Coefficient of Variation of Lifespan Across the Tree of Life: Is It a Signature of Programmed Aging?

Calculating Aging: Analysis of Survival Curves in the Norm and Pathology, Fluctuations in Mortality Dynamics, Characteristics of Lifespan Distribution, and Indicators of Lifespan Variation

The article describes the history of studies of survival data carried out at the Research Institute of Physico-Chemical Biology under the leadership of Academician V. P. Skulachev from 1970s until present, with special emphasis on the last decade. The use of accelerated failure time (AFT) model and analysis of coefficient of variation of lifespan (CVLS) in addition to the Gompertz methods of analysis, allows to assess survival curves for the presence of temporal scaling (i.e., manifestation of accelerated aging), without changing the shape of survival curve with the same coefficient of variation. A modification of the AFT model that uses temporal scaling as the null hypothesis made it possible to distinguish between the quantitative and qualitative differences in the dynamics of aging. It was also shown that it is possible to compare the data on the survival of species characterized by the survival curves of the original shape (i.e., "flat" curves without a pronounced increase in the probability of death with age typical of slowly aging species), when considering the distribution of lifespan as a statistical random variable and comparing parameters of such distribution. Thus, it was demonstrated that the higher impact of mortality caused by external factors (background mortality) in addition to the age-dependent mortality, the higher the disorder of mortality values and the greater its difference from the calculated value characteristic of developed countries (15-20%). For comparison, CVLS for the Paraguayan Ache Indians is 100% (57% if we exclude prepuberty individuals as suggested by Jones et al.). According to Skulachev, the next step is considering mortality fluctuations as a measure for the disorder of survival data. Visual evaluation of survival curves can already provide important data for subsequent analysis. Thus, Sokolov and Severin [1] found that mutations have different effects on the shape of survival curves. Type I survival curves generally retains their standard convex rectangular shape, while type II curves demonstrate a sharp increase in the mortality which makes them similar to a concave exponential curve with a stably high mortality rate. It is noteworthy that despite these differences, mutations in groups I and II are of a similar nature. They are associated (i) with "DNA metabolism" (DNA repair, transcription, and replication); (ii) protection against oxidative stress, associated with the activity of the transcription factor Nrf2, and (iii) regulation of proliferation, and (or these categories may overlap). However, these different mutations appear to produce the same result at the organismal level, namely, accelerated aging according to the Gompertz's law. This might be explained by the fact that all these mutations, each in its own unique way, either reduce the lifespan of cells or accelerate their transition to the senescent state, which supports the concept of Skulachev on the existence of multiple pathways of aging (chronic phenoptosis).

Evolution of Longevity in Tetrapods: Safety Is More Important than Metabolism Level

Various environmental morphological and behavioral factors can determine the longevity of representatives of various taxa. Long-lived species develop systems aimed at increasing organism stability, defense, and, ultimately, lifespan. Long-lived species to a different extent manifest the factors favoring longevity (gerontological success), such as body size, slow metabolism, activity of body's repair and antioxidant defense systems, resistance to toxic substances and tumorigenesis, and presence of neotenic features. In continuation of our studies of mammals, we investigated the characteristics that distinguish long-lived ectotherms (crocodiles and turtles) and compared them with those of other ectotherms (squamates and amphibians) and endotherms (birds and mammals). We also discussed mathematical indicators used to assess the predisposition to longevity in different species, including standard indicators (mortality rate, maximum lifespan, coefficient of variation of lifespan) and their derivatives. Evolutionary patterns of aging are further explained by the protective phenotypes and life history strategies. We assessed the relationship between the lifespan and various studied factors, such as body size and temperature, encephalization, protection of occupied ecological niches, presence of protective structures (for example, shells and osteoderms), and environmental temperature, and the influence of these factors on the variation of the lifespan as a statistical parameter. Our studies did not confirm the hypothesis on the metabolism level and temperature as the most decisive factors of longevity. It was found that animals protected by shells (e.g., turtles with their exceptional longevity) live longer than species that have poison or lack such protective adaptations. The improvement of defense against external threats in long-lived ectotherms is consistent with the characteristics of long-lived endotherms (for example, naked mole-rats that live in underground tunnels, or bats and birds, whose ability to fly is one of the best defense mechanisms).

Evolution of Longevity as a Species-Specific Trait in Mammals

From the evolutionary point of view, the priority problem for an individual is not longevity, but adaptation to the environment associated with the need for survival, food supply, and reproduction. We see two main vectors in the evolution of mammals. One is a short lifespan and numerous offspring ensuring reproductive success (r-strategy). The other one is development of valuable skills in order compete successfully (K-strategy). Species with the K-strategy should develop and enhance specific systems (anti-aging programs) aimed at increasing the reliability and adaptability, including lifespan. These systems are signaling cascades that provide cell repair and antioxidant defense. Hence, any arbitrarily selected long-living species should be characterized by manifestation to a different extent of the longevity-favoring traits (e.g., body size, brain development, sociality, activity of body repair and antioxidant defense systems, resistance to xenobiotics and tumor formation, presence of neotenic traits). Hereafter, we will call a set of such traits as the gerontological success of a species. Longevity is not equivalent to the evolutionary or reproductive success. This difference between these phenomena reaches its peak in mammals due to the development of endothermy and cephalization associated with the cerebral cortex expansion, which leads to the upregulated production of oxidative radicals by the mitochondria (and, consequently, accelerated aging), increase in the number of non-dividing differentiated cells, accumulation of the age-related damage in these cells, and development of neurodegenerative diseases. The article presents mathematical indicators used to assess the predisposition to longevity in different species (including the standard mortality rate and basal metabolic rate, as well as their derivatives). The properties of the evolution of mammals (including the differences between modern mammals and their ancestral forms) are also discussed.

Perspectives of Homo sapiens lifespan extension: focus on external or internal resources?

Homo sapiens and naked mole rats (Heterocephalus glaber) are vivid examples of social mammals that differ from their relatives in particular by an increased lifespan and a large number of neotenic features. An important fact for biogerontology is that the mortality rate of H. glaber (a maximal lifespan of more than 32 years, which is very large for such a small rodent) negligibly grows with age. The same is true for modern people in developed countries below the age of 60. It is important that the juvenilization of traits that separate humans from chimpanzees evolved over thousands of generations and millions of years. Rapid advances in technology resulted in a sharp increase in the life expectancy of human beings during the past 100 years. Currently, the human life expectancy has exceeded 80 years in developed countries. It cannot be excluded that the potential for increasing life expectancy by an improvement in living conditions will be exhausted after a certain period of time. New types of geroprotectors should be developed that protect not only from chronic phenoptosis gradual poisoning of the body with reactive oxygen species (ROS) but also from acute phenoptosis, where strong increase in the level of ROS immediately kills an already aged individual. Geroprotectors might be another anti-aging strategy along with neoteny (a natural physiological phenomenon) and technical progress.

A 2D analysis of correlations between the parameters of the Gompertz-Makeham model (or law?) of relationships between aging, mortality, and longevity

When mortality (μ), aging rate (γ) and age (t) are treated according to the Gompertz model μ(t) = μ₀e^γt (GM), any mean age corresponds to a manifold of paired reciprocally changing μ₀ and γ. Therefore, any noisiness of data used to derive GM parameters makes them negatively correlated. Besides this artifactual factor of the Strehler-Mildvan correlation (SMC), other factors emerge when the age-independent mortality C modifies survival according to the Gompertz-Makeham model μ(t) = C+μ₀e^γt (GMM), or body resources are partitioned between survival and protection from aging [the compensation effect of mortality (CEM)]. Theoretical curves in (γ, logμ₀) coordinates show how μ₀ decreases when γ increases upon a constant mean age. Within a species-specific range of γ, such "isoage" curves look as nearly parallel straight lines. The slopes of lines constructed by applying GM to survival curves modeled according to GMM upon changes in C are greater than the isoage slopes. When CEM is modeled, the slopes are still greater. Based on these observations, CEM is shown to contribute to SMC associated with sex differences in lifespan, with the effects of several life-extending drugs, and with recent trends in survival/mortality patterns in high-life-expectancy countries; whereas changes in C underlie differences between even high-life-expectancy countries, not only between high- and low-life-expectancy countries. Such interpretations make sense only if GM is not merely a statistical model, but rather reflects biological realities. Therefore, GM is discussed as derivable by applying certain constraints to a natural law termed the generalized Gompertz-Makeham law.