Fight Aging!

As regular readers will know, the development of a robust and cost-effective biomarker of biological age is important. At present the only way to assess the effects of a potential rejuvenation treatment on remaining life expectancy is to wait and see; that makes animal studies very slow and expensive, and human studies impractical. To speed up research, the scientific community needs a generally agreed upon assessment that can run shortly before and shortly after the application of a therapy, and that provides a good measure of biological age - of the present load of cell and tissue damage and its consequences. Here, researchers propose a largely cost-effectiveness argument for using arterial health metrics as a basis for such a biomarker. This might be good for some types of rejuvenation therapy, but it isn't hard to envisage classes of treatment that either preferentially impact the vascular system, or do little to help in that tissue. This is a challenge for any potential biomarker of aging that derives from tissue- or organ-specific measures.

Measuring aging biologically rather than chronologically provides a personalized view to an optimal, rather than "normal" or "typical" health. Throughout the course of life, each of us gradually departs from the health trajectory defined by our individual genome. Even in the case of identical twins, substantial differences in the timing of the onset development of particular aging-associated symptoms are commonplace. Hence, an adult individual's rate of ageing depends primarily on lifestyle rather than genes. The newly introduced concept of anti-aging interventions enables individuals to actively modify their lifestyles or pharmacologically correct for accumulating biochemical or functional deficits. In order to properly evaluate relative efficiency of these interactions, objective measures of attained ageing are necessary.

At best, biological age can be reflected by overall resemblance of an aged individual to an average degree of age-associated changes observed in a given population at given age. In the frame of this definition, any departure from population-wide standard of aging stems from a combination of environmental and genetic factors that either promote or delay the development and subsequent involution of various physiological systems and their capability to adapt. Therefore, a positive or a negative difference between biological and chronological age, observed in a given individual, may be interpreted as either speeding up or slowing down the ageing process, thus, providing a measure for an evaluation of one or another anti-ageing intervention.

There is a long history of attempts to determine biological age and quantify the tempo of the process of ageing. Typically, age determination utilizes one or another molecular facet of ageing, for example, the degree of the damage to cell's DNA. Among more recently developed integrative biomarkers of aging is the GlycanAge index that profiles the structural details of sugar chains attached to the conserved N-glycosylation sites of three types of IgG molecules. This index reflects the level of systemic inflammation, predicts chronological age with standard deviation of 9.7 years, and is superior to age evaluation using telomere length. Peripheral blood mononuclear cells (PBMCs) mRNAs-based "transcriptome age" index predicts chronological age with mean absolute error of 7.8 years. Even more precise PBMCs-based "epigenetic age" relies the methylation of three CpG sites located in ITGA2B, ASPA and PDE4C genes with standard deviation of less than 5 years. An increase in the number of profiled CpG dinucleotides to 353 improves epigenetics-based age estimates by decreasing an error down to 2.9 years.

It should be noted that all the techniques described above require specialized equipment and skilled laboratory personnel, thus, limiting their clinical applicability. On another end of the spectrum are age-predicting models not specifically connected to any particular mechanism of aging, for example, deep neural networks (DNNs) modules evaluating common blood biochemistry and cell count tests. Though the accuracy of this model is quite high, the number of parameters in the model is also high. Since deep neural nets are, in a nutshell, "black boxes", the dissection of these models into mechanistic insights into the process of ageing is impossible. The majority of the techniques described above have not yet entered clinical practice. The major culprits causing this lack of translation to the clinic have been a high number of the parameters requiring evaluation, and the laboratory rather than clinical nature of tests being performed. From a clinical perspective, the most convenient estimate of biological age would be the one relying on a combination of biochemical and physiological parameters typically evaluated in course of annual physical exam.

In this study, we attempt the dissection of biochemical and clinical predictors of age, the development of a predictive model for biological age, and exploration of the deviation of these predictions from chronological age in a cohort of 303 individuals. We quantified 89 clinical and biochemical parameters, then selected the top five parameters with a highest Pearson's correlation with chronological age. Importantly, all five of these parameters reflect the functioning of the cardiovascular system. The outputs of the gender-specific linear regression models predicting chronological age were compared to actual age of the subjects. Substantially higher differences between the predicted age and the calendar age were noted for patients with Type 2 Diabetes Mellitus (T2D) as compared to non-T2D controls. We believe that the proposed gender-specific models, which we named Male and Female Arterial Indices, may serve as a good approximation for an elusive biological age. Importantly, the proposed age-approximation techniques rely on functional tests which do not require specialized laboratory equipment and, therefore, could be performed in hospitals and community healthcare settings.

Link: http://dx.doi.org/10.18632/aging.101227