Abstract
Pregnancy stands at the interface of mechanics and biology. The growing fetus continuously loads the maternal organs as circulating hormone levels surge, leading to significant changes in mechanical and hormonal cues during pregnancy. In response to these cues, maternal soft tissues undergo remarkable growth and remodeling to support both mother and baby for a healthy pregnancy. We focus here on the maternal left ventricle, which increases its cardiac output and mass during pregnancy. The objective of this study is to build a multiscale cardiac growth model for pregnancy to understand how mechanical and hormonal cues interact to drive this growth process. Towards this objective, we coupled a cell signaling network model that predicts cell-level hypertrophy in response to hormones and stretch, to a compartmental model of the rat heart and circulation that predicts organ-level growth in response to hemodynamic changes. Since pregnancy is associated with a volume overloaded state and elevated hormones, we first calibrated the coupled, multiscale model to data from experimental volume overload (VO) and hormonal infusion of angiotensin 2 (AngII), estrogen (E2), and progesterone (P4). We then validated the ability of our model to capture interactions between inputs by comparing model predictions against published observations for the combinations of VO+E2 and AngII+E2. Finally, we simulated pregnancy-induced changes in hormones and hemodynamics to predict heart growth during pregnancy. Our multiscale model produced realistic heart growth consistent with experimental data. Overall, our analysis suggests that much of heart growth during pregnancy is driven by the early rise in P4, particularly during the first half of gestation. We conclude with suggestions for future experimental studies that will provide a better understanding of how hormonal and mechanical cues interact to drive pregnancy-induced heart growth.
Competing Interest Statement
The authors have declared no competing interest.
Footnotes
jsaucerman{at}virginia.edu
holmesjw{at}uab.edu
Funding: This work was supported by the National Institutes of Health (U01 HL127654).