Fight Aging!

A biomarker of aging is a a way to measure biological age, the burden of cell and tissue damage and consequent dysfunction. A biomarker that permitted the robust, quick, and cheap assessment of biological age would greatly speed up development of rejuvenation therapies. It would allow for rapid and cost-effective tests of many interventions, and the best interventions would quickly rise to prominence. At present the rigorous assessment of ways to intervene in the aging process is slow and expensive, as there is little alternative but to run life span studies. Even in mice that is prohibitively costly in time and funds for most research and development programs.

One of the more severe consequences of this state of affairs is that it takes a long time and sizable expense to weed out the less effective approaches to treatment. That this is a problem is well recognized by the scientific community, and many varied biomarkers of aging are presently under development. Perhaps the best known are the various forms of epigenetic clock, weighted algorithmic combinations of the status of DNA methylation sites that correlate with age and mortality risk. There are other approaches, though, such as combining simple measures of decline such as grip strength or inflammatory markers in blood tests. That class of methodology is explored in today's open access paper, with the focus specifically on measures adopted by the clinical community to assess frailty.

One of the concerns with the epigenetic clock, and for similar efforts using levels of blood proteins, is that it is quite unclear as to what exactly is being measured. The relationship with age and mortality emerges from the data, and it is then up to the research community to establish mechanistic connections between specific epigenetic changes and underlying processes of aging. It is quite possible that these biomarkers do not reflect all of the mechanisms of aging, and thus any use of them to assess a specific approach to rejuvenation would have to be carefully validated in parallel with the development of that therapy. This somewhat defeats the point of the exercise. When building a biomarker based on frailty indices, as here, there is at least a greater degree of confidence that it comprehensively touches on all of the contributions to aging, and we would thus expect any viable rejuvenation therapy to make a difference to the measure of age.

Age and life expectancy clocks based on machine learning analysis of mouse frailty

Biological age is an increasingly utilized concept that aims to more accurately reflect aging in an individual than the conventional chronological age. Biological measures that accurately predict health and longevity would greatly expedite studies aimed at identifying novel genetic and pharmacological disease and aging interventions. Any useful biometric or biomarker for biological age should track with chronological age and should serve as a better predictor of remaining longevity and other age-associated outcomes than does chronological age alone, even at an age when most of a population is still alive. In addition, its measurement should be non-invasive to allow for repeated measurements without altering the health or lifespan of the animal measured.

In humans, biometrics and biomarkers that meet at least some of these requirements include physiological measurements such as grip strength or gait, measures of the immune system, telomere length, advanced glycosylation end-products, levels of cellular senescence, and DNA methylation clocks. DNA methylation clocks have been adapted for mice but unfortunately these clocks are currently expensive, time consuming, and require the extraction of blood or tissue.

Frailty index assessments in humans are strong predictors of mortality and morbidity, outperforming other measures of biological age including DNA methylation clocks. Frailty indices quantify the accumulation of up to 70 health-related deficits, including laboratory test results, symptoms, diseases, and standard measures such as activities of daily living. The number of deficits an individual shows is divided by the number of items measured to give a number between 0 and 1, in which a higher number indicates a greater degree of frailty. The frailty index has been recently reverse-translated into an assessment tool for mice which includes 31 non-invasive items across a range of systems. The mouse frailty index is strongly associated with chronological age, correlated with mortality and other age-related outcomes, and is sensitive to lifespan-altering interventions. However, the power of the mouse frailty index to model biological age or predict life expectancy for an individual animal has not yet been explored.

In this study, we tracked frailty longitudinally in a cohort of aging male mice from 21 months of age until their natural deaths and employed machine learning algorithms to build two clocks: FRIGHT (Frailty Inferred Geriatric Health Timeline) age, designed to model chronological age, and the AFRAID (Analysis of Frailty and Death) clock, which was modelled to predict life expectancy. FRIGHT age reflects apparent chronological age better than the frailty index alone, while the AFRAID clock predicts life expectancy at multiple ages. These clocks were then tested for their predicitve power on cohorts of mice treated with interventions known to extend healthspan or lifespan, enalapril and methionine restriction. They accurately predicted increased healthspan and lifespan, demonstrating that an assessment of non-invasive biometrics in interventional studies can greatly accelerate the pace of discovery.