Development and cross-tissue validation of a methylation profile score for the cortisol response to stress

Hypothalamic-pituitary-adrenal axis (HPA axis) dysregulation is a risk factor for poor mental and physical health. Animal studies indicate that DNA methylation may be one mechanism through which stress can influence the function of the HPA axis, however human studies have not identified consistent individual loci. Machine learning can be used to develop methylation profile scores (MPSs), but this method has not yet been applied to HPA axis function. Using a novel machine learning pipeline, we developed an MPS to predict the salivary cortisol response (AUCi) to the Trier Social Stress Test (TSST) from whole blood Illumina Infinium HumanMethylation 450K BeadChip data (N = 84, mean age = 34, 49% female). The MPS was associated with the cortisol response in an independent, cross-tissue cohort (N = 53, mean age = 20, 51% female), both before ({beta} = 0.33, 95% CI [0.09, 0.54]) and after a social stressor ({beta} = 0.3, 95% CI [0.09, 0.47]). Functional characterisation revealed several immune, stress, and disease-related pathways and genes, including tolerance induction to self antigen, chronic myeloid leukemia, NR3C2, and PSMB4 (putatively causal in depression). We have developed and validated a novel epigenetic biomarker for stress reactivity, identifying a set of genomic loci where DNA methylation is associated with the cortisol response. Future research could investigate if HPA axis-related MPSs could be used alongside traditional risk factors to improve clinical risk assessment.