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

Abstract

Individuals with subthreshold depression have an increased risk of developing major depressive disorder (MDD). The aim of this study was to develop a prediction model to predict the probability of MDD onset in subthreshold individuals, based on their proteomic, sociodemographic and clinical data. To this end, we analysed 198 features (146 peptides representing 77 serum proteins (measured using MRM-MS), 22 sociodemographic factors and 30 clinical features) in 86 first-episode MDD patients (training set patient group), 37 subthreshold individuals who developed MDD within two or four years (extrapolation test set patient group), and 86 subthreshold individuals who did not develop MDD within four years (shared reference group). To ensure the development of a robust and reproducible model, we applied feature extraction and model averaging across a set of 100 models obtained from repeated application of group LASSO regression with ten-fold cross-validation on the training set. This resulted in a 12-feature prediction model consisting of six serum proteins (AACT, APOE, APOH, FETUA, HBA and PHLD), three sociodemographic factors (body mass index, childhood trauma and education level) and three depressive symptoms (sadness, fatigue and leaden paralysis). Importantly, the model demonstrated a fair performance in predicting future MDD diagnosis of subthreshold individuals in the extrapolation test set (AUC = 0.75), which involved going beyond the scope of the model. These findings suggest that it may be possible to detect disease indications in subthreshold individuals up to four years prior to diagnosis, which has important clinical implications regarding the identification and treatment of high-risk individuals.

Toward an Extended Definition of Major Depressive Disorder Symptomatology: Digital Assessment and Cross-validation Study

“…Across our models, the most predictive symptom of MDD was leaden paralysis, which refers to an extreme form of fatigue or heavy, leaden feelings in the arms and legs. This finding is in line with a recent study by Han et al [ 61 ], whose findings revealed that leaden paralysis was a robust and important predictor of first-onset MDD. Critically, although leaden paralysis is included in the DSM-5 specifier for atypical depression, it is not deemed a core feature of the disorder [ 20 ].…”

Quantitative Proteomic Approach for Discriminating Major Depressive Disorder and Bipolar Disorder by Multiple Reaction Monitoring-Mass Spectrometry

“…13−15 Although these studies proposed many candidates, such as C3, C4BPA, CFI, B2RAN2, ENG, RAB7A, ROCK2, XPO7, PDGF-BB, and TSP-1, their clinical relevance and significance remain unknown, necessitating further validation of these biomarkers. 13−16 Moreover, although MS-based targeted proteomic techniques have been used in studies to differentiate mood disorders from HCs or between HCs and severity of a specific mood disorder, 19,20 no study has attempted to discriminate MDD from BD using MSbased quantitative targeted proteomic methods, such as multiple reaction monitoring (MRM)-MS, at least to our knowledge.…”

A machine learning algorithm to differentiate bipolar disorder from major depressive disorder using an online mental health questionnaire and blood biomarker data

“…The returned DBS samples were analysed for neuropsychiatric biomarker levels using a validated targeted proteomic approach [27][28][29] . The method targeted 203 unique peptides representing 120 proteins (Supplementary Table 1) selected based on their association with psychiatric conditions and concentration in the blood 28 .…”