An Initial Assessment of the Phenotypic Age Metric
Today's open access paper adds to the growing number of attempts to construct a useful biomarker of aging from a combination of simple, available metrics. The Phenotypic Age measure described here uses a few fairly standard measures from blood samples as a basis, which might lead us to suspect it is heavily biased towards measuring immune system aging. Insofar as immune system function is important to overall health, and immune system function declines with age, then so far so good. The challenge with all of these potential biomarkers is less how well they do in the world of natural aging, to predict who will have a higher mortality in the years ahead, and more how they respond to specific classes of rejuvenation therapy.
The primary rationale for spending any time on the development of a biomarker of aging is to produce a fast, low-cost way of assessing the results of an alleged rejuvenation treatment. At present only life span studies can reliably determine how well such a treatment performs. Such studies are expensive and slow in mice, and out of the question in humans. This is a major impediment to progress. What is needed is an approach that enables researchers to treat older animals and people, and then a month later run a quick test to assess the results. That would speed up development in this field immensely. The work carried out in recent years on epigenetic clocks suggests that a robust biomarker of aging is a feasible goal.
The most important question remains to be answered, however: how will all of the various potential biomarkers of aging react to specific classes of rejuvenation therapy, such as senolytic drugs to clear senescent cells? A biomarker heavily based on immune cell characteristics may provide results that are of little relevance to changes taking place in the tissues of important inner organs, and vice versa. Until these interactions are well quantified by researchers, the biomarkers are not terribly useful - the output will provide a number, but what does that number really mean? Building biomarkers and building rejuvenation therapies will, at least at the outset, have to proceed in parallel, with the two sides incrementally validating one another.
One method for determining whether a person appears younger or older than expected on a biological or physiological level is to compare observable characteristics, reflecting functioning or state, to the characteristics observed in the general population for a given chronological age. A number of aging measures have been proposed using molecular variables, the most prominent being epigenetic clocks (expressed as DNA methylation age, in units of years) and leukocyte telomere length. We and others have previously shown that while these measures are phenomenal age predictors - especially DNA methylation age - their associations with aging outcomes above and beyond what is explained by chronological age is weak to moderate. Conversely, aging measures based on clinically observable data, or phenotypes, tend to be more robust predictors of aging outcomes.
The differences in prediction between these two types of measures could reflect that molecular measures may only capture one or a small number of changes involved in the multifactorial aging process, while on the other hand, clinical measures may represent the manifestations of multiple hallmarks of aging occurring at the cellular and intracellular levels. While composite scores based on traditional clinical chemistry measures are not mechanistic, their better performance and relative affordability and practicality compared to current molecular measures may make them more suitable for evaluating the effects of aging interventions on an organismal scale, and/or identifying groups at higher risk of death and disease.
Among the existing clinical measures, the majority were generated based on associations between composite variables and chronological age - with no integration of information on how the variables influence morbidity and mortality. Given that individuals vary in their rate of aging, chronological time is an imperfect proxy for building an aging measure. Recently, we developed a new metric, Phenotypic Age (in units of years), that incorporates composite clinical chemistry biomarkers based on parametrization from a Gompertz mortality model. Rather than predicting chronological age - as previous measures have done - this measure is optimized to differentiate mortality risk among persons of the same chronological age, using data from a variety of multi-system clinical chemistry biomarkers.
In general, a person's Phenotypic Age signifies the age within the general population that corresponds with that person's mortality risk. For example, two individuals may be 50 years old chronologically, but one may have a Phenotypic Age of 55 years, indicating that he/she has the average mortality risk of someone who is 55 years old chronologically, whereas the other may have a Phenotypic Age of 45 years, indicating that he/she has the average mortality risk of someone who is 45 years old chronologically.
All analyses were conducted using NHANES IV (1999-2010, an independent sample from that originally used to develop the measure). Our analytic sample consisted of 11,432 adults aged 20-84 years and 185 oldest-old adults top-coded at age 85 years. We observed a total of 1,012 deaths, ascertained over 12.6 years of follow-up. Proportional hazard models and receiver operating characteristic curves were used to evaluate all-cause and cause-specific mortality predictions. Overall, participants with more diseases had older Phenotypic Age. For instance, among young adults, those with 1 disease were 0.2 years older phenotypically than disease-free persons, and those with 2 or 3 diseases were about 0.6 years older phenotypically.
After adjusting for chronological age and sex, Phenotypic Age was significantly associated with all-cause mortality and cause-specific mortality (with the exception of cerebrovascular disease mortality). Results for all-cause mortality were robust to stratifications by age, race/ethnicity, education, disease count, and health behaviors. Further, Phenotypic Age was associated with mortality among seemingly healthy participants - defined as those who reported being disease-free and who had normal BMI - as well as among oldest-old adults, even after adjustment for disease prevalence.