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Assessing the utility of epigenetic clocks for health prediction in South Korean

Kim, Dong Jun, Kang, Joon Ho, Kim, Ji-Woong, Kim, Sun bin, Lee, Young Kee, Cheon, Myeong Jae, Lee, Byung-Chul·2024·Frontiers in Aging
DOI 10.3389/fragi.2024.1493406PMC PMC11646986
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Abstract

Epigenetic clocks have been developed to track both chronological age and biological age, which is defined by physiological biomarkers and the risk of adverse health outcomes. Epigenetic age acceleration (EAA) has been found to predict various diseases, aging-related factors, and mortality. However, epigenetic clocks have predominantly been developed with individuals of European or Hispanic ancestry, and their association with health outcomes and environmental factors has not been sufficiently assessed in East Asian populations. Here, we investigated nine epigenetic clocks: five trained on chronological age (first-generation) and four on biological age (second-generation), using DNA methylation data from blood samples of South Koreans. EAAs of second-generation epigenetic clocks reflected the risk of chronic diseases (type 2 diabetes and hypertension), levels of health-related blood markers (alanine aminotransferase, aspartate aminotransferase, high density lipoprotein, triglyceride, and high sensitivity C-reactive protein), and lung functions (percentage of predicted FEV1 and percentage of predicted FVC), while EAAs of first generation clocks did not. Using follow-up data, we also found that EAAs of second-generation clocks were associated with the time to onset risks of chronic diseases. Health behavior factors (drinking, smoking, exercise, body mass index, and waist-hip ratio), socioeconomic status (income level and educational attainment), and psychosocial status were associated with EAAs of second-generation clocks, while only smoking status was associated with EAAs of first-generation clocks. We conducted validation analyses in an independent South Korean cohort and replicated the association of EAAs with health outcomes and environmental factors. Age acceleration of epigenetic clocks is influenced by various environmental factors and can serve as an effective predictor of health in South Korea.

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FIGURE 1 Correlation matrix of epigenetic age accelerations. Positive correlations are denoted by red shades, with lighter shades signifying weaker correlation values.
FIGURE 1 Correlation matrix of epigenetic age accelerations. Positive correlations are denoted by red shades, with lighter shades signifying weaker correlation values.CC BY · PubMed Central
FIGURE 2 Forest plots for epigenetic age accelerations (EAAs) of second-generation clocks and health outcomes in KARE. Odds ratios (ORs) with 95% confidence intervals (CIs) or beta values with standard errors (SEs) were displayed along with their corresponding p-values. All regression models adjusted for age, sex, and 10 principal components, with all EAAs scaled to a mean of 0 and a standard deviation of 1. Significant results were highlighted with a blue background based on Bonferroni-corrected p-values [0.05/(4 × 9) = 1.39E-03].
FIGURE 2 Forest plots for epigenetic age accelerations (EAAs) of second-generation clocks and health outcomes in KARE. Odds ratios (ORs) with 95% confidence intervals (CIs) or beta values with standard errors (SEs) were displayed along with their corresponding p-values. All regression models adjusted for age, sex, and 10 principal components, with all EAAs scaled to a mean of 0 and a standard deviation of 1. Significant results were highlighted with a blue background based on Bonferroni-corrected p-values [0.05/(4 × 9) = 1.39E-03].CC BY · PubMed Central
FIGURE 3 Forest plots for epigenetic age accelerations (EAAs) of second-generation clocks and environmental factors in KARE. Beta values with standard errors (SEs) were displayed along with their corresponding p-values. All regression models adjusted for age, sex, and 10 principal components, with all EAAs scaled to a mean of 0 and a standard deviation of 1. Significant results were highlighted with a blue background based on Bonferroni-corrected p-values [0.05/(4 × 9) = 1.39E-03].
FIGURE 3 Forest plots for epigenetic age accelerations (EAAs) of second-generation clocks and environmental factors in KARE. Beta values with standard errors (SEs) were displayed along with their corresponding p-values. All regression models adjusted for age, sex, and 10 principal components, with all EAAs scaled to a mean of 0 and a standard deviation of 1. Significant results were highlighted with a blue background based on Bonferroni-corrected p-values [0.05/(4 × 9) = 1.39E-03].CC BY · PubMed Central

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Citations

Cites

Research works printed but not linked: 37

  1. Aryee, 2014, Minfi: a flexible and comprehensive Bioconductor package for the analysis of Infinium DNA methylation microarrays, Bioinformatics, 30, 1363, 10.1093/bioinformatics/btu049 · DOI
  2. Aune, 2021, Hypertension and the risk of all-cause and cause-specific mortality: an outcome-wide association study of 67 causes of death in the national health interview survey, Biomed. Res. Int., 2021, 9376134, 10.1155/2021/9376134 · DOI
  3. Belsky, 2022, DunedinPACE, a DNA methylation biomarker of the pace of aging, Elife, 11, e73420, 10.7554/eLife.73420 · DOI · Paper page
  4. Bernabeu, 2023, Refining epigenetic prediction of chronological and biological age, Genome Med., 15, 12, 10.1186/s13073-023-01161-y · DOI
  5. Chen, 2022, Elevated AST/ALT ratio is associated with all-cause mortality and cancer incident, J. Clin. Lab. Anal., 36, e24356, 10.1002/jcla.24356 · DOI
  6. Crimmins, 2021, Associations of age, sex, race/ethnicity, and education with 13 epigenetic clocks in a nationally representative U.S. Sample: the health and retirement study, J. Gerontol. A Biol. Sci. Med. Sci., 76, 1117, 10.1093/gerona/glab016 · DOI · Paper page
  7. Faul, 2023, Epigenetic-based age acceleration in a representative sample of older Americans: associations with aging-related morbidity and mortality, Proc. Natl. Acad. Sci. U. S. A., 120, e2215840120, 10.1073/pnas.2215840120 · DOI · Paper page
  8. Hannum, 2013, Genome-wide methylation profiles reveal quantitative views of human aging rates, Mol. Cell., 49, 359, 10.1016/j.molcel.2012.10.016 · DOI · Paper page
  9. 2015, The Health Examinees (HEXA) study: rationale, study design and baseline characteristics, Asian Pac J. Cancer Prev., 16, 1591, 10.7314/apjcp.2015.16.4.1591 · DOI
  10. Higgins-Chen, 2022, A computational solution for bolstering reliability of epigenetic clocks: implications for clinical trials and longitudinal tracking, Nat. Aging, 2, 644, 10.1038/s43587-022-00248-2 · DOI · Paper page
  11. Horvath, 2013, DNA methylation age of human tissues and cell types, Genome Biol., 14, R115, 10.1186/gb-2013-14-10-r115 · DOI · Paper page
  12. Horvath, 2016, An epigenetic clock analysis of race/ethnicity, sex, and coronary heart disease, Genome Biol., 17, 171, 10.1186/s13059-016-1030-0 · DOI
  13. Horvath, 2018, DNA methylation-based biomarkers and the epigenetic clock theory of ageing, Nat. Rev. Genet., 19, 371, 10.1038/s41576-018-0004-3 · DOI · Paper page
  14. Jain, 2022, Analysis of epigenetic age acceleration and healthy longevity among older US women, JAMA Netw. Open, 5, e2223285, 10.1001/jamanetworkopen.2022.23285 · DOI
  15. Kim, 2017, Cohort profile: the Korean Genome and Epidemiology study (KoGES) consortium, Int. J. Epidemiol., 46, 1350, 10.1093/ije/dyx105 · DOI
  16. Leek, 2012, The sva package for removing batch effects and other unwanted variation in high-throughput experiments, Bioinformatics, 28, 882, 10.1093/bioinformatics/bts034 · DOI · Paper page
  17. Levine, 2018, An epigenetic biomarker of aging for lifespan and healthspan, Aging (Albany NY), 10, 573, 10.18632/aging.101414 · DOI · Paper page
  18. Li, 2017, Hs-CRP and all-cause, cardiovascular, and cancer mortality risk: a meta-analysis, Atherosclerosis, 259, 75, 10.1016/j.atherosclerosis.2017.02.003 · DOI
  19. Lin, 2021, Associations of five obesity metrics with epigenetic age acceleration: evidence from 2,474 Taiwan Biobank participants, Obes. (Silver Spring), 29, 1731, 10.1002/oby.23255 · DOI
  20. Liu, 2013, Effects of blood triglycerides on cardiovascular and all-cause mortality: a systematic review and meta-analysis of 61 prospective studies, Lipids Health Dis., 12, 159, 10.1186/1476-511X-12-159 · DOI
  21. Lu, 2019, DNA methylation GrimAge strongly predicts lifespan and healthspan, Aging (Albany NY), 11, 303, 10.18632/aging.101684 · DOI · Paper page
  22. Maddock, 2020, DNA methylation age and physical and cognitive aging, J. Gerontol. A Biol. Sci. Med. Sci., 75, 504, 10.1093/gerona/glz246 · DOI
  23. Magnussen, 2017, FEV1 and FVC predict all-cause mortality independent of cardiac function - results from the population-based Gutenberg Health Study, Int. J. Cardiol., 234, 64, 10.1016/j.ijcard.2017.02.012 · DOI
  24. McCrory, 2021, GrimAge outperforms other epigenetic clocks in the prediction of age-related clinical phenotypes and all-cause mortality, J. Gerontol. A Biol. Sci. Med. Sci., 76, 741, 10.1093/gerona/glaa286 · DOI · Paper page
  25. Palma-Gudiel, 2020, Psychosocial stress and epigenetic aging, Int. Rev. Neurobiol., 150, 107, 10.1016/bs.irn.2019.10.020 · DOI
  26. Petrie, 2018, Diabetes, hypertension, and cardiovascular disease: clinical insights and vascular mechanisms, Can. J. Cardiol., 34, 575, 10.1016/j.cjca.2017.12.005 · DOI · Paper page
  27. Rockl, 2017, All-cause mortality in adults with and without type 2 diabetes: findings from the national health monitoring in Germany, BMJ Open Diabetes Res. Care, 5, e000451, 10.1136/bmjdrc-2017-000451 · DOI
  28. Ryan, 2020, A systematic review and meta-analysis of environmental, lifestyle, and health factors associated with DNA methylation age, J. Gerontol. A Biol. Sci. Med. Sci., 75, 481, 10.1093/gerona/glz099 · DOI
  29. Schmitz, 2022, The socioeconomic gradient in epigenetic ageing clocks: evidence from the multi-ethnic study of atherosclerosis and the health and retirement study, Epigenetics, 17, 589, 10.1080/15592294.2021.1939479 · DOI
  30. Tomo, 2024, Transfer elastic Net for developing epigenetic clocks for the Japanese population, Mathematics, 12, 2716, 10.3390/math12172716 · DOI
  31. Wang, 2024, Insights into ageing rates comparison across tissues from recalibrating cerebellum DNA methylation clock, Geroscience, 46, 39, 10.1007/s11357-023-00871-w · DOI
  32. Wu, 2019, Effect of tobacco smoking on the epigenetic age of human respiratory organs, Clin. Epigenetics, 11, 183, 10.1186/s13148-019-0777-z · DOI
  33. Yaskolka, 2021, Lifestyle weight-loss intervention may attenuate methylation aging: the CENTRAL MRI randomized controlled trial, Clin. Epigenetics, 13, 48, 10.1186/s13148-021-01038-0 · DOI
  34. Yousefi, 2022, DNA methylation-based predictors of health: applications and statistical considerations, Nat. Rev. Genet., 23, 369, 10.1038/s41576-022-00465-w · DOI
  35. Zhang, 2023, New insights into the genetics and epigenetics of aging plasticity, Genes (Basel), 14, 329, 10.3390/genes14020329 · DOI
  36. Zheng, 2024, DNA methylation clocks for estimating biological age in Chinese cohorts, Protein Cell, 15, 575, 10.1093/procel/pwae011 · DOI
  37. Zhong, 2020, HDL-C is associated with mortality from all causes, cardiovascular disease and cancer in a J-shaped dose-response fashion: a pooled analysis of 37 prospective cohort studies, Eur. J. Prev. Cardiol., 27, 1187, 10.1177/2047487320914756 · DOI

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Metadata

Authors
Kim, D. J., Kang, J. H., Kim, J.-W., Kim, S. B., Lee, Y. K., Cheon, M. J., & Lee, B.-C.
Venue
Frontiers in Aging
Publisher
Frontiers Media SA
Published
2024-12-02
Volume
5
ISSN
2673-6217
DOI
10.3389/fragi.2024.1493406
PMC
PMC11646986
Cited by
10
References
7
Licence
creativecommons.org/licenses/by/4.0/ (vor)

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