Evidence supporting the role of hypertension in the onset of migraine

Abstract

Background

The association between hypertension and migraine remains unclear.

Objective

The aim of this study employ multi-layered evidence chain that revealed the association between hypertension and migraine.

Methods

We first strictly included data from the NHANES 1999–2004 population and applied logistic regression, subgroup analysis and RCS to assess the correlation between hypertension, SBP, DBP and migraine. Meanwhile, LDSC and Mendelian randomization were conducted based on the GWAS to determine the causal relationship between hypertension and migraine. Inverse-variance weighted (IVW) was used as the primary method. Sensitivity analysis and Colocalization analysis were performed to confirm the robustness of the results. LDSC validated the genetic correlation between traits. Enrichment analysis revealed their underlying biological mechanisms.

Results

After strict inclusion in NHANES, 10,743 participants were included. The logistic regression showed a significant correlation between hypertension (OR = 1.21 [95% CI, 1.08–1.36], FDR < 0.001)、DBP (OR = 1.01 [95% CI, 1.01–1.02], FDR < 0.001) and migraine. This association did not show significant group differences in subgroup. The MR results further supported the existence of a significant causal relationship between hypertension (OR = 1.77 [95% CI, 1.43–2.30], FDR < 0.001)、DBP (OR = 1.02 [95% CI, 1.01–1.03], FDR < 0.001) and migraine onset. Additionally, the RCS analysis showed a linear relationship (P non-linear = 0.897) between the two. The LDSC result showed a significant genetic correlation between the two (Rg = 0.1092, SE = 0.028, P < 0.001).

Conclusion

The development of migraine caused by hypertension is mainly realized through high DBP.

Introduction

Migraine is a neurovascular brain disease characterized by recurrent headaches, well-known for its high disability and severe impairment of social function. According to relevant studies, it affects more than one billion people worldwide, with the largest gender difference in prevalence, 17% in women and 8.6% in men [1]. The global burden of migraine has been increasing year by year since 1990, and the age-standardized annual incidence rate of migraine increased to 1142.5/100,000 in 2019 [2]. The pathogenesis of migraine is still unclear, and its pathogenesis and progression are multifactorial, with the main risk factors including genetics, hypertension, dietary environment, sleep, and psychological factors [3, 4]. Therefore, the specific pathogenesis of migraines remains to be explored.

Epidemiological studies have demonstrated a high comorbidity between hypertension and migraine [5], but their relationship remains uncertain. Multiple cross-sectional studies have shown that the probability of combined hypertension in migraine patients is significantly higher than that in non-migraine patients, and it is positively correlated [6]. A prospective study led by C F Fagernæs et al. showed a negative correlation between hypertension and headache [7]. A review conducted by Yen-Feng Wang summarized the research on the relationship between hypertension and migraine and found that: although the relationship between hypertension and migraine is far from conclusive, most studies have shown that there is a correlation between the two, especially between high diastolic blood pressure and migraine [8]. At the same time, some antihypertensive drugs have been shown to be effective in the treatment of migraine [9, 10]. However, due to the complex relationship between migraine and hypertension that coexist in the same individual, there are many confounding factors that interfere, and it is difficult to obtain evidence of a causal relationship between them due to the chronological order of disease development.

LDSC quantifies the contributions of polygenic effects and various confounding factors, such as population stratification, using summary statistics from GWAS, We use it to assess the genetic correlation between different traits [11, 12].

Mendelian randomization (MR) is utilized to investigate causal relationships between diseases [13]. The core principle of MR is based on Mendel’s second law of independent assortment, which states that alleles are distributed randomly during meiosis and are unaffected by environmental factors. This method provides robust supplementary evidence to observational studies [14].

To assess the impact of hypertension on migraine, we first used data from the NHANES to explore the association between hypertension and migraine by adjusting for potential confounding factors. Next, we employing MR, further evaluated the causal relationship between hypertension and migraine by eliminating confounding biases and reverse causation. Sensitivity analysis and co localization studies are used to ensure the robustness of MR. We investigated the genetic correlation between the two traits using Linkage LDSC. Finally, was conducted to elucidate the underlying molecular mechanisms and biological pathways linking these two disease phenotypes (Fig. 1).

Fig. 1

Flow of overall study design

Materials and methods

Overall study design

This study systematically evaluated the potential causal relationship between hypertension and migraine by combining observational research and Mendelian randomization. To validate the robustness of MR, we performed a series of sensitivity analyses and colocalization analyses. Genetic correlation between the two phenotypes was quantified using LD score regression (LDSC). Finally, pathway enrichment analysis based on multiple databases was conducted to elucidate the underlying molecular mechanisms and biological pathways linking these two disease phenotypes.

NHANES

Study population in NHANES

NHANES is a nationally representative database that surveys the health and nutritional status of the US population, primarily using a complex multistage probability sampling design. The survey interviews and examines approximately 5,000 people each year, and includes household interviews, physical examinations, and laboratory tests [15]. Ethical approval was granted by the NHANES Institutional Review Board (protocol number 98 − 12). All participants provided written informed consent, and NCHS obtained institutional review board approval to conduct the survey. Methodological details and the survey design of the NHANES can be found at https://www.cdc.gov/nchs/nhanes/index.htm.

We used public data from participants recruited in NHANES from 1998 to 2004, which provided data on migraine. After excluding missing data on blood pressure, migraine, and other covariates of interest collected through questionnaires, physical examinations, and laboratory tests, a total of 10,743 participants were included in the final analysis.

Assessment of migraine

Migraine diagnosis was assessed through self-report on the NHANES Miscellaneous Pain Questionnaire (MPQ), with question MPQ090 “In the past 3 months, have you had severe headaches or migraines?” serving as the primary diagnostic criterion. Individuals who answered “yes” to this question were considered to have migraine. Other diagnoses (such as tension-type headache or cluster headache) could be misclassified as migraine, but the IHC did not have the additional parameters needed to classify migraine. This assessment method is consistent with previous studies [16]. According to the American Migraine Prevalence and Prevention (AMPP) study, the majority of participants with severe headache had migraine [17].

Assessment of hypertension

The protocol for measuring blood pressure followed the procedures established by the American Heart Association. The measurement was performed using a mercury sphygmomanometer by trained health professionals. After 5 min of sitting rest, readings were obtained and repeated three times at 30-second intervals. A fourth measurement was taken if necessary. To avoid the effects of blood pressure variability, the mean of SBP and DBP was calculated and applied in this study. The 2003 American JNC-7 guideline recommends that individuals with an average SBP ≥ 140 mmHg and/or average DBP ≥ 90 mmHg be defined as hypertensive. Meanwhile, participants who answered “yes” to the question “Have you ever been told you have high blood pressure?” in the self-reported Blood Pressure & Cholesterol Questionnaire (BPQ) in NHANES were also classified as hypertensive.

Other covariates used in NHANES

To control for potential confounding effects, the following demographic characteristics were adjusted based on previous studies: age, sex, family income and poverty ratio (PIR), education level, race/ethnicity, smoking status, alcohol intake, dietary intake of sodium, potassium, and calcium, and diabetes, total cholesterol, triglycerides, glomerular filtration rate, creatinine, marital status, hemoglobin, BMI, waist circumference, and inflammatory markers. Smoking status was assessed as never smoker (< 100 cigarettes smoked), former smoker (currently not smoking but smoked ≥ 100 cigarettes), or current smoker (≥ 100 cigarettes and currently smoking every day or some days). Anyone who had at least 12 drinks in the past 12 months was defined as a drinker. Race/ethnicity (non-Hispanic white, Mexican American, non-Hispanic black, other Hispanic or other race/multiple races), education level (< high school/high school graduate/> high school). Body mass index (BMI) was calculated as weight (kg) divided by height (m) squared. Diabetes was diagnosed if any of the following criteria were met: (1) participants who self-reported a diagnosis of diabetes; (2) participants who injected insulin; (3) fasting plasma glucose (FPG) level ≥ 126 mg/dL (7.0 mmol/L); (4) glycated hemoglobin (HbA1c) level ≥ 6.5% (47.5 mmol/mol); (5) use of antidiabetic medications [18]. Estimated glomerular filtration rate (eGFR) was calculated using a formula developed by the Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) [19].

Statistical analysis

Categorical variables will be presented as percentages, and continuous variables will be presented as mean ± standard deviation (SD). Pearson’s chi-square test was used to compare categorical variables between the study groups. Numerical variables were compared using the Kruskal–Wallis test and the Mann–Whitney U test. The significance level for the difference between the two groups was set at p < 0.001. Weighted multivariate regression analysis was then used to examine the associations between hypertension, SBP, DBP, and migraine. An unadjusted model was first established, followed by a model adjusted for age, sex, race, marital status, education level, PIR (poverty), smoking and drinking status, BMI, waist circumference, diabetes status, and physical activity. Model 3 further adjusted for serum total cholesterol (TC), dietary sodium, dietary potassium, dietary calcium, creatinine, hemoglobin, and glomerular filtration rate on the basis of Model 2. Results are presented as odds ratios (ORs) or b coefficients (95% confidence interval [CI]). Finally, the false discovery rate (FDR) was used to perform multiple corrections for the P values of the regression model to reduce false positives, with the significance level set at FDR < 0.05 [20]. Then, subgroup analysis was performed based on categorical variables to assess the robustness of the correlation between DBP and migraine. RCS (Restricted cubic splines) were used to explore the nonlinear relationship between DBP and migraine. Considering the complex probability cluster design of NHANES, weights were incorporated into the statistical analysis of this study. Due to the 4-year weight for the period 1999–2002 (compared to the 2-year weight, samples collected over 4 years include a larger population). Therefore, according to the NHANES guidelines, we used a 6-year weight = 2/3 the 4-year weight for the period 1999–2002 + 1/3 the 4-year weight for the period 2003–2004. All analyses were performed using R software (version 4.1.3).

Mendelian randomization

Mendelian randomization study design

A two-sample Mendelian randomization (MR) analysis was conducted to investigate the causal relationship between hypertension and migraine using genetic predictions. MR analysis relies on three key assumptions: (1) The instrumental variable (IV) used in the analysis is significantly associated with the exposure (relevance assumption); (2) The IV is not associated with any known or unknown confounders (independence assumption); (3) The IV only affects the outcome through the risk factor and not through any other direct causal pathways (exclusion restriction assumption) [21].

Exposure and outcome data

The genetic instruments for hypertension used in this study were obtained from the GWAS of the MRC Integrative Epidemiology Unit (MRC-IEU) consortium, which included data from 462,933 individuals (119,731 cases and 343,202 controls) of European descent. The genetic variants for systolic and DBP were derived from GWAS of the International Consortium for Blood Pressure (ICBP), which included 757,601 participants [22]. The aforementioned GWAS data can be obtained from the IEU OpenGWAS database (https://gwas.mrcieu.ac.uk/) (Table 1).

Table 1 Details of the GWASs included in the mendelian randomization

The GWAS summary statistics for migraine were mainly derived from the FinnGen research project (https://www.finngen.fi/fi), which originated from the Finnish national biobank network and recruited participants from 2017 to 2023. It included 333,711 participants of European descent (20,908 cases and 312,803 controls). The inclusion and exclusion criteria for participants were determined based on ICD-10 codes of the primary diagnosis during hospitalization. Each participant signed an informed consent form provided by the Finnish Biobank Act. The FinnGen study protocol was approved by the Helsinki and Uusimaa Hospital District Coordinating Ethics Committee (HUS) (number HUS/990/2017) [23] (Table 1).

Instrumental variable selection

Only SNPs that satisfy the above three assumptions will be considered as instrumental variables (IVs). This will ensure that all genetic variants used in the analysis are associated with the exposure, not with the outcome, and are not affected by confounding factors. The following are the screening criteria: (1) The P-value threshold for IV selection is (P < 5 × 10e-8), LD proxies are defined using data from 1000 European genomes; (2) A stringent linkage disequilibrium (LD) threshold is applied (R2 < 0.001, LD region width = 10000 kb), Further quality control is based on a minor allele frequency > 1%; (3) The F statistic for all SNPs is ensured to be greater than 10 based on the formula F = (beta/se)^2 to avoid bias caused by weak instrumental variables [24]. Incompatible SNPs are removed in the effect alleles of GWAS that coordinate exposure and outcome data, and palindromic SNPs with medium allele frequencies are also removed.

Statistical analysis and sensitivity analysis

In this study, Cochrane Q test was used to assess the potential heterogeneity between the estimates of each SNP. If the test result showed P value < 0.05, the RadialMR method was used to remove the outliers that may lead to the heterogeneity of the MR results [25]. At the same time, the Egger-intercept and PRESSO tests were used to test whether the Mendelian randomization assumptions were violated due to the existence of horizontal pleiotropy. If the MR analysis showed obvious horizontal pleiotropy, the MR-PRESSO method was used to remove all outliers to correct for horizontal pleiotropy [26]. In addition, the leave-one-out method was used to remove SNPs that had a large impact on the causal association effect of MR by a single SNP [27]. The MR Steiger’s directional test was used to test the causal direction by comparing the difference in the variance explained by the variants in hypertension and migraine [28]. Finally, 189 SNPs for hypertension, 373 and 386 SNPs for systolic and DBP were included as instrumental variables in the final study.

In this study, we applied MR Egger, Weighted median, Inverse variance weighted (IVW), Simple mode and Weighted mode to assess the causal relationship between hypertension, SBP, DBP and migraine. IVW can combine multiple genetic variants to obtain an overall causal estimate [29]. The false discovery rate (FDR) was applied to the P values of the main inverse-variance weighted Mendelian randomization analysis to perform multiple testing, thereby reducing the possibility of false positive results [30]. Through the above research tests, if the IVW test showed FDR < 0.05, and the effect values of the other four methods were consistent with its direction, it indicated that there was a causal relationship between the exposure and outcome in the MR analysis [31].

All statistical analyses in this study were performed using the R software packages “TwoSample MR” (version 0.5.6), “PRESSO”, “RadialMR” (version 1.1) and so on.

Linkage disequilibrium score regression

LDSC study design and process

LDSC is a method based on GWAS summary statistics used to investigate genetic correlations, primarily to elucidate the relationship between complex traits and genetic variation. It evaluates the genetic correlation between two traits by regressing LD scores against Z scores from GWAS summary statistics [11, 12]. This method is particularly widely used in genetic studies of neurological and psychiatric disorders. Information from European ancestry individuals in the 1000 Genomes Project was used as the LD reference panel [32]. The analysis was primarily conducted using LDSC (LD Score) v1.0.1 software (https://github.com/bulik/ldsc/?tab=readme-ov-file#ldsc-ld-score-v101).

Colocalization analysis

Colocalization study design and process

Colocalization analysis is based on four hypotheses: H0: Phenotype 1 (GWAS) and Phenotype 2 (QTL or GWAS) are not significantly associated with any SNPs in a specific genomic region; H1/H2: Phenotype 1 or Phenotype 2 is significantly associated with SNPs in a specific genomic region; H3: Phenotype 1 and Phenotype 2 are significantly associated with SNPs in a specific genomic region, but are driven by different causal variants; H4: Phenotype 1 and Phenotype 2 are significantly associated with SNPs in a specific genomic region, and are driven by the same causal variant. Based on these hypotheses, we focus primarily on hypothesis H4, as a higher statistical probability (PH > 0.80) strengthens the explanation of how significant signal loci influence the phenotypes. Colocalization was performed using the R package “coloc” (version 5.2.3) [33].

Enrichment analysis

Enrichment study design and process

We annotated the gene names of all Instrumental variables (IVs) significantly associated with the outcomes using the Single Nucleotide Polymorphism Database (dbSNP) from the National Human Genome Research Institute. Subsequently, Gene Ontology (GO) molecular function analysis was performed using the R package “org.Hs.eg.db” (version 3.18.0). KEGG pathway analysis was conducted to identify key metabolic pathways, and the top 20 pathways were selected for display. The significance threshold was set at P.adjust < 0.05.

Conclusion

Baseline characteristics of the migraine study population

A total of 10,743 participants were included in the cross-sectional study, and their baseline characteristics are shown in Table 2. Compared with the non-migraine group, migraineurs were more likely to be young females, who tended to be non-Hispanic white, less educated, more impoverished, less likely to drink alcohol, and currently smoke (P < 0.001). Meanwhile, they had higher BMI, lower dietary potassium intake, lower hemoglobin levels, lower SBP, and higher DBP. (P < 0.001) (Table 2).

Table 2 Baseline characteristics of the study participants

Observational study of hypertension and migraine in the NHANES

After adjusting for potential confounders, weighted multivariate adjusted logistic regression analysis results showed a significant positive association between hypertension (OR = 1.21 [95% CI, 1.08–1.36], P < 0.001) and DBP (OR = 1.01 [95% CI, 1.01–1.02], P < 0.001) and migraine risk. Additionally, in Model 1, SBP, 140 mmHg ≤ SBP < 160 mmHg, 160 mmHg ≤ SBP < 180 mmHg (OR = 0.66 [95% CI, 0.47–0.92], P < 0.05) were negatively correlated with migraine; however, after adjusting for covariates, the correlation between SBP (OR = 1.00 [95% CI, 0.99-1.00], P>0.05)、140 mmHg ≤ SBP < 160 mmHg (OR = 0.87 [95% CI, 0.69–1.11], P>0.05)、160 mmHg ≤ SBP < 180 mmHg (OR = 0.91 [95% CI, 0.62–1.34], P>0.05) and migraine became insignificant.

Meanwhile, there was no significant association between 180 mmHg ≤ SBP (OR = 1.08 [95% CI, 0.65–1.79]), 90 mmHg ≤ DBP < 100 mmHg (OR = 1.39 [95% CI, 0.95–2.05]), 100 mmHg ≤ DBP < 110 mmHg (OR = 1.01 [95% CI, 0.57–1.78]), and 110 mmHg ≤ DBP (OR = 1.80 [95% CI, 0.50–6.53]) and migraine onset (Table 3).

Table 3 Association between blood pressure categories and migraine

Furthermore, we performed a linear exploration of the relationship between DBP and migraine and found that the weighted RCS analysis showed a linear relationship (P non-linear = 0.897) between DBP and migraine after adjusting for all covariates (Fig. 2).

Fig. 2

RCS for the association between DBP and migraine

Subgroup between DBP and migraine

To further investigate the role of potential confounding factors in the association between DBP and migraine, we stratified participants into subgroups based on sex, marital status, race, alcohol consumption, education level, poverty ratio, smoking status, diabetes, and physical activity (Fig. 3). However, we did not observe any significant interactions between DBP and these potential confounders ( P of interaction > 0.05).

Fig. 3

Moderation of the DBP-Migraine Comorbidity: A Subgroup Interaction Analysis

Causal relationship between hypertension and migraine

In the above multivariate regression analysis model, we found that hypertension and SBP were positively correlated with the risk of migraine. We then used MR analysis to further infer the causal relationship between hypertension, SBP, DBP and migraine. The results showed that hypertension (OR = 1.77 [95% CI, 1.43–2.30], FDR < 0.001) and DBP (OR = 1.02 [95% CI, 1.01–1.03], FDR < 0.001) were positively correlated with the development of migraine, and the effect values of IVW were consistent with the directions of other effect values, indicating that our study was reliable. The study also showed that there was no causal relationship between SBP (OR = 1.004 [95% CI, 1.00-1.01], FDR > 0.05) and migraine. MR Steiger was used to determine whether the direction of our study was correct. Table S1 in the supplement depicts the estimates of the effect of exposure on migraine measured by different MR methods.

In addition, we conducted sensitivity analyses to assess the heterogeneity and horizontal pleiotropy of the study. The RadialMR and PRESSO methods were used to remove outliers that interfere with heterogeneity and horizontal pleiotropy. Finally, all studies showed no heterogeneity or horizontal pleiotropy. The leave-one-out method was also used to remove SNPs that had a major interference on the causal relationship of the results, avoiding the results being driven by a single SNP. The symmetry of the forest plot and funnel plot also showed the same results. Therefore, our conclusions are robust and reliable.

Colocalization analysis between DBP and migraine

Our colocalization analysis indicates that DBP and migraine are not driven by the same causal variant, with a posterior probability of H4 < 0.00516%. Under identical parameters, colocalization analysis yielded H3 > 99.9%, indicating significant SNP associations between these phenotypes but driven by distinct causal variants. This further validates the sensitivity of the Mendelian randomization analyses.

Genetic correlation between DBP and migraine

In the aforementioned multivariate regression analysis model, we found a positive correlation between hypertension, SBP, and migraine risk. Subsequently, we performed LDSC using GWAS summary data for DBP and migraine. The results showed a significant genetic correlation between the two (Rg = 0.1092, SE = 0.028, P < 0.001). This indicates that these two phenotypes may share some common genetic basis.

KEGG pathway and GO functional annotation analysis

KEGG analysis revealed that the association between DBP and migraine involves several key pathways, including hypertrophic cardiomyopathy, dilated cardiomyopathy, and the renin-angiotensin system, suggesting the significant role of vascular function and endocrine regulation in this relationship. Enriched pathways such as the PI3K-Akt signaling pathway, calcium signaling pathway, and metabolism-related pathways (e.g., cortisol synthesis and secretion) indicate a potential contribution of cellular signaling and metabolic imbalance to the pathophysiology of migraine. Additionally, neuroregulatory pathways like circadian rhythm and oxytocin signaling suggest that migraine may involve sleep mechanisms (Fig. 4).

Fig. 4

Enrichment Analysis Linking DBP and Migraine-Associated Pathways

GO functional annotation further showed that the related genes are primarily enriched in biological processes such as vascular processes, metabolic regulation, and neurodevelopment, and are significantly involved in molecular functions like anchoring protein binding and calmodulin binding, highlighting their key roles in signal transduction, cytoskeletal regulation, and molecular interactions (Fig. 4). These enrichment results provide important clues for understanding the molecular mechanisms underlying the association between DBP and migraine.

Discussion

This study integrated observational data from the NHANES and bioinformatics technology to investigate the association between blood pressure and migraine. The observational analysis demonstrated a significant linear positive correlation between DBP and migraine. MR analysis further confirmed this causal relationship. Colocalization analysis indicated that the two phenotypes do not share genetic pathogenic variants, while LDSC analysis revealed that their association stems from genetic correlation. Finally, pathway enrichment analysis was performed to elucidate the underlying molecular mechanisms.

Previous observational studies have reported an association between hypertension and migraine [31, 34, 35], whereas prospective studies have identified DBP as the primary driver of this relationship [36, 37]. This aligns with our findings, which suggest that the genetic association between hypertension and migraine is driven by DBP. This may explain why many observational studies have concluded that hypertension can lead to migraine. Additionally, our findings indicate that the two traits are not driven by shared causal variants but are associated due to genetic structure correlations, consistent with a prior genome-wide cross-phenotype meta-analysis on blood pressure and migraine [38]. We hypothesize that the mechanism by which DBP contributes to migraine may involve chronic endothelial damage, warranting further enrichment analysis.

Enrichment analysis of the association between DBP and migraine revealed multiple potential regulatory pathways, primarily involving the cardiovascular system, the renin-angiotensin system, calcium signaling pathways, and metabolism-related pathways. These findings are consistent with previous studies. Among them, there are three mechanisms that are particularly [39].

Cortical spreading depression (CSD) is a sudden, self-propagating wave of neurobiological activity that most scholars currently recognize as the primary initiating mechanism, playing a crucial role in the pathogenesis of migraine [40]. This phenomenon involves rapid changes in neuronal membrane potential, leading to simultaneous electrical activity across wide areas of cortical neurons. Studies indicate that the occurrence of CSD is closely related to disturbances in ionic homeostasis, particularly the increase in extracellular potassium concentration ([K⁺]O), which is considered a major trigger for CSD [41, 42]. This disruption of ion homeostasis can lead to rapid changes in neuronal membrane potential, triggering the spread of CSD. This, in turn, causes a significant release of neuroinflammatory factors (e.g.: CGRP), leading to central sensitization and inducing migraine. During a migraine attack, the occurrence of CSD is associated with the release of pain neurotransmitters and physiological processes such as vasodilation.

One hypothesis is the endothelial dysfunction (ED) theory. In normal individuals, the endothelial system has antithrombotic, anti-inflammatory, and antioxidant functions. It regulates vascular tone and blood pressure through the production of vasoactive substances such as endothelin-1 (ET-1), nitric oxide (NO) [43], and prostacyclin, as well as the inactivation of other factors such as bradykinin and serotonin [44]. However, when the body produces an excess of reactive oxygen species (ROS) after injury, leading to an imbalance in the redox system and oxidative stress (OS) [45], it can impair endothelium-dependent vasodilation by mediating the bioavailability of vasoactive substances. This is a key factor in the development of chronic migraine [46]. Previous evidence suggests that migraine patients have significantly elevated levels of peroxides, including oxidized low-density lipoprotein (OxLDL), and decreased activity of various antioxidant enzymes, indicating increased oxidative stress [47]. A cross-sectional study assessing vascular characteristics in migraine patients found that those with recent migraine attacks exhibited endothelial dysfunction in the peripheral circulation [48], suggesting a strong correlation between migraine and endothelial dysfunction.

Another hypothesis involves the renin-angiotensin-aldosterone system (RAAS). A meta-analysis of 94 randomized controlled trials showed that antihypertensive drugs (angiotensin-converting enzyme inhibitors, angiotensin II receptor blockers, and calcium channel blockers) reduced the prevalence of headaches by one-third compared to placebo [49], indirectly supporting the critical role of hypertension in the pathogenesis of migraine. Additionally, studies have found that the brain RAAS has extensive neurogenic effects [50]. The potential mechanism by which it contributes to migraine development involves the secretion of Ang II, leading to adrenal cardiovascular tension, vasoconstriction, and subsequent vasodilation, as well as neuroinflammatory responses induced by Ang II [51]. Hypertension is also directly associated with increased vascular stiffness and ED [52, 53].

Building upon the aforementioned research findings and hypotheses, and considering the holistic nature of the human body and the complexities of diseases, we propose an alternative hypothesis: that the DBP-RAAS system-endothelial dysfunction-CSD pathway plays a pivotal role in the development of migraine.

Hypertension can activate the renin-angiotensin-aldosterone system (RAAS), leading to elevated levels of angiotensin II (Ang II) and aldosterone. This, in turn, triggers vasoconstriction and sodium and water retention, causing an increase in blood volume and further exacerbating hypertension. During diastole, when the heart relaxes, vascular wall pressure decreases, allowing vascular smooth muscle relaxation. This mitigates the detrimental effects of endothelial injury to systole and facilitates the restoration of endothelial cell function and the production of vasoactive substances such as endothelin-1 (ET-1), nitric oxide (NO), and prostaglandins, which maintain vascular elasticity and restore function.

However, chronic elevated diastolic pressure, leads to endothelial damage, further reducing the secretion of vasoactive substances and exacerbating endothelial injury. Endothelial dysfunction and neurophysiological abnormalities result in elevated intracellular calcium (Ca2+) concentrations in glial cells, increased extracellular concentrations of potassium (K+) and neurotransmitters (e.g.: glutamate), and alterations in local cellular excitability. These changes trigger CSD in the cerebral cortex, leading to its propagation. CSD activates the trigeminal nerve afferent pathway, causing inflammatory changes in pain-sensitive meninges and initiating a cascade of neuroinflammatory reactions in pain-sensitive meningeal-related structures. These changes, in turn, generate migraine pain through central and peripheral reflex mechanisms. Previous research has established a close relationship between potassium channel regulation and endothelial dysfunction [54, 55]. Additionally, animal models have demonstrated the potent induction of CSD by endothelin-1 (a vasoconstrictor) under full perfusion conditions [56], further supporting our hypothesis. While further rigorous validation of this hypothesis is warranted, our proposed mechanism provides a novel perspective on the intricate interplay between hypertension, endothelial dysfunction, and migraine pathogenesis.

The primary strengths of this study lie in its combination of real-world observational research and MR analysis to explore the relationship between hypertension and migraine. By applying weighted multivariable-adjusted logistic regression analysis to data from the National Health and Nutrition Examination Survey (NHANES), we adjusted for various potential confounding factors, providing robust statistical support for the correlation between hypertension and migraine. This study is also the first to investigate the nonlinear relationship between DBP and migraine. Most importantly, the MR method minimizes confounding and reverse causation biases, allowing for a clearer exploration of causal relationships.

The strength of this study lies in its multi-layered evidence chain (including observational research, MR analysis, colocalization analysis, LDSC analysis and pathway enrichment analysis) that revealed the association between DBP and migraine. It confirmed that this association does not arise from shared genetic variants and elucidated potential biological pathways. However, limitations include the inherent shortcomings of the cross-sectional study design, for patients with a prior diagnosis, blood pressure measurements in this study were conducted multiple times within a single day, a common practice in large-scale epidemiological studies, This may still be influenced by the circadian rhythm of blood pressure, and the restriction of the study population primarily to individuals of European and American ancestry, requiring further validation in other ethnic groups. Future research will also explore the “DBP-RAAS-endothelial dysfunction-CSD pathway-migraine” mechanism through animal experiments.

This study identified a positive correlation between DBP and migraine risk, a pattern distinct from typical cardiovascular diseases. Based on known physiological mechanisms, we propose a novel pathogenic hypothesis emphasizing the importance of long-term DBP management in clinical practice.

Conclusion

In conclusion, this study confirms that DBP is an independent risk factor for migraine. These findings highlight the importance of long-term DBP control in the prevention of migraine.