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Can Systems Analysis Approaches Provide Insight into the Mechanisms of Aging?

Given enough data from enough old people, to what degree could modern approaches to information processing be used to derive useful information about the underlying mechanisms of aging? Such as which of the varied collection of causes and consequences involved in the biochemistry of aging are more important, how they are connected to one another, and so forth. On the one hand it seems plausible that something can be learned here, but on the other hand it seems unlikely to be as effective an approach as selectively interfering in specific mechanisms in order to observe the outcome in animal studies.

So far near all of the demonstrated approaches capable of slowing aging have involved upregulation of stress responses, something that changes near all aspects of metabolism and influences near all aspects of aging. That makes it hard to draw conclusions about the structural makeup of aging, how its distinct processes are weighted, and how they interact. But with the advent of narrow approaches such as senolytic drugs that destroy senescent cells, it becomes possible for the first time to easily affect just one aspect of aging. It will be interesting to observe the data resulting from analytical studies as they arrive in the years ahead.

Aging in most species, including humans, manifests itself as a progressive functional decline leading to the exponential increase in death risk from all causes. The mortality rate doubling time is approximately 8 years. Age-independent mortality mostly associated with violent death and infectious diseases has been progressively declining over the last century, mainly due to universal access to modern medicine and sanitation. The risks of death associated with the most prevalent age-related diseases remain very low at first, increase exponentially and dominate after the age of about 40. The incidence rates of the specific diseases, such as cancer or stroke, also accelerate after this age and double at a rate that closely tracks mortality acceleration. It is therefore, entirely plausible to think there is a single underlying driving force behind the progressive accumulation of health deficits, leading to the increased susceptibility to disease and death. This force is aging.

Although we have come to expect that physical decline is a natural consequence of aging, there is no natural law that dictates the exponential morbidity and mortality increase we observe among human populations. It is possible for death risks to increase very slowly, stay constant for extended periods, or even decline with age. Naked mole rats and the growing number of bat species are now recognized as examples of mammals that exhibit the lack of detectable mortality acceleration, or negligible senescence. Formally, this means that the mortality rate doubling time could be arbitrarily large.

We have suggested that the mortality acceleration may vanish depending on modifiable parameters, such as DNA repair or protein homeostasis maintenance efficiency, and should be, in principle, subject to manipulation. We propose to combine big data from large prospective observational studies with analytical tools borrowed from the physics of complex dynamic systems to "reverse engineer" the underlying biology behind the Gompertz law of mortality variables. This approach may yield mechanistic predictive models of aging for systematic discovery of biomarkers of aging and identification of novel therapeutic targets for future anti-aging therapies.

Link: https://doi.org/10.3389/fgene.2018.00483

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