Identification of an Immune-Neuroendocrine Biomarker Panel for Detection of Depression: A Joint Effects Statistical Approach

Abstract

Background/Aims: Less than half of depression patients are correctly diagnosed within the primary care setting. Previous proteomic studies have identified numerous immune and neuroendocrine changes in patients. However, few studies have considered the joint effects of biological molecules and their diagnostic potential. Our aim was to develop and validate a diagnostic serum biomarker panel identified through joint effects analysis of multiplex immunoassay profiling data from 1,007 clinical samples. Methods: In stage 1, we conducted a meta-analysis of two independent cohorts of 78 first-/recent-onset drug-naive/drug-free depression patients and 156 controls and applied the 10-fold cross-validation with least absolute shrinkage and selection operator regression to identify an optimal diagnostic prediction model (biomarker panel). In stage 2, we tested the discriminatory performance of this biomarker panel using the naturalistic Netherlands Study of Depression and Anxiety (NESDA) cohort of 468 depression patients and 305 controls. Results: An optimal panel of 33 immune-neuroendocrine biomarkers and gender was selected in the meta-analysis. Testing this biomarker-gender panel using the NESDA cohort resulted in a moderate to good performance to differentiate patients from controls (0.69 < AUC < 0.86), particularly the first-episode patients free of chronic non-psychiatric diseases or medications and following incorporation of sociodemographic covariates (0.76 < AUC < 0.92). Conclusion: Despite the need for additional validation studies, we demonstrated that a blood-based biomarker-sociodemographic panel can detect depression in naturalistic healthcare settings with good discriminatory power. Further refinements of blood biomarker panels aiding in the diagnosis of depression may provide a cost-effective means to increase accuracy of clinical diagnosis within the primary care setting.

The difference in serum proteomes in schizophrenia and bipolar disorder

“…The data on the levels of Alpha-2-macroglobulin in mental disorders vary substantially in various publications. The increase in Alpha-2-macroglobulin level in patients with MD was found by [90, 95, 96]. Besides, Alsaif et al [24] and Song et al [22] have found the decrease in patients with BD in comparison with healthy controls.…”

Integrating proteomic, sociodemographic and clinical data to predict future depression diagnosis in subthreshold symptomatic individuals

“…We identified six proteins (represented by six peptides) as biomarkers of MDD: AACT, APOE, APOH, FETUA, HBA and PHLD, which have functional roles in inflammatory response, lipid transport and metabolism, blood coagulation and oxygen transport 44 . Alterations in peripheral proteins involved in inflammatory response, the hypothalamic–pituitary–adrenal (HPA) axis, and carbohydrate and lipid metabolism have been reported in previous biomarker studies on depression 45–51 . Other apolipoproteins (apolipoprotein A, apolipoprotein B, apolipoprotein C-III and apolipoprotein D) have also been linked to depression 47,52–55 .…”

Differential Peripheral Proteomic Biosignature of Fluoxetine Response in a Mouse Model of Anxiety/Depression

“…Recently, Svenningsson et al (2014) showed that reduction in p11 in PBMCs could potentially predict antidepressant response to citalopram. Additionally, there is growing evidence of multiple dysregulated contributing factors, including growth factors, altered endocrine factors ( Schmidt et al, 2011 ) or immune-related pathways ( Belzeaux et al, 2016 ; Chan et al, 2016b ) in mood disorders and/or in antidepressant responses. Thus, a viable alternative to the single-biomarker approach could be the development of biomarker panels to provide coverage of multiple biological factors that contribute to the heterogeneity of MDD and treatment response ( Schmidt et al, 2011 ).…”