The Impact of New NIH Requirements on the Preclinical Research Sex Disparity — A Meta-Analysis
Author:
Nicole McGrath
Name Change:
Major:
Biology
Graduation Year:
2019
Thesis Advisor:
Lynn Atkins
Description of Publication:
Historically, women as subjects have been underrepresented in clinical research. Due to this problem, legislation was enacted by Congress in 1993 to require inclusion of women in NIH funded clinical trials. Female animals are also underrepresented in preclinical research and need to be included to ensure safe and effective drugs. Studies exclude female mammals under the assumption that the estrus cycle contributes to variability (Beery, 2011). This notion has been contradicted by several studies (Prendergast, 2014; Becker, 2016). New requirements of NIH funded researchers to consider sex as a basic biological variable were announced in 2014 (Clayton, 2014). As of June 5. 2016, all NIH grant applications must include plans to use equal numbers of both sexes and to perform statistical analysis for possible sex differences (NIH, 2016).
This study examined the impact that these requirements have had on the inclusion of both sexes and the analysis of sex differences in preclinical research. The fields of neuroscience, pharmacology, and immunology were chosen for analysis based on research indicating that they had the lowest rates of analyzing sex differences prior to the mandate (Beery, 2011). A significant increase in the inclusion of both sexes was found in all fields (p<0.001), along with a 3.58 fold increase in the proportion of articles that analyzed sex differences. NIH funded pharmacology research was more likely to include both sexes and report sex difference analyses post-mandate. However, articles still must analyze sex differences and include both sexes at a much higher rate than the current 2018 statistics calculated in this analysis (16.4% and 36.4%).Due to the recentness of the mandate, it is recommended that a follow-up study be conducted. The increases in female inclusion and sex differences analysis are promising signs of future improvement.
Location of Publication:
URL to Thesis:
https://digitalcommons.library.umaine.edu/honors/501/