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. 2015 Jul 28;112(30):E4104-10.
doi: 10.1073/pnas.1506264112. Epub 2015 Jul 6.

Quantification of Biological Aging in Young Adults

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Free PMC article

Quantification of Biological Aging in Young Adults

Daniel W Belsky et al. Proc Natl Acad Sci U S A. .
Free PMC article

Abstract

Antiaging therapies show promise in model organism research. Translation to humans is needed to address the challenges of an aging global population. Interventions to slow human aging will need to be applied to still-young individuals. However, most human aging research examines older adults, many with chronic disease. As a result, little is known about aging in young humans. We studied aging in 954 young humans, the Dunedin Study birth cohort, tracking multiple biomarkers across three time points spanning their third and fourth decades of life. We developed and validated two methods by which aging can be measured in young adults, one cross-sectional and one longitudinal. Our longitudinal measure allows quantification of the pace of coordinated physiological deterioration across multiple organ systems (e.g., pulmonary, periodontal, cardiovascular, renal, hepatic, and immune function). We applied these methods to assess biological aging in young humans who had not yet developed age-related diseases. Young individuals of the same chronological age varied in their "biological aging" (declining integrity of multiple organ systems). Already, before midlife, individuals who were aging more rapidly were less physically able, showed cognitive decline and brain aging, self-reported worse health, and looked older. Measured biological aging in young adults can be used to identify causes of aging and evaluate rejuvenation therapies.

Keywords: aging; biological aging; cognitive aging; geroscience; healthspan.

Conflict of interest statement

The authors declare no conflict of interest.

Figures

Fig. 1.
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).
Fig. 2.
Fig. 2.
Biological Age is normally distributed in a cohort of adults aged 38 y.
Fig. 3.
Fig. 3.
Healthy adults exhibit biological aging of multiple organ systems over 12 y of follow-up. Biomarker values were standardized to have mean = 0 and SD = 1 across the 12 y of follow-up (Z scores). Z scores were coded so that higher values corresponded to older levels of the biomarkers; i.e., Z scores for cardiorespiratory fitness, lung function (FEV1 and FEV1/FVC), leukocyte telomere length, creatinine clearance, and HDL cholesterol, which decline with age, were reverse coded so that higher Z scores correspond to lower levels.
Fig. 4.
Fig. 4.
Dunedin Study members with older Biological Age at 38 y exhibited an accelerated Pace of Aging from age 26–38 y. The figure shows a binned scatterplot and regression line. Plotted points show means for bins of data from 20 Dunedin Study members. Effect size and regression line were calculated from the raw data.
Fig. 5.
Fig. 5.
Healthy adults who were aging faster exhibited deficits in physical functioning relative to slower-aging peers. The figure shows binned scatter plots of the associations of Biological Age and Pace of Aging with tests of physical functioning (unipedal stance test, grooved pegboard test, grip strength) and study members’ reports of their physical limitations. In each graph, Biological Age associations are plotted on the left in blue (red regression line) and Pace of Aging associations are plotted on the right in green (navy regression line). Plotted points show means for bins of data from 20 Dunedin Study members. Effect size and regression line were calculated from the raw data.
Fig. 6.
Fig. 6.
Healthy adults who were aging faster showed evidence of cognitive decline and increased risk for stroke and dementia relative to slower-aging peers. The figure shows binned scatter plots of the associations of Biological Age and Pace of Aging with cognitive functioning and cognitive decline (Top) and with the calibers of retinal arterioles and venules (Bottom). The y axes in the graphs of cognitive functioning and cognitive decline are denominated in IQ points. The y axes in the graphs of arteriolar and venular caliber are denominated in SD units. In each graph, Biological Age associations are plotted on the left in blue (red regression line) and Pace of Aging associations are plotted on the right in green (navy regression line). Plotted points show means for bins of data from 20 Dunedin Study members. Effect size and regression line were calculated from the raw data.
Fig. 7.
Fig. 7.
Healthy adults who were aging faster felt less healthy and were rated as looking older by independent observers. The figure shows binned scatter plots of the associations of Biological Age and the Pace of Aging with self-rated health (Top) and with facial aging (Bottom). The y axes are denominated in SD units. In each graph, Biological Age associations are plotted on the left in blue (red regression line) and Pace of Aging associations are plotted on the right in green (navy regression line). Plotted points show means for bins of data from 20 Dunedin Study members. Effect size and regression line were calculated from the raw data.

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