The search for peripheral disease markers in psychiatry by genomic and proteomic approaches

Identification and Characterization of Elevated Expression of Transferrin and Its Receptor TfR1 in Mouse Models of Depression

Depression has become one of the severe mental disorders threatening global human health. In this study, we first used the proteomics approach to obtain the differentially expressed proteins in the liver between naive control and chronic social defeat stress (CSDS) induced depressed mice. We have identified the upregulation of iron binding protein transferrin (TF) in the liver, the peripheral blood, and the brain in CSDS-exposed mice. Furthermore, bioinformatics analysis of the Gene Expression Omnibus (GEO) database from various mouse models of depression revealed the significantly upregulated transcripts of TF and its receptor TfR1 in multiple brain regions in depressed mice. We also used the recombinant TF administration via the tail vein to detect its permeability through the blood-brain barrier (BBB). We demonstrated the permeability of peripheral TF into the brain through the BBB. Together, these results identified the elevated expression of TF and its receptor TfR1 in both peripheral liver and the central brain in CSDS-induced depressed mice, and peripheral administration of TF can be transported into the brain through the BBB. Therefore, our data provide a compelling information for understanding the potential role and mechanisms of the cross-talk between the liver and the brain in stress-induced depression.

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

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.

Decreased serum apolipoprotein A4 as a potential peripheral biomarker for patients with schizophrenia

Recent evidence supports an association between lipid metabolism dysfunction and the pathology of schizophrenia which has led to the search for peripheral blood-based biomarkers. The purpose of this study was to investigate the proteins involved in lipid metabolism (especially apolipoprotein) and to explore their potential as biomarkers for schizophrenia. Using multiple reaction monitoring mass spectrometry (MRM-MS), we quantified 22 proteins in serum samples of 109 healthy controls (HCs) and 111 patients with schizophrenia (SCZ), who were divided into discovery and validation sets. We found serum apolipoprotein A4 (ApoA4) to be significantly decreased in SCZ patients compared to HCs (p=1.61E-05). Moreover, the serum ApoA4 level served as an effective diagnostic tool, achieving area under the receiver operating characteristic curves (AUROC) of 0.840 in the discovery set and 0.791 in the validation set. Additionally, apolipoprotein F (ApoF), angiotensinogen (AGT), and alpha1-antichymotrypsin (ACT) levels were significantly higher in patients with schizophrenia than in healthy controls. These proteins combined with ApoA4, provided higher diagnostic accuracy for schizophrenia in the discovery set (AUROC=0.901) and in the validation set (AUROC=0.879). Our results suggest that the serum level of ApoA4 is a novel potential biomarker for schizophrenia. The proteins identified in this study expand the pool of biomarker candidates for schizophrenia and may be linked to the underlying mechanism of the disease.

Folate metabolism biomarkers from two randomised placebo-controlled clinical studies with paroxetine and venlafaxine

OBJECTIVES

Based on the hypothesis of a role for folate and vitamin B12 in major depressive disorders (MDD), we aimed at validating the association between folate pathway biomarkers and depression or antidepressant response in clinical trial populations.

METHODS

We investigated serum levels erythrocyte folate and serum levels of homocysteine, vitamin B12, and folate as disease and response biomarkers for MDD in two independent randomised, placebo-controlled clinical trials, where paroxetine or venlafaxine were used as active controls, for a total of 881 patients.

RESULTS

Significant but weak correlations between depression severity and biomarker levels could be detected in the paroxetine study for serum folate and vitamin B12, with no correlations for any biomarker in the venlafaxine study. Besides a weak association for erythrocyte folate in the venlafaxine study, no significant associations were observed between treatment response and pre-treatment levels of any of the biomarkers tested.

CONCLUSIONS

Notwithstanding the relatively large number of patients tested, we did not find consistent associations between folate biomarkers and MDD severity, or response to paroxetine and venlafaxine. Our results may be related to the particular study design or clinical population; however, our findings do not support the hypothesis of a dysfunction of one-carbon metabolism in MDD.

Comparison of serum protein profiles between major depressive disorder and bipolar disorder

BACKGROUND

Major depressive disorder and bipolar disorder are prevalent and debilitating psychiatric disorders that are difficult to distinguish, as their diagnosis is based on behavioural observations and subjective symptoms. Quantitative protein profile analysis might help to objectively distinguish between these disorders and increase our understanding of their pathophysiology. Thus, this study was conducted to compare the peripheral protein profiles between the two disorders.

METHODS

Serum samples were collected from 18 subjects with major depressive disorder and 15 subjects with bipolar disorder. After depleting abundant proteins, liquid chromatography-tandem mass spectrometry (LC-MS/MS) and label-free quantification were performed. Data-dependent acquisition data were statistically analysed from the samples of 15 subjects with major depressive disorder and 10 subjects with bipolar disorder who were psychotropic drug-free. Two-sided t-tests were performed for pairwise comparisons of proteomes to detect differentially-expressed proteins (DEPs). Ingenuity Pathway Analysis of canonical pathways, disease and functions, and protein networks based on these DEPs was further conducted.

RESULTS

Fourteen DEPs were significant between subjects with major depressive disorder and those with bipolar disorder. Ras-related protein Rab-7a (t = 5.975, p = 4.3 × 10^- 6) and Rho-associated protein kinase 2 (t = 4.782, p = 8.0 × 10^- 5) were significantly overexpressed in subjects with major depressive disorder and Exportin-7 (t = -4.520, p = 1.5 × 10^- 4) was significantly overexpressed in subjects with bipolar disorder after considering multiple comparisons. Bioinformatics analysis showed that cellular functions and inflammation/immune pathways were significantly different.

CONCLUSIONS

Ras-related protein Rab-7a, Rho-associated protein kinase 2, and Exportin-7 were identified as potential peripheral protein candidates to distinguish major depressive disorder and bipolar disorder. Further large sample studies with longitudinal designs and validation processes are warranted.

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

OBJECTIVE

This study used machine learning techniques combined with peripheral biomarker measurements to build signatures to help differentiating (1) patients with bipolar depression from patients with unipolar depression, and (2) patients with bipolar depression or unipolar depression from healthy controls.

METHODS

We assessed serum levels of interleukin-2, interleukin-4, interleukin-6, interleukin-10, tumor necrosis factor-α, interferon-γ, interleukin-17A, brain-derived neurotrophic factor, lipid peroxidation and oxidative protein damage in 54 outpatients with bipolar depression, 54 outpatients with unipolar depression and 54 healthy controls, matched by sex and age. Depressive symptoms were assessed using the Hamilton Depression Rating Scale. Variable selection was performed with recursive feature elimination with a linear support vector machine kernel, and the leave-one-out cross-validation method was used to test and validate our model.

RESULTS

Bipolar vs unipolar depression classification achieved an area under the receiver operating characteristics (ROC) curve (AUC) of 0.69, with 0.62 sensitivity and 0.66 specificity using three selected biomarkers (interleukin-4, thiobarbituric acid reactive substances and interleukin-10). For the comparison of bipolar depression vs healthy controls, the model retained five variables (interleukin-6, interleukin-4, thiobarbituric acid reactive substances, carbonyl and interleukin-17A), with an AUC of 0.70, 0.62 sensitivity and 0.7 specificity. Finally, unipolar depression vs healthy controls comparison retained seven variables (interleukin-6, Carbonyl, brain-derived neurotrophic factor, interleukin-10, interleukin-17A, interleukin-4 and tumor necrosis factor-α), with an AUC of 0.74, a sensitivity of 0.68 and 0.70 specificity.

CONCLUSION

Our findings show the potential of machine learning models to aid in clinical practice, leading to more objective assessment. Future studies will examine the possibility of combining peripheral blood biomarker data with other biological data to develop more accurate signatures.

Biomarkers for response in major depression: comparing paroxetine and venlafaxine from two randomised placebo-controlled clinical studies

The identification of biomarkers of response might speed drug development and set the premises to assist clinical practice in psychiatry. In this work, we evaluated a panel of peripheral biomarkers (including IL-6, IL-10, TNF-α, TNFRII, BDNF, CRP, MMP9 and PAI1) in depressed patients receiving paroxetine, venlafaxine, or placebo. Samples were obtained from two randomised placebo-controlled studies evaluating the efficacy and tolerability of a novel drug candidate, using either paroxetine or venlafaxine as active comparators. In both studies, the biomarker candidates were analysed in plasma collected at randomization and after 10 weeks of treatment with either placebo or active comparator (for a total of 106 and 108 subjects in the paroxetine and venlafaxine study, respectively). Data were obtained by multiplexing sandwich-ELISA system. Data were subjected to statistical analysis to assess their correlation with baseline severity and with response outcome. Increases in biomarker levels were correlated with reduction in depression severity for TNF-α, IL-6 IL-10 and CRP. Response to paroxetine treatment correlated with baseline IL-10, IL-6 and TNF-α levels, with the strongest signal being observed in males. In the venlafaxine study, a correlation was observed only between CRP level at randomisation and response, suggesting differences between the two active treatments and the two studies. Our investigations suggest that a combination of pro- and anti-inflammatory cytokines may predict response outcome in patients treated with paroxetine. The potential for IL-10, IL-6 and TNF-α as response biomarkers for a wider range of antidepressants warrants further investigations in clinical trials with other monoamine reuptake inhibitors.

Proteomics for blood biomarker exploration of severe mental illness: pitfalls of the past and potential for the future

Recent improvements in high-throughput proteomic approaches are likely to constitute an essential advance in biomarker discovery, holding promise for improved personalized care and drug development. These methodologies have been applied to study multivariate protein patterns and provide valuable data of peripheral tissues. To highlight findings of the last decade for three of the most common psychiatric disorders, namely schizophrenia (SZ), bipolar disorder (BD), and major depressive disorder (MDD), we queried PubMed. Here we delve into the findings from thirty studies, which used proteomics and multiplex immunoassay approaches for peripheral blood biomarker exploration. In an explorative approach, we ran enrichment analyses in peripheral blood according to these results and ascertained the overlap between proteomic findings and genetic loci identified in genome-wide association studies (GWAS). The studies we appraised demonstrate that proteomics for psychiatric research has been heterogeneous in aims and methods and limited by insufficient sample sizes, poorly defined case definitions, methodological inhomogeneity, and confounding results constraining the conclusions that can be extracted from them. Here, we discuss possibilities for overcoming methodological challenges for the implementation of proteomic signatures in psychiatric diagnosis and offer an outlook for future investigations. To fulfill the promise of proteomics in mental disease diagnostics, future research will need large, well-defined cohorts in combination with state-of-the-art technologies.

Plasma concentrations of BDNF and IGF-1 in abstinent cocaine users with high prevalence of substance use disorders: relationship to psychiatric comorbidity

Recent studies have identified biomarkers related to the severity and pathogenesis of cocaine addiction and common comorbid psychiatric disorders. Monitoring these plasma mediators may improve the stratification of cocaine users seeking treatment. Because the neurotrophic factors are involved in neural plasticity, neurogenesis and neuronal survival, we have determined plasma concentrations of brain-derived neurotrophic factor (BDNF), insulin-like growth factor 1 (IGF-1) and IGF-1 binding protein 3 (IGFBP-3) in a cross-sectional study with abstinent cocaine users who sought outpatient treatment for cocaine (n = 100) and age/body mass matched controls (n = 85). Participants were assessed with the diagnostic interview 'Psychiatric Research Interview for Substance and Mental Disorders'. Plasma concentrations of these peptides were not different in cocaine users and controls. They were not associated with length of abstinence, duration of cocaine use or cocaine symptom severity. The pathological use of cocaine did not influence the association of IGF-1 with age observed in healthy subjects, but the correlation between IGF-1 and IGFBP-3 was not significantly detected. Correlation analyses were performed between these peptides and other molecules sensitive to addiction: BDNF concentrations were not associated with inflammatory mediators, lipid derivatives or IGF-1 in cocaine users, but correlated with chemokines (fractalkine/CX3CL1 and SDF-1/CXCL12) and N-acyl-ethanolamines (N-palmitoyl-, N-oleoyl-, N-arachidonoyl-, N-linoleoyl- and N-dihomo-γ-linolenoyl-ethanolamine) in controls; IGF-1 concentrations only showed association with IGFBP-3 concentrations in controls; and IGFBP-3 was only correlated with N-stearoyl-ethanolamine concentrations in cocaine users. Multiple substance use disorders and life-time comorbid psychopathologies were common in abstinent cocaine users. Interestingly, plasma BDNF concentrations were exclusively found to be decreased in users diagnosed with both primary and cocaine-induced disorders for mood and anxiety disorders. In summary, BDNF, IGF-1 and IGFBP-3 were not affected by a history of pathological use of cocaine supported by the absence of associations with other molecules sensitive to cocaine addiction. However, BDNF was affected by comorbid mood disorders. Further research is necessary to elucidate the role of BDNF and IGF-1 in the transition to cocaine addiction and associated psychiatric comorbidity.

Biomarker approaches in major depressive disorder evaluated in the context of current hypotheses

Major depressive disorder is a heterogeneous disorder, mostly diagnosed on the basis of symptomatic criteria alone. It would be of great help when specific biomarkers for various subtypes and symptom clusters of depression become available to assist in diagnosis and subtyping of depression, and to enable monitoring and prognosis of treatment response. However, currently known biomarkers do not reach sufficient sensitivity and specificity, and often the relation to underlying pathophysiology is unclear. In this review, we evaluate various biomarker approaches in terms of scientific merit and clinical applicability. Finally, we discuss how combined biomarker approaches in both preclinical and clinical studies can help to make the connection between the clinical manifestations of depression and the underlying pathophysiology.

Sex differences in psychiatric comorbidity and plasma biomarkers for cocaine addiction in abstinent cocaine-addicted subjects in outpatient settings

There are sex differences in the progression of drug addiction, relapse, and response to therapies. Because biological factors participate in these differences, they should be considered when using biomarkers for addiction. In the current study, we evaluated the sex differences in psychiatric comorbidity and the concentrations of plasma mediators that have been reported to be affected by cocaine. Fifty-five abstinent cocaine-addicted subjects diagnosed with lifetime cocaine use disorders (40 men and 15 women) and 73 healthy controls (48 men and 25 women) were clinically assessed with the diagnostic interview "Psychiatric Research Interview for Substance and Mental Disorders." Plasma concentrations of chemokines, cytokines, N-acyl-ethanolamines, and 2-acyl-glycerols were analyzed according to history of cocaine addiction and sex, controlling for covariates age and body mass index (BMI). Relationships between these concentrations and variables related to cocaine addiction were also analyzed in addicted subjects. The results showed that the concentrations of chemokine (C-C motif) ligand 2/monocyte chemotactic protein-1 (CCL2/MCP-1) and chemokine (C-X-C motif) ligand 12/stromal cell-derived factor-1 (CXCL12/SDF-1) were only affected by history of cocaine addiction. The plasma concentrations of interleukin 1-beta (IL-1β), IL-6, IL-10, and tumor necrosis factor-alpha (TNFα) were affected by history of cocaine addiction and sex. In fact, whereas cytokine concentrations were higher in control women relative to men, these concentrations were reduced in cocaine-addicted women without changes in addicted men. Regarding fatty acid derivatives, history of cocaine addiction had a main effect on the concentration of each acyl derivative, whereas N-acyl-ethanolamines were increased overall in the cocaine group, 2-acyl-glycerols were decreased. Interestingly, N-palmitoleoyl-ethanolamine (POEA) was only increased in cocaine-addicted women. The covariate BMI had a significant effect on POEA and N-arachidonoyl-ethanolamine concentrations. Regarding psychiatric comorbidity in the cocaine group, women had lower incidence rates of comorbid substance use disorders than did men. For example, alcohol use disorders were found in 80% of men and 40% of women. In contrast, the addicted women had increased prevalences of comorbid psychiatric disorders (i.e., mood, anxiety, and psychosis disorders). Additionally, cocaine-addicted subjects showed a relationship between the concentrations of N-stearoyl-ethanolamine and 2-linoleoyl-glycerol and diagnosis of psychiatric comorbidity. These results demonstrate the existence of a sex influence on plasma biomarkers for cocaine addiction and on the presence of comorbid psychopathologies for clinical purposes.

Biomarkers of a five-domain translational substrate for schizophrenia and schizoaffective psychosis

BACKGROUND

The Mental Health Biomarker Project (2010-2014) selected commercial biochemistry markers related to monoamine synthesis and metabolism and measures of visual and auditory processing performance. Within a case-control discovery design with exclusion criteria designed to produce a highly characterised sample, results from 67 independently DSM IV-R-diagnosed cases of schizophrenia and schizoaffective disorder were compared with those from 67 control participants selected from a local hospital, clinic and community catchment area. Participants underwent protocol-based diagnostic-checking, functional-rating, biological sample-collection for thirty candidate markers and sensory-processing assessment.

RESULTS

Fifteen biomarkers were identified on ROC analysis. Using these biomarkers, odds ratios, adjusted for a case-control design, indicated that schizophrenia and schizoaffective disorder were highly associated with dichotic listening disorder, delayed visual processing, low visual span, delayed auditory speed of processing, low reverse digit span as a measure of auditory working memory and elevated levels of catecholamines. Other nutritional and biochemical biomarkers were identified as elevated hydroxyl pyrroline-2-one as a marker of oxidative stress, vitamin D, B6 and folate deficits with elevation of serum B12 and free serum copper to zinc ratio. When individual biomarkers were ranked by odds ratio and correlated with clinical severity, five functional domains of visual processing, auditory processing, oxidative stress, catecholamines and nutritional-biochemical variables were formed. When the strengths of their inter-domain relationships were predicted by Lowess (non-parametric) regression, predominant bidirectional relationships were found between visual processing and catecholamine domains. At a cellular level, the nutritional-biochemical domain exerted a pervasive influence on the auditory domain as well as on all other domains.

CONCLUSIONS

The findings of this biomarker research point towards a much-required advance in Psychiatry: quantification of some theoretically-understandable, translationally-informative, treatment-relevant underpinnings of serious mental illness. This evidence reveals schizophrenia and schizoaffective disorder in a somewhat different manner, as a conglomerate of several disorders many of which are not currently being assessed-for or treated in clinical settings. Currently available remediation techniques for these underlying conditions have potential to reduce treatment-resistance, relapse-prevention, cost burden and social stigma in these conditions. If replicated and validated in prospective trials, such findings will improve progress-monitoring and treatment-response for schizophrenia and schizoaffective disorder.

A Review of Biomarkers in Mood and Psychotic Disorders: A Dissection of Clinical vs. Preclinical Correlates

Despite significant research efforts aimed at understanding the neurobiological underpinnings of mood (depression, bipolar disorder) and psychotic disorders, the diagnosis and evaluation of treatment of these disorders are still based solely on relatively subjective assessment of symptoms as well as psychometric evaluations. Therefore, biological markers aimed at improving the current classification of psychotic and mood-related disorders, and that will enable patients to be stratified on a biological basis into more homogeneous clinically distinct subgroups, are urgently needed. The attainment of this goal can be facilitated by identifying biomarkers that accurately reflect pathophysiologic processes in these disorders. This review postulates that the field of psychotic and mood disorder research has advanced sufficiently to develop biochemical hypotheses of the etiopathology of the particular illness and to target the same for more effective disease modifying therapy. This implies that a "one-size fits all" paradigm in the treatment of psychotic and mood disorders is not a viable approach, but that a customized regime based on individual biological abnormalities would pave the way forward to more effective treatment. In reviewing the clinical and preclinical literature, this paper discusses the most highly regarded pathophysiologic processes in mood and psychotic disorders, thereby providing a scaffold for the selection of suitable biomarkers for future studies in this field, to develope biomarker panels, as well as to improve diagnosis and to customize treatment regimens for better therapeutic outcomes.

Urinary peptidomics identifies potential biomarkers for major depressive disorder

Major depressive disorder (MDD) is a debilitating psychiatric illness with no available objective laboratory-based diagnostic test. In this study, matrix-assisted laser desorption ionization time-of-flight mass spectrometry (MALDI-TOF MS)-based peptidomics was applied to identify potential urinary diagnostic biomarkers for MDD. A training set of 42 first-episode drug-naive MDD patients and 28 age- and gender-matched healthy controls (HC) was used to develop a peptide diagnostic pattern. Then, the diagnostic efficacy of this pattern was assessed in an independent blinded test set consisting of 24 MDD patients and 13 age- and gender-matched HC. A combination of five potential biomarkers was identified, yielding a sensitivity of 91.7% and specificity of 84.6% in the test set. Moreover, the protein precursors of four of the five peptides were identified by tandem mass spectrometric analysis: serum albumin, apolipoprotein A-I, protein AMBP, and basement membrane-specific heparan sulfate proteoglycan core protein. Taken together, the peptide pattern may be valuable for establishing an objective laboratory-based diagnostic test for MDD.

Antibodies as predictors of autoimmune diseases and cancer

BACKGROUND

Autoantibodies targeted against a variety of self-antigens are detected in autoimmune diseases and cancer. Emerging evidence has suggested the involvement of environmental factors such as infections and xenobiotics, and some dietary proteins and their antibodies in the pathogenesis of many autoimmune diseases. These antibodies appear in the blood years before presentation of symptoms in various disorders. Therefore, these antibodies may be used as biomarkers for early detection of various diseases.

OBJECTIVE

To provide an overview of antibody arrays that are measured against different human tissue antigens, crossreactive epitopes of infectious agents, dietary proteins, and haptenic chemicals in autoimmune diseases and cancer.

METHOD

Microarray analysis of antigen-antibody reaction.

CONCLUSION

The application of these antibody arrays to human autoimmune disease is expanding and is allowing for the identification of patterns or antibody signatures, thus establishing the premises for increased sensitivity and specificity of prediction, as well as positive predictive values. The presence of these antibodies would not necessarily mean that a patient would definitely become sick but may give a percentage of risk for different conditions that may develop over future months or years. Using this high-throughput microarray method, it is possible to screen rapidly for dozens of autoantibodies at low cost. This is an important factor in the implementation of autoantibody testing as a routine part of medical examinations.

Biomarkers for antipsychotic therapies

Molecular biomarkers for antipsychotic treatments have been conceptually linked to the measurements of dopamine functions, mostly D(2) receptor occupancy, either by imaging using selective PET/SPECT radioactive tracers or by assessing plasma prolactin levels. A quest for novel biomarkers was recently proposed by various academic, health service, and industrial institutions driven by the need for better treatments of psychoses. In this review we conceptualize biomarkers within the Translational Medicine paradigm whose goal was to provide support to critical decision-making in drug discovery. At first we focused on biomarkers as outcome measure of clinical studies by searching into the database clinicaltrial.gov. The results were somewhat disappointing, showing that out of 1,659 antipsychotic trials only 18 used a biomarker as an outcome measure. Several of these trials targeted plasma lipids as sentinel marker for metabolic adverse effects associated with the use of atypical antipsychotics, while only few studies were aimed to new disease specific biological markers. As an example of a mechanistic biomarker, we described the work done to progress the novel class of glycine transporter inhibitors as putative treatment for negative symptoms of schizophrenia. We also review how large-scale multiplex biological assays were applied to samples from tissues of psychiatric patients, so to learn from changes of numerous analytes (metabolic products, lipids, proteins, RNA transcripts) about the substrates involved in the disease. We concluded that a stringent implementation of these techniques could contribute to the endophenotypic characterization of patients, helping in the identification of key biomarkers to drive personalized medicine and new treatment development.

Structural brain changes in patients with recurrent major depressive disorder presenting with anxiety symptoms

BACKGROUND AND PURPOSE

Major depressive disorder (MDD) presents with extensive clinical heterogeneity. In particular, overlap with anxiety symptoms is common during depressive episodes and as a comorbid disorder. The aim of this study was to test for morphological brain differences between patients having a history of recurrent MDD with, and without, anxiety symptoms (MDD+A and MDD-A).

METHODS

T1-weighted magnetic resonance images of age-, gender- and ethnically matched groups of MDD+A (n= 49) and MDD-A (n= 96) patients were available for voxel-based morphometry analysis of regional gray matter (GM) volume differences. Brain structural images were also contrasted with 183 age-, gender-, and ethnicity-matched healthy controls.

RESULTS

MDD+A patients had greater GM volume (P(FWE) = .002) than MDD-A patients in the right temporal cortex extending from the mid-posterior superior temporal gyrus into the posterior middle and inferior temporal gyrus. The MDD patients together showed lower GM volume than healthy controls in the superior parietal lobe.

CONCLUSIONS

Regional volume differences in patients are consistent with altered neuronal or glial microstructure. The temporolateral cortical differences distinguishing the 2 MDD groups suggest neurobiological differences related to the expression of anxiety symptoms in depression and provide further rationale for considering these groups independently for therapeutic outcomes studies.

Early-life stress and antidepressants modulate peripheral biomarkers in a gene-environment rat model of depression

BACKGROUND

Availability of peripheral biomarkers for depression could aid diagnosis and help to predict treatment response. The objective of this work was to analyse the peripheral biomarker response in a gene-environment interaction model of depression. Genetically selected Flinders Sensitive Line (FSL) rats were subjected to maternal separation (MS), since early-life trauma is an important antecedent of depression. An open-ended approach based on a proteomic analysis of serum was combined with the evaluation of depression-associated proteins.

METHODS

Rats experienced MS and chronically received escitalopram (ESC) or nortryptiline (NOR). Serum proteins were compared by two-dimensional gel electrophoresis. Corticosterone, cytokines, BDNF and C-reactive protein (CRP) were measured by immunoassays.

RESULTS

Comparing FSL with the control Flinders Resistant Line (FRL), Apo-AI and Apo-AIV, alpha1-macroglobulin, glutathione peroxidase and complement-C3 were significantly modulated. Significant increases were detected in leptin, interleukin (IL) 1alpha and BDNF. CRP levels were significantly reduced. The impact of early-life stress was assessed by comparing FSL+MS versus FSL. Apo-E, alpha1-macroglobulin, complement-C3, transferrin and hemopexin were significantly modulated. The effect of stress in antidepressant response was then evaluated. In the comparison FSL+ESC+MS versus FSL+ESC, albumin, alpha1-macroglobulin, glutathione peroxidase and complement-C3 were modulated and significant reductions were detected in IL4, IL6, IL10, CRP and BDNF. By comparing FSL+NOR+MS versus FSL+NOR proteins like Apo-AIV, pyruvate dehydrogenase, alpha1-macroglobulin, transferrin and complement-C3 showed different levels.

CONCLUSIONS

Lipid metabolism and immunity proteins were differently expressed in FSL in comparison with FRL. Exposure to MS induced changes in inflammation and transport proteins which became apparent in response to antidepressant treatments. Modulated proteins could suggest biomarker studies in humans.

Plasma protein biomarkers for depression and schizophrenia by multi analyte profiling of case-control collections

Despite significant research efforts aimed at understanding the neurobiological underpinnings of psychiatric disorders, the diagnosis and the evaluation of treatment of these disorders are still based solely on relatively subjective assessment of symptoms. Therefore, biological markers which could improve the current classification of psychiatry disorders, and in perspective stratify patients on a biological basis into more homogeneous clinically distinct subgroups, are highly needed. In order to identify novel candidate biological markers for major depression and schizophrenia, we have applied a focused proteomic approach using plasma samples from a large case-control collection. Patients were diagnosed according to DSM criteria using structured interviews and a number of additional clinical variables and demographic information were assessed. Plasma samples from 245 depressed patients, 229 schizophrenic patients and 254 controls were submitted to multi analyte profiling allowing the evaluation of up to 79 proteins, including a series of cytokines, chemokines and neurotrophins previously suggested to be involved in the pathophysiology of depression and schizophrenia. Univariate data analysis showed more significant p-values than would be expected by chance and highlighted several proteins belonging to pathways or mechanisms previously suspected to be involved in the pathophysiology of major depression or schizophrenia, such as insulin and MMP-9 for depression, and BDNF, EGF and a number of chemokines for schizophrenia. Multivariate analysis was carried out to improve the differentiation of cases from controls and identify the most informative panel of markers. The results illustrate the potential of plasma biomarker profiling for psychiatric disorders, when conducted in large collections. The study highlighted a set of analytes as candidate biomarker signatures for depression and schizophrenia, warranting further investigation in independent collections.

The PsyCoLaus study: methodology and characteristics of the sample of a population-based survey on psychiatric disorders and their association with genetic and cardiovascular risk factors

BACKGROUND

The Psychiatric arm of the population-based CoLaus study (PsyCoLaus) is designed to: 1) establish the prevalence of threshold and subthreshold psychiatric syndromes in the 35 to 66 year-old population of the city of Lausanne (Switzerland); 2) test the validity of postulated definitions for subthreshold mood and anxiety syndromes; 3) determine the associations between psychiatric disorders, personality traits and cardiovascular diseases (CVD), 4) identify genetic variants that can modify the risk for psychiatric disorders and determine whether genetic risk factors are shared between psychiatric disorders and CVD. This paper presents the method as well as sociodemographic and somatic characteristics of the sample.

METHODS

All 35 to 66 year-old persons previously selected for the population-based CoLaus survey on risk factors for CVD were asked to participate in a substudy assessing psychiatric conditions. This investigation included the Diagnostic Interview for Genetic Studies to elicit diagnostic criteria for threshold disorders according to DSM-IV and algorithmically defined subthreshold syndromes. Complementary information was collected on potential risk and protective factors for psychiatric disorders, migraine and on the morbidity of first-degree relatives, whereas the collection of DNA and plasma samples was already part of the original CoLaus survey.

RESULTS

A total of 3,691 individuals completed the psychiatric evaluation (67% participation). The gender distribution of the sample did not differ significantly from that of the general population in the same age range. Although the youngest 5-year band of the cohort was underrepresented and the oldest 5-year band overrepresented, participants of PsyCoLaus and individuals who refused to participate revealed comparable scores on the General Health Questionnaire, a self-rating instrument completed at the somatic exam.

CONCLUSION

Despite limitations resulting from the relatively low participation in the context of a comprehensive and time-consuming investigation, the PsyCoLaus study should significantly contribute to the current understanding of psychiatric disorders and comorbid somatic conditions by: 1) establishing the clinical relevance of specific psychiatric syndromes below the DSM-IV threshold; 2) determining comorbidity between risk factors for CVD and psychiatric disorders; 3) assessing genetic variants associated with common psychiatric disorders and 4) identifying DNA markers shared between CVD and psychiatric disorders.