دورية أكاديمية

A novel approach to quantifying individual's biological aging using Korea's national health screening program toward precision public health.

التفاصيل البيبلوغرافية
العنوان: A novel approach to quantifying individual's biological aging using Korea's national health screening program toward precision public health.
المؤلفون: Yoo J; YooJin BioSoft, 24, Jeongbalsan-Ro Ilsandong-Gu, Goyang-Si Gyeonggi-Do, 10402, Korea., Hur J; Department of Biomedical Sciences, School of Medicine and Health Sciences, University of North Dakota, Grand Forks, ND, 58202, USA., Yoo J; YooJin BioSoft, 24, Jeongbalsan-Ro Ilsandong-Gu, Goyang-Si Gyeonggi-Do, 10402, Korea., Jurivich D; Department of Geriatrics, School of Medicine and Health Sciences, University of North Dakota, Grand Forks, ND, 58202, USA., Lee KJ; Department of Women's Rehabilitation, National Rehabilitation Center, 58, Samgaksan-Ro, Gangbuk-Gu, Seoul, 01022, Korea. drlkj4094@korea.kr.; Institute for Occupational & Environmental Health, Korea University, Seoul, 02841, Korea. drlkj4094@korea.kr.
المصدر: GeroScience [Geroscience] 2024 Jun; Vol. 46 (3), pp. 3387-3403. Date of Electronic Publication: 2024 Feb 02.
نوع المنشور: Journal Article; Research Support, Non-U.S. Gov't
اللغة: English
بيانات الدورية: Publisher: Springer International Publishing Country of Publication: Switzerland NLM ID: 101686284 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 2509-2723 (Electronic) Linking ISSN: 25092723 NLM ISO Abbreviation: Geroscience Subsets: MEDLINE
أسماء مطبوعة: Original Publication: Cham : Springer International Publishing, [2017]-
مواضيع طبية MeSH: Public Health* , Aging*/physiology, Humans ; Aged ; Proportional Hazards Models ; Biomarkers ; Republic of Korea
مستخلص: Accurate prediction of biological age can inform public health measures to extend healthy lifespans and reduce chronic conditions. Multiple theoretical models and methods have been developed; however, their applicability and accuracy are still not extensive. Here, we report Differential Aging and Health Index (DAnHI), a novel measure of age deviation, developed using physical and serum biomarkers from four million individuals in Korea's National Health Screening Program. Participants were grouped into aging statuses (< 26 vs. ≥ 26, < 27 vs. ≥ 27, …, < 75 vs. ≥ 75 years) as response variables in a binary logistic regression model with thirteen biomarkers as independent variables. DAnHI for each individual was calculated as the weighted mean of their relative probabilities of being classified into each older age status, based on model ages ranging from 26 to 75. DAnHI in our large study population showed a steady increase with the increase in age and was positively associated with death after adjusting for chronological age. However, the effect size of DAnHI on the risk of death varied according to the age group and sex. The hazard ratio was highest in the 50-59-year age group and then decreased as the individuals aged. This study demonstrates that routine health check-up biomarkers can be integrated into a quantitative measure for predicting aging-related health status and death via appropriate statistical models and methodology. Our DAnHI-based results suggest that the same level of aging-related health status does not indicate the same degree of risk for death.
(© 2024. The Author(s).)
References: The Global Burden of Disease: a critical resource for informed policymaking. https://www.healthdata.org/gbd . Accessed 23 Aug 2023.
Kontis V, Bennett JE, Mathers CD, Li G, Foreman K, Ezzati M. Future life expectancy in 35 industrialised countries: projections with a Bayesian model ensemble. Lancet. 2017;389(10076):1323–35. https://doi.org/10.1016/S0140-6736(16)32381-9 . (PMID: 10.1016/S0140-6736(16)32381-9282364645387671)
Cleries R, et al. Life expectancy and age-period-cohort effects: analysis and projections of mortality in Spain between 1977 and 2016. Public Health. 2009;123(2):156–62. https://doi.org/10.1016/j.puhe.2008.10.026 . (PMID: 10.1016/j.puhe.2008.10.02619157468)
GBDRF Collaborators. Global burden of 87 risk factors in 204 countries and territories, 1990-2019: a systematic analysis for the Global Burden of Disease Study 2019. Lancet. 2022;396(10258):1223–49. https://doi.org/10.1016/S0140-6736(20)30752-2 . (PMID: 10.1016/S0140-6736(20)30752-2)
World Health Organization. Noncommunicable diseases. 2022. https://www.who.int/news-room/fact-sheets/detail/noncommunicable-diseases . Accessed 5 Dec 2023.
Hou Y, Dan X, Babbar M, Wei Y, Hasselbalch SG, Croteau DL, Bohr VA. Ageing as a risk factor for neurodegenerative disease. Nat Rev Neurol. 2019;15(10):565–81. https://doi.org/10.1038/s41582-019-0244-7 . (PMID: 10.1038/s41582-019-0244-731501588)
Tian YE, Cropley V, Maier AB, Lautenschlager NT, Breakspear M, Zalesky A. Heterogeneous aging across multiple organ systems and prediction of chronic disease and mortality. Nat Med. 2023;29(5):1221–31. https://doi.org/10.1038/s41591-023-02296-6 . (PMID: 10.1038/s41591-023-02296-637024597)
Moqri M, et al. Biomarkers of aging for the identification and evaluation of longevity interventions. Cell. 2023;186(18):3758–75. https://doi.org/10.1016/j.cell.2023.08.003 . (PMID: 10.1016/j.cell.2023.08.00337657418)
Khan SS, Singer BD, Vaughan DE. Molecular and physiological manifestations and measurement of aging in humans. Aging Cell. 2017;16(4):624–33. https://doi.org/10.1111/acel.12601 . (PMID: 10.1111/acel.12601285441585506433)
Elliott ML, et al. Disparities in the pace of biological aging among midlife adults of the same chronological age have implications for future frailty risk and policy. Nat Aging. 2021;1(3):295–308. https://doi.org/10.1038/s43587-021-00044-4 . (PMID: 10.1038/s43587-021-00044-4337968688009092)
Dubina TL, Mints A, Zhuk EV. Biological age and its estimation. III. Introduction of a correction to the multiple regression model of biological age in cross-sectional and longitudinal studies. Exp Gerontol. 1984;19(2):133–43. https://doi.org/10.1016/0531-5565(84)90016-0 .
Hollingsworth JW, Hashizume A, Jablon S. Correlations between tests of aging in Hiroshima subjects–an attempt to define “physiologic age.” Yale J Biol Med. 1965;8(1):11–26.
Krøll J, Saxtru O. On the use of regression analysis for the estimation of human biological age. Biogerontology. 2000;1(4):363–8. (PMID: 10.1023/A:102659460225211708217)
Takeda H, Inada H, Inoue M, Yoshikawa H, Abe H. Evaluation of biological age and physical age by multiple regression analysis. Medical Informatics. 1982;7(3):221–7. (PMID: 10.3109/146392382090107207162237)
Voitenko V, Tokar A. The assessment of biological age and sex differences of human aging. Experimental Aging Res. 1983;9(4):239–44. (PMID: 10.1080/03610738308258458)
Nakamura E, Lane MA, Roth GS, Ingram DK. A strategy for identifying biomarkers of aging: further evaluation of hematology and blood chemistry data from a calorie restriction study in rhesus monkeys. Experimental Gerontol. 1998;33(5):421–43. (PMID: 10.1016/S0531-5565(97)00134-4)
Yoo J, Kim Y, Cho ER, Jee SH. Biological age as a useful index to predict seventeen-year survival and mortality in Koreans. BMC Geriatr. 2017;17(1):1–10. (PMID: 10.1186/s12877-016-0407-y)
Hochschild R. Improving the precision of biological age determinations. Part 1: a new approach to calculating biological age. Experimental Gerontol. 1989;24(4):289–300. (PMID: 10.1016/0531-5565(89)90002-8)
Hochschild R. Improving the precision of biological age determinations. Part 2: Automatic human tests, age norms and variability. Experimental Gerontol. 1989;24(4):301–16. (PMID: 10.1016/0531-5565(89)90003-X)
Hochschild R. Validating biomarkers of aging—mathematical approaches and results of a 2462-person study. Practical Handbook of Human Biologic Age Determination; CRC Press: Boca Raton, FL, USA, 1994;93–144.
Klemera P, Doubal S. A new approach to the concept and computation of biological age. Mech Ageing Dev. 2006;127(3):240–8. (PMID: 10.1016/j.mad.2005.10.00416318865)
Pyrkov TV, et al. Extracting biological age from biomedical data via deep learning: too much of a good thing? Sci Rep. 2018;8(1):5210. https://doi.org/10.1038/s41598-018-23534-9 . (PMID: 10.1038/s41598-018-23534-9295814675980076)
Mamoshina P, et al. Population Specific Biomarkers of Human Aging: A Big Data Study Using South Korean, Canadian, and Eastern European Patient Populations. J Gerontol A Biol Sci Med Sci. 2018;73(11):1482–90. https://doi.org/10.1093/gerona/gly005 . (PMID: 10.1093/gerona/gly005293405806175034)
Ashiqur Rahman S. P Giacobbi, L Pyles, C Mullett, G Doretto, and DA Adjeroh. Deep learning for biological age estimation. Brief Bioinform. 2021;22(2):1767–81. https://doi.org/10.1093/bib/bbaa021 . (PMID: 10.1093/bib/bbaa02132363395)
Cao X, et al. A Machine Learning-Based Aging Measure Among Middle-Aged and Older Chinese Adults: The China Health and Retirement Longitudinal Study. Front Med. 2021;8:698851. https://doi.org/10.3389/fmed.2021.698851 . (PMID: 10.3389/fmed.2021.698851)
Dubina TL, Dyundikova VA, Zhuk EV. Biological age and its estimation. II. Assessment of biological age of albino rats by multiple regression analysis. Exp Gerontol. 1983;18(1):5–18. https://doi.org/10.1016/0531-5565(83)90046-3 . (PMID: 10.1016/0531-5565(83)90046-36873212)
Webster IW, Logie AR. A relationship between functional age and health status in female subjects. J Gerontol. 1976;31(5):546–50. https://doi.org/10.1093/geronj/31.5.546 . (PMID: 10.1093/geronj/31.5.546950448)
Hüllermeier E, Waegeman W. Aleatoric and epistemic uncertainty in machine learning: An introduction to concepts and methods. Mach Learn. 2021;110:457–506. (PMID: 10.1007/s10994-021-05946-3)
Deelen J, et al. A metabolic profile of all-cause mortality risk identified in an observational study of 44,168 individuals. Nat Commun. 2019;10(1):3346. https://doi.org/10.1038/s41467-019-11311-9 . (PMID: 10.1038/s41467-019-11311-9314316216702196)
Butler RN, et al. Biomarkers of aging: from primitive organisms to humans. J Gerontol A Biol Sci Med Sci. 2004;59(6):B560–7. https://doi.org/10.1093/gerona/59.6.b560 . (PMID: 10.1093/gerona/59.6.b56015215265)
Johnson TE. Recent results: biomarkers of aging. Exp Gerontol. 2006;41(12):1243–6. https://doi.org/10.1016/j.exger.2006.09.006 . (PMID: 10.1016/j.exger.2006.09.00617071038)
Lara J, et al. A proposed panel of biomarkers of healthy ageing. BMC Med. 2015;13:222. https://doi.org/10.1186/s12916-015-0470-9 . (PMID: 10.1186/s12916-015-0470-9263739274572626)
Engelfriet PM, Jansen EH, Picavet HS, Dolle ME. Biochemical markers of aging for longitudinal studies in humans. Epidemiol Rev. 2013;35:132–51. https://doi.org/10.1093/epirev/mxs011 . (PMID: 10.1093/epirev/mxs011233824774707878)
Hagg S and J Jylhava. Sex differences in biological aging with a focus on human studies. Elife. 2021;10 https://doi.org/10.7554/eLife.63425 .
Pomatto LCD and KJA Davies. The role of declining adaptive homeostasis in ageing. J Physiol. 2017;595(24):7275–309. https://doi.org/10.1113/JP275072 . (PMID: 10.1113/JP275072)
Ohlsson C, Vandenput L, Tivesten A. DHEA and mortality: what is the nature of the association? J Steroid Biochem Mol Biol. 2015;145:248–53. https://doi.org/10.1016/j.jsbmb.2014.03.006 . (PMID: 10.1016/j.jsbmb.2014.03.00624704256)
Desai M, Rachet B, Coleman MP, McKee M. Two countries divided by a common language: health systems in the UK and USA. J R Soc Med. 2010;103(7):283–7. https://doi.org/10.1258/jrsm.2010.100126 . (PMID: 10.1258/jrsm.2010.100126205955322895526)
Maioli S, Leander K, Nilsson P, Nalvarte I. Estrogen receptors and the aging brain. Essays Biochem. 2021;65(6):913–25. https://doi.org/10.1042/EBC20200162 . (PMID: 10.1042/EBC20200162346234018628183)
Lizcano F, Guzman G. Estrogen Deficiency and the Origin of Obesity during Menopause. Biomed Res Int. 2014;2014:757461. https://doi.org/10.1155/2014/757461 . (PMID: 10.1155/2014/757461247342433964739)
Regitz-Zagrosek V, Lehmkuhl E, Mahmoodzadeh S. Gender aspects of the role of the metabolic syndrome as a risk factor for cardiovascular disease. Gend Med. 2007;4 Suppl B:162–77. https://doi.org/10.1016/s1550-8579(07)80056-8 . (PMID: 10.1016/s1550-8579(07)80056-8)
Mauvais-Jarvis F. Sex differences in metabolic homeostasis, diabetes, and obesity. Biol Sex Differ. 2015;6:14. https://doi.org/10.1186/s13293-015-0033-y . (PMID: 10.1186/s13293-015-0033-y263394684559072)
Sciomer S, Moscucci F, Salvioni E, Marchese G, Bussotti M, Corra U, Piepoli MF. Role of gender, age and BMI in prognosis of heart failure. Eur J Prev Cardiol. 2020;27(2):46–51. https://doi.org/10.1177/2047487320961980 . (PMID: 10.1177/2047487320961980332387367691623)
Alharbi BA, et al. Association of elderly age and chronic illnesses: Role of gender as a risk factor. J Family Med Prim Care. 2020;9(3):1684–90. https://doi.org/10.4103/jfmpc.jfmpc&#95;1060&#95;19 . (PMID: 10.4103/jfmpc.jfmpc_1060_19325096727266230)
Thomas N, Gurvich C, Kulkarni J. Sex Differences in Aging and Associated Biomarkers. Adv Exp Med Biol. 2019;1178:57–76. https://doi.org/10.1007/978-3-030-25650-0&#95;4 . (PMID: 10.1007/978-3-030-25650-0_431493222)
Food, D Administration, and NIo Health. BEST (Biomarkers, Endpoints, and other tools) resource. Silver Spring, MD: FDA-NIH Biomarker Working Group, 2016.
معلومات مُعتمدة: 10443144 Korea Health Industry Development Institute
فهرسة مساهمة: Keywords: Biological Aging; Differential Aging and Health Index; Mortality Prediction; Sex-Specific Aging
المشرفين على المادة: 0 (Biomarkers)
تواريخ الأحداث: Date Created: 20240201 Date Completed: 20240415 Latest Revision: 20240722
رمز التحديث: 20240723
مُعرف محوري في PubMed: PMC11009216
DOI: 10.1007/s11357-024-01079-2
PMID: 38302843
قاعدة البيانات: MEDLINE
الوصف
تدمد:2509-2723
DOI:10.1007/s11357-024-01079-2