Quantification of Biological Aging in Young Adults
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Quantification of biological aging in young adults Daniel W. Belskya,b,1, Avshalom Caspic,d,e,f, Renate Houtsc, Harvey J. Cohena, David L. Corcorane, Andrea Danesef,g, HonaLee Harringtonc, Salomon Israelh, Morgan E. Levinei, Jonathan D. Schaeferc, Karen Sugdenc, Ben Williamsc, Anatoli I. Yashinb, Richie Poultonj, and Terrie E. Moffittc,d,e,f aDepartment of Medicine, Duke University School of Medicine, Durham, NC 27710; bSocial Science Research Institute, Duke University, Durham, NC 27708; cDepartment of Psychology & Neuroscience, Duke University, Durham, NC 27708; dDepartment of Psychiatry & Behavioral Sciences, Duke University School of Medicine, Durham, NC 27708; eCenter for Genomic and Computational Biology, Duke University, Durham, NC 27708; fSocial, Genetic, & Developmental Psychiatry Research Centre, Institute of Psychiatry, Kings College London, London SE5 8AF, United Kingdom; gDepartment of Child & Adolescent Psychiatry, Institute of Psychiatry, King’s College London, London SE5 8AF, United Kingdom; hDepartment of Psychology, The Hebrew University of Jerusalem, Jerusalem 91905, Israel; iDepartment of Human Genetics, Gonda Research Center, David Geffen School of Medicine, University of California, Los Angeles, CA 90095; and jDepartment of Psychology, University of Otago, Dunedin 9016, New Zealand Edited by Bruce S. McEwen, The Rockefeller University, New York, NY, and approved June 1, 2015 (received for review March 30, 2015) Antiaging therapies show promise in model organism research. trajectories, before most diseases (and regimens to manage Translation to humans is needed to address the challenges of an them) become established. The main obstacle to studying aging aging global population. Interventions to slow human aging will before old age—and before the onset of age-related diseases— need to be applied to still-young individuals. However, most human is the absence of methods to quantify the Pace of Aging in aging research examines older adults, many with chronic disease. As young humans. a result, little is known about aging in young humans. We studied We studied aging in a population-representative 1972–1973 birth aging in 954 young humans, the Dunedin Study birth cohort, cohort of 1,037 young adults followed from birth to age 38 y with tracking multiple biomarkers across three time points spanning 95% retention: the Dunedin Study (SI Appendix). When they were their third and fourth decades of life. We developed and validated 38 y old, we examined their physiologies to test whether this young two methods by which aging can be measured in young adults, population would show evidence of individual variation in aging one cross-sectional and one longitudinal. Our longitudinal mea- despite remaining free of age-related disease. We next tested the sure allows quantification of the pace of coordinated physiological hypothesis that cohort members with “older” physiologies at age 38 deterioration across multiple organ systems (e.g., pulmonary, had actually been aging faster than their same chronologically aged periodontal, cardiovascular, renal, hepatic, and immune function). peers who retained “younger” physiologies; specifically, we tested We applied these methods to assess biological aging in young whether indicators of the integrity of their cardiovascular, meta- humans who had not yet developed age-related diseases. Young bolic, and immune systems, their kidneys, livers, gums, and lungs, individuals of the same chronological age varied in their “biological and their DNA had deteriorated more rapidly according to mea- aging” (declining integrity of multiple organ systems). Already, surements taken repeatedly since a baseline 12 y earlier at age 26. before midlife, individuals who were aging more rapidly were less We further tested whether, by midlife, young adults who were physically able, showed cognitive decline and brain aging, self- aging more rapidly already exhibited deficits in their physical reported worse health, and looked older. Measured biological functioning, showed signs of early cognitive decline, and looked aging in young adults can be used to identify causes of aging older to independent observers. and evaluate rejuvenation therapies. Results biological aging | cognitive aging | aging | healthspan | geroscience Are Young Adults Aging at Different Rates? Measuring the aging pro- cess is controversial. Candidate biomarkers of aging are numerous, y 2050, the world population aged 80 y and above will more Bthan triple, approaching 400 million individuals (1, 2). As the Significance population ages, the global burden of disease and disability is rising (3). From the fifth decade of life, advancing age is asso- The global population is aging, driving up age-related disease ciated with an exponential increase in burden from many different morbidity. Antiaging interventions are needed to reduce the chronic conditions (Fig. 1). The most effective means to reduce burden of disease and protect population productivity. Young disease burden and control costs is to delay this progression by people are the most attractive targets for therapies to extend extending healthspan, years of life lived free of disease and dis- healthspan (because it is still possible to prevent disease in the ability (4). A key to extending healthspan is addressing the prob- young). However, there is skepticism about whether aging – lem of aging itself (5 8). processes can be detected in young adults who do not yet have At present, much research on aging is being carried out with chronic diseases. Our findings indicate that aging processes can animals and older humans. Paradoxically, these seemingly sen- be quantified in people still young enough for prevention of sible strategies pose translational difficulties. The difficulty with age-related disease, opening a new door for antiaging thera- studying aging in old humans is that many of them already have pies. The science of healthspan extension may be focused on age-related diseases (9–11). Age-related changes to physiology the wrong end of the lifespan; rather than only studying old accumulate from early life, affecting organ systems years before humans, geroscience should also study the young. disease diagnosis (12–15). Thus, intervention to reverse or delay the march toward age-related diseases must be scheduled while Author contributions: D.W.B., A.C., R.P., and T.E.M. designed research; D.W.B., A.C., R.H., H.J.C., D.L.C., A.D., H.H., S.I., M.E.L., J.D.S., K.S., B.W., A.I.Y., R.P., and T.E.M. performed people are still young (16). Early interventions to slow aging can research; M.E.L. contributed new reagents/analytic tools; D.W.B., A.C., R.H., H.H., and be tested in model organisms (17, 18). The difficulty with these T.E.M. analyzed data; and D.W.B., A.C., and T.E.M. wrote the paper. nonhuman models is that they do not typically capture the The authors declare no conflict of interest. complex multifactorial risks and exposures that shape human This article is a PNAS Direct Submission. aging. Moreover, whereas animals’ brief lives make it feasible to Freely available online through the PNAS open access option. ’ study animal aging in the laboratory, humans lives span many 1To whom correspondence should be addressed. Email: [email protected]. years. A solution is to study human aging in the first half of the This article contains supporting information online at www.pnas.org/lookup/suppl/doi:10. life course, when individuals are starting to diverge in their aging 1073/pnas.1506264112/-/DCSupplemental. E4104–E4110 | PNAS | Published online July 6, 2015 www.pnas.org/cgi/doi/10.1073/pnas.1506264112 Downloaded by guest on October 2, 2021 PNAS PLUS Fig. 1. Burden of chronic disease rises exponentially with age. To examine the association between age and disease burden, we accessed data from the Institute for Health Metrics and Evaluation Global Burden of Disease database (www.healthdata.org/gbd) (43). Data graph (A) disability-adjusted life years (DALYs) and (B) deaths per 100,000 population by age. Bars, from bottom to top, reflect the burden of cardiovascular disease (navy), type-2 diabetes (light blue), stroke (lavender), chronic respiratory disease (red), and neurological disorders (purple). but findings are mixed (19–22). Multibiomarker algorithms have (i.e., scores were reversed for cardiorespiratory fitness, lung func- been suggested as a more reliable alternative to single-marker aging tion, leukocyte telomere length, creatinine clearance, and high indicators (23–25). A promising algorithm is the 10-biomarker US density lipoprotein cholesterol, for which values are expected to National Health and Nutrition Survey (NHANES)-based measure of decline with increasing chronological age). Even in our cohort of “Biological Age.” In more than 9,000 NHANES participants aged young adults, biomarkers showed a pattern of age-dependent de- 30–75 y at baseline, Biological Age outperformed chronological age cline in the functioning of multiple organ system over the 12-y fol- in predicting mortality over a two-decade follow-up (26). Be- low-up period (Fig. 3). Second, we used mixed-effects growth cause NHANES participants were all surveyed at one time point, models to calculate each study member’s personal slope for each of age differences in biomarker levels were not independent of co- the 18 biomarkers; 954 individuals with repeated measures of bio- hort effects; measured aging also included secular trends in en- markers contributed data to this analysis. Of the 51,516 potential vironmental