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.

NMR Spectroscopy Combined with Machine Learning Approaches for Age Prediction in Healthy and Parkinson’s Disease Cohorts through Metabolomic Fingerprints / Dimitri, G. M.; Meoni, G.; Tenori, L.; Luchinat, C.; Lio, P.. - In: APPLIED SCIENCES. - ISSN 2076-3417. - 12:18(2022). [10.3390/app12188954]

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

Lio P.
2022

Abstract

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.
2022
biological age; lipids; machine learning; metabolites; metabolomics aging; Parkinson’s disease; spectrum
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NMR Spectroscopy Combined with Machine Learning Approaches for Age Prediction in Healthy and Parkinson’s Disease Cohorts through Metabolomic Fingerprints / Dimitri, G. M.; Meoni, G.; Tenori, L.; Luchinat, C.; Lio, P.. - In: APPLIED SCIENCES. - ISSN 2076-3417. - 12:18(2022). [10.3390/app12188954]
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11573/1724050
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