Towards a major methodological shift in depression research by assessing continuous scores of recurrence of illness, lifetime and current suicidal behaviors and phenome features: focus on atherogenicity and adverse childhood experiences

Abstract

Background: The binary major depressive disorder (MDD) diagnosis is inadequate and should never be used in research. Aims: The study's objective is to explicate our novel precision nomothetic strategy for constructing depression models based on adverse childhood experiences (ACEs), lifetime and current phenome, and biomarker (atherogenicity indices) scores. Methods: This study assessed recurrence of illness (ROI: namely recurrence of depressive episodes and suicidal behaviors), lifetime and current suicidal behaviors and the phenome of depression, neuroticism, dysthymia, anxiety disorders, and lipid biomarkers (including ApoA, ApoB, free cholesterol and cholesteryl esters, triglycerides, high density lipoprotein cholesterol) in 67 normal controls and 66 MDD patients. We computed atherogenic and reverse cholesterol transport indices. Results: We were able to extract one factor from a) the lifetime phenome of depression comprising ROI, and traits such as neuroticism, dysthymia and anxiety disorders, and b) the phenome of the acute phase (based on depression, anxiety and quality of life scores). PLS analysis showed that 55.7% of the variance in the lifetime + current phenome factor was explained by increased atherogenicity, neglect and sexual abuse, while atherogenicity partially mediated the effects of neglect. Cluster analysis generated a cluster of patients with major dysmood disorder, which was externally validated by increased atherogenicity and characterized by increased scores of all clinical features. Conclusions: The outcome of depression should not be represented as a binary variable (MDD or not), but rather as multiple dimensional scores based on biomarkers, ROI, subclinical depression traits, and lifetime and current phenome scores including suicidal behaviors.

Research and Diagnostic Algorithmic Rules (RADAR) and RADAR Plots for the First Episode of Major Depressive Disorder: Effects of Childhood and Recent Adverse Experiences on Suicidal Behaviors, Neurocognition and Phenome Features

“…We have constructed RADAR graphs that contain not only clinical scores (as explained in the current study) but also diverse biomarker scores, which are altered in depression [55]. With the help of this new method, adequate clinical monitoring as well as biomarker monitoring of the disease may be performed.…”

“…With the help of this new method, adequate clinical monitoring as well as biomarker monitoring of the disease may be performed. Secondly, our methodology makes it possible for researchers to compute new risk or susceptibility biomarkers, screening and detecting biomarkers, diagnostic biomarker panels, and prognostic biomarkers of ROI, suicidal behaviors, cognitive impairments, and the phenome of MDD [55]. In addition, using our novel methodology, we are able to calculate new predictive biomarker tools that can direct personalized therapies for MDD, ROI, or suicidal tendencies [55].…”

“…Axis-2 personality disorders were not measured in the current study. Some personality traits, such as neuroticism, are comorbid with depression and are affected by ACE [55]. Therefore, it is recommended to add personality trait RADAR scores to our RADAR plots.…”

The effects of adverse childhood experiences on depression and suicidal behaviors are partially mediated by neuroticism: A subclinical manifestation of major depression

“…In fact, statistical analyses can only be conducted if the output (dependent) variable (thus: the diagnosis) is a correct model ( 58 ), which is not the case for the DSM-IV MDD diagnosis ( 8 ). Second, research assessing a lifetime diagnosis should not use a binary (one-bit of information) lifetime diagnosis, but rather validated latent variable scores that represent the lifetime trajectory and are built using the relevant lifetime indicators ( 58 ). A construct based on diverse assessments, as described in Maes et al ( 58 ), is always more precise than a binary diagnosis.…”

“…Second, research assessing a lifetime diagnosis should not use a binary (one-bit of information) lifetime diagnosis, but rather validated latent variable scores that represent the lifetime trajectory and are built using the relevant lifetime indicators ( 58 ). A construct based on diverse assessments, as described in Maes et al ( 58 ), is always more precise than a binary diagnosis. Third, as shown by the present research and Maes et al ( 58 ), the current severity of depression should not be measured by a few self-reported items (which in Kendler’s study were ordinalized and differ between the three research centers), but by a quantitative latent variable score combining depressed, anxious, and other related symptoms as assessed using interviews and self-rating scales.…”