Purpose of reviewIn this review, we illustrate and discuss the recent findings regarding the epidemiology and pathophysiology of migraine triggers and their implications in clinical practice.Recent findingsData from the literature suggest that individual triggers fail to provoke migraine attack in experimental settings. It is therefore possible that more triggers acting in combination are needed to induce an attack by promoting some degree of brain dysfunction and thus increasing the vulnerability to migraine. Caution is however needed, because some of the factors rated as triggers by the patients may actually be a component of the clinical picture of migraine attacks.SummaryTrigger factors of migraine are endogenous or exogenous elements associated with an increased likelihood of an attack in a short period of time and are reported by up to 75.9% of patients. Triggers must be differentiated from premonitory symptoms that precede the headache phase but do not have a causative role in attack provocation, being rather the very first manifestations of the attack. Identification of real triggers is an important step in the management of migraine. Vice versa, promoting an active avoiding behaviour toward factors whose role as triggers is not certain would be ineffective and even frustrating for patients.

OnabotulinumtoxinA (BonT-A) reduces migraine frequency in a considerable portion of patients with migraine. So far, predictive characteristics of response are lacking. Here, we applied machine learning (ML) algorithms to identify clinical characteristics able to predict treatment response. We collected demographic and clinical data of patients with chronic migraine (CM) or high-frequency episodic migraine (HFEM) treated with BoNT-A at our clinic in the last 5 years. Patients received BoNT-A according to the PREEMPT (Phase III Research Evaluating Migraine Prophylaxis Therapy) paradigm and were classified according to the monthly migraine days reduction in the 12 weeks after the fourth BoNT-A cycle, as compared to baseline. Data were used as input features to run ML algorithms. Of the 212 patients enrolled, 35 qualified as excellent responders to BoNT-A administration and 38 as nonresponders. None of the anamnestic characteristics were able to discriminate responders from nonresponders in the CM group. Nevertheless, a pattern of four features (age at onset of migraine, opioid use, anxiety subscore at the hospital anxiety and depression scale (HADS-a) and Migraine Disability Assessment (MIDAS) score correctly predicted response in HFEM. Our findings suggest that routine anamnestic features acquired in real-life settings cannot accurately predict BoNT-A response in migraine and call for a more complex modality of patient profiling.

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