EPIGENETIC BIOMARKERS OF POSTPARTUM DEPRESSION IDENTIFY PREMENSTRUAL DYSPHORIC DISORDER (PMDD) AND POST-MENOPAUSAL DEPRESSION (PMD)
Authors: Kaminsky *Zachary Aaron , Hantsoo Liisa , Payne Jennifer L 
Published: February 2025

Abstract

Background
Postpartum depression (PPD) occurs following periods of major hormonal flux and experiments modulating gonadal hormone levels in the humans pharmacologically can lead to the onset of depressive phenotypes(Bloch et al., 2000). Together the data suggest a class of depression driven by hormonal change, here termed ‘hormonal depression’ (Payne, Palmer and Joffe, 2009; Payne, 2019).Previous work out of our laboratories have identified epigenetic variation at the TTC9B and HP1BP3 loci that is prospectively predictive of PPD risk, validated in over 5 cohorts(Guintivano et al., 2014; Osborne et al., 2016; Kaminsky et al., 2020; Lapato et al., 2020; Payne et al., 2020).

Objectives
The primary objective was to assess the predictive efficacy of epigenetic PPD biomarkers for other hormonally driven depressions including premenstrual dysphoric disorder (PMDD) and post-menopausal depression (PMD). The secondary objective was to assess the influence of antidepressant medications and serum hormones and neuroactive metabolites on biomarker outcomes.

Methods
We applied our published PPD biomarker linear model to DNA methylation generated by targeted pyrosequencing at TTC9B and HP1BP3 in a cohort of N=55 women with and without premenstrual dysphoric disorder (PMDD). The model was evaluated in a convenience sample of publicly available DNA methylation data from N=128 women to predict current depression in post-menopausal women.

Results
In the PMDD sample, Luteal but not follicular phase samples generated an AUC of 0.71 (95% CI: 0.49-0.93 ) to distinguish N= 10 PMDD cases from N=18 controls and generated an AUC of 0.86 (95% CI: 0.70-0.86). In the PMD sample, the PPD model predicted depression status with an AUC of 0.71 (95% CI: 0.61-0.81) in N=80 women with current depression and N=48 controls. In the PMDD sample, model application to luteal phase samples distinguished between N=5 SSRI responders from N=5 non-responders (AUC= 0.80, 95% CI: 0.50-0.80). HP1BP3 DNA methylation was significantly positively associated with levels of pregnenolone (rho= 0.35, p=0.014), suggesting it may be a marker of progesterone pathway precursor levels. Additionally, follicular phase TTC9B methylation was associated with the change in log(allopregnanolone) levels from follicular to luteal phases (rho= -0.53, p=0.02), suggesting it may be a marker of altered neuroactive steroid metabolism in the progesterone pathway.

Conclusions
This study suggests epigenetic PPD biomarkers can predict other hormonal depressions like PMDD and PMD. Furthermore, they may mark a biology related to variation of gonadal hormones like pregnenolone, the precursor to progesterone, and allopregnanolone, a neuroactive steroid important for modulating mood. Allopregnanolone, a target of recently approved PPD medications like Zuranolone(Althaus et al., 2020; Deligiannidis et al., 2021), can interact with the serotonin system and may be important for antidepressant medication response(Lovick, 2013; Lü scher and Mö hler, 2019). Future work should investigate the interaction of PPD biomarkers with medication response in hormonal depressions.

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