Exploring Epigenetic Age in Response to Intensive Relaxing Training: A Pilot Study to Slow Down Biological Age

Sofia Pavanello, Manuela Campisi, Francesco Tona, Carlo Dal Lin, Sabino Iliceto, Sofia Pavanello, Manuela Campisi, Francesco Tona, Carlo Dal Lin, Sabino Iliceto

Abstract

DNA methylation (DNAm) is an emerging estimator of biological aging, i.e., the often-defined "epigenetic clock", with a unique accuracy for chronological age estimation (DNAmAge). In this pilot longitudinal study, we examine the hypothesis that intensive relaxing training of 60 days in patients after myocardial infarction and in healthy subjects may influence leucocyte DNAmAge by turning back the epigenetic clock. Moreover, we compare DNAmAge with another mechanism of biological age, leucocyte telomere length (LTL) and telomerase. DNAmAge is reduced after training in healthy subjects (p = 0.053), but not in patients. LTL is preserved after intervention in healthy subjects, while it continues to decrease in patients (p = 0.051). The conventional negative correlation between LTL and chronological age becomes positive after training in both patients (p < 0.01) and healthy subjects (p < 0.05). In our subjects, DNAmAge is not associated with LTL. Our findings would suggest that intensive relaxing practices influence different aging molecular mechanisms, i.e., DNAmAge and LTL, with a rejuvenating effect. Our study reveals that DNAmAge may represent an accurate tool to measure the effectiveness of lifestyle-based interventions in the prevention of age-related diseases.

Keywords: DNA methylation age; epigenetic age; myocardial infarction patient; relaxing training; telomerase; telomere length.

Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
Longitudinal Plan of the Study.
Figure 2
Figure 2
Correlation curves between DNAmAge and chronological age at enrolment T0 (a) versus after 60 days of relaxing practices T1 (b).
Figure 3
Figure 3
Comparison of the correlation curves between LTL and chronological age at enrolment T0 (a) versus after 60 days T1 (b) based on Chow’s test for patients. Chow Test F (2, 24 df) = 73,975 (p < 0.01).
Figure 4
Figure 4
Comparison of the correlation curves between LTL and chronological age at enrolment T0 (a) versus after 60 days T1 (b) based on Chow’s test for healthy subjects. Chow Test F (2, 8 df) = 11,889 (p < 0.05).

References

    1. Horvath S. DNA methylation age of human tissues and cell types. Genome Biol. 2013;14:R115. doi: 10.1186/gb-2013-14-10-r115.
    1. Hannum G., Guinney J., Zhao L., Zhang L., Hughes G., Sadda S., Klotzle B., Bibikova M., Fan J., Gao Y., et al. Genome-wide methylation profiles reveal quantitative views of human aging rates. Mol. Cell. 2013;49:359–367. doi: 10.1016/j.molcel.2012.10.016.
    1. Zbieć-Piekarska R., Spólnicka M., Kupiec T., Parys-Proszek A., Makowska Ż., Pałeczka A., Kucharczyk K., Płoski R., Branicki W. Development of a forensically useful age prediction method based on DNA methylation analysis. Forensic Sci. Int. Genet. 2015;17:173–179. doi: 10.1016/j.fsigen.2015.05.001.
    1. Goel N., Karir P., Garg V.K. Role of DNA methylation in human age prediction. Mech. Ageing Dev. 2017;166:33–41. doi: 10.1016/j.mad.2017.08.012.
    1. Bocklandt S., Lin W., Sehl M.E., Sánchez F.J., Sinsheimer J.S., Horvath S., Vilain E. Epigenetic predictor of age. PLoS ONE. 2011;6:e14821. doi: 10.1371/journal.pone.0014821.
    1. 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., et al. Aging of blood can be tracked by DNA methylation changes at just three CpG sites. Genome Biol. 2014;15:R24. doi: 10.1186/gb-2014-15-2-r24.
    1. Chen B.H., Marioni R.E., Colicino E., Peters M.J., Ward-Caviness C.K., Tsai P.C., Roetker N.S., Just A.C., Demerath E.W., Guan W., et al. DNA methylation-based measures of biological age: Meta-analysis predicting time to death. Aging. 2016;8:1844–1865. doi: 10.18632/aging.101020.
    1. Perna L., Zhang Y., Mons U., Holleczek B., Saum K.U., Brenner H. Epigenetic age acceleration predicts cancer, cardiovascular, and all-cause mortality in a German case cohort. Clin. Epigenet. 2016;8:64. doi: 10.1186/s13148-016-0228-z.
    1. Horvath S., Gurven M., Levine M.E., Trumble B.C., Kaplan H., Allayee H., Ritz B.R., Chen B., Lu A.T., Rickabaugh T.M., et al. An epigenetic clock analysis of race/ethnicity, sex, and coronary heart disease. Genome Biol. 2016;17:171. doi: 10.1186/s13059-016-1030-0.
    1. Marioni R.E., Shah S., McRae A.F., Chen B.H., Colicino E., Harris S.E., Gibson J., Henders A.K., Redmond P., Cox S.R., et al. DNA methylation age of blood predicts all-cause mortality in later life. Genome Biol. 2015;16:25. doi: 10.1186/s13059-015-0584-6.
    1. Fransquet P.D., Wrigglesworth J., Woods R.L., Ernst M.E., Ryan J. The epigenetic clock as a predictor of disease and mortality risk: A systematic review and meta-analysis. Clin. Epigenet. 2019;11:62. doi: 10.1186/s13148-019-0656-7.
    1. Simons R.L., Lei M.K., Beach S.R., Philibert R.A., Cutrona C.E., Gibbons F.X., Barr A. Economic hardship and biological weathering: The epigenetics of aging in a U.S. sample of black women. Soc. Sci. Med. 2016;150:192–200. doi: 10.1016/j.socscimed.2015.12.001.
    1. Quach A., Levine M.E., Tanaka T., Lu A.T., Chen B.H., Ferrucci L., Ritz B., Bandinelli S., Neuhouser M.L., Beasley J.M., et al. Epigenetic clock analysis of diet, exercise, education, and lifestyle factors. Aging. 2017;9:419–446. doi: 10.18632/aging.101168.
    1. Nwanaji-Enwerem J.C., Colicino E., Dai L., Di Q., Just A.C., Hou L., Vokonas P., De Vivo I., Lemos B., Lu Q., et al. miRNA processing gene polymorphisms, blood DNA methylation age and long-term ambient PM2.5 exposure in elderly men. Epigenomics. 2017;9:1529–1542. doi: 10.2217/epi-2017-0094.
    1. Ward-Caviness C.K., Nwanaji-Enwerem J.C., Wolf K., Wahl S., Colicino E., Trevisi L., Kloog I., Just A.C., Vokonas P., Cyrys J., et al. Long-term exposure to air pollution is associated with biological aging. Oncotarget. 2016;7:74510–74525. doi: 10.18632/oncotarget.12903.
    1. Fiorito G., McCrory C., Robinson O., Carmeli C., Rosales C.O., Zhang Y., Colicino E., Dugué P.A., Artaud F., McKay G.J., et al. Socioeconomic position, lifestyle habits and biomarkers of epigenetic aging: A multi-cohort analysis. Aging. 2019;11:2045–2070. doi: 10.18632/aging.101900.
    1. Zannas A.S., Arloth J., Carrillo-Roa T., Iurato S., Röh S., Ressler K.J., Nemeroff C.B., Smith A.K., Bradley B., Heim C., et al. Lifetime stress accelerates epigenetic aging in an urban, African American cohort: Relevance of glucocorticoid signaling. Genome Biol. 2015;16:266. doi: 10.1186/s13059-015-0828-5.
    1. Gassen N.C., Chrousos G.P., Binder E.B., Zannas A.S. Life stress, glucocorticoid signaling, and the aging epigenome: Implications for aging-related diseases. Neurosci. Biobehav. Rev. 2017;74:356–365. doi: 10.1016/j.neubiorev.2016.06.003.
    1. Spólnicka M., Pośpiech E., Adamczyk J.G., Freire-Aradas A., Pepłońska B., Zbieć-Piekarska R., Makowska Ż., Pięta A., Lareu M.V., Phillips C., et al. Modified aging of elite athletes revealed by analysis of epigenetic age markers. Aging. 2018;10:241–252. doi: 10.18632/aging.101385.
    1. Chaix R., Alvarez-López M.J., Fagny M., Lemee L., Regnault B., Davidson R.J., Lutz A., Kaliman P. Epigenetic clock analysis in long-term meditators. Psychoneuroendocrinology. 2017;85:210–214. doi: 10.1016/j.psyneuen.2017.08.016.
    1. Levine G.N., Lange R.A., Bairey-Merz C.N., Davidson R.J., Jamerson K., Mehta P.K., Michos E.D., Norris K., Ray I.B., Saban K.L., et al. Meditation and cardiovascular risk reduction: A scientific statement from the American Heart association. J. Am. Heart Assoc. 2017;6:e002218. doi: 10.1161/JAHA.117.002218.
    1. Lazar S.W., Bush G., Gollub R.L., Fricchione G.L., Khalsa G., Benson H. Functional brain mapping of the relaxation response and meditation. Neuroreport. 2000;11:1581–1585. doi: 10.1097/00001756-200005150-00042.
    1. Paul-Labrador M., Polk D., Dwyer J.H., Velasquez I., Nidich S., Rainforth M., Schneider R., Merz C.N. Effects of a randomized controlled trial of transcendental meditation on components of the metabolic syndrome in subjects with coronary heart disease. Arch. Intern. Med. 2006;166:1218–1224. doi: 10.1001/archinte.166.11.1218.
    1. Kaliman P., Alvarez-López M.J., Cosín-Tomás M., Rosenkranz M.A., Lutz A., Davidson R.J. Rapid changes in histone deacetylases and inflammatory gene expression in expert meditators. Psychoneuroendocrinology. 2014;40:96–107. doi: 10.1016/j.psyneuen.2013.11.004.
    1. Black D.S., Slavich G.M. Mindfulness meditation and the immune system: A systematic review of randomized controlled trials. Ann. N. Y. Acad. Sci. 2016;1373:13–24. doi: 10.1111/nyas.12998.
    1. Epel E.S., Lithgow G.J. Stress biology and aging mechanisms: Toward understanding the deep connection between adaptation to stress and longevity. J. Gerontol. A Biol. Sci. Med. Sci. 2014;69(Suppl. S1):S10–S16. doi: 10.1093/gerona/glu055.
    1. Jacobs T.L., Epel E.S., Lin J., Blackburn E.H., Wolkowitz O.M., Bridwell D.A., Zanesco A.P., Aichele S.R., Sahdra B.K., MacLean K.A., et al. Intensive meditation training, immune cell telomerase activity, and psychological mediators. Psychoneuroendocrinology. 2011;36:664–681. doi: 10.1016/j.psyneuen.2010.09.010.
    1. Schutte N.S., Malouff J.M. A meta-analytic review of the effects of mindfulness meditation on telomerase activity. Psychoneuroendocrinology. 2014;42:45–48. doi: 10.1016/j.psyneuen.2013.12.017.
    1. Thimmapuram J., Pargament R., Sibliss K., Grim R., Risques R., Toorens E. Effect of heartfulness meditation on burnout, emotional wellness, and telomere length in health care professionals. J. Community Hosp. Intern. Med. Perspect. 2017;7:21–27. doi: 10.1080/20009666.2016.1270806.
    1. Conklin Q.A., King B.G., Zanesco A.P., Lin J., Hamidi A.B., Pokorny J.J., Álvarez-López M.J., Cosín-Tomás M., Huang C., Kaliman P., et al. Insight meditation and telomere biology: The effects of intensive retreat and the moderating role of personality. Brain Behav. Immun. 2018;70:233–245. doi: 10.1016/j.bbi.2018.03.003.
    1. Carlson L.E., Beattie T.L., Giese-Davis J., Faris P., Tamagawa R., Fick L.J., Degelman E.S., Speca M. Mindfulness-based cancer recovery and supportive-expressive therapy maintain telomere length relative to controls in distressed breast cancer survivors. Cancer. 2015;121:476–484. doi: 10.1002/cncr.29063.
    1. Lengacher C.A., Reich R.R., Kip K.E., Barta M., Ramesar S., Paterson C.L., Moscoso M.S., Carranza I., Budhrani P.H., Kim S.J., et al. Influence of mindfulness-based stress reduction (MBSR) on telomerase activity in women with breast cancer (BC) Biol. Res. Nurs. 2014;16:438–447. doi: 10.1177/1099800413519495.
    1. Wang X., Sundquist K., Hedelius A., Palmér K., Memon A.A., Sundquist J. Leukocyte telomere length and depression, anxiety and stress and adjustment disorders in primary health care patients. BMC Psychiatr. 2017;17:148. doi: 10.1186/s12888-017-1308-0.
    1. Dal Lin C., Marinova M., Rubino G., Gola E., Brocca A., Pantano G., Brugnolo L., Sarais C., Cucchini U., Volpe B., et al. Thoughts modulate the expression of inflammatory genes and may improve the coronary blood flow in patients after a myocardial infarction. J. Tradit. Complement. Med. 2017;8:150–163. doi: 10.1016/j.jtcme.2017.04.011.
    1. Pavanello S., Stendardo M., Mastrangelo G., Bonci M., Bottazzi B., Campisi M., Nardini M., Leone R., Mantovani A., Boschetto P. Inflammatory long pentraxin 3 is associated with leukocyte telomere length in night-shift workers. Front. Immunol. 2017;8:516. doi: 10.3389/fimmu.2017.00516.
    1. Pavanello S., Angelici L., Hoxha M., Cantone L., Campisi M., Tirelli A.S., Vigna L., Pesatori A.C., Bollati V. Sterol 27-hydroxylase polymorphism significantly associates with shorter telomere, higher cardiovascular and type-2 diabetes risk in obese subjects. Front. Endocrinol. 2018;9:309. doi: 10.3389/fendo.2018.00309.
    1. Livak K.J., Schmittgen T.D. Analysis of relative gene expression data using real-time quantitative PCR and the 2(-Delta Delta C (T)) method. Methods. 2001;25:402–408. doi: 10.1006/meth.2001.1262.
    1. Klengel T., Mehta D., Anacker C., Rex-Haffner M., Pruessner J.C., Pariante C.M., Pace T.W., Mercer K.B., Mayberg H.S., Bradley B., et al. Allele-specific FKBP5 DNA demethylation mediates gene-childhood trauma interactions. Nat. Neurosci. 2013;16:33–41. doi: 10.1038/nn.3275.
    1. Bamberger C.M., Schulte H.M., Chrousos G.P. Molecular determinants of glucocorticoid receptor function and tissue sensitivity to glucocorticoids. Endocr. Rev. 1996;17:245–261. doi: 10.1210/edrv-17-3-245.
    1. Thomassin H., Flavin M., Espinás M.L., Grange T. Glucocorticoid-induced DNA demethylation and gene memory during development. EMBO J. 2001;20:1974–1983. doi: 10.1093/emboj/20.8.1974.
    1. Zannas A.S., Chrousos G.P. Epigenetic programming by stress and glucocorticoids along the human lifespan. Mol. Psychiatr. 2017;22:640–646. doi: 10.1038/mp.2017.35.
    1. Swamynathan S.K. Krüppel-like factors: Three fingers in control. Hum. Genomics. 2010;4:263–270. doi: 10.1186/1479-7364-4-4-263.
    1. Iwaya C., Kitajima H., Yamamoto K., Maeda Y., Sonoda N., Shibata H., Inoguchi T. DNA methylation of the Klf14 gene region in whole blood cells provides prediction for the chronic inflammation in the adipose tissue. Biochem. Biophys. Res. Commun. 2018;497:908–915. doi: 10.1016/j.bbrc.2017.12.104.
    1. Small K.S., Hedman A.K., Grundberg E., Nica A.C., Thorleifsson G., Kong A., Thorsteindottir U., Shin S.Y., Richards H.B., Soranzo N., et al. Identification of an imprinted master trans regulator at the KLF14 locus related to multiple metabolic phenotypes. Nat. Genet. 2011;43:561–564. doi: 10.1038/ng1011-1040c.
    1. Berdasco M., Esteller M. Hot topics in epigenetic mechanisms of aging: 2011. Aging Cell. 2012;11:181–186. doi: 10.1111/j.1474-9726.2012.00806.x.
    1. Steegenga W.T., Boekschoten M.V., Lute C., Hooiveld G.J., De Groot P.J., Morris T.J., Teschendorff A.E., Butcher L.M., Beck S., Müller M. Genome-wide age-related changes in DNA methylation and gene expression in human PBMCs. Age. 2014;36:9648. doi: 10.1007/s11357-014-9648-x.
    1. Epel E.S., Puterman E., Lin J., Blackburn E.H., Lum P.Y., Beckmann N.D., Zhu J., Lee E., Gilbert A., Rissman R.A., et al. Meditation and vacation effects have an impact on disease-associated molecular phenotypes. Transl. Psychiatr. 2016;6:e880. doi: 10.1038/tp.2016.164.
    1. Tolahunase M., Sagar R., Dada R. Impact of yoga and meditation on cellular aging in apparently healthy individuals: A prospective, open-label single-arm exploratory study. Oxid. Med. Cell Longev. 2017;2017:7928981. doi: 10.1155/2017/7928981.
    1. Vyas C.M., Hazra A., Chang S.C., Qiu W., Reynolds C.F., 3rd, Mischoulon D., Chang G., Manson J.E., De Vivo I., Okereke O.I. Pilot study of DNA methylation, molecular aging markers and measures of health and well-being in aging. Transl. Psychiatr. 2019;9:118. doi: 10.1038/s41398-019-0446-1.
    1. Marioni R.E., Harris S.E., Shah S., McRae A.F., Von Zglinicki T., Martin-Ruiz C., Wray N.R., Visscher P.M., Deary I.J. The epigenetic clock and telomere length are independently associated with chronological age and mortality. Int. J. Epidemiol. 2018;47:356. doi: 10.1093/ije/dyx233.
    1. Belsky D.W., Moffitt T.E., Cohen A.A., Corcoran D.L., Levine M.E., Prinz J.A., Schaefer J., Sugden K., Williams B., Poulton R., et al. Eleven telomere, epigenetic clock, and biomarker-composite quantifications of Biological Aging: Do They Measure the Same Thing? Am. J. Epidemiol. 2018;187:1220–1230. doi: 10.1093/aje/kwx346.
    1. Lin J., Smith D.L., Esteves K., Drury S. Telomere length measurement by qPCR—Summary of critical factors and recommendations for assay design. Psychoneuroendocrinology. 2019;99:271–278. doi: 10.1016/j.psyneuen.2018.10.005.
    1. Bischoff C., Petersen H.C., Graakjaer J., Andersen-Ranberg K., Vaupel J.W., Bohr V.A., Kølvraa S., Christensen K. No association between telomere length and survival among the elderly and oldest old. Epidemiology. 2006;17:190–194. doi: 10.1097/01.ede.0000199436.55248.10.
    1. Cassidy A., De Vivo I., Liu Y., Han J., Prescott J., Hunter D.J., Rimm E.B. Associations between diet, lifestyle factors, and telomere length in women. Am. J. Clin. Nutr. 2010;91:1273–1280. doi: 10.3945/ajcn.2009.28947.
    1. Astuti Y., Wardhana A., Watkins J., Wulaningsih W., PILAR Research Network Cigarette smoking and telomere length: A systematic review of 84 studies and meta-analysis. Environ. Res. 2017;158:480–489. doi: 10.1016/j.envres.2017.06.038.

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