CPHA

Cross-sectional analyses show that an index of aging-associated health/well-being deficits, called the "frailty index", can characterize the aging process in humans. This study provides support for such characterization from a longitudinal analysis of the frailty index properties. The data are from the National Long Term Care Survey assessed longitudinally health and functioning of the U.S. elderly in the period 1982 to 1999. In cross-sectional analysis, the frailty index exhibits accelerated increase with age till oldest-old ages (95+), with possible deceleration thereafter. Longitudinal analysis confirms the accelerated accumulation of deficits in aging individuals. The time-dynamics of the frailty index is affected by two sex-sensitive processes: i) selection of robust individuals, resulting in a decline of the mean frailty index with age, and ii) accumulation of deficits associated with physiological aging and its interaction with environment, which results in an accelerated increase of individual frailty index prior to death irrespective of chronological age. Current frailty index levels in individuals are more predictive of death than the index past values. Longitudinal analysis provides strong evidence that the cumulative index of health/well-being deficits can characterize aging-associated processes in humans and predict death better than chronological age during short-term periods.

Many longitudinal studies of aging collect genetic information only for a sub-sample of participants of the study. These data also do not include recent findings, new ideas and methodological concepts developed by distinct groups of researchers. The formal statistical analyses of genetic data ignore this additional information and therefore cannot utilize the entire research potential of the data. In this paper, we present a stochastic model for studying such longitudinal data in joint analyses of genetic and non-genetic sub-samples. The model incorporates several major concepts of aging known to date and usually studied independently. These include age-specific physiological norms, allostasis and allostatic load, stochasticity, and decline in stress resistance and adaptive capacity with age. The approach allows for studying all these concepts in their mutual connection, even if respective mechanisms are not directly measured in data (which is typical for longitudinal data available to date). The model takes into account dependence of longitudinal indices and hazard rates on genetic markers and permits evaluation of all these characteristics for carriers of different alleles (genotypes) to address questions concerning genetic influence on aging-related characteristics. The method is based on extracting genetic information from the entire sample of longitudinal data consisting of genetic and non-genetic sub-samples. Thus it results in a substantial increase in the accuracy of statistical estimates of genetic parameters compared to methods that use only information from a genetic sub-sample. Such an increase is achieved without collecting additional genetic data. Simulation studies illustrate the increase in the accuracy in different scenarios for datasets structurally similar to the Framingham Heart Study. Possible applications of the model and its further generalizations are discussed.

The deterioration of human health with age is manifested in changes of thousands of physiological and biological variables. The contribution of some of such changes to the mortality risk may be small and cannot be reliably detected by existing statistical methods. A cumulative index of health/well-being disorders, which counts changes in observed variables on the way of losing health, may be an appropriate way to take the effects of such variables into account. In this paper we investigate regularities of the aging-related changes in human health/well-being/survival status described by such an index using the new version of the quadratic hazard model of human aging and mortality. We found that the shape and the location of the mortality risk, considered as a function of the introduced health-related index, changes with age reflecting the decline in stress resistance and the age-dependence of the “optimal” health/well-being status. Comparison of these results with findings from early studies using the Cox’s-like model of risk function indicates that the results are likely to describe regularities of deterioration in human health during the aging process.

Multiple functions of the beta2-adrenergic receptor (ADRB2) and angiotensin-converting enzyme (ACE) genes warrant studies of their associations with aging-related phenotypes. We focus on multimarker analyses and analyses of the effects of compound genotypes of two polymorphisms in the ADRB2 gene, rs1042713 and rs1042714, and 11 polymorphisms of the ACE gene, on the risk of such an aging-associated phenotype as myocardial infarction (MI). We used the data from a genotyped sample of the Framingham Heart Study Offspring (FHSO) cohort (n ¼ 1500) followed for about 36 years with six examinations. The ADRB2 rs1042714 (C?G) polymorphism and two moderately correlated (r 2 ¼ 0.77) ACE polymorphisms, rs4363 (A?G) and rs12449782 (A?G), were significantly associated with risks of MI in this aging cohort in multimarker models. Predominantly linked ACE genotypes exhibited opposite effects on MI risks, e.g., the AA (rs12449782) genotype had a detrimental effect, whereas the predominantly linked AA (rs4363) genotype exhibited a protective effect. This trade-off occurs as a result of the opposite effects of rare compound genotypes of the ACE polymorphisms with a single dose of the AG heterozygote. This genetic trade-off is further augmented by the selective modulating effect of the rs1042714 ADRB2 polymorphism. The associations were not altered by adjustment for common MI risk factors. The results suggest that effects of single specific genetic variants of the ADRB2 and ACE genes on MI can be readily altered by gene-gene or=and gene-environmental interactions, especially in large heterogeneous samples. Multimarker genetic analyses should benefit studies of complex aging-associated phenotypes.

Exceptional survival results from complicated interplay between genetic and environmental factors. The effects of these factors on survival are mediated by the biological and physiological variables, which affect mortality risk. In this paper, we evaluated the role of blood glucose (BG) in exceptional survival using the Framingham heart study data for the main (FHS) and offspring (FHSO) cohorts. We found that: (1) the average cross-sectional age patterns of BG change over time; (2) the values of BG level among the longest lived individuals in this study differ for different sub-cohorts; (3) the longitudinal age patterns of BG differ from those of cross-sectional ones. We investigated mechanisms forming average age trajectories of BG in the FHS cohort. We found that the two curves: one, characterizing the average effects of allostatic adaptation, and another, minimizing mortality risk for any given age, play the central role in this process. We found that the average BG age trajectories for exceptional survivors are closer to the curve minimizing mortality risk than those of individuals having shorter life spans. We concluded that individuals whose age trajectories of BG are located around the curve minimizing chances of premature death at each given age have highest chances of reaching exceptional longevity.

Aging-related changes in a human organism follow dynamic regularities, which contribute to the observed age patterns of incidence and mortality curves. An organism's "optimal" (normal) physiological state changes with age, affecting the values of risks of disease and death. The resistance to stresses, as well as adaptive capacity, declines with age. An exposure to improper environment results in persisting deviation of individuals' physiological (and biological) indices from their normal state (due to allostatic adaptation), which, in turn, increases chances of disease and death. Despite numerous studies investigating these effects, there is no conceptual framework, which would allow for putting all these findings together, and analyze longitudinal data taking all these dynamic connections into account. In this paper we suggest such a framework, using a new version of stochastic process model of aging and mortality. Using this model, we elaborated a statistical method for analyses of longitudinal data on aging, health and longevity and tested it using different simulated data sets. The results show that the model may characterize complicated interplay among different components of aging-related changes in humans and that the model parameters are identifiable from the data.

We employ an approach based on the elaborated frailty index (FI), which is capable of taking into account variables with mild effect on the aging, health and survival outcomes, and investigate the connections between the FI, chronological age and the aging-associated outcomes in the elderly.

Relationships between aging, disease risks, and longevity are not yet well understood. For example, joint increases in cancer risk and total survival observed in many human populations and some experimental aging studies may be linked to a trade-off between cancer and aging as well as to the trade-off(s) between cancer and other diseases, and their relative impact is not clear. While the former trade-off (between cancer and aging) received broad attention in aging research, the latter one lacks respective studies, although its understanding is important for developing optimal strategies of increasing both longevity and healthy life span. In this paper, we explore the possibility of trade-offs between risks of cancer and selected major disorders. First, we review current literature suggesting that the trade-offs between cancer and other diseases may exist and be linked to the differential intensity of apoptosis. Then we select relevant disorders for the analysis (acute coronary heart disease [ACHD], stroke, asthma, and Alzheimer disease [AD]) and calculate the risk of cancer among individuals with each of these disorders, and vice versa, using the Framingham Study (5209 individuals) and the National Long Term Care Survey (NLTCS) (38,214 individuals) data. We found a reduction in cancer risk among old (80þ) men with stroke and in risk of ACHD among men (50þ) with cancer in the Framingham Study. We also found an increase in ACHD and stroke among individuals with cancer, and a reduction in cancer risk among women with AD in the NLTCS. The manifestation of trade-offs between risks of cancer and other diseases thus depended on sex, age, and study population. We discuss factors modulating the potential tradeoffs between major disorders in populations, e.g., disease treatments. Further study is needed to clarify possible impact of such trade-offs on longevity.

An important feature of aging-related deterioration in human health is the decline in organisms' resistance to stresses, which contributes to an increase in morbidity and mortality risks. In human longitudinal studies of aging, such a decline is not measured directly, so indirect methods of statistical modeling have to be used for evaluating this characteristic. Since medical interventions reflect severity of occurring health disorders, data from Medicare service use files can be used for such modeling. In this paper, we use the National Long Term Care Survey (NLTCS) data merged with the Medicare service use files to investigate dynamics of stress resistance in the U.S. elderly. We constructed individual indices of cumulative deficits and medical costs and investigated their separate and joint effects on dynamics of mortality risks using the quadratic hazard model. We found that males show a faster decline in stress resistance with age than females. Manuscript 65 -gain in remaining life expectancy after age 65 calculated using the baseline mortality (μ t 0 ) in respective models and the total mortality ( ) in the Gompertz model Mech Ageing Dev. Author manuscript; available in PMC 2009 April 1.

Background-An index of age-associated health/well-being disorders (deficits), called the "frailty index" (FI), appears to be a promising characteristic to capture dynamic variability in aging manifestations among age-peers. In this study we provide further support toward this view focusing on the analysis of the FI age patterns in the participants of the National Long Term Care Survey (NLTCS).

OBJECTIVES: To investigate the relationship between body mass index (BMI) and 9-year mortality in older (≥65) Americans with and without disability.DESIGN: Cohort study.SETTING: The unique disability-focused National Long Term Care Survey (NLTCS) data that assessed the health and well-being of older individuals in 1994 were analyzed.PARTICIPANTS: Four thousand seven hundred ninety-one individuals in the 1994 survey.MEASUREMENTS: BMI (kg/m2) was calculated from self- or proxy reports of height and weight. The analysis was adjusted for 1-year change in BMI and demographic and health-related factors, as well as reports by proxies, and death occurring during the first 2 years after the interview.RESULTS: The relative risk of death as a function of BMI formed a nonsymmetric U-shaped pattern, with larger risks associated with lower BMI (<22.0) and minimal risks for BMI of 25.0 to 34.9. (BMI 22.0–24.9 was the reference.) Adjustments for demographic and health-related factors had little effect on this pattern. Nondisabled individuals exhibited a similar U-shaped pattern but with lower risks associated with lower BMI. For disabled individuals, the mortality–risk pattern was higher for lower BMI (<22.0) and flat for higher BMI, thus exhibiting an inverse J shape. BMI patterns were age sensitive, with disability status affecting sensitivity.CONCLUSION: Overweight or mild (grade 1) obesity was not a risk factor for 9-year mortality in older Americans participating in the 1994 NLTCS. A flatter BMI pattern of the relative risk of death for disabled than for nondisabled individuals suggests that optimal body weight can be sensitive to age and health and well-being.

We present a new efficient algortithm for construction of linear latent structure (LLS) models. This algorithm reduces a problem of estimation of model parameters to a sequence of problems of linear algebra, which assures a low computational complexity and ability to handle on desktop computers data that involve up to thousands of variables.

A new method for analyzing high-dimensional categorical data, Linear Latent Structure (LLS) analysis, is presented. LLS models belong to the family of latent structure models, which are mixture distribution models constrained to satisfy the local independence assumption. LLS analysis explicitly considers a family of mixed distributions as a linear space and LLS models are obtained by imposing linear constraints on the mixing distribution.

The levels of blood glucose (BG) in humans tend to increase with age deviating from the norm specified for the young adults. Such elevation is often considered as a factor contributing to an increase in risks of disease and death. The proper use of intervention strategies coping with or preventing consequences of BG elevation requires understanding the roles of external forces and intrinsic senescence in this process. To address these issues, we performed analyses of longitudinal data on BG collected in the Framingham Heart Study using methods of descriptive statistics and statistical modeling. The approach allows us to separate effects of persistent external disturbances from "normal" aging related changes due to the senescence process. We found that the BG level corresponding to the lowest mortality risk tends to increase with age. The changes in the shape of the mortality risk with age indicate the aging related decline in resistance to stresses affecting the BG level. The results show that analyzing longitudinal data using advanced methods may substantially increase our knowledge on factors and mechanisms responsible for aging related changes in humans.

Variables measured in longitudinal studies of aging and longevity do not exhaust the list of all factors affecting health and mortality transitions. Unobserved factors generate hidden variability in susceptibility to diseases and death in populations and in age trajectories of longitudinally measured indices. Effects of such heterogeneity can be manifested not only in observed hazard rates but also in average trajectories of measured indices. Although effects of hidden heterogeneity on observed mortality rates are widely discussed, their role in forming age patterns of other aging-related characteristics (average trajectories of physiological state, stress resistance, etc.) is less clear. We propose a model of hidden heterogeneity to analyze its effects in longitudinal data. The approach takes the presence of hidden heterogeneity into account and incorporates several major concepts currently developing in aging research (allostatic load, aging-associated decline in adaptive capacity and stress-resistance, age-dependent physiological norms). Simulation experiments confirm identifiability of model's parameters.

Grade of membership (GoM) analysis was introduced in 1974 as a means of analyzing multivariate categorical data. Since then, it has been successfully applied to many problems. The primary goal of GoM analysis is to derive properties of individuals based on results of multivariate measurements; such properties are given in the form of the expectations of a hidden random variable (state of an individual) conditional on the result of observations. In this article, we present a new perspective for the GoM model, based on considering distribution laws of observed random variables as realizations of another random variable. It happens that some moments of this new random variable are directly estimable from observations. Our approach allows us to establish a number of important relations between estimable moments and values of interest, which, in turn, provides a basis for a new numerical procedure.

In this article, we present a new perspective for the GoM model, based on considering distribution laws of observed random variables as realizations of another random variable. It happens that some moments of this new random variable are directly estimable from observations. Our approach allows us to establish a number of important relations between estimable moments and values of interest, which, in turn, provides a basis for a new numerical procedure.

We calculated two--photon exchange effects for elastic electron--proton scattering at high momentum transfers. The corresponding nucleon Compton amplitude is defined by two space--like virtual photons that appear to have significant virtualities. We make predictions for a) a single--spin beam asymmetry, and b) a single--spin target asymmetry or recoil proton polarization caused by an unpolarized electron beam.