miR-221/222 Are Involved in Response to Sunitinib Treatment in Metastatic Renal Cell Carcinoma.

Sunitinib is a multitargeting tyrosine kinase inhibitor used for metastatic renal cancer. There are no biomarkers that can predict sunitinib response. Such markers are needed to avoid administration of costly medication with side effects to patients who would not benefit from it. We compared global miRNA expression between patients with a short (≤12 months) versus prolonged (>12 months) progression-free survival (PFS) under sunitinib as first-line therapy for metastatic renal cell carcinoma. We identified a number of differentially expressed miRNAs and developed miRNA statistical models that can accurately distinguish between the two groups. We validated our models in the discovery set and an independent set of 57 patients. Target prediction and pathway analysis showed that these miRNAs are involved in vascular endothelial growth factor (VEGF), TGFß, and mammalian target of rapamycin (mTOR)-mediated signaling and cell-cell communication. We tested the effect of these miRNAs on cellular proliferation and angiogenesis. We validated the negative correlation between miR-221 and its target, VEGFR2.miR-221 overexpression was associated with a poor PFS while its target, VEGFR2 was associated with longer survival. Gain of function experiments showed that miR-221 and miR-222 decreased angiogenesis and cellular proliferation in human umbilical vein endothelial cells (HUVEC) while increasing cellular proliferation in ACHN cells. miRNAs represent potential predictive markers for sunitinib response.

Discovering biomarkers for antidepressant response: protocol from the Canadian biomarker integration network in depression (CAN-BIND) and clinical characteristics of the first patient cohort.

BACKGROUND: Major Depressive Disorder (MDD) is among the most prevalent and disabling medical conditions worldwide. Identification of clinical and biological markers ("biomarkers") of treatment response could personalize clinical decisions and lead to better outcomes. This paper describes the aims, design, and methods of a discovery study of biomarkers in antidepressant treatment response, conducted by the Canadian Biomarker Integration Network in Depression (CAN-BIND). The CAN-BIND research program investigates and identifies biomarkers that help to predict outcomes in patients with MDD treated with antidepressant medication. The primary objective of this initial study (known as CAN-BIND-1) is to identify individual and integrated neuroimaging, electrophysiological, molecular, and clinical predictors of response to sequential antidepressant monotherapy and adjunctive therapy in MDD. METHODS: CAN-BIND-1 is a multisite initiative involving 6 academic health centres working collaboratively with other universities and research centres. In the 16-week protocol, patients with MDD are treated with a first-line antidepressant (escitalopram 10-20 mg/d) that, if clinically warranted after eight weeks, is augmented with an evidence-based, add-on medication (aripiprazole 2-10 mg/d). Comprehensive datasets are obtained using clinical rating scales; behavioural, dimensional, and functioning/quality of life measures; neurocognitive testing; genomic, genetic, and proteomic profiling from blood samples; combined structural and functional magnetic resonance imaging; and electroencephalography. De-identified data from all sites are aggregated within a secure neuroinformatics platform for data integration, management, storage, and analyses. Statistical analyses will include multivariate and machine-learning techniques to identify predictors, moderators, and mediators of treatment response. DISCUSSION: From June 2013 to February 2015, a cohort of 134 participants (85 outpatients with MDD and 49 healthy participants) has been evaluated at baseline. The clinical characteristics of this cohort are similar to other studies of MDD. Recruitment at all sites is ongoing to a target sample of 290 participants. CAN-BIND will identify biomarkers of treatment response in MDD through extensive clinical, molecular, and imaging assessments, in order to improve treatment practice and clinical outcomes. It will also create an innovative, robust platform and database for future research. TRIAL REGISTRATION: ClinicalTrials.gov identifier NCT01655706 . Registered July 27, 2012.

MicroRNA signature helps distinguish early from late biochemical failure in prostate cancer.

PURPOSE: Prostate-specific antigen testing has led to overtreatment of prostate cancer (PCa). Only a small subset of PCa patients will have an aggressive disease that requires intensive therapy, and there is currently no biomarker to predict disease aggressiveness at the time of surgery. MicroRNAs (miRNAs) are reported to be involved in PCa pathogenesis. METHODS: This study involved 105 participants. For the discovery phase, prostatectomy samples were dichotomized to high-risk (n = 27, biochemical failure <36 months after prostatectomy) and low-risk groups (n = 14, ≥ 36 months without biochemical failure). Expression of 754 mature miRNAs was compared between the 2 groups. Linear regression models were built to accurately predict biochemical failure risk. miRNA mimics were transfected into PCa model cell lines to test effects on proliferation and to deduce responding signaling pathways. RESULTS: We identified 25 differentially expressed miRNAs between the biochemical failure risk groups. Based on the expression of 2-3 miRNAs, 3 logistic regression models were developed, each with a high positive predictive value. Candidate miRNAs and the best-performing model were also verified on an independent PCa set. miRNA-152, featured in the models, was further investigated by using cell line models and was shown to affect cell proliferation. Predicted interaction between miR-152 and (mRNA)ERBB3 (erythroblastic leukemia viral oncogene homolog 3) was experimentally validated in vitro. CONCLUSIONS: miRNAs can help to predict biochemical failure risk at the time of prostatectomy.

Identification of differentially regulated secretome components during skeletal myogenesis.

Myogenesis is a well-characterized program of cellular differentiation that is exquisitely sensitive to the extracellular milieu. Systematic characterization of the myogenic secretome (i.e. the ensemble of secreted proteins) is, therefore, warranted for the identification of novel secretome components that regulate both the pluripotency of these progenitor mesenchymal cells, and also their commitment and passage through the differentiation program. Previously, we have successfully identified 26 secreted proteins in the mouse skeletal muscle cell line C2C12 (1). In an effort to attain a more comprehensive picture of the regulation of myogenesis by its extracellular milieu, quantitative profiling employing stable isotope labeling by amino acids in cell culture was implemented in conjunction with two parallel high throughput online reverse phase liquid chromatography-tandem mass spectrometry systems. In summary, 34 secreted proteins were quantified, 30 of which were shown to be differentially expressed during muscle development. Intriguingly, our analysis has revealed several novel up- and down-regulated secretome components that may have critical biological relevance for both the maintenance of pluripotency and the passage of cells through the differentiation program. In particular, the altered regulation of secretome components, including follistatin-like protein-1, osteoglycin, spondin-2, and cytokine-induced apoptosis inhibitor-1, along with constitutively expressed factors, such as fibulin-2, illustrate dynamic changes in the secretome that take place when differentiation to a specific lineage occurs.

FAIR in action: Brain-CODE - A neuroscience data sharing platform to accelerate brain research.

The effective sharing of health research data within the healthcare ecosystem can have tremendous impact on the advancement of disease understanding, prevention, treatment, and monitoring. By combining and reusing health research data, increasingly rich insights can be made about patients and populations that feed back into the health system resulting in more effective best practices and better patient outcomes. To achieve the promise of a learning health system, data needs to meet the FAIR principles of findability, accessibility, interoperability, and reusability. Since the inception of the Brain-CODE platform and services in 2012, the Ontario Brain Institute (OBI) has pioneered data sharing activities aligned with FAIR principles in neuroscience. Here, we describe how Brain-CODE has operationalized data sharing according to the FAIR principles. Findable-Brain-CODE offers an interactive and itemized approach for requesters to generate data cuts of interest that align with their research questions. Accessible-Brain-CODE offers multiple data access mechanisms. These mechanisms-that distinguish between metadata access, data access within a secure computing environment on Brain-CODE and data access via export will be discussed. Interoperable-Standardization happens at the data capture level and the data release stage to allow integration with similar data elements. Reusable - Brain-CODE implements several quality assurances measures and controls to maximize data value for reusability. We will highlight the successes and challenges of a FAIR-focused neuroinformatics platform that facilitates the widespread collection and sharing of neuroscience research data for learning health systems.

Rasch analyses of the Quick Inventory of Depressive Symptomatology Self-Report in neurodegenerative and major depressive disorders.

Background: Symptoms of depression are present in neurodegenerative disorders (ND). It is important that depression-related symptoms be adequately screened and monitored in persons living with ND. The Quick Inventory of Depressive Symptomatology Self-Report (QIDS-SR) is a widely-used self-report measure to assess and monitor depressive severity across different patient populations. However, the measurement properties of the QIDS-SR have not been assessed in ND. Aim: To use Rasch Measurement Theory to assess the measurement properties of the Quick Inventory of Depressive Symptomatology Self-Report (QIDS-SR) in ND and in comparison to major depressive disorder (MDD). Methods: De-identified data from the Ontario Neurodegenerative Disease Research Initiative (NCT04104373) and Canadian Biomarker Integration Network in Depression (NCT01655706) were used in the analyses. Five hundred and twenty participants with ND (Alzheimer's disease or mild cognitive impairment, amyotrophic lateral sclerosis, cerebrovascular disease, frontotemporal dementia and Parkinson's disease) and 117 participants with major depressive disorder (MDD) were administered the QIDS-SR. Rasch Measurement Theory was used to assess measurement properties of the QIDS-SR, including unidimensionality and item-level fit, category ordering, item targeting, person separation index and reliability and differential item functioning. Results: The QIDS-SR fit well to the Rasch model in ND and MDD, including unidimensionality, satisfactory category ordering and goodness-of-fit. Item-person measures (Wright maps) showed gaps in item difficulties, suggesting poor precision for persons falling between those severity levels. Differences between mean person and item measures in the ND cohort logits suggest that QIDS-SR items target more severe depression than experienced by the ND cohort. Some items showed differential item functioning between cohorts. Conclusion: The present study supports the use of the QIDS-SR in MDD and suggest that the QIDS-SR can be also used to screen for depressive symptoms in persons with ND. However, gaps in item targeting were noted that suggests that the QIDS-SR cannot differentiate participants falling within certain severity levels. Future studies would benefit from examination in a more severely depressed ND cohort, including those with diagnosed clinical depression.

Common Data Elements to Facilitate Sharing and Re-use of Participant-Level Data: Assessment of Psychiatric Comorbidity Across Brain Disorders.

The Ontario Brain Institute's "Brain-CODE" is a large-scale informatics platform designed to support the collection, storage and integration of diverse types of data across several brain disorders as a means to understand underlying causes of brain dysfunction and developing novel approaches to treatment. By providing access to aggregated datasets on participants with and without different brain disorders, Brain-CODE will facilitate analyses both within and across diseases and cover multiple brain disorders and a wide array of data, including clinical, neuroimaging, and molecular. To help achieve these goals, consensus methodology was used to identify a set of core demographic and clinical variables that should be routinely collected across all participating programs. Establishment of Common Data Elements within Brain-CODE is critical to enable a high degree of consistency in data collection across studies and thus optimize the ability of investigators to analyze pooled participant-level data within and across brain disorders. Results are also presented using selected common data elements pooled across three studies to better understand psychiatric comorbidity in neurological disease (Alzheimer's disease/amnesic mild cognitive impairment, amyotrophic lateral sclerosis, cerebrovascular disease, frontotemporal dementia, and Parkinson's disease).

An item response analysis of the motor and behavioral subscales of the unified Huntington's disease rating scale in huntington disease gene expansion carriers.

Although the Unified Huntington's Disease Rating Scale (UHDRS) is widely used in the assessment of Huntington disease (HD), the ability of individual items to discriminate individual differences in motor or behavioral manifestations has not been extensively studied in HD gene expansion carriers without a motor-defined clinical diagnosis (ie, prodromal-HD or prHD). To elucidate the relationship between scores on individual motor and behavioral UHDRS items and total score for each subscale, a nonparametric item response analysis was performed on retrospective data from 2 multicenter longitudinal studies. Motor and behavioral assessments were supplied for 737 prHD individuals with data from 2114 visits (PREDICT-HD) and 686 HD individuals with data from 1482 visits (REGISTRY). Option characteristic curves were generated for UHDRS subscale items in relation to their subscale score. In prHD, overall severity of motor signs was low, and participants had scores of 2 or above on very few items. In HD, motor items that assessed ocular pursuit, saccade initiation, finger tapping, tandem walking, and to a lesser extent, saccade velocity, dysarthria, tongue protrusion, pronation/supination, Luria, bradykinesia, choreas, gait, and balance on the retropulsion test were found to discriminate individual differences across a broad range of motor severity. In prHD, depressed mood, anxiety, and irritable behavior demonstrated good discriminative properties. In HD, depressed mood demonstrated a good relationship with the overall behavioral score. These data suggest that at least some UHDRS items appear to have utility across a broad range of severity, although many items demonstrate problematic features.

Precipitation and selective extraction of human serum endogenous peptides with analysis by quadrupole time-of-flight mass spectrometry reveals posttranslational modifications and low-abundance peptides.

The endogenous peptides of human serum may have regulatory functions, have been associated with physiological states, and their modifications may reveal some mechanisms of disease. In order to correlate levels of specific peptides with disease alongside internal standards, the polypeptides must first be reliably extracted and identified. Endogenous blood peptides can be effectively enriched by precipitation of the serum with organic solvents followed by selective extraction of peptides using aqueous solutions modified with organic solvents. Polypeptides on filter paper were assayed with Coomasie brilliant blue binding. The polypeptides were resolved by detergent tricine polyacrylamide electrophoresis and visualized by diamine silver staining. Peptides in the extracts were collected by C18 and analyzed by matrix-assisted laser desorption/ionization and liquid chromatography-electrospray ionization-tandem mass spectrometry (MS/MS) quadrupole time-of-flight MS/MS. Peptides were resolved as multiple isotopic peaks in MS mode with mass deviation of 0.1 Da or less and similar accuracy for fragments. The sensitivity of MS and MS/MS analysis was estimated to be in the picomolar range or less. The peptide composition of the extracts was dependent on solvent formulation. Multiple peptides from apolipoproteins, complement proteins, coagulation factors, and many others were identified by X!Tandem with high mass accuracy of peptide ions and fragments from collision-induced dissociation. Many previously unreported posttranslational modifications of peptides including phosphorylations, oxidations, glycosylations, and others were detected with high mass accuracy and may be of clinical importance. About 4,630 redundant peptides were identified with 99% confidence separately, and together some 1,251 distinct proteins were identified with 99% confidence or greater using the Paragon algorithm.

THE DEPRESSION INVENTORY DEVELOPMENT SCALE: Assessment of Psychometric Properties Using Classical and Modern Measurement Theory in a CAN-BIND Trial.

Objective: The goal of the Depression Inventory Development (DID) project is to develop a comprehensive and psychometrically sound rating scale for major depressive disorder (MDD) that reflects current diagnostic criteria and conceptualizations of depression. We report here the evaluation of the current DID item bank using Classical Test Theory (CTT), Item Response Theory (IRT) and Rasch Measurement Theory (RMT). Methods: The present study was part of a larger multisite, open-label study conducted by the Canadian Biomarker Integration Network in Depression (ClinicalTrials.gov: NCT01655706). Trained raters administered the 32 DID items at each of two visits (MDD: baseline, n=211 and Week 8, n=177; healthy participants: baseline, n=112 and Week 8, n=104). The DID's "grid" structure operationalizes intensity and frequency of each item, with clear symptom definitions and a structured interview guide, with the current iteration assessing symptoms related to anhedonia, cognition, fatigue, general malaise, motivation, anxiety, negative thinking, pain, and appetite. Participants were also administered the Montgomery- Åsberg Depression Rating Scale (MADRS) and Quick Inventory of Depressive Symptomatology-Self-Report (QIDS-SR) that allowed DID items to be evaluated against existing "benchmark" items. CTT was used to assess data quality/reliability (i.e., missing data, skewness, scoring frequency, internal consistency), IRT to assess individual item performance by modelling an item's ability to discriminate levels of depressive severity (as assessed by the MADRS), and RMT to assess how the items perform together as a scale to capture a range of depressive severity (item targeting). These analyses together provided empirical evidence to base decisions on which DID items to remove, modify, or advance. Results: Of the 32 DID items evaluated, eight items were identified by CTT as problematic, displaying low variability in the range of responses, floor effects, and/or skewness; and four items were identified by IRT to show poor discriminative properties that would limit their clinical utility. Five additional items were deemed to be redundant. The remaining 15 DID items all fit the Rasch model, with person and item difficulty estimates indicating satisfactory item targeting, with lower precision in participants with mild levels of depression. These 15 DID items also showed good internal consistency (alpha=0.95 and inter-item correlations ranging from r=0.49 to r=0.84) and all items were sensitive to change following antidepressant treatment (baseline vs. Week 8). RMT revealed problematic item targeting for the MADRS and QIDSSR, including an absence of MADRS items targeting participants with mild/moderate depression and an absence of QIDS-SR items targeting participants with mild or severe depression. Conclusion: The present study applied CTT, IRT, and RMT to assess the measurement properties of the DID items and identify those that should be advanced, modified, or removed. Of the 32 items evaluated, 15 items showed good measurement properties. These items (along with previously evaluated items) will provide the basis for validation of a penultimate DID scale assessing anhedonia, cognitive slowing, concentration, executive function, recent memory, drive, emotional fatigue, guilt, self-esteem, hopelessness, tension, rumination, irritability, reduced appetite, insomnia, sadness, worry, suicidality, and depressed mood. The strategies adopted by the DID process provide a framework for rating scale development and validation.

Multiple pain complaints in patients with major depressive disorder.

OBJECTIVE: To characterize the co-existence of multiple pain-related complaints in patients enrolled in a series of pharmaceutical company drug trials for the treatment of Major Depressive Disorder (MDD). METHOD: Pooled 'blinded' data from 2191 patients enrolled in randomized, multicenter, double-blind placebo-controlled studies for the treatment of MDD were analyzed. Painful symptoms were assessed using the seven pain symptoms subset of the Somatic Symptoms Inventory: 'Headache,' 'Pain in lower back,' 'Neck pain,' 'Pain in joints,' 'Soreness in muscles,' 'Pain in heart or chest,' and 'Pain or cramps in abdomen.' The 17-item Hamilton Depression Rating Scale (HAMD) was used to assess severity of depression. RESULTS: Of those meeting the study entry criteria (total HAMD score >or=15), 25% reported no pain complaints and 18% reported 1 pain compliant; the majority (57%) of patients reported the co-existence of multiple pain-related complaints, with 14%, 12%, 11%, 11%, 7%, and 3% of patients reporting 2, 3, 4, 5, 6 and 7 different pain symptoms, respectively. The number of pain-related symptoms experienced was moderately related to severity of depression (r = 0.35), with the most common pain symptom combinations being among headaches, lower back pain, neck pain, pain in joints, and soreness in muscles. CONCLUSIONS: This study supports pain as a component feature of MDD. The number of comorbid pain-related complaints, which generally increased as a function of depressive severity, should be considered in the diagnosis of depression, planning of treatment strategies, and measurement of treatment outcome.

Plasma microRNA expression levels and their targeted pathways in patients with major depressive disorder who are responsive to duloxetine treatment.

Major depressive disorder (MDD) is a complex disorder with many pathways known to contribute to its pathogenesis, such as apoptotic signaling, with antidepressants having been shown to target these pathways. In this study, we explored microRNAs as predictive markers of drug response to duloxetine, a serotonin-norepinephrine reuptake inhibiter, using peripheral blood samples from 3 independent clinical trials (NCT00635219; NCT0059991; NCT01140906) comparing 6-8 weeks of treatment with duloxetine to placebo treatment in patients with MDD. Plasma microRNA was extracted and sequenced using the Ion Proton Sequencer. Rank feature selection analysis was used to identify microRNAs in the top 10th percentile for their differentiating ability between patients who remitted and did not remit with duloxetine treatment. The results were then compared between the 3 trials to see their replicability. To further validate our findings, we reasoned that the pathways targeted by these microRNAs would be those shown to be altered in MDD in pathway enrichment analysis. Hsa-miR-23a-3p, hsa-miR-16-5p, hsa-miR-146a-5p and hsa-miR-21-5p were identified in 2 or more trials as being able to differentiate patients who would remit with duloxetine treatment using samples collected before treatment initiation, suggesting that they may be good candidates for identification of predictive biomarkers of duloxetine response. Pathway enrichment analysis further showed that microRNAs identified as differentiating for duloxetine response target the apoptosis signaling pathway. Future studies examining these microRNAs outside of a clinical trial setting and exploring their role in MDD may further our understanding of MDD and antidepressant response.

Symptomatic and Functional Outcomes and Early Prediction of Response to Escitalopram Monotherapy and Sequential Adjunctive Aripiprazole Therapy in Patients With Major Depressive Disorder: A CAN-BIND-1 Report.

OBJECTIVE: To report the symptomatic and functional outcomes in patients with major depressive disorder (MDD) during a 2-phase treatment trial and to estimate the value of early improvement after 2 weeks in predicting clinical response to escitalopram and subsequently to adjunctive treatment with aripiprazole. METHODS: Participants with MDD (N = 211) identified with the Montgomery-Asberg Depression Rating Scale (MADRS) and confirmed with the Mini-International Neuropsychiatric Interview were recruited from 6 outpatient centers across Canada (August 2013 through December 2016) and treated with open-label escitalopram (10-20 mg) for 8 weeks (Phase 1). Clinical and functional outcomes were evaluated using the MADRS, Quick Inventory of Depressive Symptomatology-Self-Rated (QIDS-SR), Sheehan Disability Scale (SDS), and Lam Employment Absence and Productivity Scale (LEAPS). Participants were evaluated at 8 and 16 weeks for clinical and functional response and remission. Phase 1 responders continued escitalopram while nonresponders received adjunctive aripiprazole (2-10 mg) for a further 8 weeks (Phase 2). RESULTS: After Phase 1, MADRS response (≥ 50% decrease from baseline) and remission (score ≤ 10) were, respectively, 47% and 31%, and SDS response (score ≤ 12) and remission (score ≤ 6) were, respectively, 53% and 24%. Response to escitalopram was maintained in 91% of participants at week 16, while 61% of the adjunctive aripiprazole group achieved MADRS response during Phase 2. Response and remission rates with the QIDS-SR were lower than with the MADRS. The LEAPS demonstrated significant occupational improvement (P < .05). Early symptomatic improvement predicted outcomes with modest accuracy. CONCLUSIONS: This study demonstrates comparable symptomatic and functional outcomes to those of other large practical-design studies. There was a high response rate with the adjunctive use of aripiprazole in escitalopram nonresponders. Given the limited value of early clinical improvement to predict outcome, integration of clinical and biological markers deserves further exploration. TRIAL REGISTRATION: ClinicalTrials.gov identifier: NCT01655706.

Symptoms of anxiety in depression: assessment of item performance of the Hamilton Anxiety Rating Scale in patients with depression.

BACKGROUND: Although diagnostically dissociable, anxiety is strongly co-morbid with depression. To examine further the clinical symptoms of anxiety in major depressive disorder (MDD), a non-parametric item response analysis on "blinded" data from four pharmaceutical company clinical trials was performed on the Hamilton Anxiety Rating Scale (HAMA) across levels of depressive severity. METHODS: The severity of depressive symptoms was assessed using the 17-item Hamilton Depression Rating Scale (HAMD). HAMA and HAMD measures were supplied for each patient on each of two post-screen visits (n=1,668 observations). Option characteristic curves were generated for all 14 HAMA items to determine the probability of scoring a particular option on the HAMA in relation to the total HAMD score. Additional analyses were conducted using Pearson's product-moment correlations. RESULTS: Results showed that anxiety-related symptomatology generally increased as a function of overall depressive severity, though there were clear differences between individual anxiety symptoms in their relationship with depressive severity. In particular, anxious mood, tension, insomnia, difficulties in concentration and memory, and depressed mood were found to discriminate over the full range of HAMD scores, increasing continuously with increases in depressive severity. By contrast, many somatic-related symptoms, including muscular, sensory, cardiovascular, respiratory, gastro-intestinal, and genito-urinary were manifested primarily at higher levels of depression and did not discriminate well at lower HAMD scores. CONCLUSIONS: These results demonstrate anxiety as a core feature of depression, and the relationship between anxiety-related symptoms and depression should be considered in the assessment of depression and evaluation of treatment strategies and outcome.

Prevalence and association of somatic symptoms in patients with Major Depressive Disorder.

BACKGROUND: Painful and non-painful somatic symptoms are often reported in patients with depressive disorder. The proper identification of depression-relevant somatic symptoms is important for the accurate diagnosis of depression, development of treatment strategies and measurement of outcome. The objective of this study was to characterize the relationship between somatic symptoms and depression in patients diagnosed with Major Depressive Disorder (MDD), using data from randomized drug trials carried out by a pharmaceutical company. METHODS: Pooled 'blinded' data from 2191 patients enrolled in randomized, multicenter, double-blind placebo-controlled studies for the treatment of MDD were analyzed. Somatic symptoms were assessed using the Somatic Symptoms Inventory (SSI) and the Hamilton Depression Rating Scale (HAMD) was used to assess symptoms of depression. RESULTS: The most common somatic symptom reported by patients with MDD was 'feeling fatigued, weak, or tired all over', with 78% of patients reporting 'moderate' levels or above. This was followed by 'feeling that not in as good physical health as most of your friends' (59%), 'not feeling well most of the time in the past few years' (54%), and 'feeling weak in parts of body' (45%). 'Headache' was the most common pain-related symptom with 43% reporting 'moderate' or above. Pearson's product-moment correlations revealed that somatic symptoms generally increased as a function of overall depressive (r=0.43), with 'feeling fatigued, weak, or tired all over' (r=0.50), 'feeling that not in as good physical health as most of your friends' (r=0.42), 'feeling weak in parts of body' (r=0.41), 'heavy feeling in arms and legs' (r=0.34), 'not feeling well most of the time in the past few years' (r=0.32), and 'headache' (r=0.31) showing the strongest correlation with overall HAMD scores. Non-parametric item response analyses showed that many somatic symptoms demonstrate good relationship between item response and the overall severity of depression. In particular, 'feeling fatigued, weak, or tired all over' exhibited good discriminative properties across the full range of severity for depression. LIMITATIONS: The analysis utilized data from a 'restricted' patient population in drug trials sponsored by a pharmaceutical company. CONCLUSIONS: These results demonstrate a high prevalence and association of somatic symptoms in patients with MDD, including feelings of fatigue, physical malaise and pain-related symptoms, which could be potentially useful in the assessment of depression and in the evaluation of treatment strategies.

The CAMH Neuroinformatics Platform: A Hospital-Focused Brain-CODE Implementation.

Investigations of mental illness have been enriched by the advent and maturation of neuroimaging technologies and the rapid pace and increased affordability of molecular sequencing techniques, however, the increased volume, variety and velocity of research data, presents a considerable technical and analytic challenge to curate, federate and interpret. Aggregation of high-dimensional datasets across brain disorders can increase sample sizes and may help identify underlying causes of brain dysfunction, however, additional barriers exist for effective data harmonization and integration for their combined use in research. To help realize the potential of multi-modal data integration for the study of mental illness, the Centre for Addiction and Mental Health (CAMH) constructed a centralized data capture, visualization and analytics environment-the CAMH Neuroinformatics Platform-based on the Ontario Brain Institute (OBI) Brain-CODE architecture, towards the curation of a standardized, consolidated psychiatric hospital-wide research dataset, directly coupled to high performance computing resources.

Brain-CODE: A Secure Neuroinformatics Platform for Management, Federation, Sharing and Analysis of Multi-Dimensional Neuroscience Data.

Historically, research databases have existed in isolation with no practical avenue for sharing or pooling medical data into high dimensional datasets that can be efficiently compared across databases. To address this challenge, the Ontario Brain Institute's "Brain-CODE" is a large-scale neuroinformatics platform designed to support the collection, storage, federation, sharing and analysis of different data types across several brain disorders, as a means to understand common underlying causes of brain dysfunction and develop novel approaches to treatment. By providing researchers access to aggregated datasets that they otherwise could not obtain independently, Brain-CODE incentivizes data sharing and collaboration and facilitates analyses both within and across disorders and across a wide array of data types, including clinical, neuroimaging and molecular. The Brain-CODE system architecture provides the technical capabilities to support (1) consolidated data management to securely capture, monitor and curate data, (2) privacy and security best-practices, and (3) interoperable and extensible systems that support harmonization, integration, and query across diverse data modalities and linkages to external data sources. Brain-CODE currently supports collaborative research networks focused on various brain conditions, including neurodevelopmental disorders, cerebral palsy, neurodegenerative diseases, epilepsy and mood disorders. These programs are generating large volumes of data that are integrated within Brain-CODE to support scientific inquiry and analytics across multiple brain disorders and modalities. By providing access to very large datasets on patients with different brain disorders and enabling linkages to provincial, national and international databases, Brain-CODE will help to generate new hypotheses about the biological bases of brain disorders, and ultimately promote new discoveries to improve patient care.

Standardization of electroencephalography for multi-site, multi-platform and multi-investigator studies: insights from the canadian biomarker integration network in depression.

Subsequent to global initiatives in mapping the human brain and investigations of neurobiological markers for brain disorders, the number of multi-site studies involving the collection and sharing of large volumes of brain data, including electroencephalography (EEG), has been increasing. Among the complexities of conducting multi-site studies and increasing the shelf life of biological data beyond the original study are timely standardization and documentation of relevant study parameters. We present the insights gained and guidelines established within the EEG working group of the Canadian Biomarker Integration Network in Depression (CAN-BIND). CAN-BIND is a multi-site, multi-investigator, and multi-project network supported by the Ontario Brain Institute with access to Brain-CODE, an informatics platform that hosts a multitude of biological data across a growing list of brain pathologies. We describe our approaches and insights on documenting and standardizing parameters across the study design, data collection, monitoring, analysis, integration, knowledge-translation, and data archiving phases of CAN-BIND projects. We introduce a custom-built EEG toolbox to track data preprocessing with open-access for the scientific community. We also evaluate the impact of variation in equipment setup on the accuracy of acquired data. Collectively, this work is intended to inspire establishing comprehensive and standardized guidelines for multi-site studies.

MicroRNAs 146a/b-5 and 425-3p and 24-3p are markers of antidepressant response and regulate MAPK/Wnt-system genes.

Antidepressants (ADs) are the most common treatment for major depressive disorder (MDD). However, only ∼30% of patients experience adequate response after a single AD trial, and this variability remains poorly understood. Here, we investigated microRNAs (miRNAs) as biomarkers of AD response using small RNA-sequencing in paired samples from MDD patients enrolled in a large, randomized placebo-controlled trial of duloxetine collected before and 8 weeks after treatment. Our results revealed differential expression of miR-146a-5p, miR-146b-5p, miR-425-3p and miR-24-3p according to treatment response. These results were replicated in two independent clinical trials of MDD, a well-characterized animal model of depression, and post-mortem human brains. Furthermore, using a combination of bioinformatics, mRNA studies and functional in vitro experiments, we showed significant dysregulation of genes involved in MAPK/Wnt signalling pathways. Together, our results indicate that these miRNAs are consistent markers of treatment response and regulators of the MAPK/Wnt systems.

The Depression Inventory Development Workgroup: A Collaborative, Empirically Driven Initiative to Develop a New Assessment Tool for Major Depressive Disorder.

The Depression Inventory Development project is an initiative of the International Society for CNS Drug Development whose goal is to develop a comprehensive and psychometrically sound measurement tool to be utilized as a primary endpoint in clinical trials for major depressive disorder. Using an iterative process between field testing and psychometric analysis and drawing upon expertise of international researchers in depression, the Depression Inventory Development team has established an empirically driven and collaborative protocol for the creation of items to assess symptoms in major depressive disorder. Depression-relevant symptom clusters were identified based on expert clinical and patient input. In addition, as an aid for symptom identification and item construction, the psychometric properties of existing clinical scales (assessing depression and related indications) were evaluated using blinded datasets from pharmaceutical antidepressant drug trials. A series of field tests in patients with major depressive disorder provided the team with data to inform the iterative process of scale development. We report here an overview of the Depression Inventory Development initiative, including results of the third iteration of items assessing symptoms related to anhedonia, cognition, fatigue, general malaise, motivation, anxiety, negative thinking, pain and appetite. The strategies adopted from the Depression Inventory Development program, as an empirically driven and collaborative process for scale development, have provided the foundation to develop and validate measurement tools in other therapeutic areas as well.