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

DOI: https://doi.org/10.1038/s41598- 020- 59738- 1

Abstract-Summary 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.

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.

The first PC (eigenvalue 4.2) showed one major pattern of clinical features posi- tively 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.

Patients with high self-efficacy and exercise levels had lower migraine-related

disability, depression, sleep quality, and somatic symptoms.

Extended: The results after excluding cases with missing data revealed findings

similar to results in which missing data were replaced by medians.

3.1 Classification

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Introduction Identifying clinically appropriate as well as naturally occurring CM clusters may help in better understanding different CM phenotypes.

According to the International Classification of Headache Disorders (ICHD-3) [88], CM is diagnosed as headache days of 15 or more in migraine sufferers out of which 8 must be migraine.

Unsupervised data reduction methods (e.g. clustering analysis) can be used to

categorize CM cases without a priori knowledge on patient classification.

While there are published studies on exploring natural clusters in episodic migraine and other headache types [72–90], there are no previous data reduction studies exclusively focused on exploring CM natural clusters.

In order to better characterize CM, we sought to identify clinically meaningful

CM clusters within our study population by using clustering analysis and PCA.

Results Psychological scores revealed that patients were mildly depressed with moderate level of somatic symptom severity.

Patients had poor sleep quality, low pain self-efficacy, low exercise minutes, and

regular lifestyle behavior (RLB) score of 18 out of 42.

Cluster I (29 patients)—the severely impacted patient featured higher levels of

depression and migraine-related disability.

Cluster II (28 patients)—the minimally impacted patient exhibited higher levels

of pain self-efficacy and exercise.

The PCA biplot revealed 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, exercise, and RLB levels.

There was positive association between depression and anxiety, pain catastroph-

izing, poor sleep quality, PTSD, somatic symptoms, migraine-related disability.

Pain self-efficacy and exercise level exhibited inverse relationship to depression,

poor sleep quality.

Discussion This study proved that CM can be classified into three naturally occurring clusters using clinical datasets.

The three clusters were found to be clinically meaningful, for example Cluster II (the minimally impacted) with higher pain self-efficacy, exercise, and regular life- style behavior (RLB) levels corresponded to lower migraine-related disability and comorbidities compared to Cluster I (the severely impacted).

The median weekly exercise of 210  min found in Cluster II (the minimally impacted) indicates that a 30-min daily exercise was associated with reduced migraine attacks in CM.

That the three clusters featured similar migraine severity and frequency but dif-

fering disability, self-efficacy and depression levels reflects CM heterogeneity.

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3 Diagnosis

Prospective studies are required to further establish temporal relationship between correlated clinical variables e.g. that self-efficacy leads to lower disabil- ity in CM.

Methods All CM patients completed online self-administered questionnaires about their demographic information, duration of CM, headache features during the previous 3  months involving monthly frequency of headache days, headache severity on numeric rating scale of 0 to 10, headache medication use, and headache-related dis- ability measured using Migraine Disability Assessment (MIDAS) [91].

Results from clustering analysis were utilized to identify natural clusters of CM patients using 14 migraine-related clinical variables (i.e. age, body mass index or BMI, monthly headache frequency, average headache severity, CM duration, depression, anxiety, pain catastrophizing, sleep quality, PTSD, somatic symptoms, migraine-related disability, pain self-efficacy, and exercise level).

Principal component analysis (PCA) was used to demonstrate the accuracy of our finding with HAC and to condense the 14 clinical variables to those explaining the largest variation.

Acknowledgement A machine generated summary based on the work of Woldeamanuel, Yohannes W.; Sanjanwala, Bharati M.; Peretz, Addie M.; Cowan, Robert P. 2020 in Scientific Reports

Characteristics of headache disorders, according to ICHD-III in an outpatient headache clinic in Sohag Governorate, Egypt