Phenotype fingerprinting of bipolar disorder prodrome

International Journal of Bipolar Disorders

Research

Srikanth Gottipati¹  Qing Zeng-Treitler²  Yan Cheng²  Yijun Shao² 

[1] Washington DC VA Medical Center, Washington, DC, USA;Washington DC VA Medical Center, Washington, DC, USA;Biomedical Informatics Center, George Washington University, 2600 Virginia Ave NW, Suite 300, 20037, Washington, DC, USA;

关键词: Bipolar disorder;    Phenotype;    Unsupervised machine learning;   

DOI  :  10.1186/s40345-023-00298-4

 received in 2023-01-30, accepted in 2023-04-14,  发布年份 2023

来源: Springer

【 摘 要 】

BackgroundDetecting 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.Methods20,000 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.ResultsOur 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.ConclusionOur 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.

【 授权许可】

CC BY   
© This is a U.S. Government work and not under copyright protection in the US; foreign copyright protection may apply 2023

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