TY - JOUR

T1 - Vertical Metabolome Transfer from Mother to Child

T2 - An Explainable Machine Learning Method for Detecting Metabolomic Heritability

AU - Lovrić, Mario

AU - Horner, David

AU - Chen, Liang

AU - Brustad, Nicklas

AU - Malby Schoos, Ann-Marie

AU - Lasky-Su, Jessica

AU - Chawes, Bo

AU - Rasmussen, Morten Arendt

N1 - Publisher Copyright: © 2024 by the authors.

PY - 2024

Y1 - 2024

N2 - Vertical transmission of metabolic constituents from mother to child contributes to the manifestation of disease phenotypes in early life. This study probes the vertical transmission of metabolites from mothers to offspring by utilizing machine learning techniques to differentiate between true mother–child dyads and randomly paired non-dyads. Employing random forests (RF), light gradient boosting machine (LGBM), and logistic regression (Elasticnet) models, we analyzed metabolite concentration discrepancies in mother–child pairs, with maternal plasma sampled at 24 weeks of gestation and children’s plasma at 6 months. The propensity of vertical transfer was quantified, reflecting the likelihood of accurate mother–child matching. Our findings were substantiated against an external test set and further verified through statistical tests, while the models were explained using permutation importance and SHapley Additive exPlanations (SHAP). The best model was achieved using RF, while xenobiotics were shown to be highly relevant in transfer. The study reaffirms the transmission of certain metabolites, such as perfluorooctanoic acid (PFOA), but also reveals additional insights into the maternal influence on the child’s metabolome. We also discuss the multifaceted nature of vertical transfer. These machine learning-driven insights complement conventional epidemiological findings and offer a novel perspective on using machine learning as a methodology for understanding metabolic interactions.

AB - Vertical transmission of metabolic constituents from mother to child contributes to the manifestation of disease phenotypes in early life. This study probes the vertical transmission of metabolites from mothers to offspring by utilizing machine learning techniques to differentiate between true mother–child dyads and randomly paired non-dyads. Employing random forests (RF), light gradient boosting machine (LGBM), and logistic regression (Elasticnet) models, we analyzed metabolite concentration discrepancies in mother–child pairs, with maternal plasma sampled at 24 weeks of gestation and children’s plasma at 6 months. The propensity of vertical transfer was quantified, reflecting the likelihood of accurate mother–child matching. Our findings were substantiated against an external test set and further verified through statistical tests, while the models were explained using permutation importance and SHapley Additive exPlanations (SHAP). The best model was achieved using RF, while xenobiotics were shown to be highly relevant in transfer. The study reaffirms the transmission of certain metabolites, such as perfluorooctanoic acid (PFOA), but also reveals additional insights into the maternal influence on the child’s metabolome. We also discuss the multifaceted nature of vertical transfer. These machine learning-driven insights complement conventional epidemiological findings and offer a novel perspective on using machine learning as a methodology for understanding metabolic interactions.

KW - childhood

KW - PFOA

KW - PFOS

KW - pregnancy

KW - propensity

U2 - 10.3390/metabo14030136

DO - 10.3390/metabo14030136

M3 - Journal article

C2 - 38535296

AN - SCOPUS:85188928594

VL - 14

JO - Metabolites

JF - Metabolites

SN - 2218-1989

IS - 3

M1 - 136

ER -