Amniotic fluid metabolic biomarkers of fetal physiology and pregnancy success
Year: 2025
Type: article
Abstract: Amniotic fluid (AF) profiling provides a minimally invasive window into early fetal physiology. We characterized the AF metabolome from first trimester (Day 68) Holstein dairy heifers (n = 45), considering fetal sex, conception method [in vitro fertilization vs. artificial insemination (AI)], and eventual pregnancy outcome as key variables. Multivariate statistics uncovered differentially abundant metabolites for each comparison-including markers that preceded spontaneous abortion-independently of recipient age, weight, gestation length, or fetal genetics. Thereafter, a machine learning algorithm using panels of six metabolites accurately predicted fetal sex (AUROC = 0.76; P = 0.023) and pregnancy viability (AUROC = 0.81; P = 0.018), while corroborating conception method (AUROC = 0.91; P = 0.001). External validation using AF (Day 42) from an independent cohort of beef heifers (n = 22) reproduced the fetal sex classifier with similarly high sensitivity and specificity (AUROC = 0.85, P = 0.029). These findings reveal metabolic signatures that forecast fetal sex and pregnancy viability, while confirming distinct metabolic imprints of assisted-conception modalities. These data lay the groundwork for next-generation AF prenatal diagnostics in veterinary and human obstetrics.
Source: Biology of Reproduction
Authors: Victor A Absalón-Medina, Rodrigo V Sala, Daniela C Pereira, Vanessa C Fricke, Iebu Devkota, Zachary L Bonomo, Dailin M Fuego, Michael McDonald, José M Sánchez, Maria B Rabaglino, Antonios Matsakas, Anastasios Vourekas, Xing Fu, Rocio M Rivera, Patrick Lonergan, Pablo J Ross, Constantine A Simintiras
Institutions: The Ohio State University, STgenetics (United States), Louisiana State University, Louisiana State University Agricultural Center, University College Dublin, Consejo Superior de Investigaciones Científicas, Instituto Nacional de Investigación y Tecnología Agraria y Alimentaria, Utrecht University, Manchester Metropolitan University, University of Missouri, University of California, Davis
Language: English
Subfield: Agronomy and Crop Science
Domain: Life Sciences
Open Access status: green
OpenAlex ID: W4415507220