Biology subjects

Rutenberg, A.

Publications and source records attributed to Rutenberg, A..

3 recordsLinked to original sources

bioRxiv2026

Age-related biological dysregulation quantified using metabolomics is composed of distinct subsystems with synergistic associations to health outcomes: An analysis of the Canadian Longitudinal Study on Aging

Resilience is a fundamental component of healthy aging, whereby an organism can efficiently respond to external stressors and return to a resting biological and physiological state, known as homeostasis. This capacity declines with age in a heterogenous manner and is aggravated by biological dysregulation, a detrimental state broadly associated with health risk factors and poor outcomes. However, the underlying structure of dysregulation remains unclear, particularly in the context of older adult health trajectories. Here, we take a novel systems-based approach to the quantification of dysregulation using metabolomic data from n=9,992 older adults enrolled in the Canadian Longitudinal Study on Aging. We show that dysregulation across different biological systems is heritable, and exhibit distinct associations with health risk factors, indicators and outcomes, and DNA methylation patterns. Importantly, the effect of systems-specific dysregulation is cumulative, resulting in a significantly higher risk of disease incidence and death in older adults. Our findings broaden our understanding of biological dysregulation as a health state and demonstrate the value of system-specific dysregulation for effective strategies in personalized medicine and risk stratification.

physiology↗

bioRxiv2023

Using deep-learning to obtain calibrated individual disease and ADL damage transition probabilities between successive ELSA waves

We predictively model damage transition probabilities for binary health outputs of 19 diseases and 25 activities of daily living states (ADLs) between successive waves of the English Longitudinal Study of Aging (ELSA). Model selection between deep neural networks (DNN), random forests, and logistic regression found that a simple one-hidden layer 128-node DNN was best able to predict future health states (AUC [≥] 0.91) and average damage probabilities (R2 [≥] 0.92). Feature selection from 134 explanatory variables found that 33 variables are sufficient to predict all disease and ADL states well. Deciles of predicted damage transition probabilities were well calibrated, but correlations between predicted health states were stronger than observed. The hazard ratios (HRs) between high-risk deciles and the average were between 3 and 10; high prevalence damage transitions typically had smaller HRs. Model predictions were good across all individual ages. A simple one-hidden layer DNN predicts multiple binary diseases and ADLs with well calibrated damage and repair transition probabilities.

biophysics↗

bioRxiv2020

Generating individual aging trajectories with a network model using cross-sectional data

We develop a computational model of human aging that generates individual health trajectories with a set of observed health attributes. Our model consists of a network of interacting health attributes that stochastically damage with age to form health deficits, leading to eventual mortality. We train and test the model for two different cross-sectional observational aging studies that include simple binarized clinical indicators of health. In both studies, we find that cohorts of simulated individuals generated from the model resemble the observed cross-sectional data in both health characteristics and mortality. We can generate large numbers of synthetic individual aging trajectories with our weighted network model. Predicted average health trajectories and survival probabilities agree well with the observed data.

systems biology↗