Attempting to Build a Biomarker of Aging from Standard Blood Test Metrics

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.

Link: http://onlinelibrary.wiley.com/doi/10.1111/acel.12557/full

Comments

Surprising no mention of red blood cell width average as a predictor of death. There is plenty of data on this topic. Here are a couple reports:

Arch Intern Med. 2009 Mar 9;169(5):515-23. doi: 10.1001/archinternmed.2009.11.
Red blood cell distribution width and the risk of death in middle-aged and older adults.
Patel KV1, Ferrucci L, Ershler WB, Longo DL, Guralnik JM.
Author information
Abstract
BACKGROUND:
Red blood cell distribution width (RDW), a component of an electronic complete blood count, is a measure of heterogeneity in the size of circulating erythrocytes. In patients with symptomatic cardiovascular disease (CVD), RDW is associated with mortality. However, it has not been demonstrated that RDW is a predictor of mortality independent of nutritional deficiencies or in the general population.
METHODS:
Red blood cell distribution width was measured in a national sample of 8175 community-dwelling adults 45 years or older who participated in the 1988-1994 National Health and Nutrition Examination Survey; mortality follow-up occurred through December 31, 2000. Deaths from all causes, CVD, cancer, and other causes were examined as a function of RDW.
RESULTS:
Higher RDW values were strongly associated with an increased risk of death. Compared with the lowest quintile of RDW, the following were adjusted hazard ratios (HRs) for all-cause mortality (and 95% confidence intervals [CIs]): second quintile, HR, 1.1 (95% CI, 0.9-1.3); third quintile, HR, 1.2 (95% CI, 1.0-1.4); fourth quintile, HR, 1.4 (95% CI, 1.2-1.8); and fifth quintile, HR, 2.1 (95% CI, 1.7-2.6). For every 1% increment in RDW, all-cause mortality risk increased by 22% (HR, 1.22; 95% CI, 1.15-1.30; P < .001). Even when analyses were restricted to nonanemic participants or to those in the reference range of RDW (11%-15%) without iron, folate, or vitamin B(12) deficiency, RDW remained strongly associated with mortality. The prognostic effect of RDW was observed in both middle-aged and older adults for multiple causes of death.
CONCLUSION:
Red blood cell distribution width is a widely available test that is a strong predictor of mortality in the general population of adults 45 years or older.

J Gerontol A Biol Sci Med Sci. 2010 Mar;65(3):258-65. doi: 10.1093/gerona/glp163. Epub 2009 Oct 30.
Red cell distribution width and mortality in older adults: a meta-analysis.
Patel KV1, Semba RD, Ferrucci L, Newman AB, Fried LP, Wallace RB, Bandinelli S, Phillips CS, Yu B, Connelly S, Shlipak MG, Chaves PH, Launer LJ, Ershler WB, Harris TB, Longo DL, Guralnik JM.
Author information
Abstract
BACKGROUND:
Red cell distribution width (RDW) is a quantitative measure of variability in the size of circulating erythrocytes with higher values reflecting greater heterogeneity in cell sizes. Recent studies have shown that higher RDW is associated with increased mortality risk in patients with clinically significant cardiovascular disease (CVD). Whether RDW is prognostic in more representative community-based populations is unclear.
METHODS:
Seven relevant community-based studies of older adults with RDW measurement and mortality ascertainment were identified. Cox proportional hazards regression and meta-analysis on individual participant data were performed.
RESULTS:
Median RDW values varied across studies from 13.2% to 14.6%. During 68,822 person-years of follow-up of 11,827 older adults with RDW measured, there was a graded increased risk of death associated with higher RDW values (p < .001). For every 1% increment in RDW, total mortality risk increased by 14% (adjusted hazard ratio [HR]: 1.14; 95% confidence interval [CI]: 1.11-1.17). In addition, RDW was strongly associated with deaths from CVD (adjusted HR: 1.15; 95% CI: 1.12-1.25), cancer (adjusted HR: 1.13; 95% CI: 1.07-1.20), and other causes (adjusted HR: 1.13; 95% CI: 1.07-1.18). Furthermore, the RDW-mortality association occurred in all major demographic, disease, and nutritional risk factor subgroups examined. Among the subset of 1,603 older adults without major age-associated diseases, RDW remained strongly associated with total mortality (adjusted HR: 1.32; 95% CI: 1.21-1.44).
CONCLUSIONS:
RDW is a routinely reported test that is a powerful predictor of mortality in community-dwelling older adults with and without age-associated diseases. The biologic mechanisms underlying this association merit investigation.

Posted by: Bill Sardi at January 8th, 2017 9:39 AM

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