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Background <p>White matter hyperintensities (WMHs) are prevalent in migraine, yet their relationship to specific migraine features remains poorly defined. Prior studies have used coarse phenotyping and linear methods, leaving potential threshold effects and the broader migraine phenotype unexplored. We applied an explainable machine-learning framework to characterise associations between migraine features and WMH volume and to identify data-driven phenotypes.</p> Methods <p>Baseline data from two ongoing randomised controlled trials in Hong Kong Chinese women with episodic migraine were used for analyses. Participants underwent 3T brain magnetic resonance imaging (MRI) and WMH were quantified from manually corrected segmentations by radiologists. Migraine features (attack frequency, duration, pain intensity, aura subtypes, accompanying symptoms, triggers, and acute medication use) were derived from prospective three-month diaries and structured interviews. An XGBoost model was developed with nested five-fold cross-validation and interpreted using SHapley Additive exPlanations (SHAP). Unsupervised K-means clustering on patient-level SHAP values identified phenotypes.</p> Results <p>The analytic sample comprised 171 women (median age 52.0 years [IQR 38.5–60.0]) with a median WMH volume of 0.90 mL (IQR 0.56–1.31). The model explained modest variance (R² = 0.221) and achieved an area under the curve of 0.801 (95% CI 0.726–0.876) for classifying high WMH burden. SHAP dependence analyses revealed several non-linear patterns of migraine features on WMH. Attack frequency and duration showed apparent saturation, plateauing above approximately 5 attacks/month and 24&#xa0;h/month, respectively; pain intensity showed an L-shaped threshold pattern above a Numerical Rating Scale score of approximately 5. Somatosensory aura was associated with higher WMH volume. Three phenotypes emerged: Age-Dominant (<i>n</i> = 16; older, low burden, low WMH), Cumulative Risk (<i>n</i> = 59; older with high vascular and migraine burden, highest WMH volume), and Migraine-Dominant (<i>n</i> = 96; younger, migraine as the primary driver, intermediate WMH).</p> Conclusions <p>Explainable machine learning revealed threshold associations, including saturation patterns for attack frequency and duration and an L-shaped pattern for pain intensity, together with a pattern linking somatosensory aura to higher WMH volume, which may help reconcile longstanding discrepancies in the migraine-WMH literature. Findings from this study are hypothesis-generating and warrant validation in larger, longitudinal, and multi-ethnic cohorts.</p>

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Explainable machine learning reveals threshold associations and distinct phenotypes linking migraine features to white matter hyperintensities in women with episodic migraine

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年8月4日

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  • Qingling Yang,
  • Sen Deng,
  • Jing Qin,
  • Harry Haoxiang Wang,
  • Kin Cheung,
  • Yao Jie Xie

摘要

Background

White matter hyperintensities (WMHs) are prevalent in migraine, yet their relationship to specific migraine features remains poorly defined. Prior studies have used coarse phenotyping and linear methods, leaving potential threshold effects and the broader migraine phenotype unexplored. We applied an explainable machine-learning framework to characterise associations between migraine features and WMH volume and to identify data-driven phenotypes.

Methods

Baseline data from two ongoing randomised controlled trials in Hong Kong Chinese women with episodic migraine were used for analyses. Participants underwent 3T brain magnetic resonance imaging (MRI) and WMH were quantified from manually corrected segmentations by radiologists. Migraine features (attack frequency, duration, pain intensity, aura subtypes, accompanying symptoms, triggers, and acute medication use) were derived from prospective three-month diaries and structured interviews. An XGBoost model was developed with nested five-fold cross-validation and interpreted using SHapley Additive exPlanations (SHAP). Unsupervised K-means clustering on patient-level SHAP values identified phenotypes.

Results

The analytic sample comprised 171 women (median age 52.0 years [IQR 38.5–60.0]) with a median WMH volume of 0.90 mL (IQR 0.56–1.31). The model explained modest variance (R² = 0.221) and achieved an area under the curve of 0.801 (95% CI 0.726–0.876) for classifying high WMH burden. SHAP dependence analyses revealed several non-linear patterns of migraine features on WMH. Attack frequency and duration showed apparent saturation, plateauing above approximately 5 attacks/month and 24 h/month, respectively; pain intensity showed an L-shaped threshold pattern above a Numerical Rating Scale score of approximately 5. Somatosensory aura was associated with higher WMH volume. Three phenotypes emerged: Age-Dominant (n = 16; older, low burden, low WMH), Cumulative Risk (n = 59; older with high vascular and migraine burden, highest WMH volume), and Migraine-Dominant (n = 96; younger, migraine as the primary driver, intermediate WMH).

Conclusions

Explainable machine learning revealed threshold associations, including saturation patterns for attack frequency and duration and an L-shaped pattern for pain intensity, together with a pattern linking somatosensory aura to higher WMH volume, which may help reconcile longstanding discrepancies in the migraine-WMH literature. Findings from this study are hypothesis-generating and warrant validation in larger, longitudinal, and multi-ethnic cohorts.