Linearized Growth Curve,Proper Age and Age at Menarche

A mathematical model of the linearized growth curve and its physiological interpretation by the introduction of proper age, which is proportional to the chronological age, are presented here. In the second phase, but not in the first phase, this constant of proportionality is highly correlated with the age at menarche.     

The Mouse Age Phenome Knowledgebase and Disease-Specific Inter-Species Age Mapping

Background

Similarities between mice and humans lead to generation of many mouse models of human disease. However, differences between the species often result in mice being unreliable as preclinical models for human disease. One difference that might play a role in lowering the predictivity of mice models to human diseases is age. Despite the important role age plays in medicine, it is too often considered only casually when considering mouse models.

Methods

We developed the mouse-Age Phenotype Knowledgebase, which holds knowledge about age-related phenotypic patterns in mice. The knowledgebase was extensively populated with literature-derived data using text mining techniques. We then mapped between ages in humans and mice by comparing the age distribution pattern for 887 diseases in both species.

Results

The knowledgebase was populated with over 9800 instances generated by a text-mining pipeline. The quality of the data was manually evaluated, and was found to be of high accuracy (estimated precision >86%). Furthermore, grouping together diseases that share similar age patterns in mice resulted in clusters that mirror actual biomedical knowledge. Using these data, we matched age distribution patterns in mice and in humans, allowing for age differences by shifting either of the patterns. High correlation (r²>0.5) was found for 223 diseases. The results clearly indicate a difference in the age mapping between different diseases: age 30 years in human is mapped to 120 days in mice for Leukemia, but to 295 days for Anemia. Based on these results we generated a mice-to-human age map which is publicly available.

Conclusions

We present here the development of the mouse-APK, its population with literature-derived data and its use to map ages in mice and human for 223 diseases. These results present a further step made to bridging the gap between humans and mice in biomedical research.     

Age and immunity

Longitudinal studies are defining progressive alterations to the immune system associated with increased mortality in the very elderly. Many of these changes are exacerbated by or even caused by chronic T cell stimulation by persistent antigen, particularly from Cytomegalovirus. The composition of T cell subsets, their functional integrity and representation in the repertoire are all markedly influenced by age and by CMV. How these findings relate to epidemiological, functional, genetic, genomic and proteomic studies of human T cell immunosenescence was the subject of intense debate at an international conference held just before Christmas 2005 in the Black Forest.     

Age Distribution of Cancer: The Incidence Turnover at Old Age

In recent years data on cancer incidence in the USA, the Netherlands, and in Hong Kong indicate a flattening and perhaps a turnover at advanced age, but no model has been successful in fitting this data and thus providing clues to the underlying biology. In this work we assume these data are reliable and free from bias. We find that a Beta distribution fits SEER age-specific cancer incidence data for all adult cancers extremely well, and its interpretation as a model leads to the possibility that there is a beneficial cancer extinction process that becomes important at elevated age. Particularly evident from the data is the apparent remarkable uniformity of adult cancers peaking in incidence at about the same age, including cancers in other countries. Possible biological mechanisms include increasing apoptosis and cell senescence with age. Further, the model suggests that cancer is not inevitable at advanced age, but reaches a maximum cumulative probability of affliction with any cancer of about 70% for men and 53% for women in the US, and much smaller values for individual cancers.     

Age estimation of zebra

A method of zebra age determination, using the decreasing height of Ml in a wear model, was compared with the method of visual assessment of incisor changes as used by Klingel & Klingel (1966). Good agreement between the methods was found.