Development and validation of blood-based proteomic biomarker-sociodemographic diagnostic prediction models to identify major depressive disorder among symptomatic individuals

Major depressive disorder (MDD) is a highly prevalent and disabling condition with a complex pathophysiology that has not been fully elucidated to date. While the socioeconomic burden of the disease is significant, many individuals remain undiagnosed or misdiagnosed. This is largely because the current diagnostic approach that relies on clinical evaluations of signs and symptoms can be subjective, and time and resources tend to be rather limited in primary care where the majority seek help for depression. Therefore, there is a significant and pressing need for an objective, reliable and readily accessible diagnostic test to enable earlier and more accurate diagnosis of MDD. In particular, as individuals experiencing subthreshold levels of depressive symptoms have an increased risk of developing MDD, it would be clinically relevant for such a diagnostic test to be able to identify depressed patients and/or individuals with high risks of incident MDD among symptomatic individuals. This thesis sought to develop risk prediction models that could potentially be utilised within a clinical setting to facilitate earlier and more accurate diagnosis of MDD. Such models were used to obtain probability estimates of the investigated individuals having or developing MDD based on their blood-based proteomic profiles and other characteristics, including sociodemographic and lifestyle factors. A targeted mass spectrometry approach was used to measure the abundances of a panel of peptides representing proteins, many of which have been previously associated with psychiatric disorders. Biomarkers were investigated in serum samples, which are widely used for blood-based biomarker discovery, as well as in dried blood spot samples, which are relatively novel in the field and carry several advantages. Importantly, this thesis focused on adopting appropriate statistical methods to ensure that the diagnostic predictions made by the models were accurate and reproducible, by addressing problems of model overfitting and model selection uncertainty. A particularly significant aspect of this was the development and application of a multimodel-based approach combining feature extraction and model averaging, which resulted in improved model predictive performance and generalisability. Diagnostic prediction models based on serum proteomic, sociodemographic/lifestyle and clinical data were shown to be able to differentiate between subthreshold symptomatic individuals who developed and did not develop MDD. Additionally, diagnostic prediction models based on dried blood spot proteomic and digital mental health assessment data were shown to be able to identify currently depressed patients without an existing MDD diagnosis as well as currently not depressed patients with an existing MDD diagnosis among subthreshold symptomatic individuals. These results clearly demonstrate the potential of such prediction models to be used as an aid to the diagnosis of MDD in clinical practice, especially within the primary care setting. Moreover, MDD was found to be associated with several blood-based proteomic biomarkers, which mainly represented an immune/inflammatory profile, as well as with various other patient features, most notably body mass index and childhood trauma. Although further investigations are needed, these associations reveal disturbances in the stress response pathways involving the hypothalamic-pituitary-adrenal axis in the pathophysiology of depression

EEG Asymmetries in Survivors of Severe Motor Accidents: Association with Posttraumatic Stress Disorder and its Treatment as well as Posttraumatic Growth: EEG Asymmetries in Survivors of Severe Motor Accidents: Association with Posttraumatic Stress Disorder and its Treatment as well as Posttraumatic Growth

Severe motor vehicle accidents (MVAs) represent one of the most often occurring psychological traumas, and are a leading cause of Posttraumatic Stress Disorder (PTSD). However, not all persons develop PTSD after traumatic events and a great proportion of patients who show symptoms initially recover over time. This has stimulated research of psychological and biological factors that explain development and maintenance of the disorder. Fortunately, this highly distressing condition can be effectively treated, e.g. via cognitive behavioral therapy (CBT). However, brain mechanisms underlying changes due to psychological therapy in PTSD are almost unknown (Roffman, Marci, Glick, Dougherty, & Rauch, 2005). On the other hand there are observations of positive changes following trauma called Posttraumatic Growth (PTG), which have stimulated research of associated psychological processes and factors. However, there is a lack of research about the relation of biological variables (e.g. measures of brain function) and PTG. Theories of brain asymmetry and emotion (Davidson, 1998b, 2004b; Heller, Koven, & Miller, 2003) propose that asymmetries of brain activation are related to certain features of human emotion (e.g. valence, approach or withdrawal tendencies, arousal). Whereas an enormous increase in the understanding of structural and functional abnormalities in PTSD could be achieved in the last decades due to neuroimaging research, there are still numerous unanswered questions. Especially, there is only little research explicitly examining activation asymmetries in PTSD. Furthermore, as mentioned, research is sparse investigating alterations of brain function that are associated with successful psychological treatment of PTSD. Finally, there is no published study examining how measures of brain function are related to PTG. This thesis presents 3 studies investigating electroencephalographic (EEG) asymmetries in survivors of severe motor vehicle accidents. The first part of the thesis (chapter 2) is devoted to a literature review about description (chapter 2.1), epidemiology (chapter 2.2 and 2.3), risk factors (chapter 2.4), psychological theories (chapter 2.5), biological mechanisms particularly neuroimaging findings (chapter 2.6), and treatment of PTSD (chapter 2.7.). Chapter 2.8 gives a short review on definition and research of Posttraumatic Growth. Chapter 2.9 provides an overview of models and research regarding brain asymmetry and emotion. In chapter 3.1, a study is presented that investigated hemispheric asymmetries (EEG alpha) among MVA survivors with PTSD, with subsyndromal PTSD, and without PTSD as well as non-exposed healthy controls during a baseline condition and in response to neutral, positive, negative, and trauma-related pictures (study I). Next, the findings of study II are presented (chapter 3.2). This study examined the effect of cognitive behavioral therapy on measures of EEG activity. Therefore, EEG activity before and after CBT in comparison to an assessment only Wait-list condition was measured. In chapter 3.3 a correlational study (study III) is presented that examined the relationship between frontal brain asymmetry and selfreported posttraumatic growth after severe MVAs. Finally, in chapter 4 the findings are summarized and discussed with respect to (1) the state/trait debate in frontal asymmetry research and (2) current psychological theories of PTSD and PTG. In addition, the use of neuroscientific research for psychotherapy is discussed. Suggestions are presented for future goals for “brain” research of PTSD and treatment of PTSD.Schwere Verkehrsunfälle stellen eines der am häufigsten vorkommenden psychologischen Traumata dar, und sind eine Hauptursache der Posttraumatischen Belastungsstörung (PTBS). Jedoch entwickeln nicht alle Personen nach traumatischen Ereignissen eine PTBS und bei einem Großteil remittieren anfängliche PTBS-Symptome. Dies stimulierte die Erforschung von psychologischen und biologischen Faktoren, die die Entstehung und Aufrechterhaltung der PTBS erklären. Glücklicherweise kann die PTBS effektiv, z.B über die kognitive Verhaltenstherapie (KVT), behandelt werden. Jedoch sind Gehirnmechanismen, die mit klinischen Änderungen aufgrund der psychologischen Therapie in PTSD einhergehen, nahezu unbekannt (Roffman, Marci, Glick, Dougherty, Rauch, 2005). Auf der anderen Seite gibt es Berichte von positiven Änderungen nach traumatischen Ereignissen, die als Posttraumatische Reifung (PTR) bezeichent werden. Dies hat in kürzerer Vergangenheit die Forschung von verbundenen psychologischen Prozessen und Faktoren stimuliert. Jedoch gibt es kaum Untersuchungen über die Beziehung von biologischen Variablen (z.B Messungen der Gehirnfunktion) und PTR. Diese Arbeit präsentiert 3 Studien, die electroenzephalographische (EEG) Asymmetrien bei Opfern schwerer Verkehrsunfälle untersuchten. Der erste Teil der Arbeit (Kapitel 2) widmet sich einer Literaturrezension über: die Beschreibung (Kapitel 2.1), Epidemiologie (Kapitel 2.2 und 2.3), Risikofaktoren (Kapitel 2.4), psychologische Theorien (Kapitel 2.5), biologische Mechanismen besonders Neuroimaging Ergebnisse (Kapitel 2.6), und Behandlung der PTBS (Kapitel 2.7.). Kapitel 2.8 gibt einen kurzen Überblick über die Definition und Forschung zur Posttraumatischen Reifung. Kapitel 2.9 gibt eine Übersicht zu aktuellen Modellen und empirischen Befunden bezüglich Gehirnasymmetrien und Emotionen. Kapitel 3.1 präsentiert eine Studie, in der hemisphärische Asymmetrien (im EEG-Alpha Band) bei Unfallopfern mit PTBS, subsyndromaler PTBS, und ohne PTBS sowie gesunden Kontrollpersonen ohne Unfall untersucht wurden: während einer Ruhemessung und einer Emotionsinduktions-bedingung (neutrale, positive, negative und trauma-spezifische Bilder) (Studie I). Danach werden die Ergebnisse der Studie II (Kapitel 3.2) präsentiert. Hier wurde die Wirkung der kognitiven Verhaltenstherapie auf Messungen der EEG-Aktivität untersucht. Deshalb wurde EEG-Aktivität vor und nach einer KVT im Vergleich mit einer Warten-Gruppe gemessen. Kapitel 3.3 präsentiert eine Korellationsanalyse (Studie III), bei der die Beziehung zwischen der frontalen Gehirnasymmetrie und posttraumatischer Reifung untersucht wurde. Am Ende der Arbeit (Kapitel 4) werden die Ergebnisse zusammengefasst und in Bezug auf (1) die state/trait-Debatte im Rahmen der Asymmetrie-Forschung diskutiert sowie (2) ein Bezug zu aktuellen psychologische Theorien von PTSD und PTG hergestellt. Außerdem wird der Nutzen von neurobiologischer Forschung für die Psychotherapie besprochen. Dabei werden Vorschläge für zukünftige Projekte für die "Gehirn"-Forschung im Zusammenhang mit der PTBS, deren Behandlung und PTG gemacht

Searching for patomechanisms of late life minor depression

The doctoral dissertation: Searching for pathomechanisms of late-life minor depression – a combined MRI, biomarker and meta-analytic study was one of the first studies investigating the underlying pathophysiology of minor depression. The dissertation comprises a systematic review of the prevalence rates of minor depression, two meta-analyses of peripheral BDNF changes in major depressive disorder, as well as two original studies investigating serum BDNF, S100B and NSE levels and gray matter changes in minor depression. The limitations of studies and proposed improvements to the study design are discussed extensively.:1. INTRODUCTION 1.1 Motivation 1.1.1 Minor depression in the spectrum of psychiatric disorders 1.1.2 Minor depression is prevalent but unrecognized. 1.2 Theoretical background 1.2.1 Overview of depression hypotheses 1.2.2 Neurotrophic hypothesis of depressive disorders 1.2.3 Glial hypothesis of depressive disorders 1.2.4 Structural neuroimaging changes in major depression 1.3. Rationale and hypotheses of the empirical studies 1.3.1 Research questions 1.3.2 Research hypotheses 2. EMPIRICAL STUDIES 2.1 The prevalence of minor depression in the late life 2.2 The meta-analysis of BDNF changes in mood disorders 2.3 The meta-analysis of BDNF changes following ECT in depression 2.4 Serum biomarkers in minor depression 2.5 Structural brain imaging in minor depression 3. GENERAL DISCUSSION 3.1 Summary of results 3.2 Implications for research 3.3 Implications for clinical studies SUMMARY REFERENCES APPENDIX A: DECLARATION OF CONTRIBUTION APPENDIX B: STATEMENT OF AUTHORSHIP APPENDIX C: CURRICULUM VITAEAPPENDIX D: ACADEMIC CONTRIBUTIONS APPENDIX E: ACKNOWLEDGMEN

Prediction of depressive symptoms from socioeconomic data and DNA methylation signatures in depression

Depression is an increasingly common mental disorder associated with substantial deficits in the quality of life of the patient and increased mortality risk. Several Genomic-Wide Association Studies (GWAS) have been performed to identify genes associated with depression, but its partial heritability among other characteristics suggests the involvement of epigenetic changes in the origin of the disease. Since has been proven a discrepancy between objective and subjective cognition in Major Depressive Disorder (MDD) patients, it is necessary to find a system to detect the disease from a biological perspective. Using a Canadian community-based cohort (n=94) containing DNA methylation data, stratified for early-life socioeconomic status, and assessed for depressive symptoms with the Center for Epidemiologic Studies Depression (CES-D) scale, a differential methylation analysis was performed. From this analysis, 31 cytosine guanine dinucleotides (CpG) were identified as differentially methylated in patients showing depressive symptoms from patients not showing those symptoms. The analysis was performed separating patients by gender and taking the variable age as a covariate. From the socioeconomic and biomolecular variables, and identified CpG sites, a random forest classifier was developed to create a depressive symptoms prediction tool. The resulting algorithm has an accuracy of 73.74% (repeated 15-fold cross-validation, with 3 repeats). The web application Desypre (http://desypre.000webhostapp.com/) was created to allow the public use of the classifier.La depresión es un trastorno mental cada vez más común asociado con déficits sustanciales en la calidad de vida del paciente y un mayor riesgo de mortalidad. Se han realizado varios estudios de asociación genómica (GWAS) para identificar genes asociados con la depresión, pero su heredabilidad parcial entre otras características sugiere la existencia de cambios epigenéticos en el origen de la enfermedad. Dado que se ha demostrado una discrepancia entre la cognición objetiva y subjetiva en pacientes con trastorno depresivo mayor (MDD), es necesario encontrar un sistema para detectar la enfermedad desde una perspectiva biológica. Utilizando una cohorte basada en una comunidad canadiense (n = 94) que contiene datos de metilación del ADN, estratificada para el estado socioeconómico temprano y evaluada para síntomas depresivos con la escala para Depresión del Centro para Estudios Epidemiológicos (CES-D), se realizó un análisis de metilación diferencial. A partir de este análisis, se identificaron 31 dinucleótidos citosina-guanina (CpG) como metilados diferencialmente en pacientes que muestran síntomas depresivos contra pacientes que no muestran esos síntomas. El análisis se realizó separando a los pacientes por género y tomando la variable edad como una covariable. A partir de las variables socioeconómicas y biomoleculares, y los sitios de CpG identificados, se desarrolló un clasificador Random Forest con el objetivo de crear una herramienta de predicción de síntomas depresivos. El algoritmo resultante tiene una precisión del 73,74% (validación 15 veces cruzada con 3 repeticiones). La aplicación web Desypre (http://desypre.000webhostapp.com/) se creó para permitir el uso público del clasificador.La depressió és un trastorn mental cada vegada més comú associat amb dèficits substancials en la qualitat de vida del pacient i un major risc de mortalitat. S'han realitzat diversos estudis d'associació genòmica (GWAS) per identificar gens associats amb la depressió, però la seva heretabilitat parcial entre altres característiques suggereix l'existència de canvis epigenètics en l'origen de la malaltia. Atès que s'ha demostrat una discrepància entre la cognició objectiva i subjectiva en pacients amb trastorn depressiu major (MDD), cal trobar un sistema per detectar la malaltia des d'una perspectiva biològica. Utilitzant una cohort basada en una comunitat canadenca (n = 94) que conté dades de metilació de l'ADN, estratificada per a l'estat socioeconòmic d'hora i avaluada per símptomes depressius amb l'escala per Depressió el Centre per a Estudis Epidemiològics (CES-D), es va realitzar una anàlisi de metilació diferencial. A partir d'aquesta anàlisi, es van identificar 31 dinucleòtids citosina-guanina (CpG) com metilats diferencialment en pacients que mostren símptomes depressius contra pacients que no mostren aquests símptomes. L'anàlisi es va realitzar separant als pacients per gènere i prenent la variable edat com una covariable. A partir de les variables socioeconòmiques i biomoleculars, i els llocs de CpG identificats, es va desenvolupar un classificador Random Forest amb l'objectiu de crear una eina de predicció de símptomes depressius. L'algorisme resultant té una precisió de l'73,74% (validació 15 vegades creuada amb 3 repeticions). L'aplicació web Desypre (http://desypre.000webhostapp.com/) es va crear per permetre l'ús públic de el classificador

Investigating the Transcriptome Signature of Depression: Employing Co-expression Network, Candidate Pathways and Machine Learning Approaches

Depression is the leading cause of disability worldwide and is one of the major contributors to the overall global burden of disease. Despite significant advances in elucidating the neurobiology of depression in recent years, the molecular factors involved in the pathophysiology of depression remain poorly understood. Chapter 1: An overview of Major Depressive Disorder (MDD) from epidemiological and clinical perspectives with a summary of the current knowledge of the underlying biology is provided. A review of the major pathophysiological hypotheses of MDD highlights a need for a more comprehensive approach that allows studying complex molecular interactions involved in depression. Chapter 2: Transcriptome signature of depression was examined using the measure of replication at individual gene level across different tissues and cell types in both brain and periphery. Fifty-seven replicated genes were reported as differentially expressed in the brain and 21 in peripheral tissues. In-silico functional characterisation of these genes was provided, implicating shared pathways in a comorbid phenotype of depression and cardiovascular disease. Chapter 3: The molecular basis of MDD using co-expression network analysis was investigated. The Weighed Gene Co-expression Network Analysis (WGCNA) allowed for studying complex interactions between individual genes influencing biological pathways in MDD. Utilising the Sydney Memory and Aging Study (sMAS) and the Older Australian Twin Study (OATS) as discovery and replication cohorts respectively, it was found that the eigengenes of four clusters containing over 3,000 highly co-regulated genes are involved in 13 immune- and pathogen-related pathways and associated with recurrent MDD. However, the findings were not replicated on an independent cohort at the network level. Chapter 4: Using a machine learning (ML) approach, a predictive model was built to identify the genome-wide gene expression markers of recurrent MDD. Fuzzy Forests (FF) is a novel ML algorithm, which works in conjunction with WGCNA and was designed to reduce the bias seen in feature selection caused by the presence of correlated transcripts in transcriptome data. FF correctly classified 63% of recurrently depressed individuals in test data using the single top predictive feature (TFRC, encodes for transferrin receptor). This suggests that TFRC can represent a putative marker for recurrent MDD. Chapter 5: Following the findings on immune-related pathways being associated with recurrent MDD in the elderly (Chapter 3), the role of these pathways in recurrent MDD was examined at individual gene levels in an independent cohort (OATS). To target the immune pathways, all known genes (KEGG) involved in these 13 pathways were selected and a differential expression analysis was conducted on 1,302 candidates between individuals with recurrent MDD and those without. We found that CD14 was significantly downregulated in recurrent MDD (FDR < 5%). Considering the key role of CD14 for facilitating the innate immune response, we suggest that CD14 can potentially serve as a peripheral marker of immune dysregulation in recurrent MDD. Chapter 6: A discussion on obtained findings is provided and future directions are outlined with a particular focus on how co-expression network and machine learning approaches that can enhance translation of molecular findings into clinical translation.Thesis (Ph.D.) -- University of Adelaide, Adelaide Medical School, 201

Latent Classes of Self-Reported Adolescent Depression in a Clinical In-Patient Population

The depressive disorders are among the most common mental health problems with substantial financial and quality-of-life costs. Depression has generated considerable debate as to the underlying structure and the taxonomy continues to be frequently debated. Adolescents who meet diagnostic criteria for major depressive disorder often experience anxiety (and vice versa). Emerging statistical approaches such as latent class analysis (LCA) have utility for understanding the underlying structure of depression as well as the co-occurrence of depression and anxiety. An LCA of adolescents with depression would add to our conceptual understanding of the disorder(s) and facilitate treatments of adolescents with depression and potentially those with co-occurring anxiety symptoms. The current study adds to the body of literature on the latent structure of depression and co-occurring anxiety of a juvenile in-patient sample. LCA was conducted on an in-patient sample of juveniles (N = 722). Analyses yielded six distinct classes or subtypes of depression that were different from each other on overall symptom severity as well as the presence or absence of anhedonia. Results may have implications regarding subtypes of adolescent depression, comorbidity of anxiety, and our understanding of the taxonomic structure of categorical versus dimensional aspects of depression diagnosis. Results suggest subclinical features of anxiety commonly co-occur with depression among juveniles, suggesting a common construct of adolescent distress made up of both depression and anxiety