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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...

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Autores principales: Shao, Yijun, Cheng, Yan, Gottipati, Srikanth, Zeng-Treitler, Qing
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer Berlin Heidelberg 2023
Materias:
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.
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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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