Observable traits predict changes in cognitive and physical health

We all age differently.

A new measurement system based on phenotypic (observable) data can identify individuals at risk for adverse health outcomes based on their computed “aging score.” After collecting these data from nearly 1,000 people aged 24 to 93, NIA-funded researchers found that individuals with higher biological aging scores exhibited faster physical and cognitive decline, developed multiple health conditions, and had shorter lifespans. The approach may be a better predictor of health outcomes over time than the traditional focus on a person’s chronological age, which is based on birthdate. Findings from the study were published in Nature Aging.

NIA’s Baltimore Longitudinal Study of Aging (BLSA), the United States’ longest-running scientific study of human aging, has shown that the manifestations of aging are highly variable across individuals. Because people age differently, chronological age alone does not provide a complete picture of the influences on and the effects of aging. Phenotypes, which are observable traits based on genes and the environment’s impact on those genes, may provide insight into biological aging. Phenotypes could reveal biological aging at the cellular and molecular level, and indicate how fast health changes will occur, such as the progression of chronic disease and decline in physical and cognitive function.

For this phenotypic study, researchers from NIA, Johns Hopkins Bloomberg School of Public Health, Yale School of Medicine, and the University of Maryland School of Medicine used data from 968 BLSA participants. The researchers organized the phenotypic data into four groups: body composition such as waist size, energetics such as oxygen consumption, homeostatic mechanisms such as blood pressure, and neuroplasticity/neurodegeneration such as brain volume and nerve firing.

For each phenotype, the researchers measured the difference between an individual’s changes over time and the sex- and age-specific average changes over time in the study population. Notably, by using these changes over time as a reference, the resulting phenotypic scores accounted for nonlinear rates of change. These nonlinear rates are important because certain measures of aging, such as fitness, do not change in a linear way over time. The study also included changes in mobility and cognitive testing, the number of medical conditions reported by participants, and participants’ lifespan.

The researchers averaged individual phenotypic scores within each phenotype group, then averaged the four group scores to find a participant’s longitudinal (over time) phenotypic-aging score. Those with higher scores, representing a faster rate of phenotypic aging than the general population, had a more rapid decline in functional aging, a speedier increase in their number of medical conditions, and a shorter lifespan. This longitudinal approach showed stronger associations with changes in physical and cognitive functions than aging measurements that use data from a single point in time. Next research steps could include linking the phenotypic-aging score with cellular and molecular measurements to enhance understanding of the biology of aging.

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