Psychiatry research · 2026
Comparison of demographic and clinical features in adolescent-onset vs. adult-onset bipolar disorder an analysis of BIPAS Phase II data.
СтатьяСравнительное исследованиеНазвание и аннотация: Перевод в очереди
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Zhao Guoqing, Wang Zuowei, Zhu Yuncheng, Wang Xiaoping, Xu Guiyun, Pan Miao, Chen Zhiyu, Li Wenfei, Li Mingli, Zhang Yong, Chen Jingxu, Chen Xiuzhe, Li Na, Sun Jing, Zhang Jian, Hu Shaohua, Gan Zhaoyu, Qin Yan, Wang Yumei, Ma Yantao, Li Xiaohong, Jiao Zhian, Fang Yiru
Аннотация
OBJECTIVE: The literature on large-scale studies of Chinese patients with adolescent-onset bipolar disorder (adolescent-onset BD) was limited. Based on the analysis of the National Bipolar Mania Pathway Survey (BIPAS) Phase II data, we examined the demographic and clinical characteristics of adults with adolescent-onset BD.
METHODS: Among 899 participants diagnosed with BD from 20 mental health services, demographics and clinical data were collected at screening. Comparisons were made using chi-square (or Fisher's exact) tests and ANOVA. Multivariate logistic regression identified independent factors for adolescent-onset BD, and a CHAID decision tree analysis (SPSS) was constructed to detect risk factors.
RESULTS: In the sample, 360 (40 %) had adolescent-onset BD and 539 (60 %) adult-onset BD. Significant differences between the two groups were observed in current age, number of episodes, years of education, gender, age of onset, education level, marital status, occupation, comorbid chronic physical illness, and first episode type. Stratified analysis also revealed significant differences between adolescent-onset BD I and BD II. Multivariate logistic regression identified younger onset age, more frequent episodes, lower education level, marital status, occupation, first episode type, and prior hospitalization as independent factors for adolescent-onset BD. The decision tree model selected current age as the first splitting variable, followed by occupation and marital status as the second, and years of education and prior hospitalization as the third.
CONCLUSIONS: Adolescent-onset BD exhibits distinct demographic and clinical features compared to adult-onset BD. Early recognition and tailored treatment strategies may improve prognosis and outcomes in this population.
METHODS: Among 899 participants diagnosed with BD from 20 mental health services, demographics and clinical data were collected at screening. Comparisons were made using chi-square (or Fisher's exact) tests and ANOVA. Multivariate logistic regression identified independent factors for adolescent-onset BD, and a CHAID decision tree analysis (SPSS) was constructed to detect risk factors.
RESULTS: In the sample, 360 (40 %) had adolescent-onset BD and 539 (60 %) adult-onset BD. Significant differences between the two groups were observed in current age, number of episodes, years of education, gender, age of onset, education level, marital status, occupation, comorbid chronic physical illness, and first episode type. Stratified analysis also revealed significant differences between adolescent-onset BD I and BD II. Multivariate logistic regression identified younger onset age, more frequent episodes, lower education level, marital status, occupation, first episode type, and prior hospitalization as independent factors for adolescent-onset BD. The decision tree model selected current age as the first splitting variable, followed by occupation and marital status as the second, and years of education and prior hospitalization as the third.
CONCLUSIONS: Adolescent-onset BD exhibits distinct demographic and clinical features compared to adult-onset BD. Early recognition and tailored treatment strategies may improve prognosis and outcomes in this population.