Revealing the impact of lifestyle stressors on the risk of adverse pregnancy outcomes with multitask machine learning.

Autor:	Becker M; Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University, Palo Alto, CA, United States.; Department of Pediatrics, Stanford University, Palo Alto, CA, United States.; Department of Biomedical Data Science, Stanford University, Palo Alto, CA, United States.; Chair for Intelligent Data Analytics, Institute for Visual and Analytic Computing, Department of Computer Science and Electrical Engineering, University of Rostock, Rostock, Germany., Dai J; Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University, Palo Alto, CA, United States.; Department of Pediatrics, Stanford University, Palo Alto, CA, United States.; Department of Biomedical Data Science, Stanford University, Palo Alto, CA, United States., Chang AL; Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University, Palo Alto, CA, United States.; Department of Pediatrics, Stanford University, Palo Alto, CA, United States.; Department of Biomedical Data Science, Stanford University, Palo Alto, CA, United States., Feyaerts D; Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University, Palo Alto, CA, United States., Stelzer IA; Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University, Palo Alto, CA, United States., Zhang M; Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University, Palo Alto, CA, United States.; Department of Pediatrics, Stanford University, Palo Alto, CA, United States.; Department of Biomedical Data Science, Stanford University, Palo Alto, CA, United States., Berson E; Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University, Palo Alto, CA, United States.; Department of Pediatrics, Stanford University, Palo Alto, CA, United States.; Department of Pathology, Stanford University, Palo Alto, CA, United States., Saarunya G; Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University, Palo Alto, CA, United States.; Department of Pediatrics, Stanford University, Palo Alto, CA, United States.; Department of Biomedical Data Science, Stanford University, Palo Alto, CA, United States., De Francesco D; Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University, Palo Alto, CA, United States.; Department of Pediatrics, Stanford University, Palo Alto, CA, United States.; Department of Biomedical Data Science, Stanford University, Palo Alto, CA, United States., Espinosa C; Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University, Palo Alto, CA, United States.; Department of Pediatrics, Stanford University, Palo Alto, CA, United States.; Department of Biomedical Data Science, Stanford University, Palo Alto, CA, United States., Kim Y; Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University, Palo Alto, CA, United States.; Department of Pediatrics, Stanford University, Palo Alto, CA, United States.; Department of Biomedical Data Science, Stanford University, Palo Alto, CA, United States., Marić I; Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University, Palo Alto, CA, United States.; Department of Pediatrics, Stanford University, Palo Alto, CA, United States.; Department of Biomedical Data Science, Stanford University, Palo Alto, CA, United States., Mataraso S; Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University, Palo Alto, CA, United States.; Department of Pediatrics, Stanford University, Palo Alto, CA, United States.; Department of Biomedical Data Science, Stanford University, Palo Alto, CA, United States., Payrovnaziri SN; Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University, Palo Alto, CA, United States.; Department of Pediatrics, Stanford University, Palo Alto, CA, United States.; Department of Biomedical Data Science, Stanford University, Palo Alto, CA, United States., Phongpreecha T; Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University, Palo Alto, CA, United States.; Department of Biomedical Data Science, Stanford University, Palo Alto, CA, United States.; Department of Pathology, Stanford University, Palo Alto, CA, United States., Ravindra NG; Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University, Palo Alto, CA, United States.; Department of Pediatrics, Stanford University, Palo Alto, CA, United States.; Department of Biomedical Data Science, Stanford University, Palo Alto, CA, United States., Shome S; Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University, Palo Alto, CA, United States.; Department of Pediatrics, Stanford University, Palo Alto, CA, United States.; Department of Biomedical Data Science, Stanford University, Palo Alto, CA, United States., Tan Y; Department of Microbiology & Immunology, Stanford University, Palo Alto, CA, United States.; Baxter Laboratory for Stem Cell Biology, Stanford University, Palo Alto, CA, United States., Thuraiappah M; Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University, Palo Alto, CA, United States.; Department of Pediatrics, Stanford University, Palo Alto, CA, United States.; Department of Biomedical Data Science, Stanford University, Palo Alto, CA, United States., Xue L; Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University, Palo Alto, CA, United States.; Department of Pediatrics, Stanford University, Palo Alto, CA, United States.; Department of Biomedical Data Science, Stanford University, Palo Alto, CA, United States., Mayo JA; Department of Pediatrics, Stanford University, Palo Alto, CA, United States., Quaintance CC; Department of Pediatrics, Stanford University, Palo Alto, CA, United States., Laborde A; Department of Pediatrics, Stanford University, Palo Alto, CA, United States., King LS; Department of Psychology, Stanford University, Palo Alto, CA, United States., Dhabhar FS; Department of Psychiatry & Behavioral Science, University of Miami, Miami, FL, United States.; Department of Microbiology & Immunology, University of Miami, Miami, FL, United States.; Sylvester Comprehensive Cancer Center, University of Miami, Miami, FL, United States.; Miller School of Medicine, University of Miami, Miami, FL, United States., Gotlib IH; Department of Psychology, Stanford University, Palo Alto, CA, United States., Wong RJ; Department of Biomedical Data Science, Stanford University, Palo Alto, CA, United States., Angst MS; Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University, Palo Alto, CA, United States., Shaw GM; Department of Pediatrics, Stanford University, Palo Alto, CA, United States., Stevenson DK; Department of Pediatrics, Stanford University, Palo Alto, CA, United States., Gaudilliere B; Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University, Palo Alto, CA, United States.; Department of Pediatrics, Stanford University, Palo Alto, CA, United States., Aghaeepour N; Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University, Palo Alto, CA, United States.; Department of Pediatrics, Stanford University, Palo Alto, CA, United States.; Department of Biomedical Data Science, Stanford University, Palo Alto, CA, United States.
Jazyk:	angličtina
Zdroj:	Frontiers in pediatrics [Front Pediatr] 2022 Dec 13; Vol. 10, pp. 933266. Date of Electronic Publication: 2022 Dec 13 (Print Publication: 2022).
DOI:	10.3389/fped.2022.933266
Abstrakt:	Psychosocial and stress-related factors (PSFs), defined as internal or external stimuli that induce biological changes, are potentially modifiable factors and accessible targets for interventions that are associated with adverse pregnancy outcomes (APOs). Although individual APOs have been shown to be connected to PSFs, they are biologically interconnected, relatively infrequent, and therefore challenging to model. In this context, multi-task machine learning (MML) is an ideal tool for exploring the interconnectedness of APOs on the one hand and building on joint combinatorial outcomes to increase predictive power on the other hand. Additionally, by integrating single cell immunological profiling of underlying biological processes, the effects of stress-based therapeutics may be measurable, facilitating the development of precision medicine approaches. Objectives: The primary objectives were to jointly model multiple APOs and their connection to stress early in pregnancy, and to explore the underlying biology to guide development of accessible and measurable interventions. Materials and Methods: In a prospective cohort study, PSFs were assessed during the first trimester with an extensive self-filled questionnaire for 200 women. We used MML to simultaneously model, and predict APOs (severe preeclampsia, superimposed preeclampsia, gestational diabetes and early gestational age) as well as several risk factors (BMI, diabetes, hypertension) for these patients based on PSFs. Strongly interrelated stressors were categorized to identify potential therapeutic targets. Furthermore, for a subset of 14 women, we modeled the connection of PSFs to the maternal immune system to APOs by building corresponding ML models based on an extensive single cell immune dataset generated by mass cytometry time of flight (CyTOF). Results: Jointly modeling APOs in a MML setting significantly increased modeling capabilities and yielded a highly predictive integrated model of APOs underscoring their interconnectedness. Most APOs were associated with mental health, life stress, and perceived health risks. Biologically, stressors were associated with specific immune characteristics revolving around CD4/CD8 T cells. Immune characteristics predicted based on stress were in turn found to be associated with APOs. Conclusions: Elucidating connections among stress, multiple APOs simultaneously, and immune characteristics has the potential to facilitate the implementation of ML-based, individualized, integrative models of pregnancy in clinical decision making. The modifiable nature of stressors may enable the development of accessible interventions, with success tracked through immune characteristics. Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. (© 2022 Becker, Dai, Chang, Feyaerts, Stelzer, Zhang, Berson, Saarunya, De Francesco, Espinosa, Kim, Marić, Mataraso, Payrovnaziri, Phongpreecha, Ravindra, Shome, Tan, Thuraiappah, Xue, Mayo, Quaintance, Laborde, King, Dhabhar, Gotlib, Wong, Angst, Shaw, Stevenson, Gaudilliere and Aghaeepour.)
Databáze:	MEDLINE
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