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Epigenetic Responses to Nonchemical Stressors: Potential Molecular Links to Perinatal Health Outcomes

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Abstract

Purpose of Review

We summarize the recent literature investigating exposure to four nonchemical stressors (financial stress, racism, psychosocial stress, and trauma) and DNA methylation, miRNA expression, and mRNA expression. We also highlight the relationships between these epigenetic changes and six critical perinatal outcomes (preterm birth, low birth weight, preeclampsia, gestational diabetes, childhood allergic disease, and childhood neurocognition).

Recent Findings

Multiple studies have found financial stress, psychosocial stress, and trauma to be associated with DNA methylation and/or miRNA and mRNA expression. Fewer studies have investigated the effects of racism. The majority of studies assessed epigenetic or genomic changes in maternal blood, cord blood, or placenta. Several studies included multi-OMIC assessments in which DNA methylation and/or miRNA expression were associated with gene expression. There is strong evidence for the role of epigenetics in driving the health outcomes considered. A total of 22 biomarkers, including numerous HPA axis genes, were identified to be epigenetically altered by both stressors and outcomes. Epigenetic changes related to inflammation, the immune and endocrine systems, and cell growth and survival were highlighted across numerous studies.

Summary

Maternal exposure to nonchemical stressors is associated with epigenetic and/or genomic changes in a tissue-specific manner among inflammatory, immune, endocrine, and cell growth-related pathways, which may act as mediating pathways to perinatal health outcomes. Future research can test the mediating role of the specific biomarkers identified as linked with both stressors and outcomes. Understanding underlying epigenetic mechanisms altered by nonchemical stressors can provide a better understanding of how chemical and nonchemical exposures interact.

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Data Availability

The supplemental files containing detailed information about each included study are available online. The original search data are available upon request.

References

Papers of particular interest, published recently, have been highlighted as: • Of importance

  1. Rider CV, Dourson ML, Hertzberg RC, Mumtaz MM, Price PS, Simmons JE. Incorporating nonchemical stressors into cumulative risk assessments. Toxicol Sci. 2012;127(1):10–7.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  2. Gee GC, Payne-Sturges DC. Environmental health disparities: A framework integrating psychosocial and environmental concepts. Environ Health Perspect. 2004;112(17):1645–53.

    Article  PubMed  PubMed Central  Google Scholar 

  3. Morello-Frosch R, Shenassa ED. The environmental “riskscape” and social inequality: Implications for explaining maternal and child health disparities. Environ Health Perspect. 2006;114(8):1150–3.

    Article  PubMed  PubMed Central  Google Scholar 

  4. Wright RJ. Moving towards making social toxins mainstream in children’s environmental health. Curr Opin Pediatr. 2009;21(2):222–9.

    Article  PubMed  PubMed Central  Google Scholar 

  5. Zhang P, Carlsten C, Chaleckis R, Hanhineva K, Huang M, Isobe T, et al. Defining the scope of exposome studies and research needs from a multidisciplinary perspective. Environ Sci Technol Lett. 2021;8(10):839–52.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Blumenshine P, Egerter S, Barclay CJ, Cubbin C, Braveman PA. Socioeconomic disparities in adverse birth outcomes: A systematic review. Am J Prev Med. 2010;39(3):263–72.

    Article  PubMed  Google Scholar 

  7. Jardine J, Walker K, Gurol-Urganci I, Webster K, Muller P, Hawdon J, et al. Adverse pregnancy outcomes attributable to socioeconomic and ethnic inequalities in England: A national cohort study. Lancet. 2021;398(10314):1905–12.

    Article  PubMed  Google Scholar 

  8. Shah PS, Shah J. Knowledge Synthesis Group on Determinants of Preterm LBWB Maternal exposure to domestic violence and pregnancy and birth outcomes: A systematic review and meta-analyses. J Womens Health (Larchmt). 2010;19(11):2017–31.

    Article  PubMed  Google Scholar 

  9. Suglia SF, Sapra KJ, Koenen KC. Violence and cardiovascular health: A systematic review. Am J Prev Med. 2015;48(2):205–12.

    Article  PubMed  PubMed Central  Google Scholar 

  10. Paradies Y. A systematic review of empirical research on self-reported racism and health. Int J Epidemiol. 2006;35(4):888–901.

    Article  PubMed  Google Scholar 

  11. Pachter LM, Coll CG. Racism and child health: A review of the literature and future directions. J Dev Behav Pediatr. 2009;30(3):255–63.

    Article  PubMed  PubMed Central  Google Scholar 

  12. Bailey ZD, Krieger N, Agenor M, Graves J, Linos N, Bassett MT. Structural racism and health inequities in the USA: Evidence and interventions. Lancet. 2017;389(10077):1453–63.

    Article  PubMed  Google Scholar 

  13. Ashrap P, Aker A, Watkins DJ, Mukherjee B, Rosario-Pabon Z, Velez-Vega CM, et al. Psychosocial status modifies the effect of maternal blood metal and metalloid concentrations on birth outcomes. Environ Int. 2021;149:106418.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Tamayo YOM, Tellez-Rojo MM, Trejo-Valdivia B, Schnaas L, Osorio-Valencia E, Coull B, et al. Maternal stress modifies the effect of exposure to lead during pregnancy and 24-month old children’s neurodevelopment. Environ Int. 2017;98:191–7.

    Article  Google Scholar 

  15. Maranville AR, Ting T-F, Zhang Y. An environmental justice analysis: Superfund sites and surrounding communities in Illinois. Environmental Justice. 2009;2(2):49–58

  16. EPA. Population surrounding 1,857 Superfund Remedial Sites 2020 [Available from: https://www.epa.gov/sites/default/files/2015-09/documents/webpopulationrsuperfundsites9.28.15.pdf.

  17. Trottier BA, Niehoff NM, Keil AP, Jones RR, Levine KE, MacNell NS, et al. Residential proximity to metal-containing superfund sites and their potential as a source of disparities in metal exposure among U.S. women. Environ Health Perspect. 2023;131(3):37701.

    Article  CAS  PubMed  Google Scholar 

  18. Mijal RS, Holzman CB. Blood cadmium levels in women of childbearing age vary by race/ethnicity. Environ Res. 2010;110(5):505–12.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Geron M, Cowell W, Amarasiriwardena C, Andra SS, Carroll K, Kloog I, et al. Racial/ethnic and neighborhood disparities in metals exposure during pregnancy in the Northeastern United States. Sci Total Environ. 2022;820:153249.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Schaaf JM, Liem SM, Mol BW, Abu-Hanna A, Ravelli AC. Ethnic and racial disparities in the risk of preterm birth: A systematic review and meta-analysis. Am J Perinatol. 2013;30(6):433–50.

    PubMed  Google Scholar 

  21. Suresh S, Amegashie C, Patel E, Nieman KM, Rana S. Racial disparities in diagnosis, management, and outcomes in preeclampsia. Curr Hypertens Rep. 2022;24(4):87–93.

    Article  PubMed  Google Scholar 

  22. Getahun D, Nath C, Ananth CV, Chavez MR, Smulian JC. Gestational diabetes in the United States: Temporal trends 1989 through 2004. Am J Obstet Gynecol. 2008;198(5):525e1-5.

    Article  Google Scholar 

  23. Joseph RM, O’Shea TM, Allred EN, Heeren T, Kuban KK. Maternal educational status at birth, maternal educational advancement, and neurocognitive outcomes at age 10 years among children born extremely preterm. Pediatr Res. 2018;83(4):767–77.

    Article  PubMed  Google Scholar 

  24. Hill TD, Graham LM, Divgi V. Racial disparities in pediatric asthma: A review of the literature. Curr Allergy Asthma Rep. 2011;11(1):85–90.

    Article  PubMed  Google Scholar 

  25. Bollati V, Baccarelli A. Environmental epigenetics. Heredity (Edinb). 2010;105(1):105–12.

    Article  CAS  PubMed  Google Scholar 

  26. DeLano K, Folger AT, Ding L, Ji H, Yolton K, Ammerman RT, et al. Associations between maternal community deprivation and infant DNA methylation of the SLC6A4 gene. Front Public Health. 2020;8:557195.

    Article  PubMed  PubMed Central  Google Scholar 

  27. Coker ES, Gunier R, Huen K, Holland N, Eskenazi B. DNA methylation and socioeconomic status in a Mexican-American birth cohort. Clin Epigenetics. 2018;10:61.

    Article  PubMed  PubMed Central  Google Scholar 

  28. Santos HP Jr, Bhattacharya A, Martin EM, Addo K, Psioda M, Smeester L, et al. Epigenome-wide DNA methylation in placentas from preterm infants: Association with maternal socioeconomic status. Epigenetics. 2019;14(8):751–65. An EWAS study that found placental CpG methylation alterations in relation to maternal various measures of socioeconomic status.

    Article  PubMed  PubMed Central  Google Scholar 

  29. Appleton AA, Lin B, Kennedy EM, Holdsworth EA. Maternal depression and adverse neighbourhood conditions during pregnancy are associated with gestational epigenetic age deceleration. Epigenetics. 2022;17(13):1905–19.

    Article  PubMed  PubMed Central  Google Scholar 

  30. Clark J, Bulka CM, Martin CL, Roell K, Santos HP, O’Shea TM, et al. Placental epigenetic gestational aging in relation to maternal sociodemographic factors and smoking among infants born extremely preterm: A descriptive study. Epigenetics. 2022;17(13):2389–403.

    Article  PubMed  PubMed Central  Google Scholar 

  31. Santos HP Jr, Nephew BC, Bhattacharya A, Tan X, Smith L, Alyamani RAS, et al. Discrimination exposure and DNA methylation of stress-related genes in Latina mothers. Psychoneuroendocrinology. 2018;98:131–8. One of the only studies we could locate that examines discrimination, rather than self-reported race, and DNA methylation.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Palma-Gudiel H, Cordova-Palomera A, Eixarch E, Deuschle M, Fananas L. Maternal psychosocial stress during pregnancy alters the epigenetic signature of the glucocorticoid receptor gene promoter in their offspring: A meta-analysis. Epigenetics. 2015;10(10):893–902.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Nazzari S, Grumi S, Mambretti F, Villa M, Giorda R, Provenzi L, et al. Maternal and infant NR3C1 and SLC6A4 epigenetic signatures of the COVID-19 pandemic lockdown: When timing matters. Transl Psychiatry. 2022;12(1):386.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Wiley KS, Camilo C, Gouveia G, Euclydes V, Panter-Brick C, Matijasevich A, et al. Maternal distress, DNA methylation, and fetal programing of stress physiology in Brazilian mother-infant pairs. Dev Psychobiol. 2023;65(1):e22352.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Provenzi L, Mambretti F, Villa M, Grumi S, Citterio A, Bertazzoli E, et al. Hidden pandemic: COVID-19-related stress, SLC6A4 methylation, and infants’ temperament at 3 months. Sci Rep. 2021;11(1):15658.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  36. Zhang W, Li Q, Deyssenroth M, Lambertini L, Finik J, Ham J, et al. Timing of prenatal exposure to trauma and altered placental expressions of hypothalamic-pituitary-adrenal axis genes and genes driving neurodevelopment. J Neuroendocrinol. 2018;30(4):e12581.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. Hjort L, Rushiti F, Wang SJ, Fransquet P, P Krasniqi S, I Çarkaxhiu S, et al. Intergenerational effects of maternal post-traumatic stress disorder on offspring epigenetic patterns and cortisol levels. Epigenomics. 2021;13(12):967–80.

    Article  CAS  PubMed  Google Scholar 

  38. Grasso DJ, Drury S, Briggs-Gowan M, Johnson A, Ford J, Lapidus G, et al. Adverse childhood experiences, posttraumatic stress, and FKBP5 methylation patterns in postpartum women and their newborn infants. Psychoneuroendocrinology. 2020;114:104604.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. Euclydes V, Gomes C, Gouveia G, Gastaldi VD, Feltrin AS, Camilo C, et al. Gestational age acceleration is associated with epigenetic biomarkers of prenatal physiologic stress exposure. Clin Epigenetics. 2022;14(1):152.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  40. Clukay CJ, Hughes DA, Rodney NC, Kertes DA, Mulligan CJ. DNA methylation of methylation complex genes in relation to stress and genome-wide methylation in mother-newborn dyads. Am J Phys Anthropol. 2018;165(1):173–82.

    Article  PubMed  Google Scholar 

  41. Clukay CJ, Hughes DA, Kertes DA, Mulligan CJ. Associations between maternal psychosocial stress, DNA methylation, and newborn birth weight identified by investigating methylation at individual, regional, and genome levels. Hum Biol. 2019;91(2):117–31. A thorough study that provides evidence for epigenetic mediation of the effect of multiple kinds of stress, chronic and acute (war trauma, sexual violence), on newborn birthweight.

    Article  PubMed  Google Scholar 

  42. Nowak AL, Anderson CM, Mackos AR, Neiman E, Gillespie SL. Stress during pregnancy and epigenetic modifications to offspring DNA: A systematic review of associations and implications for preterm birth. J Perinat Neonatal Nurs. 2020;34(2):134–45.

    Article  PubMed  PubMed Central  Google Scholar 

  43. Palma-Gudiel H, Fananas L, Horvath S, Zannas AS. Psychosocial stress and epigenetic aging. Int Rev Neurobiol. 2020;150:107–28.

    Article  CAS  PubMed  Google Scholar 

  44. Jahnke JR, Teran E, Murgueitio F, Cabrera H, Thompson AL. Maternal stress, placental 11beta-hydroxysteroid dehydrogenase type 2, and infant HPA axis development in humans: Psychosocial and physiological pathways. Placenta. 2021;104:179–87.

    Article  CAS  PubMed  Google Scholar 

  45. Bierer LM, Bader HN, Daskalakis NP, Lehrner A, Provencal N, Wiechmann T, et al. Intergenerational effects of maternal holocaust exposure on FKBP5 methylation. Am J Psychiatry. 2020;177(8):744–53.

    Article  PubMed  Google Scholar 

  46. Folger AT, Nidey N, Ding L, Ji H, Yolton K, Ammerman RT, et al. Association between maternal adverse childhood experiences and neonatal SCG5 DNA methylation-effect modification by prenatal home visiting. Am J Epidemiol. 2022;191(4):636–45.

    Article  PubMed  Google Scholar 

  47. Mavioglu RN, Ramo-Fernandez L, Gumpp AM, Kolassa IT, Karabatsiakis A. A history of childhood maltreatment is associated with altered DNA methylation levels of DNA methyltransferase 1 in maternal but not neonatal mononuclear immune cells. Front Psychiatry. 2022;13:945343.

    Article  PubMed  PubMed Central  Google Scholar 

  48. Montoya-Williams D, Quinlan J, Clukay C, Rodney NC, Kertes DA, Mulligan CJ. Associations between maternal prenatal stress, methylation changes in IGF1 and IGF2, and birth weight. J Dev Orig Health Dis. 2018;9(2):215–22.

    Article  CAS  PubMed  Google Scholar 

  49. Manuck TA, Eaves LA, Rager JE, Sheffield-Abdullah K, Fry RC. Nitric oxide-related gene and microRNA expression in peripheral blood in pregnancy vary by self-reported race. Epigenetics. 2022;17(7):731–45.

    Article  PubMed  Google Scholar 

  50. Alvarado-Flores F, Savelyeva A, Chu T, Sadovsky Y, Amutah-Onukagha N, O’Tierney-Ginn P. Placental miRNAs targeting cellular stress response pathways are highly expressed in non-Hispanic Black people. Reprod Sci. 2022;29(7):2043–50.

    Article  CAS  PubMed  Google Scholar 

  51. Foley HB, Howe CG, Eckel SP, Chavez T, Gevorkian L, Reyes EG, et al. Depression, perceived stress, and distress during pregnancy and EV-associated miRNA profiles in MADRES. J Affect Disord. 2023;323:799–808.

    Article  CAS  PubMed  Google Scholar 

  52. Aushev VN, Li Q, Deyssenroth M, Zhang W, Finik J, Hurd YL, et al. Placental gene network modules are associated with maternal stress during pregnancy and infant temperament. FASEB J. 2021;35(10):e21922.

    Article  CAS  PubMed  Google Scholar 

  53. Breen MS, Wingo AP, Koen N, Donald KA, Nicol M, Zar HJ, et al. Gene expression in cord blood links genetic risk for neurodevelopmental disorders with maternal psychological distress and adverse childhood outcomes. Brain Behav Immun. 2018;73:320–30.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  54. Xu W, Ru P, Gu Z, Zhang R, Pang X, Huang Y, et al. Comprehensive analysis of differently expressed and methylated genes in preeclampsia. Comput Math Methods Med. 2020;2020:2139270.

    Article  PubMed  PubMed Central  Google Scholar 

  55. Cirkovic A, Garovic V, Milin Lazovic J, Milicevic O, Savic M, Rajovic N, et al. Systematic review supports the role of DNA methylation in the pathophysiology of preeclampsia: A call for analytical and methodological standardization. Biol Sex Differ. 2020;11(1):36.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  56. Lim JH, Kang YJ, Bak HJ, Kim MS, Lee HJ, Kwak DW, et al. Epigenome-wide DNA methylation profiling of preeclamptic placenta according to severe features. Clin Epigenetics. 2020;12(1):128.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  57. Cruz JO, Conceicao I, Tosatti JAG, Gomes KB, Luizon MR. Global DNA methylation in placental tissues from pregnant with preeclampsia: A systematic review and pathway analysis. Placenta. 2020;101:97–107.

    Article  CAS  PubMed  Google Scholar 

  58. Li Y, Cui S, Shi W, Yang B, Yuan Y, Yan S, et al. Differential placental methylation in preeclampsia, preterm and term pregnancies. Placenta. 2020;93:56–63.

    Article  CAS  PubMed  Google Scholar 

  59. Wang J, Song H, Zhang Y. Comprehensive analysis of gene expression and DNA methylation for preeclampsia progression. J Chin Med Assoc. 2021;84(4):410–7.

    Article  CAS  PubMed  Google Scholar 

  60. Jiang L, Chang R, Liu J, Xin H. Methylation-based epigenetic studies and gene integration analysis of preeclampsia. Ann Transl Med. 2022;10(24):1342.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  61. He J, Liu K, Hou X, Lu J. Comprehensive analysis of DNA methylation and gene expression profiles in gestational diabetes mellitus. Medicine (Baltimore). 2021;100(26):e26497.

    Article  CAS  PubMed  Google Scholar 

  62. Wang WJ, Huang R, Zheng T, Du Q, Yang MN, Xu YJ, et al. Genome-wide placental gene methylations in gestational diabetes mellitus, fetal growth and metabolic health biomarkers in cord blood. Front Endocrinol (Lausanne). 2022;13:875180.

    Article  PubMed  Google Scholar 

  63. Lu S, Wang J, Kakongoma N, Hua W, Xu J, Wang Y, et al. DNA methylation and expression profiles of placenta and umbilical cord blood reveal the characteristics of gestational diabetes mellitus patients and offspring. Clin Epigenetics. 2022;14(1):69.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  64. Zhang Y, Zhang T, Chen Y. Comprehensive analysis of gene expression profiles and DNA methylome reveals Oas1, Ppie, Polr2g as pathogenic target genes of gestational diabetes mellitus. Sci Rep. 2018;8(1):16244.

    Article  PubMed  PubMed Central  Google Scholar 

  65. Chen M, Yan J, Han Q, Luo J, Zhang Q. Identification of hub-methylated differentially expressed genes in patients with gestational diabetes mellitus by multi-omic WGCNA basing epigenome-wide and transcriptome-wide profiling. J Cell Biochem. 2020;121(5–6):3173–84.

    Article  CAS  PubMed  Google Scholar 

  66. Zhu W, Shen Y, Liu J, Fei X, Zhang Z, Li M, et al. Epigenetic alternations of microRNAs and DNA methylation contribute to gestational diabetes mellitus. J Cell Mol Med. 2020;24(23):13899–912.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  67. Knijnenburg TA, Vockley JG, Chambwe N, Gibbs DL, Humphries C, Huddleston KC, et al. Genomic and molecular characterization of preterm birth. Proc Natl Acad Sci U S A. 2019;116(12):5819–27. A multi-OMIC study of preterm birth, helpful as an example of integration of genomic data with transcriptomic and epigenomic markers in relation to a health outcome.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  68. Hong X, Sherwood B, Ladd-Acosta C, Peng S, Ji H, Hao K, et al. Genome-wide DNA methylation associations with spontaneous preterm birth in US blacks: Findings in maternal and cord blood samples. Epigenetics. 2018;13(2):163–72.

    Article  PubMed  PubMed Central  Google Scholar 

  69. Kheirkhah Rahimabad P, Arshad SH, Holloway JW, Mukherjee N, Hedman A, Gruzieva O, et al. Association of maternal DNA methylation and offspring birthweight. Reprod Sci. 2021;28(1):218–27.

    Article  CAS  PubMed  Google Scholar 

  70. Kupers LK, Monnereau C, Sharp GC, Yousefi P, Salas LA, Ghantous A, et al. Meta-analysis of epigenome-wide association studies in neonates reveals widespread differential DNA methylation associated with birthweight. Nat Commun. 2019;10(1):1893.

    Article  PubMed  PubMed Central  Google Scholar 

  71. Nowak AL, Anderson CM, Ford JL, Mackos A, Ohm J, Saadat N, et al. DNA methylation patterns of glucocorticoid pathway genes in preterm birth among Black women. Biol Res Nurs. 2022;24(4):493–502.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  72. Clark J, Avula V, Ring C, Eaves LA, Howard T, Santos HP, et al. Comparing the predictivity of human placental gene, microRNA, and CpG methylation signatures in relation to perinatal outcomes. Toxicol Sci. 2021;183(2):269–84. This study examines multiple molecular biomarkers (mRNA, miRNA, CpG methylation) and multiple critical outcomes (birth weight, placenta weight, placental damage and placental inflammation; thus, it provides an example of how to integrate across multi-OMICs.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  73. Wang XM, Tian FY, Fan LJ, Xie CB, Niu ZZ, Chen WQ. Comparison of DNA methylation profiles associated with spontaneous preterm birth in placenta and cord blood. BMC Med Genomics. 2019;12(1):1.

    Article  PubMed  PubMed Central  Google Scholar 

  74. Merid SK, Novoloaca A, Sharp GC, Kupers LK, Kho AT, Roy R, et al. Epigenome-wide meta-analysis of blood DNA methylation in newborns and children identifies numerous loci related to gestational age. Genome Med. 2020;12(1):25.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  75. Lancaster EE, Lapato DM, Jackson-Cook C, Strauss JF 3rd, Roberson-Nay R, York TP. Maternal biological age assessed in early pregnancy is associated with gestational age at birth. Sci Rep. 2021;11(1):15440.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  76. Alfano R, Chadeau-Hyam M, Ghantous A, Keski-Rahkonen P, Chatzi L, Perez AE, et al. A multi-omic analysis of birthweight in newborn cord blood reveals new underlying mechanisms related to cholesterol metabolism. Metabolism. 2020;110:154292.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  77. Caramaschi D, Neumann A, Cardenas A, Tindula G, Alemany S, Zillich L, et al. Meta-analysis of epigenome-wide associations between DNA methylation at birth and childhood cognitive skills. Mol Psychiatry. 2022;27(4):2126–35.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  78. Han L, Kaushal A, Zhang H, Kadalayil L, Duan J, Holloway JW, et al. DNA methylation at birth is associated with childhood serum immunoglobulin E levels. Epigenet Insights. 2021;14:25168657211008108.

    Article  PubMed  PubMed Central  Google Scholar 

  79. Bahado-Singh RO, Vishweswaraiah S, Aydas B, Radhakrishna U. Placental DNA methylation changes and the early prediction of autism in full-term newborns. PLoS ONE. 2021;16(7):e0253340.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  80. Tilley SK, Martin EM, Smeester L, Joseph RM, Kuban KCK, Heeren TC, et al. Placental CpG methylation of infants born extremely preterm predicts cognitive impairment later in life. PLoS ONE. 2018;13(3):e0193271.

    Article  PubMed  PubMed Central  Google Scholar 

  81. Meakin CJ, Martin EM, Santos HP Jr, Mokrova I, Kuban K, O’Shea TM, et al. Placental CpG methylation of HPA-axis genes is associated with cognitive impairment at age 10 among children born extremely preterm. Horm Behav. 2018;101:29–35.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  82. Freedman AN, Clark J, Eaves LA, Roell K, Oran A, Koval L, et al. A multi-omic approach identifies an autism spectrum disorder (ASD) regulatory complex of functional epimutations in placentas from children born preterm. Autism Res. 2023;16(5):918–934. 

  83. Santos HP Jr, Bhattacharya A, Joseph RM, Smeester L, Kuban KCK, Marsit CJ, et al. Evidence for the placenta-brain axis: Multi-omic kernel aggregation predicts intellectual and social impairment in children born extremely preterm. Mol Autism. 2020;11(1):97.

    Article  PubMed  Google Scholar 

  84. Subramanian A, Weiss D, Nyhan K, Dewan A, Jukic AMZ. Circulating miRNAs in the first trimester and pregnancy complications: A systematic review. Epigenetics. 2023;18(1):2152615.

    Article  PubMed  Google Scholar 

  85. Jin M, Xu Q, Li J, Xu S, Tang C. Micro-RNAs in human pla- 1008 centa: Tiny molecules, immense power. Molecules. 2022;27(18):5943. 

  86. Lewis KA, Chang L, Cheung J, Aouizerat BE, Jelliffe-Pawlowski LL, McLemore MR, et al. Systematic review of transcriptome and microRNAome associations with gestational diabetes mellitus. Front Endocrinol (Lausanne). 2022;13:971354.

    Article  PubMed  Google Scholar 

  87. Li J, Gan B, Lu L, Chen L, Yan J. Expression of microRNAs in patients with gestational diabetes mellitus: A systematic review and meta-analysis. Acta Diabetol. 2023;60(4):461–9.

    Article  CAS  PubMed  Google Scholar 

  88. Masete M, Dias S, Malaza N, Adam S, Pheiffer C. A big role for microRNAs in gestational diabetes mellitus. Front Endocrinol (Lausanne). 2022;13:892587.

    Article  PubMed  Google Scholar 

  89. Mavreli D, Theodora M, Avgeris M, Papantoniou N, Antsaklis P, Daskalakis G, et al. First trimester maternal plasma aberrant miRNA expression associated with spontaneous preterm birth. Int J Mol Sci. 2022;23(23):14972.

  90. Wommack JC, Trzeciakowski JP, Miranda RC, Stowe RP, Ruiz RJ. Micro RNA clusters in maternal plasma are associated with preterm birth and infant outcomes. PLoS ONE. 2018;13(6):e0199029.

    Article  PubMed  PubMed Central  Google Scholar 

  91. Illarionov RA, Pachuliia OV, Vashukova ES, Tkachenko AA, Maltseva AR, Postnikova TB, et al. Plasma miRNA profile in high risk of preterm birth during early and mid-pregnancy. Genes (Basel). 2022;13(11).

  92. Paquette AG, Shynlova O, Wu X, Kibschull M, Wang K, Price ND, et al. MicroRNA-transcriptome networks in whole blood and monocytes of women undergoing preterm labour. J Cell Mol Med. 2019;23(10):6835–45.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  93. Cook J, Bennett PR, Kim SH, Teoh TG, Sykes L, Kindinger LM, et al. First trimester circulating microRNA biomarkers predictive of subsequent preterm delivery and cervical shortening. Sci Rep. 2019;9(1):5861.

    Article  PubMed  PubMed Central  Google Scholar 

  94. Manuck TA, Eaves LA, Rager JE, Fry RC. Mid-pregnancy maternal blood nitric oxide-related gene and miRNA expression are associated with preterm birth. Epigenomics. 2021;13(9):667–82.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  95. Garcia-Beltran C, Carreras-Badosa G, Bassols J, Malpique R, Plou C, de Zegher F, et al. microRNAs in newborns with low birth weight: Relation to birth size and body composition. Pediatr Res. 2022;92(3):829–37.

    Article  CAS  PubMed  Google Scholar 

  96. Payton A, Clark J, Eaves L, Santos HP Jr, Smeester L, Bangma JT, et al. Placental genomic and epigenomic signatures associated with infant birth weight highlight mechanisms involved in collagen and growth factor signaling. Reprod Toxicol. 2020;96:221–30.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  97. Kennedy EM, Hermetz K, Burt A, Everson TM, Deyssenroth M, Hao K, et al. Placental microRNA expression associates with birthweight through control of adipokines: Results from two independent cohorts. Epigenetics. 2021;16(7):770–82.

    Article  PubMed  Google Scholar 

  98. Burris HH, Gerson KD, Woodward A, Redhunt AM, Ledyard R, Brennan K, et al. Cervical microRNA expression and spontaneous preterm birth. Am J Obstet Gynecol MFM. 2023;5(1):100783.

    Article  CAS  PubMed  Google Scholar 

  99. Howe CG, Foley HB, Kennedy EM, Eckel SP, Chavez TA, Faham D, et al. Extracellular vesicle microRNA in early versus late pregnancy with birth outcomes in the MADRES study. Epigenetics. 2022;17(3):269–85.

    Article  PubMed  Google Scholar 

  100. Fudono A, Imai C, Takimoto H, Tarui I, Aoyama T, Yago S, et al. Trimester-specific associations between extracellular vesicle microRNAs and fetal growth. J Matern Fetal Neonatal Med. 2022;35(25):8728–34.

    Article  CAS  PubMed  Google Scholar 

  101. Beheshti R, Stone S, Chandran D, Hicks SD. Multi-omic profiles in infants at risk for food reactions. Genes (Basel). 2022;13(11).

  102. Beheshti R, Halstead ES, McKeone D, Hicks SD. Multi-omic factors associated with future wheezing in infants. Pediatr Res. 2023;93(3):579–85.

    Article  PubMed  Google Scholar 

  103. Freedman AN, Eaves LA, Rager JE, Gavino-Lopez N, Smeester L, Bangma J, et al. The placenta epigenome-brain axis: Placental epigenomic and transcriptomic responses that preprogram cognitive impairment. Epigenomics. 2022;14(15):897–911.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  104. Chen M, Yan J. A preliminary integrated analysis of miRNA-mRNA expression profiles reveals a role of miR-146a-3p/TRAF6 in plasma from gestational diabetes mellitus patients. Ginekol Pol. 2023.

  105. Wang L, Zhang L, Fan Y, Peng Y, Song D, Fu J, et al. Human placenta-based genome-wide mRNA sequencing to identify TEK/IGF1/CSF1/ANGPT2 as crucial segments in the pathogenesis of pre-eclampsia. Front Genet. 2022;13:944932.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  106. Guo R, Teng Z, Wang Y, Zhou X, Xu H, Liu D. Integrated learning: Screening optimal biomarkers for identifying preeclampsia in placental mRNA samples. Comput Math Methods Med. 2021;2021:6691096.

    Article  PubMed  PubMed Central  Google Scholar 

  107. Cao M, Zhang L, Lin Y, Li Z, Xu J, Shi Z, et al. Differential mRNA and long noncoding RNA expression profiles in umbilical cord blood exosomes from gestational diabetes mellitus patients. DNA Cell Biol. 2020;39(11):2005–16.

    Article  CAS  PubMed  Google Scholar 

  108. Paquette AG, Shynlova O, Kibschull M, Price ND, Lye SJ, Global Alliance to Prevent Prematurity and Stillbirth Systems Biology of Preterm Birth Team, Comparative analysis of gene expression in maternal peripheral blood and monocytes during spontaneous preterm labor. Am J Obstet Gynecol. 2018;218(3):345 e1-e30.

  109. Yoo JY, Hyeon DY, Shin Y, Kim SM, You YA, Kim D, et al. Integrative analysis of transcriptomic data for identification of T-cell activation-related mRNA signatures indicative of preterm birth. Sci Rep. 2021;11(1):2392.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  110. Deyssenroth MA, Peng S, Hao K, Lambertini L, Marsit CJ, Chen J. Whole-transcriptome analysis delineates the human placenta gene network and its associations with fetal growth. BMC Genomics. 2017;18(1):520.

    Article  PubMed  PubMed Central  Google Scholar 

  111. Chatterjee S, Zeng X, Ouidir M, Tesfaye M, Zhang C, Tekola-Ayele F. Sex-specific placental gene expression signatures of small for gestational age at birth. Placenta. 2022;121:82–90.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  112. Lambertini L, Li Q, Ma Y, Zhang W, Hao K, Marsit C, et al. Placental imprinted gene expression mediates the effects of maternal psychosocial stress during pregnancy on fetal growth. J Dev Orig Health Dis. 2019;10(2):196–205. The only study we located that formally assessed mediation of a nonchemical stressor effect by an epigenetic/transcriptomic marker.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  113. Deyssenroth MA, Li Q, Lacasana M, Nomura Y, Marsit C, Chen J. Expression of placental regulatory genes is associated with fetal growth. J Perinat Med. 2017;45(7):887–93.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  114. Kim MA, Lee EJ, Yang W, Shin HY, Kim YH, Kim JH. Identification of a novel gene signature in second-trimester amniotic fluid for the prediction of preterm birth. Sci Rep. 2022;12(1):3085.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  115. Rosa MJ, Lee AG, Wright RJ. Evidence establishing a link between prenatal and early-life stress and asthma development. Curr Opin Allergy Clin Immunol. 2018;18(2):148–58.

    Article  PubMed  PubMed Central  Google Scholar 

  116. Marable CA, Roell K, Kuban K, O’Shea TM, Fry RC. Placental transcriptional signatures associated with cerebral white matter damage in the neonate. Front Neurosci. 2022;16:1017953.

    Article  PubMed  PubMed Central  Google Scholar 

  117. Palma-Gudiel H, Cirera F, Crispi F, Eixarch E, Fananas L. The impact of prenatal insults on the human placental epigenome: A systematic review. Neurotoxicol Teratol. 2018;66:80–93.

    Article  CAS  PubMed  Google Scholar 

  118. Subramanian I, Verma S, Kumar S, Jere A, Anamika K. Multi-omics data integration, interpretation, and its application. Bioinform Biol Insights. 2020;14:1177932219899051.

    Article  PubMed  PubMed Central  Google Scholar 

  119. Mo Q, Wang S, Seshan VE, Olshen AB, Schultz N, Sander C, et al. Pattern discovery and cancer gene identification in integrated cancer genomic data. Proc Natl Acad Sci U S A. 2013;110(11):4245–50.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  120. Provenzi L, Grumi S, Altieri L, Bensi G, Bertazzoli E, Biasucci G, et al. Prenatal maternal stress during the COVID-19 pandemic and infant regulatory capacity at 3 months: A longitudinal study. Dev Psychopathol. 2021;35(1):35–43.

    Article  PubMed  Google Scholar 

  121. Clark-Boucher D, Zhou X, Du J, Liu Y, Needham BL, Smith JA, et al. Methods for mediation analysis with high-dimensional DNA methylation data: Possible choices and comparison. medRxiv, 2023;02(10):23285764.

  122. Gao Y, Yang H, Fang R, Zhang Y, Goode EL, Cui Y. Testing mediation effects in high-dimensional epigenetic studies. Front Genet. 2019;10:1195.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  123. Fang R, Yang H, Gao Y, Cao H, Goode EL, Cui Y. Gene-based mediation analysis in epigenetic studies. Brief Bioinform. 2021;22(3):bbaa113.

  124. Peng C, Wang J, Asante I, Louie S, Jin R, Chatzi L, et al. A latent unknown clustering integrating multi-omics data (LUCID) with phenotypic traits. Bioinformatics. 2020;36(3):842–50.

    Article  CAS  PubMed  Google Scholar 

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Funding

This research was supported in part by funding from the National Institutes of Health (NIH) (P42-ES031007, UH-OD023348).

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Authors and Affiliations

  1. Department of Environmental Sciences and Engineering, Gillings School of Global Public Health, University of North Carolina, Chapel Hill, NC, USA

    Lauren A. Eaves, Cailee E. Harrington & Rebecca C. Fry

  2. Institute for Environmental Health Solutions, Gillings School of Global Public Health, University of North Carolina, Chapel Hill, NC, USA

    Lauren A. Eaves & Rebecca C. Fry

  3. Curriculum in Toxicology and Environmental Medicine, School of Medicine, University of North Carolina, Chapel Hill, NC, USA

    Rebecca C. Fry

  4. Department of Pediatrics, School of Medicine, University of North Carolina, Chapel Hill, NC, USA

    Rebecca C. Fry

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  1. Lauren A. Eaves
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LE, CH, and RF wrote the main manuscript text. LE and CH prepared the tables. LE prepared the figures. All authors reviewed the manuscript.

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Correspondence to Rebecca C. Fry.

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Eaves, L.A., Harrington, C.E. & Fry, R.C. Epigenetic Responses to Nonchemical Stressors: Potential Molecular Links to Perinatal Health Outcomes. Curr Envir Health Rpt (2024). https://doi.org/10.1007/s40572-024-00435-w

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