Chapter 177
Changing Aging — 14
In a UK Biobank cohort of nearly forty-two thousand participants, the assay results for three thousand plasma proteins were used to predict the subsequent occurrence of over two hundred common and rare diseases during a ten-year follow-up. Similarly, this high-throughput proteomic approach, with over twenty-nine hundred blood proteins, integrated with genomic and electronic health record data and analyzed with machine learning, was applied to a prospective cohort of more than twenty-five thousand participants showing predictive power across most diseases. Other plasma protein profiles have been used to predict dementia more than a decade before diagnosis. A study of more than fifty-two thousand healthy adults with over fourteen hundred plasma proteins found four (GFAP, NEFL, GD15, and LTBP2) that were highly specific for dementia prediction. A plasma protein risk score may help to predict hip fracture, a major age-related condition with a high fatality rate.
If validated for actionability at the individual level for improving health outcomes, I would consider the plasma protein organ clocks a major advance for tracking the aging process. By providing specificity of risk at the organ level for an individual, this would be paired with actionable steps known to decelerate aging—the right information about the right organ in the right person—exemplifying preventive individualized medicine. Further, organ clocks may be especially useful for regulatory authorities as a surrogate metric for a drug’s specific therapeutic antiaging effect. Overall, resetting an organ clock in a person is a less ambitious, more attainable goal than trying to do so at the whole-body level.
TISSUE-SPECIFIC AGING
Much of the aging process is tissue specific. To understand why some people aged eighty and older are super brain-agers, compared with age-matched controls, comprehensive assessment revealed more brain gray matter volume, slower gray matter atrophy, faster movement speed, more preserved white matter microstructure, and better mental health. Notably, there was no difference in APOE4 carriers, amyloid accumulation in the brain, or lifestyle factors between groups. In a similar study, the super agers’ brains had the look of fifty- to sixty-year-old brains instead of their eighty-year-old chronological age, as did their memory function. These reports provided structural brain differences and correlation with cognitive function, but they didn’t help explain why some people have such a slow brain-aging process. Of interest is the finding of a markedly different trajectory of brain aging from tissue methylation (known as HistoAge clock) compared with the Horvath and related whole-body epigenetic clocks, which aligns well with the concept of organ- (and tissue-) specific aging patterns. So does a study of over four hundred omic features in more than four thousand people that showed again that organs are aging at very different rates within individuals.
The mounting evidence supports a “middle-aging” brain, like the second wave of plasma proteins of aging, when there is a turning point for multiple processes (fig. 12.5) that can be tracked, and for which there are some modifiable risk factors. This long period, ten to twenty years before neurodegenerative disease takes hold, provides a big opportunity to change the course of a person’s natural history of cognitive function decline.