Citation

Hack LM, Nishitani S, Knight AK, Kilaru V, Maddox SA, Seligowski AV, Jovanovic T, Ressler KJ, Smith AK, Michopoulos V. Compr. Psychoneuroendocrinol. 2021; 6: e100045.

Copyright

(Copyright © 2021, Elsevier Publishing)

DOI

10.1016/j.cpnec.2021.100045

PMID

unavailable

Abstract

17β-estradiol (E2) levels in women correlate with multiple neuropsychiatric symptoms, including those that are stress-related. Furthermore, prior work from our group has demonstrated that E2 status influences DNA methylation (DNAm) across the genome. We developed and validated a DNAm-based predictor of E2 (one of four naturally occurring estrogens) using a training set of 183 females and a test set of 79 females from the same traumatized cohort. We showed that predicted E2 levels were highly correlated with measured E2 concentrations in our testing set (r ​= ​0.75, p ​= ​1.8e-15). We further demonstrated that predicted E2 concentrations, in combination with measured values, negatively correlated with current post-traumatic stress disorder (PTSD) (β ​= ​−0.38, p ​= ​0.01) and major depressive disorder (MDD) diagnoses (β ​= ​−0.45, p ​= ​0.02), as well as a continuous measure of PTSD symptom severity (β ​= ​−2.3, p ​= ​0.007) in females. Finally, we tested our predictor in an independent data set (n ​= ​85) also comprised of recently traumatized female subjects to determine if the predictor would generalize to a different population than the one on which it was developed. We found that the correlation between predicted and actual E2 concentrations in the external validation data set was also high (r ​= ​0.48, p ​= ​3.0e-6). While further validation is warranted, a DNAm predictor of E2 concentrations will advance our understanding of hormone-epigenetic interactions. Furthermore, such a DNAm predictor may serve as an epigenetic proxy for E2 concentrations and thus provide an important biomarker to better evaluate the contribution of E2 to current and potentially future psychiatric symptoms in samples for which E2 is not measured.

Language: en

Keywords

Biomarker; DNA methylation; Estrogen; Machine learning; Random forests (RFs); Trauma