Graduation Year

2025

Document Type

Dissertation

Degree

Ph.D.

Degree Name

Doctor of Philosophy (Ph.D.)

Degree Granting Department

Public Health

Major Professor

Monica Uddin, Ph.D.

Committee Member

Derek Wildman, Ph.D.

Committee Member

Brendan Walker, Ph.D.

Committee Member

Chengqi Wang, Ph.D.

Keywords

Depression, Predictive Biomarkers, Transcranial Magnetic Stimulation, Treatment Resistance

Abstract

Major depressive disorder (MDD) is a common and debilitating mental disorder associated with a significant disease burden and economic cost. Despite ample evidence documenting the efficacy of a diverse array of established MDD therapies, a large proportion of patients are inadequately responsive to initial treatment attempts, with first-line treatments leading to remission in only ~30% of the patients. Importantly, about a third of patients even fail to achieve symptom improvement after two or more antidepressant trials, resulting in what is commonly defined as treatment-resistant depression (TRD). The ability to identify which treatments are most likely to elicit a positive response in a given patient remains an elusive goal in psychiatry, highlighting the urgent need to identify biomarkers of MDD treatment outcomes, remission, and relapse. Moreover, although a substantial body of work has investigated MDD disease trajectory in both clinical and epidemiologic contexts, little research has examined biologic predictors of MDD disease trajectory in naturalistic (i.e. community-based) settings.

Recently, epigenetic profiles have shown promise as predictors of MDD treatment outcomes and may show promise as predictors of MDD disease trajectory. Epigenetic factors are stable but modifiable chemical modifications that serve as mechanisms by which the environment can moderate the effects of genes. DNA methylation (DNAm) is generally regarded as the most stable epigenetic mark, and while much work has focused on this mechanism in relation to MDD, only few studies have investigated DNAm in relation to treatment outcomes and TRD. Converging evidence suggests that baseline DNAm could be leveraged as possible predictors of antidepressant and electroconvulsive therapy response but a significant gap in the literature exists about the use of DNAm profiles in the context of transcranial magnetic stimulation (TMS) - a treatment modality that involves stimulating the brain via a magnetic field resulting in acute depolarization and altered electrical activity in the underlying neurons. Although TMS is highly effective in reducing depressive symptoms, a substantial fraction (~30%) of patients do not respond to this treatment, highlighting the need to identify whether epigenetic profiles predict response to TMS prior to treatment.

Relatedly, DNAm-based epigenetic factors have been used to characterize MDD disease trajectory, yet other epigenetic factors-in particular microRNAs (miRNAs) have yet to be explored outside of cross-sectional and/or clinical settings. Regarding miRNAs, these dynamic epigenetic factors have been shown to be involved with the dysregulation of known pathways implicated in the pathophysiology of MDD, yet such classifications do not apply to 100% of MDD patients, suggesting heterogeneity in the underlying characteristics of the disease. Additionally, the associational nature of current MDD miRNA studies prohibits the determination of whether observed miRNA expression profiles associated with MDD are predictive of, or a consequence of, the disorder.

In the Introduction in Chapter One, I provide a brief overview of MDD and its treatment approaches, discuss the concept of treatment resistance and explain the unmet need for reliable predictors of MDD disease trajectory and treatment outcomes. Additionally, I give a brief overview of epigenetic regulation in MDD and how it relates to its disease pathogenesis and trajectory. In Chapter Two, I discuss how epigenetic mechanism may help inform the development of predictive biomarkers by critically evaluating studies to date that investigated the best documented epigenetic mechanism, DNAm, in relation to treatment outcomes. While this avenue of research is in its infancy, it provides a new approach to improve personalized medicine that could be explored in other treatment strategies such a TMS. In Chapter Three, I apply genome-scale and targeted approaches to prospectively characterize blood-derived, DNAm-based epigenetic profiles among patients with TRD who are undergoing TMS treatment. I investigate whether distinct methylation patterns, captured through differentially methylated regions and methylation levels at targeted probes in the peripheral blood of patients with TRD can distinguish between TMS treatment-responders and nonresponders. While I identify distinct DNAm-based epigenetic signatures among TMS treatment outcomes, future studies leveraging larger sample sizes and employing longitudinal designs will be critical to the development of DNAm biomarkers in the context of TMS.

In Chapter Four, I leverage two-timepoint miRNA sequencing data from the Detroit Neighborhood Health Study, a population-representative cohort of adults residing in Detroit, Michigan, to characterize miRNA expression profiles associated with changes in depressive symptoms. I hypothesized that changes in depression symptom severity are associated with changes in miRNA expression over time and identify miRNAs whose expression change trajectories are explained by symptom improvement or worsening.

With these insights in mind, the overall goal of this dissertation work is to evaluate the epigenetic mechanism DNAm in relation to MDD treatment outcomes; characterize DNAm-based epigenetic profiles that distinguish between responders vs. nonresponders to TMS, a promising treatment in TRD populations; and elucidate the role of microRNA in MDD development and disease trajectory. This work provides an innovative approach to characterize epigenetics mechanism involved in the disease trajectory of MDD, that may inform other future efforts within the framework of precision psychiatry: a framework that seeks to use biomarkers to help guide treatment choices and optimize therapy response, which I discuss in the conclusion in Chapter Five.

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