Nagy, Tamás and Gonda, Xénia and Gézsi, András and Eszlári, Nóra and Hullám, Gábor István and González-Colom, Rubèn and Mäkinen, Hannu and Paajanen, Teemu and Török, Dóra and Gál, Zsófia and Petschner, Péter and Cano, Isaac and Kuokkanen, Mikko and Schmidt, Carsten O. and Van der Auwera, Sandra and Roca, Josep and Antal, Péter and Juhász, Gabriella (2025) Pharmacological profiling of major depressive disorder-related multimorbidity clusters. EUROPEAN NEUROPSYCHOPHARMACOLOGY, 96. pp. 71-83. ISSN 0924-977X
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Abstract
We previously identified seven distinct multimorbidity clusters associated with major depressive disorder through a comprehensive analysis of 1.2 million individuals of multiple cohorts. These clusters, characterized by unique clinical, genetic, and psychiatric and somatic illness risk profiles, implicate divergent treatment pathways and disease management strategies. This study aims to deepen the understanding of these clusters by analyzing drug prescriptions, evaluating the effectiveness of antidepressant treatment strategies, and identifying potential markers for personalized medicine. Utilizing drug prescription data in the format of ATC codes, we performed epidemiological assessments, including multimorbidity (number of diseases), polypharmacy (number of chemical substances), and drug burden (number of prescriptions) analyses across the clusters. We applied linear regression models to assess strength and predictive capability of cluster membership on various metrics, and logistic regression to explore associations with treatment-resistant depression. We also quantified and visualized common antidepressant treatment sequences within each cluster. Our findings indicate significant variations in polypharmacy and drug burden across clusters, with distinct patterns emerging that correlate with the clusters’ profiles. Clusters liable to multimorbidity have higher drug burden, even after correction for number of diseases. Furthermore, the three clusters with higher risk for MDD showed different antidepressant treatment profiles; two required significantly more antidepressant prescriptions and had a higher risk for TRD. 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.
| Item Type: | Article |
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| Additional Information: | Funding Agency and Grant Number: Hungarian National Research, Development, and Innovation Office [K 139330, PD 146014, PD 134449]; Hungarian Brain Research Program 3.0 [NAP2022-I-4/2022]; Ministry of Innovation and Technology of Hungary from the National Research, Development and Innovation Fund [TKP2021-EGA-25, TKP2021-EGA-02]; European Union [RRF-2.3.1-21-2022-00004]; Janos Bolyai Research Scholarship of the Hungarian Academy of Sciences; Academy of Finland [ERAPERMED2019-108]; Catalan Department of Health [ERAPERMED2019-108, SLD002/19/000002]; [2019-2.1.7-ERA-NET-2020-00005]; [OTKA K 143391]; [EKOP-2024-68] Funding text: This study was supported by the Hungarian National Research, Development, and Innovation Office 2019-2.1.7-ERA-NET-2020-00005 under the frame of ERA PerMed (ERAPERMED2019-108) ; by the Hungarian National Research, Development, and Innovation Office OTKA K 143391, K 139330, PD 146014, and PD 134449 grants; by the Hungarian Brain Research Program 3.0 (NAP2022-I-4/2022) ; and by TKP2021-EGA-25 and TKP2021-EGA-02, supported by the Ministry of Innovation and Technology of Hungary from the National Research, Development and Innovation Fund, under the TKP2021-EGA funding scheme; and the European Union project [RRF-2.3.1-21-2022-00004] within the framework of the Artificial Intelligence National Laboratory. Dora Torok is supported by EKOP-2024-68. Nora Eszlari is supported by the Janos Bolyai Research Scholarship of the Hungarian Academy of Sciences. The study was funded by the Academy of Finland under the frame of ERA PerMed (TRAJECTOME project, ERAPERMED2019-108) . The Catalan cohort was extracted from the Catalan Health Surveillance System database, owned and managed by the Catalan Health Service, with the earnest collaboration of the Digitalization for the Sustainability of the Healthcare (DS3) -IDIBELL group. The study was supported by the Catalan Department of Health (SLD002/19/000002) under the frame of ERA PerMed (ERAPERMED2019-108) . |
| Uncontrolled Keywords: | Multimorbidity, Pharmacology, Major depressive disorder, Antidepressants |
| Subjects: | R Medicine / orvostudomány > RM Therapeutics. Pharmacology / terápia, gyógyszertan |
| SWORD Depositor: | MTMT SWORD |
| Depositing User: | MTMT SWORD |
| Date Deposited: | 04 Sep 2025 07:06 |
| Last Modified: | 04 Sep 2025 07:06 |
| URI: | https://real.mtak.hu/id/eprint/223428 |
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