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Related Concept Videos

Aging01:26

Aging

352
Aging is a complex biological phenomenon influenced by various processes that affect cellular and systemic functions. Several prominent theories attempt to explain its mechanisms, highlighting cellular limitations, oxidative damage, and hormonal changes as central factors in aging.
Cellular Clock Theory
The cellular clock theory posits that the human lifespan is closely tied to the finite capacity of cells to divide, a phenomenon governed by telomeres, which are protective caps at the ends of...
352

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A Metabolomic Aging Clock Using Human Cerebrospinal Fluid.

Nathan Hwangbo1, Xinyu Zhang2, Daniel Raftery2

  • 1Department of Statistics and Applied Probability, University of California, Santa Barbara, USA.

The Journals of Gerontology. Series A, Biological Sciences and Medical Sciences
|August 12, 2021
PubMed
Summary
This summary is machine-generated.

This study developed biological age prediction models using cerebrospinal fluid (CSF) metabolomics. These models accurately predict chronological age and show altered age-metabolome relationships in neurodegenerative diseases.

Keywords:
Aging clockBiomarkerCerebrospinal fluidMetabolomics

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Area of Science:

  • Gerontology and Neuroscience
  • Metabolomics and Biomarker Discovery
  • Biostatistics and Computational Biology

Background:

  • Quantifying physiological aging is crucial for understanding age-related diseases and healthy aging variations.
  • Omic clocks, using regression models on omics data to predict chronological age, offer potential measures of biological age.
  • Residual variation in predicted age from omics data correlates with health outcomes.

Purpose of the Study:

  • To develop predictive models for chronological age using metabolomic and lipidomic profiles of cerebrospinal fluid (CSF).
  • To investigate if these models reveal differences in the age-metabolome relationship in Alzheimer's and Parkinson's disease cohorts.
  • To identify specific metabolites and metabolic pathways associated with chronological age in healthy individuals.

Main Methods:

  • Development of multivariate predictive models for age using mass spectrometry-based metabolomic and lipidomic data from human CSF.
  • Application of these models to predict the age of subjects with Alzheimer's and Parkinson's disease.
  • Analysis of control subjects to identify age-associated metabolites and pathways, including the carnitine shuttle, sucrose, biopterin, vitamin E, tryptophan, and tyrosine.

Main Results:

  • Metabolite and lipid data from CSF generally predicted chronological age within 10 years in healthy subjects.
  • An increased prediction error was observed when applying the models to cohorts with Alzheimer's and Parkinson's disease.
  • Specific metabolic pathways and metabolites, including the carnitine shuttle and vitamin E metabolism, were strongly associated with chronological age in controls.

Conclusions:

  • Metabolomic profiling of CSF can serve as a basis for biological age prediction.
  • The relationship between the metabolome and chronological age appears to differ in the presence of Alzheimer's and Parkinson's disease.
  • This study highlights the potential of metabolomic age prediction models in aging research and disease, while acknowledging statistical challenges like data imputation and drift correction.