Frailty and migraine are significant health burdens, yet their life-course relationship remains poorly defined. Identifying predictors of migraine risk, particularly in aging populations, is crucial for developing targeted preventive strategies. This multi-level study leveraged data from the Global Burden of Disease Study (GBD, 1990–2017), the US National Health and Nutrition Examination Survey (NHANES, 1999–2004), and the China Health and Retirement Longitudinal Study (CHARLS, 2011–2018). We employed spatial and cross-sectional analyses to identify the global patterns and individual-level associations, and conducted a longitudinal cohort analysis to assess cumulative migraine risk by frailty index (FI) category. Age–FI interaction effects were examined using a Linear Generalized Additive Model (LinearGAM), and longitudinal associations were evaluated with CausalForest analysis. A significant negative correlation was observed in adults aged ≥ 70 years, with age acting as an important effect modifier. The association between FI and migraine showed considerable heterogeneity across age groups. Even after full adjustment, frail individuals had significantly higher migraine prevalence. Longitudinal assessment confirmed a substantially elevated cumulative risk of migraine in frail participants. Machine learning analysis further identified a sharp increase in migraine risk once FI exceeded the exploratory threshold of 0.6. Frailty influences migraine through complex mechanisms, with age serving as a critical effect modifier. The FI is a potential, scalable predictor of migraine risk. Incorporating frailty assessment could refine prevention strategies, particularly in younger populations where migraine incidence is rising.
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- Article
Frailty Index as a Predictor of Migraine Risk Across the Life Course: A Multi-Level Study from Global Spatial Patterns to Individual Longitudinal Associations
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Received: 10 May 2026 | Revised: 02 Sep 2026 | Accepted: 07 Sep 2026 | Published: 16 Sep 2026
Abstract
Keywords
frailty index | migraine | age | multi-level study | machine learning
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