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Transl. Med."],"published-print":{"date-parts":[[2026,1,21]]},"abstract":"<jats:p>Neonatal prematurity leads to considerable morbidity and mortality, partly because of acquired conditions such as bronchopulmonary dysplasia (BPD), intraventricular hemorrhage (IVH), necrotizing enterocolitis (NEC), and retinopathy of prematurity (ROP). Standard gestational age and birthweight-based classifications of prematurity inadequately capture the variation in newborns\u2019 health outcomes, creating an urgent need to develop risk stratification tools for vulnerable newborn infants to initiate the most appropriate care pathways as early as possible. We hypothesized that the metabolic profiles of newborn infants capture additional risk information beyond current measures. A total of 13,536 newborn screening (NBS) blood spot tests from preterm infants in California with linked clinical outcomes of prematurity were used to develop an NBS-based metabolic health index to stratify preterm infants at risk for BPD, IVH, NEC, and ROP (12,096 cases with one or more conditions and 1440 controls) through a deep learning model that provides a single index score in tandem with subgroup discovery to identify individuals with the strongest metabolite biomarker signals for adverse outcomes of prematurity. This metabolic health index captured risk signals that were distinct from gestational age and birthweight and outperformed other machine learning algorithms and clinical risk variable-based models in stratifying at-risk individuals for adverse outcomes of prematurity. The metabolic health index was externally validated in an independent retrospective cohort of 3299 very premature newborns from Ontario, Canada (2117 cases and 1182 controls), which recapitulated common metabolic risk subgroups. In summary, combining widespread metabolite screening with deep learning established a generalizable biological risk metric of prematurity.<\/jats:p>","DOI":"10.1126\/scitranslmed.adv4942","type":"journal-article","created":{"date-parts":[[2026,1,21]],"date-time":"2026-01-21T18:58:22Z","timestamp":1769021902000},"update-policy":"https:\/\/doi.org\/10.34133\/aaas_crossmark","source":"Crossref","is-referenced-by-count":4,"title":["Quantitative assessment of neonatal health using dried blood spot metabolite profiles and deep learning"],"prefix":"10.1126","volume":"18","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1716-0134","authenticated-orcid":true,"given":"Alan L.","family":"Chang","sequence":"first","affiliation":[{"name":"Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University School of Medicine, Stanford, CA 94305, USA."},{"name":"Department of Biomedical Data Science, Stanford University, Stanford, CA 94305, USA."},{"name":"Department of Pediatrics, 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