AUTHOR=Li Hui , Xu Xiaonuo , Zhou Jiying , Dong Liang 

TITLE=Cluster and network analysis of non-headache symptoms in migraine patients reveals distinct subgroups based on onset age and vestibular-cochlear symptom interconnection

JOURNAL=Frontiers in Neurology

VOLUME=Volume 14 - 2023

YEAR=2023

URL=https://www.frontiersin.org/journals/neurology/articles/10.3389/fneur.2023.1184069

DOI=10.3389/fneur.2023.1184069

ISSN=1664-2295

ABSTRACT=Objective: The present study endeavors to identify natural subgroups of migraine patients based on the patterns of non-headache symptoms, utilizing unsupervised machine learning technique. Subsequently, network analysis was performed to estimate the structure of symptoms and explore the potential pathophysiology of these findings.
Method: A total of 475 patients who met the diagnostic criteria for migraine were surveyed face-to-face during the period of 2019 to 2022. The survey included demographic and symptom data. The Kamila clustering algorithm generated four different solutions, from which two cluster solutions were adapted. Network analysis was then conducted using BGGM to estimate the symptom structure of the two groups, and the two structures were compared globally and pair-wisely.
Result: Cluster analysis identified two distinct groups. Participants assigned to cluster two exhibited a later onset of migraine symptoms, accompanied by a longer course of migraine, a higher frequency of headache attacks per month, and a greater tendency towards medication overuse. As for symptoms, participants belonging to cluster one demonstrated a higher frequency of nausea, vomiting, and phonophobia, compared to those in the other group. The onset age of migraine served as an effective division between the two groups. The network analysis revealed a different symptom structure between the two groups globally, while the pairwise differences indicated an increasing connection between tinnitus and dizziness, and a decreasing connection between tinnitus and hearing loss in the early-onset group.
Conclusion: Utilizing unsupervised machine learning and network analysis, we have identified two distinct non-headache symptom structures of migraine patients with early-onset age and late-onset age. Our findings suggest that cochlear symptoms may differ in the context of different onset ages of migraine patients, which may contribute to a better understanding of the pathology of cochlear symptoms in migraine.