Stress and Its Consequences—Biological Strain

David K. Stevenson; Ian H. Gotlib; Jessica L. Buthmann; Ivana Marié; Nima Aghaeepour; Brice Gaudilliere; Martin S. Angst; Gary L. Darmstadt; Maurice L. Druzin; Ronald J. Wong; Gary M. Shaw; Michael Katz

doi:10.1055/a-1798-1602

American Journal of Perinatology, Table of Contents

Am J Perinatol 2024; 41(09): 1282-1284
DOI: 10.1055/a-1798-1602

Commentary

Stress and Its Consequences—Biological Strain

Authors

David K. Stevenson

¹Division of Neonatal and Developmental Medicine, Department of Pediatrics, Stanford University School of Medicine, Stanford, California
Ian H. Gotlib

²Department of Psychology, Stanford University School of Humanities and Science, Stanford, California
Jessica L. Buthmann

²Department of Psychology, Stanford University School of Humanities and Science, Stanford, California
Ivana Marié

¹Division of Neonatal and Developmental Medicine, Department of Pediatrics, Stanford University School of Medicine, Stanford, California
Nima Aghaeepour

³Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University School of Medicine, Stanford, California
Brice Gaudilliere

³Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University School of Medicine, Stanford, California
Martin S. Angst

³Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University School of Medicine, Stanford, California
Gary L. Darmstadt

¹Division of Neonatal and Developmental Medicine, Department of Pediatrics, Stanford University School of Medicine, Stanford, California
Maurice L. Druzin

⁴Department of Obstetrics and Gynecology—Maternal-Fetal Medicine, Stanford University School of Medicine, Stanford, California
Ronald J. Wong

¹Division of Neonatal and Developmental Medicine, Department of Pediatrics, Stanford University School of Medicine, Stanford, California
Gary M. Shaw

¹Division of Neonatal and Developmental Medicine, Department of Pediatrics, Stanford University School of Medicine, Stanford, California
Michael Katz

¹Division of Neonatal and Developmental Medicine, Department of Pediatrics, Stanford University School of Medicine, Stanford, California

Abstract

Understanding the role of stress in pregnancy and its consequences is important, particularly given documented associations between maternal stress and preterm birth and other pathological outcomes. Physical and psychological stressors can elicit the same biological responses, known as biological strain. Chronic stressors, like poverty and racism (race-based discriminatory treatment), may create a legacy or trajectory of biological strain that no amount of coping can relieve in the absence of larger-scale socio-behavioral or societal changes. An integrative approach that takes into consideration simultaneously social and biological determinants of stress may provide the best insights into the risk of preterm birth. The most successful computational approaches and the most predictive machine-learning models are likely to be those that combine information about the stressors and the biological strain (for example, as measured by different omics) experienced during pregnancy.

Keywords

pregnancy - preterm birth - omics - machine learning

Full Text

References

References
1 Holmes TH, Rahe RH. The social readjustment rating scale. J Psychosom Res 1967; 11 (02) 213-218
2 Braveman PA, Arkin E, Proctor D, Kauh T, Holm N. Systemic and structural racism: definitions, examples, health damages, and approaches to dismantling. Health Aff (Millwood) 2022; 41 (02) 171-178
3 Wadhwa PD, Entringer S, Buss C, Lu MC. The contribution of maternal stress to preterm birth: issues and considerations. Clin Perinatol 2011; 38 (03) 351-384
4 Singh GK. Trends and social inequalities in maternal mortality in the United States, 1969–2018. Int J MCH AIDS 2021; 10 (01) 29-42
5 Becker M, Mayo JA, Phogat NK. et al. Deleterious and Protective Psychosocial and Stress-Related Factors Predict Risk of Spontaneous Preterm Birth. Am J Perinatol 2023; 40 (01) 74-88
6 Hobel CJ, Goldstein A, Barrett ES. Psychosocial stress and pregnancy outcome. Clin Obstet Gynecol 2008; 51 (02) 333-348
7 Hedegaard M, Henriksen TB, Secher NJ, Hatch MC, Sabroe S. Do stressful life events affect duration of gestation and risk of preterm delivery?. Epidemiology 1996; 7 (04) 339-345
8 Guardino CM, Schetter CD. Coping during pregnancy: a systematic review and recommendations. Health Psychol Rev 2014; 8 (01) 70-94
9 Paarlberg KM, Vingerhoets AJ, Passchier J, Dekker GA, Van Geijn HP. Psychosocial factors and pregnancy outcome: a review with emphasis on methodological issues. J Psychosom Res 1995; 39 (05) 563-595
10 Mesner O, Davis A, Casman E. et al. Using graph learning to understand adverse pregnancy outcomes and stress pathways. PLoS One 2019; 14 (09) e0223319
11 Ghaemi MS, DiGiulio DB, Contrepois K. et al. Multiomics modeling of the immunome, transcriptome, microbiome, proteome and metabolome adaptations during human pregnancy. Bioinformatics 2019; 35 (01) 95-103
12 Jehan F, Sazawal S, Baqui AH. et al; Alliance for Maternal and Newborn Health Improvement, the Global Alliance to Prevent Prematurity and Stillbirth, and the Prematurity Research Center at Stanford University. Multiomics characterization of preterm birth in low- and middle-income countries. JAMA Netw Open 2020; 3 (12) e2029655
13 Ando K, Hédou JJ, Feyaerts D. et al. A peripheral immune signature of labor induction. Front Immunol 2021; 12: 725989
14 Stelzer IA, Ghaemi MS, Han X. et al. Integrated trajectories of the maternal metabolome, proteome, and immunome predict labor onset. Sci Transl Med 2021; 13 (592) eabd9898
15 Cao-Lei L, Massart R, Suderman MJ. et al. DNA methylation signatures triggered by prenatal maternal stress exposure to a natural disaster: project Ice Storm. PLoS One 2014; 9 (09) e107653
16 Zucchi FC, Yao Y, Ward ID. et al. Maternal stress induces epigenetic signatures of psychiatric and neurological diseases in the offspring. PLoS One 2013; 8 (02) e56967
17 Paxman EJ, Boora NS, Kiss D. et al. Prenatal maternal stress from a natural disaster alters urinary metabolomic profiles in project ice storm participants. Sci Rep 2018; 8 (01) 12932
18 Cunningham JP, Ghahramani Z. Linear dimensionality reduction: survey, insights, and generalizations. J Mach Learn Res 2015; 16: 2859-2900
19 Hastie T, Tibshirani R, Wainwright M. Statistical Learning with Sparsity: The Lasso and Generalization. Boca Raton, FL:: CRC Press;; 2015
20 Ruiz C, Zitnik M, Leskovec J. Identification of disease treatment mechanisms through the multiscale interactome. Nat Commun 2021; 12 (01) 1796
21 Espinosa C, Becker M, Marić I. et al; Prematurity Research Center at Stanford. Data-driven modeling of pregnancy-related complications. Trends Mol Med 2021; 27 (08) 762-776
22 Kazzazi F. The automation of doctors and machines: a classification for AI in medicine (ADAM framework). Future Healthc J 2021; 8 (02) e257-e262
23 Giurgescu C, Misra DP, Slaughter-Acey JC. et al. Neighborhoods, racism, stress, and preterm birth among African American women: a review. West J Nurs Res 2022; 44 (01) 101-110
24 Lopez M, Ruiz MO, Rovnaghi CR. et al. The social ecology of childhood and early life adversity. Pediatr Res 2021; 89 (02) 353-367
25 Stevenson DK, Wong RJ, Aghaeepour N. et al. Towards personalized medicine in maternal and child health: integrating biologic and social determinants. Pediatr Res 2021; 89 (02) 252-258
26 Ajilore O, Thames AD. The fire this time: the stress of racism, inflammation and COVID-19. Brain Behav Immun 2020; 88: 66-67
27 Aghaeepour N, Ganio EA, Mcilwain D. et al. An immune clock of human pregnancy. Sci Immunol 2017; 2 (15) eaan2946
28 Aghaeepour N, Lehallier B, Baca Q. et al. A proteomic clock of human pregnancy. Am J Obstet Gynecol 2018; 218 (03) 347.e1-347.e14
29 Peterson LS, Stelzer IA, Tsai AS. et al. Multiomic immune clockworks of pregnancy. Semin Immunopathol 2020; 42 (04) 397-412