Suggested Biomarkers for Major Depressive Disorder

Hacımusalar, Yunus; Eşel, Ertuğrul

doi:10.5152/npa.2017.19482

Cited by 51 publications

(54 citation statements)

References 180 publications

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“…In addition, other researchers have confirmed neuronal atrophy in the granular cell and pyramidal cell layers of the hippocampus, along with a reduction in hippocampal volume in both patients with depression and animal models (Abdallah et al, 2015;Hacimusalar and Eşel, 2018). Likewise, in this experiment, the weight of rat hippocampus decreased in response to CUMS, indirectly indicating hippocampal atrophy.…”

Section: Discussionsupporting

confidence: 77%

N-Palmitoylethanolamide Exerts Antidepressant-Like Effects in Rats: Involvement of PPARα Pathway in the Hippocampus

Wang

et al. 2019

J Pharmacol Exp Ther

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N-Palmitoylethanolamide (PEA), an endocannabinoid-like molecule, participates in controlling behaviors associated with mental disorders as an endogenous neuroprotective factor. On the basis of accumulating evidence and our previous data, we tested the hypothesis that the antidepressantlike effects of PEA observed during chronic unpredictable mild stress (CUMS) are mediated by possible targets in the peroxisome proliferator-activated receptor alpha (PPARa) pathway. In this study, rats were subjected to 35 days of CUMS and treated with drugs such as PEA (2.5, 5.0, or 10 mg/kg, by mouth), fluoxetine (10 mg/kg, by mouth), or the combination of PEA and MK886 (1-[(4-chlorophenyl) methyl]-3-[(1,1-dimethylethyl) thio]-a,a-dimethyl-5-(1-methylethyl)-1H-indole-2-propanoic acid). After behavioral tests, the animals were sacrificed and their hippocampi were dissected for subsequent studies. PEA normalized weight gain, sucrose preferences, locomotor activity in an open-field test, and levels of the PPARa mRNA and protein in the hippocampus, and it reduced serum adrenocorticotropic hormone (ACTH) and corticosterone (CORT) levels in rats subjected to CUMS. PEA reversed the abnormal levels of several oxidative stress biomarkers and increased the concentrations of two neurotrophic factors in the hippocampus of CUMS-induced rats. In addition, PEA alleviated the decrease in hippocampal weight. However, the aforementioned effects of PEA were completely or partially abolished by MK886, a selective PPARa antagonist. On the basis of these findings, the PPARa pathway in the hippocampus is a possible target of the antidepressant effects of PEA, and the maintenance of a stable hypothalamicpituitary-adrenal axis, the antioxidant defenses, and normalization of neurotrophic factor levels in the hippocampus are involved in this process.

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Section: Discussionsupporting

confidence: 77%

N-Palmitoylethanolamide Exerts Antidepressant-Like Effects in Rats: Involvement of PPARα Pathway in the Hippocampus

Wang

et al. 2019

J Pharmacol Exp Ther

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“…The main obstacles in this area may consist of the lack of a suitable animal model of depression, the inclusion of a set of biologically and clinically heterogeneous disorders in MDD, the presence of different subtypes and the continual change of this subgrouping, the high incidence of comorbidities of MDD with many other physical or psychiatric disorders, and the lack of specificity and sensitivity rates of a single biomarker. Many authors have suggested that a wider and multivariate approach could be more useful, including a combination of neuro-imaging, genetic, epigenetic, proteomic and metabolomic approaches (Hacimusalar and Eşel, 2018 ).…”

Section: Literature Reviewmentioning

confidence: 99%

Improving Diagnosis of Depression With XGBOOST Machine Learning Model and a Large Biomarkers Dutch Dataset (n = 11,081)

Sharma

Verbeke

2020

Front. Big Data

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Machine Learning has been on the rise and healthcare is no exception to that. In healthcare, mental health is gaining more and more space. The diagnosis of mental disorders is based upon standardized patient interviews with defined set of questions and scales which is a time consuming and costly process. Our objective was to apply the machine learning model and to evaluate to see if there is predictive power of biomarkers data to enhance the diagnosis of depression cases. In this research paper, we aimed to explore the detection of depression cases among the sample of 11,081 Dutch citizen dataset. Most of the earlier studies have balanced datasets wherein the proportion of healthy cases and unhealthy cases are equal but in our study, the dataset contains only 570 cases of self-reported depression out of 11,081 cases hence it is a class imbalance classification problem. The machine learning model built on imbalance dataset gives predictions biased toward majority class hence the model will always predict the case as no depression case even if it is a case of depression. We used different resampling strategies to address the class imbalance problem. We created multiple samples by under sampling, over sampling, over-under sampling and ROSE sampling techniques to balance the dataset and then, we applied machine learning algorithm “Extreme Gradient Boosting” (XGBoost) on each sample to classify the mental illness cases from healthy cases. The balanced accuracy, precision, recall and F1 score obtained from over-sampling and over-under sampling were more than 0.90.

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“…Biomarkers are quantifiable characteristics of biological processes, which could prove helpful in improving diagnostic objectivity of MDD and TRD as well as help in personalizing its treatment. For MDD, no specific biomarkers have yet been found, though several markers have been shown to be potential candidates, such as brain-derived neurotrophic factor (BDNF), interleukins (IL) 1 and 6, tumor necrosis factor (TNF), malondialdehyde (MDA), hypothalamic-pituitary-adrenal (HPA) activity, and cortisol responses [ 86 , 87 ]. Every biomarker as a standalone shows a low sensitivity and specificity, partly explained by the heterogeneity of MDD.…”

Section: Towards a More Personalized Dbs Treatment Approach For Trmentioning

confidence: 99%

Deep Brain Stimulation for Treatment-Resistant Depression: Towards a More Personalized Treatment Approach

Roet

Boonstra

Şahin

et al. 2020

JCM

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Major depressive disorder (MDD) affects approximately 4.4% of the world’s population. One third of MDD patients do not respond to routine psychotherapeutic and pharmacotherapeutic treatment and are said to suffer from treatment-resistant depression (TRD). Deep brain stimulation (DBS) is increasingly being investigated as a treatment modality for TRD. Although early case studies showed promising results of DBS, open-label trials and placebo-controlled studies have reported inconsistent outcomes. This has raised discussion about the correct interpretation of trial results as well as the criteria for patient selection, the choice of stimulation target, and the optimal stimulation parameters. In this narrative review, we summarize recent studies of the effectiveness of DBS in TRD and address the relation between the targeted brain structures and clinical outcomes. Elaborating upon that, we hypothesize that the effectiveness of DBS in TRD can be increased by a more personalized and symptom-based approach. This may be achieved by using resting-state connectivity mapping for neurophysiological subtyping of TRD, by using individualized tractography to help decisions about stimulation target and electrode placement, and by using a more detailed registration of symptomatic improvements during DBS, for instance by using ‘experience sampling’ methods.

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Suggested Biomarkers for Major Depressive Disorder

Cited by 51 publications

References 180 publications

N-Palmitoylethanolamide Exerts Antidepressant-Like Effects in Rats: Involvement of PPARα Pathway in the Hippocampus

N-Palmitoylethanolamide Exerts Antidepressant-Like Effects in Rats: Involvement of PPARα Pathway in the Hippocampus

Improving Diagnosis of Depression With XGBOOST Machine Learning Model and a Large Biomarkers Dutch Dataset (n = 11,081)

Deep Brain Stimulation for Treatment-Resistant Depression: Towards a More Personalized Treatment Approach

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