ArticleProceedings of the National Academy of Sciences of the United States of America2023
Machine learning estimation of human body time using metabolomic profiling.
Article in Proceedings of the National Academy of Sciences of the United States of America, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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Who cites it
14 citing papers in PubMed.
- Leveraging the Metabolic Fingerprint of Sleep Deprivation and Sleep Restriction for Forensic Applications: A Machine Learning Study in Oral Fluid Metabolomics.Journal of proteome research · 2026Article
- Metabolomics and Human Health: Progress, Insights, Challenges and the Concept of a Healthy Metabolome.Biomolecules · 2026Review
- HairTime: A noninvasive assay for estimating circadian phase from a single hair sample.Proceedings of the National Academy of Sciences of the United States of America · 2026Article
- Performance of Blood-Based Biomarkers for Human Circadian Pacemaker Phase: Training Sets Matter As Much As Feature-Selection Methods.Journal of biological rhythms · 2025Article
- Article
- Daily rhythm in DNA methylation and the effect of total sleep deprivation.Journal of sleep research · 2025Article
- Integrating Assessment of Circadian Rhythmicity to Improve Treatment Outcomes for Circadian Rhythm Sleep-Wake Disorders: Updates on New Treatments.Current sleep medicine reports · 2025Review
- Challenges and opportunities for statistical power and biomarker identification arising from rhythmic variation in proteomics.Npj biological timing and sleep · 2025Article
- Circadian rhythms in solid organ transplantation.The Journal of heart and lung transplantation : the official publication of the International Society for Heart Transplantation · 2024Review
- Learning from circadian rhythm to transform cancer prevention, prognosis, and survivorship care.Trends in cancer · 2024Review
- Circadian protein expression patterns in healthy young adults.Sleep health · 2024Article
- Circadian dysfunction and cardio-metabolic disorders in humans.Frontiers in endocrinology · 2024Review
- Metabolic pathway analysis using stable isotopes in patients with cancer.Nature reviews. Cancer · 2023Review
- Machine learning estimation of human body time using metabolomic profiling.Proceedings of the National Academy of Sciences of the United States of America · 2023Article
Corrections and comments
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Authors and funding
8 authors.
Funding
Abstract
Circadian rhythms influence physiology, metabolism, and molecular processes in the human body. Estimation of individual body time (circadian phase) is therefore highly relevant for individual optimization of behavior (sleep, meals, sports), diagnostic sampling, medical treatment, and for treatment of circadian rhythm disorders. Here, we provide a partial least squares regression (PLSR) machine learning approach that uses plasma-derived metabolomics data in one or more samples to estimate dim light melatonin onset (DLMO) as a proxy for circadian phase of the human body. For this purpose, our protocol was aimed to stay close to real-life conditions. We found that a metabolomics approach optimized for either women or men under entrained conditions performed equally well or better than existing approaches using more labor-intensive RNA sequencing-based methods. Although estimation of circadian body time using blood-targeted metabolomics requires further validation in shift work and other real-world conditions, it currently may offer a robust, feasible technique with relatively high accuracy to aid personalized optimization of behavior and clinical treatment after appropriate validation in patient populations.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.