. 2025 Nov 27;16(1):11688.

doi: 10.1038/s41467-025-66755-z.

Human plasma proteomic profile of clonal hematopoiesis

Zhi Yu^{1

2

3}, Amélie Vromman⁴, Ngoc Quynh H Nguyen⁵, Art Schuermans^{2

3

6}, Linke Li^{1

2}, Thiago Rentz⁴, Tetsushi Nakao^{2

3

4}, Shamsudheen K Vellarikkal⁴, Md Mesbah Uddin^{2

3}, Abhishek Niroula^{2

7

8}, Gabriel Griffin^{2

9

10}, Michael C Honigberg^{2

3

11}, Amy E Lin¹², Christopher J Gibson^{2

13}, Daniel H Katz^{14

15}, Usman A Tahir¹⁴, Shi Fang⁵, Jacqueline S Dron^{2

3}, Michael Pan^{3

11}, Sara Haidermota^{2

3}, Shriienidhie Ganesh^{2

3}, Tajmara Antoine^{2

3}, Joshua Weinstock¹⁶, Thomas R Austin^{17

18}, Ramachandran S Vasan^{19

20}, Gina M Peloso²¹, Whitney Hornsby^{2

3}, Peter Ganz²², JoAnn E Manson^{23

24}, Bernhard Haring²⁵, Charles Kooperberg²⁶, Alexander P Reiner^{26

27}, Joshua C Bis¹⁷, Bruce M Psaty^{17

18

27}, Yuan-I Min²⁸, Adolfo Correa²⁸, Leslie A Lange²⁹, Wendy S Post³⁰, Jerome I Rotter³¹, Stephen S Rich³², James G Wilson¹⁴, Benjamin L Ebert^{2

11

13}, Bing Yu⁵, Christie M Ballantyne³³, Josef Coresh³⁴, Vijay G Sankaran^{2

35

36

37

38}, Alexander G Bick³⁹, Siddhartha Jaiswal⁴⁰, Robert E Gerszten^{11

14}; NHLBI Trans-Omics for Precision Medicine; Peter Libby⁴, Rajat M Gupta^{2

4}, Pradeep Natarajan^{41

42

43}

Collaborators, Affiliations

Collaborators

NHLBI Trans-Omics for Precision Medicine:
Usman A Tahir, Robert E Gerszten

Affiliations

¹ Clinical and Translational Epidemiology Unit, Massachusetts General Hospital, Boston, MA, USA.
² Broad Institute of MIT and Harvard, Cambridge, MA, USA.
³ Cardiovascular Research Center and Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA.
⁴ Division of Cardiovascular Medicine, Brigham and Women's Hospital Heart & Vascular Center, Boston, MA, USA.
⁵ Department of Epidemiology, Human Genetics and Environmental Sciences, School of Public Health, University of Texas Health Science Center at Houston, Houston, TX, USA.
⁶ Faculty of Medicine, KU Leuven, Leuven, Belgium.
⁷ Department of Medical Biochemistry and Cell Biology, Institute of Biomedicine, University of Gothenburg, Gothenburg, Sweden.
⁸ SciLifeLab, University of Gothenburg, Gothenburg, Sweden.
⁹ Department of Pathology, Brigham and Women's Hospital, Boston, MA, USA.
¹⁰ Department of Pathology, Dana-Farber Cancer Institute, Boston, MA, USA.
¹¹ Department of Medicine, Harvard Medical School, Boston, MA, USA.
¹² Division of Cardiology, Section of Cardio-Oncology and Immunology, University of California-San Francisco, San Francisco, CA, USA.
¹³ Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA.
¹⁴ Division of Cardiovascular Medicine, Beth Israel Deaconess Medical Center, Boston, MA, USA.
¹⁵ Department of Cardiovascular Medicine, Stanford University, Stanford, CA, USA.
¹⁶ Department of Human Genetics, Emory University School of Medicine, Atlanta, GA, USA.
¹⁷ Cardiovascular Health Research Unit, Department of Medicine, University of Washington, Seattle, WA, USA.
¹⁸ Department of Epidemiology, University of Washington, Seattle, WA, USA.
¹⁹ University of Texas Health Sciences Center, San Antonio, TX, USA.
²⁰ Framingham Heart Study, Framingham, MA, USA.
²¹ Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA.
²² Division of Cardiology, Zuckerberg San Francisco General Hospital and Department of Medicine, University of California, San Francisco, CA, USA.
²³ Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA.
²⁴ Division of Preventive Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA.
²⁵ Department of Medicine IV, Clinic Hietzing, Vienna, Austria.
²⁶ Public Health Sciences Division, Fred Hutchinson Cancer Center, Seattle, WA, USA.
²⁷ Department of Health Systems and Population Health, University of Washington, Seattle, WA, USA.
²⁸ Department of Medicine, University of Mississippi Medical Center, Jackson, MS, USA.
²⁹ Department of Medicine, Division of Biomedical Informatics and Personalized Medicine, University of Colorado Anschutz Medical Campus, Denver, CO, USA.
³⁰ Division of Cardiology, Department of Medicine, Johns Hopkins University, Baltimore, MD, USA.
³¹ The Institute for Translational Genomics and Population Sciences, Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA, USA.
³² Department of Genome Sciences, University of Virginia, Charlottesville, VA, USA.
³³ Department of Medicine, Baylor College of Medicine, Houston, TX, USA.
³⁴ Departments of Population Health and Medicine, NYU Grossman School of Medicine, New York, NY, USA.
³⁵ Division of Hematology and Oncology, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA.
³⁶ Department of Pediatric Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA, USA.
³⁷ Harvard Stem Cell Institute, Cambridge, MA, USA.
³⁸ Howard Hughes Medical Institute, Boston, MA, USA.
³⁹ Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA.
⁴⁰ Department of Pathology and Institute for Stem Cell Biology and Regenerative Medicine, Stanford University School of Medicine, Stanford, CA, USA.
⁴¹ Broad Institute of MIT and Harvard, Cambridge, MA, USA. pnatarajan@mgh.harvard.edu.
⁴² Cardiovascular Research Center and Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA. pnatarajan@mgh.harvard.edu.
⁴³ Department of Medicine, Harvard Medical School, Boston, MA, USA. pnatarajan@mgh.harvard.edu.

PMID: 41309676
PMCID: PMC12748892
DOI: 10.1038/s41467-025-66755-z

Human plasma proteomic profile of clonal hematopoiesis

Zhi Yu et al. Nat Commun. 2025.

. 2025 Nov 27;16(1):11688.

doi: 10.1038/s41467-025-66755-z.

Authors

Collaborators

NHLBI Trans-Omics for Precision Medicine:
Usman A Tahir, Robert E Gerszten

Affiliations

¹ Clinical and Translational Epidemiology Unit, Massachusetts General Hospital, Boston, MA, USA.
² Broad Institute of MIT and Harvard, Cambridge, MA, USA.
³ Cardiovascular Research Center and Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA.
⁴ Division of Cardiovascular Medicine, Brigham and Women's Hospital Heart & Vascular Center, Boston, MA, USA.
⁵ Department of Epidemiology, Human Genetics and Environmental Sciences, School of Public Health, University of Texas Health Science Center at Houston, Houston, TX, USA.
⁶ Faculty of Medicine, KU Leuven, Leuven, Belgium.
⁷ Department of Medical Biochemistry and Cell Biology, Institute of Biomedicine, University of Gothenburg, Gothenburg, Sweden.
⁸ SciLifeLab, University of Gothenburg, Gothenburg, Sweden.
⁹ Department of Pathology, Brigham and Women's Hospital, Boston, MA, USA.
¹⁰ Department of Pathology, Dana-Farber Cancer Institute, Boston, MA, USA.
¹¹ Department of Medicine, Harvard Medical School, Boston, MA, USA.
¹² Division of Cardiology, Section of Cardio-Oncology and Immunology, University of California-San Francisco, San Francisco, CA, USA.
¹³ Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA.
¹⁴ Division of Cardiovascular Medicine, Beth Israel Deaconess Medical Center, Boston, MA, USA.
¹⁵ Department of Cardiovascular Medicine, Stanford University, Stanford, CA, USA.
¹⁶ Department of Human Genetics, Emory University School of Medicine, Atlanta, GA, USA.
¹⁷ Cardiovascular Health Research Unit, Department of Medicine, University of Washington, Seattle, WA, USA.
¹⁸ Department of Epidemiology, University of Washington, Seattle, WA, USA.
¹⁹ University of Texas Health Sciences Center, San Antonio, TX, USA.
²⁰ Framingham Heart Study, Framingham, MA, USA.
²¹ Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA.
²² Division of Cardiology, Zuckerberg San Francisco General Hospital and Department of Medicine, University of California, San Francisco, CA, USA.
²³ Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA.
²⁴ Division of Preventive Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA.
²⁵ Department of Medicine IV, Clinic Hietzing, Vienna, Austria.
²⁶ Public Health Sciences Division, Fred Hutchinson Cancer Center, Seattle, WA, USA.
²⁷ Department of Health Systems and Population Health, University of Washington, Seattle, WA, USA.
²⁸ Department of Medicine, University of Mississippi Medical Center, Jackson, MS, USA.
²⁹ Department of Medicine, Division of Biomedical Informatics and Personalized Medicine, University of Colorado Anschutz Medical Campus, Denver, CO, USA.
³⁰ Division of Cardiology, Department of Medicine, Johns Hopkins University, Baltimore, MD, USA.
³¹ The Institute for Translational Genomics and Population Sciences, Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA, USA.
³² Department of Genome Sciences, University of Virginia, Charlottesville, VA, USA.
³³ Department of Medicine, Baylor College of Medicine, Houston, TX, USA.
³⁴ Departments of Population Health and Medicine, NYU Grossman School of Medicine, New York, NY, USA.
³⁵ Division of Hematology and Oncology, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA.
³⁶ Department of Pediatric Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA, USA.
³⁷ Harvard Stem Cell Institute, Cambridge, MA, USA.
³⁸ Howard Hughes Medical Institute, Boston, MA, USA.
³⁹ Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA.
⁴⁰ Department of Pathology and Institute for Stem Cell Biology and Regenerative Medicine, Stanford University School of Medicine, Stanford, CA, USA.
⁴¹ Broad Institute of MIT and Harvard, Cambridge, MA, USA. pnatarajan@mgh.harvard.edu.
⁴² Cardiovascular Research Center and Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA. pnatarajan@mgh.harvard.edu.
⁴³ Department of Medicine, Harvard Medical School, Boston, MA, USA. pnatarajan@mgh.harvard.edu.

PMID: 41309676
PMCID: PMC12748892
DOI: 10.1038/s41467-025-66755-z

Abstract

Plasma proteomic profiles associated with subclinical somatic mutations in blood cells may offer insights into downstream clinical consequences. Here we explore these patterns in clonal hematopoiesis of indeterminate potential (CHIP), which is linked to several cancer and non-cancer outcomes, including coronary artery disease (CAD). Among 61,833 participants (3881 with CHIP) from TOPMed and UK Biobank (UKB) with blood-based DNA sequencing and proteomic measurements (1,148 proteins by SomaScan in TOPMed and 2917 proteins by Olink in UKB), we identify 32 and 345 proteins from TOPMed and UKB, respectively, associated with CHIP and most prevalent driver genes (DNMT3A, TET2, and ASXL1). These associations show substantial heterogeneity by driver genes, sex, and race, and were enriched for immune response and inflammation pathways. Mendelian randomization in humans, coupled with ELISA in hematopoietic Tet2-/- vs wild-type mice validation, disentangle causal proteomic perturbations from TET2 CHIP. Lastly, we identify plasma proteins shared between CHIP and CAD.

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Conflict of interest statement

Competing interests: A.E.L. is currently a member of TenSixteen Bio, outside of the submitted work. B.L.E. has received research funding from Celgene, Deerfield, Novartis, and Calico and consulting fees from GRAIL. He is a member of the scientific advisory board and shareholder for Neomorph Inc., Big Sur Bio, Skyhawk Therapeutics, and Exo Therapeutics. B.M.P. serves on the Steering Committee of the Yale Open Data Access Project funded by Johnson & Johnson. J.C. is a scientific advisor to SomaLogic. M.C.H. reports consulting fees from Comanche Biopharma, research support from Genentech, and site principal investigator work for Novartis. P.L. is an unpaid consultant to, or involved in clinical trials for Amgen, AstraZeneca, Baim Institute, Beren Therapeutics, Esperion Therapeutics, Genentech, Kancera, Kowa Pharmaceuticals, Medimmune, Merck, Moderna, Novo Nordisk, Novartis, Pfizer, and Sanofi-Regeneron. P.L. is a member of the scientific advisory board for Amgen, Caristo Diagnostics, Cartesian Therapeutics, CSL Behring, DalCor Pharmaceuticals, Dewpoint Therapeutics, Eulicid Bioimaging, Kancera, Kowa Pharmaceuticals, Olatec Therapeutics, Medimmune, Novartis, PlaqueTec, TenSixteen Bio, Soley Thereapeutics, and XBiotech, Inc. P.L.‘s laboratory has received research funding in the last 2 years from Novartis, Novo Nordisk, and Genentech. P.L. is on the Board of Directors of XBiotech, Inc. P.L. has a financial interest in Xbiotech, a company developing therapeutic human antibodies, in TenSixteen Bio, a company targeting somatic mosaicism and clonal hematopoiesis of indeterminate potential (CHIP) to discover and develop novel therapeutics to treat age-related diseases, and in Soley Therapeutics, a biotechnology company that is combining artificial intelligence with molecular and cellular response detection for discovering and developing new drugs, currently focusing on cancer therapeutics. P.L.‘s interests were reviewed and are managed by Brigham and Women’s Hospital and Mass General Brigham in accordance with their conflict-of-interest policies. P.N. reports investigator-initiated grants from Amgen, Apple, Boston Scientific, Novartis, and AstraZeneca, personal fees from Allelica, Apple, AstraZeneca, Blackstone Life Sciences, Foresite Labs, Genentech, and Novartis, scientific board membership for Esperion Therapeutics, geneXwell, and TenSixteen Bio, and spousal employment at Vertex, all unrelated to the present work. P.N., A.G.B., S.J., and B.L.E. are scientific co-founders of TenSixteen Bio, and P.L. is an advisor to TenSixteen Bio. TenSixteen Bio is a company focused on clonal hematopoiesis but had no role in the present work. S.J. is on advisory boards for Novartis, AVRO Bio, and Roche Genentech, reports speaking fees and a honorarium from GSK, and is on the scientific advisory board of Bitterroot Bio. The remaining authors declare no conflicts of interests.

Figures

**Fig. 1. Scheme of the study design.**
We assessed the associations of CHIP and driver gene-specific CHIP subtypes (*DNMT3A, TET2, ASXL1*, and *JAK2*) with 1148 circulating proteins quantified by the SomaScan platform in 12,911 participants from TOPMed cohorts and 2923 circulating proteins quantified by Olink in 49,217 participants from UK Biobank. Causal relations for the associations were examined through genetic causal inference using Mendelian randomization and murine experiments contrasting plasma protein levels between *Tet2*^+/+ mice and control mice using ELISA. Pathway analyses were conducted using IPA tools. Finally, we investigated the associations between prevalent CAD and proteomics, identifying shared proteins associated with both CAD and any examined CHIP variable. CAD Coronary artery disease, CHIP Clonal hematopoiesis of indeterminate potential, TOPMed Trans-Omics for Precision Medicine, ELISA enzyme-linked immunosorbent assay. Parts of this figure have been created with BioRender.com.

**Fig. 2. CHIP and proteomics in TOPMed cohorts and UK Biobank.**
A CHIP prevalence increased with donor age at the time of blood sampling. The center line represents the general additive model spline, and the shaded region is the 95% confidence interval (N_ARIC = 8188; N_CHS = 1689; N_JHS = 2058; N_MESA = 976; N_UKB = 49,217). B More than 90% of individuals with CHIP had only one somatic CHIP driver mutation variant identified. C Counts for four driver genes, *DNMT3A, TET2, ASXL1*, and *JAK2*, of CHIP mutations. D CHIP clone size heterogeneity as measured by variant allele fraction (VAF) by CHIP driver gene. Violin plot spanning minimum and maximum values; the white dot indicates median; Kruskal–Wallis tests show that the VAF significantly varies for *DNMT3A* and *TET2*. E Platform and panel used for proteomics measurement by each cohort (for ARIC, CHS, JHS, and MESA, which utilized SomaScan for proteomics measurements, discovery analyses were performed using only the 1148 proteins common to both the 5 k and 1.3 k panels). CHIP: Clonal hematopoiesis of indeterminate potential.

**Fig. 3. Meta-analyzed associations between CHIP mutations and circulating proteome quantified by SomaScan in TOPMed cohorts.**
A All participants (N = 12,911). B Male participants only (N = 5616) vs. Female participants only (N = 7295). C Black participants only (N = 4452) vs. White participants only (N = 8076). Proteins that are associated at FDR = 0.05 level (for 4560 testings) are labeled with the corresponding SomaScan targets and colored in blue, red, green, and orange, indicating significant associations with composite CHIP, *DNMT3A*, *TET2*, and *ASXL1*, respectively. Associations were assessed through linear regression models adjusting for age at sequencing, sex (if applicable), self-reported race (if applicable), batch (if applicable), type 2 diabetes status, smoker status, first ten principal components of genetic ancestry, and PEER factors (the number of PEER factors varies by cohorts based on the sizes of study populations: 50 for JHS, MESA, and CHS; 70 for ARIC AA; 120 for ARIC EA). AA African Ancestry, ARIC Atherosclerosis Risk in Communities, CHIP Clonal hematopoiesis of indeterminate potential, CHS Cardiovascular Heart Study, EA European Ancestry, FDR False discovery rate, JHS Jackson Heart Study, MESA Multi-Ethnic Study of Atherosclerosis, PEER Probabilistic estimation of expression residuals.

**Fig. 4. Associations between CHIP mutations and circulating proteome quantified by Olink in UK Biobank.**
A All participants (N = 41,022). B Male participants only (N = 18,831) vs. Female participants only (N = 22,191). Proteins that are associated at FDR = 0.005 level (for 11,668 testings) are labeled with the corresponding Olink targets and colored in blue, red, purple, and green, indicating significant associations with composite CHIP, *DNMT3A*, *TET2*, and *ASXL1*, respectively. Associations were assessed through linear regression models adjusting for age at sequencing, sex, self-reported British White ancestry (if applicable), type 2 diabetes status, current smoker status, first ten principal components of genetic ancestry, and 150 PEER factors. CHIP Clonal hematopoiesis of indeterminate potential, FDR False discovery rate, PEER Probabilistic estimation of expression residuals.

**Fig. 5. Estimation of bi-directional genetic causal effects between CHIP mutations and associated proteins.**
A Proteins quantified by SomaScan in TOPMed cohorts. B Proteins quantified by Olink in UK Biobank. The GWAS summary statistics for CHIP were from our CHIP GWAS meta-analysis for 648,992 multi-ancestry individuals in the UK Biobank, TOPMed, Vanderbilt BioVU, and Mass General Brigham Biobank. For genetic instruments of proteins, we obtained SomaScan pQTL data from 35,892 Icelanders and Olink pQTL data from 48,922 UK Biobank-Pharma Proteomics Project participants who had their circulating proteomes profiled. All GWAS summary statistics assumed an additive genetic model. For both A and B, we examined CHIP mutations’ genetic causal effects on proteins and proteins’ genetic causal effects on CHIP mutations. Only CHIP mutation-protein pairs that were significantly associated at FDR = 0.05 level were examined. CHIP mutations were limited to overall CHIP, *DNMT3A*, and *TET2* given the availability of GWAS. Some proteins were not examined as no valid instruments were available. A two-sided, inverse-variance weighted Mendelian randomization approach was used for the analysis. Points represent the beta (β) coefficients and the error bars represent the 95% confidence intervals. Exact p-values for the causal estimates are shown either in the figure (P > 0.001) or in Supplemental Data (P < 0.001).

**Fig. 6. ELISA results of *Tet2*^−/− and WT mice by sex for selected plasma proteins whose level changes are associated with and causal by *TET2* in human.**
A A protein of which the causal role of *TET2* is supported in both SomaScan and Olink, LCN2. B A protein of which the causal role of *TET2* is supported in SomaScan only, MPO. C A protein of which the causal role of *TET2* is supported in Olink only, Flt3l. Data are presented as mean values ± SEM, with each point representing a single biological replicate (one mouse). For each experimental group (defined by *Tet2*^−/− vs WT and sex), sample sizes ranged from 8 to 10 mice, based on availability at the time of the experiment. Statistical significance was determined using a two-way ANOVA. Significance is indicated as **P < 0.05, ***P < 0.01, and ****P < 0.0001; NS not significant, Flt3l FMS-related tyrosine kinase 3 ligand, LCN Lipocalin 2, MPO Myeloperoxidase, WT wild-type.

**Fig. 7. Significantly enriched and modulated pathways were identified among proteins associated with CHIP driver genes.**
Significantly enriched and modulated pathways corresponding to CHIP-associated proteins were derived based on known genetic and molecular relationships using IPA. The input was the Z-scores of the associations between major CHIP driver genes, i.e., *DNMT3A*, *TET2*, and *ASXL1*, and proteins that were significant at the P = 0.05 level. The listed pathways fulfill two criteria: (1) within the top 30 most significantly enriched pathways by input proteins based on IPA analysis (enrichment p-value calculated using a right-tailed Fisher’s Exact Test, with adjustment for multiple comparisons using the Benjamini–Hochberg method; P < 0.05) and (2) being significantly modulated, either inhibited or activated, based on IPA analysis (Z > 1.96). The orange indicates predicted activation, and the blue indicates predicted inhibition. The darker the color, the stronger the modulation effect. A Significantly modulated canonical pathways implicated among proteins associated with *DNMT3A*. B Significantly modulated canonical pathways implicated among proteins associated with *TET2*. C Significantly modulated canonical pathways implicated among proteins associated with *ASXL1*. CHIP Clonal hematopoiesis of indeterminate potential, IPA Ingenuity Pathway Analysis.

**Fig. 8. Upset plot showing overlapped and non-overlapped associated proteins between CHIP variables and CAD.**
A total of 68 proteins were associated with both prevalent CAD and any of the CHIP variables (composite CHIP, *DNMT3A, TET2*, and *ASXL1*) at P = 0.05 level. For both CHIP variables and CAD, associations were assessed through linear regression models adjusting for age at sequencing, sex, race, batch (if applicable), type 2 diabetes status, smoker status, and the first ten principal components of genetic ancestry. PEER factors (the number of PEER factors varies by cohorts based on the sizes of study populations: 50 for JHS, MESA, and CHS; 70 for ARIC AA; 120 for ARIC EA) were adjusted in CHIP analysis only, but not CAD analysis; this is because around 1/3 of them were associated with CAD, but in general not with CHIP. AA African ancestry, ARIC Atherosclerosis Risk in Communities, CAD Coronary artery disease, CHIP clonal hematopoiesis of indeterminate potential, CHS Cardiovascular Heart Study, EA European ancestry, FDR False discovery rate, JHS Jackson Heart Study, MESA Multi-Ethnic Study of Atherosclerosis, PEER Probabilistic estimation of expression residuals.

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Update of

Human Plasma Proteomic Profile of Clonal Hematopoiesis.
Yu Z, Vromman A, Nguyen NQH, Schuermans A, Rentz T, Vellarikkal SK, Uddin MM, Niroula A, Griffin G, Honigberg MC, Lin AE, Gibson CJ, Katz DH, Tahir U, Fang S, Haidermota S, Ganesh S, Antoine T, Weinstock J, Austin TR, Ramachandran VS, Peloso GM, Hornsby W, Ganz P, Manson JE, Haring B, Kooperberg CL, Reiner AP, Bis JC, Psaty BM, Min YI, Correa A, Lange LA, Post WS, Rotter JI, Rich SS, Wilson JG, Ebert BL, Yu B, Ballantyne CM, Coresh J, Sankaran VG, Bick AG, Jaiswal S, Gerszten RE; NHLBI Trans-Omics for Precision Medicine; Libby P, Gupta RM, Natarajan P. Yu Z, et al. bioRxiv [Preprint]. 2024 Oct 31:2023.07.25.550557. doi: 10.1101/2023.07.25.550557. bioRxiv. 2024. Update in: Nat Commun. 2025 Nov 27;16(1):11688. doi: 10.1038/s41467-025-66755-z. PMID: 39554199 Free PMC article. Updated. Preprint.

References

1. Steensma, D. P. et al. Clonal hematopoiesis of indeterminate potential and its distinction from myelodysplastic syndromes. Blood126, 9–16 (2015). - DOI - PMC - PubMed
1. Xie, M. et al. Age-related mutations associated with clonal hematopoietic expansion and malignancies. Nat. Med.20, 1472–1478 (2014). - DOI - PMC - PubMed
1. Genovese, G. et al. Clonal hematopoiesis and blood-cancer risk inferred from blood DNA sequence. N. Engl. J. Med.371, 2477–2487 (2014). - DOI - PMC - PubMed
1. Jaiswal, S. et al. Age-related clonal hematopoiesis associated with adverse outcomes. N. Engl. J. Med.371, 2488–2498 (2014). - DOI - PMC - PubMed
1. Kim, P. G. et al. Dnmt3a-mutated clonal hematopoiesis promotes osteoporosis. J. Exp. Med.218, e20211872 (2021). - DOI - PMC - PubMed

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Human plasma proteomic profile of clonal hematopoiesis

Collaborators

Affiliations

Human plasma proteomic profile of clonal hematopoiesis

Authors

Collaborators

Affiliations

Abstract

Conflict of interest statement

Figures

Update of

References

MeSH terms

Substances

Grants and funding

LinkOut - more resources

Full Text Sources

Medical

Molecular Biology Databases

Miscellaneous