Abstract
Background: Life-spanning population survivorship curves (the number of survivors versus age) are conventionally regarded as a demographic issue. Most often, the term hazard, the relative mortality per age-interval, is used as a typical survivorship parameter. Population survivorship curves are construed from cross-sectional data (single event per individual; here, mortality). Objective: We tested (quantitatively) how Gompertz’ law describes the mortality pattern of a wide variety of organisms, some of them fed with lifespan affecting diets. Moreover, we tested (semi-quantitatively) whether Gompertz’ law describes the disintegration of a (biological) small-world network. Methods: The Gompertz tests, explored in demographic data of humans (male/female) and 4 animal species (mice, honeybees, fruit flies, houseflies), were analyzed with conventional software. The Gompertz law was examined in a small-world network model. Results: Gompertz' law applies to all cohorts; thus, with or without exposure to experimental conditions. It describes in all cohorts old-age slowing of mortality. Gompertz’ law is compatible with a gradual and random increase of connections in the network model. Conclusion: Old-age deceleration of mortality is a characteristic of many populations. Aging has to be understood as a lifetime increasing of excitatory or, alternatively, of decreasing inhibitory (biological) connections, thereby facilitating pathogenic mechanisms.
Highlights
Population survivorship curves are used to assess quantitative parameters associated with aging or interventions affecting the longevity of human and animal populations [1 4]
Old-age deceleration of mortality is a characteristic of many populations
Aging has to be understood as a lifetime increasing of excitatory or, alternatively, of decreasing inhibitory connections, thereby facilitating pathogenic mechanisms
Summary
Population survivorship curves are used to assess quantitative parameters associated with aging or interventions affecting the longevity of human and animal populations [1 4]. These curves, showing all-cause mortality, do not disclose a specific pathology. Survivorship patterns have been explained by assuming individual (often Gaussian) variability in, among others, frailty, genes or defective proteins [5, 6]. In a protective environment (i.e. with little external causes of death such as severe infections, starvation, and accidents), survivorship curves are population-specific characteristics. Population survivorship curves are construed from cross-sectional data (single event per individual; here, mortality)
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