A broad variety of aging clocks have been created in recent years. Such a clock is produced via machine learning techniques applied to any sufficiently complex set of biological data that tends to change with age. Everything from imaging to blood chemistry to omics data sets can and has been used for this purpose. A reference data set is processed to derive combinations of measurements that predict chronological age, or mortality risk, or some other output. A good clock then produces similar results in other data sets. A potentially useful clock also has the characteristic that a predicted clock age higher than chronological age correlates with a greater risk of mortality and age-related disease.
It is proposed that aging clocks are a measurement of biological age. If starting out with the very simple concept that biological aging is an increase in the risk of mortality due to intrinsic causes, then clocks that show correlation between clock age and mortality risk can reasonably be considered a first step in that direction. At any more detailed level of inquiry, however, it becomes a great deal less clear as to whether any given clock is actually decent measure of biological age. It is also difficult to gain consensus on how exactly to define biological age in any more detailed way. This is in part because aging is very complex. Any given clock is probably only sampling the consequences of some of the mechanisms involved. Can we trust that any given clock will correctly predict the outcome of a therapy that only affects one mechanism of aging, such as a senolytic drug that clears senescent cells from aged tissues? Not without actually running a lengthy study to find out.
Thus researchers are at present accumulating as much data as they can on the way in which various mainstream aging clocks behave in response to interventions and circumstances. In today's open access paper, for example, researchers look at how clock age correlates with mortality risk following surgery in older people. It is well known that surgery is an increasingly hazardous choice at older ages, but really the hazard scales with damage and dysfunction, not age. The level of accumulated damage and dysfunction varies from person to person of a given age, and thus perhaps clocks can help to better assess the risk attending some of the hard medical choices that have to be made in later life.
Surgery is very common and risky for older adults. Though surgical procedures on aging patients occur commonly, the outcomes are mixed. After major surgery, the risk of poor functional recovery, long-term disability, institutionalization, and 1-year mortality is significant. Surgery contributes to significant physiologic stress, and resilience to and recovery from physiological stress impacts postoperative outcomes. Chronological age (time since birth) is an insufficient proxy for surgical resilience, and restricting surgical care by chronological age is imprecise and potentially harmful. Instead, biological estimates of aging, termed biological age, provide a quantitative metric of aging for people across the entire age spectrum, and may better reflect vulnerability to surgical stress.
We evaluated PhenoAge, a validated biological age metric, in an international multi-cohort study comprising over 430,000 surgical patients across the UK, USA, and South Korea. In the UK Biobank (N = 291,845), PhenoAge was a robust, independent predictor of 1-year mortality (odds ratio, OR = 1.043), major adverse cardiovascular events (OR = 1.041), and 30-day readmission (OR = 1.02), even after adjusting for chronological age, Fried Frailty Index, Charlson comorbidity score, American Society of Anesthesiologists (ASA) physical status, surgical complexity, and other common surgical risk factors. "Fast Agers" faced a 49% higher risk of mortality than "Normal Agers". The main findings were replicated across three independent international cohorts (MOVER, OR = 1.03; Weill Cornell, OR = 1.036; INSPIRE, OR = 1.05), and validated prospectively at a large academic medical center, where PhenoAge predicted acute 3-day complications (OR 1.20).
View the full article at FightAging
