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Personalized Medicine Approaches to Depression Prevention, Diagnosis, and Therapeutics
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Personalized Medicine Approaches to Depression Prevention, Diagnosis, and Therapeutics

A special issue of Journal of Personalized Medicine (ISSN 2075-4426). This special issue belongs to the section "Methodology, Drug and Device Discovery".

Deadline for manuscript submissions: closed (20 February 2022) | Viewed by 20295

Special Issue Information

Dear Colleagues,

The use of modern “omics” technologies permits more accurate medical diagnosis and tailored therapeutics. Major depressive disorder (MDD) is remarkably common, yet is often resistant to effective treatment because of its diverse presentation and complex pathogenesis, defined by both genetic and environmental factors. Recent developments on the neurobiological basis of MDD suggest that a number of novel genetic loci may be associated with MDD and related conditions. Promising work suggests that a better understanding of the risk profiles, symptom-based phenotypes, and genetics could be used to improve prevention, identification, and treatment. At the same time, depression is a profoundly human experience, and translating precision medicine approaches into effective personalized medicine must incorporate individual nuances and therapeutic relationships. This Special Issue of the Journal of Personalized Medicine is focused on describing the current state of personalized medicine approaches to MDD, as well as the promise of modern “omics” technologies in its prevention, detection, and treatment.

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Journal of Personalized Medicine is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Major depressive disorder
  • Personalized medicine
  • Genetics
  • Genomics
  • Prevention
  • Detection
  • Treatment

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Published Papers (5 papers)

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Research

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19 pages, 4677 KiB  
Article
A Selective Histamine H4 Receptor Antagonist, JNJ7777120, Role on glutamate Transporter Activity in Chronic Depression
by Yesim Yeni, Zeynep Cakir, Ahmet Hacimuftuoglu, Ali Taghizadehghalehjoughi, Ufuk Okkay, Sidika Genc, Serkan Yildirim, Yavuz Selim Saglam, Daniela Calina, Aristidis Tsatsakis and Anca Oana Docea
J. Pers. Med. 2022, 12(2), 246; https://doi.org/10.3390/jpm12020246 - 9 Feb 2022
Cited by 12 | Viewed by 3051
Abstract
Glutamate release and reuptake play a key role in the pathophysiology of depression. glutamatergic nerves in the hippocampus region are modulated by histaminergic afferents. Excessive accumulation of glutamate in the synaptic area causes degeneration of neuron cells. The H4 receptor is defined as [...] Read more.
Glutamate release and reuptake play a key role in the pathophysiology of depression. glutamatergic nerves in the hippocampus region are modulated by histaminergic afferents. Excessive accumulation of glutamate in the synaptic area causes degeneration of neuron cells. The H4 receptor is defined as the main immune system histamine receptor with a pro-inflammatory role. To understand the role of this receptor, the drug JNJ7777120 was used to reveal the chronic depression-glutamate relationship. We have important findings showing that the H4 antagonist increases the glutamate transporters’ instantaneous activity. In our experiment, it has been shown that blocking the H4 receptor leads to increased neuron cell viability and improvement in behavioral ability due to glutamate. Therefore, JNJ can be used to prevent neurotoxicity, inhibit membrane phospholipase activation and free radical formation, and minimize membrane disruption. In line with our findings, results have been obtained that indicate that JNJ will contribute to the effective prevention and treatment of depression. Full article
(This article belongs to the Special Issue Personalized Medicine Approaches to Depression Prevention, Diagnosis, and Therapeutics)
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9 pages, 666 KiB  
Article
Transdiagnostic Symptom Subtypes to Predict Response to Therapeutic Transcranial Magnetic Stimulation in Major Depressive Disorder and Posttraumatic Stress Disorder
by Camila Cosmo, Yosef A. Berlow, Katherine A. Grisanzio, Scott L. Fleming, Abdullah P. Rashed Ahmed, McKenna C. Brennan, Linda L. Carpenter and Noah S. Philip
J. Pers. Med. 2022, 12(2), 224; https://doi.org/10.3390/jpm12020224 - 6 Feb 2022
Viewed by 2327
Abstract
The diagnostic categories in psychiatry often encompass heterogeneous symptom profiles associated with differences in the underlying etiology, pathogenesis and prognosis. Prior work demonstrated that some of this heterogeneity can be quantified though dimensional analysis of the Depression Anxiety Stress Scale (DASS), yielding unique [...] Read more.
The diagnostic categories in psychiatry often encompass heterogeneous symptom profiles associated with differences in the underlying etiology, pathogenesis and prognosis. Prior work demonstrated that some of this heterogeneity can be quantified though dimensional analysis of the Depression Anxiety Stress Scale (DASS), yielding unique transdiagnostic symptom subtypes. This study investigated whether classifying patients according to these symptom profiles would have prognostic value for the treatment response to therapeutic transcranial magnetic stimulation (TMS) in comorbid major depressive disorder (MDD) and posttraumatic stress disorder (PTSD). A linear discriminant model was constructed using a simulation dataset to classify 35 participants into one of the following six pre-defined symptom profiles: Normative Mood, Tension, Anxious Arousal, Generalized Anxiety, Anhedonia and Melancholia. Clinical outcomes with TMS across MDD and PTSD were assessed. All six symptom profiles were present. After TMS, participants with anxious arousal were less likely to achieve MDD remission compared to other subtypes (FET, odds ratio 0.16, p = 0.034), exhibited poorer PTSD symptom reduction (21% vs. 46%; t (33) = 2.025, p = 0.051) and were less likely to complete TMS (FET, odds ratio 0.066, p = 0.011). These results offer preliminary evidence that classifying individuals according to these transdiagnostic symptom profiles may offer a simple method to inform TMS treatment decisions. Full article
(This article belongs to the Special Issue Personalized Medicine Approaches to Depression Prevention, Diagnosis, and Therapeutics)
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18 pages, 762 KiB  
Article
A Patient Stratification Approach to Identifying the Likelihood of Continued Chronic Depression and Relapse Following Treatment for Depression
by Rob Saunders, Zachary D. Cohen, Gareth Ambler, Robert J. DeRubeis, Nicola Wiles, David Kessler, Simon Gilbody, Steve D. Hollon, Tony Kendrick, Ed Watkins, David Richards, Sally Brabyn, Elizabeth Littlewood, Debbie Sharp, Glyn Lewis, Steve Pilling and Joshua E. J. Buckman
J. Pers. Med. 2021, 11(12), 1295; https://doi.org/10.3390/jpm11121295 - 4 Dec 2021
Cited by 13 | Viewed by 3622
Abstract
Background: Subgrouping methods have the potential to support treatment decision making for patients with depression. Such approaches have not been used to study the continued course of depression or likelihood of relapse following treatment. Method: Data from individual participants of seven randomised controlled [...] Read more.
Background: Subgrouping methods have the potential to support treatment decision making for patients with depression. Such approaches have not been used to study the continued course of depression or likelihood of relapse following treatment. Method: Data from individual participants of seven randomised controlled trials were analysed. Latent profile analysis was used to identify subgroups based on baseline characteristics. Associations between profiles and odds of both continued chronic depression and relapse up to one year post-treatment were explored. Differences in outcomes were investigated within profiles for those treated with antidepressants, psychological therapy, and usual care. Results: Seven profiles were identified; profiles with higher symptom severity and long durations of both anxiety and depression at baseline were at higher risk of relapse and of chronic depression. Members of profile five (likely long durations of depression and anxiety, moderately-severe symptoms, and past antidepressant use) appeared to have better outcomes with psychological therapies: antidepressants vs. psychological therapies (OR (95% CI) for relapse = 2.92 (1.24–6.87), chronic course = 2.27 (1.27–4.06)) and usual care vs. psychological therapies (relapse = 2.51 (1.16–5.40), chronic course = 1.98 (1.16–3.37)). Conclusions: Profiles at greater risk of poor outcomes could benefit from more intensive treatment and frequent monitoring. Patients in profile five may benefit more from psychological therapies than other treatments. Full article
(This article belongs to the Special Issue Personalized Medicine Approaches to Depression Prevention, Diagnosis, and Therapeutics)
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Review

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17 pages, 292 KiB  
Review
Machine Learning-Based Behavioral Diagnostic Tools for Depression: Advances, Challenges, and Future Directions
by Thalia Richter, Barak Fishbain, Gal Richter-Levin and Hadas Okon-Singer
J. Pers. Med. 2021, 11(10), 957; https://doi.org/10.3390/jpm11100957 - 26 Sep 2021
Cited by 23 | Viewed by 5356
Abstract
The psychiatric diagnostic procedure is currently based on self-reports that are subject to personal biases. Therefore, the diagnostic process would benefit greatly from data-driven tools that can enhance accuracy and specificity. In recent years, many studies have achieved promising results in detecting and [...] Read more.
The psychiatric diagnostic procedure is currently based on self-reports that are subject to personal biases. Therefore, the diagnostic process would benefit greatly from data-driven tools that can enhance accuracy and specificity. In recent years, many studies have achieved promising results in detecting and diagnosing depression based on machine learning (ML) analysis. Despite these favorable results in depression diagnosis, which are primarily based on ML analysis of neuroimaging data, most patients do not have access to neuroimaging tools. Hence, objective assessment tools are needed that can be easily integrated into the routine psychiatric diagnostic process. One solution is to use behavioral data, which can be easily collected while still maintaining objectivity. The current paper summarizes the main ML-based approaches that use behavioral data in diagnosing depression and other psychiatric disorders. We classified these studies into two main categories: (a) laboratory-based assessments and (b) data mining, the latter of which we further divided into two sub-groups: (i) social media usage and movement sensors data and (ii) demographic and clinical information. The paper discusses the advantages and challenges in this field and suggests future research directions and implementations. The paper’s overarching aim is to serve as a first step in synthetizing existing knowledge about ML-based behavioral diagnosis studies in order to develop interventions and individually tailored treatments in the future. Full article
(This article belongs to the Special Issue Personalized Medicine Approaches to Depression Prevention, Diagnosis, and Therapeutics)
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Other

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13 pages, 752 KiB  
Systematic Review
The Contribution of “Individual Participant Data” Meta-Analyses of Psychotherapies for Depression to the Development of Personalized Treatments: A Systematic Review
by Pim Cuijpers, Marketa Ciharova, Soledad Quero, Clara Miguel, Ellen Driessen, Mathias Harrer, Marianna Purgato, David Ebert and Eirini Karyotaki
J. Pers. Med. 2022, 12(1), 93; https://doi.org/10.3390/jpm12010093 - 11 Jan 2022
Cited by 32 | Viewed by 4964
Abstract
While randomized trials typically lack sufficient statistical power to identify predictors and moderators of outcome, ”individual participant data” (IPD) meta-analyses, which combine primary data of multiple randomized trials, can increase the statistical power to identify predictors and moderators of outcome. We conducted a [...] Read more.
While randomized trials typically lack sufficient statistical power to identify predictors and moderators of outcome, ”individual participant data” (IPD) meta-analyses, which combine primary data of multiple randomized trials, can increase the statistical power to identify predictors and moderators of outcome. We conducted a systematic review of IPD meta-analyses on psychological treatments of depression to provide an overview of predictors and moderators identified. We included 10 (eight pairwise and two network) IPD meta-analyses. Six meta-analyses showed that higher baseline depression severity was associated with better outcomes, and two found that older age was associated with better outcomes. Because power was high in most IPD meta-analyses, non-significant findings are also of interest because they indicate that these variables are probably not relevant as predictors and moderators. We did not find in any IPD meta-analysis that gender, education level, or relationship status were significant predictors or moderators. This review shows that IPD meta-analyses on psychological treatments can identify predictors and moderators of treatment effects and thereby contribute considerably to the development of personalized treatments of depression. Full article
(This article belongs to the Special Issue Personalized Medicine Approaches to Depression Prevention, Diagnosis, and Therapeutics)
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