PhD Scientific Days 2026

Mental Health Sciences 4.

Pharmacological profiling of major depressive disorder-related multimorbidity clusters

Előadó neve

Nagy, Tamás

Neptune code

NCF0AL

Előadó munkahelye

Department of Pharmacodynamics, Faculty of Pharmaceutical Sciences, Semmelweis University

Előadó telefonszáma

+36202729066

Előadó e-mail címe

tonyo199606@gmail.com

Az előadás címe

Pharmacological profiling of major depressive disorder-related multimorbidity clusters

Szerző(k) neve és munkahelye

Tamás Nagy¹, Gabriella Juhasz¹, Nora Eszlari¹, Peter Antal²

1: Department of Pharmacodynamics, Faculty of Pharmaceutical Sciences, Semmelweis University
2: Department of Artificial Intelligence and Systems Engineering, Budapest University of Technology and Economics

Bemutatás módja

Szóbeli

Szekció

Mental Health Sciences 4.

Language of the presentation

English

Preferred session

Mental Health Sciences

Összefoglaló szövege

Introduction Major depressive disorder (MDD) is clinically heterogeneous, with approximately one-third of patients showing treatment resistance. The TRAJECTOME project identified seven MDD-related multimorbidity clusters from longitudinal disease trajectories of 1.2 million individuals across three European cohorts using Bayesian non-parametric modelling. Each cluster showed distinct clinical and genetic profiles, but their non-genetic risk factor and pharmacological treatment patterns remained uncharacterized.

Aims To characterize the non-genetic risk factor profiles of the seven clusters and to profile their pharmacological treatment patterns, including polypharmacy, drug burden, antidepressant strategies, and treatment-resistant depression (TRD) prevalence.

Methods Non-genetic risk factor analysis used linear regression models with cluster membership log-odds as dependent variables in the UK Biobank (N=249,167) and Finnish THL cohorts. Pharmacological profiling utilized ATC-coded prescription data across three cohorts, assessing polypharmacy, drug burden trajectories, and TRD prevalence. Antidepressant treatment sequences were mapped using drug-tree analysis, and receptor-level synaptic target profiles were constructed.

Result Low-risk clusters (1-4) showed favourable lifestyle profiles, while high-risk clusters (5-6) accumulated adverse factors including stress, poor sleep, physical inactivity, and elevated BMI. Cluster 7 showed mixed non-genetic factors despite strong genetic inflammation-related loading. Polypharmacy and drug burden differences persisted after correction for comorbidity count. TRD prevalence was 22% (Cluster 5) and 19% (Cluster 6) versus 12% in low-risk clusters. Cluster 5 showed the highest TCA burden with broad synaptic target engagement, while Cluster 7 showed treatment patterns similar to low-risk clusters.

Conclusion MDD-related multimorbidity clusters have distinct, replicable non-genetic risk factor and pharmacological profiles. These findings support multimorbidity-based stratification for personalized depression treatment.

Funding Hungarian National Research, Development, and Innovation Office (2019-2.1.7-ERA-NET-2020-00005, ERA PerMed TRAJECTOME project).

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.

phd.section.field

after finishing doctoral studies with absolutorium (PhD)

Kind

Szabad

Status

elfogadva

Accepted presentation method

szóbeli

Előadás fájl jóváhagyás

nem rendelkezett róla

Előadó

8084

Start

16:00

End

16:10