Is early life body weight a predictor of longevity and tumor risk in rats?

Predictive Models of Life Span in Old Female Mice Based on Immune, Redox, and Behavioral Parameters

The development of mathematical models capable of predicting the lifespan of animals is growing. However, there are no studies that compare the predictive power of different sets of parameters depending on the age of the animals. The aim of the present study is to test whether mathematical models for life span prediction developed in adult female mice based on immune, redox, and behavioral parameters can predict life span in old animals and to develop new models in old mice. For this purpose, 29 variables, including parameters of immune function, redox state, and behavioral ones, were evaluated in old female Swiss mice (80 ± 4 weeks). Life span was registered when they died naturally. Firstly, we observed that the models developed in adults were not able to accurately predict the life span of old mice. Therefore, the immunity (adjusted R2 = 73.6%), redox (adjusted R2 = 46.5%), immunity-redox (adjusted R2 = 96.4%), and behavioral (adjusted R2 = 67.9%) models were developed in old age. Finally, the models were validated in another batch of mice. The developed models in old mice show certain similarities to those in adults but include different immune, redox, and behavioral markers, which highlights the importance of age in the prediction of life span.

Modeling of senescent cell dynamics predicts a late‐life decrease in cancer incidence

Current oncogenic theories state that tumors arise from cell lineages that sequentially accumulate (epi)mutations, progressively turning healthy cells into carcinogenic ones. While those models found some empirical support, they are little predictive of intraspecies age-specific cancer incidence and of interspecies cancer prevalence. Notably, in humans and lab rodents, a deceleration (and sometimes decline) of cancer incidence rate has been found at old ages. Additionally, dominant theoretical models of oncogenesis predict that cancer risk should increase in large and/or long-lived species, which is not supported by empirical data. Here, we explore the hypothesis that cellular senescence could explain those incongruent empirical patterns. More precisely, we hypothesize that there is a trade-off between dying of cancer and of (other) ageing-related causes. This trade-off between organismal mortality components would be mediated, at the cellular scale, by the accumulation of senescent cells. In this framework, damaged cells can either undergo apoptosis or enter senescence. Apoptotic cells lead to compensatory proliferation, associated with an excess risk of cancer, whereas senescent cell accumulation leads to ageing-related mortality. To test our framework, we build a deterministic model that first describes how cells get damaged, undergo apoptosis, or enter senescence. We then translate those cellular dynamics into a compound organismal survival metric also integrating life-history traits. We address four different questions linked to our framework: can cellular senescence be adaptive, do the predictions of our model reflect epidemiological patterns observed among mammal species, what is the effect of species sizes on those answers, and what happens when senescent cells are removed? Importantly, we find that cellular senescence can optimize lifetime reproductive success. Moreover, we find that life-history traits play an important role in shaping the cellular trade-offs. Overall, we demonstrate that integrating cellular biology knowledge with eco-evolutionary principles is crucial to solve parts of the cancer puzzle.

When will my mouse die? Life span prediction based on immune function, redox and behavioural parameters in female mice at the adult age

The identification of predictive markers of life span would help to unravel the underlying mechanisms influencing ageing and longevity. For this aim, 30 variables including immune functions, inflammatory-oxidative stress state and behavioural characteristics were investigated in ICR-CD1 female mice at the adult age (N = 38). Mice were monitored individually until they died and individual life spans were registered. Multiple linear regression was carried out to construct an Immunity model (adjusted R² = 75.8%) comprising Macrophage chemotaxis and phagocytosis and Lymphoproliferation capacity, a Redox model (adjusted R² = 84.4%) involving Reduced Glutathione and Malondialdehyde concentrations and Glutathione Peroxidase activity and a Behavioural model (adjusted R² = 79.8%) comprising Internal Locomotion and Time spent in open arms indices. In addition, a Combined model (adjusted R² = 92.4%) and an Immunity-Redox model (adjusted R² = 88.7%) were also constructed by combining the above-mentioned selected variables. The models were also cross-validated using two different sets of female mice (N = 30; N = 40). Correlation between predicted and observed life span was 0.849 (P < 0.000) for the Immunity model, 0.691 (P < 0.000) for the Redox, 0.662 (P < 0.000) for the Behavioural and 0.840 (P < 0.000) for the Immunity-Redox model. Thus, these results provide a new perspective on the use of immune function, redox and behavioural markers as prognostic tools in ageing research.

How Much Should We Weigh for a Long and Healthy Life Span? The Need to Reconcile Caloric Restriction versus Longevity with Body Mass Index versus Mortality Data

Total caloric restriction (CR) without malnutrition is a well-established experimental approach to extend life span in laboratory animals. Although CR in humans is capable of shifting several endocrinological parameters, it is not clear where the minimum inflection point of the U-shaped curve linking body mass index (BMI) with all-cause mortality lies. The exact trend of this curve, when used for planning preventive strategies for public health is of extreme importance. Normal BMI ranges from 18.5 to 24.9; many epidemiological studies show an inverse relationship between mortality and BMI inside the normal BMI range. Other studies show that the lowest mortality in the entire range of BMI is obtained in the overweight range (25-29.9). Reconciling the extension of life span in laboratory animals by experimental CR with the BMI-mortality curve of human epidemiology is not trivial. In fact, one interpretation is that the CR data are identifying a known: "excess fat is deleterious for health"; although a second interpretation may be that: "additional leanness from a normal body weight may add health and life span delaying the process of aging." This short review hope to start a discussion aimed at finding the widest consensus on which weight range should be considered the "healthiest" for our species, contributing in this way to the picture of what is the correct life style for a long and healthy life span.

Genetic regulation of life span, metabolism, and body weight in Pohn, a new wild-derived mouse strain

Quantitative trait loci (QTL) of longevity identified in human and mouse are significantly colocalized, suggesting that common mechanisms are involved. However, the limited number of strains that have been used in mouse longevity studies undermines the ability to identify longevity genes. We crossed C57BL/6J mice with a new wild-derived strain, Pohn, and identified two life span QTL-Ls1 and Ls2. Interestingly, homologous human longevity QTL colocalize with Ls1. We also defined new QTL for metabolic heat production and body weight. Both phenotypes are significantly correlated with life span. We found that large clone ratio, an in vitro indicator for cellular senescence, is not correlated with life span, suggesting that cell senescence and intrinsic aging are not always associated. Overall, by using Pohn mice, we identified new QTL for longevity-related traits, thus facilitating the exploration of the genetic regulation of aging.

How long will my mouse live? Machine learning approaches for prediction of mouse life span

Prediction of individual life span based on characteristics evaluated at middle-age represents a challenging objective for aging research. In this study, we used machine learning algorithms to construct models that predict life span in a stock of genetically heterogeneous mice. Life-span prediction accuracy of 22 algorithms was evaluated using a cross-validation approach, in which models were trained and tested with distinct subsets of data. Using a combination of body weight and T-cell subset measures evaluated before 2 years of age, we show that the life-span quartile to which an individual mouse belongs can be predicted with an accuracy of 35.3% (±0.10%). This result provides a new benchmark for the development of life-span–predictive models, but improvement can be expected through identification of new predictor variables and development of computational approaches. Future work in this direction can provide tools for aging research and will shed light on associations between phenotypic traits and longevity.

Coevolution of telomerase activity and body mass in mammals: from mice to beavers

Telomerase is repressed in the majority of human somatic tissues. As a result human somatic cells undergo replicative senescence, which plays an important role in suppressing tumorigenesis, and at the same time contributes to the process of aging. Repression of somatic telomerase activity is not a universal phenomenon among mammals. Mice, for example, express telomerase in somatic tissues, and mouse cells are immortal when cultured at physiological oxygen concentration. What is the status of telomerase in other animals, beyond human and laboratory mouse, and why do some species evolve repression of telomerase activity while others do not? Here we discuss the data on telomere biology in various mammalian species, and a recent study of telomerase activity in a large collection of wild rodent species, which showed that telomerase activity coevolves with body mass, but not lifespan. Large rodents repress telomerase activity, while small rodents maintain high levels of telomerase activity in somatic cells. We discuss a model that large body mass presents an increased cancer risk, which drives the evolution of telomerase suppression and replicative senescence.

Cancer in rodents: does it tell us about cancer in humans?

Information obtained from animal models (mostly mice and rats) has contributed substantially to the development of treatments for human cancers. However, important interspecies differences have to be taken into account when considering the mechanisms of cancer development and extrapolating the results from mice to humans. Comparative studies of cancer in humans and animal models mostly focus on genetic factors. This review discusses the bio-epidemiological aspects of cancer manifestation in humans and rodents that have been underrepresented in the literature.