Maternal adaptation to pregnancy is at least in part influenced by fetal gender
Bibliographic record
Abstract
An age-old question, 'is it a boy or a girl?' may be of greater significance than we have assumed! Differences in fetal growth rates by gender have been recognised for decades; however, the reasons why such differences exist remain poorly understood. Constitutional, genetic, metabolic, and many other factors have been proposed as affecting fetal growth and gender-related growth differences. The importance of placental function in fetal growth is unquestionable. Although each fetus has its own phenotype, it has typically been assumed that it is maternal factors that exhibit the greatest influence on placental function, and thereby upon fetal development. Examples include the patterns of fetal growth seen in mothers with medical issues such as diabetes, hypertension, or those with a pro-thrombotic disposition; however, recent data highlight the importance of maternal–fetal interactions, and furthermore what role fetal gender may have to play in these. Differential responses in fetal growth have been observed between genders with respect to maternal height and weight (Lampl et al. Am J Hum Biol 2010;22:431–443). Rather than the expected (that fetal weight would correlate directly with maternal height and weight), this study showed contrasting, gender-specific, growth patterns that varied not only with time but also with maternal height and weight independent of each other. Recent publications have demonstrated more direct associations between fetal gender and placental function. It appears that male fetuses are better able to maintain placental growth than females in the presence of maternal pre-eclampsia (Roland et al. Pregnancy Hypertension 2013;3:95). Prior data from the current authors suggest that fetal gender is associated with differences in placental function from early pregnancy, as demonstrated by observed differences in first-trimester placental biomarkers in uncomplicated pregnancies (Brown Placenta 2014;35:359–64). Although these differences may be superseded in pathological states, important gender influences persist nonetheless, with maternal blood pressures in complicated pregnancies (including pre-eclampsia) being higher in pregnancies with female fetuses (Brown et al. Pregnancy Hypertension 2015;5:31–2). In this present study (Brown et al. BJOG 2016; 123:1087–95) the authors have evaluated maternal vascular adaptation to pregnancy through the assessment of uterine artery Doppler and blood pressure, and have shown that pregnancies with male fetuses were more frequently associated with evidence of poor maternal vascular adaptation. Offering an important insight into the function of the fetoplacental unit, these data represents another piece in an as yet incomplete puzzle. The interactions between mother and fetus appear even more complex than was previously thought, and the balances and interplays that exist between the two appear themselves subject to modification by disease states. Further studies may highlight the roles that not only fetal gender but other fetal characteristics play. An understanding of how these might interact with various parental factors may provide further insights into the regulation of placental function and subsequent fetal development in both normal and complicated pregnancies. An improved understanding of these interrelationships is required in order for us to be better able to influence higher risk pregnancies and improve outcomes. None declared. Completed disclosure of interests form available to view online as supporting information.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".