Method and Computer System for Dialog Optimization of Aging Biomarker Panels for Biological Age Assessment.

A concept, method, algorithm, and computer system (CS) of step-by-step dialog optimization of biomarker (BM) panels for assessing human biological age (BA) according to a number of universal criteria based on incomplete and noisy data have been developed. This system provides the ability to automatically build BM panels for BA assessment and to increase the accuracy of BA determination while reducing the number of measured BMs. The optimization criteria are as follows: high correlation of BMs with chronological age (CA); minimum size of BM panels, obtained by rejecting highly cross-correlated BMs; high accuracy of BA assessment; high accuracy of BA/CA dependency interpolation; absence of outliers in BM values, which reduce the BA assessment accuracy; rejection of panels resulting in a high standard deviation for the BA-CA difference; and possible additional criteria entered by the researcher according to the task specifics. The CS input consists of data on physiological, biochemical, and other BMs that change with age. The CS output is a panel of BMs optimized according to the specified optimization criteria. The CS is user-friendly. It allows the user to add optimization criteria that the researcher considers to be important or to remove criteria that the user considers incorrect. The CS may be used in solving practical problems of anti-aging medicine, such as the treatment and prevention of age-related chronic non-infectious diseases representing the main causes of death. The authors' point of view on the role and place of BA diagnostics in this area is discussed.

How regularities of mortality statistics explain why we age despite having potentially ageless somatic stem cells.

Researchers working in the area of ageing have found numerous manifestations of this process at the molecular biological level, including DNA and protein damage, accumulation of metabolic by-products, lipids peroxidation, macromolecular cross-linking, non-enzymatic glycosylation, anti-oxidant/pro-oxidant misbalance, rising of pro-inflammatory cytokines, etc. This results in an increase in the proportion of cells in growth arrest, reduction of the rate of information processing, metabolic rate decrease, and decrease in rates of other processes characterizing dynamic aspects of the organism's interaction with its environment. Such staggering multilevel diversity in manifestation of senescence precludes (without methodology of systems biology) development of a correct understanding of its primary causes and does not allow for developing approaches capable of postponing ageing or reducing organisms' ageing rate to attain health preservation. Moreover, it turns out that damage production and damage elimination processes, the misbalance of which results in the ageing process, can to a large extent be regulated by external signals. The purpose of this report is to provide evidence supporting this view and its compatibility with the regularities of mortality statistics, because the main idea is very simple. Even potentially a non-senescent but certainly not immortal body must start to age under inadequate conditions (like a non-melting piece of ice taken out from the deepfreeze inevitably start to melt at the temperatures above zero Celsius). This conclusion is totally consistent with existing patterns of mortality and with agelessness potential of somatic stem cells. Therefore, there is no need to build up and explore too complicated, computational and sophisticated systems models of intrinsic ageing to understand the origin of this mainly extrinsic root cause of natural ageing, which is controlled by environmental signals. In our case, a simple phenomenological black-box approach with Input-Output analysis is ample. Here Input refers to the environmentally dependent initial force of mortality, whereas Output is a rate of age-related increase of mortality force.

Natural aging as as a sequential poly-systemic syndrome.

We review the progression of aging as a sequential development of multiple syndromes analogous to other diseases. This generalized approach may allow practicing physicians to consider the signs of aging as manifestations of a poly-syndrome disease and facilitate prevention, diagnosis and treatment of common aging-related dysfunctions.

Mortality Trajectories at Exceptionally High Ages: A Study of Supercentenarians.

The growing number of persons surviving to age 100 years and beyond raises questions about the shape of mortality trajectories at exceptionally high ages, and this problem may become significant for actuaries in the near future. However, such studies are scarce because of the difficulties in obtaining reliable age estimates at exceptionally high ages. The current view about mortality beyond age 110 years suggests that death rates do not grow with age and are virtually flat. The same assumption is made in the new actuarial VBT tables. In this paper, we test the hypothesis that the mortality of supercentenarians (persons living 110+ years) is constant and does not grow with age, and we analyze mortality trajectories at these exceptionally high ages. Death records of supercentenarians were taken from the International Database on Longevity (IDL). All ages of supercentenarians in the database were subjected to careful validation. We used IDL records for persons belonging to extinct birth cohorts (born before 1895) since the last deaths in IDL were observed in 2007. We also compared our results based on IDL data with a more contemporary database maintained by the Gerontology Research Group (GRG). First we attempted to replicate findings by Gampe (2010), who analyzed IDL data and came to the conclusion that "human mortality after age 110 is flat." We split IDL data into two groups: cohorts born before 1885 and cohorts born in 1885 and later. Hazard rate estimates were conducted using the standard procedure available in Stata software. We found that mortality in both groups grows with age, although in older cohorts, growth was slower compared with more recent cohorts and not statistically significant. Mortality analysis of more numerous 1884-1894 birth cohort with the Akaike goodness-of-fit criterion showed better fit for the Gompertz model than for the exponential model (flat mortality). Mortality analyses with GRG data produced similar results. The remaining life expectancy for the 1884-1894 birth cohort demonstrates rapid decline with age. This decline is similar to the computer-simulated trajectory expected for the Gompertz model, rather than the extremely slow decline in the case of the exponential model. These results demonstrate that hazard rates after age 110 years do not stay constant and suggest that mortality deceleration at older ages is not a universal phenomenon. These findings may represent a challenge to the existing theories of aging and longevity, which predict constant mortality in the late stages of life. One possibility for reconciliation of the observed phenomenon and the existing theoretical consideration is a possibility of mortality deceleration and mortality plateau at very high yet unobservable ages.

Historical Evolution of Old-Age Mortality and New Approaches to Mortality Forecasting.

Knowledge of future mortality levels and trends is important for actuarial practice but poses a challenge to actuaries and demographers. The Lee-Carter method, currently used for mortality forecasting, is based on the assumption that the historical evolution of mortality at all age groups is driven by one factor only. This approach cannot capture an additive manner of mortality decline observed before the 1960s. To overcome the limitation of the one-factor model of mortality and to determine the true number of factors underlying mortality changes over time, we suggest a new approach to mortality analysis and forecasting based on the method of latent variable analysis. The basic assumption of this approach is that most variation in mortality rates over time is a manifestation of a small number of latent variables, variation in which gives rise to the observed mortality patterns. To extract major components of mortality variation, we apply factor analysis to mortality changes in developed countries over the period of 1900-2014. Factor analysis of time series of age-specific death rates in 12 developed countries (data taken from the Human Mortality Database) identified two factors capable of explaining almost 94 to 99 percent of the variance in the temporal changes of adult death rates at ages 25 to 85 years. Analysis of these two factors reveals that the first factor is a "young-age" or background factor with high factor loadings at ages 30 to 45 years. The second factor can be called an "oldage" or senescent factor because of high factor loadings at ages 65 to 85 years. It was found that the senescent factor was relatively stable in the past but now is rapidly declining for both men and women. The decline of the senescent factor is faster for men, although in most countries, it started almost 30 years later. Factor analysis of time series of age-specific death rates conducted for the oldest-old ages (65 to 100 years) found two factors explaining variation of mortality at extremely old ages in the United States. The first factor is comparable to the senescent factor found for adult mortality. The second factor, however, is specific to extreme old ages (96 to 100 years) and shows peaks in 1960 and 2000. Although mortality below 90 to 95 years shows a steady decline with time driven by the senescent factor, mortality of centenarians does not decline and remains relatively stable. The approach suggested in this paper has several advantages. First, it is able to determine the total number of independent factors affecting mortality changes over time. Second, this approach allows researchers to determine the time interval in which underlying factors remain stable or undergo rapid changes. Most methods of mortality projections are not able to identify the best base period for mortality projections, attempting to use the longest-possible time period instead. We observe that the senescent factor of mortality continues to decline, and this decline does not demonstrate any indications of slowing down. At the same time, mortality of centenarians does not decline and remains stable. The lack of mortality decline at extremely old ages may diminish anticipated longevity gains in the future.

The Future of Human Longevity.

Recent scientific publications suggest that human longevity records stopped increasing. Our finding that the mortality of centenarians has not decreased noticeably in recent decades (despite a significant mortality decline in younger age groups) is consistent with this suggestion. However, there is no convincing evidence that we have reached the limit of human life span. The future of human longevity is not fixed and will depend on human efforts to extend life span.