Revealing the impact of lifestyle stressors on the risk of adverse pregnancy outcomes with multitask machine learning
Authors: Becker Martin , Dai Jennifer , Chang Alan L. , Feyaerts Dorien , Stelzer Ina A. , Zhang Miao , Berson Eloise , Saarunya Geetha , De Francesco Davide , Espinosa Camilo , Kim Yeasul , Marić Ivana , Mataraso Samson , Payrovnaziri Seyedeh Neelufar , Phongpreecha Thanaphong , Ravindra Neal G. , Shome Sayane , Tan Yuqi , Thuraiappah Melan , Xue Lei , Mayo Jonathan A. , Quaintance Cecele C. , Laborde Ana , King Lucy S. , Dhabhar Firdaus S. , Gotlib Ian H. , Wong Ronald J. , Angst Martin S. , Shaw Gary M. , Stevenson David K. , Gaudilliere Brice , Aghaeepour Nima 
Published: December 2022

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.