Abstract
Convergent evidence strongly suggests major depressive disorder is heterogeneous in its etiology and clinical characteristics. Depression biomarkers hold potential for identifying etiological subtypes, improving diagnostic accuracy, predicting treatment response, and personalization of treatment. Human plasma contains numerous sterols that have not been systematically studied. Changes in cholesterol concentrations have been implicated in suicide and depression, suggesting plasma sterols may be depression biomarkers. Here, we investigated associations between plasma levels of 34 sterols (measured by mass spectrometry) and scores on the Quick Inventory of Depressive Symptomatology-Self Report (QIDS-SR16) scale in 3117 adult participants in the Dallas Heart Study, an ethnically diverse, population-based cohort. We built a random forest model using feature selection from a pool of 43 variables including demographics, general health indicators, and sterol concentrations. This model comprised 19 variables, 13 of which were sterol concentrations, and explained 15.5% of the variation in depressive symptoms. Desmosterol concentrations below the fifth percentile (1.9 ng/mL, OR 1.9, 95% CI 1.2–2.9) were significantly associated with depressive symptoms of at least moderate severity (QIDS-SR16 score ≥10.5). This is the first study reporting a novel association between plasma concentrations cholesterol precursors and depressive symptom severity.
Highlights
Major depressive disorder (MDD) is a highly prevalent disease and a leading cause of disability worldwide [1]
We determined that 13.7% of the participants had not answered one or more questions on the QIDS-SR16
We used a machine learning approach to investigate whether plasma sterols concentrations are associated with depressive symptoms in an epidemiological cohort
Summary
Major depressive disorder (MDD) is a highly prevalent disease and a leading cause of disability worldwide [1]. MDD is heterogeneous in its etiology and clinical characteristics [2]. Depression biomarkers hold potential for identifying etiological subtypes, improving diagnostic accuracy, predicting treatment response, and personalization of treatment. Changes in various biochemical pathways have been associated with MDD, including inflammatory, neurotrophic, and hypothalamic-pituitary-adrenal (HPA) axis alterations [3]. Plasma lipids have received relatively little attention as depression biomarkers despite a long-recognized association between low plasma cholesterol, suicidal behavior, and depression [4,5,6,7,8,9,10,11].
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