arXiv subjects

Andrew Rutenberg

Publications and source records attributed to Andrew Rutenberg.

7 recordsLinked to original sources

arXiv2025

Inferring and predicting Fried physical frailty phenotype deficits

We predict the Fried physical frailty phenotype health deficits (FPFP5: slow gait, weakness, weight loss, low activity, and exhaustion) using two measures of frailty: frailty index (FI) or frailty phenotype (FP). The FP theorizes that the FPFP5 are mutually dependent through shared etiology and positive feedbacks, so that the total number of FPFP5 deficits (NFPFP5) should be highly predictive of existing deficits. Alternatively, the FI theorizes that strong mutual dependencies exist between \emph{all} age-related health deficits, so that the FI would be more predictive. We investigated predictive models of FPFP5 using FI or NFPFP5 in the Health and Retirement Study (HRS), the English Longitudinal Study of Aging (ELSA), and the National Health and Nutrition Examination Survey (NHANES). We find that the FI, chronological age, and current deficit state are all important predictors of future FPFP5 deficits. Notably, the FI consistently out-performed NFPFP5, raising questions regarding FPFP5 causal connections and how best to measure the physical component of frailty. We discuss implications for both FPFP5 forecasting, and inference when data are missing or incomplete.

q-bio.QM↗

arXiv2024

Aging health dynamics cross a tipping point near age 75

Aging includes both continuous gradual decline, such as in physiological function, together with major deficit onset events such as morbidity, disability and ultimately death. These deficit events are stochastic and include non-linear feedbacks, making health trajectory forecasting challenging. We propose a framework for modelling the gradual effects of aging together with health deficit onset events, as reflected in the frailty index (FI) - a quantitative measure of overall age-related health. We model damage and repair dynamics of the FI from individual health transitions within two large longitudinal studies of aging health, the Health and Retirement Study (HRS) and the English Longitudinal Study of Ageing (ELSA), which together included N = 47592 individuals. We find that both damage resistance (robustness) and damage recovery (resilience) rates decline smoothly with both increasing age and with increasing FI, for both sexes. This leads to two distinct dynamical states: a robust and resilient young state of stable good health (low FI) and an older state that drifts towards poor health (high FI). These two health states are separated by a sharp transition near age 75. Since FI accumulation risk accelerates dramatically across this tipping point, ages 70-80 are crucial for understanding and forecasting late-life decline in health.

q-bio.QM↗

arXiv2021

Interpretable machine learning for high-dimensional trajectories of aging health

We have built a computational model for individual aging trajectories of health and survival, which contains physical, functional, and biological variables, and is conditioned on demographic, lifestyle, and medical background information. We combine techniques of modern machine learning with an interpretable interaction network, where health variables are coupled by explicit pair-wise interactions within a stochastic dynamical system. Our dynamic joint interpretable network (DJIN) model is scalable to large longitudinal data sets, is predictive of individual high-dimensional health trajectories and survival from baseline health states, and infers an interpretable network of directed interactions between the health variables. The network identifies plausible physiological connections between health variables as well as clusters of strongly connected health variables. We use English Longitudinal Study of Aging (ELSA) data to train our model and show that it performs better than multiple dedicated linear models for health outcomes and survival. We compare our model with flexible lower-dimensional latent-space models to explore the dimensionality required to accurately model aging health outcomes. Our DJIN model can be used to generate synthetic individuals that age realistically, to impute missing data, and to simulate future aging outcomes given arbitrary initial health states.

q-bio.QM↗

arXiv2020

The potential for complex computational models of aging

The gradual accumulation of damage and dysregulation during the aging of living organisms can be quantified. Even so, the aging process is complex and has multiple interacting physiological scales -- from the molecular to cellular to whole tissues. In the face of this complexity, we can significantly advance our understanding of aging with the use of computational models that simulate realistic individual trajectories of health as well as mortality. To do so, they must be systems-level models that incorporate interactions between measurable aspects of age-associated changes. To incorporate individual variability in the aging process, models must be stochastic. To be useful they should also be predictive, and so must be fit or parameterized by data from large populations of aging individuals. In this perspective, we outline where we have been, where we are, and where we hope to go with such computational models of aging. Our focus is on data-driven systems-level models, and on their great potential in aging research.

q-bio.QM↗

arXiv2016

Network model of human aging: frailty limits and information measures

Aging is associated with the accumulation of damage throughout a persons life. Individual health can be assessed by the Frailty Index (FI). The FI is calculated simply as the proportion $f$ of accumulated age related deficits relative to the total, leading to a theoretical maximum of $f \leq 1$. Observational studies have generally reported a much more stringent bound, with $f \leq f_{max} <1$. The value of $f_{max}$ in observational studies appears to be non-universal, but $f_{max} \approx 0.7$ is often reported. A previously developed network model of individual aging was unable to recover $f_{max}<1$ while retaining the other observed phenomenology of increasing $f$ and mortality rates with age. We have developed a computationally accelerated network model that also allows us to tune the scale-free network exponent $\alpha$. The network exponent $\alpha$ significantly affects the growth of mortality rates with age. However, we are only able to recover $f_{max}$ by also introducing a deficit sensitivity parameter $1-q$, which is equivalent to a false-negative rate $q$. Our value of $q=0.3$ is comparable to finite sensitivities of age-related deficits with respect to mortality that are often reported in the literature. In light of non-zero $q$, we use mutual information $I$ to provide a non-parametric measure of the predictive value of the FI with respect to individual mortality. We find that $I$ is only modestly degraded by $q<1$, and this degradation is mitigated when increasing number of deficits are included in the FI. We also find that the information spectrum, i.e. the mutual information of individual deficits vs connectivity, has an approximately power-law dependence that depends on the network exponent $\alpha$. Mutual information $I$ is therefore a useful tool for characterizing the network topology of aging populations.

q-bio.PE↗

arXiv2011

Monodisperse domains by proteolytic control of the coarsening instability

The coarsening instability typically disrupts steady-state cluster-size distributions. We show that degradation coupled to the cluster size, such as arising from biological proteolysis, leads to a novel fixed-point cluster size. Stochastic evaporative and condensative fluxes determine the width of the fixed-point size distribution. At the fixed-point, we show how the peak size and width depend on number, interactions, and proteolytic rate. This proteolytic size-control mechanism is consistent with the phenomenology of pseudo-pilus length control in the general secretion pathway of bacteria.

physics.bio-ph↗

arXiv2005

Maximally-fast coarsening algorithms

We present maximally-fast numerical algorithms for conserved coarsening systems that are stable and accurate with a growing natural time-step $Δt=A t_s^{2/3}$. For non-conserved systems, only effectively finite timesteps are accessible for similar unconditionally stable algorithms. We compare the scaling structure obtained from our maximally-fast conserved systems directly against the standard fixed-timestep Euler algorithm, and find that the error scales as $\sqrt{A}$ -- so arbitrary accuracy can be achieved.

cond-mat.mtrl-sci↗