Cephalalgia : an international journal of headache · 2026
Prediction of migraine treatment response with clinical and genetic data: A machine learning analysis of the HUNT study.
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Abildsnes Håkon Kvisle, Danelakis Antonios, Giri Samita, Giles Dominic, Matharu Manjit, Nachev Parashkev, Bjørk Marte Helene, Winsvold Bendik S, Hagen Knut, Børte Sigrid, Vidal Joan Crespi, Tronvik Erling, Stubberud Anker
Аннотация
AIM: Migraine is highly heritable, yet no machine learning models for predicting treatment responses have incorporated genetic data. We aimed to develop machine learning models including both genetic and clinical information to predict treatment responses of migraine preventives and treatment retention of triptans.
METHODS: This was a cross-sectional population-based machine learning analysis of the Trøndelag Health Studies and the national prescription registry. Clinical predictors included demographics, serum metabolic measures, comorbidities, mental health and lifestyle factors. Genetic predictors included 108 migraine risk loci. Preventive response was defined as ≥50% reduction in triptan dispensations after first preventive dispensation, and triptan retention as ≥3 dispensations. The dataset was split into training (70%), validation (10%) and test set (20%). A series of standard and causal machine learning architectures were trained and optimized on the training and validation set, and the best model was evaluated on the held-out test data. Shapley Additive exPlanation values identified key predictors.
RESULTS: In total, 475 participants tried a preventive (amitriptyline, beta-blocker, candesartan or topiramate) and 565 a triptan (371 sumatriptan) during the project period. Using only clinical features, the best models achieved area under curve of 0.56-0.69 for the preventives, and 0.56 for triptans. When combining clinical and genetic data, the area under curve was unchanged or reduced in all cases except for amitriptyline (0.80) and sumatriptan (0.55). The most important predictors were hormone-related features, lipid levels and hemodynamic parameters.
CONCLUSION: We developed models that predicted treatment responses to amitriptyline, beta-blockers, candesartan and topiramate with modest accuracy. Incorporating genetic data did not enhance performance, although the limited sample size and the uncertain accuracy of cohort definition and treatment response assessment prevent firm conclusions.
METHODS: This was a cross-sectional population-based machine learning analysis of the Trøndelag Health Studies and the national prescription registry. Clinical predictors included demographics, serum metabolic measures, comorbidities, mental health and lifestyle factors. Genetic predictors included 108 migraine risk loci. Preventive response was defined as ≥50% reduction in triptan dispensations after first preventive dispensation, and triptan retention as ≥3 dispensations. The dataset was split into training (70%), validation (10%) and test set (20%). A series of standard and causal machine learning architectures were trained and optimized on the training and validation set, and the best model was evaluated on the held-out test data. Shapley Additive exPlanation values identified key predictors.
RESULTS: In total, 475 participants tried a preventive (amitriptyline, beta-blocker, candesartan or topiramate) and 565 a triptan (371 sumatriptan) during the project period. Using only clinical features, the best models achieved area under curve of 0.56-0.69 for the preventives, and 0.56 for triptans. When combining clinical and genetic data, the area under curve was unchanged or reduced in all cases except for amitriptyline (0.80) and sumatriptan (0.55). The most important predictors were hormone-related features, lipid levels and hemodynamic parameters.
CONCLUSION: We developed models that predicted treatment responses to amitriptyline, beta-blockers, candesartan and topiramate with modest accuracy. Incorporating genetic data did not enhance performance, although the limited sample size and the uncertain accuracy of cohort definition and treatment response assessment prevent firm conclusions.