Display options
Share it on

Pediatr Res. 2021 Jan;89(2):252-258. doi: 10.1038/s41390-020-0981-8. Epub 2020 May 26.

Towards personalized medicine in maternal and child health: integrating biologic and social determinants.

Pediatric research

David K Stevenson, Ronald J Wong, Nima Aghaeepour, Ivana Maric, Martin S Angst, Kevin Contrepois, Gary L Darmstadt, Maurice L Druzin, Michael L Eisenberg, Brice Gaudilliere, Ronald S Gibbs, Ian H Gotlib, Jeffrey B Gould, Henry C Lee, Xuefeng B Ling, Jonathan A Mayo, Mira N Moufarrej, Cecele C Quaintance, Stephen R Quake, David A Relman, Marina Sirota, Michael P Snyder, Karl G Sylvester, Shiying Hao, Paul H Wise, Gary M Shaw, Michael Katz

Affiliations

  1. Department of Pediatrics, Stanford University School of Medicine, Stanford, CA, 94305, USA.
  2. Department of Pediatrics, Stanford University School of Medicine, Stanford, CA, 94305, USA. [email protected].
  3. Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University School of Medicine, Stanford, CA, 94305, USA.
  4. Stanford Center for Genomics and Personalized Medicine, Department of Genetics, Stanford University School of Medicine, Stanford, CA, 94305, USA.
  5. Department of Obstetrics and Gynecology, Stanford University School of Medicine, Stanford, CA, 94305, USA.
  6. Department of Urology, Stanford University School of Medicine, Stanford, CA, 94305, USA.
  7. Department of Psychology, Stanford University School of Humanities and Science, Stanford, CA, 94305, USA.
  8. Department of Surgery, Stanford University School of Medicine, Stanford, CA, 94305, USA.
  9. Clinical and Translational Research Program, Betty Irene Moore Children's Heart Center, Lucile Packard Children's Hospital, Palo Alto, CA, 94306, USA.
  10. Departments of Bioengineering and Applied Physics, Stanford University, and Chan Zuckerberg Biohub, Stanford, CA, 94305, USA.
  11. Department of Medicine, Stanford University School of Medicine, Stanford, CA, 94305, USA.
  12. Infectious Diseases Section, Veterans Affairs Palo Alto Health Care System, Palo Alto, CA, 94306, USA.
  13. Bakar Computational Health Sciences Institute, University of California, San Francisco, San Francisco, CA, USA.
  14. Department of Pediatrics, University of California, San Francisco, San Francisco, CA, USA.
  15. Department of Cardiothoracic Surgery, Stanford University School of Medicine, Stanford, CA, 94305, USA.

PMID: 32454518 PMCID: PMC8061757 DOI: 10.1038/s41390-020-0981-8

[No abstract available.]

References

  1. Stevenson, D. K. et al. Transdisciplinary translational science and the case of preterm birth. J. Perinatol. 33, 251–258 (2013). - PubMed
  2. Iams, J. D. et al. The length of the cervix and the risk of spontaneous premature delivery. National Institute of Child Health and Human Development Maternal Fetal Medicine Unit Network. N. Engl. J. Med. 334, 567–572 (1996). - PubMed
  3. Meis, P. J. et al. Prevention of recurrent preterm delivery by 17 alpha-hydroxyprogesterone caproate. N. Engl. J. Med. 348, 2379–2385 (2003). - PubMed
  4. Hoffman, M. K. et al. Low-dose aspirin for the prevention of preterm delivery in nulliparous women with a singleton pregnancy (ASPIRIN): a randomised, double-blind, placebo-controlled trial. Lancet 39, 285–293 (2020). - PubMed
  5. Ghaemi, M. S. et al. Multiomics modeling of the immunome, transcriptome, microbiome, proteome and metabolome adaptations during human pregnancy. Bioinformatics 35, 95–103 (2019). - PubMed
  6. Muglia, L. J. & Katz, M. The enigma of spontaneous preterm birth. N. Engl. J. Med 362, 529–535 (2010). - PubMed
  7. Romero, R., Dey, S. K. & Fisher, S. J. Preterm labor: one syndrome, many causes. Science 345, 760–765 (2014). - PubMed
  8. Stevenson, D. K. et al. Understanding health disparities. J. Perinatol. 39, 354–358 (2019). - PubMed
  9. Yudell, M., Roberts, D., DeSalle, R. & Tishkoff, S. Science and Society. Taking race out of human genetics. Science 351, 564–565 (2016). - PubMed
  10. Li, J. et al. Natural selection has differentiated the progesterone receptor among human populations. Am. J. Hum. Genet. 103, 45–57 (2018). - PubMed
  11. Stevenson, D. K. et al. The contributions of genetics to premature birth. Pediatr. Res. 85, 416–417 (2019). - PubMed
  12. Gracie, S. et al. An integrated systems biology approach to the study of preterm birth using “-omic” technology—a guideline for research. BMC Pregnancy Childbirth 11, 71 (2011). - PubMed
  13. Goldenberg, R. L., Culhane, J. F., Iams, J. D. & Romero, R. Epidemiology and causes of preterm birth. Lancet 371, 75–84 (2008). - PubMed
  14. Wallenstein, M. B., Shaw, G. M. & Stevenson, D. K. Preterm birth as a calendar event or immunologic anomaly. JAMA Pediatr. 170, 525–526 (2016). - PubMed
  15. Zhao, H., Ozen, M., Wong, R. J. & Stevenson, D. K. Heme oxygenase-1 in pregnancy and cancer: similarities in cellular invasion, cytoprotection, angiogenesis, and immunomodulation. Front. Pharm. 5, 295 (2014). - PubMed
  16. Trowsdale, J. & Betz, A. G. Mother’s little helpers: mechanisms of maternal-fetal tolerance. Nat. Immunol. 7, 241–246 (2006). - PubMed
  17. Ozen, M., Zhao, H., Lewis, D. B., Wong, R. J. & Stevenson, D. K. Heme oxygenase and the immune system in normal and pathological pregnancies. Front. Pharm. 6, 84 (2015). - PubMed
  18. Druckmann, R. & Druckmann, M. A. Progesterone and the immunology of pregnancy. J. Steroid Biochem. Mol. Biol. 97, 389–396 (2005). - PubMed
  19. Bygren, L. O., Kaati, G. & Edvinsson, S. Longevity determined by paternal ancestors’ nutrition during their slow growth period. Acta Biotheor. 49, 53–59 (2001). - PubMed
  20. Maric, I. et al. Data-driven queries between medications and spontaneous preterm birth among 2.5 million pregnancies. Birth Defects Res. 111, 1145–1153 (2019). - PubMed
  21. Greenberg, D. R. et al. Disease burden in offspring is associated with changing paternal demographics in the United States. Andrology https://doi.org/10.1111/andr.12700 (2019). - PubMed
  22. Mayo, J. A., Lu, Y., Stevenson, D. K., Shaw, G. M. & Eisenberg, M. L. Parental age and stillbirth: a population-based cohort of nearly 10 million California deliveries from 1991 to 2011. Ann. Epidemiol. 31, 32–37 e32 (2019). - PubMed
  23. Khandwala, Y. S. et al. Association of paternal age with perinatal outcomes between 2007 and 2016 in the United States: population based cohort study. BMJ 363, k4372 (2018). - PubMed
  24. Northstone, K., Golding, J., Davey Smith, G., Miller, L. L. & Pembrey, M. Prepubertal start of father’s smoking and increased body fat in his sons: further characterisation of paternal transgenerational responses. Eur. J. Hum. Genet. 22, 1382–1386 (2014). - PubMed
  25. Moss, J. L. & Harris, K. M. Impact of maternal and paternal preconception health on birth outcomes using prospective couples’ data in Add Health. Arch. Gynecol. Obstet. 291, 287–298 (2015). - PubMed
  26. Shaw, G. M. et al. Residential agricultural pesticide exposures and risks of preeclampsia. Environ. Res. 164, 546–555 (2018). - PubMed
  27. Shaw, G. M. et al. Residential agricultural pesticide exposures and risks of spontaneous preterm birth. Epidemiology 29, 8–21 (2018). - PubMed
  28. Sirota, M. et al. Enabling precision medicine in neonatology, an integrated repository for preterm birth research. Sci. Data 5, 180219 (2018). - PubMed
  29. Callahan, B. J. et al. Replication and refinement of a vaginal microbial signature of preterm birth in two racially distinct cohorts of US women. Proc. Natl. Acad. Sci. USA 114, 9966–9971 (2017). - PubMed
  30. DiGiulio, D. B. et al. Temporal and spatial variation of the human microbiota during pregnancy. Proc. Natl. Acad. Sci. USA 112, 11060–11065 (2015). - PubMed
  31. Ngo, T. T. M. et al. Noninvasive blood tests for fetal development predict gestational age and preterm delivery. Science 360, 1133–1136 (2018). - PubMed
  32. Pan, W. et al. Simultaneously monitoring immune response and microbial infections during pregnancy through plasma cfRNA sequencing. Clin. Chem. 63, 1695–1704 (2017). - PubMed
  33. Goltsman, D. S. A. et al. Metagenomic analysis with strain-level resolution reveals fine-scale variation in the human pregnancy microbiome. Genome Res. 28, 1467–1480 (2018). - PubMed
  34. Fan, H. C. & Quake, S. R. Sensitivity of noninvasive prenatal detection of fetal aneuploidy from maternal plasma using shotgun sequencing is limited only by counting statistics. PLoS ONE 5, e10439 (2010). - PubMed
  35. Koh, W. et al. Single cell gene transcriptomes derived from human cervical and uterine tissue during pregnancy. Adv. Biosyst. 3, 1800336 (2019). - PubMed
  36. Aghaeepour, N. et al. An immune clock of human pregnancy. Sci. Immunol. 2, eaan2946 (2017). - PubMed
  37. Peterson, L. S. et al. Multiomic immune clockworks of pregnancy. Semin. Immunopathol. https://doi.org/10.1007/s00281-019-00772-1 (2020). - PubMed
  38. Bandura, D. R. et al. Mass cytometry: technique for real time single cell multitarget immunoassay based on inductively coupled plasma time-of-flight mass spectrometry. Anal. Chem. 81, 6813–6822 (2009). - PubMed
  39. Bendall, S. C. et al. Single-cell mass cytometry of differential immune and drug responses across a human hematopoietic continuum. Science 332, 687–696 (2011). - PubMed
  40. Gaudilliere, B. et al. Implementing mass cytometry at the bedside to study the immunological basis of human diseases: distinctive immune features in patients with a history of term or preterm birth. Cytom. A 87, 817–829 (2015). - PubMed
  41. Nadeau-Vallee, M. et al. Novel noncompetitive IL-1 receptor-biased ligand prevents infection- and inflammation-induced preterm birth. J. Immunol. 195, 3402–3415 (2015). - PubMed
  42. Quiniou, C. et al. Development of a novel noncompetitive antagonist of IL-1 receptor. J. Immunol. 180, 6977–6987 (2008). - PubMed
  43. Han, X. et al. Differential dynamics of the maternal immune system in healthy pregnancy and preeclampsia. Front. Immunol. 10, 1305 (2019). - PubMed
  44. Rosenzwajg, M. et al. Immunological and clinical effects of low-dose interleukin-2 across 11 autoimmune diseases in a single, open clinical trial. Ann. Rheum. Dis. 78, 209–217 (2019). - PubMed
  45. Klatzmann, D. & Abbas, A. K. The promise of low-dose interleukin-2 therapy for autoimmune and inflammatory diseases. Nat. Rev. Immunol. 15, 283–294 (2015). - PubMed
  46. Ferrero, D. M. et al. Cross-country individual participant analysis of 4.1 million singleton births in 5 countries with very high human development index confirms known associations but provides no biologic explanation for 2/3 of all preterm births. PLoS ONE 11, e0162506 (2016). - PubMed
  47. Akolekar, R., Syngelaki, A., Poon, L., Wright, D. & Nicolaides, K. H. Competing risks model in early screening for preeclampsia by biophysical and biochemical markers. Fetal Diagn. Ther. 33, 8–15 (2013). - PubMed
  48. Francisco, C., Wright, D., Benko, Z., Syngelaki, A. & Nicolaides, K. H. Competing-risks model in screening for pre-eclampsia in twin pregnancy according to maternal factors and biomarkers at 11-13 weeks’ gestation. Ultrasound Obstet. Gynecol. 50, 589–595 (2017). - PubMed
  49. Oskovi Kaplan, Z. A. & Ozgu-Erdinc, A. S. Prediction of preterm birth: maternal characteristics, ultrasound markers, and biomarkers: an updated overview. J. Pregnancy 2018, 8367571 (2018). - PubMed
  50. Stout, M. J. et al. First trimester serum analytes, maternal characteristics and ultrasound markers to predict pregnancies at risk for preterm birth. Placenta 34, 14–19 (2013). - PubMed
  51. Hastie, T., Tibshirani, R. & Freidman, J. The Elements of Statistical Learning 2nd edn (Springer-Verlag, Switzerland, 2009). - PubMed
  52. Stone, M. Cross-validatory choice and assessment of statistical predictions. J. R. Stat. Soc.: Ser. B (Methodol.) 38, 102 (1976). - PubMed
  53. Wolpert, D. H. Stacked generalization. Neural Netw. 5, 241–259 (1992). - PubMed
  54. Breiman, L. Stacked regressions. Mach. Learn. 24, 49–64 (1996). - PubMed
  55. Vapnik, V. N. The Nature of Statistical Learning Theory 2nd edn. (Springer, New York, 1995). - PubMed
  56. Koller, D. Probabilistic Graphical Models Principles and Techniques (Massachusetts Institute of Technology, Boston, 2009). - PubMed
  57. Sinoquet, C. in Probabilistic Graphical Models for Genetics, Genomics, and Postgenomics (ed. Mourad, R.) 3−49 (Oxford University Press, London, 2014). - PubMed
  58. Tibshirani, R. & Friedman, J. A pliable lasso. Preprint at https://arxiv.org/abs/1712.00484 (2018). - PubMed
  59. Tibshirani, R. Regression shrinkage and selection via the lasso. J. R. Stat. Soc.: Ser. B (Methodol.) 58, 267 (1996). - PubMed
  60. Hastie, T. & Tibshirani, R. Varying-coefficient models. J. R. Stat. Soc.: Ser. B (Methodol.) 55, 757 (1993). - PubMed
  61. Lewis, C., Hoggatt, K. J. & Ritz, B. The impact of different causal models on estimated effects of disinfection by-products on preterm birth. Environ. Res. 111, 371–376 (2011). - PubMed
  62. Koopman, J. S. & Lynch, J. W. Individual causal models and population system models in epidemiology. Am. J. Public Health 89, 1170–1174 (1999). - PubMed
  63. Barlas, Y. & Carpenter, S. Philosophical roots of model validation: two paradigms. Syst. Dyn. Rev. 6, 148–166 (1990). - PubMed
  64. Le, B. L., Iwatani, S., Wong, R. J., Stevenson, D. K. & Sirota, M. Computational discovery of therapeutic candidates for preventing preterm birth. JCI Insight 5, 133761 (2020). - PubMed
  65. Beck, A. F. et al. The color of health: how racism, segregation, and inequality affect the health and well-being of preterm infants and their families. Pediatr. Res. 87, 227–234 (2020). - PubMed
  66. Wise, P. H. The anatomy of a disparity in infant mortality. Annu. Rev. Public Health 24, 341–362 (2003). - PubMed
  67. Owen, C. M., Goldstein, E. H., Clayton, J. A. & Segars, J. H. Racial and ethnic health disparities in reproductive medicine: an evidence-based overview. Semin. Reprod. Med. 31, 317–324 (2013). - PubMed
  68. Goetz, L. H. & Schork, N. J. Personalized medicine: motivation, challenges, and progress. Fertil. Steril. 109, 952–963 (2018). - PubMed
  69. Weil, A. R. Precision medicine. Health Aff. (Millwood) 37, 687 (2018). - PubMed
  70. Minor, L. & Rees, M. Discovering Precision Health: Predict, Prevent, and Cure to Advance Health and Well-Being (Wiley-Blackwell, New Jersey, 2020). - PubMed
  71. Leon, L. J. et al. Preeclampsia and cardiovascular disease in a large uk pregnancy cohort of linked electronic health records: a CALIBER study. Circulation 140, 1050–1060 (2019). - PubMed

MeSH terms

Publication Types

Grant support