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dc.contributor.authorWoelders, Tom
dc.contributor.authorRevell, Victoria
dc.contributor.authorMiddleton, Benita
dc.contributor.authorAckermann, Katrin
dc.contributor.authorKayser, Manfred
dc.contributor.authorRaynaud, Florence
dc.contributor.authorSkene, Debra
dc.contributor.authorHut, Roelof
dc.identifier.citationWoelders , T , Revell , V , Middleton , B , Ackermann , K , Kayser , M , Raynaud , F , Skene , D & Hut , R 2023 , ' Machine learning estimation of human body time using metabolomic profiling ' , Proceedings of the National Academy of Sciences of the United States of America , vol. 120 , no. 18 , e2212685120 .
dc.descriptionThis work was supported in part by the Netherlands Forensic Institute, Netherlands Genomics Initiative/Netherlands Organization for Scientific Research within the framework of the Forensic Genomics Consortium Netherlands, the 6th Framework project EUCLOCK (018741), and UK Biotechnology and Biological Sciences Research Council Grant BB/I019405/1. Additional funding was received from the Cancer Research UK Cancer Therapeutics Unit award (Ref: C2739/A22897) and a Cancer Therapeutics Centre award (Ref: C309/A25144 to FR), the NWO-STW Perspective Program grant ‘OnTime’ (project 12185 to TW and RAH).en
dc.description.abstractCircadian 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.
dc.relation.ispartofProceedings of the National Academy of Sciences of the United States of Americaen
dc.subjectDim light melatonin onseten
dc.subjectMachine learningen
dc.subjectHuman body timeen
dc.subjectCircadian phaseen
dc.subjectRC0321 Neuroscience. Biological psychiatry. Neuropsychiatryen
dc.titleMachine learning estimation of human body time using metabolomic profilingen
dc.typeJournal articleen
dc.contributor.institutionUniversity of St Andrews. School of Chemistryen
dc.contributor.institutionUniversity of St Andrews. Institute of Behavioural and Neural Sciencesen
dc.description.statusPeer revieweden

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