Data-Driven Modeling of Pregnancy-Related Complications
Authors: Camilo Espinosa, Martin Becker, Ivana Marić, Ronald J. Wong, Gary M. Shaw, Brice Gaudilliere, Nima Aghaeepour, David K. Stevenson, Ina A. Stelzer, Laura S. Peterson, Alan L. Chang, Maria Xenochristou, Thanaphong Phongpreecha, Davide De Francesco, Michael Katz, Yair J. Blumenfeld, Martin S. Angst
Journal: Trends in Molecular Medicine (2021)

A multitude of clinical, biological, environmental, and demographic factors influence the trajectory of a pregnancy. Maternal genetics, environment, stress, nutrition, medical history, socioeconomic status, and racial and ethnic background all play a role in determining the success of a pregnancy.Diverse data sources are available for the study of pregnancy and prediction of adverse outcomes, including electronic health records (EHRs) and administrative claims data, high-throughput multiomics data for characterizing biological systems, and more complex sources like time series, imaging and video data, and text.Recent advances in multiview, multitask, and deep learning allow joint modeling across data sources as well as across outcomes and demonstrate the vast potential of such integrated approaches. A healthy pregnancy depends on complex interrelated biological adaptations involving placentation, maternal immune responses, and hormonal homeostasis. Recent advances in high-throughput technologies have provided access to multiomics biological data that, combined with clinical and social data, can provide a deeper understanding of normal and abnormal pregnancies. Integration of these heterogeneous datasets using state-of-the-art machine-learning methods can enable the prediction of short- and long-term health trajectories for a mother and offspring and the development of treatments to prevent or minimize complications. We review advanced machine-learning methods that could: provide deeper biological insights into a pregnancy not yet unveiled by current methodologies; clarify the etiologies and heterogeneity of pathologies that affect a pregnancy; and suggest the best approaches to address disparities in outcomes affecting vulnerable populations. A healthy pregnancy depends on complex interrelated biological adaptations involving placentation, maternal immune responses, and hormonal homeostasis. Recent advances in high-throughput technologies have provided access to multiomics biological data that, combined with clinical and social data, can provide a deeper understanding of normal and abnormal pregnancies. Integration of these heterogeneous datasets using state-of-the-art machine-learning methods can enable the prediction of short- and long-term health trajectories for a mother and offspring and the development of treatments to prevent or minimize complications. We review advanced machine-learning methods that could: provide deeper biological insights into a pregnancy not yet unveiled by current methodologies; clarify the etiologies and heterogeneity of pathologies that affect a pregnancy; and suggest the best approaches to address disparities in outcomes affecting vulnerable populations. Pregnancy is a complex process in which each stage – implantation, decidualization, placentation, organogenesis, the establishment of the maternal–fetal interface, fetal growth, and, finally, parturition – depends on the successful completion of the previous stage [1.Cha J. et al.Mechanisms of implantation: strategies for successful pregnancy.Nat. Med. 2012; 18: 1754-1767Crossref PubMed Scopus (603) Google Scholar]. The exact etiologies of most adverse pregnancy outcomes remain unknown, challenging the development of therapeutic interventions. Preterm birth (PTB), defined as a birth before 37 weeks of gestation, is the leading cause of global infant morbidity and mortality. Spontaneous PTB (sPTB) has many antecedents and has been attributed to various pathological mechanisms including intra-amniotic infection, disruption of maternal–fetal tolerance, vascular disorders, myometrial stretching, and cervical incompetence, thus appearing to be a syndrome with many causes [2.Romero R. et al.Preterm labor: one syndrome, many causes.Science. 2014; 345: 760-765Crossref PubMed Scopus (865) Google Scholar]. Moreover, the current definition of sPTB, based solely on a gestational age (GA) of less than 37 weeks, is insufficient to precisely identify neonates who will develop adverse outcomes. Further challenging this definition is the inability to accurately estimate GA in low- and middle-income countries (LMICs) [3.Rosenberg R.E. et al.Determining gestational age in a low-resource setting: validity of last menstrual period.J. Health Popul. Nutr. 2009; 27: 332-338PubMed Google Scholar]. Similarly, there is growing evidence that the two main types of preeclampsia – early versus late onset – in addition to having different risk factors have different pathophysiologies [4.Tarca A.L. et al.The prediction of early preeclampsia: results from a longitudinal proteomics study.PLoS One. 2019; 14e0217273Crossref PubMed Scopus (27) Google Scholar]. Various adverse pregnancy outcomes – fetal growth restriction (FGR), PTB, preterm premature rupture of membranes (PPROM), late spontaneous abortion, and placental abruption – have been associated with a common culprit: an abnormal placenta [5.Ilekis J.V. et al.Placental origins of adverse pregnancy outcomes: potential molecular targets: an Executive Workshop Summary of the Eunice Kennedy Shriver National Institute of Child Health and Human Development.Am. J. Obstet. Gynecol. 2016; 215: S1-S46Abstract Full Text Full Text PDF PubMed Scopus (128) Google Scholar,6.Khalil A. et al.Change in the incidence of stillbirth and preterm delivery during the COVID-19 pandemic.JAMA. 2020; 324: 705-706Crossref Scopus (81) Google Scholar]. These findings point to the complexity of these outcomes and of pregnancy itself. In addition to – and perhaps as a consequence of – this inherent complexity of pregnancy, the corresponding adverse outcomes are influenced by various interrelated factors, including genetic and immunological predisposition as well as medical history and social determinants (e.g., race/ethnicity, socioeconomic status, education) [7.Stevenson D.K. et al.Towards personalized medicine in maternal and child health: integrating biologic and social determinants.Pediatr. Res. 2020; (Published online May 26, 2020. https://doi.org/10.1038/s41390-020-0981-8)Crossref Scopus (1) Google Scholar]. New diseases and environmental factors – such as coronavirus disease 2019 (COVID-19) and climate change – can affect pregnancy in many complex ways [6.Khalil A. et al.Change in the incidence of stillbirth and preterm delivery during the COVID-19 pandemic.JAMA. 2020; 324: 705-706Crossref Scopus (81) Google Scholar,8.Bekkar B. et al.Association of air pollution and heat exposure with preterm birth, low birth weight, and stillbirth in the US: a systematic review.JAMA Netw. Open. 2020; 3e208243Crossref PubMed Scopus (38) Google Scholar,9.Rylander C. et al.Climate change and the potential effects on maternal and pregnancy outcomes: an assessment of the most vulnerable – the mother, fetus, and newborn child.Glob. Health Action. 2013; 619538Crossref PubMed Scopus (0) Google Scholar]. Disparities in outcomes often seem to be related to racial, socioeconomic, and geographic factors [10.Stevenson D.K. et al.Understanding health disparities.J. Perinatol. 2019; 39: 354-358Crossref PubMed Scopus (6) Google Scholar]. However, the assessment of a woman's predisposition to a specific complication based on clinical, demographic, and social data – a key step – is limited in its predictive accuracy [11.Poon L.C. Nicolaides K.H. Early prediction of preeclampsia.Obstet. Gynecol. Int. 2014; 2014297397Crossref PubMed Google Scholar]. The recent availability of high-throughput, molecular-level data from genomic, transcriptomic, proteomic, metabolomic, and single-cell immunological measurements, together with advanced computational and statistical tools, has provided an opportunity for analyses of these comprehensive and detailed datasets and for the integration of biological and nonbiological biomarkers. Such integrated approaches can lead to more accurate inferences about the diversity and multiplicity of causes of pathologies related to pregnancy, providing more specific and generalizable signatures of pregnancy-related pathologies. In this review, we provide an overview of major clinical challenges in pregnancy and the available data sources to study pregnancy, as well as state-of-the-art machine-learning (see Glossary) methodology to analyze these data. We close with an outlook on how a combination of diverse data sources and advanced machine learning can yield a holistic view of pregnancy. The establishment, maintenance, and completion of a healthy pregnancy depends on a series of interrelated biological adaptations. Derangements in the processes of placentation, maternal immune adaptation, and hormonal homeostasis, among others, contribute to the pathogenesis of adverse outcomes [12.Peterson L.S. et al.Multiomic immune clockworks of pregnancy.Semin. Immunopathol. 2020; 42: 397-412Crossref PubMed Scopus (12) Google Scholar]. For example, during pregnancy the maternal immune system must maintain tolerance to the fetoplacental unit while still protecting the mother and fetus against invading pathogens. Failure to do so is strongly implicated in almost all adverse outcomes of pregnancy [12.Peterson L.S. et al.Multiomic immune clockworks of pregnancy.Semin. Immunopathol. 2020; 42: 397-412Crossref PubMed Scopus (12) Google Scholar,13.Han X. et al.Differential dynamics of the maternal immune system in healthy pregnancy and preeclampsia.Front. Immunol. 2019; 10: 1305Crossref PubMed Scopus (15) Google Scholar]. Similarly, pregnancy stages are orchestrated in part by specific chronological changes in the maternal endocrine system, and any dysregulation of hormonal homeostasis can lead to pathogenesis [14.Stelzer I.A. Arck P.C. Immunity and the endocrine system.in: Encyclopedia of Immunobiology. Elsevier, 2016: 73-85Crossref Scopus (2) Google Scholar,15.Solano M.E. Arck P.C. Steroids, pregnancy and fetal development.Front. Immunol. 2019; 10: 3017Crossref PubMed Scopus (6) Google Scholar]. Pathogenic processes can interfere with the critical biological adaptations during pregnancy and lead to pregnancy-related complications. PTB, preeclampsia, and FGR arise from such processes and complicate 15–20% of pregnancies. While most of these disorders present clinically in the third trimester, treatment after the onset of clinical symptoms results in only mild to moderate improvements. Because implantation, uterine remodeling, and placental development occur early in pregnancy, identifying molecular changes weeks before the clinical onset of disease has the potential to dramatically improve our diagnostic and therapeutic capacities and decrease the associated maternal and neonatal morbidity and mortality. Prematurity remains a leading cause of neonatal morbidity and mortality, and children born preterm often face lifelong challenges including permanent physical and neurodevelopmental disabilities [16.Martin J.A. et al.Births: final data for 2018.Natl. Vital Stat. Rep. 2019; 68: 1-47Google Scholar]. PTB etiologies are often categorized into different subgroups [17.Goldenberg R.L. et al.Epidemiology and causes of preterm birth.Lancet. 2008; 371: 75-84Abstract Full Text Full Text PDF PubMed Scopus (4088) Google Scholar,18.Romero R. et al.The preterm parturition syndrome.BJOG. 2006; 113: 17-42Crossref PubMed Scopus (808) Google Scholar]. Iatrogenic PTB is defined as a PTB in which physicians either induce labor or proceed with Cesarean delivery at early GAs in response to a severe underlying maternal and/or fetal complication. sPTB results from either PPROM or spontaneous preterm contractions/preterm labor (PTL). Although infection and inflammation have traditionally been thought to mediate a significant proportion of sPTBs, recent advances have also highlighted the role of pathological immune responses, maternal sociodemographic stressors (including bias and racism) [19.Traylor C.S. et al.Effects of psychological stress on adverse pregnancy outcomes and non-pharmacologic approaches for reduction: an expert review.Am. J. Obstet. Gynecol. MFM. 2020; 2100229Abstract Full Text Full Text PDF PubMed Google Scholar,20.Krieger N. et al.Structural racism, historical redlining, and risk of preterm birth in New York City, 2013–2017.Am. J. Public Health. 2020; 110: 1046-1053Crossref PubMed Scopus (25) Google Scholar], cervical disorders (e.g., cervical incompetence), and uterine overdistension [as is the case in multiple gestations or in polyhydramnios (excess amniotic fluid volume)]. Unfortunately, irrespective of the etiology, PTB remains extremely difficult to predict, partly because of heterogeneous and multifactorial underlying mechanisms and partly because clinicians lack the necessary tools to identify these mechanisms prior to the onset of clinical symptoms. Moreover, treatment of PTL and/or PPROM using tocolytics, antibiotics, or other measures has only limited benefit in delaying delivery once clinical symptoms have begun [21.American College of Obstetricians and Gynecologists' Committee on Practice Bulletins – Obstetrics Practice bulletin no. 171: management of preterm labor.Obstet. Gynecol. 2016; 128: e155-e164Crossref PubMed Scopus (0) Google Scholar]. Preeclampsia is a multiorgan disorder complicating 2–8% of pregnancies [22.American College of Obstetricians and Gynecologists' Committee on Practice Bulletins – Obstetrics Gestational hypertension and preeclampsia: ACOG practice bulletin, number 222.Obstet. Gynecol. 2020; 135: e237-e260Crossref PubMed Scopus (0) Google Scholar]. Although the hallmarks of preeclampsia are new-onset maternal hypertension and proteinuria, preeclampsia can have significant adverse effects on a variety of organs including the central nervous system, lungs, liver, kidneys, and heart, all mediated via generalized vasoconstriction [22.American College of Obstetricians and Gynecologists' Committee on Practice Bulletins – Obstetrics Gestational hypertension and preeclampsia: ACOG practice bulletin, number 222.Obstet. Gynecol. 2020; 135: e237-e260Crossref PubMed Scopus (0) Google Scholar]. Preeclampsia may present as early- or late-onset preeclampsia, with the early-onset form indicative of more severe disease and with increased rates of complications [23.Wójtowicz A. et al.Early- and late-onset preeclampsia: a comprehensive cohort study of laboratory and clinical findings according to the New ISHHP Criteria.Int. J. Hypertens. 2019; 20194108271Crossref PubMed Scopus (10) Google Scholar]. While the clinical manifestations of preeclampsia occur most often in the third trimester, it is hypothesized that the pathophysiology of preeclampsia begins in the first trimester. Specifically, abnormal invasion of the placental trophoblasts into the uteroplacental interface is believed to result in maldevelopment of the spiral arteries and hence lead to inadequate perfusion of the placenta [24.Chaiworapongsa T. et al.Pre-eclampsia part 1: current understanding of its pathophysiology.Nat. Rev. Nephrol. 2014; 10: 466-480Crossref PubMed Scopus (502) Google Scholar]. This early-onset abnormal placentation cascade can also have direct third trimester ramifications as preeclampsia can result in placental abruption, PTB, and/or FGR. The adverse effects of preeclampsia are likely to extend beyond maternal and neonatal morbidity. Recent studies have begun to elucidate a link between preeclampsia and long-term maternal and offspring cardiovascular disease, likely to occur via direct and permanent cardiac remodeling [25.Andraweera P.H. Lassi Z.S. Cardiovascular risk factors in offspring of preeclamptic pregnancies – systematic review and meta-analysis.J. Pediatr. 2019; 208: 104-113.e6Abstract Full Text Full Text PDF PubMed Google Scholar,26.Yilgwan C.S. et al.Profile of congenital heart disease in infants born following exposure to preeclampsia.PLoS One. 2020; 15e0229987Crossref PubMed Scopus (1) Google Scholar]. Therefore, the identification of novel predictive tools, which allow timely interventions, may ultimately lead not only to a reduction in maternal and neonatal morbidity but also to improvements in the long-term health of women. Although FGR is often defined as an estimated fetal weight less than the tenth percentile [27.Society for Maternal–Fetal Medicine et al.Society for Maternal–Fetal Medicine Consult Series #52: diagnosis and management of fetal growth restriction: (replaces Clinical Guideline Number 3, April 2012).Am. J. Obstet. Gynecol. 2020; 223: B2-B17Abstract Full Text Full Text PDF Scopus (0) Google Scholar], most cases are not pathological but rather 'constitutional', based on maternal or paternal size. Pathological FGR complicates 3–5% of pregnancies and results from abnormal genetics, infections [e.g., cytomegalovirus (CMV)], anatomical malformations, placental pathologies, and/or underlying maternal disorders leading to decreased placental perfusion [28.Iams J.D. Small for gestational age (SGA) and fetal growth restriction (FGR).Am. J. Obstet. Gynecol. 2010; 202: 513Abstract Full Text Full Text PDF PubMed Scopus (0) Google Scholar]. The long-term neonatal and pediatric effects of FGR have been well characterized and include physical, metabolic, and neurodevelopmental challenges [27.Society for Maternal–Fetal Medicine et al.Society for Maternal–Fetal Medicine Consult Series #52: diagnosis and management of fetal growth restriction: (replaces Clinical Guideline Number 3, April 2012).Am. J. Obstet. Gynecol. 2020; 223: B2-B17Abstract Full Text Full Text PDF Scopus (0) Google Scholar]. While most FGR is diagnosed in the third trimester, predicting FGR in the first or second trimester is difficult, and there are no proven therapies to alter fetal growth in utero. Interestingly, there is a link between FGR and metabolic syndrome in the offspring, highlighting a vicious cycle in which a pathological pregnancy adversely affects the long-term health and possible eventual pregnancy of the offspring [29.Crispi F. et al.Long-term cardiovascular consequences of fetal growth restriction: biology, clinical implications, and opportunities for prevention of adult disease.Am. J. Obstet. Gynecol. 2018; 218: S869-S879Abstract Full Text Full Text PDF PubMed Scopus (86) Google Scholar]. Stillbirth – fetal death after 20 weeks of gestation – remains a significant global health burden in both LMICs and high-income countries (HICs) [30.Blencowe H. et al.National, regional, and worldwide estimates of stillbirth rates in 2015, with trends from 2000: a systematic analysis.Lancet Glob. Health. 2016; 4: e98-e108Abstract Full Text Full Text PDF PubMed Scopus (387) Google Scholar,31.Smith L.K. et al.Quantifying the burden of stillbirths before 28 weeks of completed gestational age in high-income countries: a population-based study of 19 European countries.Lancet. 2018; 392: 1639-1646Abstract Full Text Full Text PDF PubMed Scopus (18) Google Scholar]. While the cause of intrauterine fetal death is multifactorial, common complications that can lead to stillbirth include fetal congenital anomalies, placental pathologies, maternal health disorders, and obstetric syndromes [32.Stillbirth Collaborative Research Network Writing GroupCauses of death among stillbirths.JAMA. 2011; 306: 2459-2468Crossref PubMed Google Scholar]. Furthermore, a pregnancy that ends with a stillbirth will have long-term physical and psychological consequences on the mother's health and may endanger the success of future pregnancies [33.Wall-Wieler E. et al.Severe maternal morbidity among stillbirth and live birth deliveries in California.Obstet. Gynecol. 2019; 134: 310-317Crossref PubMed Scopus (10) Google Scholar,34.Nonyane B.A.S. et al.Pregnancy intervals after stillbirth, neonatal death and spontaneous abortion and the risk of an adverse outcome in the next pregnancy in rural Bangladesh.BMC Pregnancy Childbirth. 2019; 19: 62Crossref PubMed Scopus (1) Google Scholar]. Various population-level data sources have been used to identify factors that impact maternal and offspring health. One of the main such sources – electronic health records (EHRs) – includes demographics, anthropometrics, diagnoses, vital signs, medications, and various clinical measurements, offering a detailed look into a patient's medical history. Administrative insurance claims data, containing large sample sizes over large geographic areas but with less granularity, have been successfully mined to create and analyze pregnancy cohorts [35.Ailes E.C. et al.Using insurance claims data to identify and estimate critical periods in pregnancy: an application to antidepressants.Birth Defects Res. A Clin. Mol. Teratol. 2016; 106: 927-934Crossref PubMed Scopus (37) Google Scholar, 36.MacDonald S.C. et al.Pregnancy outcomes in women with multiple sclerosis.Am. J. Epidemiol. 2019; 188: 57-66Crossref PubMed Scopus (9) Google Scholar, 37.Marić I. et al.Data-driven queries between medications and spontaneous preterm birth among 2.5 million pregnancies.Birth Defects Res. 2019; 111: 1145-1153Crossref PubMed Scopus (2) Google Scholar]. Studies involving EHR-based models for the prediction of gestational diabetes mellitus (GDM), preeclampsia, and PTB illustrate how such data sources are relevant in the context of reproductive health [38.Gao C. et al.Deep learning predicts extreme preterm birth from electronic health records.J. Biomed. Inform. 2019; 100103334Crossref PubMed Scopus (4) Google Scholar, 39.Marić I. et al.Early prediction of preeclampsia via machine learning.Am. J. Obstet. Gynecol. MFM. 2020; 2100100Abstract Full Text Full Text PDF PubMed Google Scholar, 40.Qiu H. et al.Electronic health record driven prediction for gestational diabetes mellitus in early pregnancy.Sci. Rep. 2017; 716417Crossref PubMed Scopus (13) Google Scholar]. EHRs can also be used to characterize the currently unknown pharmacological effects that a broad range of drugs might have on the physiology of pregnancy [41.Boland M.R. et al.Development of a machine learning algorithm to classify drugs of unknown fetal effect.Sci. Rep. 2017; 712839Crossref PubMed Scopus (8) Google Scholar,42.Challa A.P. et al.EHRs could clarify drug safety in pregnant people.Nat. Med. 2020; 26: 820-821Crossref PubMed Scopus (0) Google Scholar]. Well-established measures of health such as hematology panels and urinalyses continue to provide useful information when assessing maternal and fetal health. For instance, higher neutrophil counts and neutrophil-to-lymphocyte ratios – both inflammatory markers – have been associated with the development of GDM and decreased fetal growth [43.Levy O. et al.Early predictors of small-for-gestational-age neonates using non-invasive, low-cost, and readily available hematological markers.Int. J. Gynaecol. Obstet. 2020; 150: 340-345Crossref PubMed Scopus (0) Google Scholar]. Additionally, proteinuria and bacteriuria, determined through urinalysis and urine culture, respectively, are important predictors of life-threatening complications like preeclampsia [44.Tanacan A. et al.The importance of proteinuria in preeclampsia and its predictive role in maternal and neonatal outcomes.Hypertens. Pregnancy. 2019; 38: 111-118Crossref PubMed Scopus (3) Google Scholar,45.Kaduma J. et al.Urinary tract infections and preeclampsia among pregnant women attending two hospitals in Mwanza City, Tanzania: a 1:2 matched case-control study.Biomed. Res. Int. 2019; 20193937812Crossref PubMed Scopus (1) Google Scholar]. Advances in artificial intelligence (AI) have recently enabled clinicians to use some of these traditional, low-cost tests in more sophisticated predictive and diagnostic models than previously possible [46.Burton R.J. et al.Using artificial intelligence to reduce diagnostic workload without compromising detection of urinary tract infections.BMC Med. Inform. Decis. Mak. 2019; 19: 171Crossref PubMed Scopus (17) Google Scholar]. Recent results have revealed that psychological, nutritional, and other lifestyle-related factors can affect the course of a pregnancy. Stress – broadly defined using clinical diagnoses of depression and anxiety and physiological measurements such as elevated blood pressure – can affect fetal development with consequences that extend into adulthood [47.Coussons-Read M.E. Effects of prenatal stress on pregnancy and human development: mechanisms and pathways.Obstet. Med. 2013; 6: 52-57Crossref PubMed Google Scholar,48.Weber K.A. et al.Periconceptional stressors and social support and risk for adverse birth outcomes.BMC Pregnancy Childbirth. 2020; 20: 487Crossref PubMed Scopus (1) Google Scholar]. Increased levels of stress have been associated with preeclampsia and PTB through poorly understood biological mechanisms [49.Lilliecreutz C. et al.Effect of maternal stress during pregnancy on the risk for preterm birth.BMC Pregnancy Childbirth. 2016; 16: 5Crossref PubMed Scopus (14) Google Scholar, 50.Rejnö G. et al.Maternal anxiety, depression and asthma and adverse pregnancy outcomes – a population based study.Sci. Rep. 2019; 913101Crossref PubMed Scopus (5) Google Scholar, 51.Walsh K. et al.Maternal prenatal stress phenotypes associate with fetal neurodevelopment and birth outcomes.Proc. Natl. Acad. Sci. U. S. A. 2019; 116: 23996-24005Crossref PubMed Scopus (31) Google Scholar]. The influence of maternal nutrition on fetal development is better characterized [52.Wu G. et al.Biological mechanisms for nutritional regulation of maternal health and fetal development.Paediatr. Perinat. Epidemiol. 2012; 26: 4-26Crossref PubMed Scopus (96) Google Scholar]. Both malnutrition and obesity affect the adapted maternal metabolism in pregnancy and correlate with infertility, PTB, and FGR [53.AlSaif S. et al.A short review of adipokines, smooth muscle and uterine contractility.Life Sci. 2015; 125: 2-8Crossref PubMed Scopus (9) Google Scholar,54.Connor K.L. et al.Maternal metabolic, immune, and microbial systems in late pregnancy vary with malnutrition in mice.Biol. Reprod. 2018; 98: 579-592PubMed Google Scholar]. Similarly, micronutrient deficiencies, particularly vitamin D, are epidemiologically linked to preeclampsia, GDM, and developmental fetal defects, demonstrating the importance of maternal nutrition during gestation [55.Carmichael S.L. et al.Women's periconceptional diet and risk of biliary atresia in offspring.Birth Defects Res. 2018; 110: 994-1000Crossref PubMed Scopus (0) Google Scholar, 56.Weber K.A. et al.Nutrient intake in women before conception and risks of anophthalmia and microphthalmia in their offspring.Birth Defects Res. 2018; 110: 863-870Crossref PubMed Scopus (5) Google Scholar, 57.Lu M. et al.Association between vitamin D status and the risk of gestational diabetes mellitus: a meta-analysis.Arch. Gynecol. Obstet. 2016; 293: 959-966Crossref PubMed Scopus (66) Google Scholar, 58.Murthi P. et al.Role of the placental vitamin D receptor in modulating feto-placental growth in fetal growth restriction and preeclampsia-affected pregnancies.Front. Physiol. 2016; 7: 43Crossref PubMed Google Scholar]. Racial and ethnic background, socioeconomic status, and living environment act via complex mechanisms that make measurements of their direct effects on maternal and fetal health difficult [59.Workalemahu T. et al.Genetic and environmental influences on fetal growth vary during sensitive periods in pregnancy.Sci. Rep. 2018; 8: 7274Crossref PubMed Scopus (5) Google Scholar]. For example, a recent study found two distinct pathways by which stress can lead to complications during pregnancy demarcated by racial and ethnic lines, such that Hispanic and African-American women progress towards the same adverse outcomes fueled by similar stressors, but in biologically different ways [60.Mesner O. et al.Using graph learning to understand adverse pregnancy outcomes and stress pathways.PLoS One. 2019; 14e0223319Crossref PubMed Scopus (1) Google Scholar]. Lower socioeconomic status and levels of education decrease access to prenatal care (even when costs are not prohibitive) to the detriment of the mother and her offspring [61.Kim M.K. et al.Socioeconomic status can affect pregnancy outcomes and complications, even with a universal healthcare system.Int. J. Equity Health. 2018; 17: 2Crossref PubMed Scopus (28) Google Scholar,62.Fasina F. et al.Impact of mothers' socio-demographic factors and antenatal clinic attendance on neonatal mortality in Nigeria.Cogent Soc. Sci. 2020; 61747328Crossref Scopus (1) Google Scholar]. Exposures to air pollution, heavy metals, and toxic compounds also often distribute across socioeconomic and geographic lines and have been associated with increased risks of developmental defects and other adverse outcomes [63.Berger K. et al.Prenatal phthalate, paraben, and phenol exposure and childhood allergic and respiratory outcomes: evaluating exposure to chemical mixtures.Sci. Total Environ. 2020; 725138418Crossref PubMed Scopus (5) Google Scholar, 64.Zheng T. et al.Effects of environmental exposures on fetal and childhood growth trajectories.Ann. Glob. Health. 2016; 82: 41-99Crossref PubMed Google Scholar, 65.Patel J. et al.Maternal occupational exposure to polycyclic aromatic hydrocarbons and the risk of isolated congenital heart defects among offspring.Environ. Res. 2020; 186109550Crossref PubMed Scopus (1) Google Scholar, 66.Choi G. et al.Maternal exposure to outdoor air pollution and congenital limb deficiencies in the National Birth Defects Prevention Study.Environ. Res. 2019; 179108716Crossref PubMed Scopus (3) Google Scholar, 67.Stingone J.A. et al.Maternal exposure to nitrogen dioxide, intake of methyl nutrients, and congenital heart defects in offspring.Am. J. Epidemiol. 2017; 186: 719-729Crossref PubMed Scopus (16) Google Scholar]. Machine learning based on clinical and social determinants to model adverse pregnancy outcomes yields promising results that – dependent on the data used – can enhance our ability to predict adverse outcomes and enable deeper insights into the underlying processes of pregnancy. However, they have not yet been shown to