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

Abstract

Because major depressive disorder (MDD) and bipolar disorder (BD) manifest with similar symptoms, misdiagnosis is a persistent issue, necessitating their differentiation through objective methods. This study was aimed to differentiate between these disorders using a targeted proteomic approach. Multiple reaction monitoring-mass spectrometry (MRM-MS) analysis was performed to quantify protein targets regarding the two disorders in plasma samples of 270 individuals (90 MDD, 90 BD, and 90 healthy controls (HCs)). In the training set (72 MDD and 72 BD), a generalizable model comprising nine proteins was developed. The model was evaluated in the test set (18 MDD and 18 BD). The model demonstrated a good performance (area under the curve (AUC) >0.8) in discriminating MDD from BD in the training (AUC = 0.84) and test sets (AUC = 0.81) and in distinguishing MDD from BD without current hypomanic/manic/mixed symptoms (90 MDD and 75 BD) (AUC = 0.83). Subsequently, the model demonstrated excellent performance for drug-free MDD versus BD (11 MDD and 10 BD) (AUC = 0.96) and good performance for MDD versus HC (AUC = 0.87) and BD versus HC (AUC = 0.86). Furthermore, the nine proteins were associated with neuro, oxidative/nitrosative stress, and immunity/inflammation-related biological functions. This proof-of-concept study introduces a potential model for distinguishing between the two disorders.

Integrating proteomic and clinical data to discriminate major psychiatric disorders: Applications for major depressive disorder, bipolar disorder, and schizophrenia

“…Furthermore, the network was related to significant canonical pathways including complement and coagulation cascade dysregulation, neural signalling, and oxidative and inflammatory pathways, which has been replicated in previous studies (Figure 3). 2,9 Especially, reelin signalling was a significant canonical pathway, which is known to regulate neuronal migration and synaptogenesis in the brain, and has been linked to MDD, BD, and SCZ. 10 Through proteomic profiling, analytically stable plasma proteome (902 quantified proteins) were constructed in each pooled sample for the four groups (Table S12 and Figure S14A-D).…”

“…Multiprotein‐marker (MPM) models were constructed by LASSO (least absolute shrinkage and selection operator) with 100‐repeated 5‐fold cross‐validations, additionally with feature extraction and weighted model averaging, 2 in the training sets (Table S6 and Figure S7A–C) . After evaluating model performances in the validation sets based on selection fractions, the simplest models (selection fraction = 1) were selected, as the performances only mildly increased with selection fraction ≥.8 (Figure 1A–C ; Figure S8A–C) .…”

“…Due to different analytical methods, the corresponding proteins differed with our previous study for discriminating MDD versus BD except for ITIH2. 2 However, the current models were constructed with larger samples and expanded targets, and validated in an independent set; implying greater reproducibility. For each MPM model, the direction of each average coefficient corresponded to the alteration in expression (fold‐change) (Figure 1A–C ).…”

Evaluation of a Nondepleted Plasma Multiprotein-Based Model for Discriminating Psychiatric Disorders Using Multiple Reaction Monitoring-Mass Spectrometry: Proof-of-Concept Study

“…To develop discriminative models for each pairwise group comparison, least absolute shrinkage and selection operator (LASSO) based on feature extraction and weighted model averaging, , which had been referred in our previous studies, was used to decrease overfitting by simultaneous shrinkage of the coefficients and model construction. 3-fold cross-validation (100 repetitions) was used to determine the optimized value of the shrinkage parameter, lambda, which resulted in the most regularized model.…”

Discovery and replication of blood-based proteomic signature of PTSD in 9/11 responders

“…To date, proteomics has advanced the understanding of several psychiatric disorders, including schizophrenia, bipolar disorder, and depression [8][9][10]. For example, proteomics profiles derived from machine learning approaches have been found to discriminate between bipolar disorder and major depressive disorder in several independent studies, with a range from AUC = 0.67 [11] to AUC = 0.81 [12].…”