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Report Finds Natural Clusters of Chronic Migraine Phenotypes

Gianna Melillo
Researchers were able to use questionnaire responses to accurately determine natural clusters of chronic migraine phenotypes, according to a study published in Scientific Reports.
Researchers were able to use questionnaire responses to accurately determine natural clusters of chronic migraine phenotypes, according to a study published in Scientific Reports.

One hundred patients with chronic migraine (CM) enrolled at the Stanford Headache Clinic between January 2015 and May 2019 completed online self-administered questionnaires about their symptoms, demographic information, and migraine duration. The majority of participating CM patients were middle-aged, female and mildly overweight.

Results from the cross-sectional clinical study allowed researchers to identify 3 major clusters of chronic migraineurs. Cluster 1 (29 patients) consisted of the severely impacted patients and featured higher levels of depression and migraine-related disability. Cluster 2 (28 patients) was made up of minimally impacted patients who exhibited higher levels of pain self-efficacy and exercise, and while cluster 3 (43 patients) included moderately impacted patients who exhibited features ranging between clusters 1 and 2.

“Cluster 1 CM patients had 4 times higher odds of having medication-overuse headache (MOH) compared to cluster 2 CM patients (P = .02),” the researchers said. In addition, intermedian comparison found statistically significant differences between clusters 1 and 2 among the following variables:
  • Depression
  • Migraine-related disability
  • Pain self-efficacy
  • Exercise minutes
Results also showed statistically significant associations between migraine frequency and migraine-related disability along with a “positive association between depression and anxiety, pain catastrophizing, poor sleep quality, posttraumatic stress disorder, somatic symptoms, and migraine-related disability.” The researchers determined that increased pain self-efficacy and exercises were associated with lower psychological comorbidities and migraine burden.

Because CM can be classified into 3 naturally occurring clusters, the researchers argue the results support the link between lifestyle behaviors and migraine self-management. In addition to enabling targeted interventions for behavioral change among chronic migraineurs, “our cluster identification can be applied to discover biomarkers (eg, genes, proteins) linked to a specific cluster,” they said.

They continued, “Our study clearly showed significant heterogeneity in patient characteristics and comorbidities…improved recognition of such heterogeneity may lead to more potent treatment by personalizing headache care to better fit CM patient profiles.”

Based on these findings, the researchers are working on developing a supervised learning algorithm to generate a model to predict CM classification. Baseline classification will aid in identifying treatment nonresponders, responders, and super-responders in clinical trials.  

Reference 

Woldeamanuel YW, Sanjanwala BM, Peretz AM, Cowan RP. Exploring natural clusters of chronic migraine phenotypes: a cross-sectional clinical study [published online February 18, 2020]. Sci Rep. doi: 10.1038/s41598-020-59738-1.

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