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Read More. World-class research. The incidence of specific diseases, such as cancer or stroke, also accelerates after the age of about 40 and doubles at a rate that mirrors the mortality-rate doubling time. It is therefore, entirely plausible to think that there is a single underlying process, the driving force behind the progressive reduction of the organism's health leading to the increased susceptibility to diseases and death; aging.
There is, however, no fundamental law of nature requiring exponential morbidity and mortality risk trajectories.
The acceleration of mortality is thus the most important characteristics of the aging process. It varies dramatically even among closely related mammalian species and hence appears to be a tunable phenotype. Here, we follow how big data from large human medical studies, and analytical approaches borrowed from physics of complex dynamic systems can help to reverse engineer the underlying biology behind Gompertz mortality law.
With such an approach we hope to generate predictive models of aging for systematic discovery of biomarkers of aging followed by identification of novel therapeutic targets for future anti-aging interventions. 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 Gompertz, 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 Gavrilov and Gavrilova, ; Partridge et al. 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 Barzilai and Rennert, ; Zenin et al.
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 Vaupel et al.
Naked mole rats Buffenstein, ; Ruby et al. Formally, this means that the mortality rate doubling time could be arbitrarily large. In Kogan et al. This approach may yield mechanistic predictive models of aging for systematic discovery of biomarkers of aging, identification of novel therapeutic targets for future anti-aging therapies.
Each dot on the graph represents the averaged position of a person's organism state representations derived from one-week long physical activity tracks of NHANES participants, stratified into sex- and age-matched cohorts Pyrkov et al. The data distribution paints a complex multidimensional picture beyond the obvious overall decline in physical activity levels in the sick and elderly. On the coarse-grained level, however, the life history appears as a well-defined trajectory in the physiological parameters space.
Figure 1. B The trajectory of aging is shown superimposed on the potential energy landscape vertical axis , which provides a schematic visualization of the constraints provided by the underlying regulatory network. Each dot represents the physical activity state vectors of an age-and sex-matched cohort of NHANES participants men, diamonds; women, circles.
Cohorts were categorized by one year increments. The axes in the horizontal plane are i biological age in years , and ii biological age-independent mortality. The stability basin A is separated from the unstable region C by the potential energy barrier B; The figures are adopted from Pyrkov et al. In the dynamics systems theory framework, the restriction of the variation in physiological variables to the low-dimensional aging trajectory has a deep physical significance. Biological systems consist of strongly interacting components built from an enormous number of individual parts and thus belong to the realm of statistical physics or physical kinetics.
The necessity for the correlation between the vital physiological variables over spatial and time scales, representing the organism's size and lifespan, as well as evolutionary pressure, drives the underlying regulatory networks to criticality Hidalgo et al. The order parameter, associated with the unstable phase, is the emergent organism level property characterized by extensive relaxation time, amplified response to perturbations, and coinciding, approximately, with the first principal component score.
This is, therefore, a natural biomarker of age, or the biological age, that can be approximately identified in any sufficiently large dataset by means of PCA. It is closely related to Strehler-Mildvan vitality Strehler and Mildvan, deficit, a qualitative measure of deviations from the youthful state. The time scales involved with the biological age dynamics are long compared to mortality rate doubling time and naturally correspond to the life stages spanning development Krotov et al.
The DNAm clock predicts all-cause mortality in later life better than chronological age Marioni et al. The biological age acceleration BAA is defined as the difference between the biological age estimation of an individual and the average biological age prediction in the sex- and the age-matched cohort. This indicator is elevated in patients with chronic diseases, such as HIV Zhang et al. The BAA predicts healthspan Pyrkov et al.
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It is therefore, plausible to think that chronic age-associated diseases share components of their genetic architecture, which further supports the hypothesis of shared underlying mechanisms. Future understanding of genetic factors of predispositions to chronic diseases and accelerated aging can help to improve the accuracy of Health Risk Assessment HRA in life insurance, personal wealth management, and retirement planning applications. Popular biological age models are trained to predict chronological age, however, often fail to fully capture signatures of mortality and incidence of diseases.
This deficiency can be addressed with log-linear risk models using, if available, the clinical or death registry to produce a biological age estimation in the form of log-hazard ratio Liu et al. In Pyrkov et al. The organism state dynamics in the highly multidimensional space of all possible biological measurements is constrained by an unstable effective potential defined by the underlying regulatory interactions.
The systematic shifts and fluctuations on top of the aging drift represent the organism's responses to perturbations, such as diseases or lifestyles, such as smoking. Survival depends on the shape of the potential barriers separating the healthy aging individuals from the dynamically unstable regions, see Figure 1B.
As the organism state changes, the nature of the regulatory interactions also vary: it is natural to assume that there is at least one potential barrier, with the activation energy decreasing as a function of biological age. Accordingly, the Gompertz mortality law arises from the exponentially increasing chances of a stochastic activation over the lowest of the barriers and transiting into a relatively short-lived state characterized by the complete loss of dynamic stability, multiple morbidity, and death. In this picture, the increase in biological age is not an indicator of any specific disease.
Instead, it drives the build-up of functional deficits, loss of resilience, and exponentially rising risks of incidence of chronic diseases. The form of the effective potential constraining the evolution of physiological state variables on the time scales relevant to aging and diseases broadly suggests that there could be two possible strategies for human life extension. One option would be to target resilience with interventions that increase the height of the barrier with the least activation energy at any given age without counter-acting the aging drift see Figure 1B.
To our knowledge, there are few examples pointing to such a possibility. It appears from the analysis that smoking does not affect the aging drift but instead, reduces the resilience, thus increasing the chances of disease and death Pyrkov et al. The effect of smoking is reversible, with individuals who quit smoking before a certain age experiencing a similar life expectancy to their peers who have never smoked Taylor et al. There is experimental evidence that caloric restriction in flies produces another example of reversible short-term death risk without appreciable changes in the rate of aging Mair et al.
The other possibility would be to introduce a therapy aimed at the reduction of biological age itself.
This option is considerably more attractive, since it would imply an action against the slowest mode causally involved in the loss of resilience and hence would produce a long-lasting effect on healthspan and survival. The intervention would mitigate health deficits, delay the onset of chronic diseases and henceforth bring substantial improvements in quality of life.
A transient rapamycin treatment in mice leads to a significant life extension, changes the disease incidence statistics long after the cessation of the treatment Bitto et al. Reverse engineering is easier than invention from scratch, which is why advanced electronic devices or military machines are guarded secrets. Any proposal involving biological reverse engineering and subsequent targeting of the regulatory subsystem responsible for the control of the aging process necessarily implies data acquisition.
The Cell Nucleus and Aging: Tantalizing Clues and Hopeful Promises
Aging models are then inferred from the data to identify aging regulators or potential anti-aging therapies. The physical kinetics equations are signal-agnostic, and hence the choice of the specific biological variables for the analysis should be driven by additional requirements such as data quality, availability, and actionability. Other important factors include the ease of preclinical validation and the expected regulatory burden. Biological studies involving a large number of samples are costly and logistically involved. The criticality of the underlying regulatory network dynamics greatly facilitates the analysis, since it implies a separation of scales between aging dynamics and considerably faster reversible responses of the organism to specific stress factors.
Therefore, it should be possible to obtain a sufficiently complete quantitative picture of the aging process, including the system of regulators of aging, in a cost-efficient way from a minimum number of samples representing aging organisms. For example, the increasing number of available genomes of exceptionally old and hence successfully aging individuals can provide an insight on the genetic architecture of exceptional life- and health- spans by use of Genome-Wide Association Studies GWAS. The genetic variants associated with extreme lifespan, including parental longevity Joshi et al.
If combined with large drug perturbation databases, such as the Broad Institute CMAP, the results of genetic studies could be used for transcriptomic GWAS-imputation followed by ranking small molecular compounds as potential life-extending interventions, or drug repositioning So et al.
Mining transcriptomic signatures of drug perturbations to counter aging drift in gene expression levels has a long history of success in model organisms see e. Redirecting existing drug therapies for new applications is particularly attractive since it potentially sidesteps the target ID, and validation steps although many drugs are well-characterized, permitting a robust target hypothesis.
Once the efficacy of the predicted drugs is confirmed in animal studies, FDA approval could be safely expedited for human clinical trials. Some specific genetic variants from the GWAS could hint at attractive targets for future genetic therapies against aging. Alternatively, a sufficiently large dataset of gene expression in a cohort of aging human subjects may yield an entirely new set of targets for a genetic intervention, including RNA interference Wittrup and Lieberman, , gene editing Cox et al.