Metabolic prediction of maturity at birth in pigs

Metabolic prediction of maturity at birth in pigs | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Metabolic prediction of maturity at birth in pigs Elise Maigné, Nathalie Marty-Gasset, Laure Gress, Cécile Canlet, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9150369/v1 This work is licensed under a CC BY 4.0 License Status: Under Revision Version 1 posted 14 You are reading this latest preprint version Abstract Improving piglet survival is a key objective for breeders. Piglets that have not yet fully developed are more likely to die prematurely. Here, focus was to better characterize maturity at birth. Very immature piglets exhibit a distinctive head morphology with a reminiscent of a dolphin's, with prominent eyes. This study proposed integrating phenotyping data with blood sampling to develop a predictive metabolic signature of piglet maturity at birth. Following analysis of the head morphology, the study categorized 278 newborns (99 Landrace, 87 Large White, 92 LR×LW) according to their maturity level. Furthermore, a metabolomic analysis was also performed by 1H-NMR on blood samples (serum) collected on piglets in the hours following birth. The raw spectra were analyzed using the R package ASICS. The following statistics were based on 55 metabolites with non-zero variance. A subset of 14 metabolites was selected to develop a predictive model based on random Forests and GLM methods. The two models accurately predict 100\% of the severe immaturity status in both the training and test samples. Some piglets that are morphologically classified as mature may be metabolically immature. The 14-metabolite signature can qualify the maturity with a qualitative score as mature or not, and two quantitative scores, a mean predicted value and a stability of the prediction, which allow the confidence of the prediction to be assessed. The predictive model was applied to an independent dataset of blood collected on different farms and from piglets of different genetic origins. This allowed the relevance of the model to be evaluated, taking into account other phenotypes related to the status of birth piglets, such as birth weight, and body mass index. Genetic selection for survival at birth and growth is primarily based on the measurement of birth weight. As these traits are correlated, it is important to unravel these correlations to understand the underlying molecular mechanisms. The identification of a molecular signature could facilitate future experiments aimed at deciphering the genetic architecture of complex traits, such as maturity. Therefore, we have developed a minimally invasive blood sample that allows for low-cost, user-friendly metabolic analysis of serum. While maturity is typically defined at the biometric level, we propose a novel approach to define this complex trait at the metabolic level. Biological sciences/Biological techniques Biological sciences/Computational biology and bioinformatics Biological sciences/Genetics Biological sciences/Zoology Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Revision Version 1 posted Editorial decision: Revision requested 06 May, 2026 Reviews received at journal 01 May, 2026 Reviews received at journal 21 Apr, 2026 Reviews received at journal 07 Apr, 2026 Reviewers agreed at journal 07 Apr, 2026 Reviewers agreed at journal 05 Apr, 2026 Reviewers agreed at journal 05 Apr, 2026 Reviewers agreed at journal 05 Apr, 2026 Reviewers agreed at journal 03 Apr, 2026 Reviewers invited by journal 03 Apr, 2026 Editor assigned by journal 02 Apr, 2026 Editor invited by journal 02 Apr, 2026 Submission checks completed at journal 27 Mar, 2026 First submitted to journal 27 Mar, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9150369","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":619207011,"identity":"0b5658e7-358f-413f-8be1-d2894ca20f5a","order_by":0,"name":"Elise Maigné","email":"","orcid":"","institution":"Mathematiques et Informatique Appliquées de Toulouse","correspondingAuthor":false,"prefix":"","firstName":"Elise","middleName":"","lastName":"Maigné","suffix":""},{"id":619207012,"identity":"8ac8a227-0fad-4274-a0b8-150a48c7c292","order_by":1,"name":"Nathalie Marty-Gasset","email":"","orcid":"","institution":"Génétique Physiologie et Systèmes 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Piglets that have not yet fully developed are more likely to die prematurely. Here, focus was to better characterize maturity at birth. Very \u0026nbsp;immature piglets exhibit a distinctive head morphology with a reminiscent of a dolphin's, with prominent eyes. This study proposed integrating phenotyping data with blood sampling to develop a predictive metabolic signature of piglet maturity at birth.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFollowing analysis of the head morphology, the study categorized 278 newborns (99 Landrace, 87 Large White, 92 LR×LW) according to their maturity level. Furthermore, a metabolomic analysis was also performed by 1H-NMR on blood samples (serum) collected on piglets in the hours following birth. The raw spectra were analyzed using the R package ASICS. The following statistics were based on 55 metabolites with non-zero variance. A subset of 14 metabolites was selected to develop a predictive model based on random Forests and GLM methods. The two models accurately predict 100\\% of the severe immaturity status in both the training and test samples. Some piglets that are morphologically classified as mature may be metabolically immature. The 14-metabolite signature can qualify the maturity with a qualitative score as mature or not, and two quantitative scores, a mean predicted value and a stability of the prediction, which allow the confidence of the prediction to be assessed. The predictive model was applied to an independent dataset of blood collected on different farms and from piglets of different genetic origins. This allowed the relevance of the model to be evaluated, taking into account other phenotypes related to the status of birth piglets, such as birth weight, and body mass index.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eGenetic selection for survival at birth and growth is primarily based on the measurement of birth weight. 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