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Phenotype fingerprinting of bipolar disorder prodrome
BACKGROUND: Detecting prodromal symptoms of bipolar disorder (BD) has garnered significant attention in recent research, as early intervention could potentially improve therapeutic efficacy and improve patient outcomes. The heterogeneous nature of the prodromal phase in BD, however, poses considerab...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer Berlin Heidelberg
2023
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Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10195932/ https://www.ncbi.nlm.nih.gov/pubmed/37202607 http://dx.doi.org/10.1186/s40345-023-00298-4 |
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author | Shao, Yijun Cheng, Yan Gottipati, Srikanth Zeng-Treitler, Qing |
author_facet | Shao, Yijun Cheng, Yan Gottipati, Srikanth Zeng-Treitler, Qing |
author_sort | Shao, Yijun |
collection | PubMed |
description | BACKGROUND: Detecting prodromal symptoms of bipolar disorder (BD) has garnered significant attention in recent research, as early intervention could potentially improve therapeutic efficacy and improve patient outcomes. The heterogeneous nature of the prodromal phase in BD, however, poses considerable challenges for investigators. Our study aimed to identify distinct prodromal phenotypes or "fingerprints" in patients diagnosed with BD and subsequently examine correlations between these fingerprints and relevant clinical outcomes. METHODS: 20,000 veterans diagnosed with BD were randomly selected for this study. K-means clustering analysis was performed on temporal graphs of the clinical features of each patient. We applied what we call “temporal blurring” to each patient image in order to allow clustering to focus on the clinical features, and not cluster patients based upon their varying temporal patterns in diagnosis, which lead to the desired types of clusters. We evaluated several outcomes including mortality rate, hospitalization rate, mean number of hospitalizations, mean length of stay, and the occurrence of a psychosis diagnosis within one year following the initial BD diagnosis. To determine the statistical significance of the observed differences for each outcome, we conducted appropriate tests, such as ANOVA or Chi-square. RESULTS: Our analysis yielded 8 clusters which appear to represent distinct phenotypes with differing clinical attributes. Each of these clusters also has statistically significant differences across all outcomes (p < 0.0001). The clinical features in many of the clusters were consistent with findings in the literature concerning prodromal symptoms in patients with BD. One cluster, notably characterized by patients lacking discernible prodromal symptoms, exhibited the most favorable results across all measured outcomes. CONCLUSION: Our study successfully identified distinct prodromal phenotypes in patients diagnosed with BD. We also found that these distinct prodromal phenotypes are associated with different clinical outcomes. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s40345-023-00298-4. |
format | Online Article Text |
id | pubmed-10195932 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Springer Berlin Heidelberg |
record_format | MEDLINE/PubMed |
spelling | pubmed-101959322023-05-20 Phenotype fingerprinting of bipolar disorder prodrome Shao, Yijun Cheng, Yan Gottipati, Srikanth Zeng-Treitler, Qing Int J Bipolar Disord Research BACKGROUND: Detecting prodromal symptoms of bipolar disorder (BD) has garnered significant attention in recent research, as early intervention could potentially improve therapeutic efficacy and improve patient outcomes. The heterogeneous nature of the prodromal phase in BD, however, poses considerable challenges for investigators. Our study aimed to identify distinct prodromal phenotypes or "fingerprints" in patients diagnosed with BD and subsequently examine correlations between these fingerprints and relevant clinical outcomes. METHODS: 20,000 veterans diagnosed with BD were randomly selected for this study. K-means clustering analysis was performed on temporal graphs of the clinical features of each patient. We applied what we call “temporal blurring” to each patient image in order to allow clustering to focus on the clinical features, and not cluster patients based upon their varying temporal patterns in diagnosis, which lead to the desired types of clusters. We evaluated several outcomes including mortality rate, hospitalization rate, mean number of hospitalizations, mean length of stay, and the occurrence of a psychosis diagnosis within one year following the initial BD diagnosis. To determine the statistical significance of the observed differences for each outcome, we conducted appropriate tests, such as ANOVA or Chi-square. RESULTS: Our analysis yielded 8 clusters which appear to represent distinct phenotypes with differing clinical attributes. Each of these clusters also has statistically significant differences across all outcomes (p < 0.0001). The clinical features in many of the clusters were consistent with findings in the literature concerning prodromal symptoms in patients with BD. One cluster, notably characterized by patients lacking discernible prodromal symptoms, exhibited the most favorable results across all measured outcomes. CONCLUSION: Our study successfully identified distinct prodromal phenotypes in patients diagnosed with BD. We also found that these distinct prodromal phenotypes are associated with different clinical outcomes. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s40345-023-00298-4. Springer Berlin Heidelberg 2023-05-18 /pmc/articles/PMC10195932/ /pubmed/37202607 http://dx.doi.org/10.1186/s40345-023-00298-4 Text en © This is a U.S. Government work and not under copyright protection in the US; foreign copyright protection may apply 2023 https://creativecommons.org/licenses/by/4.0/Open AccessThis 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 licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence 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 licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Research Shao, Yijun Cheng, Yan Gottipati, Srikanth Zeng-Treitler, Qing Phenotype fingerprinting of bipolar disorder prodrome |
title | Phenotype fingerprinting of bipolar disorder prodrome |
title_full | Phenotype fingerprinting of bipolar disorder prodrome |
title_fullStr | Phenotype fingerprinting of bipolar disorder prodrome |
title_full_unstemmed | Phenotype fingerprinting of bipolar disorder prodrome |
title_short | Phenotype fingerprinting of bipolar disorder prodrome |
title_sort | phenotype fingerprinting of bipolar disorder prodrome |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10195932/ https://www.ncbi.nlm.nih.gov/pubmed/37202607 http://dx.doi.org/10.1186/s40345-023-00298-4 |
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