Amniotic fluid metabolic biomarkers of fetal physiology and pregnancy success†.

Journal: Biology of reproduction
Published Date: Mar 16, 2026

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.

Authors

  • Victor A Absalon-Medina
    ST Genetics, Ohio Heifer Center, South Charleston, OH, 45368, USA.
  • Rodrigo V Sala
    ST Genetics, Ohio Heifer Center, South Charleston, OH, 45368, USA.
  • Daniela C Pereira
    ST Genetics, Navasota, TX, 77868, USA.
  • Vanessa C Fricke
    ST Genetics, Ohio Heifer Center, South Charleston, OH, 45368, USA.
  • Iebu Devkota
    School of Animal Sciences, Agricultural Center, Louisiana State University, Baton Rouge, LA, 70803, USA.
  • Zachary L Bonomo
    School of Animal Sciences, Agricultural Center, Louisiana State University, Baton Rouge, LA, 70803, USA.
  • Dailin M Fuego
    School of Animal Sciences, Agricultural Center, Louisiana State University, Baton Rouge, LA, 70803, USA.
  • Michael McDonald
    School of Agriculture and Food Science, University College Dublin, Belfield, Dublin, C04V1W8, Ireland.
  • José M Sánchez
    Instituto Nacional de Investigación y Tecnología Agraria y Alimentaria (INIA), Madrid, 28040, Spain.
  • Maria B Rabaglino
    Quantitative Genetics, Bioinformatics and Computational Biology Group, Department of Applied Mathematics and Computer Science, Technical University of Denmark, Lyngby, Denmark.
  • Antonios Matsakas
    Department of Life Sciences, Manchester Metropolitan University, Manchester, M1 5GD, United Kingdom.
  • Anastasios Vourekas
    Department of Biological Sciences, Louisiana State University, Baton Rouge, LA, 70803, USA.
  • Xing Fu
    Group of Theoretical Biology, School of Life Sciences, Sun Yat-sen University, Guangzhou 510275, China.
  • Rocio M Rivera
    Division of Animal Sciences, University of Missouri, Columbia, MO, 65211, USA.
  • Patrick Lonergan
    School of Agriculture and Food Science, University College Dublin, Dublin, Ireland.
  • Pablo J Ross
    ST Genetics, Navasota, TX, 77868, USA.
  • Constantine A Simintiras
    School of Animal Sciences, Agricultural Center, Louisiana State University, Baton Rouge, LA, 70803, USA.

Keywords

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