Background: Despite overwhelming evidence that major depression is highly heritable, recent studies have localized only a single depression-related locus reaching genome-wide significance and have yet to identify a causal gene. Focusing on family-based studies of quantitative intermediate phenotypes or endophenotypes, in tandem with studies of unrelated individuals using categorical diagnoses, should improve the likelihood of identifying major depression genes. However, there is currently no empirically derived statistically rigorous method for selecting optimal endophentypes for mental illnesses. Here, we describe the endophenotype ranking value, a new objective index of the genetic utility of endophenotypes for any heritable illness.
Methods: Applying endophenotype ranking value analysis to a high-dimensional set of over 11,000 traits drawn from behavioral/neurocognitive, neuroanatomic, and transcriptomic phenotypic domains, we identified a set of objective endophenotypes for recurrent major depression in a sample of Mexican American individuals (n = 1122) from large randomly selected extended pedigrees.
Results: Top-ranked endophenotypes included the Beck Depression Inventory, bilateral ventral diencephalon volume, and expression levels of the RNF123 transcript. To illustrate the utility of endophentypes in this context, each of these traits were utlized along with disease status in bivariate linkage analysis. A genome-wide significant quantitative trait locus was localized on chromsome 4p15 (logarithm of odds = 3.5) exhibiting pleiotropic effects on both the endophenotype (lymphocyte-derived expression levels of the RNF123 gene) and disease risk.
Conclusions: The wider use of quantitative endophenotypes, combined with unbiased methods for selecting among these measures, should spur new insights into the biological mechanisms that influence mental illnesses like major depression.
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http://dx.doi.org/10.1016/j.biopsych.2011.08.022 | DOI Listing |
J Clin Psychol
January 2025
Department of Clinical Psychology and Psychobiology, The Institute of Neurosciences, Universitat de Barcelona, Barcelona, Spain.
Based on the repertory grid technique, we developed Explore Your Meanings (EYME), a digital platform that helps patients explore identity values and internal conflicts using virtual reality (VR). EYME was part of a research project treating depression in young adults, including 10 weekly, 1-h sessions aimed at changing personal constructs-cognitive schemas that shape how individuals interpret reality. We present the case of Mary, a 21-year-old woman diagnosed with persistent major depressive disorder and social phobia.
View Article and Find Full Text PDFNeuroradiology
January 2025
Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, 400016, China.
Introduction: Bipolar disorder (BD) and major depressive disorder (MDD) have overlapping clinical presentations which may make it difficult for clinicians to distinguish them potentially resulting in misdiagnosis. This study combined structural MRI and machine learning techniques to determine whether regional morphological differences could distinguish patients with BD and MDD.
Methods: A total of 123 participants, including BD (n = 31), MDD (n = 48), and healthy controls (HC, n = 44), underwent high-resolution 3D T1-weighted imaging.
Nord J Psychiatry
January 2025
Mental Health Services, Psychiatry East, Copenhagen University Hospital, Region Zealand, Denmark.
Purpose: To describe the prevalence of self-reported depression in a socioeconomically deprived area in Denmark and the association to socioeconomic position (SEP) defined by education, occupation, and being in financial strain.
Method: 13,955 adults completing the Major Depression Inventory (MDI) in the Lolland-Falster Health Study questionnaire were included.Multivariate logistic regression on symptoms of depression based on MDI sum score and ICD-10 scores, associated to education, occupation, and financial strain - unadjusted and adjusted for sex and age group.
BMC Psychiatry
January 2025
Department of Neurology, Shenzhen People's Hospital (The Second Clinical Medical College, Jinan University; The First Affiliated Hospital, Southern University of Science and Technology), Shenzhen, Guangdong, China.
Background: The neurasthenia-depression controversy has lasted for several decades. It is challenging to solve the argument by symptoms alone for syndrome-based disease classification. Our aim was to identify objective electroencephalography (EEG) measures that can differentiate neurasthenia from major depressive disorder (MDD).
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