NMR Spectroscopy Combined with Machine Learning Approaches for Age Prediction in Healthy and Parkinson’s Disease Cohorts through Metabolomic Fingerprints

Biological aging can be affected by several factors such as drug treatments and pathological conditions. Metabolomics can help in the estimation of biological age by analyzing the differences between predicted and actual chronological age in different subjects. In this paper, we compared three diffe...

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Bibliographic Details
Published in:Applied sciences 2022-09, Vol.12 (18), p.8954
Main Authors: Dimitri, Giovanna Maria, Meoni, Gaia, Tenori, Leonardo, Luchinat, Claudio, Lió, Pietro
Format: Article
Language:English
Subjects:
Age
Age determination
Aging
Biological activity
Biomarkers
Brain research
DNA methylation
Epigenetics
Ethics
Gastrointestinal surgery
High density lipoprotein
Learning algorithms
lipids
Lipoproteins
Machine learning
Magnetic resonance spectroscopy
Metabolites
Metabolomics
metabolomics aging
NMR
NMR spectroscopy
Nuclear magnetic resonance
Parkinson's disease
spectrum
Spectrum analysis
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Summary:Biological aging can be affected by several factors such as drug treatments and pathological conditions. Metabolomics can help in the estimation of biological age by analyzing the differences between predicted and actual chronological age in different subjects. In this paper, we compared three different and well-known machine learning approaches—SVM, ElasticNet, and PLS—to build a model based on the 1H-NMR metabolomic data of serum samples, able to predict chronological age in control individuals. Then, we tested these models in two pathological cohorts of de novo and advanced PD patients. The discrepancies observed between predicted and actual age in patients are interpreted as a sign of a (pathological) biological aging process.
ISSN:2076-3417
2076-3417
DOI:10.3390/app12188954