Genetic and Demographic Factors That Influence the Pain and Progress of Labor

There is enormous variability among women in the progress of normal labor. Labor requires complex integrated interplay between the decidua, uterine cervix and myometrium that can take minutes, days or weeks to occur and is incompletely understood. Understanding the biological variables that underlie differences in labor progress has been hampered by the lack of appropriate models that allow sensitive statistical analysis. Identification of genetic and physiognomic factors that impact normal labor progress will allow for individualization of labor management and better use of societal resources.

Structural models of labor progress were first proposed by Friedman in the 1950s at Columbia University. Aspects of Friedman's model, such as the deceleration phase, have been debated since that time but Friedman's model allowed for identification and quantification of the latent and active phase of labor in populations. These concepts have been modified by the World Health Organization as the WHO Partogram, the use of which has resulted in reduced requirement of oxytocin and reduced incidence of cesarean section.

Dr. Flood's group has developed a continuous bi-exponential model of labor progress and sigmoidal model for labor pain that the investigators have statistically and experimentally validated in several independent databases. The investigators model can be used both prospectively in an individual labor and with large cohorts to identify variables that significantly affect the progress of labor.

The investigators have found in a previous work that parturients who carry G at the 27th amino acid beta-2 adrenergic receptors (β2AR) developed labor pain more rapidly that parturients with the common allele [1]. and the investigators also have found that catechol-O-methyltransferase (COMT) rs4633 genotype TT resulted in a slower latent phase rate, and oxytocin receptor rs53576 genotype GG transitioned to active labor earlier [2].

In this new project, the investigators are planning to use bigger data base, to detect further genes associations, and tested some pharmacogenetic variations that could explain the different response to same medications and doses among patients.