Using Machine Learning to Identify Predictors of Maternal and Infant Hair Cortisol Concentration Before and During...

Area of Science:

• Endocrinology
• Psychoneuroendocrinology
• Developmental Psychology

Background:

• Hair cortisol concentration (HCC) is a biomarker for chronic stress and hypothalamic-pituitary-adrenal (HPA) axis activity.
• Maternal and infant HCC may be linked due to shared environmental and biological factors.
• The COVID-19 pandemic presented a significant ecological stressor impacting maternal and infant well-being.

Purpose of the Study:

• To examine HCC as a retrospective indicator of HPA axis activity in mothers and infants during the COVID-19 pandemic.
• To compare HCC between cohorts recruited before and during the pandemic.
• To identify psychological stress predictors of maternal and infant HCC and assess mother-infant HCC covariation.

Main Methods:

• Comparative analysis of HCC in two postpartum cohorts (pre-pandemic vs. pandemic).
• Machine learning application to identify psychological stress indicators predicting HCC.
• Statistical testing for within-dyad covariation in maternal and infant HCC.

Main Results:

• The study will compare HCC across pre-pandemic and pandemic-recruited mother-infant dyads.
• Machine learning will identify key psychological stress predictors (perceived stress, parenting stress, depressive symptoms) of HCC.
• Covariation in maternal and infant HCC will be analyzed in relation to maternal stress and pandemic experiences.

Conclusions:

• Findings will elucidate the utility of HCC in reflecting psychological stress during the peripartum period.
• The study will provide insights into the impact of large-scale stressors like the COVID-19 pandemic on maternal and infant HPA axis activity.
• Results will inform future research utilizing hair cortisol as a biomarker for chronic stress in dyads.