LipidClock: A Lipid-Based Predictor of Biological Age

Abstract

Complexity is a fundamental feature of biological systems. Omics techniques like lipidomics can simultaneously quantify many thousands of molecules, thereby directly capturing the underlying biological complexity. However, this approach transfers the original biological complexity to the resulting datasets, posing challenges in data reduction and analysis. Aging is a prime example of a process that exhibits complex behaviour across multiple scales of biological organisation. The aging process is characterised by slow, cumulative and detrimental changes that are driven by intrinsic biological stochasticity and mediated through non-linear interactions and feedback within and between these levels of organization (ranging from metabolites, macromolecules, organelles and cells to tissue and organs). Only collectively and over long timeframes do these changes manifest as the exponential increases in morbidity and mortality that define biological aging, making aging a problem more difficult to study than the aetiologies of specific diseases. But aging’s time dependence can also be exploited to extract key insights into its underlying biology. Here we explore this idea by using data on changes in lipid composition across the lifespan of an organism to construct and test a LipidClock to predict biological age in the nematode Caenorhabdits elegans. The LipidClock consist of a feature transformation via Principal Component Analysis followed by Elastic Net regression and yields and Mean Absolute Error of 1.45 days for wild type animals and 4.13 days when applied to mutant strains with lifespans that are substantially different from that of wild type. Gompertz aging rates predicted by the LipidClock can be used to simulate survival curves that are in agreement with those from lifespan experiments.

A lipidome Aging Clock shows Age Acceleration in individuals with Autism

“…As a result, two models were developed for comparative analysis. The first model, referred to as Elastic PCA, utilizes elastic net in combination with PCA, which is a widely utilized approach in literature (27, 45, 66). The second model is an XGBoost model, which is also able to pick up nonlinear signals and can be interpreted using SHAP values.…”

“…Subsequently, various other aging clocks using DNA methylation data have been developed and proposed (23, 24). This has been followed by the development of aging clocks utilizing other omics data, such as transcriptomics (25), proteomics (26), lipidomics (27), or a combination of various data sources (28). Most of these models are considered first-generation aging clocks as they predict chronological age.…”

Lipidomic profiling reveals age-dependent changes in complex plasma membrane lipids that regulate neural stem cell aging

“…By performing lipidomic analysis on isolated plasma membrane, we find that plasma membrane lipids largely contribute to these age-related lipidomic changes. Together, these comprehensive datasets allow us to generate robust “lipidomic aging signatures” 89 that discriminate between young and old quiescent NSCs and can be leveraged to predict the impact of specific interventions. Prior lipidomics analyses have been conducted in cultured cells (e.g.…”

“…In parallel, lipidomics studies have also been performed on whole organisms or organs (e.g. whole brains) in aging or Alzheimer’s disease 29, 31–34, 64, 89, 96–101 , but without cellular resolution. Thus, our lipidomics datasets represent the first systematic examination of the global lipidome of aging NSCs, and more generally of aging cells, in vitro and in vivo .…”

A metabolic atlas of mouse aging

“…However, epigenetic clocks were not developed to identify therapeutic targets or mechanisms of aging. Clocks built on downstream biological processes such as changes in levels of lipids 27 , proteins 28 , RNA 29,30 or metabolites, could provide these mechanistic and therapeutic insights.…”