Differential biomarker signatures in unipolar and bipolar depression: A machine learning approach

MicroRNA classification and discovery for major depressive disorder diagnosis: Towards a robust and interpretable machine learning approach

BACKGROUND

Major depressive disorder (MDD) is notably underdiagnosed and undertreated due to its complex nature and subjective diagnostic methods. Biomarker identification would help provide a clearer understanding of MDD aetiology. Although machine learning (ML) has been implemented in previous studies to study the alteration of microRNA (miRNA) levels in MDD cases, clinical translation has not been feasible due to the lack of interpretability (i.e. too many miRNAs for consideration) and stability.

METHODS

This study applied logistic regression (LR) model to the blood miRNA expression profile to differentiate patients with MDD (n = 60) from healthy controls (HCs, n = 60). Embedded (L1-regularised logistic regression) feature selector was utilised to extract clinically relevant miRNAs, and optimized for clinical application.

RESULTS

Patients with MDD could be differentiated from HCs with the area under the receiver operating characteristic curve (AUC) of 0.81 on testing data when all available miRNAs were considered (which served as a benchmark). Our LR model selected miRNAs up to 5 (known as LR-5 model) emerged as the best model because it achieved a moderate classification ability (AUC = 0.75), relatively high interpretability (feature number = 5) and stability (ϕ̂Z=0.55) compared to the benchmark. The top-ranking miRNAs identified by our model have demonstrated associations with MDD pathways involving cytokine signalling in the immune system, the reelin signalling pathway, programmed cell death and cellular responses to stress.

CONCLUSION

The LR-5 model, which is optimised based on ML design factors, may lead to a robust and clinically usable MDD diagnostic tool.

Inflammatory mediators in major depression and bipolar disorder

Major depressive disorder (MDD) and bipolar disorder (BD) are highly disabling illnesses defined by different psychopathological, neuroimaging, and cognitive profiles. In the last decades, immune dysregulation has received increasing attention as a central factor in the pathophysiology of these disorders. Several aspects of immune dysregulations have been investigated, including, low-grade inflammation cytokines, chemokines, cell populations, gene expression, and markers of both peripheral and central immune activation. Understanding the distinct immune profiles characterizing the two disorders is indeed of crucial importance for differential diagnosis and the implementation of personalized treatment strategies. In this paper, we reviewed the current literature on the dysregulation of the immune response system focusing our attention on studies using inflammatory markers to discriminate between MDD and BD. High heterogeneity characterized the available literature, reflecting the heterogeneity of the disorders. Common alterations in the immune response system include high pro-inflammatory cytokines such as IL-6 and TNF-α. On the contrary, a greater involvement of chemokines and markers associated with innate immunity has been reported in BD together with dynamic changes in T cells with differentiation defects during childhood which normalize in adulthood, whereas classic mediators of immune responses such as IL-4 and IL-10 are present in MDD together with signs of immune-senescence.

Depression recognition using voice-based pre-training model

The early screening of depression is highly beneficial for patients to obtain better diagnosis and treatment. While the effectiveness of utilizing voice data for depression detection has been demonstrated, the issue of insufficient dataset size remains unresolved. Therefore, we propose an artificial intelligence method to effectively identify depression. The wav2vec 2.0 voice-based pre-training model was used as a feature extractor to automatically extract high-quality voice features from raw audio. Additionally, a small fine-tuning network was used as a classification model to output depression classification results. Subsequently, the proposed model was fine-tuned on the DAIC-WOZ dataset and achieved excellent classification results. Notably, the model demonstrated outstanding performance in binary classification, attaining an accuracy of 0.9649 and an RMSE of 0.1875 on the test set. Similarly, impressive results were obtained in multi-classification, with an accuracy of 0.9481 and an RMSE of 0.3810. The wav2vec 2.0 model was first used for depression recognition and showed strong generalization ability. The method is simple, practical, and applicable, which can assist doctors in the early screening of depression.

The Immunoseasonal Theory of Psychiatric Disorders

Although the influence of the weather on the well-being and mental health of psychiatric patients has been widely seen, the relationships between various seasonal weather factors and depressive, manic, anxiety, and psychotic states have not been systematized in the literature. The current article describes the seasonal changes in weather-related immune responses and their impact on the development of episodes of depression, mania, psychosis, and anxiety, highlighting the T-helper 1 (Th1) and Th2 immune balance as their potential trigger. In autumn-winter depression, the hyperactivation of the Th1 system, possibly by microbial/airborne pathogens, may lead to the inflammatory inhibition of prefrontal activity and the subcortical centers responsible for mood, drive, and motivation. Depressive mood periods are present in most people suffering from schizophrenia. In the spring and summertime, when the compensating anti-Th1 property of the Th2 immune system is activated, it decreases the Th1 response. In individuals immunogenetically susceptible to psychosis and mania, the inhibition of Th1 by the Th2 system may be excessive and lead to Th2-related frontal and subcortical hyperactivation and subsequent psychosis. In people suffering from bipolar disorder, hyperintense changes in white matter may be responsible for the partial activation of subcortical areas, preventing full paranoid psychosis. Thus, psychosis may be mood-congruent in affective disorders.

Machine Learning for the Prediction of Depression Progression from Inflammation Markers

Major depressive disorder is one of the major contributors to disability worldwide with an estimated prevalence of 4%. Depression is a heterogeneous disease often characterized by an undefined pathogenesis and multifactorial phenotype that complicate diagnosis and follow-up. Translational research and identification of objective biomarkers including inflammation can assist clinicians in diagnosing depression and disease progression. Investigating inflammation markers using machine learning methods combines recent understanding of the pathogenesis of depression associated with inflammatory changes as part of chronic disease progression that aims to highlight complex interactions. In this paper, 721 patients attending a diabetes health screening clinic (DiabHealth) were classified into no depression (none) to minimal depression (none-minimal), mild depression, and moderate to severe depression (moderate-severe) based on the Patient Health Questionnaire (PHQ-9). Logistic Regression, K-nearest Neighbors, Support Vector Machine, Random Forest, Multi-layer Perceptron, and Extreme Gradient Boosting were applied and compared to predict depression level from inflammatory marker data that included C-reactive protein (CRP), Interleukin (IL)-6, IL-1β, IL-10, Complement Component 5a (C5a), D-Dimer, Monocyte Chemoattractant Protein (MCP)-1, and Insulin-like Growth Factor (IGF)-1. MCP-1 and IL-1β were the most significant inflammatory markers for the classification performance of depression level. Extreme Gradient Boosting outperformed the models achieving the highest accuracy and Area Under the Receiver Operator Curve (AUC) of 0.89 and 0.95, respectively.Clinical Relevance- The findings of this study show the potential of machine learning models to aid in clinical practice, leading to a more objective assessment of depression level based on the involvement of MCP-1 and IL-1β inflammatory markers with disease progression.

Differences in intracellular protein levels in monocytes and CD4+ lymphocytes between bipolar depressed patients and healthy controls: A pilot study with tyramine-based signal-amplified flow cytometry

BACKGROUND

Molecular biomarkers for bipolar disorder (BD) that distinguish it from other manifestations of depressive symptoms remain unknown. The aim of this study was to determine if a very sensitive tyramine-based signal-amplification technology for flow cytometry (CellPrint™) could facilitate the identification of cell-specific analyte expression profiles of peripheral blood cells for bipolar depression (BPD) versus healthy controls (HCs).

METHODS

The diagnosis of psychiatric disorders was ascertained with Mini International Neuropsychiatric Interview for DSM-5. Expression levels for eighteen protein analytes previously shown to be related to bipolar disorder were assessed with CellPrint™ in CD4⁺ T cells and monocytes of bipolar patients and HCs. Implementation of protein-protein interaction (PPI) network and pathway analysis was subsequently used to identify new analytes and pathways for subsequent interrogations.

RESULTS

Fourteen drug-naïve or -free patients with bipolar I or II depression and 17 healthy controls (HCs) were enrolled. The most distinguishable changes in analyte expression based on t-tests included GSK3β, HMGB1, IRS2, phospho-GSK3αβ, phospho-RELA, and TSPO in CD4⁺ T cells and calmodulin, GSK3β, IRS2, and phospho-HS1 in monocytes. Subsequent PPI and pathway analysis indicated that prolactin, leptin, BDNF, and interleukin-3 signal pathways were significantly different between bipolar patients and HCs.

LIMITATION

The sample size of the study was small and 2 patients were on medications.

CONCLUSION

In this pilot study, CellPrint™ was able to detect differences in cell-specific protein levels between BPD patients and HCs. A subsequent study including samples from patients with BPD, major depressive disorder, and HCs is warranted.

Optimizing multi-domain hematologic biomarkers and clinical features for the differential diagnosis of unipolar depression and bipolar depression

There is a lack of objective features for the differential diagnosis of unipolar and bipolar depression, especially those that are readily available in practical settings. We investigated whether clinical features of disease course, biomarkers from complete blood count, and blood biochemical markers could accurately classify unipolar and bipolar depression using machine learning methods. This retrospective study included 1160 eligible patients (918 with unipolar depression and 242 with bipolar depression). Patient data were randomly split into training (85%) and open test (15%) sets 1000 times, and the average performance was reported. XGBoost achieved the optimal open-test performance using selected biomarkers and clinical features-AUC 0.889, sensitivity 0.831, specificity 0.839, and accuracy 0.863. The importance of features for differential diagnosis was measured using SHapley Additive exPlanations (SHAP) values. The most informative features include (1) clinical features of disease duration and age of onset, (2) biochemical markers of albumin, low density lipoprotein (LDL), and potassium, and (3) complete blood count-derived biomarkers of white blood cell count (WBC), platelet-to-lymphocyte ratio (PLR), and monocytes (MONO). Overall, onset features and hematologic biomarkers appear to be reliable information that can be readily obtained in clinical settings to facilitate the differential diagnosis of unipolar and bipolar depression.

Serum NOX1 and Raftlin as new potential biomarkers of Major Depressive Disorder: A study in treatment-naive first episode patients

INTRODUCTION

Biological factors are known to be important in understanding the pathogenesis of Major Depressive Disorder (MDD). Oxidative stress and neuroinflammation pathways are likely to play a critical role here.

METHODS

We undertook a study to investigate two novel biomarkers - serum NADPH oxidase 1 (NOX1) and Raftlin levels - in treatment-naive, smoking-free first episode patients with MDD compared to healthy controls (HCs) matched for age, sex and body mass index.

RESULTS

We found increased NOX1 and Raftlin levels in MDD patients compared to HCs. Both parameters showed very good diagnostic performance in the MDD group. In addition, we found a significant positive correlation between depression severity (HAMD) scores and both biomarker levels in the patient group.

CONCLUSION

To the best of our knowledge, this is the first human study to evaluate serum NOX1 and Raftlin levels in depression. NOX1, an important source of reactive oxygen species (ROS), and Raftlin, which may play a role in the inflammatory process, represent novel potential biomarkers of MDD. These findings support the implication of oxidative stress and inflammatory processes in patients with MDD, and indicate that the deteriorated ROS-antioxidant balance can be regulated via NOX1 in patients with depression.

Is SARS-CoV-2 a Risk Factor of Bipolar Disorder?-A Narrative Review

For 2.5 years we have been facing the coronavirus disease (COVID-19) and its health, social and economic effects. One of its known consequences is the development of neuropsychiatric diseases such as anxiety and depression. However, reports of manic episodes related to COVID-19 have emerged. Mania is an integral part of the debilitating illness-bipolar disorder (BD). Due to its devastating effects, it is therefore important to establish whether SARS-CoV-2 infection is a causative agent of this severe mental disorder. In this narrative review, we discuss the similarities between the disorders caused by SARS-CoV-2 and those found in patients with BD, and we also try to answer the question of whether SARS-CoV-2 infection may be a risk factor for the development of this affective disorder. Our observation shows that disorders in COVID-19 showing the greatest similarity to those in BD are cytokine disorders, tryptophan metabolism, sleep disorders and structural changes in the central nervous system (CNS). These changes, especially intensified in severe infections, may be a trigger for the development of BD in particularly vulnerable people, e.g., with family history, or cause an acute episode in patients with a pre-existing BD.

Oxidative stress parameters and antioxidants in adults with unipolar or bipolar depression versus healthy controls: Systematic review and meta-analysis

BACKGROUND

To study differences in oxidative stress markers and antioxidants among patients with bipolar depression (BPD) and unipolar depression (UPD).

METHODS

Data sources. Electronic MEDLINE/PubMed/Cochrane Library/Scopus/TripDatabase database search until 30/06/2021.

STUDY SELECTION

Included were articles comparing antioxidant or oxidative stress markers between adults with BPD or UPD and healthy controls (HCs).

DATA EXTRACTION

Two authors extracted data independently. Random effects meta-analysis, calculating standardized mean differences for results from ≥3 studies.

RESULTS

Oxidative stress markers reported in 40 studies -1 published repeatedly- (UPD, studies = 30 n = 3072; their HCs, n = 2856; BPD, studies = 11 n = 393; their HCs, n = 540; with 1 study reporting on both UPD and BPD) included thiobarbituric acid reactive substances (TBARS), antioxidant uric acid and antioxidant-enhancing enzymes superoxide dismutase (SOD), catalase (CAT) and glutathione-peroxidase (GPX). Compared with HCs, UPD and BPD were associated with significantly higher levels of TBARS, without differences between UPD and BPD (P = 0.11). Compared with HCs, UPD and BPD did not differ regarding the activity of the CAT (P = 0.28), SOD (P = 0.87) and GPX (P = 0.25) enzymes. However, uric acid levels were significantly higher vs HCs in BPD than in UPD among adult patients (P = 0.004). Results were heterogenous, which, for some parameters, decreased after stratification by the blood source (serum, plasma red blood cells, whole blood).

LIMITATIONS

The main limitations are the small number of studies/participants in the BPD subgroup, and heterogeneity of the results.

SUMMATIONS

Both BPD and UPD may be associated with an impaired oxidative stress balance, with significantly higher uric acid levels vs. HCs in UPD than in BPD.

Peripheral biomarkers to predict the diagnosis of bipolar disorder from major depressive disorder in adolescents

The onset of bipolar disorder (BD) occurs in childhood or adolescence in half of the patients. Early stages of BD usually present depressive episodes, which makes it difficult to be distinguished from major depressive disorder (MDD). Objective biomarkers for discriminating BD from MDD in adolescent patients are limited. We collected basic demographic data and the information of the first blood examination performed after the admission to psychiatry unit of BD and MDD inpatients during 2009-2018. We recruited 261 adolescents (aged from 10 to 18), including 160 MDD and 101 BD. Forward-Stepwise Selection of binary logistic regression was used to construct predictive models for the total sample and subgroups by gender. Independent external validation was made by 255 matched patients from another hospital in China. Regression models of total adolescents, male and female subgroups showed accuracy of 73.3%, 70.6% and 75.2%, with area under curves (AUC) as 0.785, 0.816 and 0.793, respectively. Age, direct bilirubin (DBIL), lactic dehydrogenase (LDH), free triiodothyronine (FT3) and C-reactive protein (CRP) were final factors included into the models. The discrimination was well at external validation (AUC = 0.714). This study offers the evidence that accessible information of common clinical laboratory examination might be valuable in distinguishing BD form MDD in adolescents. With good diagnostic accuracies and external validation, the total regression equation might potentially be applied to individualized clinical inferences on adolescent BD patients.

Prediction of the trajectories of depressive symptoms among children in the adolescent brain cognitive development (ABCD) study using machine learning approach

BACKGROUND

Depression often first emerges during adolescence and evidence shows that the long-term patterns of depressive symptoms over time are heterogeneous. It is meaningful to predict the trajectory of depressive symptoms in adolescents to find early intervention targets.

METHODS

Based on the Adolescent Brain Cognitive Development Study, we included 4962 participants aged 9-10 who were followed-up for 2 years. Trajectories of depressive symptoms were identified by Latent Class Growth Analyses (LCGA). Four types of machine learning models were built to predict the identified trajectories and to obtain variables with predictive value based on the best performance model.

RESULTS

Of all participants, 536 (10.80%) were classified as increasing, 269 (5.42%) as persistently high, 433 (8.73%) as decreasing, and 3724 (75.05%) as persistently low by LCGA. Gradient Boosting Machine (GBM) model got the highest discriminant performance. Sleep quality, parental emotional state and family financial adversities were the most important predictors and three resting state functional magnetic resonance imaging functional connectivity data were also helpful to distinguish trajectories.

LIMITATION

We only have depressive symptom scores at three time points. Some valuable predictors are not specific to depression. External validation is an important next step. These predictors should not be interpreted as etiology and some variables were reported by parents/caregivers.

CONCLUSION

Using GBM combined with baseline characteristics, the trajectories of depressive symptoms with two years among adolescents aged 9-10 years can be well predicted, which might further facilitate the identification of adolescents at high risk of depressive symptoms and development of effective early interventions.

Machine learning approaches for prediction of bipolar disorder based on biological, clinical and neuropsychological markers: a systematic review and meta-analysis

Applying machine learning (ML) to objective markers may overcome prognosis uncertainty due to the subjective nature of the diagnosis of bipolar disorder (BD). This PRISMA-compliant meta-analysis provides new systematic evidence of the BD classification accuracy reached by different markers and ML algorithms. We focused on neuroimaging, electrophysiological techniques, peripheral biomarkers, genetic data, neuropsychological or clinical measures, and multimodal approaches. PubMed, Embase and Scopus were searched through 3^rd December 2020. Meta-analyses were performed using random-effect models. Overall, 81 studies were included in this systematic review and 65 in the meta-analysis (11,336 participants, 3,903 BD). The overall pooled classification accuracy was 0.77 (95%CI[0.75;0.80]). Despite subgroup analyses for diagnostic comparison group, psychiatric disorders, marker, ML algorithm, and validation procedure were not significant, linear discriminant analysis significantly outperformed support vector machine for peripheral biomarkers (p=0.03). Sample size was inversely related to accuracy. Evidence of publication bias was detected. Ultimately, although ML reached a high accuracy in differentiating BD from other psychiatric disorders, best practices in methodology are needed for the advancement of future studies.

A Predictive Model of Risk Factors for Conversion From Major Depressive Disorder to Bipolar Disorder Based on Clinical Characteristics and Circadian Rhythm Gene Polymorphisms

BACKGROUND

Bipolar disorder (BD) is easy to be misdiagnosed as major depressive disorder (MDD), which may contribute to a delay in treatment and affect prognosis. Circadian rhythm dysfunction is significantly associated with conversion from MDD to BD. So far, there has been no study that has revealed a relationship between circadian rhythm gene polymorphism and MDD-to-BD conversion. Furthermore, the prediction of MDD-to-BD conversion has not been made by integrating multidimensional data. The study combined clinical and genetic factors to establish a predictive model through machine learning (ML) for MDD-to-BD conversion.

METHOD

By following up for 5 years, 70 patients with MDD and 68 patients with BD were included in this study at last. Single nucleotide polymorphisms (SNPs) of the circadian rhythm genes were selected for detection. The R software was used to operate feature screening and establish a predictive model. The predictive model was established by logistic regression, which was performed by four evaluation methods.

RESULTS

It was found that age of onset was a risk factor for MDD-to-BD conversion. The younger the age of onset, the higher the risk of BD. Furthermore, suicide attempts and the number of hospitalizations were associated with MDD-to-BD conversion. Eleven circadian rhythm gene polymorphisms were associated with MDD-to-BD conversion by feature screening. These factors were used to establish two models, and 4 evaluation methods proved that the model with clinical characteristics and SNPs had the better predictive ability.

CONCLUSION

The risk factors for MDD-to-BD conversion have been found, and a predictive model has been established, with a specific guiding significance for clinical diagnosis.

Blood cytokines differentiate bipolar disorder and major depressive disorder during a major depressive episode: Initial discovery and independent sample replication

Bipolar disorder (BD) diagnosis currently relies on assessment of clinical symptoms, mainly retrospective and subject to memory bias. BD is often misdiagnosed as Major Depressive Disorder (MDD) resulting in ineffective treatment and worsened clinical outcome. The primary purpose of this study was to identify blood biomarkers that discriminate MDD from BD patients when in a depressed state. We have used clinical data and serum samples from two independent naturalistic cohorts of patients with a Major Depressive Episode (MDE) who fulfilled the criteria of either BD or MDD at inclusion. The discovery and replication cohorts consisted of 462 and 133 patients respectively. Patients were clinically assessed using standard diagnostic interviews, and clinical variables including current treatments were recorded. Blood was collected and serum assessed for levels of 31 cytokines using a sensitive multiplex assay. A penalized logistic regression model combined with nonparametric bootstrap was subsequently used to identify cytokines associated with BD. Interleukin (IL)-6, IL-10, IL-15, IL-27 and C-X-C ligand chemokine (CXCL)-10 were positively associated with BD in the discovery cohort. Of the five cytokines identified as discriminant features in the discovery cohort, IL-10, IL-15 and IL-27 were also positively associated with BD in the replication cohort therefore providing an external validation to our finding. Should our results be validated in a prospective cohort, they could provide new insights into the pathophysiological mechanisms of mood disorders.

Machine Learning-Based Behavioral Diagnostic Tools for Depression: Advances, Challenges, and Future Directions

The psychiatric diagnostic procedure is currently based on self-reports that are subject to personal biases. Therefore, the diagnostic process would benefit greatly from data-driven tools that can enhance accuracy and specificity. In recent years, many studies have achieved promising results in detecting and diagnosing depression based on machine learning (ML) analysis. Despite these favorable results in depression diagnosis, which are primarily based on ML analysis of neuroimaging data, most patients do not have access to neuroimaging tools. Hence, objective assessment tools are needed that can be easily integrated into the routine psychiatric diagnostic process. One solution is to use behavioral data, which can be easily collected while still maintaining objectivity. The current paper summarizes the main ML-based approaches that use behavioral data in diagnosing depression and other psychiatric disorders. We classified these studies into two main categories: (a) laboratory-based assessments and (b) data mining, the latter of which we further divided into two sub-groups: (i) social media usage and movement sensors data and (ii) demographic and clinical information. The paper discusses the advantages and challenges in this field and suggests future research directions and implementations. The paper's overarching aim is to serve as a first step in synthetizing existing knowledge about ML-based behavioral diagnosis studies in order to develop interventions and individually tailored treatments in the future.

Neutrophil-to-Lymphocyte Ratio, a Novel Inflammatory Marker, as a Predictor of Bipolar Type in Depressed Patients: A Quest for Biological Markers

(1) Background: Recent research suggests inflammation as a factor involved in the pathophysiology of mood disorders. Neutrophil-to-lymphocyte (NLR), monocyte-to-lymphocyte (MLR), platelet-to-lymphocyte (PLR), and systemic immune-inflammatory (SII) index ratios have been studied as peripheral markers of inflammation in bipolar and major depressive disorders. The purpose of this study is to comparatively analyze these inflammatory ratios among manic episodes of bipolar disorder, bipolar depression and unipolar depression. (2) Methods: 182 patients were retrospectively included in the study and divided into three groups: 65 manic patients, 34 patients with bipolar depression, and 83 unipolar depressive patients. White blood cells, neutrophils, monocytes, lymphocytes, and platelets were retrieved from the patients' database. NLR, MLR, PLR, and SII index were calculated using these parameters. (3) Results: Patients with manic episodes had elevated NLR (p < 0.001), MLR (p < 0.01), PLR (p < 0.05), and SII index (p < 0.001) compared to unipolar depression and increased NLR (p < 0.05) and SII index (p < 0.05) when compared to bipolar depression. NLR (p < 0.01) and SII index (p < 0.05) were higher in the bipolar depression than unipolar depression. NLR is an independent predictor of the bipolar type of depression in depressive patients. (4) Conclusions: The results confirm the role of inflammation in the pathophysiology of mood disorders and suggest the ability of NLR as a marker for the differentiation of bipolar from unipolar depression.

Biological Pathways Associated with Neuroprogression in Bipolar Disorder

There is evidence suggesting clinical progression in a subset of patients with bipolar disorder (BD). This progression is associated with worse clinical outcomes and biological changes. Molecular pathways and biological markers of clinical progression have been identified and may explain the progressive changes associated with this disorder. The biological basis for clinical progression in BD is called neuroprogression. We propose that the following intertwined pathways provide the biological basis of neuroprogression: inflammation, oxidative stress, impaired calcium signaling, endoplasmic reticulum and mitochondrial dysfunction, and impaired neuroplasticity and cellular resilience. The nonlinear interaction of these pathways may worsen clinical outcomes, cognition, and functioning. Understanding neuroprogression in BD is crucial for identifying novel therapeutic targets, preventing illness progression, and ultimately promoting better outcomes.

Using classification and regression tree modeling to investigate appetite hormones and proinflammatory cytokines as biomarkers to differentiate bipolar I depression from major depressive disorder

BACKGROUND

Altered immunity and metabolic profiles have been compared between bipolar depression (BD) and major depressive disorder (MDD). This study aimed at developing a composite predictor of appetite hormones and proinflammatory cytokines to differentiate BD from MDD.

METHODS

This cross-sectional study enrolled patients with BD and those with MDD aged 20 to 59 years and displaying depressive episodes. Clinical characteristics (age, sex, body mass index, and depression severity), cytokines (C-reactive protein, interleukin [IL]-2, IL-6, tumor necrosis factor [TNF]-α, P-selectin, and monocyte chemoattractant protein), and appetite hormones (leptin, adiponectin, ghrelin, and insulin) were assessed as potential predictors using a classification and regression tree (CRT) model for differentiating BD from MDD.

RESULTS

The predicted probability of a composite predictor of ghrelin and TNF-α was significantly greater (for BD: area under curve = 0.877; for MDD: area under curve = 0.914) than that of any one marker (all P > .05) to distinguish BD from MDD. The most powerful predictors for diagnosing BD were high ghrelin and TNF-α levels, whereas those for MDD were low ghrelin and TNF-α levels.

CONCLUSION

A composite predictor of ghrelin and TNF-α driven by CRT could assist in the differential diagnosis of BD from MDD with high specificity. Further clinical studies are warranted to validate our results and to explore underlying mechanisms.

Personalized Medicine Using Neuroimmunological Biomarkers in Depressive Disorders

Major depressive disorder (MDD) is associated with increased suicidal risk and reduced productivity at work. Neuroimmunology, the study of the immune system and nervous system, provides further insight into the pathogenesis and outcome of MDD. Cytokines are the main modulators of neuroimmunology, and their levels are somewhat entangled in depressive disorders as they affect depressive symptoms and are affected by antidepressant treatment. The use of cytokine-derived medication as a treatment option for MDD is currently a topic of interest. Although not very promising, cytokines are also considered as possible prognostic or diagnostic markers for depression. The machine learning approach is a powerful tool for pattern recognition and has been used in psychiatry for finding useful patterns in data that have translational meaning and can be incorporated in daily clinical practice. This review focuses on the current knowledge of neuroimmunology and depression and the possible use of machine learning to widen our understanding of the topic.

The role of cognitive functions in the diagnosis of bipolar disorder: A machine learning model

BACKGROUND

Considering the clinical heterogeneity of the bipolar disorder, difficulties are encountered in making the correct diagnosis. Although a number of common findings have been found in studies on the neurocognitive profile of bipolar disorder, the search for a neurocognitive endophenotype has failed. The aim of this study is to separate bipolar disorder patients from healthy controls with higher accuracy by using a broader neurocognitive evaluation and a novel machine-learning algorithm.

METHODS

Individuals who presented to the Bipolar Outpatient Clinic of the Medical Faculty of Eskişehir Osmangazi University and met the inclusion criteria of the research are included in the study. Six neurocognitive tests from the CANTAB test battery were used for neurocognitive evaluation, Polyhedral Conic Functions algorithm was used to classify the participants.

RESULTS

Bipolar disorder patients differentiated from healthy controls with an accuracy of 78 %.

DISCUSSION

Our study presents a prediction algorithm that separates bipolar disorder from healthy controls with high accuracy by using CANTAB neurocognitive battery.