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Abstract
Meconium, a non-invasive biomaterial reflecting prenatal substance accumulation, could provide valuable insights into neonatal health. However, the comprehensive protein profile of meconium across gestational ages remains unclear. Here, we conducted an extensive proteomic analysis of first meconium from 259 newborns across varied gestational ages to delineate protein composition and elucidate its relevance to neonatal diseases. The first meconium samples were collected, with the majority obtained before feeding, and the mean time for the first meconium passage from the anus was 11.9 ± 9.47 h. Our analysis revealed 5370 host-derived meconium proteins, which varied depending on sex and gestational age. Specifically, meconium from preterm infants exhibited elevated concentrations of proteins associated with the extracellular matrix. Additionally, the protein profiles of meconium also exhibited unique variations depending on both specific diseases, including gastrointestinal diseases, congenital heart diseases, and maternal conditions. Furthermore, we developed a machine learning model to predict gestational ages using meconium proteins. Our model suggests that newborns with gastrointestinal diseases and congenital heart diseases may have immature gastrointestinal systems. These findings highlight the intricate relationship between clinical parameters and meconium protein composition, offering potential for a novel approach to assess neonatal gastrointestinal health.
Meconium, a non-invasive biomaterial reflecting prenatal substance accumulation, holds promise for offering valuable insights into neonatal health. Here, the authors perform an extensive proteomic analysis of meconium samples collected from 259 newborns spanning various gestational ages.
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1 The University of Tokyo, Department of Pediatrics, Faculty of Medicine, Tokyo, Japan (GRID:grid.26999.3d) (ISNI:0000 0001 2169 1048)
2 Kazusa DNA Research Institute, Department of Applied Genomics, Chiba, Japan (GRID:grid.410858.0) (ISNI:0000 0000 9824 2470)
3 Chiba University, Institute for Advanced Academic Research (IAAR), Chiba, Japan (GRID:grid.136304.3) (ISNI:0000 0004 0370 1101); Chiba University, Department of Artificial Intelligence Medicine, Graduate School of Medicine, Chiba, Japan (GRID:grid.136304.3) (ISNI:0000 0004 0370 1101); Osaka University, Premium Research Institute for Human Metaverse Medicine (WPI-PRIMe), Suita, Japan (GRID:grid.136593.b) (ISNI:0000 0004 0373 3971)
4 The University of Tokyo, Department of Pediatric Surgery, Faculty of Medicine, Tokyo, Japan (GRID:grid.26999.3d) (ISNI:0000 0001 2169 1048)
5 International University of Health and Welfare, Department of Obstetrics and Gynecology, Faculty of Medicine, Chiba, Japan (GRID:grid.411731.1) (ISNI:0000 0004 0531 3030)
6 Chiba University, Institute for Advanced Academic Research (IAAR), Chiba, Japan (GRID:grid.136304.3) (ISNI:0000 0004 0370 1101); Chiba University, Department of Artificial Intelligence Medicine, Graduate School of Medicine, Chiba, Japan (GRID:grid.136304.3) (ISNI:0000 0004 0370 1101); RIKEN Information R&D and Strategy Headquarters, RIKEN, Advanced Data Science Project, Kanagawa, Japan (GRID:grid.7597.c) (ISNI:0000 0000 9446 5255)
7 The University of Tokyo, Department of Pediatric Surgery, Faculty of Medicine, Tokyo, Japan (GRID:grid.26999.3d) (ISNI:0000 0001 2169 1048); Gunma Children’s Medical Center, Department of Surgery, Gunma, Japan (GRID:grid.410822.d) (ISNI:0000 0004 0595 1091)