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LipidClock: A Lipid-Based Predictor of Biological Age
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 d...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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Frontiers Media S.A.
2022
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Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9261347/ https://www.ncbi.nlm.nih.gov/pubmed/35821819 http://dx.doi.org/10.3389/fragi.2022.828239 |
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author | Unfried, Maximilian Ng, Li Fang Cazenave-Gassiot, Amaury Batchu, Krishna Chaithanya Kennedy, Brian K. Wenk, Markus R. Tolwinski, Nicholas Gruber, Jan |
author_facet | Unfried, Maximilian Ng, Li Fang Cazenave-Gassiot, Amaury Batchu, Krishna Chaithanya Kennedy, Brian K. Wenk, Markus R. Tolwinski, Nicholas Gruber, Jan |
author_sort | Unfried, Maximilian |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-9261347 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-92613472022-07-11 LipidClock: A Lipid-Based Predictor of Biological Age Unfried, Maximilian Ng, Li Fang Cazenave-Gassiot, Amaury Batchu, Krishna Chaithanya Kennedy, Brian K. Wenk, Markus R. Tolwinski, Nicholas Gruber, Jan Front Aging Aging 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. Frontiers Media S.A. 2022-05-27 /pmc/articles/PMC9261347/ /pubmed/35821819 http://dx.doi.org/10.3389/fragi.2022.828239 Text en Copyright © 2022 Unfried, Ng, Cazenave-Gassiot, Batchu, Kennedy, Wenk, Tolwinski and Gruber. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Aging Unfried, Maximilian Ng, Li Fang Cazenave-Gassiot, Amaury Batchu, Krishna Chaithanya Kennedy, Brian K. Wenk, Markus R. Tolwinski, Nicholas Gruber, Jan LipidClock: A Lipid-Based Predictor of Biological Age |
title | LipidClock: A Lipid-Based Predictor of Biological Age |
title_full | LipidClock: A Lipid-Based Predictor of Biological Age |
title_fullStr | LipidClock: A Lipid-Based Predictor of Biological Age |
title_full_unstemmed | LipidClock: A Lipid-Based Predictor of Biological Age |
title_short | LipidClock: A Lipid-Based Predictor of Biological Age |
title_sort | lipidclock: a lipid-based predictor of biological age |
topic | Aging |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9261347/ https://www.ncbi.nlm.nih.gov/pubmed/35821819 http://dx.doi.org/10.3389/fragi.2022.828239 |
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