Iron concentration in the ventral pallidum of chronic migraine with medication-overuse headache

BACKGROUND: Chronic migraine (CM) imposes a significant disease burden and frequently co-occurs with medication-overuse headache (MOH). MOH is a secondary headache disorder occurring especially in individuals with primary migraine and associated with excessive analgesic use. Impaired iron homeostasis may be related to the medication overuse behavior, however, its' pathophysiology is complex and incompletely understood. This study examines regional iron deposition in the subcortical nuclei in two comparisons: (1) controls versus chronic migraine patients; and (2) CM patients with medication-overuse headache (wMOH) versus those without MOH (woMOH). Furthermore, it investigates the association between iron deposition and disease outcomes. METHODS: We recruited 38 controls and 72 CM patients (40 wMOH, 32 woMOH). All participants underwent MRI and assessments of migraine duration, analgesic use, and health metrics. CM patients with MOH were further evaluated for one-year relapse rates and Severity of Dependence Scale scores. Quantitative susceptibility mapping (QSM) was used to measure iron content in subcortical regions, and correlations with clinical variables. ROC curve analysis was performed to evaluate the QSM's predictive value for MOH relapse. RESULTS: CM patients with MOH had lower iron content in the ventral pallidum (VeP, 0.178 ± 0.112 ppm vs. 0.183 ± 0.082 ppm, p < 0.001) compared to those without MOH. Notably, VeP iron content negatively correlated with analgesic excessive use (r = -0.557, p < 0.001) and relapse rates (r = -0.360, p < 0.05). ROC analyses demonstrated significant discriminative capacity for relapse prediction (VeP: AUC = 0.900, 95% CI [0.773-0.999], p < 0.001) in wMOH. CONCLUSIONS: The reduced iron in subcortical nuclei, especially the VeP, relates to analgesic excessive use and relapse in MOH. This insight enhances our understanding of MOH's neurobiological basis and supports the potential development of QSM-based biomarkers for relapse risk prediction.