Background: Irritable bowel syndrome (IBS) is an under-diagnosed common health problem that impairs quality of life. Migraine and IBS are comorbid disorders that are triggered by foods. We aim to investigate IBS frequency in medication overuse headache (MOH) patients and identify food triggers and food avoidance behavior. Methods: Participants who completed the cross-sectional, observational and online survey were included (n = 1118). Demographic data, comorbid disorders, medications used, presence of headache, the diagnostic features of headache and IBS, migraine related subjective cognitive symptoms scale (MigSCog), consumption behavior of patients regarding 125 food/food additives and food triggers were asked about in the questionnaire. Results: Migraine and MOH diagnoses were made in 88% and 30.7% of the participants, respectively. Non-steroidal anti-inflammatory drugs (NSAIDs) were the main overused drug (89%) in MOH patients. IBS symptoms were present in 35.8% of non-headache sufferers, 52% of migraine patients and 65% of MOH patients. Specific food triggers for MOH patients were dopaminergic and frequently consumed as healthy foods such as banana, apple, cherry, apricot, watermelon, olive, ice cream and yogurt. MigSCog scores were significantly higher in episodic migraine and MOH patients when IBS symptoms coexisted. Conclusions: The frequency of IBS was higher in MOH patients compared to migraine patients. Coexistence of IBS seems to be a confounding factor for cognitive functions. MOH specific triggers were mostly dopaminergic foods, whereas migraine specific food triggers were mostly histaminergic and processed foods. Personalized diets focusing on food triggers and interference with leaky gut must be integrated to MOH and migraine treatment to achieve sustainable management of these disorders.

Purpose of Reviews: Headaches represent a prevalent and burdensome health condition, affecting individuals of all ages worldwide. While dietary factors have been implicated in headache pathophysiology, the association between dairy consumption and headaches remains controversial and inadequately understood. This comprehensive review systematically examines the existing literature to elucidate the relationship between dairy intake and headaches, addressing methodological challenges, potential biases, and gaps in the current knowledge. Recent Findings: A thorough search of electronic databases identified relevant observational studies, clinical trials, and mechanistic investigations exploring the impact of dairy consumption on headache incidence, frequency, severity, and duration. Methodological considerations, including study design, measurement of exposure and outcome variables, confounding factors, and sources of bias, were critically evaluated to assess the strength of evidence and validity of findings. Despite heterogeneity across studies, emerging evidence suggests a complex and multifaceted relationship between dairy intake and headaches, influenced by individual characteristics, dietary patterns, headache subtype, and study context. While some studies report a positive association between dairy consumption and headaches, others indicate no significant effect or potential therapeutic benefits of dairy restriction. Mechanistic insights suggest plausible biological mechanisms, including neuroinflammatory pathways, neurotransmitter modulation, vascular effects, and gut-brain interactions, which may mediate the observed associations. Summary: Future research directions encompass longitudinal studies, mechanistic investigations, stratified analyses, randomized controlled trials, and exploration of the gut microbiota to further elucidate the underlying mechanisms and inform evidence-based dietary recommendations for headache management. This integrative review underscores the importance of interdisciplinary collaboration and personalized approaches to address the complex interplay between diet, headaches, and overall health.

While triggering by physical activity is a criterion for the diagnosis of migraine, evaluations regarding fear of movement related to physical activity are lacking in the literature. This study aimed to investigate the validity and reliability of the Turkish version of the Tampa Scale of Kinesiophobia, adapted to headache (TSK-H), in patients with episodic migraine (EM) and chronic migraine (CM). This questionnaire is a newly developed version adapted for headache, based on but different from the original TSK used in low back pain, and has not been previously validated in Turkish for headache. Patients between the ages of 18–70 were included in the study. Demographic-clinical information was obtained after expert committee evaluation and cross-cultural adaptation of the questionnaire. Numerical Pain Rating Scale (NPRS), Migraine Disability Assessment (MIDAS), Headache Impact Test-6 (HIT-6), Hospital Anxiety and Depression Scale (HADS) and TSK-H were evaluated for structural validity. Exploratory Factor Analysis (EFA) and Confirmatory Factor Analysis (CFA) were performed. Intraclass Correlation Coefficient (ICC) and Cronbach’s α values ​​were analyzed for reliability. A total of 329 patients (n = 148 EM, n = 181 CM) with a mean age of 37.2 ± 10.6 years for EM and 39.8 ± 13.1 years for CM were included in the study. Headache intensity on NPRS was 6.89 ± 1.86 for EM and 7.56 ± 2.10 for CM. Item-total correlations ranged from − 0.030 to 0.758. Cronbach’s α was 0.925. TSK-H had good test-retest reliability (ICC = 0.866, n = 151) and high internal consistency (Cronbach’s α = 0.928). TSK-H was associated with NPRS, MIDAS, HIT-6 and HADS (p < 0.000 for each one). EFA identified a four-factor structure explaining 58.64% of the variance. CFA remained within acceptable limits. The Turkish version of the 23-item TSK-H is a valid and reliable tool for assessing kinesiophobia in patients with migraine and is significantly associated with physical, functional and psychosocial factors.

Introduction: Migraine is a common neurological disease with a variety of sensory symptoms including pain, visual disturbances, nausea, vomiting, dizziness, hypersensitivity to light, sound and smell. Migraine patients can perceive some sensory signals as harmful and stressful due to differences in sensory processing. These signals can be a source of stress and anxiety for migraine patients. Related to these, the aim of our study was to examine the relationship between sensory profiles and pain characteristics and anxiety and depression findings in migraine patients. Materials and Methods: A total of 28 female migraine patients were evaluated for pain characteristics, sensory modulation with the Sensory Profile Questionnaire, and depression and anxiety with Anxiety and Depression Scale. In order to investigate the relationships between these variables, correlation analyses were conducted. Results: Our results suggest that migraine patients may have sensory processing disorders due to differences in their sensory profiles. Sensory profile scores representing sensory processing of migraine patients were found to be different from norm values. Migraine that starts at a younger age and lasts longer is associated with sensory processing disorders. In addition, patients with higher sensory sensitivity and sensory avoidance have more anxiety. Conclusion: Our study results will shed light on treatments that holistically address the physical, cognitive, and emotional factors of migraine patients depending on the relationships between sensory processing, anxiety and depression states and pain characteristics. Keywords: Migraine, sensory processing, pain, anxiety, depression

Migraine can impair productivity, work performance, and daily functioning in working-age adults. While higher Central Sensitization Inventory (CSI) scores have been observed in migraine chronicity, work-related stress, and musculoskeletal pain are also common among desk-based workers and may play a role in exacerbating migraine symptoms. However, these factors have rarely been studied together in this population. This cross-sectional study aimed to compare central sensitization, work-related stress, and musculoskeletal pain symptoms in desk-based workers with and without migraine, to examine the relationships among them, and to identify the determinants of central sensitization in individuals with migraine. This cross-sectional study included 228 desk-based workers: a control group (CG) without migraine (n = 84), episodic migraine (EM) (n = 74), and chronic migraine (CM) (n = 70). Participants completed the CSI, General Work Stress Scale (GWSS), Nordic Musculoskeletal Questionnaire (NMQ), Migraine Disability Assessment (MIDAS), and Headache Impact Test-6 (HIT-6). Group comparisons were performed using ANOVA or Kruskal-Wallis tests. Spearman coefficients were used in correlation analyses. Multiple linear regression was applied to identify factors associated with CSI. Both migraine groups scored higher on the CSI compared to the CG (p < 0.001), with CM showing the highest CSI scores. This difference remained significant after adjusting for confounders. GWSS scores were also higher in both migraine groups than the CG (p < 0.001) and remained significant after adjustment. NMQ;3-items were significantly higher in the migraine groups compared to the CG (p ≤ 0.005), independent of confounders. In the migraine groups, higher CSI scores showed a positive correlation with GWSS, MIDAS, HIT-6, and NMQ. In multiple linear regression analysis, GWSS (β = 0.380), number of painful body regions during the past 12 months (β = 0.382), MIDAS (β = 0.230), and the number of headache days per month (β = 0.170) were identified as variables independently associated with CSI scores (all p < 0.05). Desk-based workers with migraine had higher central sensitization, greater work-related stress, and more widespread musculoskeletal pain symptoms compared to those without migraine. In desk-based workers with migraine, central sensitization was more pronounced in CM than EM, suggesting a potential role in migraine chronicity. These findings support multifaceted management strategies for desk-based workers with migraine, including headache-stress management, musculoskeletal rehabilitation, and Sustainable Development Goal-3, "to ensure healthy lives and promote well-being for all ages." NCT07554664 (registration date: 21.04.2026).

Background/Hypothesis: Differentiating primary from secondary headaches remains challenging due to overlapping symptoms and the subjective nature of pain assessment. Machine learning (ML) combined with explainable artificial intelligence (XAI) offers a promising approach for improving diagnostic accuracy while maintaining clinical interpretability. The study aimed to develop an interpretable ML model to differentiate primary from secondary headaches and to identify the relative diagnostic contributions of individual clinical features. Methods: This cross-sectional study analyzed 1219 patients using routine clinical history variables, including 21 triggers, 15 associated symptoms, temporal patterns, pain intensity (visual analog scale [VAS]), and demographic factors. Data were obtained from the Global Migraine and Pain Society (GMPS) registry and the epikriz.com/headache platform, restricted to patients managed by specialist headache neurologists at two headache clinics in Türkiye. The binary classification outcome was clinically assigned headache label: primary versus secondary headache disorder (primary headache designated as the positive class). An eXtreme Gradient Boosting (XGBoost) classifier was developed with Random Over-Sampling Examples to address class imbalance, which was applied exclusively to the training partition. Model interpretability was achieved using SHapley Additive exPlanations (SHAP) and Local Interpretable Model-agnostic Explanations (LIME). Results: The model demonstrated excellent discrimination (AUC = 0.905), with sensitivity of 90.8%, specificity of 87.5%, precision of 99.7%, and an F1-score of 0.950. Associated symptom burden was the strongest predictor of primary headache classification (mean absolute SHAP = 1.075), driven principally by nausea, phonophobia, and photophobia. Trigger burden ranked second (mean absolute SHAP = 0.790), with stress, hunger, and odors most prevalent. Two laterality features independently discriminated headache type: strictly unilateral side-shifting (mean absolute SHAP = 0.545) and unilateral-to-bilateral transition (mean absolute SHAP = 0.481), both favoring primary classification. Higher diurnal pattern scores were associated with secondary headache disorders (mean absolute SHAP = 0.257). VAS contributed minimally (mean absolute SHAP = 0.101), suggesting that the syndromic profile outperforms pain severity as a diagnostic discriminator in this cohort. Conclusion/Interpretation: In this cohort, an ML model incorporating trigger profiles, associated symptoms, and temporal headache characteristics demonstrated good discrimination between clinically assigned primary and secondary headache labels. Associated symptom burden and trigger burden were more influential in model classification than pain intensity. This finding aligns with clinical practice, where headache diagnosis relies on symptom patterns and triggers rather than pain intensity alone. XAI frameworks may help make such classification models more clinically interpretable, although further external validation is required.