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Related Experiment Video

Updated: Aug 1, 2025

Identification and Quantification of Deranged Metabolites in Critically Ill Patients Using NMR-Based Metabolomics
11:02

Identification and Quantification of Deranged Metabolites in Critically Ill Patients Using NMR-Based Metabolomics

Published on: November 29, 2024

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Machine learning estimation of human body time using metabolomic profiling.

Tom Woelders1, Victoria L Revell2, Benita Middleton2

  • 1Chronobiology unit, Groningen Institute of Evolutionary Life Sciences, University of Groningen, 9700 CC Groningen, the Netherlands.

Proceedings of the National Academy of Sciences of the United States of America
|April 24, 2023
PubMed
Summary

Estimating your body

Keywords:
circadian phasedim light melatonin onsethuman body timemachine learningmetabolomics

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Area of Science:

  • Chronobiology and metabolomics
  • Biomarker discovery for circadian rhythms

Background:

  • Circadian rhythms impact human physiology, metabolism, and molecular functions.
  • Accurate estimation of an individual's body time (circadian phase) is crucial for personalized health optimization, diagnostics, and managing circadian rhythm disorders.

Purpose of the Study:

  • To develop and validate a machine learning approach using plasma metabolomics for estimating circadian phase.
  • To establish a robust and feasible method for determining dim light melatonin onset (DLMO) as a proxy for circadian phase.

Main Methods:

  • Utilized partial least squares regression (PLSR), a machine learning technique.
  • Employed plasma-derived metabolomics data from one or more samples.
  • Designed the protocol to closely mimic real-life conditions.

Main Results:

  • The metabolomics approach demonstrated high accuracy in estimating DLMO.
  • Performance was comparable or superior to existing RNA sequencing-based methods.
  • Sex-specific optimized models performed well under entrained conditions.

Conclusions:

  • Plasma metabolomics offers a robust and feasible method for estimating circadian body time.
  • This approach can aid in personalized behavior optimization and clinical treatment.
  • Further validation is needed for shift work and diverse real-world conditions.