Dataset Information

Plasma microRNA Array Analysis Identifies Overexpressed miR-19b-3p as a Biomarker of Bipolar Depression Distinguishing From Unipolar Depression.

ABSTRACT: Objectives:The clinical characteristics of bipolar disorder (current major depressive episode) (BD) overlap with unipolar depressive disorder (UD), which makes it difficult to perform an accurate diagnosis. We identified plasma microRNAs (miRNAs) that distinguished BD from UD and explored the relationship between miRNA expression levels and clinical characteristics. Methods:Total miRNAs from blood plasma from seven UD patients, seven BD patients, and six controls were analyzed. The identified miRNAs were validated in a separate population group. Depression severity and early life adversities were assessed. Bioinformatic analysis was conducted to investigate the target genes that were identified and the pathways associated with the altered miRNAs. Results:Compared to controls, 42 miRNAs were differentially expressed in patients. miR-19b-3p, miR-3921, and miR-1180-3p were selected to validate the microarray results. Only miR-19b-3p was validated as down-regulated in patients. The primary predicted genes associated with miR-19b-3p were MAPK1, PTEN, and PRKAA1. The most relevant KEGG pathways included mTOR, FoxO, and the PI3-K/Akt signaling pathway. BD patients were more likely to have higher expression levels of miR-19b-3p and more severe childhood trauma experience compared to UD patients. Conclusions:Plasma miR-19b-3p is a potential non-invasive biomarker that might be useful in distinguishing UD from BD. miR-19b3p was predicted to be involved in the pathway of inflammatory dysregulation associated with experiencing early childhood trauma.

SUBMITTER: Chen Y

PROVIDER: S-EPMC7432143 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

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Plasma microRNA Array Analysis Identifies Overexpressed miR-19b-3p as a Biomarker of Bipolar Depression Distinguishing From Unipolar Depression.

Chen Yu Y Shi Jiabo J Liu Haiyan H Wang Qiang Q Chen Xiangxiang X Tang Hao H Yan Rui R Yao Zhijian Z Lu Qing Q

Frontiers in psychiatry 20200811

<h4>Objectives</h4>The clinical characteristics of bipolar disorder (current major depressive episode) (BD) overlap with unipolar depressive disorder (UD), which makes it difficult to perform an accurate diagnosis. We identified plasma microRNAs (miRNAs) that distinguished BD from UD and explored the relationship between miRNA expression levels and clinical characteristics.<h4>Methods</h4>Total miRNAs from blood plasma from seven UD patients, seven BD patients, and six controls were analyzed. Th ...[more]

PMID: 33192625

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Project description:The structural abnormalities in the brain that accurately differentiate unipolar depression (UD) and bipolar depression (BD) remain unidentified.First, to investigate and compare morphometric changes in UD and BD, and to replicate the findings at 2 independent neuroimaging sites; second, to differentiate UD and BD using multivariate pattern classification techniques.In a 2-center cross-sectional study, structural gray matter data were obtained at 2 independent sites (Pittsburgh, Pennsylvania, and Münster, Germany) using 3-T magnetic resonance imaging. Voxel-based morphometry was used to compare local gray and white matter volumes, and a novel pattern classification approach was used to discriminate between UD and BD, while training the classifier at one imaging site and testing in an independent sample at the other site. The Pittsburgh sample of participants was recruited from the Western Psychiatric Institute and Clinic at the University of Pittsburgh from 2008 to 2012. The Münster sample was recruited from the Department of Psychiatry at the University of Münster from 2010 to 2012. Equally divided between the 2 sites were 58 currently depressed patients with bipolar I disorder, 58 age- and sex-matched unipolar depressed patients, and 58 matched healthy controls.Magnetic resonance imaging was used to detect structural differences between groups. Morphometric analyses were applied using voxel-based morphometry. Pattern classification techniques were used for a multivariate approach.At both sites, individuals with BD showed reduced gray matter volumes in the hippocampal formation and the amygdala relative to individuals with UD (Montreal Neurological Institute coordinates x?=?-22, y?=?-1, z?=?20; k?=?1938 voxels; t?=?4.75), whereas individuals with UD showed reduced gray matter volumes in the anterior cingulate gyrus compared with individuals with BD (Montreal Neurological Institute coordinates x?=?-8, y?=?32, z?=?3; k?=?979 voxels; t?=?6.37; all corrected P?<?.05). Reductions in white matter volume within the cerebellum and hippocampus were found in individuals with BD. Pattern classification yielded up to 79.3% accuracy (P?<?.001) by differentiating the 2 depressed groups, training and testing the classifier at one site, and up to 69.0% accuracy (P?<?.001), training the classifier at one imaging site (Pittsburgh) and testing it at the other independent sample (Münster). Medication load did not alter the pattern of results.Individuals with UD and those with BD are differentiated by structural abnormalities in neural regions supporting emotion processing. Neuroimaging and multivariate pattern classification techniques are promising tools to differentiate UD from BD and show promise as future diagnostic aids.

Project description:Background Circulating microRNAs are emerging biomarkers for heart failure (HF). Our study aimed to assess the prognostic value of microRNA signature that is differentially expressed in patients with acute HF. Methods and Results Our study comprised a screening cohort of 15 patients with AHF and 5 controls, a PCR-discovery cohort of 50 patients with AHF and 26 controls and a validation cohort of 564 patients with AHF from registered study DRAGON-HF (Diagnostic, Risk Stratification and Prognostic Value of Novel Biomarkers in Patients With Heart Failure). Through screening by RNA-sequencing and verification by reverse-transcription quantitative polymerase chain reaction, 9 differentially expressed microRNAs were verified (miR-939-5p, miR-1908-5p, miR-7706, miR-101-3p, miR-144-3p, miR-4732-3p, miR-3615, miR-484 and miR-19b-3p). Among them, miR-19b-3p was identified as the microRNA signature with the highest fold-change of 8.4 and the strongest prognostic potential (area under curve with 95% CI, 0.791, 0.654-0.927). To further validate its prognostic value, in the validation cohort, the baseline level of miR-19b-3p was measured. During a follow-up period of 19.1 (17.7, 20.7) months, primary end point comprising of all-cause mortality or readmission due to HF occurred in 48.9% patients, while patients in the highest quartile of miR-19b-3p level presented the worst survival (Log-rank P<0.001). Multivariate Cox model showed that the level of miR-19b-3p could independently predict the occurrence of primary end point (adjusted hazard ratio,1.39; 95% CI, 1.18-1.64). In addition, miR-19b-3p positively correlated with soluble suppression of tumorigenicity 2 and echocardiographic indexes of left ventricular hypertrophy. Conclusions Circulating miR-19b-3p could be a valuable prognostic biomarker for AHF. In addition, a high level of circulating miR-19b-3p might indicate ventricular hypertrophy in AHF subjects. Registration URL: https://www.clinicaltrials.gov. Unique Identifier: NCT03727828.

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Dataset Information

Plasma microRNA Array Analysis Identifies Overexpressed miR-19b-3p as a Biomarker of Bipolar Depression Distinguishing From Unipolar Depression.

Publications

Plasma microRNA Array Analysis Identifies Overexpressed miR-19b-3p as a Biomarker of Bipolar Depression Distinguishing From Unipolar Depression.

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