PhD Scientific Days 2024
Budapest, 9-10 July 2024
Mental Health Sciences III.
Pharmacological Profiling of Depression-Related Comorbidity Clusters in the UK Biobank: Insights from Temporal and Drug Prescription Data
Előadó neve
Nagy, Tamás, MSc
Neptun code
NCF0AL
Előadó munkahelye
Semmelweis University
Előadó telefonszáma
+36202729066
Előadó e-mail címe
tonyo199606@gmail.com
Az előadás címe
Pharmacological Profiling of Depression-Related Comorbidity Clusters in the UK Biobank: Insights from Temporal and Drug Prescription Data
Szerző(k) neve és munkahelye
Tamás Nagy1, Nóra Eszlári1, Péter Antal2, Gabriella Juhász1
1: Semmelweis University
2: Budapest University of Technology and Economics
Bemutatás módja
Szóbeli
Szekció
Mental Health Sciences III.
Language of the presentation
Hungarian
Preferred session
Mental Health Sciences
Összefoglaló szövege
Introduction: We previously identified seven distinct multimorbidity clusters associated with major depressive disorder (MDD) through a comprehensive analysis of 1.2 million individuals of multiple cohorts [1]. These clusters, characterized by unique clinical, genetic, and risk profiles, suggest divergent treatment pathways and disease management strategies.
Aims: This study aims to deepen the understanding of these clusters by analyzing drug prescriptions, evaluating the effectiveness of treatment strategies, and identifying potential markers for personalized medicine.
Methods: Utilizing drug prescription data in the format of ATC codes, we performed epidemiological assessments, including multimorbidity, polypharmacy, and drug burden analyses across the clusters. We applied linear regression models to assess the strength and predictive capability of cluster membership, and logistic regression to explore associations with treatment-resistant depression (TRD). We also quantified and visualized common antidepressant treatment sequences within each cluster.
Results: Preliminary findings indicate significant variations in drug types and quantities across clusters, with distinct patterns emerging that correlate with the clusters’ profiles. We provided a clear visual representation of treatment pathways, highlighting both common and divergent antidepressant strategies among the clusters.
Conclusion: The detailed pharmacological profiling presented in this study not only corroborates the initial cluster definitions but also enhances our predictive capabilities for treatment outcomes in MDD. By linking pharmacological data with comorbidity profiles, we pave the way for targeted therapeutic interventions.
Funding: 2019-2.1.7-ERA-NET-2020-00005 under the frame of ERAPERMED2019-108; NKFIH K 143391, PD 146014, and PD 134449 grants; Hungarian Brain Research Program 3.0 (NAP2022-I-4/2022); TKP2021-EGA-25, TKP2021-EGA-02. NE is supported by the János Bolyai Research Scholarship of the HAS. TN is supported by the SE 250+ scholarship.
References
[1] Gabriella Juhasz, Andras Gezsi, Sandra Van der Auwera et al. Unique genetic and risk-factor profiles in multimorbidity clusters of depression-related disease trajectories from a study of 1.2 million subjects, 01 August 2023, PREPRINT (Version 1) available at Research Square [https://doi.org/10.21203/rs.3.rs-3199113/v1]
University
Semmelweis University
Supervisor
Gabriella Juhász
Publication of my abstract
I do not give consent to the publication of my abstract on the website of the congress.
Kind
Szabad
Status
elfogadva
Accepted presentation method
szóbeli
Előadás fájl jóváhagyás
nem rendelkezett róla
Előadó
8084
Start
16:15
End
16:25