PhD Scientific Days 2023

Mental Health Sciences I.

Pharmacological Characterization of Multimorbidity-Based Clusters in Depression

Előadó neve

Nagy, Tamás, MSc

Neptun code

NCF0AL

Előadó munkahelye

Department of Pharmacodynamics, Faculty of Pharmacy, Semmelweis University

Előadó telefonszáma

+36202729066

Előadó e-mail címe

nagy.tamas96@phd.semmelweis.hu

Az előadás címe

Pharmacological Characterization of Multimorbidity-Based Clusters in Depression

Szerző(k) neve és munkahelye

Tamas Nagy1,2,3, Andras Gezsi2, Gabor Hullam2, Nora Eszlari1,3, Peter Antal2, Gabriella Juhasz1,3

1 Department of Pharmacodynamics, Faculty of Pharmacy, Semmelweis University, Nagyvárad tér 4., H-1089 Budapest, Hungary
2 Department of Measurement and Information Systems, Budapest University of Technology and Economics, Műegyetem rkp. 3., H-1111 Budapest, Hungary
3 NAP3.0-SE Neuropsychopharmacology Research Group, Hungarian Brain Research Program, Semmelweis University, Üllői út 26., H-1085 Budapest, Hungary

Bemutatás módja

Szóbeli

Szekció

Mental Health Sciences I.

Language of the presentation

Hungarian

Preferred session

Mental Sciences

Összefoglaló szövege

Introduction: Depression is a heterogeneous disorder often associated with various comorbidities, leading to the need for personalized therapeutic approaches. In a previous study, we identified seven clusters of depression-related comorbidities in the UK Biobank population using Bayesian network analysis. This study aims to analyse the pharmacological patterns within these clusters, focusing on polypharmacy and multimorbidity to elucidate the importance of personalized medicine in depression treatment.

Aims: The primary objective of this study is to describe the temporal patterns and indicators related to drug consumption within each cluster, which may inform personalized therapeutic strategies in depression.

Method: We used continuous cluster membership variables as weighting factors and binarized them for further analysis. We extracted pharmacological data from the UK Biobank from 1996-2016, acknowledging the limitations of incomplete data from both ends. Descriptive and temporal statistical analyses were conducted to compare the clusters in terms of multimorbidity, drug consumption indicators (number of drug purchases, polypharmacy) and their ratios.

Results: The consumption patterns across the seven clusters showed both similarities and differences, with varying levels of polypharmacy and multimorbidity observed. The regression models revealed significant associations between cluster membership metrics and drug consumption indicators, highlighting the potential influence of comorbidities on pharmacological profiles. However, the polypharmacy/multimorbidity ratio varied among clusters, indicating differential treatment needs and emphasizing the importance of personalized medicine in managing depression and its comorbidities.

Conclusion: Our findings underscore the heterogeneity of depression and its associated comorbidities, revealing distinct pharmacological patterns across multimorbidity-based clusters. The observed differences in drug consumption patterns within each cluster may inform the development of tailored therapeutic approaches for depression treatment.

Funding:
2019-2.1.7-ERA-NET-2020-00005 (ERAPERMED2019-108); OTKA K143391 and PD134449; NAP2022-I-4/2022; TKP2021-EGA-25; ÚNKP-22-4-II-SE-1

University and Doctoral School

Semmelweis University, Doctoral School of Mental Health Sciences

Supervisor

Dr. Gabriella Juhász, Dr. Péter Antal

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

6859

Start

16:15

End

16:30

Authors (legacy)

Tamas Nagy1,2,3, Andras Gezsi2, Gabor Hullam2, Nora Eszlari1,3, Peter Antal2, Gabriella Juhasz1,3

1 Department of Pharmacodynamics, Faculty of Pharmacy, Semmelweis University, Nagyvárad tér 4., H-1089 Budapest, Hungary
2 Department of Measurement and Information Systems, Budapest University of Technology and Economics, Műegyetem rkp. 3., H-1111 Budapest, Hungary
3 NAP3.0-SE Neuropsychopharmacology Research Group, Hungarian Brain Research Program, Semmelweis University, Üllői út 26., H-1085 Budapest, Hungary