The biological age of the heart is consistently younger than chronological age

Sofia Pavanello, Manuela Campisi, Assunta Fabozzo, Giorgia Cibin, Vincenzo Tarzia, Giuseppe Toscano, Gino Gerosa, Sofia Pavanello, Manuela Campisi, Assunta Fabozzo, Giorgia Cibin, Vincenzo Tarzia, Giuseppe Toscano, Gino Gerosa

Abstract

Chronological age represents the main factor in donor selection criteria for organ transplantation, however aging is very heterogeneous. Defining the biological aging of individual organs may contribute to supporting this process. In this study we examined the biological age of the heart [right (RA)/left atrium (LA)] and peripheral blood leucocytes in the same subject, and compared these to assess whether blood mirrors cardiac biological aging. Biological aging was studied in 35 donors (0.4-72 years) by exploring mitotic and non-mitotic pathways, using telomere length (TL) and age-dependent methylation changes in certain CpG loci (DNAmAge). Heart non-mitotic DNAmAge was strongly younger than that of both blood (- 10 years, p < 0.0001) and chronological age (- 12 years, p < 0.0001). Instead, heart and blood mitotic age (TL) were similar, and there was no difference in DNAmAge and TL between RA and LA. DNAmAge negatively correlated with TL in heart and blood (p ≤ 0.01). Finally, blood and heart TL (p < 0.01) and DNAmAge (p < 0.0001) were correlated. Therefore, blood can be a proxy indicator of heart biological age. While future investigation on post-transplant graft performance in relation to biological aging is still needed, our study could contribute to opening up novel basic and clinical research platforms in the field of organ transplantation.

Conflict of interest statement

The authors declare no competing interests.

Figures

Figure 1
Figure 1
In (A) and (B), non-parametric linear regression plots showing correlation between donor chronological age and DNAmAge of the right atrium (RA) and the left atrium (LA) (Kendall’s rank correlation coefficient tau b for RA = 0.741, for LA = 0.852). In (C) and (D), non-parametric linear regression plots showing the correlation between AgeAcc and chronological age of RA in A and LA donors in B (Kendall’s rank correlation coefficient tau b = − 0.589 and tau b = − 0.578). In (E) and (F), non-parametric linear regression plots showing the correlation between DNAmAge of the circulating blood leucocytes (indicated as “blood age”) and the chronological age of the donor in A (Kendall’s rank correlation coefficient tau b = 0.842), whereas in B no correlation is shown between chronological age and blood AgeAcc (Kendall’s rank correlation coefficient tau b = 0.011).
Figure 2
Figure 2
In (A) and (B), non-parametric linear regression plots showing correlation between donor telomere length (T/S) and donor DNAmAge of the right atrium (RA) and the left atrium (LA) (Kendall’s rank correlation coefficient tau b for RA = − 0.440, for LA = − 0.317). In (C), non-parametric linear regression plots showing the correlation between donor telomere length and DNAmAge of the circulating blood leucocytes (indicated as “Blood T/S” and “Blood DNAmAge”) (Kendall’s rank correlation coefficient tau b = − 0.347). Mean, Standard Error (SE) and 95% coefficient intervals (CI) are represented as green, pink and black lines, respectively.
Figure 3
Figure 3
In (A) and (B), non-parametric linear regression plots showing correlation between telomere length (T/S) of the Right Atrium (RA) and the Left Atrium (LA) and donor chronological age (Kendall’s rank correlation coefficient tau b for RA = − 0.446, for LA = − 0.304). In (C), non-parametric linear regression plots showing the correlation between telomere length (T/S) of the circulating blood leucocytes (indicated as “blood T/S”) and the chronological age of the donors (Kendall’s rank correlation coefficient tau b = − 0.472). Mean, Standard Error (SE) and 95% coefficient intervals (CI) are represented as green, pink and black lines, respectively.
Figure 4
Figure 4
In (A) and (B), non-parametric linear regression plots showing correlation between donor DNAmAge of the circulating blood leucocytes (indicated as “Blood DNAmAge”) and the DNAmAge of the Right Atrium (RA) and Left Atrium (LA) (Kendall’s rank correlation coefficient tau b for RA = 0.735, for LA = 0.852). In (C) and (D), non-parametric linear regression plots showing correlation between donor TL of the circulating blood leucocytes (indicated as “Blood T/S”) and the TL of the Right Atrium (RA) and Left Atrium (LA) (Kendall’s rank correlation coefficient tau b for RA = 0.645 and for LA = 0.438). Mean, Standard Error (SE) and 95% coefficient intervals (CI) are represented as green, pink and black lines, respectively.

References

    1. Mehra MR, et al. The 2016 International Society for Heart Lung Transplantation listing criteria for heart transplantation: a 10-year update. J. Heart Lung Transplant. 2016;35:1–23.
    1. Lowsky DJ, Olshansky SJ, Bhattacharya J, Goldman DP. Heterogeneity in healthy aging. J. Gerontol. A Biol. Sci. Med. Sci. 2014;69:640–649.
    1. Pavanello S, et al. Shorter telomere length in peripheral blood lymphocytes of workers exposed to polycyclic aromatic hydrocarbons. Carcinogenesis. 2010;31:216–221.
    1. Pavanello S, et al. Inflammatory long pentraxin 3 is associated with leukocyte telomere length in night-shift workers. Front. Immunol. 2017;8:516.
    1. Pavanello S, et al. Sterol 27-hydroxylase polymorphism significantly associates with shorter telomere, higher cardiovascular and type-2 diabetes risk in obese subjects. Front. Endocrinol. (Lausanne) 2018;9:309.
    1. Shiels PG, Ritzau-Reid K. Biological aging, inflammation and nutrition: how might they impact on systemic sclerosis? Curr. Aging Sci. 2015;8:123–130.
    1. López-Otín C, Blasco MA, Partridge L, Serrano M, Kroemer G. The hallmarks of aging. Cell. 2013;153:1194–1217.
    1. Blackburn EH, Epel ES, Lin J. Human telomere biology: a contributory and interactive factor in aging, disease risks, and protection. Science. 2015;350:1193–1198.
    1. Sidler C, Kovalchuk O, Kovalchuk I. Epigenetic regulation of cellular senescence and aging. Front Genet. 2017;8:138.
    1. Müezzinler A, Zaineddin AK, Brenner H. A systematic review of leukocyte telomere length and age in adults. Ageing Res. Rev. 2013;12:509–519.
    1. Arbeev KG, et al. Association of leukocyte telomere length with mortality among adult participants in 3 longitudinal studies. JAMA Netw. Open. 2020;3:e200023.
    1. Mons U, et al. Leukocyte telomere length and all-cause, cardiovascular disease, and cancer mortality: results from individual-participant-data meta-analysis of 2 large prospective cohort studies. Am. J. Epidemiol. 2017;185:1317–1326.
    1. Lowe D, Horvath S, Raj K. Epigenetic clock analyses of cellular senescence and ageing. Oncotarget. 2016;7:8524–8531.
    1. Horvath S, Raj K. DNA methylation-based biomarkers and the epigenetic clock theory of ageing. Nat. Rev. Genet. 2018;19:371–384.
    1. Horvath S. DNA methylation age of human tissues and cell types. Genome Biol. 2013;14:R115.
    1. Hannum G, et al. Genome-wide methylation profiles reveal quantitative views of human aging rates. Mol. Cell. 2013;49:359–367.
    1. Zbieć-Piekarska R, et al. Development of a forensically useful age prediction method based on DNA methylation analysis. Forensic Sci. Int. Genet. 2015;17:173–179.
    1. Weidner CI, et al. Aging of blood can be tracked by DNA methylation changes at just three CpG sites. Genome Biol. 2014;15:R24.
    1. Pavanello S, Campisi M, Tona F, Dal Lin C, Iliceto S. Exploring epigenetic age in response to intensive relaxing training: a pilot study to slow down biological age. Int. J. Environ. Res. Public Health. 2019;16:3074.
    1. Jung SE, et al. DNA methylation of the ELOVL2, FHL2, KLF14, C1orf132/MIR29B2C, and TRIM59 genes for age prediction from blood, saliva, and buccal swab samples. Forensic Sci. Int. Genet. 2019;38:1–8.
    1. Daunay A, Baudrin LG, Deleuze JF, How-Kit A. Evaluation of six blood-based age prediction models using DNA methylation analysis by pyrosequencing. Sci. Rep. 2019;9:8862.
    1. Cen BH, et al. DNA methylation-based measures of biological age: meta-analysis predicting time to death. Aging (Albany NY) 2016;8:1844–1865.
    1. Fransquet PD, Wrigglesworth J, Woods RL, Ernst ME, Ryan J. The epigenetic clock as a predictor of disease and mortality risk: a systematic review and meta-analysis. Clin. Epigenetics. 2019;11:62.
    1. Zannas AS, et al. Lifetime stress accelerates epigenetic aging in an urban, African American cohort: relevance of glucocorticoid signaling. Genome Biol. 2015;16:266.
    1. Ward-Caviness CK, Nwanaji-Enwerem JC, Wolf K, et al. Long-term exposure to air pollution is associated with biological aging. Oncotarget. 2016;7:74510–74525.
    1. Quach A, et al. Epigenetic clock analysis of diet, exercise, education, and lifestyle factors. Aging (Albany NY) 2017;9:419–446.
    1. Bell CG, et al. DNA methylation aging clocks: challenges and recommendations. Genome Biol. 2019;20:249.
    1. Herbstman JB, et al. Predictors and consequences of global DNA methylation in cord blood and at three years. PLoS ONE. 2013;8:e72824.
    1. Martino DJ, et al. Evidence for age-related and individual-specific changes in DNA methylation profile of mononuclear cells during early immune development in humans. Epigenetics. 2011;6:1085–1094.
    1. Fraga MF, et al. Epigenetic differences arise during the lifetime of monozygotic twins. Proc. Natl. Acad. Sci. USA. 2005;102:10604–10609.
    1. Tan Q, et al. Epigenetic drift in the aging genome: a ten-year follow-up in an elderly twin cohort. Int. J. Epidemiol. 2016;45:1146–1158.
    1. Beltrami AP, et al. Adult cardiac stem cells are multipotent and support myocardial regeneration. Cell. 2003;114:763–776.
    1. Breitling LP, et al. Frailty is associated with the epigenetic clock but not with telomere length in a German cohort. Clin. Epigenetics. 2016;8:21.
    1. Levine ME, et al. Menopause accelerates biological aging. Proc. Natl. Acad. Sci. USA. 2016;113:9327–9332.
    1. Ambatipudi S, et al. DNA methylome analysis identifies accelerated epigenetic ageing associated with postmenopausal breast cancer susceptibility. Eur. J. Cancer. 2017;75:299–307.
    1. Levine ME, Lu AT, Bennett DA, Horvath S. Epigenetic age of the pre-frontal cortex is associated with neuritic plaques, amyloid load, and Alzheimer’s disease related cognitive functioning. Aging (Albany NY) 2015;7:1198–1211.
    1. Marioni RE, et al. The epigenetic clock and telomere length are independently associated with chronological age and mortality. Int. J. Epidemiol. 2018;45:424–432.
    1. Belsky DW, et al. Telomere, epigenetic clock, and biomarker-composite quantifications of biological aging: do they measure the same thing? Am. J. Epidemiol. 2018;187:1220–1230.
    1. Verhulst S, et al. Commentary: the reliability of telomere length measurements. Int. J. Epidemiol. 2015;44:1683–1686.
    1. Tolios A, Teupser D, Holdt LM. Preanalytical conditions and DNA isolation methods affect telomere length quantification in whole blood. PLoS ONE. 2015;10:e0143889.
    1. BLUEPRINT Consortium Quantitative comparison of DNA methylation assays for biomarker development and clinical applications. Nat. Biotechnol. 2016;34:726–737.
    1. Jylhävä J, Pedersen NL, Hägg S. Biological age predictors. EBioMedicine. 2017;21:29–36.
    1. Soma-Pillay P, Nelson-Piercy C, Tolppanen H, Mebazaa A. Physiological changes in pregnancy. Cardiovasc. J. Afr. 2016;27:89–94.
    1. Kuzawa CW, Adair LS, Borja J, Mcdade TW. C-reactive protein by pregnancy and lactational status among Filipino young adult women. Am. J. Hum. Biol. 2013;25:131–134.
    1. Ardehali A, et al. Ex-vivo perfusion of donor hearts for human heart transplantation (PROCEED II): a prospective, open-label, multicentre, randomised non-inferiority trial. Lancet. 2015;385:2577–2584.

Source: PubMed

3
Abonner