Is it possible to assemble a useful biomarker of biological aging from a combination of existing metrics easily obtained via blood tests? This is an open question, but a number of research groups have made the attempt. To be useful, it would have to work at least as well as the DNA methylation biomarkers currently under development. The combination of metrics outlined in this open access paper is a start in that direction, but much more work and validation is needed. A robust, discriminating biomarker that reflects biological age, the level of molecular damage to cells and tissues and consequences thereof, would allow faster development, verification, and improvement of rejuvenation therapies. Without such a tool, it is very slow and expensive to determine the degree to which any particular candidate therapy has beneficial long-term effects on healthy life span. That in turn makes it hard to discard less effective approaches in favor of more effective approaches, and the greater cost means that less progress is made for a given investment in research and development.

The steady increase in human average life expectancy in the 20th century is considered one of the greatest accomplishments of public health. Improved life expectancy has also led to a steady growth in the population of older people, age-related illnesses and disabilities, and consequently the need for prevention strategies and interventions that promote healthy aging. A challenge in assessing the effect of such interventions is ‘what to measure’. Chronological age is not a sufficient marker of an individual’s functional status and susceptibility to aging-related diseases and disabilities. As has been said many times, people can age very differently from one another. Individual biomarkers show promise in capturing specificity of biological aging, and the scientific literature is rich in examples of biomarkers that correlate with physical function, anabolic response, and immune aging. However, single biomarker correlations with complex phenotypes that have numerous and complex underlying mechanisms is limited by poor specificity.

Moving from a simple approach based on one biomarker at a time to a systems analysis approach that simultaneously integrates multiple biological markers provides an opportunity to identify comprehensive biomarker signatures of aging. Analogous to this approach, molecular signatures of gene expression have been correlated with age and survival, and a regression model based on gene expression predicts chronological age with substantial accuracy, although differences between predicted and attained age could be attributed to some aging-related diseases. The well-known DNA methylation clock developed by Horvath has been argued to predict chronological age. Alternative approaches that aggregate the individual effects of multiple biological and physiological markers into an ‘aging score’ have also been proposed. These various aging scores do not attempt to capture the heterogeneity of aging. In addition, many of these aging scores use combinations of molecular and phenotypic markers and do not distinguish between the effects and the causes of aging.

Here we propose a system-type analysis of 19 circulating biomarkers to discover different biological signatures of aging. The biomarkers were selected based upon their noted quantitative change with age and specificity for inflammatory, hematological, metabolic, hormonal, or kidney functions. The intuition of the approach is that in a sample of individuals of different ages, there will be an ‘average distribution’ of these circulating biomarkers that represents a prototypical signature of average aging. Additional signatures of biomarkers that may correlate to varying aging patterns, for example, disease-free aging, or aging with increased risk for diabetes or cardiovascular disease (CVD), will be characterized by a departure of subsets of the circulating biomarkers from the average distribution. We implemented this approach using data from the Long Life Family Study (LLFS), a longitudinal family-based study of healthy aging and longevity that enrolled individuals with ages ranging between 30 and 110 years.

We used an agglomerative algorithm to group LLFS participants into clusters thus yielding 26 different biomarker signatures. To test whether these signatures were associated with differences in biological aging, we correlated them with longitudinal changes in physiological functions and incident risk of cancer, cardiovascular disease, type 2 diabetes, and mortality using longitudinal data collected in the LLFS. Signature 2 was associated with significantly lower mortality, morbidity, and better physical function relative to the most common biomarker signature in LLFS, while nine other signatures were associated with less successful aging, characterized by higher risks for frailty, morbidity, and mortality. The predictive values of seven signatures were replicated in an independent data set from the Framingham Heart Study with comparable significant effects, and an additional three signatures showed consistent effects. This analysis shows that various biomarker signatures exist, and their significant associations with physical function, morbidity, and mortality suggest that these patterns represent differences in biological aging.


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