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Developing symptom clusters: linking inflammatory biomarkers to depressive symptom profiles
Considering the burden of depression and the lack of efficacy of available treatments, there is a need for biomarkers to predict tailored or personalized treatments. However, identifying reliable biomarkers for depression has been challenging, likely owing to the vast symptom heterogeneity and high...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8971490/ https://www.ncbi.nlm.nih.gov/pubmed/35361785 http://dx.doi.org/10.1038/s41398-022-01900-6 |
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author | Franklyn, Sabina I. Stewart, Jayme Beaurepaire, Cecile Thaw, Emily McQuaid, Robyn J. |
author_facet | Franklyn, Sabina I. Stewart, Jayme Beaurepaire, Cecile Thaw, Emily McQuaid, Robyn J. |
author_sort | Franklyn, Sabina I. |
collection | PubMed |
description | Considering the burden of depression and the lack of efficacy of available treatments, there is a need for biomarkers to predict tailored or personalized treatments. However, identifying reliable biomarkers for depression has been challenging, likely owing to the vast symptom heterogeneity and high rates of comorbidity that exists. Examining biomarkers that map onto dimensions of depression as well as shared symptoms/constructs that cut across disorders could be most effective for informing personalized treatment approaches. With a sample of 539 young adults, we conducted a principal component analysis (PCA) followed by hierarchical cluster analysis to develop transdiagnostic clusters of depression and anxiety symptoms. We collected blood to assess whether neuroendocrine (cortisol) and inflammatory profiles (C-reactive protein (CRP), Interleukin (IL)-6, and tumor necrosis factor (TNF) – α) could be used to differentiate symptom clusters. Six distinct clusters were identified that differed significantly on symptom dimensions including somatic anxiety, general anxiety, anhedonia, and neurovegetative depression. Moreover, the neurovegetative depression cluster displayed significantly elevated CRP levels compared to other clusters. In fact, inflammation was not strongly associated with overall depression scores or severity, but rather related to specific features of depression marked by eating, appetite, and tiredness. This study emphasizes the importance of characterizing the biological underpinnings of symptom dimensions and subtypes to better understand the etiology of complex mental health disorders such as depression. |
format | Online Article Text |
id | pubmed-8971490 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-89714902022-04-20 Developing symptom clusters: linking inflammatory biomarkers to depressive symptom profiles Franklyn, Sabina I. Stewart, Jayme Beaurepaire, Cecile Thaw, Emily McQuaid, Robyn J. Transl Psychiatry Article Considering the burden of depression and the lack of efficacy of available treatments, there is a need for biomarkers to predict tailored or personalized treatments. However, identifying reliable biomarkers for depression has been challenging, likely owing to the vast symptom heterogeneity and high rates of comorbidity that exists. Examining biomarkers that map onto dimensions of depression as well as shared symptoms/constructs that cut across disorders could be most effective for informing personalized treatment approaches. With a sample of 539 young adults, we conducted a principal component analysis (PCA) followed by hierarchical cluster analysis to develop transdiagnostic clusters of depression and anxiety symptoms. We collected blood to assess whether neuroendocrine (cortisol) and inflammatory profiles (C-reactive protein (CRP), Interleukin (IL)-6, and tumor necrosis factor (TNF) – α) could be used to differentiate symptom clusters. Six distinct clusters were identified that differed significantly on symptom dimensions including somatic anxiety, general anxiety, anhedonia, and neurovegetative depression. Moreover, the neurovegetative depression cluster displayed significantly elevated CRP levels compared to other clusters. In fact, inflammation was not strongly associated with overall depression scores or severity, but rather related to specific features of depression marked by eating, appetite, and tiredness. This study emphasizes the importance of characterizing the biological underpinnings of symptom dimensions and subtypes to better understand the etiology of complex mental health disorders such as depression. Nature Publishing Group UK 2022-03-31 /pmc/articles/PMC8971490/ /pubmed/35361785 http://dx.doi.org/10.1038/s41398-022-01900-6 Text en © The Author(s) 2022, corrected publication 2022 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Article Franklyn, Sabina I. Stewart, Jayme Beaurepaire, Cecile Thaw, Emily McQuaid, Robyn J. Developing symptom clusters: linking inflammatory biomarkers to depressive symptom profiles |
title | Developing symptom clusters: linking inflammatory biomarkers to depressive symptom profiles |
title_full | Developing symptom clusters: linking inflammatory biomarkers to depressive symptom profiles |
title_fullStr | Developing symptom clusters: linking inflammatory biomarkers to depressive symptom profiles |
title_full_unstemmed | Developing symptom clusters: linking inflammatory biomarkers to depressive symptom profiles |
title_short | Developing symptom clusters: linking inflammatory biomarkers to depressive symptom profiles |
title_sort | developing symptom clusters: linking inflammatory biomarkers to depressive symptom profiles |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8971490/ https://www.ncbi.nlm.nih.gov/pubmed/35361785 http://dx.doi.org/10.1038/s41398-022-01900-6 |
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