No abstract

Reliability theory is a general theory about systems failure. It allows researchers to predict the age-related failure kinetics for a system of given architecture (reliability structure) and given reliability of its components. Reliability theory predicts that even those systems that are entirely composed of non-aging elements (with a constant failure rate) will nevertheless deteriorate (fail more often) with age, if these systems are redundant in irreplaceable elements. Aging, therefore, is a direct consequence of systems redundancy. Reliability theory also predicts the late-life mortality deceleration with subsequent leveling-o!, as well as the late-life mortality plateaus, as an inevitable consequence of redundancy exhaustion at extreme old ages. The theory explains why mortality rates increase exponentially with age (the Gompertz law) in many species, by taking into account the initial -aws (defects) in newly formed systems. It also explains why organisms &&prefer'' to die according to the Gompertz law, while technical devices usually fail according to the Weibull (power) law. Theoretical conditions are speci"ed when organisms die according to the Weibull law: organisms should be relatively free of initial #aws and defects. The theory makes it possible to "nd a general failure law applicable to all adult and extreme old ages, where the Gompertz and the Weibull laws are just special cases of this more general failure law. The theory explains why relative di!erences in mortality rates of compared populations (within a given species) vanish with age, and mortality convergence is observed due to the exhaustion of initial di!erences in redundancy levels. Overall, reliability theory has an amazing predictive and explanatory power with a few, very general and realistic assumptions. Therefore, reliability theory seems to be a promising approach for developing a comprehensive theory of aging and longevity integrating mathematical methods with speci"c biological knowledge. Academic Press

Reliability theory is a general theory about systems failure. It allows researchers to predict the age-related failure kinetics for a system of given architecture (reliability structure) and given reliability of its components. Reliability theory predicts that even those systems that are entirely composed of non-aging elements (with a constant failure rate) will nevertheless deteriorate (fail more often) with age, if these systems are redundant in irreplaceable elements. Aging, therefore, is a direct consequence of systems redundancy. Reliability theory also predicts the late-life mortality deceleration with subsequent leveling-o!, as well as the late-life mortality plateaus, as an inevitable consequence of redundancy exhaustion at extreme old ages. The theory explains why mortality rates increase exponentially with age (the Gompertz law) in many species, by taking into account the initial -aws (defects) in newly formed systems. It also explains why organisms &&prefer'' to die according to the Gompertz law, while technical devices usually fail according to the Weibull (power) law. Theoretical conditions are speci"ed when organisms die according to the Weibull law: organisms should be relatively free of initial #aws and defects. The theory makes it possible to "nd a general failure law applicable to all adult and extreme old ages, where the Gompertz and the Weibull laws are just special cases of this more general failure law. The theory explains why relative di!erences in mortality rates of compared populations (within a given species) vanish with age, and mortality convergence is observed due to the exhaustion of initial di!erences in redundancy levels. Overall, reliability theory has an amazing predictive and explanatory power with a few, very general and realistic assumptions. Therefore, reliability theory seems to be a promising approach for developing a comprehensive theory of aging and longevity integrating mathematical methods with speci"c biological knowledge. Academic Press

Observational data have shown that some cancer survivors develop chronic conditions like frailty, sarcopenia, cardiac dysfunction, and mild cognitive impairment earlier and/or at a greater burden than similarly aged individuals never diagnosed with cancer or exposed to systemic or targeted cancer therapies. In aggregate, cancer- and treatment-related physical, cognitive, and psychosocial late- and long-term morbidities experienced by cancer survivors are hypothesized to represent accelerated or accentuated aging trajectories. However, conceptual, measurement, and methodological challenges have constrained efforts to identify, predict, and mitigate aging-related consequences of cancer and cancer treatment. In July 2018, the National Cancer Institute convened basic, clinical, and translational science experts for a think tank titled “Measuring Aging and Identifying Aging Phenotypes in Cancer Survivors.” Through the resulting deliberations, several research and resource needs were identified, including longitudinal studies to examine aging trajectories that include detailed data from before, during, and after cancer treatment; mechanistic studies to elucidate the pathways that lead to the emergence of aging phenotypes in cancer survivors; long-term clinical surveillance to monitor survivors for late-emerging effects; and tools to integrate multiple data sources to inform understanding of how cancer and its therapies contribute to the aging process. Addressing these needs will help expand the evidence base and inform strategies to optimize healthy aging of cancer survivors.

The purpose of this article is to provide students and researchers entering the field of aging studies with an introduction to the evolutionary theories of aging, as well as to orient them in the abundant modern scientific literature on evolutionary gerontology. The following three major evolutionary theories of aging are discussed: 1) the theory of programmed death suggested by August Weismann, 2) the mutation accumulation theory of aging suggested by Peter Medawar, and 3) the antagonistic pleiotropy theory of aging suggested by George Williams. We also discuss a special case of the antagonistic pleiotropy theory, the disposable soma theory developed by Tom Kirkwood and Robin Holliday. The theories are compared with each other as well as with recent experimental findings. At present the most viable evolutionary theories are the mutation accumulation theory and the antagonistic pleiotropy theory; these theories are not mutually exclusive, and they both may become a part of a future unifying theory of aging.Evolutionary theories of aging are useful because they open new oppor-tunities for further research by suggesting testable predictions, but they have also been harmful in the past when they were used to impose limitations on aging studies. At this time, the evolutionary theories of aging are not ultimate completed theories, but rather a set of ideas that themselves require further elaboration and validation. This theoretical review article is written for a wide readership.

Accurate estimates of mortality at advanced ages are essential to improving forecasts of mortality and the population size of the oldest old age group. However, estimation of hazard rates at extremely old ages poses serious challenges to researchers: (1) The observed mortality deceleration may be at least partially an artifact of mixing different birth cohorts with different mortality (heterogeneity effect); (2) standard assumptions of hazard rate estimates may be invalid when risk of death is extremely high at old ages and (3) ages of very old people may be exaggerated. One way of obtaining estimates of mortality at extreme ages is to pool together international records of persons surviving to extreme ages with subsequent efforts of strict age validation. This approach helps researchers to resolve the third of the above-mentioned problems but does not resolve the first two problems because of inevitable data heterogeneity when data for people belonging to different birth cohorts and countries are pooled together. In this paper we propose an alternative approach, which gives an opportunity to resolve the first two problems by compiling data for more homogeneous single-year birth cohorts with hazard rates measured at narrow (monthly) age intervals. Possible ways of resolving the third problem of hazard rate estimation are elaborated. This approach is based on data from the Social Security Administration Death Master File (DMF). Some birth cohorts covered by DMF could be studied by the method of extinct generations. Availability of month of birth and month of death information provides a unique opportunity to obtain hazard rate estimates for every month of age. Study of several single-year extinct birth cohorts shows that mortality trajectory at advanced ages follows the Gompertz law up to the ages 102–105 years without a noticeable deceleration. Earlier reports of mortality deceleration (deviation of mortality from the Gompertz law) at ages below 100 appear to be artifacts of mixing together several birth cohorts with different mortality levels and using cross-sectional instead of cohort data. Age exaggeration and crude assumptions applied to mortality estimates at advanced ages may also contribute to mortality underestimation at very advanced ages.

Mortality from ill-defined conditions in Russia has the fastest rate of increase compared to all other major causes of death. High proportion of deaths in this category is indicative for low quality of mortality statistics. This article examines the trends and possible causes of mortality from ill-defined conditions in Russia. During 1991During -2005, mortality from ill-defined conditions in Russia increased in all age groups. The pace of increase was particularly high at working ages and the mean expected age at death from ill-defined conditions has shifted to younger ages, particularly for men. The analysis of individual medical death certificates issued in Kirov and Smolensk regions of Russia demonstrate that 89-100% of working-age deaths from ill-defined conditions correspond to human bodies found in a state of decomposition. Data from Smolensk region shows that over 60% of these decedents were unemployed. Temporal trends of mortality from ill-defined conditions and injuries of undetermined intent in Moscow city suggest that deaths from the latter cause were probably misclassified as ill-defined conditions. This practice can lead to underestimation of mortality from external causes. Growing number of socially isolated marginalized people in Russia and insufficient investigation of the circumstances of their death contribute to the observed trends in mortality from ill-defined conditions.

The main cause that hampered many previous biodemographic studies of human longevity is the lack of appropriate data. At the same time, many existing data resources (millions of genealogical records) are under-utilized, because their very existence is not widely known, let alone the quality and scientific value of these data sets are not yet validated. The purpose of this work is to review the data resources that could be used in familial studies of human longevity. This is an extended and supplemented version of the previous study made by the authors upon the request of the National Institute on Aging (1998 NIH Professional Service Contract). The review describes: (1) data resources developed for biodemographic studies, (2) data collected in the projects on historical demography, (3) data resources for long lived individuals and their families, (4) publicly available computerized genealogical data resources, (5) published genealogical and family history data. The review also contains the description of databases developed by the participants of the Research Workshops "Genes, Genealogies, and Longevity" organized by the Max Planck Institute for Demographic Research.