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Papers: 15 Feb 2020 - 21 Feb 2020

Human Studies


2020 Feb 18

Sci Rep



Exploring Natural Clusters of Chronic Migraine Phenotypes: A Cross-Sectional Clinical Study.


Woldeamanuel YW, Sanjanwala BM, Peretz AM, Cowan RP
Sci Rep. 2020 Feb 18; 10(1):2804.
PMID: 32071349.


Heterogeneity in chronic migraine (CM) presents significant challenge for diagnosis, management, and clinical trials. To explore naturally occurring clusters of CM, we utilized data reduction methods on migraine-related clinical dataset. Hierarchical agglomerative clustering and principal component analyses (PCA) were conducted to identify natural clusters in 100 CM patients using 14 migraine-related clinical variables. Three major clusters were identified. Cluster I (29 patients) – the severely impacted patient featured highest levels of depression and migraine-related disability. Cluster II (28 patients) – the minimally impacted patient exhibited highest levels of self-efficacy and exercise. Cluster III (43 patients) – the moderately impacted patient showed features ranging between Cluster I and II. The first 5 principal components (PC) of the PCA explained 65% of variability. The first PC (eigenvalue 4.2) showed one major pattern of clinical features positively loaded by migraine-related disability, depression, poor sleep quality, somatic symptoms, post-traumatic stress disorder, being overweight and negatively loaded by pain self-efficacy and exercise levels. CM patients can be classified into three naturally-occurring clusters. Patients with high self-efficacy and exercise levels had lower migraine-related disability, depression, sleep quality, and somatic symptoms. These results may ultimately inform different management strategies.