Dataset Information

Alcohol Induced Depression: Clinical, Biological and Genetic Features.

ABSTRACT: BACKGROUND:In clinical practice, there is the need to have clinical and biological markers to identify induced depression. The objective was to investigate clinical, biological and genetic differences between Primary Major Depression (Primary MD) and Alcohol Induced MD (AI-MD). METHODS:Patients, of both genders, were recruited from psychiatric hospitalisation units. The PRISM instrument was used to establish the diagnoses. Data on socio-demographic/family history, clinical scales for depression, anxiety, personality and stressful life events were recorded. A blood test was performed analysing biochemical parameters and a Genome Wide Association Study (GWAS) to identify genetic markers associated with AI-MD. RESULTS:A total of 80 patients were included (47 Primary MD and 33 AI-MD). The AI-MD group presented more medical comorbidities and less family history of depression. There were differences in traumatic life events, with higher scores in the AI-MD (14.21 ± 11.35 vs. 9.30 ± 7.38; p = 0.021). DSM-5 criteria were different between groups with higher prevalence of weight changes and less anhedonia, difficulties in concentration and suicidal thoughts in the AI-MD. None of the genetic variants reached significance beyond multiple testing thresholds; however, some suggestive variants were observed. CONCLUSIONS:This study has found clinical and biological features that may help physicians to identify AI-MD and improve its therapeutic approach.

SUBMITTER: Farre A

PROVIDER: S-EPMC7465278 | biostudies-literature | 2020 Aug

REPOSITORIES: biostudies-literature

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Alcohol Induced Depression: Clinical, Biological and Genetic Features.

Farré Adriana A Tirado Judit J Spataro Nino N Alías-Ferri María M Torrens Marta M Fonseca Francina F

Journal of clinical medicine 20200818 8

<h4>Background</h4>In clinical practice, there is the need to have clinical and biological markers to identify induced depression. The objective was to investigate clinical, biological and genetic differences between Primary Major Depression (Primary MD) and Alcohol Induced MD (AI-MD).<h4>Methods</h4>Patients, of both genders, were recruited from psychiatric hospitalisation units. The PRISM instrument was used to establish the diagnoses. Data on socio-demographic/family history, clinical scales ...[more]

PMID: 32824737

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Project description:ObjectivesPost-stroke depression (PSD) is a common and serious psychiatric complication which hinders functional recovery and social participation of stroke patients. Stroke is characterized by dynamic changes in metabolism and hemodynamics, however, there is still a lack of metabolism-associated effective and reliable diagnostic markers and therapeutic targets for PSD. Our study was dedicated to the discovery of metabolism related diagnostic and therapeutic biomarkers for PSD.MethodsExpression profiles of GSE140275, GSE122709, and GSE180470 were obtained from GEO database. Differentially expressed genes (DEGs) were detected in GSE140275 and GSE122709. Functional enrichment analysis was performed for DEGs in GSE140275. Weighted gene co-expression network analysis (WGCNA) was constructed in GSE122709 to identify key module genes. Moreover, correlation analysis was performed to obtain metabolism related genes. Interaction analysis of key module genes, metabolism related genes, and DEGs in GSE122709 was performed to obtain candidate hub genes. Two machine learning algorithms, least absolute shrinkage and selection operator (LASSO) and random forest, were used to identify signature genes. Expression of signature genes was validated in GSE140275, GSE122709, and GSE180470. Gene set enrichment analysis (GSEA) was applied on signature genes. Based on signature genes, a nomogram model was constructed in our PSD cohort (27 PSD patients vs. 54 controls). ROC curves were performed for the estimation of its diagnostic value. Finally, correlation analysis between expression of signature genes and several clinical traits was performed.ResultsFunctional enrichment analysis indicated that DEGs in GSE140275 enriched in metabolism pathway. A total of 8,188 metabolism associated genes were identified by correlation analysis. WGCNA analysis was constructed to obtain 3,471 key module genes. A total of 557 candidate hub genes were identified by interaction analysis. Furthermore, two signature genes (SDHD and FERMT3) were selected using LASSO and random forest analysis. GSEA analysis found that two signature genes had major roles in depression. Subsequently, PSD cohort was collected for constructing a PSD diagnosis. Nomogram model showed good reliability and validity. AUC values of receiver operating characteristic (ROC) curve of SDHD and FERMT3 were 0.896 and 0.964. ROC curves showed that two signature genes played a significant role in diagnosis of PSD. Correlation analysis found that SDHD (r = 0.653, P < 0.001) and FERM3 (r = 0.728, P < 0.001) were positively related to the Hamilton Depression Rating Scale 17-item (HAMD) score.ConclusionA total of 557 metabolism associated candidate hub genes were obtained by interaction with DEGs in GSE122709, key modules genes, and metabolism related genes. Based on machine learning algorithms, two signature genes (SDHD and FERMT3) were identified, they were proved to be valuable therapeutic and diagnostic biomarkers for PSD. Early diagnosis and prevention of PSD were made possible by our findings.

Project description:BackgroundPsychotic major depression (PMD) is a subtype of depression with a poor prognosis. Previous studies have failed to find many differences between patients with PMD and those with non-psychotic major depression (NMD) or schizophrenia (SZ). We compared sociodemographic factors (including season of conception) and clinical characteristics between patients with PMD, NMD, and schizophrenia. Our aim was to provide data to help inform clinical diagnoses and future etiology research.MethodsThis study used data of all patients admitted to Shandong Mental Health Center from June 1, 2016 to December 31, 2017. We analyzed cases who had experienced an episode of PMD (International Classification of Diseases, Tenth Revision codes F32.3, F33.3), NMD (F32.0-2/9, F33.0-2/9), and SZ (F20-20.9). Data on sex, main discharge diagnosis, date of birth, ethnicity, family history of psychiatric diseases, marital status, age at first onset, education, allergy history, and presence of trigger events were collected. Odds ratios (OR) were calculated using logistic regression analyses. Missing values were filled using the k-nearest neighbor method.ResultsPMD patients were more likely to have a family history of psychiatric diseases in their first-, second-, and third-degree relatives ([OR] 1.701, 95% confidence interval [CI] 1.019-2.804) and to have obtained a higher level of education (OR 1.451, 95% CI 1.168-1.808) compared with depression patients without psychotic features. Compared to PMD patients, schizophrenia patients had lower education (OR 0.604, 95% CI 0.492-0.741), were more often divorced (OR 3.087, 95% CI 1.168-10.096), had a younger age of onset (OR 0.934, 95% CI 0.914-0.954), less likely to have a history of allergies (OR 0.604, 95% CI 0.492-0.741), and less likely to have experienced a trigger event 1 year before first onset (OR 0.420, 95% CI 0.267-0.661). Season of conception, ethnicity, and sex did not differ significantly between PMD and NMD or schizophrenia and PMD.ConclusionsPMD patients have more similarities with NMD patients than SZ patients in terms of demographic and clinical characteristics. The differences found between PMD and SZ, and PMD and NMD correlated with specificity of the diseases. Furthermore, allergy history should be considered in future epidemiological studies of psychotic disorders.

Dataset Information

Alcohol Induced Depression: Clinical, Biological and Genetic Features.

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Alcohol Induced Depression: Clinical, Biological and Genetic Features.

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