Identification of Differential MicroRNAs in Cerebrospinal Fluid and Serum of Patients with Major Depressive Disorder

Major depression is a debilitating disease. To date, the development of biomarkers of major depressive disorder (MDD) remains a challenge. Recently, alterations in the expression of microRNAs (miRNAs) from post-mortem brain tissue and peripheral blood have been linked to MDD. The goals of this study were to detect the differential miRNAs in cerebrospinal fluid (CSF) and serum of MDD patients. First, the relative expression levels of 179 miRNAs (relative high levels in serum) were analyzed by miRNA PCR Panel in the CSF of MDD patients. Then, the differentially altered miRNAs from CSF were further assessed by qRT-PCR in the serum of the same patients. Finally, the serum differentially altered miRNAs were further validated by qRT-PCR in the serum of another MDD patients. The CSF-results indicated that 11 miRNAs in MDD patients were significantly higher than these in control subjects, and 5 miRNAs were significantly lower than these in control subjects. The serum-results from the same patients showed that 3 miRNAs (miR-221-3p, miR-34a-5p, and let-7d-3p) of the 11 miRNAs were significantly higher than these in control subjects, and 1 miRNA (miR-451a) of 5 miRNAs was significantly lower than these in control subjects. The up-regulation of miR-221-3p, miR-34a-5p, let-7d-3p and down-regulation of miR-451a was further validated in another 32 MDD patients. ROC analysis showed that the area under curve of let-7d-3p, miR-34a-5p, miR-221-3p and miR-451a was 0.94, 0.98, 0.97 and 0.94, with specificity of 90.48%, 95.24%, 90.48% and 90.48%, and sensitivity of 93.75%, 96.88%, 90.63% and 84.85%, respectively. In addition, target gene prediction found that the altered miRNAs are involved in affecting some important genes and pathway related to MDD. Our results suggested that differentially altered miRNAs in CSF might be involved in MDD, and serum miR-221-3p, miR-34a-5p, let-7d-3p, and miR-451a might be able to serve as biomarkers for MDD.     

Identification and Validation of Urinary Metabolite Biomarkers for Major Depressive Disorder

Major depressive disorder (MDD) is a widespread and debilitating mental disorder. However, there are no biomarkers available to aid in the diagnosis of this disorder. In this study, a nuclear magnetic resonance spectroscopy–based metabonomic approach was employed to profile urine samples from 82 first-episode drug-naïve depressed subjects and 82 healthy controls (the training set) in order to identify urinary metabolite biomarkers for MDD. Then, 44 unselected depressed subjects and 52 healthy controls (the test set) were used to independently validate the diagnostic generalizability of these biomarkers. A panel of five urinary metabolite biomarkers—malonate, formate, N-methylnicotinamide, m-hydroxyphenylacetate, and alanine—was identified. This panel was capable of distinguishing depressed subjects from healthy controls with an area under the receiver operating characteristic curve (AUC) of 0.81 in the training set. Moreover, this panel could classify blinded samples from the test set with an AUC of 0.89. These findings demonstrate that this urinary metabolite biomarker panel can aid in the future development of a urine-based diagnostic test for MDD.Major depressive disorder (MDD)¹ is a debilitating mental disorder affecting up to 15% of the general population and accounting for 12.3% of the global burden of disease (1, 2). Currently, the diagnosis of MDD still relies on the subjective identification of symptom clusters rather than empirical laboratory tests. The current diagnostic modality results in a considerable error rate (3), as the clinical presentation of MDD is highly heterogeneous and the current symptom-based method is not capable of adequately characterizing this heterogeneity (4). An approach that can be used to circumvent these limitations is to identify disease biomarkers to support objective diagnostic laboratory tests for MDD.Metabonomics, which can measure the small molecules in given biosamples such as plasma and urine without bias (5), has been extensively used to characterize the metabolic changes of diseases and thus facilitate the identification of novel disease-specific signatures as putative biomarkers (6–10). Nuclear magnetic resonance (NMR) spectroscopy–based metabonomic approaches characterized by sensitive, high-throughput molecular screening have been employed previously in identifying novel biomarkers for a variety of neuropsychiatric disorders, including stroke, bipolar disorder, and schizophrenia (11–13).Specifically with regard to MDD, several animal studies have already characterized the metabolic changes in the blood and urine (14–19). These studies provide valuable clues as to the pathophysiological mechanism of MDD. However, no study has been designed with the aim of diagnosing this disease. Recently, using an NMR-based metabonomic approach, this research group identified a unique plasma metabolic signature that enables the discrimination of MDD from healthy controls with both high sensitivity and specificity (20). These findings motivated further study on urinary diagnostic metabolite biomarkers for MDD, which would be more valuable from a clinical applicability standpoint, as urine can be more non-invasively collected. Moreover, previous studies have also demonstrated the feasibility of identifying diagnostic metabolite biomarkers of psychiatric disorders in the urine. For example, using an NMR-based metabonomics approach, Yap et al. (21) identified a unique urinary metabolite signature that clearly discriminated autism patients from healthy controls. As systemic metabolic disturbances have been observed in the urine of a depressed animal model, it is likely that diagnostic metabolite markers for MDD can be detected in human urine.Therefore, in this study, NMR spectroscopy combined with multivariate pattern recognition techniques were used to profile 82 first-episode drug-naïve MDD subjects and 82 healthy controls (the training set) in order to identify potential metabolite biomarkers for MDD. Furthermore, 44 unselected MDD subjects and 52 healthy controls (the test set) were employed to independently validate the diagnostic performance of these urinary metabolite biomarkers.     

Predictors of the Onset of Manic Symptoms and a (Hypo)Manic Episode in Patients with Major Depressive Disorder

Objective

One third of patients with a major depressive episode also experience manic symptoms or, even, a (hypo)manic episode. Retrospective studies on the temporal sequencing of symptomatology suggest that the majority of these patients report depressive symptoms before the onset of manic symptoms. However, prospective studies are scarce and this study will, therefore, prospectively examine the onset of either manic symptoms or a (hypo)manic episode in patients with a major depressive disorder. In addition, we will consider the impact of a large set of potential risk factors on both outcomes.

Methodology

Four-year follow-up data were used to determine the onset of manic symptoms as well as a CIDI-based (hypo)manic episode in a large sample (n = 889, age: 18–65 years) of outpatients with a major depressive disorder and without manic symptoms at baseline. Baseline vulnerability (i.e., sociodemographics, family history of depression, childhood trauma, life-events) and clinical (i.e., isolated manic symptoms, depression characteristics, and psychiatric comorbidity) factors were considered as potential risk factors.

Results

In our sample of depressed patients, 15.9% developed manic symptoms and an additional 4.7% developed a (hypo)manic episode during four years. Baseline isolated manic symptoms and comorbid alcohol dependence predicted both the onset of manic symptoms and a (hypo)manic episode. Low education only predicted the onset of manic symptoms, whereas male gender, childhood trauma and severity of depressive symptoms showed strong associations with, especially, the onset of (hypo)manic episodes.

Conclusions

A substantial proportion (20.6%) of patients with a major depressive disorder later developed manic symptoms or a (hypo)manic episode. Interestingly, some identified risk factors differed for the two outcomes, which may indicate that pathways leading to the onset of manic symptoms or a (hypo)manic episode might be different. Our findings indirectly support a clinical staging model.     

The Efficacy of Neurofeedback in Patients with Major Depressive Disorder: An Open Labeled Prospective Study

The purpose of this study was to evaluate the effect of neurofeedback on depressive symptoms and electrophysiological disturbances in patients with major depressive disorder. We recruited participants suffering from depression to evaluate efficacy of left prefrontal beta with alpha/theta training. An 8-week, prospective, open-label study was undertaken. Twenty participants were recruited. The treatment protocol was twice or three times a week training of beta at F3 with alpha/theta at Pz for 8 weeks. When every visit, patients were received beta training for 30 min, and then alpha/theta training for 30 min. Baseline, 4 and 8 week scores of; the Hamilton rating scale for Depression (HAM-D), the Hamilton rating scale for Anxiety (HAM-A), the Beck Depression Inventory (BDI)-II, the Beck Anxiety Inventory (BAI), Clinical global impression-severity (CGI-S), and pre- and post-treatment resting state EEGs were compared. Interhemispheric alpha power asymmetry (A score) was computed for homologous sites F3–F4. Pre- and post-training clinical assessments revealed significant improvements in HAM–D, HAM-A, BDI, and CGI-S scores. Cumulative response rates by HAM-D were 35.0 and 75.0 % at 4 and 8 weeks, respectively, corresponding cumulative remission rates by HAM-D were 15.0 and 55.0 %, respectively. No significant differences were found between pre- and post-treatment A score. Neurofeedback treatment could improve depressive symptoms significantly. In addition, anxiety symptoms and clinical illness severity decreased significantly after neurofeedback treatment. Despite its several limitations, such as, small sample size and lack of a control group, this study suggested neurofeedback has significant effects in patients with major depressive disorder.     

基于内在功能连接推定抑郁症脑网络效率的改变

本文研究了在保留最大化内在功能连接条件下抑郁症患者脑网络效率的改变,并探索了改变的拓扑效率和抑郁症病理学之间的关系.为此,我们收集了20例抑郁症患者和20例在年龄、性别和教育水平相匹配的健康被试的静息态功能磁共振图像数据.图论分析显示,与健康对照组比较,抑郁症患者的节点效率减少在左海马旁回、右杏仁核,左颞横回和左颞极(颞中回)减少.减少的节点效率表明,在抑郁症患者脑网络中这些区域传送信息到其他区域的能力减弱.此外,发现局部效率降低在左内侧额上回、左眶部额上回、右回直肌、左杏仁核、右顶上回、左丘脑和左颞极(颞中回).并且发现左内侧额上回、左杏仁核、左丘脑与PHQ-9得分呈负相关.降低的局部效率表明抑郁症患者脑网络中这些区域的局部网络信息传送能力受到抑制.这些结果进一步确认在抑郁症患者中涉及情感信息处理的前额-丘脑-边缘区域被破坏.我们的发现为抑郁症病人的辅助诊断提供了新的潜在生物学标记物.     

Joint Analysis of Psychiatric Disorders Increases Accuracy of Risk Prediction for Schizophrenia,Bipolar Disorder,and Major Depressive Disorder

Genetic risk prediction has several potential applications in medical research and clinical practice and could be used, for example, to stratify a heterogeneous population of patients by their predicted genetic risk. However, for polygenic traits, such as psychiatric disorders, the accuracy of risk prediction is low. Here we use a multivariate linear mixed model and apply multi-trait genomic best linear unbiased prediction for genetic risk prediction. This method exploits correlations between disorders and simultaneously evaluates individual risk for each disorder. We show that the multivariate approach significantly increases the prediction accuracy for schizophrenia, bipolar disorder, and major depressive disorder in the discovery as well as in independent validation datasets. By grouping SNPs based on genome annotation and fitting multiple random effects, we show that the prediction accuracy could be further improved. The gain in prediction accuracy of the multivariate approach is equivalent to an increase in sample size of 34% for schizophrenia, 68% for bipolar disorder, and 76% for major depressive disorders using single trait models. Because our approach can be readily applied to any number of GWAS datasets of correlated traits, it is a flexible and powerful tool to maximize prediction accuracy. With current sample size, risk predictors are not useful in a clinical setting but already are a valuable research tool, for example in experimental designs comparing cases with high and low polygenic risk.     

The Functional Significance of MicroRNA-29c in Patients with Colorectal Cancer: A Potential Circulating Biomarker for Predicting Early Relapse

Background

The recurrence of colorectal cancer (CRC) is frequent within the first year of curative resection surgery and may be unavoidable. microRNAs have been suggested to play roles in carcinogenesis and cancer recurrence. We recently identified microRNA-29c (miRNA-29c) as a predictor of early recurrence in CRC. In the present study, we further investigated the functions and serum level of miRNA-29c in relation to early recurrence of CRC.

Methods

First we further confirmed overexpression of miRNA-29c in non-early relapse subjects. Gain-of-function in vitro studies were used to evaluate the effect of miRNA-29c on cell proliferation, migration, invasion, and cell cycle progression. The colon cancer cell line Caco2 and a stable clone overexpressing miRNA-29c were xenografted to evaluate the in vivo effect of miRNA-29c in null mice. Finally, circulating miRNA-29c was investigated as a potential biomarker for identifying early relapse.

Results

miRNA-29c expression significantly decreased during early relapse compared to non-early relapse in UICC stage II and III CRC patients (P = 0.021). In vitro studies showed that overexpression of miRNA-29c inhibited cell proliferation and migration. The cell cycle studies also revealed that miRNA-29c caused an accumulation of the G1 and G2 population. In vivo, miRNA-29c suppressed tumor growth in null mice. The serum miRNA-29c increased significantly in early relapsed patients compared to non-early elapsed patients (P = 0.012).

Conclusions

miRNA-29c shows anti-tumorigenesis activity, and preoperative circulating miRNA-29c levels can be used to predict postoperative early relapse of CRC.     

The Papez Circuit in First-Episode,Treatment-Naive Adults with Major Depressive Disorder: Combined Atlas-Based Tract-Specific Quantification Analysis and Voxel-Based Analysis

Previous findings suggest that the Papez Circuit may have a role in major depressive disorders. We used atlas-based tract-specific quantification analysis and voxel-based analysis to examine the integrity of white matter tracts involved in mood regulation (including tracts in the Papez Circuit). Diffusion tensor imaging acquired from 35 first-episode, treatment-naive adults with major depressive disorders and 34 healthy adult controls were compared. Our statistical approach compared structural integrity of 11 major white matter tracts between the major depressive disorder and adult controls, as well as illness duration influence in patients. Fractional anisotropy was decreased in the hippocampal cingulum and in the anterior thalamic radiation according to both analytical approaches, all of which were important tracts included in the Papez Circuit. Our results support the role of the Papez Circuit in major depressive disorders with the minimal probability of false positive due to similar findings in both analyses that have complementary advantages. Dysfunction of the Papez Circuit may be a potential marker for studying the pathogenesis of major depressive disorders.     

Influence of the CYP2D6 Isoenzyme in Patients Treated with Venlafaxine for Major Depressive Disorder: Clinical and Economic Consequences

Background

Antidepressant drugs are the mainstay of drug therapy for sustained remission of symptoms. However, the clinical results are not encouraging. This lack of response could be due, among other causes, to factors that alter the metabolism of the antidepressant drug. Objective: to evaluate the impact of concomitant administration of CYP2D6 inhibitors or substrates on the efficacy, tolerability and costs of patients treated with venlafaxine for major depressive disorder in clinical practice.

Methods

We designed an observational study using the medical records of outpatients. Subjects aged ≥18 years who started taking venlafaxine during 2008–2010 were included. Three study groups were considered: no combinations (reference), venlafaxine-substrate, and venlafaxine-inhibitor. The follow-up period was 12 months. The main variables were: demographic data, comorbidity, remission (Hamilton <7), response to treatment, adverse events and costs. The statistical analysis included logistic regression models and ANCOVA, with p values <0.05 considered significant.

Results

A total of 1,115 subjects were recruited. The mean age was 61.7 years and 75.1% were female. Approximately 33.3% (95% CI: 30.5 to 36.1) were receiving some kind of drug combination (venlafaxine-substrate: 23.0%, and venlafaxine-inhibitor: 10.3%). Compared with the venlafaxine-substrate and venlafaxine-inhibitor groups, patients not taking concomitant drugs had a better response to therapy (49.1% vs. 39.9% and 34.3%, p<0.01), greater remission of symptoms (59.9% vs. 50.2% and 43.8%, p<0.001), fewer adverse events (1.9% vs. 7.0% and 6.1%, p<0.05) and a lower mean adjusted cost (€2,881.7 vs. €4,963.3 and €7,389.1, p<0.001), respectively. All cost components showed these differences.

Conclusions

The patients treated with venlafaxine alone showed a better response to anti-depressant treatment, greater remission of symptoms, a lower incidence of adverse events and lower healthcare costs.     

Adjunctive Atypical Antipsychotic Treatment for Major Depressive Disorder: A Meta-Analysis of Depression,Quality of Life,and Safety Outcomes

Background

Atypical antipsychotic medications are widely prescribed for the adjunctive treatment of depression, yet their total risk–benefit profile is not well understood. We thus conducted a systematic review of the efficacy and safety profiles of atypical antipsychotic medications used for the adjunctive treatment of depression.

Methods and Findings

We included randomized trials comparing adjunctive antipsychotic medication to placebo for treatment-resistant depression in adults. Our literature search (conducted in December 2011 and updated on December 14, 2012) identified 14 short-term trials of aripiprazole, olanzapine/fluoxetine combination (OFC), quetiapine, and risperidone. When possible, we supplemented published literature with data from manufacturers'' clinical trial registries and US Food and Drug Administration New Drug Applications. Study duration ranged from 4 to 12 wk. All four drugs had statistically significant effects on remission, as follows: aripiprazole (odds ratio [OR], 2.01; 95% CI, 1.48–2.73), OFC (OR, 1.42; 95% CI, 1.01–2.0), quetiapine (OR, 1.79; 95% CI, 1.33–2.42), and risperidone (OR, 2.37; 95% CI, 1.31–4.30).The number needed to treat (NNT) was 19 for OFC and nine for each other drug. All drugs with the exception of OFC also had statistically significant effects on response rates, as follows: aripiprazole (OR, 2.07; 95% CI, 1.58–2.72; NNT, 7), OFC (OR, 1.30, 95% CI, 0.87–1.93), quetiapine (OR, 1.53, 95% CI, 1.17–2.0; NNT, 10), and risperidone (OR, 1.83, 95% CI, 1.16–2.88; NNT, 8). All four drugs showed statistically significant effects on clinician-rated depression severity measures (Hedges'' g ranged from 0.26 to 0.48; mean difference of 2.69 points on the Montgomery–Asberg Depression Rating Scale across drugs). On measures of functioning and quality of life, these medications produced either no benefit or a very small benefit, except for risperidone, which had a small-to-moderate effect on quality of life (g = 0.49).Treatment was linked to several adverse events, including akathisia (aripiprazole), sedation (quetiapine, OFC, and aripiprazole), abnormal metabolic laboratory results (quetiapine and OFC), and weight gain (all four drugs, especially OFC). Shortcomings in study design and data reporting, as well as use of post hoc analyses, may have inflated the apparent benefits of treatment and reduced the apparent incidence of adverse events.

Conclusions

Atypical antipsychotic medications for the adjunctive treatment of depression are efficacious in reducing observer-rated depressive symptoms, but clinicians should interpret these findings cautiously in light of (1) the small-to-moderate-sized benefits, (2) the lack of benefit with regards to quality of life or functional impairment, and (3) the abundant evidence of potential treatment-related harm. Please see later in the article for the Editors'' Summary     

Decreased Serum Levels of Mature Brain-Derived Neurotrophic Factor (BDNF), but Not Its Precursor proBDNF, in Patients with Major Depressive Disorder

Background

Meta-analyses have identified serum levels of brain-derived neurotrophic factor (BDNF) as a potential biomarker for major depressive disorder (MDD). However, at the time, commercially available human ELISA kits are unable to distinguish between proBDNF (precursor of BDNF) and mature BDNF because of limited BDNF antibody specificity. In this study, we examined whether serum levels of proBDNF, mature BDNF, and matrix metalloproteinase-9 (MMP-9), which converts proBDNF to mature BDNF, are altered in patients with MDD.

Methodology/Principal Findings

Sixty-nine patients with MDD and 78 age- and gender-matched healthy subjects were enrolled. Patients were evaluated using 17 items on the Structured Interview Guide for the Hamilton Depression Rating Scale. Cognitive impairment was evaluated using the CogState battery. Serum levels of proBDNF, mature BDNF, and MMP-9 were measured using ELISA kits. Serum levels of mature BDNF in patients with MDD were significantly lower than those of normal controls. In contrast, there was no difference in the serum levels of proBDNF and MMP-9 between patients and normal controls. While neither proBDNF nor mature BDNF serum levels was associated with clinical variables, there were significant correlations between MMP-9 serum levels and the severity of depression, quality of life scores, and social function scores in patients.

Conclusions/Significance

These findings suggest that mature BDNF may serve as a biomarker for MDD, and that MMP-9 may play a role in the pathophysiology of MDD. Further studies using larger sample sizes will be needed to investigate these results.     

The Influence of 5-HTTLPR Genotype on the Association between the Plasma Concentration and Therapeutic Effect of Paroxetine in Patients with Major Depressive Disorder

Introduction

The efficacy of treatment with selective serotonin reuptake inhibitors in patients with major depressive disorder (MDD) can differ depending on the patient''s serotonin transporter-linked polymorphic region (5-HTTLPR) genotype, and the effects of varying plasma concentrations of drugs can also vary. We investigated the association between the paroxetine plasma concentration and clinical response in patients with different 5-HTTLPR genotypes.

Methods

Fifty-one patients were enrolled in this study. The Montgomery-Asberg Depression Rating Scale (MADRS) was used to evaluate patients at 0, 1, 2, 4, and 6 weeks. The patients'' paroxetine plasma concentrations at week 6 were measured using high-performance liquid chromatography. Additionally, their 5-HTTLPR polymorphisms (alleles S and L) were analyzed using a polymerase chain reaction with specific primers. We divided the participants into two groups based on their L haplotype: the SS group and the SL and LL group. We performed single and multiple regression analyses to investigate the associations between MADRS improvement and paroxetine plasma concentrations or other covariates for each group.

Results

There were no significant differences between the two groups with regard to demographic or clinical data. In the SS group, the paroxetine plasma concentration was significantly negatively correlated with improvement in MADRS at week 6. In the SL and LL group, the paroxetine plasma concentration was significantly positively correlated with improvement in MADRS at week 6 according to the results of the single regression analysis; however, it was not significantly correlated with improvement in MADRS at week 6 according to the results of the multiple regression analysis.

Conclusion

Among patients with MDD who do not respond to paroxetine, a lower plasma concentration or a lower oral dose of paroxetine might be more effective in those with the SS genotype, and a higher plasma concentration might be more effective in those with the SL or LL genotype.     

Genetic and Environmental Risk for Chronic Pain and the Contribution of Risk Variants for Major Depressive Disorder: A Family-Based Mixed-Model Analysis

BackgroundChronic pain is highly prevalent and a significant source of disability, yet its genetic and environmental risk factors are poorly understood. Its relationship with major depressive disorder (MDD) is of particular importance. We sought to test the contribution of genetic factors and shared and unique environment to risk of chronic pain and its correlation with MDD in Generation Scotland: Scottish Family Health Study (GS:SFHS). We then sought to replicate any significant findings in the United Kingdom Biobank study.ConclusionsGenetic factors, as well as chronic pain in a partner or spouse, contribute substantially to the risk of chronic pain for an individual. Chronic pain is genetically correlated with MDD, has a polygenic architecture, and is associated with polygenic risk of MDD.     

Reduced SNP Panels for Genetic Identification and Introgression Analysis in the Dark Honey Bee (Apis mellifera mellifera)

Beekeeping activities, especially queen trading, have shaped the distribution of honey bee (Apis mellifera) subspecies in Europe, and have resulted in extensive introductions of two eastern European C-lineage subspecies (A. m. ligustica and A. m. carnica) into the native range of the M-lineage A. m. mellifera subspecies in Western Europe. As a consequence, replacement and gene flow between native and commercial populations have occurred at varying levels across western European populations. Genetic identification and introgression analysis using molecular markers is an important tool for management and conservation of honey bee subspecies. Previous studies have monitored introgression by using microsatellite, PCR-RFLP markers and most recently, high density assays using single nucleotide polymorphism (SNP) markers. While the latter are almost prohibitively expensive, the information gained to date can be exploited to create a reduced panel containing the most ancestry-informative markers (AIMs) for those purposes with very little loss of information. The objective of this study was to design reduced panels of AIMs to verify the origin of A. m. mellifera individuals and to provide accurate estimates of the level of C-lineage introgression into their genome. The discriminant power of the SNPs using a variety of metrics and approaches including the Weir & Cockerham’s F_ST, an F_ST-based outlier test, Delta, informativeness (I_n), and PCA was evaluated. This study shows that reduced AIMs panels assign individuals to the correct origin and calculates the admixture level with a high degree of accuracy. These panels provide an essential tool in Europe for genetic stock identification and estimation of admixture levels which can assist management strategies and monitor honey bee conservation programs.     

Screening for Major Depressive Disorder with the Patient Health Questionnaire (PHQ-9 and PHQ-2) in an Outpatient Clinic Staffed by Primary Care Physicians in Japan: A Case Control Study

Objective

The Patient Health Questionnaire (PHQ-9) is a self-report questionnaire commonly used to screen for depression, with ≥8–11 generally recommended as the cut-off. In Japan, studies of the validity of the PHQ-9 and PHQ-2 have been limited. In this study, we examined the utility of the PHQ-9 and PHQ-2 at an outpatient clinic in a Medical University Hospital in Japan.

Methods

New consecutive outpatients were included in the study. We administered the PHQ-9 to 574 patients, and acquired complete PHQ-9 and PHQ-2 data for 521 patients. Major depressive disorders were diagnosed according to the DSM-IV-TR.

Results

Forty-two patients were diagnosed with major depressive disorders. The mean PHQ-9 (15.7) and PHQ-2 (3.8) scores of the patients with major depressive disorders were significantly higher than the scores of the patients without depression (6.0 (PHQ-9) and 1.8 (PHQ-2)). The best cut-off points for the PHQ-9 and PHQ-2 summary scores were ≥11 (sensitivity 0.76, specificity 0.81) and ≥3 (sensitivity 0.76, specificity 0.82), respectively. No relationship was observed between the age and PHQ-9 scores.

Conclusion

The PHQ-9 and PHQ-2 were useful instruments for screening for major depressive disorders. The best cut-off point for the PHQ-9 summary score should be ≥11 to detect depression in the primary care setting in Japan.     

Identification and Validation of Potential Biomarkers for the Detection of Dysregulated microRNA by qPCR in Patients with Colorectal Adenocarcinoma

Colorectal cancer represents a lethal disease that has raised concern and has attracted significant attention. Adenocarcinoma is the most common type of colorectal cancer (CRC). MicroRNAs are thought to be potential biomarkers of CRC. Many researchers have focused on the expression pattern of miRNAs in CRC. However, previous studies did not pay particular attention to the effects of the degree of differentiation of the cancer with respect to the miRNA expression profile. First, this study compared the expression level of 1547 miRNAs by qRT-PCR in Colorectal adenocarcinoma tissues to that in paired normal tissues. In all, 93 miRNAs were identified that were significantly dysregulated in Colorectal adenocarcinoma relative to normal tissues (P<0.05). Then, we analyzed their potential as cancer biomarkers by ROC analysis, and the result revealed that three miRNAs with high sensitivity and specificity are suitable as biomarkers for the diagnosis of CRC (the value of the AUC was greater than 0.7). Interestingly, previous reports of 23 of these miRNAs have been scarce. Furthermore, we wanted to analyze the difference between well- and moderately differentiated cancers, and as expected, 58 miRNAs showed significant dysregulation. Importantly, 32 miRNAs were able to not only distinguish cancer tissues from normal tissues, but they were also able to identify well- and moderately differentiated cancers. In conclusion, the degree of differentiation has an important influence on the miRNA expression pattern. To avoid misdiagnoses and missed diagnoses, tumors of different degrees of differentiation should be treated differently when miRNAs are used as cancer biomarkers.