Immunometabolic Pathways: Investigating Mediators of Major Depressive Disorder and Atherosclerotic Cardiovascular Disease Comorbidity
Background: Major depressive disorder (MDD) and cardiovascular diseases (CVDs) often co-occur whereby comorbidity results in poorer clinical outcomes, presumably due to shared immunometabolic pathways. Identifying shared biomarkers for MDD-CVD comorbidity may provide targets for prevention or treatm...
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Elsevier
2025-09-01
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| Series: | Biological Psychiatry Global Open Science |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2667174325000825 |
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| author | Angela Koloi Nabila P.R. Siregar Rick Quax Antonis I. Sakellarios Femke Lamers Arja Rydin Kevin Dobretz Costas Papaloukas Dimitrios I. Fotiadis Jos A. Bosch |
| author_facet | Angela Koloi Nabila P.R. Siregar Rick Quax Antonis I. Sakellarios Femke Lamers Arja Rydin Kevin Dobretz Costas Papaloukas Dimitrios I. Fotiadis Jos A. Bosch |
| author_sort | Angela Koloi |
| collection | DOAJ |
| description | Background: Major depressive disorder (MDD) and cardiovascular diseases (CVDs) often co-occur whereby comorbidity results in poorer clinical outcomes, presumably due to shared immunometabolic pathways. Identifying shared biomarkers for MDD-CVD comorbidity may provide targets for prevention or treatment. Methods: Using data from the NESDA (Netherlands Study of Depression and Anxiety) (n = 2256, 66.3% female, mean age 41.86 ± 13.08 years at baseline), validated with the UK Biobank (UKB) data (n = 35,668, 56.14% female, mean age 63.95 ± 7.74 years), this study aimed to identify 1) biomarkers, closely associated with current MDD, and 2) longitudinal pathways linking MDD and atherosclerotic CVD. Plasma metabolites (nuclear magnetic resonance) and inflammatory markers were used as exposures within a machine learning framework. Influential biomarkers were integrated into a temporal network analysis linking MDD to subsequent CVDs, exploring longitudinal pathways through causal discovery, validated by sensitivity analysis and centrality assessment. External validation included mediation and regression analysis adjusting for covariates. Results: Network analysis identified stable direct paths from MDD to CVDs via tumor necrosis factor α (TNF-α), tyrosine, and fatty acids and indirect paths via acetate, high-density lipoprotein (HDL) diameter, interleukin 6, AGP, high-sensitivity C-reactive protein, and low-density lipoprotein triglycerides. Among these, acetate, tyrosine, AGP (α1-acid glycoprotein), and HDL diameter potentially mediated the MDD-CVD connection, given that these were identified as key nodes within the network. UKB validation confirmed HDL diameter (β = 0.004) and AGP (β = 0.003) as significant depression-CVD mediators (both p < .001), after adjusting for age, sex, deprivation index, alcohol consumption, smoking status, physical activity, and body mass index. Conclusions: These analyses identified biomarkers shared in MDD and CVDs and may drive comorbid pathology risk. |
| format | Article |
| id | doaj-art-612d7e71af284259a99f2a823a1867bf |
| institution | OA Journals |
| issn | 2667-1743 |
| language | English |
| publishDate | 2025-09-01 |
| publisher | Elsevier |
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| series | Biological Psychiatry Global Open Science |
| spelling | doaj-art-612d7e71af284259a99f2a823a1867bf2025-08-20T02:07:35ZengElsevierBiological Psychiatry Global Open Science2667-17432025-09-015510052810.1016/j.bpsgos.2025.100528Immunometabolic Pathways: Investigating Mediators of Major Depressive Disorder and Atherosclerotic Cardiovascular Disease ComorbidityAngela Koloi0Nabila P.R. Siregar1Rick Quax2Antonis I. Sakellarios3Femke Lamers4Arja Rydin5Kevin Dobretz6Costas Papaloukas7Dimitrios I. Fotiadis8Jos A. Bosch9Unit of Medical Technology and Intelligent Information Systems, Department of Materials Science and Engineering, University of Ioannina, Ioannina, Greece; Department of Biological Applications and Technology, University of Ioannina, Ioannina, Greece; Department of Clinical Psychology, University of Amsterdam, Amsterdam, the Netherlands; Department of Medical Psychology, Amsterdam University Medical Center, Amsterdam, the Netherlands; Address correspondence to Angela Koloi, M.D.Computational Science Laboratory, Institute of Informatics, University of Amsterdam, Amsterdam, the NetherlandsComputational Science Laboratory, Institute of Informatics, University of Amsterdam, Amsterdam, the NetherlandsUnit of Medical Technology and Intelligent Information Systems, Department of Materials Science and Engineering, University of Ioannina, Ioannina, Greece; Biomedical Engineering of the Department of Mechanical Engineering and Aeronautics, University of Patras, Patras, GreeceDepartment of Psychiatry, Amsterdam University Medical Center, VU Amsterdam, Amsterdam, the Netherlands; Amsterdam Public Health, Mental Health Program, Amsterdam, the NetherlandsDepartment of Psychiatry, Amsterdam University Medical Center, VU Amsterdam, Amsterdam, the Netherlands; Amsterdam Public Health, Mental Health Program, Amsterdam, the NetherlandsCardiology, Geneva University Hospitals, Geneva, Switzerland; Department of Medicine, University of Geneva, Geneva, SwitzerlandUnit of Medical Technology and Intelligent Information Systems, Department of Materials Science and Engineering, University of Ioannina, Ioannina, Greece; Department of Biological Applications and Technology, University of Ioannina, Ioannina, Greece; Biomedical Research Institute, Foundation for Research and Technology - Hellas, Ioannina, GreeceUnit of Medical Technology and Intelligent Information Systems, Department of Materials Science and Engineering, University of Ioannina, Ioannina, Greece; Biomedical Research Institute, Foundation for Research and Technology - Hellas, Ioannina, GreeceDepartment of Clinical Psychology, University of Amsterdam, Amsterdam, the Netherlands; Department of Medical Psychology, Amsterdam University Medical Center, Amsterdam, the NetherlandsBackground: Major depressive disorder (MDD) and cardiovascular diseases (CVDs) often co-occur whereby comorbidity results in poorer clinical outcomes, presumably due to shared immunometabolic pathways. Identifying shared biomarkers for MDD-CVD comorbidity may provide targets for prevention or treatment. Methods: Using data from the NESDA (Netherlands Study of Depression and Anxiety) (n = 2256, 66.3% female, mean age 41.86 ± 13.08 years at baseline), validated with the UK Biobank (UKB) data (n = 35,668, 56.14% female, mean age 63.95 ± 7.74 years), this study aimed to identify 1) biomarkers, closely associated with current MDD, and 2) longitudinal pathways linking MDD and atherosclerotic CVD. Plasma metabolites (nuclear magnetic resonance) and inflammatory markers were used as exposures within a machine learning framework. Influential biomarkers were integrated into a temporal network analysis linking MDD to subsequent CVDs, exploring longitudinal pathways through causal discovery, validated by sensitivity analysis and centrality assessment. External validation included mediation and regression analysis adjusting for covariates. Results: Network analysis identified stable direct paths from MDD to CVDs via tumor necrosis factor α (TNF-α), tyrosine, and fatty acids and indirect paths via acetate, high-density lipoprotein (HDL) diameter, interleukin 6, AGP, high-sensitivity C-reactive protein, and low-density lipoprotein triglycerides. Among these, acetate, tyrosine, AGP (α1-acid glycoprotein), and HDL diameter potentially mediated the MDD-CVD connection, given that these were identified as key nodes within the network. UKB validation confirmed HDL diameter (β = 0.004) and AGP (β = 0.003) as significant depression-CVD mediators (both p < .001), after adjusting for age, sex, deprivation index, alcohol consumption, smoking status, physical activity, and body mass index. Conclusions: These analyses identified biomarkers shared in MDD and CVDs and may drive comorbid pathology risk.http://www.sciencedirect.com/science/article/pii/S2667174325000825BiomarkersCVDMachine learningMDDMediators |
| spellingShingle | Angela Koloi Nabila P.R. Siregar Rick Quax Antonis I. Sakellarios Femke Lamers Arja Rydin Kevin Dobretz Costas Papaloukas Dimitrios I. Fotiadis Jos A. Bosch Immunometabolic Pathways: Investigating Mediators of Major Depressive Disorder and Atherosclerotic Cardiovascular Disease Comorbidity Biological Psychiatry Global Open Science Biomarkers CVD Machine learning MDD Mediators |
| title | Immunometabolic Pathways: Investigating Mediators of Major Depressive Disorder and Atherosclerotic Cardiovascular Disease Comorbidity |
| title_full | Immunometabolic Pathways: Investigating Mediators of Major Depressive Disorder and Atherosclerotic Cardiovascular Disease Comorbidity |
| title_fullStr | Immunometabolic Pathways: Investigating Mediators of Major Depressive Disorder and Atherosclerotic Cardiovascular Disease Comorbidity |
| title_full_unstemmed | Immunometabolic Pathways: Investigating Mediators of Major Depressive Disorder and Atherosclerotic Cardiovascular Disease Comorbidity |
| title_short | Immunometabolic Pathways: Investigating Mediators of Major Depressive Disorder and Atherosclerotic Cardiovascular Disease Comorbidity |
| title_sort | immunometabolic pathways investigating mediators of major depressive disorder and atherosclerotic cardiovascular disease comorbidity |
| topic | Biomarkers CVD Machine learning MDD Mediators |
| url | http://www.sciencedirect.com/science/article/pii/S2667174325000825 |
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