[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-127527-en":3,"doc-seo-127527-105":30,"detail-sidebar-cat-0-en-105":96},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},127527,687207017582,"Himbo","https://ap-avatar.wpscdn.com/davatar_994ba38a5ba835b3df7d355c54d3ed8d",8,"Research & Report","Machine learning estimation of human body time using metabolomic profiling - research article","Circadian rhythms shape physiology, metabolism, and molecular processes, making an accurate estimate of individual body time (circadian phase) important for optimizing behavior, diagnostic sampling, medical treatment, and circadian disorder management. The study presents a partial least squares regression (PLSR) machine learning framework using plasma-derived metabolomics to predict dim light melatonin onset (DLMO) as a proxy for circadian phase. The metabolomics workflow under entrained conditions matched or outperformed RNA-sequencing based approaches. Further validation is required for shift work and other real-world contexts, where metabolomics may still provide a robust and feasible technique with high accuracy after validation.","University of Groningen  \nMachine learning estimation of human body time using metabolomic profiling  \nWoelders, Tom; Revell, Victoria L. ; Middleton, Benita; Ackermann, Katrin; Kayser, Manfred; Raynaud, Florence I. ; Skene, Debra J. ; Hut, Roelof A.  \nPublished in:  \nProceedings of the National Academy of Sciences of the United States of America  \nDOI:  \n10.1073/pnas.2212685120  \nIMPORTANT NOTE: You are advised to consult the publisher's version (publisher's PDF) if you wish to cite from it. Please check the document version below.  \nDocument Version  \nPublisher's PDF, also known as Version of record  \nPublication date: 2023  \nLink to publication in University of Groningen/UMCG research database  \nCitation for published version (APA):  \nWoelders, T. , Revell, V. L. , Middleton, B. , Ackermann, K. , Kayser, M. , Raynaud, F. I. , Skene, D. J. , & Hut, R. A. (2023) . Machine learning estimation of human body time using metabolomic profiling. Proceedings of the National Academy of Sciences of the United States of America, 120(18), Article e2212685120 .  \n[https://doi.org/10.1073/pnas.2212685120](https://doi.org/10.1073/pnas.2212685120)  \nCopyright  \nOther than for strictly personal use, it is not permitted to download or to forward/distribute the text or part of it without the consent of the author(s) and/or copyright holder(s), unless the work is under an open content license (like Creative Commons) .  \nThe publication may also be distributed here under the terms of Article 25fa of the Dutch Copyright Act, indicated by the “Taverne” license. More information can be found on the University of Groningen website: [https://www.rug.nl/library/open-access/self-archiving-pure/taverne](https://www.rug.nl/library/open-access/self-archiving-pure/taverne)amendment.  \nTake-down policy  \nIf you believe that this document breaches copyright please contact us providing details, and we will remove access to the work immediately and investigate your claim.  \nDownloaded from the University of Groningen/U MCG research database (Pure): [http://www.rug. nl/research/portal. For technical reasons the](http://www.rug. nl/research/portal. For technical reasons the)[ ](http://www.rug. nl/research/portal. For technical reasons the)[number of authors shown on this cover page is limited to 10 maximum.](number of authors shown on this cover page is limited to 10 maximum.)  \nDownload date: 01-01-2026  \nDownloaded from [https://www.pnas.org by BIBLIO DER RIJKSUNIVERSITEIT DEPT ACQUISITION on May 15](https://www.pnas.org by BIBLIO DER RIJKSUNIVERSITEIT DEPT ACQUISITION on May 15), 2023 from IP address [129.125.19.61](129.125.19.61).  \nRESEARCH ARTICLE  \nMEDICAL SCIENCES  \nMachine learning estimation of human body time using metabolomic profiling  \nTom Woeldersa,1, Victoria L. Revellb,2, Benita Middletonb, Katrin Ackermannc,3, Manfred Kayserc , Florence I. Raynaudd , Debra J. Skeneb , and Roelof A. Huta,4  \nEdited by Joseph Takahashi, The University of Texas Southwestern Medical Center, Dallas, TX; received July 23, 2022; accepted March 6, 2023  \nCircadian 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 datain 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 ","cbCaiuotf19fO3SG","https://ap.wps.com/l/cbCaiuotf19fO3SG","pdf",2176204,1,10,"English","en",105,"# Introduction\n## Circadian rhythms and body time estimation\n## Chronomedicine and DLMO as a proxy\n# Methods\n## Plasma-derived metabolomics and PLSR approach\n## Protocol designed to reflect real-life conditions\n# Results and Discussion\n## Performance versus RNA sequencing methods\n## Applicability to real-world settings and validation needs","[{\"question\":\"What does the study use to estimate human circadian phase?\",\"answer\":\"It uses plasma-derived metabolomics data and a partial least squares regression (PLSR) machine learning approach to estimate dim light melatonin onset (DLMO).\"},{\"question\":\"Why is DLMO important in this context?\",\"answer\":\"DLMO is used as a proxy for circadian phase, reflecting the timing of pineal melatonin synthesis under controlled lighting conditions.\"},{\"question\":\"How does the metabolomics method compare with RNA-sequencing based approaches?\",\"answer\":\"Under entrained conditions, a metabolomics approach optimized for either women or men performed equally well or better than existing methods relying on more labor-intensive RNA sequencing.\"},{\"question\":\"What limitations or future work are mentioned?\",\"answer\":\"The approach requires further validation in shift work and other real-world conditions before broader clinical application.\"}]","Machine learning estimation of human body time using metabolomic profiling - research article | PDF",1785939780,25,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":91,"head_meta":93,"extra_data":95,"updated_unix":28},"machine-learning-estimation-of-human-body-time-using-metabolomic-profiling-research-article","",{"@graph":36,"@context":90},[37,54,69],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/machine-learning-estimation-of-human-body-time-using-metabolomic-profiling-research-article/127527/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":63,"encodingFormat":61,"isAccessibleForFree":64,"interactionStatistic":65},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-23","2026-08-05",true,{"@type":66,"interactionType":67,"userInteractionCount":20},"InteractionCounter",{"@type":68},"ViewAction",{"@type":70,"mainEntity":71},"FAQPage",[72,78,82,86],{"name":73,"@type":74,"acceptedAnswer":75},"What does the study use to estimate human circadian phase?","Question",{"text":76,"@type":77},"It uses plasma-derived metabolomics data and a partial least squares regression (PLSR) machine learning approach to estimate dim light melatonin onset (DLMO).","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"Why is DLMO important in this context?",{"text":81,"@type":77},"DLMO is used as a proxy for circadian phase, reflecting the timing of pineal melatonin synthesis under controlled lighting conditions.",{"name":83,"@type":74,"acceptedAnswer":84},"How does the metabolomics method compare with RNA-sequencing based approaches?",{"text":85,"@type":77},"Under entrained conditions, a metabolomics approach optimized for either women or men performed equally well or better than existing methods relying on more labor-intensive RNA sequencing.",{"name":87,"@type":74,"acceptedAnswer":88},"What limitations or future work are mentioned?",{"text":89,"@type":77},"The approach requires further validation in shift work and other real-world conditions before broader clinical application.","https://schema.org",{"og:url":52,"og:type":92,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":94,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":97},[98,102,106,110,115,120,125,128,133,136,139],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":99,"show_sort_weight":100,"slug":101},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":103,"show_sort_weight":104,"slug":105},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},"Exam",70,"exam",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},5,"Comic",60,"comic",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},6,"Technology",50,"technology",{"id":121,"doc_module":4,"doc_module_name":46,"category_name":122,"show_sort_weight":123,"slug":124},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":126,"slug":127},30,"research-report",{"id":129,"doc_module":4,"doc_module_name":46,"category_name":130,"show_sort_weight":131,"slug":132},9,"Religion & Spirituality",20,"religion-spirituality",{"id":131,"doc_module":4,"doc_module_name":46,"category_name":134,"show_sort_weight":131,"slug":135},"World Cup","world-cup",{"id":21,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":21,"slug":138},"Lifestyle","lifestyle",{"id":140,"doc_module":4,"doc_module_name":46,"category_name":141,"show_sort_weight":111,"slug":142},19,"General","general"]