Model Of Biological Aging Research Articles

Biological Aging Parameters Can Be Improved After Autologous Adipose-Derived Stem Cell Injection.

Biological aging (BA) is a comprehensive assessment tool for elderly persons. The authors aimed to develop a rat model that can be used to assess BA by evaluating various blood, biochemical, and hormonal parameters and demonstrate that the intravenous administration of autologous adipose-derived stem cells (ADSCs) improves BA. Twelve elderly (aged 20 months) male Sprague-Dawley rats were used in this study and divided into 2 groups: autologous ADSC administration (n = 6) and saline administration (n = 6). The complete blood count, biochemical and hormonal parameters, and antioxidant potential were evaluated before harvesting the rat inguinal fat tissue and intravenous ADSC administration as well as at 1, 3, and 5 weeks after ADSC administration. Adipose-derived stem cells administration regulated blood content, biochemical parameters, renal function, and antioxidant enzymes in elderly rats. Furthermore, changes in several hormonal levels were identified in the ADSC administration group compared with the saline administration group. An assessment model of BA in elderly rats was successfully developed after the intravenous administration of autologous ADSCs. The authors suggest that intravenously injected ADSC treatment may be a valuable method to improve BA.

Selection of a set of biomarkers and comparisons of biological age estimation models for Korean men.

Biological age (BA) represents the rate of the senescence with a set of biomarkers. The BA prediction models have not been compared to obtain an optimal BA prediction model with BA biomarkers for Korean men. The study aims to obtain a set of BA biomarkers and compare three of the reported statistical approaches for an optimal BA prediction model. The Korea National Health and Nutrition Examination Surveys data of 2009 to 2011 were used to select six BA biomarkers from 940 healthy subjects aged between 30 to 80 years. The multiple linear regression (MLR), principal component analysis (PCA), and Klemera and Doubal methods (KDM) were used to obtain three BA prediction models. Correlation coefficients (r) with 95% confidence intervals (CI) and regression slopes were assessed. One of the Euro Quality of Life-5 Dimensions, mobility, was compared for feasibility test of each BA models. KDM showed greatest correlation (r=0.88 [P<0.05]) with smallest 95% CI and regression slope (1.00). PCA also showed strong correlation (r=0.79 [P<0.05]) with small 95% CI and regression slope (0.94). MLR (r=0.68 [P<0.05]) showed over and underestimated BA results at the end of the age spectrum. Estimations of BA were most reliable with KDM. The PCA and MLR approaches were comparatively simple to devise for Korean men.

Extracting biological age from biomedical data via deep learning: too much of a good thing?

Age-related physiological changes in humans are linearly associated with age. Naturally, linear combinations of physiological measures trained to estimate chronological age have recently emerged as a practical way to quantify aging in the form of biological age. In this work, we used one-week long physical activity records from a 2003–2006 National Health and Nutrition Examination Survey (NHANES) to compare three increasingly accurate biological age models: the unsupervised Principal Components Analysis (PCA) score, a multivariate linear regression, and a state-of-the-art deep convolutional neural network (CNN). We found that the supervised approaches produce better chronological age estimations at the expense of a loss of the association between the aging acceleration and all-cause mortality. Consequently, we turned to the NHANES death register directly and introduced a novel way to train parametric proportional hazards models suitable for out-of-the-box implementation with any modern machine learning software. As a demonstration, we produced a separate deep CNN for mortality risks prediction that outperformed any of the biological age or a simple linear proportional hazards model. Altogether, our findings demonstrate the emerging potential of combined wearable sensors and deep learning technologies for applications involving continuous health risk monitoring and real-time feedback to patients and care providers.

Biological age as a health index for mortality and major age-related disease incidence in Koreans: National Health Insurance Service - Health screening 11-year follow-up study.

PurposeA comprehensive health index is needed to measure an individual’s overall health and aging status and predict the risk of death and age-related disease incidence, and evaluate the effect of a health management program. The purpose of this study is to demonstrate the validity of estimated biological age (BA) in relation to all-cause mortality and age-related disease incidence based on National Sample Cohort database.Patients and methodsThis study was based on National Sample Cohort database of the National Health Insurance Service – Eligibility database and the National Health Insurance Service – Medical and Health Examination database of the year 2002 through 2013. BA model was developed based on the National Health Insurance Service – National Sample Cohort (NHIS – NSC) database and Cox proportional hazard analysis was done for mortality and major age-related disease incidence.ResultsFor every 1 year increase of the calculated BA and chronological age difference, the hazard ratio for mortality significantly increased by 1.6% (1.5% in men and 2.0% in women) and also for hypertension, diabetes mellitus, heart disease, stroke, and cancer incidence by 2.5%, 4.2%, 1.3%, 1.6%, and 0.4%, respectively (p<0.001).ConclusionEstimated BA by the developed BA model based on NHIS – NSC database is expected to be used not only as an index for assessing health and aging status and predicting mortality and major age-related disease incidence, but can also be applied to various health care fields.

Common methods of biological age estimation.

At present, no single indicator could be used as a golden index to estimate aging process. The biological age (BA), which combines several important biomarkers with mathematical modeling, has been proposed for >50 years as an aging estimation method to replace chronological age (CA). The common methods used for BA estimation include the multiple linear regression (MLR), the principal component analysis (PCA), the Hochschild’s method, and the Klemera and Doubal’s method (KDM). The fundamental differences in these four methods are the roles of CA and the selection criteria of aging biomarkers. In MLR and PCA, CA is treated as the selection criterion and an independent index. The Hochschild’s method and KDM share a similar concept, making CA an independent variable. Previous studies have either simply constructed the BA model by one or compared the four methods together. However, reviews have yet to illustrate and compare the four methods systematically. Since the BA model is a potential estimation of aging for clinical use, such as predicting onset and prognosis of diseases, improving the elderly’s living qualities, and realizing successful aging, here we summarize previous BA studies, illustrate the basic statistical steps, and thoroughly discuss the comparisons among the four common BA estimation methods.

Models for estimating the metabolic syndrome biological age as the new index for evaluation and management of metabolic syndrome.

PurposeThis study aims to propose a metabolic syndrome (MS) biological age model, through which overall evaluation and management of the health status and aging state in MS can be done easily. Through this model, we hope to provide a novel evaluation and management health index that can be utilized in various health care fields.Patient and methodsMS parameters from American Heart Association/National Heart, Lung, and Blood Institute guidelines in 2005 were used as biomarkers for the estimation of MS biological age. MS biological age model development was done by analyzing data of 263,828 participants and clinical application of the developed MS biological age was assessed by analyzing the data of 188,886 subjects.ResultsThe principal component accounted for 36.1% in male and 38.9% in female of the total variance in the battery of five variables. The correlation coefficient between corrected biological age and chronological age in males and females were 0.711 and 0.737, respectively. Significant difference for mean MS biological age and chronological age between the three groups, normal, at risk and MS, was seen (P<0.001).ConclusionFor the comprehensive approach in MS management, MS biological age is expected to be additionally utilized as a novel evaluation and management index along with the traditional MS diagnosis.

Selection of an optimal set of biomarkers and comparative analyses of biological age estimation models in Korean females

To date, an optimal working model which predicts biological age (BA) with a set of working biomarkers has not been devised to represent the Korean female population. Accuracy of prediction and applicability are required of an optimal set of commonly assessed biomarkers to provide information on the health status. The goal of this study was to identify a set of biomarkers that represent the aging process and to develop and compare different BA prediction models to elucidate the most fitting and applicable model for providing information on health status in the Korean female population. Using a series of selection processes, eight clinically assessable variables were selected by analyzing relations between 31 clinical variables and chronologic age in 912 normal, healthy individuals among 3642 female participants with ages ranging from 30 to 80 years. The multiple linear regression (MLR), principal component analysis (PCA), and the Klemera-Doubal (KDM) statistical methods were applied to obtain three different sets of BA prediction models. These three models were assessed by calculating and performing the coefficient determinations (r2), regression slopes, effect sizes, pairwise t-tests, and Bland-Altman plots. The BA models were further compared for the applicability by calculating the BAs of clinical risk groups. MLR showed the narrowing effects at the either ends of the age spectrum with greatest effect sizes. PCA showed the greatest degree of dispersion and deviation from the regression center. These MLR and PCA trends were also exhibited by clinically risk groups. In conclusion, the KDM BA prediction model based on the selected biomarkers was found to provide the most reliable and stable results for the practical assessment of BA.

Simulations of populations of Sapajus robustus in a fragmented landscape

The study of populations subject to the phenomenon of loss and fragmentation of habitat, transforming continuous areas into small ones, usually surrounded by anthropogenic matrices, has been the focus of many researches within the scope of conservation. The objective of this study was to develop a computer model by introducing modifications to the renowned Penna model for biological aging, in order to evaluate the behavior of populations subjected to the effects of fragmented environments. As an object of study, it was used biological data of the robust tufted capuchin (Sapajus robustus), an endangered primate species whose geographical distribution within the Atlantic Rain Forest is part of the backdrop of intense habitat fragmentation. The simulations showed the expected behavior based on the three main aspects that affects populations under intense habitat fragmentation: the population density, area and conformation of the fragments and deleterious effects due the low genetic variability in small and isolated populations. The model showed itself suitable to describe changes in viability and population dynamics of the species crested capuchin considering critical levels of survival in a fragmented environment and also, actions in order to preserve the species should be focused not only on increasing available area but also in dispersion dynamics.

An agent-based computational model for tuberculosis spreading on age-structured populations

In this work we present an agent-based computational model to study the spreading of the tuberculosis (TB) disease on age-structured populations. The model proposed is a merge of two previous models: an agent-based computational model for the spreading of tuberculosis and a bit-string model for biological aging. The combination of TB with the population aging, reproduces the coexistence of health states, as seen in real populations. In addition, the universal exponential behavior of mortalities curves is still preserved. Finally, the population distribution as function of age shows the prevalence of TB mostly in elders, for high efficacy treatments.

Epigenetic contribution to age distribution of mortality within the Penna model.

Some modifications of the simple asexual Penna model, enriched by epigenetic contributions, are presented. The standard bit-string Penna model of biological aging and population evolution is based on an inherited DNA structure which defines the future life of a newly born individuals, when genes are activated by the biological clock, and the predefined genetic death is fully controlled by the number of defected genes. Epigenomes allow to introduce additional mechanism of gene activation or silencing without affecting the DNA genome itself. It may be either inherited or may reflect external, environmental factors. In the presented model, information read from the introduced epigenome may alter gene expression that may be stopped or re-activated. We concentrate on the influence of epigenetics on the age a distribution of genetic mortality m(a). Changes in m(a) are strong for the case of inherited epigenetic contribution with nearly perfect inheritance and 'positive' epigenome that partly ignores the 'bad' mutations. We conclude that the epigenetic contribution may influence population structure m(a) and could be, at least partly, responsible for deviation of m(a) distribution from the Gompertz law. In short, we claim that proposed epigenetic contribution may be seen as a candidate for possible explanation of observed deviation from the Gompertz law, also among senior members of society. A very simple model was used in this paper and many crucial mechanisms of biological aging were omitted. Therefore, further work based on a more realistic models is necessary.

KNDC1 knockdown protects human umbilical vein endothelial cells from senescence.

KNDC1 (kinase noncatalytic C-lobe domain containing 1), a brain-specific Ras guanine nucleotide exchange factor, controls the negative regulation of neuronal dendrite growth. However, the effect of KNDC1 on cellular senescence remains to be elucidated. The present study investigated the impact of KNDC1 knockdown on human endothelial cell senescence and the mechanisms underlying this effect. Human umbilical vein endothelial cells (HUVECs) cultured in vitro were used as a model of biological aging. Senescence-associated β-galactosidase staining was used to detect cellular senescence and flow cytometry was employed to determine cell cycle progression. Quantitative polymerase chain reaction (qPCR) and western blot analysis were utilized to investigate mRNA transcription and protein expression. In the HUVECs, a senescence-like phenotypes developed with increasing passage number in vitro, which were associated with a progressive increase in the transcription and expression of KNDC1. KNDC1 knockdown promoted cell proliferation and partially reversed cellular senescence and cell cycle arrest in the G0/G1 phase in aging HUVECs. Investigations into the mechanism underlying this effect demonstrated that KNDC1 knockdown promoted HUVEC proliferation via the extracellular signal-regulated kinase signaling pathway and delayed HUVEC senescence by inhibiting the p53-p21-p16 transduction cascade. In addition, the promotion of the capillary tube network formation and the increased expression of endothelial nitric oxide synthase revealed that the activity and function of endothelial cells were enhanced. In conclusion, KNDC1 knockdown delayed endothelial cell senescence and promoted HUVEC activity and function. These results demonstrated that KNDC1 may be a novel therapeutic target for the development of agents to extend human life.

Diminishing glutathione availability and age-associated decline in neuronal excitability

Oxidative stress is frequently implicated in diminished electrical excitability of aging neurons yet the foundations of this phenomenon are poorly understood. This study explored links between alterations in cellular thiol-redox state and age-associated decline in electrical excitability in identified neurons (right pedal dorsal 1 [RPeD1]) of the gastropod Lymnaea stagnalis. Intracellular thiol redox state was modulated with either dithiothreitol or membrane permeable ethyl ester of the antioxidant glutathione (et-GSH). Neuronal antioxidant demand was manipulated through induction of lipid peroxidation with 2,2′-azobis-2-methyl-propanimidamide-dihydrochloride (AAPH). Glutathione synthesis was manipulated with buthionine sulfoximine (BSO). We show that; glutathione content of snail brains declines with age, whereas pyroglutamate content increases; treatment with AAPH and BSO alone aggravated the natural low excitability state of old RPeD1, but only the combination of AAPH + BSO affected electrical excitability of young RPeD1; et-GSH reversed this effect in young RPeD1; et-GSH and dithiothreitol treatment reversed age-associated low excitability of old RPeD1. Together, these data argue for a tight association between glutathione availability and the regulation of neuronal electrical excitability and indicate perturbation of cellular thiol-redox metabolism as a key factor in neuronal functional decline in this gastropod model of biological aging.

Cellular senescence in the Penna model of aging

Cellular senescence is thought to play a major role in age-related diseases, which cause nearly 67% of all human deaths worldwide. Recent research in mice showed that exercising mice had higher levels of telomerase, an enzyme that helps maintain telomere length, than nonexercising mice. A commonly used model for biological aging was proposed by Penna. I propose a modification of the Penna model that incorporates cellular senescence and find an analytical steady-state solution following Coe, Mao, and Cates [Phys. Rev. Lett. 89, 288103 (2002)]. I find that models corresponding to delayed cellular senescence have younger populations that live longer. I fit the model to the United Kingdom's death distribution, which the original Penna model cannot do.

On Evolution of Migrating Population with Two Competing Species

A computer experiment study of population evolution and its dynamics is pre- sented for two competing species (A and B) which share two habitats (1 and 2) of a limited environmental capacity. The Penna model of biological aging, based on the concept of defective mutation accumulation, was adopted for mi- grating population. In this paper, we assume and concentrate on the case when only one species (A) is mobile. For isolated habitats and for any initial popu- lation, we get at equilibrium spatial population distribution (A, B) in which A occupies location '1' only, while B-species is the ultimate winner in '2'. This is achieved by suitable choice of model parameters so habitat '1' is more at- tractive for species 'A' while location '2' is more advantageous to 'B'. However, population distribution begins to dier when migration between habitats is al-

A probabilistic model of biological ageing of the lungs for analysing the effects of smoking, asthma and COPD

BackgroundAlthough a large body of literature is available that describes the effects of smoking, asthma and COPD on lung function, most studies are restricted to a small age range and to one factor. As a consequence, available results are incomplete and often difficult to compare, also due to the ways the effects are expressed. Furthermore, current approaches consider one type of measurement only or several types separately.MethodsWe propose a probabilistic model that expresses the effects as number of years added to chronological age or, in other words, that estimates the biological age of the lungs. Using biological age as a measure of the effects has the advantage of facilitating the understanding of their severity and comparison of results. In our model, chronological age and other factors affecting the health status of the lungs generate biological age, which in turn generates lung function measurements. This structure enables the use of multiple types of measurement to obtain a more precise estimate of the effects and parameter sharing for characterization over large age ranges and of co-occurrence of factors with little data. We treat the parameters that model smoking habits and lung diseases as random variables to obtain uncertainty in the estimated effects.ResultsWe use the model to investigate the effects of smoking, asthma and COPD on the TwinsUK Registry. Our results suggest that the combination of smoking with lung disease(s) has higher effect than smoking or lung disease(s) alone, and that in smokers, co-occurrence of asthma and COPD is more detrimental than asthma or COPD alone.ConclusionsThe proposed model or other models based on a similar approach could be of help in improving the understanding of factors affecting lung function by enabling characterizations over large age ranges and of co-occurrence of factors with little data and the use of multiple types of measurement. The software implementing the model can be downloaded at the first author’s webpage.

Evolution of species from Darwin theory: A simple model

Evolution of species is a complex phenomenon. Some theoretical models take into account evolution of species, like the Bak–Sneppen model that obtain punctuated equilibrium from self-organized criticality and the Penna model for biological aging that consists in a bit-string model subjected to aging, reproduction and death. In this work we propose a simple model to study different scenarios used to simulate the evolution of species. This model is based on Darwin’s ideas of evolution. The present findings show that punctuated equilibria and stasis seem to be obtained directly from the mutation, selection of parents and the genetic crossover, and are very close to the fossil data analysis.

Numerical solution of the Penna model of biological aging with age-modified mutation rate

In this paper we present results of numerical calculation of the Penna bit-string model of biological aging, modified for the case of a -dependent mutation rate m(a), where a is the parent's age. The mutation rate m(a) is the probability per bit of an extra bad mutation introduced in offspring inherited genome. We assume that m(a) increases with age a. As compared with the reference case of the standard Penna model based on a constant mutation rate m , the dynamics of the population growth shows distinct changes in age distribution of the population. Here we concentrate on mortality q(a), a fraction of items eliminated from the population when we go from age (a) to (a+1) in simulated transition from time (t) to next time (t+1). The experimentally observed q(a) dependence essentially follows the Gompertz exponential law for a above the minimum reproduction age. Deviation from the Gompertz law is however observed for the very old items, close to the maximal age. This effect may also result from an increase in mutation rate m with age a discussed in this paper. The numerical calculations are based on analytical solution of the Penna model, presented in a series of papers by Coe et al. [J. B. Coe, Y. Mao, and M. E. Cates, Phys. Rev. Lett. 89, 288103 (2002)]. Results of the numerical calculations are supported by the data obtained from computer simulation based on the solution by Coe et al.

Stability of the analytical solution of Penna model of biological aging

There are some analytical solutions of the Penna model of biological aging; here, we discuss the approach by Coe et al. (Phys. Rev. Lett. 89, 288103, 2002), based on the concept of self-consistent solution of a master equation representing the Penna model. The equation describes transition of the population distribution at time t to next time step (t + 1). For the steady state, the population n(a, l, t) at age a and for given genome length l becomes time-independent. In this paper we discuss the stability of the analytical solution at various ranges of the model parameters--the birth rate b or mutation rate m. The map for the transition from n(a, l, t) to the next time step population distribution n(a + 1, l, t + 1) is constructed. Then the fix point (the steady state solution) brings recovery of Coe et al. results. From the analysis of the stability matrix, the Lyapunov coefficients, indicative of the stability of the solutions, are extracted. The results lead to phase diagram of the stable solutions in the space of model parameters (b, m, h), where h is the hunt rate. With increasing birth rate b, we observe critical b (0) below which population is extinct, followed by non-zero stable single solution. Further increase in b leads to typical series of bifurcations with the cycle doubling until the chaos is reached at some b (c). Limiting cases such as those leading to the logistic model are also discussed.

A Sensitivity Analysis of an Integrated Modelling Approach to Assess the Risk of Wind-borne Spread of Foot-and-mouth Disease Virus from Infected Premises

The sensitivity of an integrated model to assess the potential for wind-borne spread of foot-and-mouth disease (FMD) to variations in key parameters controlling different physical and biological processes was evaluated. The estimated number of farms at risk is sensitive to the virus strain used and the accompanying effective contact rate. The C Noville strain increased the estimated number of exposed farms ranked as high and medium risk of being infected by a factor of 5, compared to the baseline, based on the O UKG 2001 strain. The inclusion of a model for biological ageing of the virus can also have a significant effect on the concentration patterns arising from transport and dispersion of the virus. Its inclusion has the practical advantage of markedly reducing the time required for the calculations. The estimated number of farms affected by exposure to high and medium virus concentrations is not grossly sensitive to attenuation caused by temperature or relative humidity effects. Changes in susceptibility to infection, as determined by the parameter θ in the exposure-risk model, does not change the configuration of the virus plumes, but it does change the distribution of farms at risk by risk category. These findings suggest that a good understanding of characteristics (excretion rates from infected animals, susceptibility of different species to infection, virus survival, etc.) of the virus strain involved in an FMD outbreak is necessary to provide a reliable assessment of the risk of wind-borne spread. In the event of an incursion of FMD, provision for laboratory studies on the virus will be an essential component of the disease response and should be factored into contingency plans.

The Penna Model of Biological Aging

This review deals with computer simulation of biological aging, particularly with the Penna model of 1995. They are based on the mutation accumulation theory of half a century ago. The results agree well with demographical reality, and also with the seemingly contradictory influence of predators on the aging of prey.