Journal Article

Using graph learning to understand adverse pregnancy outcomes and stress pathways

Mesner, Octavio, Casman, Elizabeth, Simhan, Hyagriv, Krishnamurti, Tamar, Davis, Alex, Shalizi, Cosma, Borders, Ann, Keenan-Devlin, Lauren

2019

Overview

To identify pathways between stress indicators and adverse pregnancy outcomes, we applied a nonparametric graph-learning algorithm, PC-KCI, to data from an observational prospective cohort study. The Measurement of Maternal Stress study (MOMS) followed 744 women with a singleton intrauterine pregnancy recruited between June 2013 and May 2015. Infant adverse pregnancy outcomes were prematurity (<37 weeks' gestation), infant days spent in hospital after birth, and being small for gestational age (percentile gestational weight at birth). Maternal adverse pregnancy outcomes were pre-eclampsia, gestational diabetes, and gestational hypertension. PC-KCI replicated well-established pathways, such as the relationship between gestational weeks and preterm premature rupture of membranes. PC-KCI also identified previously unobserved pathways to adverse pregnancy outcomes, including 1) a link between hair cortisol levels (at 12-21 weeks of pregnancy) and pre-eclampsia; 2) two pathways to preterm birth depending on race, with one linking Hispanic race, pre-gestational diabetes and gestational weeks, and a second pathway linking black race, hair cortisol, preeclampsia, and gestational weeks; and 3) a relationship between maternal childhood trauma, perceived social stress in adulthood, and low weight for gestational age. Our approach confirmed previous findings and identified previously unobserved pathways to adverse pregnancy outcomes. It presents a method for a global assessment of a clinical problem for further study of possible causal pathways.

• Publisher: Public Library of Science,Public Library of Science (PLoS)
• Subject: Abortion, Spontaneous - diagnosis / Abortion, Spontaneous - epidemiology / Abortion, Spontaneous - metabolism / Adult / Algorithms / Analysis / Anxiety / Biology and Life Sciences / Biomarkers - metabolism / Birth / Childhood / Children / Cortisol / Data collection / Data mining / Delivery, Obstetric / Diabetes mellitus / Diabetes, Gestational - diagnosis / Diabetes, Gestational - epidemiology / Diabetes, Gestational - metabolism / Eclampsia / Engineering / Female / Generalized linear models / Gestation / Gestational Age / Gestational diabetes / Gestational hypertension / Glucocorticoids / Graph theory / Gynecology / Hair / Hair - chemistry / Hair - metabolism / Health aspects / Humans / Hydrocortisone - metabolism / Hypertension / Hypertension, Pregnancy-Induced - diagnosis / Hypertension, Pregnancy-Induced - epidemiology / Hypertension, Pregnancy-Induced - metabolism / Infant, Low Birth Weight / Infant, Newborn / Infant, Premature / Infants / Live Birth / Machine learning / Maternal & child health / Mediation / Medicine and Health Sciences / Membranes / Obstetrics / People and Places / Pre-eclampsia / Pre-Eclampsia - diagnosis / Pre-Eclampsia - epidemiology / Pre-Eclampsia - metabolism / Preeclampsia / Pregnancy / Pregnancy complications / Pregnant women / Premature birth / Premature rupture of the membrane / Prospective Studies / Proteins / Psychological aspects / Public policy / Race / Researchers / Risk factors / Small for gestational age / Social interactions / Social Sciences / Statistics, Nonparametric / Stillbirth / Stress / Stress (Psychology) / Stress, Psychological - diagnosis / Stress, Psychological - epidemiology / Stress, Psychological - metabolism / Studies / Systematic review / Trauma / United States - epidemiology / Variables / Weight / Women / Womens health