Background: In major depressive disorder (MDD), only ~35% achieve remission after first-line antidepressant therapy. Using UK Biobank data, we identify sociodemographic, clinical, and genetic predictors of antidepressant response through self-reported outcomes, aiming to inform personalized treatment strategies.
Methods: In UK Biobank Mental Health Questionnaire 2, participants with MDD reported whether specific antidepressants helped them. We tested whether retrospective lifetime response to four selective serotonin reuptake inhibitors (SSRIs) ( = 19,516) - citalopram ( = 8335), fluoxetine ( = 8476), paroxetine ( = 2297) and sertraline ( = 5883) - was associated with sociodemographic (e.g. age, gender) and clinical factors (e.g. episode duration). Genetic analyses evaluated the association between CYP2C19 variation and self-reported response, while polygenic score (PGS) analysis assessed whether genetic predisposition to psychiatric disorders and antidepressant response predicted self-reported SSRI outcomes.
Results: 71%-77% of participants reported positive responses to SSRIs. Non-response was significantly associated with alcohol and illicit drug use (OR = 1.59, = 2.23 × 10), male gender (OR = 1.25, = 8.29 × 10), and lower-income (OR = 1.35, = 4.22 × 10). The worst episode lasting over 2 years (OR = 1.93, = 3.87 × 10) and no mood improvement from positive events (OR = 1.35, = 2.37 × 10) were also associated with non-response. CYP2C19 poor metabolizers had nominally higher non-response rates (OR = 1.31, = 1.77 × 10). Higher PGS for depression (OR = 1.08, = 3.37 × 10) predicted negative SSRI outcomes after multiple testing corrections.
Conclusions: Self-reported antidepressant response in the UK Biobank is influenced by sociodemographic, clinical, and genetic factors, mirroring clinical response measures. While positive outcomes are more frequent than remission reported in clinical trials, these self-reports replicate known treatment associations, suggesting they capture meaningful aspects of antidepressant effectiveness from the patient's perspective.
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http://dx.doi.org/10.1017/S0033291725000388 | DOI Listing |
Eur J Nutr
March 2025
Nestlé Institute of Health Sciences, Nestlé Research, Lausanne, Switzerland.
Purpose: In the current study we evaluated a blend of ingredients containing mulberry leaf extract (to lower postprandial glucose of the evening meal), tryptophan (facilitator of the sleep initiation) to benefit sleep initiation and quality in adults with self-reported difficulties with sleep initiation.
Methods: Forty-three adults aged between 25 and 50 years enrolled in a randomized, crossover, double-blind, controlled trial. Participants received standardized meals with a glycemic load of 55 ± 10% and were assigned to receive treatment comprising a combination of mulberry leaf extract (750 mg), whey protein containing 120 mg tryptophan, zinc (1.
Eur Arch Psychiatry Clin Neurosci
March 2025
Institute of General Practice and Family Medicine, LMU University Hospital, LMU, Munich, Germany.
We present a cross-sectional analysis of 1391 outpatients and 280 inpatients participating in subprojects of the Research Training Group POKAL, of whom 1609 had a PHQ-9 score ≥ 5 and 62 reported depression with antidepressant use. Antidepressant use was lower among outpatients than inpatients (28.5% vs.
View Article and Find Full Text PDFPsychol Med
March 2025
Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK.
Background: In major depressive disorder (MDD), only ~35% achieve remission after first-line antidepressant therapy. Using UK Biobank data, we identify sociodemographic, clinical, and genetic predictors of antidepressant response through self-reported outcomes, aiming to inform personalized treatment strategies.
Methods: In UK Biobank Mental Health Questionnaire 2, participants with MDD reported whether specific antidepressants helped them.
Actas Esp Psiquiatr
March 2025
Severe Female Ward, Wenzhou Seventh People's Hospital, 325000 Wenzhou, Zhejiang, China.
Background: Major depressive disorder (MDD) is a prevalent and debilitating mental health condition, necessitating early detection and effective treatment strategies. Near-infrared spectroscopy (NIRS) is a promising neuroimaging technique for monitoring cerebral hemodynamics and may serve as an objective biomarker for MDD diagnosis and treatment efficacy. This study aimed to investigate the utility of NIRS in the early detection and longitudinal monitoring of antidepressant treatment efficacy in MDD patients.
View Article and Find Full Text PDFCommun Med (Lond)
March 2025
Department of Computer Science and Sanghani Center for AI and Data Analytics, Virginia Tech, Blacksburg, VA, USA.
Background: Machine learning (ML) based mortality prediction models can be immensely useful in intensive care units. Such a model should generate warnings to alert physicians when a patient's condition rapidly deteriorates, or their vitals are in highly abnormal ranges. Before clinical deployment, it is important to comprehensively assess a model's ability to recognize critical patient conditions.
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