Chiang, Chia‐Chun

White matter hyperintensities (WMHs) are common MRI findings and established markers of cerebral small-vessel disease associated with stroke, cognitive decline, and vascular risk factors. Several studies have suggested that individuals with migraine, particularly migraine with aura (MA), may have an increased burden of WMHs compared with individuals without migraine. Early population-based studies and meta-analyses reported a higher prevalence of WMHs in migraine, whereas more recent large neuroimaging cohorts have reported conflicting findings or no association. Differences in study design, migraine ascertainment, MRI acquisition parameters, WMH quantification methods, and adjustment for vascular risk factors complicate interpretation of the existing literature. An updated systematic review focusing on methodologically comparable population-based cohorts is therefore warranted. The primary objective of this systematic review and meta-analysis is to determine whether adults with migraine have a higher prevalence or burden of WMHs on MRI than non-migraine controls. Secondary objectives include evaluating associations according to migraine subtype (MA vs. migraine without aura and episodic vs. chronic migraine), sex, WMH location, age, and MRI acquisition or WMH quantification methods, as well as examining longitudinal WMH progression where available. We will include population-based cross-sectional or longitudinal cohort studies of adults undergoing brain MRI with sequences suitable for WMH detection. Migraine exposure may be defined using International Classification of Headache Disorders criteria, structured interviews, validated questionnaires, physician diagnosis, or ICD codes. Controls must originate from the same source population. Searches will be conducted in PubMed/MEDLINE, EMBASE, Web of Science, and Scopus. Study selection, data extraction, and risk-of-bias assessment will be performed independently by two reviewers using Covidence, and risk of bias will be evaluated using the ROBINS-E tool. Because WMH burden is reported using heterogeneous metrics across studies, the primary synthesis will use a precision-weighted direction-of-effect approach, in which each study contributes the direction of the association, weighted by statistical precision. Where sufficiently comparable outcomes are available, random-effects meta-analysis will be performed. Sensitivity analyses will explore the influence of diagnostic criteria, MRI field strength, and confounder adjustment. This review will provide an updated and methodologically rigorous synthesis of the evidence on the association between migraine and WMHs.

Aim To determine whether there is a difference in the time to complete a rapid number naming task during the interictal state compared to prodrome, migraine headache, postdrome phases of migraine and non-migraine headache, and characterize non-headache symptoms experienced during these phases. Methods This prospective longitudinal cohort study evaluated 30 adults with migraine with or without aura who completed diary entries and a timed rapid number naming task at least three times daily for up to four months from October 2020 to July 2022 at a tertiary headache center. Diary entries included self-report of current migraine phase (having a migraine headache, non-migraine headache, prodrome, postdrome, or interictal baseline) and detailed symptoms experienced. The outcome of interest for the timed rapid number naming task was the time to complete the task with zero errors. Results Compared to the interictal baseline, the time with zero errors for the rapid number naming task was significantly slower during the prodrome (median (Q1, Q3) 1.0 s slower (0.4, 2.9), p -value 0.010), migraine headache (median (Q1, Q3) 3.8 s slower (2.6, 7.5), p -value <0.001), and postdrome (median (Q1, Q3) 0.9 s slower (0.2, 2.1), p -value 0.011) phases of migraine, and non-migraine headache attacks (median (Q1, Q3) 1.2 s slower (0.1, 3.1), p -value 0.006). For those with migraine with aura (N = 15), the time with zero errors was significantly slower during all phases compared to interictal phase. For those with migraine without aura (N = 15), the time with zero errors was significantly slower only during the migraine headache, but not other phases compared to the interictal phase. The top 5 most reported non-headache symptoms during any phase of migraine and non-migraine headache attacks included fatigue, neck stiffness, light sensitivity, blurred vision/vision changes, and difficulty concentrating. Conclusions Among the overall participant cohort, the time with zero errors to complete a rapid number naming task was significantly slower during all ictal phases compared to interictal phase. This was also the case in the migraine with aura cohort but in the migraine without aura cohort there was only a significant difference during the migraine headache phase versus interictal phase. There is overlap in non-headache symptoms between the prodrome, migraine headache, postdrome, and non-migraine headache phases, which may make distinguishing between these phases difficult. Combining subjective data, such as symptom diaries, with objective measures, such as rapid number naming tasks, may allow for more refined assessment of migraine phases and distinguishing migraine from non-migraine headache attacks.

Abstract Objective To investigate the ability of artificial intelligence‐enabled electrocardiogram (AI‐ECG) atrial fibrillation (AF) prediction model output and delta age, defined as AI‐estimated age minus chronological age, to predict adverse vascular outcomes in patients with migraine. Background Migraine, especially migraine with aura, is associated with an increased risk of cardiovascular events. Using normal sinus rhythm ECG, our institution has developed AI‐ECG models that predict the probability of AF and estimate patients' age, and from that, delta age (estimated age minus actual age) has been shown to reflect cardiovascular health and physiologic aging. Methods We conducted a retrospective cohort study that included adult patients with migraine who had at least one digital, standard 12‐lead ECG from 2000 to 2020 at any Mayo Clinic site. The first normal sinus rhythm ECG was defined as the index ECG, and those with adverse vascular events or AF prior to, or within 3 days after, index ECG were excluded. AI‐ECG AF prediction model output and delta age were obtained from the index ECG. The primary outcome was a composite endpoint of adverse vascular events that included acute myocardial infarction, acute ischemic stroke, venous thromboembolism, and death. New‐onset AF was also recorded as a secondary outcome. Results A total of 29,928 patients were included. The mean age was 44.3 ± 14.5 years, and the median follow‐up time was 54 months. During the study period, 4662 (15.6%) patients had adverse vascular events, and 1384 (4.6%) patients developed new‐onset AF. A higher AF prediction model output was associated with a higher risk of adverse vascular outcomes (adjusted hazard ratio [aHR]:1.15 [95% confidence interval: 1.12 to 1.18] for every 10% increase in prediction model output), as was a larger delta age (aHR:1.16 [1.12 to 1.21] for every 10‐year increase in delta age). Moreover, AI‐ECG AF prediction model outputs of ≥1% (aHR: 1.45 [1.36 to 1.54]) and delta age ≥−1 years (aHR: 1.09 [1.02 to 1.16]) were found to be the optimal cutoffs to dichotomize patients into high‐risk and low‐risk groups for adverse vascular events. Higher AI‐ECG AF prediction model output at baseline also predicted a higher likelihood of having new‐onset AF during the study period (aHR: 1.31 [1.26 to 1.37]), and the development of new‐onset AF was associated with a higher risk of adverse vascular events (aHR: 2.43 [2.17 to 2.73]). Conclusions For patients with migraine, a higher AI‐ECG AF prediction model output and delta age are predictive of adverse vascular outcomes. A higher AF prediction model output is also predictive of future AF development. Furthermore, we identified the cutoffs for both AI‐ECG models that can distinguish patients between high‐ and low‐risk groups. Our results showed that AI‐ECG models could inform clinical practice and identify patients with migraine at risk for future adverse vascular events.