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Laboratory experiments show us that the deleterious character of accumulated novel age-specific mutations is reduced and made less variable with increased age. While theories of aging predict that the frequency of deleterious mutations at mutation-selection equilibrium will increase with the mutation's age of effect, they do not account for these age-related changes in the distribution of de novomutational effects. Furthermore, no model predicts why this dependence of mutational effects upon age exists. Because the nature of mutational distributions plays a critical role in shaping patterns of senescence, we need to develop aging theory that explains and incorporates these effects. Here we propose a model that explains the age dependency of mutational effects by extending Fisher's geometrical model of adaptation to include a temporal dimension. Using a combination of simple analytical arguments and simulations, we show that our model predicts age-specific mutational distributions that are consistent with observations from mutationaccumulation experiments. Simulations show us that these age-specific mutational effects may generate patterns of senescence at mutation-selection equilibrium that are consistent with observed demographic patterns that are otherwise difficult to explain.

An optimal aging theory, in which there is an endogenous rise in mortality rates with age, is presented. The model expects maximum fertility to occur at an age when somatic growth ends. How biology can be integrated with economics is also explained.

The process of aging causes significant alterations in the facial appearance of individuals. When compared with other sources of variation in face images, appearance variation due to aging displays some unique characteristics. For example, aging variation is specific to a given individual; it occurs slowly and is affected significantly by other factors, such as health, gender, and lifestyle. Changes in facial appearance due to aging can even affect discriminatory facial features, resulting in deterioration of the ability of humans and machines to identify aged individuals. In this paper, we describe how the effects of aging on facial appearance can be explained using learned age transformations and present experimental results to show that reasonably accurate estimates of age can be made for unseen images. We also show that we can improve our results by taking into account the fact that different individuals age in different ways and by considering the effect of lifestyle. Our proposed framework can be used for simulating aging effects on new face images in order to predict how an individual might look like in the future or how he/she used to look in the past. The methodology presented has also been used for designing a face recognition system, robust to aging variation. In this context, the perceived age of the subjects in the training and test images is normalized before the training and classification procedure so that aging variation is eliminated. Experimental results demonstrate that, when age normalization is used, the performance of our face recognition system can be improved. Index Terms--Aging variation, statistical face models, face recognition.

Health planning efforts for the population age 65 and over have been hampered continually by the lack of reliable estimates of the noninstitutionalized long-term care population. Until recently national estimates were virtually nonexistent, and reliable small area estimates remain unavailable. However, with the recent publication of several national surveys and the 1990 Census, synthetic estimates can be made for states and counties by using multivariate methods to model functional dependency at the national level, and then applying the predicted probabilities to corresponding state and county data. Using the 1984 National Health Interview Survey's Supplement on Aging and the 1986 Area Health Resources File System, we have produced log-linear regression models that include demographic and contextual variables as predictors of functional dependency among the noninstitutionalized population age 65 and over. Age, sex, race, and the percent of the 65 and over population who reside in poverty were found to be significant predictors of functional dependency. Applying these models to 1986 Medicare Enrollment Statistics, regression-adjusted synthetic estimates of two levels of functional dependency were produced for all states and--as examples of how the rates can be used to produce additional synthetic estimates--the largest County in each state. We also Produced point estimates and standard errors for the national prevalence of functional dependency among the noninstitutionalized population age 65 and over. (Am J Public Health 1990;81:335-343), Accurate estimation of the proportion of the population over age 65 that consists of functionally dependent people is necessary for health care planning, yet no clearly superior way of arriving at such an estimate has been developed. A method that uses log-linear regression models to estimate the numbers of noninstitutionalized dependent people over 65 is presented and applied to data drawn from several national and local surveys. This model allows for adjustments in the estimates due to several ''real-life'' factors, such as the effect of poverty on other factors that influence dependence (age, sex, race), and the fact that a small proportion (5 percent) of people older than 65 are not enrolled in Medicare. The results showed that age, sex, race, and poverty were the most important predictors of functional dependency. Using these variables, estimates of two levels of dependency were calculated for the individual states and selected counties. The two levels were ADL-dependent (people who have difficulty with the activities of daily life, such as dressing or eating), and those who could perform the activities of daily life but could not, for reasons of mobility, prepare meals, shop, or perform housework. The estimates are likely to prove useful for predicting the number of older people within a region who are likely to require long-term care. (Consumer Summary produced by Reliance Medical Information, Inc.)

'It's like trying to understand human psychology by interviewing two brothers,' says Harvard biologist Steven Austad. Austad is criticizing gerontology's traditional reliance on laboratory mice and rats to study the [...]

Allostatic load (AL) has been proposed as a new conceptualization of cumulative biological burden exacted on the body through attempts to adapt to life's demands. Using a multisystem summary measure of AL, we evaluated its capacity to predict four categories of health outcomes, 7 years after a baseline survey of 1,189 men and women age 70-79. Higher baseline AL scores were associated with significantly increased risk for 7-year mortality as well as declines in cognitive and physical functioning and were marginally associated with incident cardiovascular disease events, independent of standard socio-demographic characteristics and baseline health status. The summary AL measure was based on 10 parameters of biological functioning, four of which are primary mediators in the cascade from perceived challenges to downstream health outcomes. Six of the components are secondary mediators reflecting primarily components of the metabolic syndrome (syndrome X). AL was a better predictor of mortality and decline in physical functioning than either the syndrome X or primary mediator components alone. The findings support the concept of AL as a measure of cumulative biological burden.

Author(s): Susan E. Howlett [sup.1] [sup.2] , Kenneth Rockwood [sup.1] [sup.2] Author Affiliations: (1) Dalhousie University, Halifax, Canada (2) University of Manchester, UK Good preclinical models of ageing are needed [...]