The influence of weather on migraine - are migraine attacks predictable?

Abstract

Objective: The study aimed at elucidating a potential correlation between specific meteorological variables and the prevalence and intensity of migraine attacks as well as exploring a potential individual predictability of a migraine attack based on meteorological variables and their changes.

Methods: Attack prevalence and intensity of 100 migraineurs were correlated with atmospheric pressure, relative air humidity, and ambient temperature in 4-h intervals over 12 consecutive months. For each correlation, meteorological parameters at the time of the migraine attack as well as their variation within the preceding 24 h were analyzed. For migraineurs showing a positive correlation, logistic regression analysis was used to assess the predictability of a migraine attack based on meteorological information.

Results: In a subgroup of migraineurs, a significant weather sensitivity could be observed. In contrast, pooled analysis of all patients did not reveal a significant association. An individual prediction of a migraine attack based on meteorological data was not possible, mainly as a result of the small prevalence of attacks.

Interpretation: The results suggest that only a subgroup of migraineurs is sensitive to specific weather conditions. Our findings may provide an explanation as to why previous studies, which commonly rely on a pooled analysis, show inconclusive results. The lack of individual attack predictability indicates that the use of preventive measures based on meteorological conditions is not feasible.

Figure 1

Distribution of migraine events. (A) Number of migraine events for each study participant. (B) Distribution of migraine events over all recorded time points. Stacks indicate migraine intensities with 1 representing the lowest and 5 the highest pain intensity.

Figure 2

Overall distribution of P-values. The figure shows the distribution of the P-values calculated for all 100 patients. The horizontal line displays the expected number of P-values for a uniform distribution between 0 and 1. Skewing toward smaller P-values indicates a significant association.

Figure 3

Clustering of patients with similar weather sensitivity. The rows of the illustrated heat map refer to patients, columns to weather variables (k = 21; pressure: 1–7, temperature: 14–20, humidity: 27–33). Dark red color refers to a strong association between weather data and migraine events.