) of 1.25 (95% CI, 1.03-1.50; P = .02) compared with intermediate diet quality (eFigure 6 in the Supplement). Healthy diet quality compared with intermediate diet quality was associated with a lower CHIP prevalence (95% CI, 0.72-1.03; P = .16), but this difference was not statistically significant. These results were robust to a sensitivity analysis in which the model was adjusted for prevalent hypertension and hyperlipidemia as well as systolic blood pressure, total cholesterol, triglyceride, and blood glucose levels and to a separate sensitivity analysis in which individuals with ischemic stroke and heart failure were excluded (eTable 4 and eTable 5 in the Supplement). The findings were replicated using alternate coding of diet quality as a continuous variable (eTable 6 and eFigure 7 in the Supplement).

    We next assessed whether diet quality categories were associated with specific CHIP-associated genes. We assessed DNMT3A, TET2, and ASXL1, the 3 genes with the most commonly observed CHIP-associated variations. Among 162 participants with CHIP who had an unhealthy diet, 95 individuals (58.6%) had DNMT3A variations, 26 individuals (16.0%) had TET2 variations, and 10 individuals (6.2%) had ASXL1 variations. No statistically significant differences were observed between the distribution of CHIP-associated genes across dietary classes using χ2 testing (eTable 7 and eFigure 8 in the Supplement).

    Diet Quality and Adverse Cardiovascular Outcomes

    A total of 3855 participants (8.7%) experienced incident CV events that occurred over a median of 10.0 years (IQR, 9.6-10.4 years). Of those, 276 of 2271 individuals (12.2%) had an unhealthy diet, 3382 of 38 552 individuals (8.8%) had an intermediate diet, and 197 of 3288 individuals (6.0%) had a healthy diet (eTable 8 in the Supplement). Improvement in diet quality category was associated with fewer incident events, with events occurring in 276 of 2271 participants (12.2%) with unhealthy diets, 3382 of 38 552 participants (8.8%) with intermediate diets, and 197 of 3288 participants (6.0%) with healthy diets (P < .001 for trend). This association remained after multivariable adjustment using a Cox proportional hazards model. Compared with an intermediate diet, an unhealthy diet was associated with a higher risk of CV events (hazard ratio [HR], 1.29; 95% CI, 1.14-1.46; P < .001), and a healthy diet was associated with a lower risk of CV events (HR, 0.75; 95% CI, 0.65-0.87; P < .001) (eFigure 9 in the Supplement).

    Adverse Cardiovascular Outcomes by Diet Quality

    After multivariable adjustment, the presence of CHIP was independently associated with an increased risk of incident CV events (HR, 1.24; 95% CI, 1.11-1.39; P < .001) (Table 2). In a combined model, both CHIP and diet quality were independently associated with incident CV events. Compared with participants without CHIP who had an intermediate diet, those with CHIP who had an unhealthy diet had a higher risk of experiencing a CV event (HR, 1.52; 95% CI, 1.04-2.22). Those with CHIP who had an intermediate diet had a CV event risk (HR, 1.24; 95% CI, 1.10-1.40) similar to those without CHIP who had an unhealthy diet (HR, 1.29; 95% CI, 1.13-1.47). Individuals with CHIP who had a healthy diet also had a CV event risk (HR, 0.99; 95% CI, 0.62-1.58) that was similar to those without CHIP who had an intermediate diet (reference group). Those without CHIP who had a healthy diet had the lowest risk of a CV event (HR, 0.75; 95% CI, 0.64-0.87) (Figure 3; eFigure 10 in the Supplement). We observed similar relative diet quality–based stratification, albeit at systematically lower absolute event rates, among individuals with vs without CHIP. After excluding individuals with cardiac magnetic resonance imaging data, we found similar adverse CV event stratification by presence of CHIP and diet quality, suggesting the results were not observed because of immortal time bias (eTable 9 in the Supplement).

    Discussion

    In this cohort study, an unhealthy diet that was low in fruits and vegetables and high in red meat was associated with an increased likelihood of the presence of CHIP, which is a known risk factor for both hematologic cancer and CVD.7,14 Among individuals with CHIP, those with healthier diets had the fewest incident CV events; however, this finding was not statistically significant. These results have several implications for understanding the associations between CHIP, health-associated behaviors, and CVD.

    First, dietary patterns and content were found to be associated with the prevalence of CHIP, which is a novel CV risk factor of emerging importance. Deep targeted sequencing has indicated that most middle-aged adults have quiescent blood cell clones with cancer-associated variations; however, the additional factors and exposures that alter fitness and produce further clonal expansion to meet the criteria for CHIP are not yet understood.45,46 Putative mechanisms for diet include regulation of the bone marrow microenvironment, hematopoietic stem cell pool maintenance, and dietary associations with inflammation.47,48 Nutrients (such as vitamins B12, A, D, C, and folate) participate substantially in hematopoiesis.49,50 Vitamin C is a cofactor in the enzymatic activity of the tet family of DNA hydroxylases, of which TET2 is a negative regulator of hematopoietic stem cell self-renewal.51 In addition to consequences for hematopoietic stem cell regeneration and differentiation, a healthy diet high in fruits, vegetables, fiber, protein, and health-promoting fats is associated with lower inflammatory markers and healthy hematopoietic stem cells.47,52-54

    Second, a diet low in fruits and vegetables and high in red and processed meats has been previously associated with increased CVD and hematologic cancer rates and, based on the present findings, has now been associated with increased CHIP prevalence, which is a shared risk factor for CVD and hematologic cancer.4,29,55-61 In vitro and animal studies have indicated that common inflammatory pathways can promote myeloid differentiation and clonal expansion of TET2 variant cell lines.38,62 Blood levels of interleukin 6, tumor necrosis factor α, leptin, and C-reactive protein are all increased in the sera of obese individuals.58,63 Increases in inflammatory cytokines such as tumor necrosis factor α have been reported to favor TET2-variant clonal hematopoiesis.62 In contrast, healthy diets high in fruits and vegetables have been associated with reduced cancer rates and lower systemic markers of inflammation.24,29,64-66

    Third, the results of the current study support the notion that dietary pattern may mitigate the risk of excess CVD among individuals with CHIP. With the increasingly widespread use of next-generation sequencing in research studies and clinical settings, CHIP is increasingly being identified in asymptomatic individuals.12 Although there are several hypotheses regarding anti-inflammatory therapies, approved dedicated therapies for the reduction of CHIP-associated CVD are currently absent.10,11,13,14,67 Notably, this study found a graded additive association between improved diet quality and reduced incident CV events among individuals with CHIP. In the current analysis, a healthy diet was associated with a substantially lower CV event risk at the general population level. Given the increased CVD risk conferred by CHIP, the absolute risk reduction associated with diet modification may be greater among those with CHIP. Disclosure of an accumulation of CVD risk factors has been reported to promote greater adherence to dietary interventions.68 In a clinical context, when the absolute risk reduction of CVD is expected to be greater, individuals may be more willing to adopt new therapies and recommendations.68 Although prospective clinical trials will be necessary to establish efficacy, dietary interventions may provide a low-risk therapeutic intervention for individuals with CHIP.

    Fourth, a dietary history survey is an inexpensive and widely available screening tool that may be used to optimize the diagnostic yield of CHIP testing. Conventional laboratory testing, including assessments of complete blood cell count and high-sensitivity C-reactive protein, does not accurately identify individuals with CHIP.7,11,12 However, the data from the present study suggest that dietary quality may be added to the constellation of risk factors for CHIP that may be used to enhance diagnostic yield.6-8,11,18,19,69 Requiring further study is male sex as a risk factor. In the primary analysis, consistent with previous studies, male sex was associated with an increased risk of CHIP; however, in sensitivity analyses that adjusted for systolic blood pressure, cholesterol, triglyceride, and blood glucose levels, sex was no longer a statistically significant covariate, although diet quality remained significant.7 In addition to smoking, dietary history represents an independent modifiable lifestyle factor that may help to prioritize individuals for CHIP testing.

    Limitations

    This study has limitations. First, the observational cross-sectional analysis cannot determine whether the association between diet quality and CHIP prevalence is causal. Longitudinal assessments of the evolution of CHIP are necessary to confirm the proposed temporal association between dietary patterns and the development of CHIP. Second, despite extensive covariate adjustment, the possibility of residual confounding from unmeasured variables cannot be ruled out. Third, this study was underpowered to test the consequences of specific dietary components as well as gene-specific associations. Fourth, although these observational analyses suggest that diet can be used to stratify CHIP-associated CV events, prospective randomized clinical trials are necessary to establish that improvements in diet quality reduce the CHIP-associated risk of CV events.

    Conclusions

    Clonal hematopoiesis of indeterminate potential is a newly recognized risk factor for CVD that is higher among individuals with unhealthy diets. Among individuals with CHIP, diet quality remains an independent risk factor that can be used to stratify CVD risk.

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    Article Information

    Accepted for Publication: April 15, 2021.

    Published Online: June 9, 2021. doi:10.1001/jamacardio.2021.1678

    Corresponding Author: Pradeep Natarajan, MD, MMSc, Cardiovascular Research Center, Massachusetts General Hospital, 185 Cambridge St, CPZN 3.184, Boston, MA 02114 (pnatarajan@mgh.harvard.edu).

    Author Contributions: Dr Bhattacharya had full access to all of the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.

    Concept and design: Bhattacharya, Libby, Ebert, Bick, Natarajan.

    Acquisition, analysis, or interpretation of data: Bhattacharya, Zekavat, Uddin, Pirruccello, Niroula, Gibson, Griffin, Libby, Bick, Natarajan.

    Drafting of the manuscript: Bhattacharya, Zekavat, Libby, Bick, Natarajan.

    Critical revision of the manuscript for important intellectual content: All authors.

    Statistical analysis: Bhattacharya, Zekavat, Libby, Bick.

    Obtained funding: Libby, Natarajan.

    Administrative, technical, or material support: Pirruccello, Gibson, Libby, Natarajan.

    Supervision: Ebert, Natarajan.

    Conflict of Interest Disclosures: Dr Bhattacharya reported receiving personal fees from Casana outside the submitted work. Dr Zekavat reported receiving grants from the National Heart, Lung, and Blood Institute during the conduct of the study. Dr Pirruccello reported receiving personal fees from Maze Therapeutics outside the submitted work. Dr Niroula reported receiving grants from the Knut and Alice Wallenberg Foundation outside the submitted work. Dr Griffin reported receiving personal fees from Moderna Therapeutics outside the submitted work. Dr Libby reported serving as an unpaid consultant and/or scientific advisory board member for Amgen, AstraZeneca, the Baim Institute for Clinical Research, Beren Therapeutics, Corvidia Therapeutics, DalCor Pharmaceuticals, Esperion Therapeutics, Genentech, IFM Therapeutics, Ionis Pharmaceuticals, Kancera, Kowa Pharmaceuticals America, MedImmune, Merck & Co, Novartis, Novo Nordisk, Olatec Therapeutics, Pfizer, Sanofi–Regeneron Pharmaceuticals, and XBiotech during the conduct of the study and having a patent pending for the use of canakinumab, receiving laboratory research funding from Novartis, and serving on the board of directors and having a financial interest in XBiotech outside the submitted work. Dr Ebert reported receiving grants from Celgene Corp, Deerfield Management, and Novartis; receiving consulting fees from GRAIL; and serving on the scientific boards and owning equity in Exo Therapeutics, Neomorph, and Skyhawk Therapeutics outside the submitted work. Dr Natarajan reported receiving grants from Amgen, Apple, AstraZeneca, Boston Scientific, and Novartis and personal fees from Apple, Blackstone Life Sciences, Genentech, and Novartis outside the submitted work. No other disclosures were reported.

    Funding/Support: This work was supported by grant T32HL007208 from the National Institutes of Health (Dr Bhattacharya); grants R01 HL148565, R01HL142711, R01HL148565, and R01HL148050 from the National Heart, Lung, and Blood Institute (Dr Natarajan); grant TNE-18CVD04 from the Fondation Leducq (Dr Natarajan); a Hassenfeld Scholar Award from Massachusetts General Hospital (Dr Natarajan); and a John S. LaDue Memorial Fellowship in Cardiovascular Research (Dr Pirruccello).

    Role of the Funder/Sponsor: The funding organizations had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.

    References
    1.
    Hindy  G, Aragam  KG, Ng  K,  et al.  Genome-wide polygenic score, clinical risk factors, and long-term trajectories of coronary artery disease.   Arterioscler Thromb Vasc Biol. 2020;40(11):2738-2746. doi:10.1161/ATVBAHA.120.314856 PubMedGoogle ScholarCrossref
    2.
    GBD 2019 Diseases and Injuries Collaborators.  Global burden of 369 diseases and injuries in 204 countries and territories, 1990-2019: a systematic analysis for the Global Burden of Disease Study 2019.   Lancet. 2020;396(10258):1204-1222. doi:10.1016/S0140-6736(20)30925-9 PubMedGoogle ScholarCrossref
    3.
    Virani  SS, Alonso  A, Benjamin  EJ,  et al; American Heart Association Council on Epidemiology and Prevention Statistics Committee and Stroke Statistics Subcommittee.  Heart disease and stroke statistics—2020 update: a report from the American Heart Association.   Circulation. 2020;141(9):e139-e596. doi:10.1161/CIR.0000000000000757 PubMedGoogle ScholarCrossref
    4.
    Libby  P, Ridker  PM, Maseri  A.  Inflammation and atherosclerosis.   Circulation. 2002;105(9):1135-1143. doi:10.1161/hc0902.104353 PubMedGoogle ScholarCrossref
    5.
    North  BJ, Sinclair  DA.  The intersection between aging and cardiovascular disease.   Circ Res. 2012;110(8):1097-1108. doi:10.1161/CIRCRESAHA.111.246876 PubMedGoogle ScholarCrossref
    6.
    Genovese  G, Kahler  AK, Handsaker  RE,  et al.  Clonal hematopoiesis and blood-cancer risk inferred from blood DNA sequence.   N Engl J Med. 2014;371(26):2477-2487. doi:10.1056/NEJMoa1409405 PubMedGoogle ScholarCrossref
    7.
    Jaiswal  S, Fontanillas  P, Flannick  J,  et al.  Age-related clonal hematopoiesis associated with adverse outcomes.   N Engl J Med. 2014;371(26):2488-2498. doi:10.1056/NEJMoa1408617 PubMedGoogle ScholarCrossref
    8.
    Xie  M, Lu  C, Wang  J,  et al.  Age-related mutations associated with clonal hematopoietic expansion and malignancies.   Nat Med. 2014;20(12):1472-1478. doi:10.1038/nm.3733 PubMedGoogle ScholarCrossref
    9.
    Khetarpal  SA, Qamar  A, Bick  AG,  et al.  Clonal hematopoiesis of indeterminate potential reshapes age-related CVD: JACC review topic of the week.   J Am Coll Cardiol. 2019;74(4):578-586. doi:10.1016/j.jacc.2019.05.045 PubMedGoogle ScholarCrossref
    10.
    Abplanalp  WT, Mas-Peiro  S, Cremer  S, John  D, Dimmeler  S, Zeiher  AM.  Association of clonal hematopoiesis of indeterminate potential with inflammatory gene expression in patients with severe degenerative aortic valve stenosis or chronic postischemic heart failure.   JAMA Cardiol. 2020;5(10):1-6. doi:10.1001/jamacardio.2020.2468 PubMedGoogle ScholarCrossref
    11.
    Bick  AG, Pirruccello  JP, Griffin  GK,  et al.  Genetic interleukin 6 signaling deficiency attenuates cardiovascular risk in clonal hematopoiesis.   Circulation. 2020;141(2):124-131. doi:10.1161/CIRCULATIONAHA.119.044362 PubMedGoogle ScholarCrossref
    12.
    Bick  AG, Weinstock  JS, Nandakumar  SK,  et al; NHLBI Trans-Omics for Precision Medicine Consortium.  Inherited causes of clonal haematopoiesis in 97,691 whole genomes.   Nature. 2020;586(7831):763-768. doi:10.1038/s41586-020-2819-2 PubMedGoogle ScholarCrossref
    13.
    Fuster  JJ, MacLauchlan  S, Zuriaga  MA,  et al.  Clonal hematopoiesis associated with TET2 deficiency accelerates atherosclerosis development in mice.   Science. 2017;355(6327):842-847. doi:10.1126/science.aag1381 PubMedGoogle ScholarCrossref
    14.
    Jaiswal  S, Natarajan  P, Silver  AJ,  et al.  Clonal hematopoiesis and risk of atherosclerotic cardiovascular disease.   N Engl J Med. 2017;377(2):111-121. doi:10.1056/NEJMoa1701719 PubMedGoogle ScholarCrossref
    15.
    Natarajan  P, Jaiswal  S, Kathiresan  S.  Clonal hematopoiesis: somatic mutations in blood cells and atherosclerosis.   Circ Genom Precis Med. 2018;11(7):e001926. doi:10.1161/CIRCGEN.118.001926 PubMedGoogle Scholar
    16.
    King  KY, Huang  Y, Nakada  D, Goodell  MA.  Environmental influences on clonal hematopoiesis.   Exp Hematol. 2020;83:66-73. doi:10.1016/j.exphem.2019.12.005PubMedGoogle Scholar
    17.
    Jaiswal  S, Ebert  BL.  Clonal hematopoiesis in human aging and disease.   Science. 2019;366(6465):eaan4673. doi:10.1126/science.aan4673 PubMedGoogle Scholar
    18.
    Honigberg  MC, Zekavat  SM, Niroula  A,  et al.  Premature menopause, clonal hematopoiesis, and coronary artery disease in postmenopausal women.   Circulation. 2021;143(5):410-423. doi:10.1161/CIRCULATIONAHA.120.051775 PubMedGoogle Scholar
    19.
    Bick  AG, Popadin  K, Thorball  CW,  et al.  Increased CHIP prevalence amongst people living with HIV.   medRxiv. Preprint posted online November 7, 2020. doi:10.1101/2020.11.06.20225607 Google Scholar
    20.
    Bolton  KL, Ptashkin  RN, Gao  T,  et al.  Cancer therapy shapes the fitness landscape of clonal hematopoiesis.   Nat Genet. 2020;52(11):1219-1226. doi:10.1038/s41588-020-00710-0 PubMedGoogle ScholarCrossref
    21.
    Coombs  CC, Zehir  A, Devlin  SM,  et al.  Therapy-related clonal hematopoiesis in patients with non-hematologic cancers is common and associated with adverse clinical outcomes.   Cell Stem Cell. 2017;21(3):374-382. doi:10.1016/j.stem.2017.07.010 PubMedGoogle ScholarCrossref
    22.
    Zink  F, Stacey  SN, Norddahl  GL,  et al.  Clonal hematopoiesis, with and without candidate driver mutations, is common in the elderly.   Blood. 2017;130(6):742-752. doi:10.1182/blood-2017-02-769869 PubMedGoogle ScholarCrossref
    23.
    Folsom  AR, Yatsuya  H, Nettleton  JA, Lutsey  PL, Cushman  M, Rosamond  WD; ARIC Study Investigators.  Community prevalence of ideal cardiovascular health, by the American Heart Association definition, and relationship with cardiovascular disease incidence.   J Am Coll Cardiol. 2011;57(16):1690-1696. doi:10.1016/j.jacc.2010.11.041 PubMedGoogle ScholarCrossref
    24.
    Hosseini  B, Berthon  BS, Saedisomeolia  A,  et al.  Effects of fruit and vegetable consumption on inflammatory biomarkers and immune cell populations: a systematic literature review and meta-analysis.   Am J Clin Nutr. 2018;108(1):136-155. doi:10.1093/ajcn/nqy082 PubMedGoogle ScholarCrossref
    25.
    Sotos-Prieto  M, Bhupathiraju  SN, Mattei  J,  et al.  Association of changes in diet quality with total and cause-specific mortality.   N Engl J Med. 2017;377(2):143-153. doi:10.1056/NEJMoa1613502 PubMedGoogle ScholarCrossref
    26.
    Khera  AV, Emdin  CA, Drake  I,  et al.  Genetic risk, adherence to a healthy lifestyle, and coronary disease.   N Engl J Med. 2016;375(24):2349-2358. doi:10.1056/NEJMoa1605086 PubMedGoogle ScholarCrossref
    27.
    Dias  JA, Wirfalt  E, Drake  I,  et al.  A high quality diet is associated with reduced systemic inflammation in middle-aged individuals.   Atherosclerosis. 2015;238(1):38-44. doi:10.1016/j.atherosclerosis.2014.11.006 PubMedGoogle ScholarCrossref
    28.
    Giugliano  D, Ceriello  A, Esposito  K.  The effects of diet on inflammation: emphasis on the metabolic syndrome.   J Am Coll Cardiol. 2006;48(4):677-685. doi:10.1016/j.jacc.2006.03.052 PubMedGoogle ScholarCrossref
    29.
    Li  J, Lee  DH, Hu  J,  et al.  Dietary inflammatory potential and risk of cardiovascular disease among men and women in the U.S.   J Am Coll Cardiol. 2020;76(19):2181-2193. doi:10.1016/j.jacc.2020.09.535 PubMedGoogle ScholarCrossref
    30.
    Reedy  J, Krebs-Smith  SM, Miller  PE,  et al.  Higher diet quality is associated with decreased risk of all-cause, cardiovascular disease, and cancer mortality among older adults.   J Nutr. 2014;144(6):881-889. doi:10.3945/jn.113.189407 PubMedGoogle ScholarCrossref
    31.
    Penuelas  J, Krisztin  T, Obersteiner  M,  et al.  Country-level relationships of the human intake of N and P, animal and vegetable food, and alcoholic beverages with cancer and life expectancy.   Int J Environ Res Public Health. 2020;17(19):7240. doi:10.3390/ijerph17197240 PubMedGoogle ScholarCrossref
    32.
    Keaver  L, Ruan  M, Chen  F,  et al.  Plant- and animal-based diet quality and mortality among US adults: a cohort study.   Br J Nutr. 2020;1-11. doi:10.1017/S0007114520003670 PubMedGoogle Scholar
    33.
    Diallo  A, Deschasaux  M, Latino-Martel  P,  et al.  Red and processed meat intake and cancer risk: results from the prospective NutriNet-Santé cohort study.   Int J Cancer. 2018;142(2):230-237. doi:10.1002/ijc.31046 PubMedGoogle ScholarCrossref
    34.
    Fung  TT, Hu  FB, McCullough  ML, Newby  PK, Willett  WC, Holmes  MD.  Diet quality is associated with the risk of estrogen receptor–negative breast cancer in postmenopausal women.   J Nutr. 2006;136(2):466-472. doi:10.1093/jn/136.2.466 PubMedGoogle ScholarCrossref
    35.
    Norat  T, Lukanova  A, Ferrari  P, Riboli  E.  Meat consumption and colorectal cancer risk: dose-response meta-analysis of epidemiological studies.   Int J Cancer. 2002;98(2):241-256. doi:10.1002/ijc.10126 PubMedGoogle ScholarCrossref
    36.
    Tavani  A, La Vecchia  C, Gallus  S,  et al.  Red meat intake and cancer risk: a study in Italy.   Int J Cancer. 2000;86(3):425-428. doi:10.1002/(SICI)1097-0215(20000501)86:3<425::AID-IJC19>3.0.CO;2-S PubMedGoogle ScholarCrossref
    37.
    Herault  A, Binnewies  M, Leong  S,  et al.  Myeloid progenitor cluster formation drives emergency and leukaemic myelopoiesis.   Nature. 2017;544(7648):53-58. doi:10.1038/nature21693 PubMedGoogle ScholarCrossref
    38.
    Meisel  M, Hinterleitner  R, Pacis  A,  et al.  Microbial signals drive pre-leukaemic myeloproliferation in a Tet2-deficient host.   Nature. 2018;557(7706):580-584. doi:10.1038/s41586-018-0125-z PubMedGoogle ScholarCrossref
    39.
    Bycroft  C, Freeman  C, Petkova  D,  et al.  The UK Biobank resource with deep phenotyping and genomic data.   Nature. 2018;562(7726):203-209. doi:10.1038/s41586-018-0579-z PubMedGoogle ScholarCrossref
    40.
    Van Hout  CV, Tachmazidou  I, Backman  JD,  et al; Geisinger-Regeneron DiscovEHR Collaboration; Regeneron Genetics Center.  Exome sequencing and characterization of 49,960 individuals in the UK Biobank.   Nature. 2020;586(7831):749-756. doi:10.1038/s41586-020-2853-0 PubMedGoogle ScholarCrossref
    41.
    US Department of Health and Human Services; US Department of Agriculture. 2015-2020 Dietary Guidelines for Americans. 8th ed. US Department of Health and Human Services and US Department of Agriculture; December 2015. Accessed March 15, 2020. https://health.gov/sites/default/files/2019-09/2015-2020_Dietary_Guidelines.pdf
    42.
    Liu  B, Young  H, Crowe  FL,  et al.  Development and evaluation of the Oxford WebQ, a low-cost, web-based method for assessment of previous 24 h dietary intakes in large-scale prospective studies.   Public Health Nutr. 2011;14(11):1998-2005. doi:10.1017/S1368980011000942 PubMedGoogle ScholarCrossref
    43.
    Greenwood  DC, Hardie  LJ, Frost  GS,  et al.  Validation of the Oxford WebQ online 24-hour dietary questionnaire using biomarkers.   Am J Epidemiol. 2019;188(10):1858-1867. doi:10.1093/aje/kwz165 PubMedGoogle ScholarCrossref
    44.
    Toft  U, Kristoffersen  LH, Lau  C, Borch-Johnsen  K, Jorgensen  T.  The Dietary Quality Score: validation and association with cardiovascular risk factors: the Inter99 study.   Eur J Clin Nutr. 2007;61(2):270-278. doi:10.1038/sj.ejcn.1602503 PubMedGoogle ScholarCrossref
    45.
    Young  AL, Challen  GA, Birmann  BM, Druley  TE.  Clonal haematopoiesis harbouring AML-associated mutations is ubiquitous in healthy adults.   Nat Commun. 2016;7:12484. doi:10.1038/ncomms12484 PubMedGoogle ScholarCrossref
    46.
    Watson  CJ, Papula  AL, Poon  GYP,  et al.  The evolutionary dynamics and fitness landscape of clonal hematopoiesis.   Science. 2020;367(6485):1449-1454. doi:10.1126/science.aay9333 PubMedGoogle ScholarCrossref
    47.
    Bujko  K, Cymer  M, Adamiak  M, Ratajczak  MZ.  An overview of novel unconventional mechanisms of hematopoietic development and regulators of hematopoiesis—a roadmap for future investigations.   Stem Cell Rev Rep. 2019;15(6):785-794. doi:10.1007/s12015-019-09920-4 PubMedGoogle ScholarCrossref
    48.
    Nahrendorf  M, Swirski  FK.  Lifestyle effects on hematopoiesis and atherosclerosis.   Circ Res. 2015;116(5):884-894. doi:10.1161/CIRCRESAHA.116.303550 PubMedGoogle ScholarCrossref
    49.
    Cabezas-Wallscheid  N, Buettner  F, Sommerkamp  P,  et al.  Vitamin A–retinoic acid signaling regulates hematopoietic stem cell dormancy.   Cell. 2017;169(5):807-823. doi:10.1016/j.cell.2017.04.018 PubMedGoogle ScholarCrossref
    50.
    Cortes  M, Chen  MJ, Stachura  DL,  et al.  Developmental vitamin D availability impacts hematopoietic stem cell production.   Cell Rep. 2016;17(2):458-468. doi:10.1016/j.celrep.2016.09.012 PubMedGoogle ScholarCrossref
    51.
    Cimmino  L, Dolgalev  I, Wang  Y,  et al.  Restoration of TET2 function blocks aberrant self-renewal and leukemia progression.   Cell. 2017;170(6):1079-1095. doi:10.1016/j.cell.2017.07.032 PubMedGoogle ScholarCrossref
    52.
    Burns  SS, Kapur  R.  Putative mechanisms underlying cardiovascular disease associated with clonal hematopoiesis of indeterminate potential.   Stem Cell Reports. 2020;15(2):292-306. doi:10.1016/j.stemcr.2020.06.021 PubMedGoogle ScholarCrossref
    53.
    Pietras  EM, Mirantes-Barbeito  C, Fong  S,  et al.  Chronic interleukin-1 exposure drives haematopoietic stem cells towards precocious myeloid differentiation at the expense of self-renewal.   Nat Cell Biol. 2016;18(6):607-618. doi:10.1038/ncb3346 PubMedGoogle ScholarCrossref
    54.
    Singh  RK, Chang  HW, Yan  D,  et al.  Influence of diet on the gut microbiome and implications for human health.   J Transl Med. 2017;15(1):73. doi:10.1186/s12967-017-1175-y PubMedGoogle ScholarCrossref
    55.
    Baena Ruiz  R, Salinas Hernandez  P.  Diet and cancer: risk factors and epidemiological evidence.   Maturitas. 2014;77(3):202-208. doi:10.1016/j.maturitas.2013.11.010 PubMedGoogle ScholarCrossref
    56.
    English  DR, MacInnis  RJ, Hodge  AM, Hopper  JL, Haydon  AM, Giles  GG.  Red meat, chicken, and fish consumption and risk of colorectal cancer.   Cancer Epidemiol Biomarkers Prev. 2004;13(9):1509-1514.PubMedGoogle Scholar
    57.
    Hu  FB, Willett  WC.  Optimal diets for prevention of coronary heart disease.   JAMA. 2002;288(20):2569-2578. doi:10.1001/jama.288.20.2569 PubMedGoogle ScholarCrossref
    58.
    Koene  RJ, Prizment  AE, Blaes  A, Konety  SH.  Shared risk factors in cardiovascular disease and cancer.   Circulation. 2016;133(11):1104-1114. doi:10.1161/CIRCULATIONAHA.115.020406 PubMedGoogle ScholarCrossref
    59.
    Libby  P.  Inflammation and cardiovascular disease mechanisms.   Am J Clin Nutr. 2006;83(2):456S-460S. doi:10.1093/ajcn/83.2.456S PubMedGoogle ScholarCrossref
    60.
    Swerdlow  DI, Holmes  MV, Kuchenbaecker  KB,  et al; Interleukin-6 Receptor Mendelian Randomisation Analysis (IL6R MR) Consortium.  The interleukin-6 receptor as a target for prevention of coronary heart disease: a mendelian randomisation analysis.   Lancet. 2012;379(9822):1214-1224. doi:10.1016/S0140-6736(12)60110-X PubMedGoogle Scholar
    61.
    Wood  AD, Strachan  AA, Thies  F,  et al.  Patterns of dietary intake and serum carotenoid and tocopherol status are associated with biomarkers of chronic low-grade systemic inflammation and cardiovascular risk.   Br J Nutr. 2014;112(8):1341-1352. doi:10.1017/S0007114514001962 PubMedGoogle ScholarCrossref
    62.
    Abegunde  SO, Buckstein  R, Wells  RA, Rauh  MJ.  An inflammatory environment containing TNFα favors Tet2-mutant clonal hematopoiesis.   Exp Hematol. 2018;59:60-65. doi:10.1016/j.exphem.2017.11.002 PubMedGoogle ScholarCrossref
    63.
    Ferrucci  L, Fabbri  E.  Inflammageing: chronic inflammation in ageing, cardiovascular disease, and frailty.   Nat Rev Cardiol. 2018;15(9):505-522. doi:10.1038/s41569-018-0064-2 PubMedGoogle ScholarCrossref
    64.
    Haring  B, Reiner  AP, Liu  J,  et al.  Healthy lifestyle and clonal hematopoiesis of indeterminate potential: results from the Women’s Health Initiative.   J Am Heart Assoc. 2021;10(5):e018789. doi:10.1161/JAHA.120.018789 PubMedGoogle Scholar
    65.
    Hematdar  Z, Ghasemifard  N, Phishdad  G, Faghih  S.  Substitution of red meat with soybean but not non-soy legumes improves inflammation in patients with type 2 diabetes; a randomized clinical trial.   J Diabetes Metab Disord. 2018;17(2):111-116. doi:10.1007/s40200-018-0346-6 PubMedGoogle ScholarCrossref
    66.
    Shah  B, Newman  JD, Woolf  K,  et al.  Anti-inflammatory effects of a vegan diet versus the American Heart Association–recommended diet in coronary artery disease trial.   J Am Heart Assoc. 2018;7(23):e011367. doi:10.1161/JAHA.118.011367 PubMedGoogle Scholar
    67.
    Sidlow  R, Lin  AE, Gupta  D,  et al.  The clinical challenge of clonal hematopoiesis, a newly recognized cardiovascular risk factor.   JAMA Cardiol. 2020;5(8):958-961. doi:10.1001/jamacardio.2020.1271 PubMedGoogle Scholar
    68.
    Navar  AM, Wang  TY, Mi  X,  et al.  Influence of cardiovascular risk communication tools and presentation formats on patient perceptions and preferences.   JAMA Cardiol. 2018;3(12):1192-1199. doi:10.1001/jamacardio.2018.3680 PubMedGoogle ScholarCrossref
    69.
    Dharan  NJ, Yeh  P, Bloch  M,  et al; ARCHIVE Study Group.  Age-related clonal haematopoiesis is more prevalent in older adults with HIV: the ARCHIVE study.   medRxiv. Preprint posted online November 22, 2020. doi:10.1101/2020.11.19.20235069Google Scholar
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