Amniotic fluid metabolic biomarkers of fetal physiology and pregnancy success

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.

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Publication Details

Journal
Biology of Reproduction
Published
2025-10-18
DOI
https://doi.org/10.1093/biolre/ioaf236
Primary Topic
Pregnancy and preeclampsia studies
Type
article
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article

Amniotic fluid metabolic biomarkers of fetal physiology and pregnancy success

Anastassios Vourekas, María Belén Rabaglino, Xing Fu, Michael McDonald et al.
Biology of Reproduction
Pregnancy and preeclampsia studies
article

Amniotic fluid metabolic biomarkers of fetal physiology and pregnancy success

Anastassios Vourekas, María Belén Rabaglino, Xing Fu, Michael McDonald, Daniela C. Pereira, V. C. Fricke, Victor Antonio Absalón-Medina, Antonios Matsakas, Constantine A. Simintiras, Pablo J. Ross, Rocío Melissa Rivera, Dailin Fuego, José María Sánchez, Patrick Lonergan, Rodrigo Vasconcellos Sala, Iebu Devkota, Zachary L Bonomo
article en

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.

Biology of ReproductionVol. 114(3)
University College Dublin (IE), Manchester Metropolitan University (GB), Louisiana State University (US), Louisiana State University Agricultural Center (US), University of Applied Sciences Utrecht (NL), Utrecht University (NL), Instituto Nacional de Investigación y Tecnología Agraria y Alimentaria (ES), Heifer International (US), STgenetics (United States) (US), The Ohio State University (US), University of Missouri (US), University of California, Davis (US)
Openalex Percentile: Top 62%
Pregnancy and preeclampsia studies
DOISourceOpenAlex
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