Cited by 837

The DOI matches its Crossref record; 10 of the 10 references it cites were found in a registry or an index.

Aging of blood can be tracked by DNA methylation changes at just three CpG sites

Weidner, Carola Ingrid, Lin, Qiong, Koch, Carmen Maike, Eisele, Lewin, Beier, Fabian, Ziegler, Patrick, Bauerschlag, Dirk Olaf, Jöckel, Karl-Heinz, Erbel, Raimund, Mühleisen, Thomas Walter, Zenke, Martin, Brümmendorf, Tim Henrik, Wagner, Wolfgang·2014·Genome Biology

Abstract

Background: Human aging is associated with DNA methylation changes at specific sites in the genome. These epigenetic modifications may be used to track donor age for forensic analysis or to estimate biological age. Results: We perform a comprehensive analysis of methylation profiles to narrow down 102 age-related CpG sites in blood. We demonstrate that most of these age-associated methylation changes are reversed in induced pluripotent stem cells (iPSCs). Methylation levels at three age-related CpGs - located in the genes ITGA2B, ASPA and PDE4C - were subsequently analyzed by bisulfite pyrosequencing of 151 blood samples. This epigenetic aging signature facilitates age predictions with a mean absolute deviation from chronological age of less than 5 years. This precision is higher than age predictions based on telomere length. Variation of age predictions correlates moderately with clinical and lifestyle parameters supporting the notion that age-associated methylation changes are associated more with biological age than with chronological age. Furthermore, patients with acquired aplastic anemia or dyskeratosis congenita - two diseases associated with progressive bone marrow failure and severe telomere attrition - are predicted to be prematurely aged. Conclusions: Our epigenetic aging signature provides a simple biomarker to estimate the state of aging in blood. Age-associated DNA methylation changes are counteracted in iPSCs. On the other hand, over-estimation of chronological age in bone marrow failure syndromes is indicative for exhaustion of the hematopoietic cell pool. Thus, epigenetic changes upon aging seem to reflect biological aging of blood.

Overview

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1186/gb-2014-15-2-r24

Cites

10 of 10 references verified0 citation errors · 0 not checkable

Statistics

Verification

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Citations

Cites

Flagged first, then by citations

Show all 10 references

Research works printed but not linked: 49

  1. Song Z, Von FG, Liu Y, Kraus JM, Torrice C, Dillon P, Rudolph-Watabe M, Ju Z, Kestler HA, Sanoff H, Lenhard RK: Lifestyle impacts on the aging-associated expression of biomarkers of DNA damage and telomere dysfunction in human blood. Aging Cell. 2010, 9: 607-615. 10.1111/j.1474-9726.2010.00583.x. · DOI · Paper page
  2. Blasco MA: Telomeres and human disease: ageing, cancer and beyond. Nat Rev Genet. 2005, 6: 611-622. 10.1038/nrg1656. · DOI · Paper page
  3. Cortopassi GA, Shibata D, Soong NW, Arnheim N: A pattern of accumulation of a somatic deletion of mitochondrial DNA in aging human tissues. Proc Natl Acad Sci U S A. 1992, 89: 7370-7374. 10.1073/pnas.89.16.7370. · DOI
  4. Zubakov D, Liu F, van Zelm MC, Vermeulen J, Oostra BA, van Duijn CM, Driessen GJ, van Dongen JJ, Kayser M, Langerak AW: Estimating human age from T-cell DNA rearrangements. Curr Biol. 2010, 20: R970-R971. 10.1016/j.cub.2010.10.022. · DOI
  5. Helfman PM, Bada JL: Aspartic acid racemisation in dentine as a measure of ageing. Nature. 1976, 262: 279-281. · DOI
  6. Odetti P, Rossi S, Monacelli F, Poggi A, Cirnigliaro M, Federici M, Federici A: Advanced glycation end products and bone loss during aging. Ann N Y Acad Sci. 2005, 1043: 710-717. 10.1196/annals.1333.082. · DOI
  7. Meissner C, Ritz-Timme S: Molecular pathology and age estimation. Forensic Sci Int. 2010, 203: 34-43. 10.1016/j.forsciint.2010.07.010. · DOI
  8. Christensen BC, Houseman EA, Marsit CJ, Zheng S, Wrensch MR, Wiemels JL, Nelson HH, Karagas MR, Padbury JF, Bueno R, Sugarbaker DJ, Yeh RF, Wiencke JK, Kelsey KT: Aging and environmental exposures alter tissue-specific DNA methylation dependent upon CpG island context. PLoS Genet. 2009, 5: e1000602-10.1371/journal.pgen.1000602. · DOI
  9. Alisch RS, Barwick BG, Chopra P, Myrick LK, Satten GA, Conneely KN, Warren ST: Age-associated DNA methylation in pediatric populations. Genome Res. 2012, 22: 623-632. 10.1101/gr.125187.111. · DOI
  10. Bork S, Pfister S, Witt H, Horn P, Korn B, Ho AD, Wagner W: DNA methylation pattern changes upon long-term culture and aging of human mesenchymal stromal cells. Aging Cell. 2010, 9: 54-63. 10.1111/j.1474-9726.2009.00535.x. · DOI
  11. Rakyan VK, Down TA, Maslau S, Andrew T, Yang TP, Beyan H, Whittaker P, McCann OT, Finer S, Valdes AM, Leslie RD, Deloukas P, Spector TD: Human aging-associated DNA hypermethylation occurs preferentially at bivalent chromatin domains. Genome Res. 2010, 20: 434-439. 10.1101/gr.103101.109. · DOI
  12. Teschendorff AE, Menon U, Gentry-Maharaj A, Ramus SJ, Weisenberger DJ, Shen H, Campan M, Noushmehr H, Bell CG, Maxwell AP, Savage DA, Mueller-Holzner E, Marth C, Kocjan G, Gayther SA, Jones A, Beck S, Wagner W, Laird PW, Jacobs IJ, Widschwendter M: Age-dependent DNA methylation of genes that are suppressed in stem cells is a hallmark of cancer. Genome Res. 2010, 20: 440-446. 10.1101/gr.103606.109. · DOI · Paper page
  13. Maegawa S, Hinkal G, Kim HS, Shen L, Zhang L, Zhang J, Zhang N, Liang S, Donehower LA, Issa JP: Widespread and tissue specific age-related DNA methylation changes in mice. Genome Res. 2010, 20: 332-340. 10.1101/gr.096826.109. · DOI · Paper page
  14. Beerman I, Bock C, Garrison BS, Smith ZD, Gu H, Meissner A, Rossi DJ: Proliferation-dependent alterations of the DNA methylation landscape underlie hematopoietic stem cell aging. Cell Stem Cell. 2013, 12: 413-425. 10.1016/j.stem.2013.01.017. · DOI · Paper page
  15. Hannum G, Guinney J, Zhao L, Zhang L, Hughes G, Sadda S, Klotzle B, Bibikova M, Fan JB, Gao Y, Deconde R, Chen M, Rajapakse I, Friend S, Ideker T, Zhang K: Genome-wide methylation profiles reveal quantitative views of human aging rates. Mol Cell. 2013, 49: 359-367. 10.1016/j.molcel.2012.10.016. · DOI · Paper page
  16. Koch CM, Wagner W: Epigenetic-aging-signature to determine age in different tissues. Aging (Albany NY). 2011, 3: 1018-1027. · DOI
  17. Chen YA, Choufani S, Ferreira JC, Grafodatskaya D, Butcher DT, Weksberg R: Sequence overlap between autosomal and sex-linked probes on the Illumina HumanMethylation27 microarray. Genomics. 2011, 97: 214-222. 10.1016/j.ygeno.2010.12.004. · DOI · Paper page
  18. Adkins RM, Thomas F, Tylavsky FA, Krushkal J: Parental ages and levels of DNA methylation in the newborn are correlated. BMC Med Genet. 2011, 12: 47- · DOI
  19. Bibikova M, Le J, Barnes R, Saedinia-Melnyk S, Shou L, Zhen R, Gunderson KL: Genome-wide DNA methylation profiling using Infinium assay. Epigenomics. 2009, 1: 177-200. 10.2217/epi.09.14. · DOI
  20. Ku M, Koche RP, Rheinbay E, Mendenhall EM, Endoh M, Mikkelsen TS, Presser A, Nusbaum C, Xie X, Chi AS, Adli M, Kasif S, Ptaszek LM, Cowan CA, Lander ES, Koseki H, Bernstein BE: Genomewide analysis of PRC1 and PRC2 occupancy identifies two classes of bivalent domains. PLoS Genet. 2008, 4: e1000242-10.1371/journal.pgen.1000242. · DOI
  21. Pan G, Tian S, Nie J, Yang C, Ruotti V, Wei H, Jonsdottir GA, Stewart R, Thomson JA: Whole-genome analysis of histone H3 lysine 4 and lysine 27 methylation in human embryonic stem cells. Cell Stem Cell. 2007, 1: 299-312. 10.1016/j.stem.2007.08.003. · DOI · Paper page
  22. Teschendorff AE, West J, Beck S: Age-associated epigenetic drift: implications, and a case of epigenetic thrift?. Hum Mol Genet. 2013, 22: R7-R15. 10.1093/hmg/ddt375. · DOI
  23. Day K, Waite LL, Thalacker-Mercer A, West A, Bamman MM, Brooks JD, Myers RM, Absher D: Differential DNA methylation with age displays both common and dynamic features across human tissues that are influenced by CpG landscape. Genome Biol. 2013, 14: R102-10.1186/gb-2013-14-9-r102. · DOI
  24. Horvath S, Zhang Y, Langfelder P, Kahn RS, Boks MP, Van EK, van den Berg LH, Ophoff RA: Aging effects on DNA methylation modules in human brain and blood tissue. Genome Biol. 2012, 13: R97-10.1186/gb-2012-13-10-r97. · DOI
  25. Koch CM, Joussen S, Schellenberg A, Lin Q, Zenke M, Wagner W: Monitoring of cellular senescence by DNA-methylation at specific CpG sites. Aging Cell. 2012, 11: 366-369. 10.1111/j.1474-9726.2011.00784.x. · DOI
  26. Koch C, Reck K, Shao K, Lin Q, Joussen S, Ziegler P, Walenda G, Drescher W, Opalka B, May T, Brummendorf T, Zenke M, Saric T, Wagner W: Pluripotent stem cells escape from senescence-associated DNA methylation changes. Genome Res. 2013, 23: 248-259. 10.1101/gr.141945.112. · DOI
  27. Mallon BS, Chenoweth JG, Johnson KR, Hamilton RS, Tesar PJ, Yavatkar AS, Tyson LJ, Park K, Chen KG, Fann YC, McKay RD: StemCellDB: the human pluripotent stem cell database at the National Institutes of Health. Stem Cell Res. 2013, 10: 57-66. 10.1016/j.scr.2012.09.002. · DOI
  28. Marion RM, Strati K, Li H, Tejera A, Schoeftner S, Ortega S, Serrano M, Blasco MA: Telomeres acquire embryonic stem cell characteristics in induced pluripotent stem cells. Cell Stem Cell. 2009, 4: 141-154. 10.1016/j.stem.2008.12.010. · DOI · Paper page
  29. Passtoors WM, Boer JM, Goeman JJ, Akker EB, Deelen J, Zwaan BJ, Scarborough A, Breggen R, Vossen RH, Houwing-Duistermaat JJ, Ommen GJ, Westendorp RG, Heemst D, Craen AJ, White AJ, Gunn DA, Beekman M, Slagboom PE: Transcriptional profiling of human familial longevity indicates a role for ASF1A and IL7R. PLoS ONE. 2012, 7: e27759-10.1371/journal.pone.0027759. · DOI
  30. MacKinney AA: Effect of aging on the peripheral blood lymphocyte count. J Gerontol. 1978, 33: 213-216. 10.1093/geronj/33.2.213. · DOI
  31. Mahlknecht U, Kaiser S: Age-related changes in peripheral blood counts in humans. Exp Ther Med. 2010, 1: 1019-1025. · DOI
  32. Reinius LE, Acevedo N, Joerink M, Pershagen G, Dahlen SE, Greco D, Soderhall C, Scheynius A, Kere J: Differential DNA methylation in purified human blood cells: implications for cell lineage and studies on disease susceptibility. PLoS ONE. 2012, 7: e41361-10.1371/journal.pone.0041361. · DOI
  33. Bocker MT, Hellwig I, Breiling A, Eckstein V, Ho AD, Lyko F: Genome-wide promoter DNA methylation dynamics of human hematopoietic progenitor cells during differentiation and aging. Blood. 2011, 117: e182-e189. 10.1182/blood-2011-01-331926. · DOI
  34. Schmermund A, Möhlenkamp S, Stang A, Grönemeyer D, Seibel R, Hirche H, Mann K, Siffert W, Lauterbach K, Siegrist J, Jöckel KH, Erbel R: Assessment of clinically silent atherosclerotic disease and established and novel risk factors for predicting myocardial infarction and cardiac death in healthy middle-aged subjects: rationale and design of the Heinz Nixdorf Recall Study. Risk Factors, Evaluation of Coronary Calcium and Lifestyle. Am Heart J. 2002, 144: 212-218. 10.1067/mhj.2002.123579. · DOI
  35. Flegal KM, Kit BK, Orpana H, Graubard BI: Association of all-cause mortality with overweight and obesity using standard body mass index categories: a systematic review and meta-analysis. JAMA. 2013, 309: 71-82. 10.1001/jama.2012.113905. · DOI · Paper page
  36. Pavanello S, Hoxha M, Dioni L, Bertazzi PA, Snenghi R, Nalesso A, Ferrara SD, Montisci M, Baccarelli A: Shortened telomeres in individuals with abuse in alcohol consumption. Int J Cancer. 2011, 129: 983-992. 10.1002/ijc.25999. · DOI · Paper page
  37. Lee M, Martin H, Firpo MA, Demerath EW: Inverse association between adiposity and telomere length: The Fels Longitudinal Study. Am J Hum Biol. 2011, 23: 100-106. 10.1002/ajhb.21109. · DOI
  38. Kuningas M, Altmae S, Uitterlinden AG, Hofman A, van Duijn CM, Tiemeier H: The relationship between fertility and lifespan in humans. Age (Dordr). 2011, 33: 615-622. 10.1007/s11357-010-9202-4. · DOI
  39. Brummendorf TH, Balabanov S: Telomere length dynamics in normal hematopoiesis and in disease states characterized by increased stem cell turnover. Leukemia. 2006, 20: 1706-1716. 10.1038/sj.leu.2404339. · DOI
  40. Calado RT, Young NS: Telomere diseases. N Engl J Med. 2009, 361: 2353-2365. 10.1056/NEJMra0903373. · DOI
  41. Thompson RF, Atzmon G, Gheorghe C, Liang HQ, Lowes C, Greally JM, Barzilai N: Tissue-specific dysregulation of DNA methylation in aging. Aging Cell. 2010, 9: 506-518. 10.1111/j.1474-9726.2010.00577.x. · DOI
  42. Liu L, Rando TA: Manifestations and mechanisms of stem cell aging. J Cell Biol. 2011, 193: 257-266. 10.1083/jcb.201010131. · DOI
  43. Rando TA, Chang HY: Aging, rejuvenation, and epigenetic reprogramming: resetting the aging clock. Cell. 2012, 148: 46-57. 10.1016/j.cell.2012.01.003. · DOI · Paper page
  44. Kirkwood TB: Understanding the odd science of aging. Cell. 2005, 120: 437-447. 10.1016/j.cell.2005.01.027. · DOI · Paper page
  45. Brummendorf TH, Maciejewski JP, Mak J, Young NS, Lansdorp PM: Telomere length in leukocyte subpopulations of patients with aplastic anemia. Blood. 2001, 97: 895-900. 10.1182/blood.V97.4.895. · DOI · Paper page
  46. Erbel R, Möhlenkamp S, Möbus S, Schmermund A, Lehmann N, Stang A, Dragano N, Grönemeyer D, Seibel R, Kälsch H, Bröcker-Preuss M, Mann K, Siegrist J, Jöckel KH: Coronary risk stratification, discrimination, and reclassification improvement based on quantification of subclinical coronary atherosclerosis: the Heinz Nixdorf Recall study. J Am Coll Cardiol. 2010, 56: 1397-1406. 10.1016/j.jacc.2010.06.030. · DOI
  47. Zeeberg BR, Feng W, Wang G, Wang MD, Fojo AT, Sunshine M, Narasimhan S, Kane DW, Reinhold WC, Lababidi S, Bussey KJ, Riss J, Barrett JC, Weinstein JN: GoMiner: a resource for biological interpretation of genomic and proteomic data. Genome Biol. 2003, 4: R28-10.1186/gb-2003-4-4-r28. · DOI
  48. Kuhn M: Building predictive models in R using the caret package. J Statist Software. 2008, 28: 1-26. · DOI · Paper page
  49. Beier F, Balabanov S, Buckley T, Dietz K, Hartmann U, Rojewski M, Kanz L, Schrezenmeier H, Brummendorf TH: Accelerated telomere shortening in glycosylphosphatidylinositol (GPI)-negative compared with GPI-positive granulocytes from patients with paroxysmal nocturnal hemoglobinuria (PNH) detected by proaerolysin flow-FISH. Blood. 2005, 106: 531-533. 10.1182/blood-2004-10-3996. · DOI

Cited by

Sorted by citations

See all 837

Metadata

Authors
Weidner, C. I., Lin, Q., Koch, C. M., Eisele, L., Beier, F., Ziegler, P., Bauerschlag, D. O., Jöckel, K.-H., Erbel, R., Mühleisen, T. W., Zenke, M., Brümmendorf, T. H., & Wagner, W.
Venue
Genome Biology
Published
2014-02-03
Volume
15
Issue
2
ISSN
1474-760X
Cited by
837
References
10
Language
en

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review