The 89th Annual Meeting of the American Association of Physical Anthropologists (2020)


Day of menstrual cycle does not have a significant effect on urinary cortisol levels in a sample of Polish American women

MEREDITH A. WILSON1, KATHARINE NM. LEE1, MARY P. ROGERS1 and KATHRYN BH. CLANCY1,2.

1Department of Anthropology, University of Illinois, Urbana-Champaign, 2Beckman Institute, University of Illinois, Urbana-Champaign

April 16, 2020 , Platinum Ballroom Add to calendar

With the National Institute of Health recognizing the need for more inclusion of female and menstruating subjects in animal and clinical research, understanding how and if the menstrual cycle effects biological outcomes is particularly important. In this study, we examined whether day of cycle had an effect on urinary cortisol levels. We collected daily urine samples for one full cycle from 24 healthy, Polish-American women (age=18-45), living in urban regions of the United States. Participants were instructed to collect a urine sample daily, starting the first day of menstruation until the start of their next period, resulting in n=555 samples (some days of cycle missing). Mid cycle estradiol drop was used to estimate timing of ovulation, which was coded as day 0. We performed a generalized linear model to test the effect of day of cycle on cortisol levels and found a near significant effect (p=.067). However, to account for individual variation, we tested a second model including individual. There were no significant effects (day of cycle, p=.336). Our results suggest that controlling for cycle day or phase may not be necessary when collecting urinary cortisol from a group of healthy, homogenous adults. Additionally, omitting cycling participants from research because of concerns of increased variation or fears of the menstrual cycle impacting results may be unjustified and should be further explored.

Research supported by the NSF (BCS-1317140, DDRIG BCS-1732117, DDRIG BCS-1650839, GRFP DGE-1144245), Wenner-Gren Dissertation Fieldwork Grants, APS Lewis and Clark Fund, Beckman Institute CS/AI Award, Sigma Xi & more.