Stochastic model for analysis of longitudinal data on aging and mortality

Yashin, Anatoli I.; Arbeev, Konstantin G.; Akushevich, Igor; Kulminski, Alexander M.; Akushevich, Lucy; Ukraintseva, Svetlana V.

doi:10.1016/j.mbs.2006.11.006

Cited by 72 publications

(136 citation statements)

References 34 publications

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“…Further, these statistical improvements can also drive new biomedical insights and provide avenues for testing those hypotheses. Much previous work has demonstrated the utility of physiological history for understanding the processes that drive aging and the relationship between disease risk and specific physiological measurements (Yashin et al ., 2006, 2007; Arbeev et al ., 2011). Our results add to this understanding by demonstrating clearly that early‐life physiology informs late‐life survival and that cumulative historical exposure can be a useful variable to track in addition to the present state of an individual's physiology.…”

Section: Discussionmentioning

confidence: 99%

“…In particular, modeling physiological change as a dynamic system allowed mortality risk to be successfully modeled as a function of the difference between an individuals' current physiological state and the ideal state for an individual of that age (Yashin et al ., 2007; Arbeev et al ., 2011). Related analyses classified individuals' likely lifespans according to trajectories of physiological indices (Yashin et al ., 2006, 2010).…”

Section: Introductionmentioning

confidence: 99%

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Predicting all‐cause mortality from basic physiology in the Framingham Heart Study

Zhang

Pincus

2015

Aging Cell

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SummaryUsing longitudinal data from a cohort of 1349 participants in the Framingham Heart Study, we show that as early as 28–38 years of age, almost 10% of variation in future lifespan can be predicted from simple clinical parameters. Specifically, we found diastolic and systolic blood pressure, blood glucose, weight, and body mass index (BMI) to be relevant to lifespan. These and similar parameters have been well‐characterized as risk factors in the relatively narrow context of cardiovascular disease and mortality in middle to old age. In contrast, we demonstrate here that such measures can be used to predict all‐cause mortality from mid‐adulthood onward. Further, we find that different clinical measurements are predictive of lifespan in different age regimes. Specifically, blood pressure and BMI are predictive of all‐cause mortality from ages 35 to 60, while blood glucose is predictive from ages 57 to 73. Moreover, we find that several of these parameters are best considered as measures of a rate of ‘damage accrual’, such that total historical exposure, rather than current measurement values, is the most relevant risk factor (as with pack‐years of cigarette smoking). In short, we show that simple physiological measurements have broader lifespan‐predictive value than indicated by previous work and that incorporating information from multiple time points can significantly increase that predictive capacity. In general, our results apply equally to both men and women, although some differences exist.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Predicting all‐cause mortality from basic physiology in the Framingham Heart Study

Zhang

Pincus

2015

Aging Cell

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“…a h t ðtÞ is a matrix function of negative feedback coefficients, and components of the vector function f 1h t ðtÞ characterize the effects of allostatic adaptation on physiological state (Yashin et al, 2007(Yashin et al, , 2008. All other notations are the same as in Eq.…”

Section: Modelmentioning

confidence: 99%

“…Woodbury and Manton (1977) suggested a stochastic process model to analyze longitudinal data, relying on the conditional Gaussian property of the distribution of physiological variables among survivors. The model describes physiological changes using stochastic differential equations, and the respective stochastic processes are Markov (Yashin, 1980(Yashin, , 1985Yashin and Manton, 1997;Akushevich et al, 2005;Yashin et al, 2007Yashin et al, , 2008Arbeev et al, 2009). Bagdonavicius and Nikulin (2009) introduced ''degradation modelling'' into the analysis of failure time data using different ideas.…”

Section: Introductionmentioning

confidence: 99%

Joint Analysis of Health Histories, Physiological State, and Survival

Yashin

Akushevich

Arbeev

et al. 2011

Mathematical Population Studies

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Data on individual health histories, age trajectories of physiological or biological variables, and mortality allow for the study of the joint evolution of health and physiological states and their effects on mortality. Individual health and physiological trajectories are described using a stochastic process with two mutuallydependent continuous and jumping components. The parameters of this process and mortality rate are identified from the data in which the continuous component is measured in discrete times, and transitions of jumping process are observed.

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“…The behaviour of these equation systems can be sensitive to noisy measurement signals. While stochastic differential equations directly model the noisy nature of expression data, they scale even less well to larger systems [39] . Dealing better with the high-dimensional nature of microarray data, methods from multivariate statistics have thus been applied to microarray time-course analysis [40,41] .…”

Section: Timed Knock-downs and Dynamic Effectsmentioning

confidence: 99%

RNA Interference in Ageing Research – A Mini-Review

et al. 2010

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Background: The search for genetic mechanisms affecting life-span and ageing represents an important part of ageing research, especially since the discovery of single-gene mutations with dramatic effects on these traits. Due to its relative ease of use and its power to specifically target arbitrary genes, RNA interference (RNAi) has rapidly been adopted as a technique for silencing gene expression. The feasibility of genome-wide RNAi screens potentially much simplifies the identification of novel ageing-related genes. Objective: In a review of applications of RNAi in ageing research with a focus on the model organisms Caenorhabditis elegans and Drosophila melanogaster and discussing recent technical developments, we aim to highlight the current and future impact of this technology in the field. Method: We show how RNAi has successfully been used to complement classic mutant studies. Moreover, we discuss the novel opportunities and challenges of an application of RNAi in genome-wide screens in D. melanogaster, which has become possible with the recent availability of a comprehensive transgenic RNAi library for the fly. We highlight, in particular, how the flexible control of RNAi induction can support the study of dynamic processes like ageing through specific experiments and the development of matching computational methods. In an overview of complementary approaches we discuss the challenge of extracting insight from the high-dimensional measurement datasets that are required for the study of dynamic effects and interaction dependencies. Conclusion: RNAi has emerged as a powerful tool for the study of ageing, allowing the further characterization of the roles of specific genes in the ageing process as well as the efficient identification of new genes implicated. RNAi has contributed to our understanding of age-related diseases especially by making genes amenable to manipulation for which mutants were not easily available. Recent developments enable genome-wide screens with unprecedented temporal and spatial control of RNAi induction. Specific RNAi time-course experiments provide an opportunity for the analysis of high-resolution gene expression profiles capturing the dynamics of ageing-relevant processes and gene interactions. Research exploiting new avenues opened by the growing RNAi toolbox will considerably contribute to the next steps in researching the genetics of ageing and age-related diseases.

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Stochastic model for analysis of longitudinal data on aging and mortality

Cited by 72 publications

References 34 publications

Predicting all‐cause mortality from basic physiology in the Framingham Heart Study

Predicting all‐cause mortality from basic physiology in the Framingham Heart Study

Joint Analysis of Health Histories, Physiological State, and Survival

RNA Interference in Ageing Research – A Mini-Review

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