Deffur, A. | bioRxiv Science

bioRxiv2023

Wearable-ome meets epigenome: A novel approach to measuring biological age with wearable devices.

Aging is an inevitable process of cellular and physiological decline. These markers of age can be measured on the molecular and functional level. Wearable devices offer a non-invasive continuous measure of physiological and behavioural features and how they pertain to aging. Wearable data can be used to extrapolate information derived from epigenetic biological age predictions and its underlying biology. LifeQ-enabled wearable devices were worn for 40 days to harvest data on 48 human participants. Thereafter blood was drawn and methylation levels determined using the Illumina EPIC array. Multiple epigenetic clock ages were calculated and compared with wearable features. Activity minutes correlated with VO2 max (p = 0.003), subendocardial viability ratio (SEVR, p < 0.01), blood pressure index (BPI, p = 0.02), resting heart rate (RHR, p < 0.01) and heart outflow (HO, p < 0.01). Sedentary time correlated with RHR (p < 0.01), VO2 max (p = 0.01), SEVR (p = 0.04), and HO (p = 0.04). VO2 max, SEVR, small artery resistance (SAR), BPI and large artery stiffness index (LASI) correlated with multiple epigenetic age clock outputs and chronological age but were most strongly correlated with PCPhenoAge. VO2 max, (p = 0.04) RHR (p < 0.01) and LASI (p = 0.04) were significantly correlated with PCPhenoAge acceleration. Weighted gene correlation network analysis (WGCNA) of the differentially methylated positions of PCPhenoAge acceleration was used to construct modules, identifying 3 modules correlating with wearable features. Behavioural features impact physiological state, measured by the wearable, which are associated with epigenetic age and age acceleration. Signal from the underlying biology of age acceleration can be picked up by the wearable, presenting a case that wearable devices can capture portions of biological aging.

bioinformatics↗

bioRxiv2018

ANIMA: Association Network Integration for Multiscale Analysis

Contextual functional interpretation of -omics data derived from clinical samples is a classical and difficult problem in computational systems biology. The measurement of thousands of datapoints on single samples has become routine but relating big data datasets to the complexities of human pathobiology is an area of ongoing research. Complicating this is the fact that many publically available datasets use bulk transcriptomics data from complex tissues like blood. The most prevalent analytic approaches derive molecular signatures of disease states or apply modular analysis frameworks to the data. Here we show, using a network-based data integration method using clinical phenotype and microarray data as inputs, that we can reconstruct multiple features (or endophenotypes) of disease states at various scales of organization, from transcript abundance patterns of individual genes through co-expression patterns of groups of genes to patterns of cellular behavior in whole blood samples, both in single experiments as well as in a meta-analysis of multiple datasets.

systems biology↗