The purpose of this study is to validate a set of signatures, based on a panel of proteomic markers, that discriminate BDI, BDII, and MDD in people seeking treatment for a depressive episode.
This is a hypothesis-driven confirmatory study to validate the diagnostic signature (model) for distinguishing BDI from MDD that also aims to optimize the models to discriminate BDII from MDD and BDI. A binary classification model, using linear discriminant analysis and based on 13 a priori-defined proteomic markers will aim to distinguish BDI from MDD. An alternative binary classification model based on multiple logistic regression and using 10 a priori -defined proteomic markers will aim for the same result. To improve the predictive performance of the signatures, items from self-report mood rating scales and treatment-emergent changes in proteomic markers will be analyzed. In addition, the study will examine if baseline or early treatment-emergent changes in proteomic markers predict treatment response.
Study Type
OBSERVATIONAL
Enrollment
261
Proteomic assay
University of Iowa Health Care, Department of Psychiatry
Iowa City, Iowa, United States
University of Minnesota (UMN) Department of Psychiatry
Minneapolis, Minnesota, United States
Lindner Center of HOPE/University of Cincinnati College of Medicine
Mason, Ohio, United States
University of Pittsburgh Western Psychiatric Institute and Clinic
Pittsburgh, Pennsylvania, United States
Agreement between the model derived diagnosis (based on panel of serum proteomic markers) and the clinical diagnosis (confirmed by the SCID DSM-5)
Linear discriminant analysis and multiple logistic regression will be used to create three diagnostic models for the proteomic markers (BDI vs MDD, BDI vs BDII and BDII vs MDD). The patient's model diagnosis will be compared to the patient's clinical diagnoses (based on SCID DSM -5) and the proportion of concordant classifications will be calculated.
Time frame: Baseline
Self-report clinical rating scales (IDS-SR30, PHQ-9, MDQ, HCL-32 and TEMPS-A)
Additional clinical characterization of the patient and their depressive episode will be obtained through analysis of the self-report clinical rating scales and used to optimize the predictive performance of the proteomic signatures.
Time frame: Baseline, Week 2 and Week 8
Changes in proteomic markers at Week 2 and Week 8
Proteomic markers will be analyzed at weeks 2 and 8 to observe for any treatment-emergent changes to increase the predictive validity of the proteomic signatures.
Time frame: Week 2 and Week 8
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University of Texas Health Science Center
San Antonio, Texas, United States