ArticlePsychiatry investigation2020
Personalized Psychiatry and Depression: The Role of Sociodemographic and Clinical Variables.
Article in Psychiatry investigation, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT05708222 (Investigation of the Likelihood of a Current Major Depressive Episode in Individuals Referred to Sleep Clinics for Polysomnography), which is not on this map. Cited by 20 papers, 1 of them a synthesis that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Investigation of the Likelihood of a Current Major Depressive Episode in Individuals Referred to Sleep Clinics for Polysomnography (PSG) Assessment Using the MEB-001 Device.
Who cites it
20 citing papers in PubMed, 1 synthesis or guideline pooled it, 43 citations in OpenAlex.
- Blood-based biomarkers of antidepressant response to ketamine and esketamine: A systematic review and meta-analysis.Molecular psychiatry · 2022Pooled it
- Perceived criticism and depressive symptoms among adults aged 50 years and older: a 17-year population-based cohort study.Translational psychiatry · 2025Article
- Sociodemographic, clinical, and genetic factors associated with self-reported antidepressant response outcomes in the UK Biobank.Psychological medicine · 2025Article
- Clinical association between current depressive symptoms and odds ratio product in US sleep centers.Frontiers in sleep · 2025Article
- Observational
- Developing an individualized treatment rule for Veterans with major depressive disorder using electronic health records.Molecular psychiatry · 2024Article
- Potential correlations between abnormal homogeneity of default mode network and personality or lipid level in major depressive disorder.Brain and behavior · 2024Article
- Neural signatures of emotional biases predict clinical outcomes in difficult-to-treat depression.Research directions. Depression · 2024Article
- Anhedonia in Major Depressive Disorder: Prevalence and Treatment Expectations and Satisfaction with Treatment Goals Among Patients and Physicians in Asia-Pacific.Neuropsychiatric disease and treatment · 2024Article
- Development of a model to predict antidepressant treatment response for depression among Veterans.Psychological medicine · 2023Article
- Influence of Sociodemographic, Premorbid, and Injury-Related Factors on Post-Traumatic Stress, Anxiety, and Depression after Traumatic Brain Injury.Journal of clinical medicine · 2023Article
- An exploratory machine learning approach to identify placebo responders in pharmacological binge eating disorder trials.Clinical and translational science · 2022Article
- Prevalence and Predictive Factors of Masked Depression and Anxiety among Jordanian and Palestinian Couples: A Cross-Sectional Study.Healthcare (Basel, Switzerland) · 2022Article
- The complex relationship between gut microbiota dysregulation and mood disorders: A narrative review.Current research in neurobiology · 2022Review
- Tricyclic antidepressants for major depressive disorder: a comprehensive evaluation of current practice in the Netherlands.BMC psychiatry · 2021Article
- Predicting Depression in Community Dwellers Using a Machine Learning Algorithm.Diagnostics (Basel, Switzerland) · 2021Article
- Pathophysiology of Major Depression by Clinical Stages.Frontiers in psychology · 2021Article
- How to Utilize Clinical and Genetic Information for Personalized Treatment of Major Depressive Disorder: Step by Step Strategic Approach.Clinical psychopharmacology and neuroscience : the official scientific journal of the Korean College of Neuropsychopharmacology · 2020Review
- Major Depressive Disorder: Current Advances and Paradigm Shifts.Psychiatry investigation · 2020Article
- Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors at 2 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Despite several pharmacological options, the clinical outcomes of major depressive disorder (MDD) are often unsatisfactory. Personalized psychiatry attempts to tailor therapeutic interventions according to each patient's unique profile and characteristics. This approach can be a crucial strategy in improving pharmacological outcomes in MDD and overcoming trial-and-error treatment choices. In this narrative review, we evaluate whether sociodemographic (i.e., gender, age, race/ethnicity, and socioeconomic status) and clinical [i.e., body mass index (BMI), severity of depressive symptoms, and symptom profiles] variables that are easily assessable in clinical practice may help clinicians to optimize the selection of antidepressant treatment for each patient with MDD at the early stages of the disorder. We found that several variables were associated with poorer outcomes for all antidepressants. However, only preliminary associations were found between some clinical variables (i.e., BMI, anhedonia, and MDD with melancholic/atypical features) and possible benefits with some specific antidepressants. Finally, in clinical practice, the assessment of sociodemographic and clinical variables considered in our review can be valuable for early identification of depressed individuals at high risk for poor responses to antidepressants, but there are not enough data on which to ground any reliable selection of specific antidepressant class or compounds. Recent advances in computational resources, such as machine learning techniques, which are able to integrate multiple potential predictors, such as individual/ clinical variables, biomarkers, and genetic factors, may offer future reliable tools to guide personalized antidepressant choice for each patient with MDD.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.