I participated in the HPP in the beginning, went to a meeting, and submitted a funding proposal. I have lost touch since. I don't know all that has been achieved, or how the program has evolved. My impression in the beginning was that there was a strong emphasis on obstetric outcomes and technologies that could produce, for the benefit of the physician, rapid and clinically meaningful results. Innovation in technology is exciting but I did not see a place for myself at the table.
I work on the placenta in the domain of public health. I am a proponent of improving methodologies and approaches for etiologic research in human populations, and with an eye towards population-level intervention. I would be happy to represent the perspective of the public health scientist in this group. Many of our objectives are overlapping across fields (basic biology, clinical medicine, public health). Yet, we may also have unique perspectives and approaches that could all play a role in moving the field forward.
Work that my group is working on in the context of a placenta-centric R01 project, includes:
• methods for biomarker-based studies in the first trimester of pregnancy to connect maternal exposures (endocrine disruption, maternal stress, racism, infection), circulating and placental tissue biomarkers and child outcomes: absolute quantitative western blot using a standard curve method, identifying differences in biomarker associations by placental tissue type, comparing first trimester and term. We also have generated transcriptome datasets (effects of phthalates on placental gene expression) which inform our biomarker studies. So far, we have not found the transcriptome studies to be reproducible. I see above there is a push towards high dimensional data sets. The methods to anlayze these types of data to generate reproducible findings are not as well developed as the methods to analyze lower dimensional datasets.
• applying statistical methods to improve causal inference in observational settings with low dimensional data and transcriptome data: DAG (directed acyclic graph) theory, causal mediation, shape detection to identify non-linear relationships, longitudinal analysis, mixture modeling. I realize there is a push to apply machine learning in the statement above. Machine learning is an exciting direction as a prediction tool. It is not clear how much it can teach us about causality.
• As a field, I think we are moving past in vitro modeling of the human placenta based on monolayer cultures, but I am not sure if the method published by this group (PMID: 32908314) is going to work for our group in the form that it has been proposed. It would be helpful to have a focused discussion on this issue, and specifically with respect to primary first trimester placental tissue culture.
• Our current R01 project includes detailed analyses of human chorionic gonadotropin (hCG) which is an extremely rich source of information on diverse types of placental function in pregnancy.In these studies, we are finding differences between hCG-alpha and hCG-beta that are giving rise to new hypotheses and directions for our research, including extending outcomes to include maternal postpartum health.
• I learned recently that statewide prenatal screening programs are eliminating the serum screening analytes (hCG, PAPP-A, Estriol, Inhibin-A, AFP) in favor of cell free fetal DNA exclusively. I think this is something that the HPP should discuss. There is no comparison in terms of the type of information provided in the two types of screening. I am showing in my research the utility of serum screening data in public health research. It would be a HUGE loss if those data are no longer collected.
**take what you want here! I defer to you as to how our agendas may or may not match up.