Phenotype fingerprinting of bipolar disorder prodrome
Abstract 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 co...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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SpringerOpen
2023-05-01
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| Series: | International Journal of Bipolar Disorders |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s40345-023-00298-4 |
| _version_ | 1797823105431764992 |
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| author | Yijun Shao Yan Cheng Srikanth Gottipati Qing Zeng-Treitler |
| author_facet | Yijun Shao Yan Cheng Srikanth Gottipati Qing Zeng-Treitler |
| author_sort | Yijun Shao |
| collection | DOAJ |
| description | Abstract 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. |
| first_indexed | 2024-03-13T10:18:11Z |
| format | Article |
| id | doaj.art-b8c2c130a522445da446b9f15883c13e |
| institution | Directory Open Access Journal |
| issn | 2194-7511 |
| language | English |
| last_indexed | 2024-03-13T10:18:11Z |
| publishDate | 2023-05-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | International Journal of Bipolar Disorders |
| spelling | doaj.art-b8c2c130a522445da446b9f15883c13e2023-05-21T11:05:35ZengSpringerOpenInternational Journal of Bipolar Disorders2194-75112023-05-0111111010.1186/s40345-023-00298-4Phenotype fingerprinting of bipolar disorder prodromeYijun Shao0Yan Cheng1Srikanth Gottipati2Qing Zeng-Treitler3Washington DC VA Medical CenterWashington DC VA Medical CenterWashington DC VA Medical CenterWashington DC VA Medical CenterAbstract 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.https://doi.org/10.1186/s40345-023-00298-4Bipolar disorderPhenotypeUnsupervised machine learning |
| spellingShingle | Yijun Shao Yan Cheng Srikanth Gottipati Qing Zeng-Treitler Phenotype fingerprinting of bipolar disorder prodrome International Journal of Bipolar Disorders Bipolar disorder Phenotype Unsupervised machine learning |
| 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 | Bipolar disorder Phenotype Unsupervised machine learning |
| url | https://doi.org/10.1186/s40345-023-00298-4 |
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