Association of diet quality, dietary acid load and antioxidant index with migraine severity, disability, and duration: a cross-sectional study

Abstract

Background

Migraine disproportionately affects women and may be modulated by dietary factors. This cross-sectional study investigated associations between diet quality (Alternative Healthy Eating Index, AHEI), dietary acid load (Net Endogenous Acid Production, NEAP), and dietary antioxidant capacity (Dietary Antioxidant Index, DAI) with migraine pain intensity, disability, and headache duration in Iranian women.

Methods

A total of 280 women aged 18–50 years with migraine (diagnosed per ICHD-3 criteria) were recruited from neurology clinics in Zanjan, Iran (August 2024–June 2025). Dietary intake was assessed using a validated 168-item food frequency questionnaire. AHEI, NEAP, and DAI scores were calculated and stratified into tertiles. Outcomes included pain intensity (Visual Analog Scale [VAS]: mild [1–3; reference], moderate [4–7], severe [8–10]), disability (Migraine Disability Assessment Scale [MIDAS]: none [0–5; reference], mild [6–10], moderate [11–20], severe [> 20]), and mean headache duration (hours). Multivariable-adjusted multinomial logistic regression (for VAS and MIDAS) and linear regression (for duration) were used to estimate odds ratios (ORs) and β coefficients with 95% confidence intervals (CIs), comparing the middle (T2) and highest (T3) tertiles versus the lowest (T1; reference), adjusted for age, BMI, physical activity, prophylactic medication use, and socioeconomic status. P-for-trend was calculated using tertile medians as continuous variables.

Results

Higher AHEI tertiles showed graded protective associations with severe pain (T3 OR 0.69, 95% CI 0.51–0.89, p = 0.010; P-for-trend = 0.009) and shorter headache duration (T3 β − 1.58, 95% CI − 2.75 to − 0.41, p = 0.009; P-for-trend = 0.008). Higher NEAP tertiles were linked to increased severe pain (T3 OR 1.38, 95% CI 1.08–1.66, p = 0.021; P-for-trend = 0.018), severe disability (T3 OR 1.29, 95% CI 1.02–1.56, p = 0.044; P-for-trend = 0.039), and longer duration (T3 β 1.28, 95% CI 0.25–2.31, p = 0.015; P-for-trend = 0.013). Higher DAI tertiles demonstrated the strongest graded reductions in moderate pain (T3 OR 0.59, 95% CI 0.41–0.83, p = 0.035; P-for-trend = 0.012), severe pain (T3 OR 0.47, 95% CI 0.33–0.74, p = 0.024; P-for-trend = 0.007), moderate disability (T3 OR 0.73, 95% CI 0.52–0.97, p = 0.048; P-for-trend = 0.031), severe disability (T3 OR 0.69, 95% CI 0.47–0.91, p = 0.038; P-for-trend = 0.022), and shorter duration (T3 β − 1.34, 95% CI − 2.33 to − 0.35, p = 0.008; P-for-trend = 0.006).

Conclusions

Better diet quality and higher antioxidant capacity were associated with reduced migraine burden, while higher dietary acid load correlated with increased burden. These findings highlight potential graded associations and support the need for prospective studies to establish causality and evaluate dietary interventions in women with migraine.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12883-026-04690-2.

Introduction

Migraine is a debilitating neurological disorder characterized by recurrent moderate to severe headaches, often accompanied by nausea, photophobia, and phonophobia, significantly impairing quality of life [1]. Globally, it affects approximately 15% of adults, with a higher prevalence in women (up to 21%) compared to men (10%) [2]. Recent data show stable age-standardized prevalence over three decades, but a pronounced burden in women of reproductive age (18–44 years), with three-month prevalence exceeding 20% in some cohorts [3, 4]. In Iran, migraine prevalence among women is estimated at 18–25%, contributing to substantial socioeconomic costs through lost productivity and healthcare utilization [5]. The impact is exacerbated during hormonal transitions such as puberty and menopause, where up to 60% of women report menstrual-related migraine exacerbations, underscoring the need for targeted interventions in this demographic [6].

Emerging evidence highlights the potentially modifiable role of diet in migraine pathophysiology, potentially through mechanisms involving inflammation, oxidative stress, and acid-base balance [7–9]. Diet quality, as assessed by indices like the Alternative Healthy Eating Index (AHEI), emphasizes consumption of fruits, vegetables, whole grains, and healthy fats while limiting processed meats and sodium, and has been inversely associated with migraine severity and frequency [10–13]. For instance, a cross-sectional study among Iranian women demonstrated that high AHEI adherence reduced migraine duration by 43%, attributing this to anti-inflammatory effects [8, 10, 14]. Similarly, dietary acid load, estimated using the Net Endogenous Acid Production (NEAP) score, reflects the acidogenic potential of protein-rich diets relative to alkalizing nutrients like potassium, and has been positively linked to migraine odds and clinical features [15–17].

Recent case-control and cross-sectional studies in Iranian populations have shown that elevated DAI score increases migraine headache frequency and severity, possibly via metabolic acidosis-induced cortical hyperexcitability [11, 18]. Furthermore, the Dietary Antioxidant Index (DAI), which aggregates standardized intakes of antioxidants such as vitamins C, E, A, selenium, zinc, and β-carotene, has demonstrated protective associations against migraine [19, 20]. Cross-sectional analyses from the National Health and Nutrition Examination Survey (NHANES) and Iranian cohorts indicate that higher DAI or Composite Dietary Antioxidant Index (CDAI) scores correlate with lower migraine attack frequency and severity, likely by mitigating oxidative stress in the trigeminovascular system [21].

Despite these insights, gaps persist in the literature. Most studies have focused on isolated dietary components or patterns like the Mediterranean or DASH diets, with limited integration of comprehensive indices such as AHEI, NEAP, and DAI in a single framework [13, 22]. Moreover, evidence from Middle Eastern populations, particularly Iranian women where cultural dietary habits may influence acid load and antioxidant intake, remains scarce [9, 17, 18]. Few investigations have simultaneously examined associations with multiple migraine outcomes, including pain intensity (Visual Analog Scale, VAS), disability (Migraine Disability Assessment Scale, MIDAS), and headache duration, while adjusting for confounders like socioeconomic status and physical activity [23].

To address these gaps, the present cross-sectional study aimed to investigate the relationships between diet quality (AHEI), dietary acid load (NEAP), and dietary antioxidant capacity (DAI) with migraine severity, disability, and duration among adult Iranian women. We hypothesized that higher AHEI and DAI scores would be inversely associated, while higher NEAP scores would be positively associated, with adverse migraine characteristics, independent of potential confounders.

Methods

Study design and participants

This cross-sectional study was conducted to investigate the relationships between dietary quality, dietary acid load, and dietary antioxidant capacity with migraine characteristics among adult women. Participants were recruited consecutively from specialized neurology clinics affiliated with Zanjan University of Medical Sciences in Zanjan, Iran, between August 2024 and June 2025. Eligible individuals included women aged 18 to 50 years who had been diagnosed with migraine according to the International Classification of Headache Disorders (ICHD-3) criteria [24] by a board-certified neurologist. To minimize recall bias and ensure homogeneity, we included only those presenting for their initial evaluation at the clinic and reporting at least three migraine episodes in the preceding month. Exclusion criteria encompassed pregnant or lactating women, those with comorbid chronic conditions (such as hypertension, diabetes, or renal disease) that could influence dietary patterns or migraine symptoms, individuals on specialized diets (e.g., ketogenic or vegan), and those with incomplete questionnaire responses or implausible energy intakes (below 800 kcal/day or above 4500 kcal/day). These energy intake thresholds were selected to identify misreporting, based on standard cut-offs used in Iranian FFQ studies for women aged 18–50, adapted from the Goldberg method for plausible energy reporting in similar demographics [25–27]. All participants provided written informed consent prior to enrollment. The study protocol received approval from the Ethics Committee of Zanjan University of Medical Sciences (IR.ZUMS.REC.1403.041). The procedures adhered to the principles outlined in the Declaration of Helsinki. Prior to participation, all individuals provided written informed consent, affirming their voluntary participation after being fully informed about the study’s purpose, methodology, potential risks, and benefits.

Variables and data collection

Demographic and clinical information was gathered through structured interviews conducted by trained research assistants. This included age, marital status, education level, employment, smoking history, family history of migraine, and medication use for migraine prophylaxis or acute treatment. Socioeconomic status was quantified using a weighted composite score derived from education, occupation, and household income. Points were assigned as per validated methods in Iranian health studies [28]: education level of the head of household (weighted 9.8: illiterate 0, primary 2, secondary 4, diploma 6, university or higher 9.8), occupation of the head of household (weighted 7.35: unemployed/housewife 0, manual 2.45, clerical/self-employed 4.9, professional 7.35), and household monthly income (weighted 3.975: low tertile 0, middle 1.325, high 3.975). The total score (range 0-21.125) was calculated by summing the weighted points, with the mean 15.26 ± 1.65 in our sample, and categorized into low, medium, and high tertiles for analysis.

Physical activity levels were assessed via the short-form International Physical Activity Questionnaire (IPAQ) [29], using the validated Persian version [30], with total scores calculated in metabolic equivalent task-minutes per week (MET-min/week) and then averaged to daily values (MET-min/day) for reporting. Levels were classified as low (< 600 MET-min/week), moderate (600–3000 MET-min/week), or high (> 3000 MET-min/week).

Anthropometric measurements were performed in a standardized manner. Height was measured to the nearest 0.1 cm using a wall-mounted stadiometer (Seca 206, Germany) with participants barefoot and in light clothing. Weight was recorded to the nearest 0.1 kg on a digital scale (Seca 808, Germany). Body mass index (BMI) was calculated as weight in kilograms divided by height in meters squared. Waist circumference was measured at the midpoint between the lower rib margin and iliac crest, and hip circumference at the widest point over the buttocks, both to the nearest 0.5 cm using a non-elastic tape.

Assessment of dietary intakes

Dietary intake during the preceding year was assessed using a previously published, validated semi-quantitative food frequency questionnaire (FFQ) containing 168 food items, developed for the Tehran Lipid and Glucose Study [31]. This FFQ, which has been validated for use in Iranian populations, was used to capture dietary patterns relevant to the study population. Participants reported consumption frequency (daily, weekly, monthly, or rarely/never) and portion sizes for each item, which were converted to daily gram intakes using household measures. Nutrient analyses were performed with Nutritionist IV software (First Databank, San Bruno, CA, USA), modified for Iranian foods. Total energy intake and macronutrient/micro-nutrient values were derived accordingly.

Assessment of adherence to alternative healthy eating index‑2010

Dietary quality was assessed using the Alternative Healthy Eating Index (AHEI-2010), which originally comprises eleven components (vegetables, fruits, whole grains, nuts and legumes, long-chain n-3 fatty acids, polyunsaturated fatty acids, sugar-sweetened beverages and fruit juices, red/processed meat, trans fatty acids, alcohol, and sodium), each scored on a 0–10 scale, yielding a total score from 0 to 110 [32]. However, trans fatty acids were excluded due to limited availability of accurate trans-fat content data for Iranian foods in the Nutritionist IV software. Alcohol intake was also excluded because the validated 168-item FFQ, derived from the Tehran Lipid and Glucose Study and tailored to typical Iranian dietary patterns, does not include specific questions on alcohol consumption. This omission is intentional, as alcohol is prohibited under Iranian law and Islamic religious principles, leading to negligible intake and a high likelihood of underreporting or reluctance to disclose even clandestine use among respondents. Consequently, the modified AHEI was based on nine components, with the total score calculated as the unscaled sum of individual component scores (ranging from 0 to 90, to maintain proportionality without artificial adjustment). This modification has been employed in prior Iranian studies to adapt the index to cultural and data constraints, preserving its utility for relative associations within similar populations [33–36].

Participants were then categorized into deciles based on their energy-adjusted consumption of each component. Decile-based scoring was chosen to minimize misclassification compared to quantitative categorization. For beneficial components (fruits, vegetables, whole grains, nuts and legumes, long-chain n-3 fatty acids, and PUFAs), participants in the highest decile of intake received a score of 10, while those in the lowest decile were assigned a score of 0. For detrimental components (red and processed meats, sugar-sweetened beverages and fruit juices, trans fatty acids, and sodium), scoring was reversed: the lowest decile received a score of 10, and the highest decile a score of 0. Intermediate deciles were scored proportionally.

Assessment of dietary acid load and dietary antioxidant index

Dietary acid load was estimated via the Net Endogenous Acid Production (NEAP) score as: NEAP (mEq/day) = (54.5 × protein intake [g/day] / potassium intake [mEq/day]) − 10.2. This formula accounts for the acidogenic potential of protein relative to potassium’s alkalizing effect [37].

DAI was computed to reflect the overall antioxidant capacity of the diet. Standardized intakes of key antioxidants (vitamins C, E, A, selenium, zinc, and β-carotene) were summed after normalization (subtracting the global mean and dividing by the global standard deviation from reference data), with higher DAI scores denoting greater antioxidant potential, as follows [38]:

The global mean and standard deviation for each antioxidant were derived from a multi-national reference population of approximately 2,340 adults (mixed gender, aged 18–65 years) from 6 countries (United States, France, United Kingdom, Germany, Greece, and Spain), as reported in the DAI validation study. This reference data, based on dietary surveys from cohorts like NHANES and EPIC, ensures standardization across diverse populations while allowing replication in similar contexts [39, 40].

Evaluation of migraine severity and duration

Migraine severity and impact were evaluated by a neurologist using validated tools. Pain intensity was measured with the Visual Analog Scale (VAS), a 10-cm line where participants marked their average pain level over the past month (0 = no pain, 10 = worst pain), categorized as mild [1–3], moderate [4–7], or severe [8–10, 41–43]. This categorization was chosen as it is commonly employed in migraine studies, including Iranian cohorts, to provide clinically meaningful distinctions—mild pain often not requiring intervention, moderate encompassing a range typically managed with acute therapies, and severe indicating potential need for prophylactic treatment—while aligning with validated approaches for headache pain monitoring [42]. Disability was assessed via the Migraine Disability Assessment Scale (MIDAS), grading the impact on daily activities over three months into none (0–5), mild [6–10], moderate [11–20], or severe (> 20). The mean duration of each headache attack (in hours) was self-reported based on the last month’s episodes [44].

Addressing bias

To reduce selection bias, we employed consecutive sampling from multiple clinics serving diverse socioeconomic groups in Zanjan. Information bias was mitigated by using validated questionnaires and training interviewers to standardize data collection. Potential confounding was addressed in analyses by adjusting for variables such as age, BMI, physical activity, medication use, and socioeconomic score, selected based on prior literature and univariate associations (p < 0.20).

Study size

The sample size was determined a priori using GPower software (version 3.1.9.7) for logistic regression, assuming a moderate effect size (odds ratio of 1.5 for the association between dietary indices and severe migraine, based on preliminary data from prior similar studies in Iranian populations [10, 11, 18], such as observed associations between high AHEI adherence and reduced migraine duration by 43% [10] or elevated NEAP and increased migraine odds [11, 18]), a two-sided alpha of 0.05, power of 80%, and accounting for up to 10% attrition. This yielded a minimum of 250 participants; we recruited 280 to enhance precision and allow for subgroup analyses. Post-hoc power calculations were also performed using GPower to assess power for the observed effect sizes (ORs ranging from 0.47 to 1.38), approximating multinomial logistic models with binomial logistic tests (base prevalence ~ 30% for severe outcomes, based on literature [45]). These confirmed adequate power (> 80%) for stronger effects (e.g., OR = 0.47) and ~ 70–80% for weaker ones (e.g., OR = 1.38), supporting the detection of significant associations (see Supplementary Table S3 for details). Note that post-hoc power is a function of observed p-values and is presented for transparency, though it is not recommended as a primary interpretive tool due to its redundancy with significance testing [46, 47].

Statistical analyses

Continuous variables are presented as means ± standard deviations (SD), and categorical variables as frequencies (percentages). Dietary intakes were adjusted for total energy intake using the residual method where applicable. The Alternative Healthy Eating Index (AHEI), Net Endogenous Acid Production (NEAP), and Dietary Antioxidant Index (DAI) scores were calculated and stratified into tertiles. Participant characteristics and dietary variables across tertiles of each dietary index were compared using one-way analysis of variance (ANOVA) for continuous variables and Pearson’s χ² test for categorical variables.

Multinomial logistic regression models were used to estimate odds ratios (ORs) with 95% confidence intervals (CIs) for migraine pain intensity categories (Visual Analog Scale [VAS]: mild pain [1–3; reference within pain outcomes], moderate pain [4–7], severe pain [8–10]) and disability categories (Migraine Disability Assessment Scale [MIDAS]: none [0–5; reference within disability outcomes], mild [6–10], moderate [11–20], severe [> 20]). Linear regression models were used to estimate β coefficients with 95% CIs for mean headache duration per attack (hours).

The lowest tertile (T1) of each dietary index served as the reference category. Primary results are presented as multivariable-adjusted associations for the middle (T2) and highest (T3) tertiles versus T1, with p-values for each tertile comparison. Adjusted models included age (continuous, years), body mass index (BMI, continuous, kg/m²), physical activity (continuous, MET-min/day), prophylactic medication use (yes/no), and socioeconomic status (continuous score). Crude models showed similar directional associations with slightly wider confidence intervals. P-for-trend was calculated by assigning the median value of each tertile as a continuous variable in the adjusted regression models to test for linear dose-response relationships across dietary index categories.

Potential confounding was addressed by adjusting for variables selected based on prior literature demonstrating their influence on both dietary patterns and migraine outcomes in similar populations [45, 48, 49] and univariate associations with the exposures (dietary indices) or outcomes (VAS, MIDAS, duration) at p < 0.20. Univariate analyses (one-way ANOVA for continuous variables and chi-square tests for categorical variables across dietary index tertiles) identified these variables as potential confounders. Other collected variables, such as smoking status and family history of migraine, were evaluated but not included in the final models due to non-significant univariate associations (p > 0.20) and limited evidence of strong confounding in women-specific migraine-diet studies [50, 51]. Normality was assessed using Kolmogorov-Smirnov tests; non-normal variables were log-transformed if necessary. Multicollinearity was evaluated using variance inflation factors (VIF < 5 for all models).

All analyses were two-tailed with statistical significance set at p < 0.05 and performed using IBM SPSS Statistics version 26.0 (IBM Corp., Armonk, NY, USA). No formal correction for multiple comparisons was applied, as the analyses were hypothesis-driven and focused on a limited set of a priori associations; however, this is acknowledged as a limitation, with post-hoc sensitivity using conservative Bonferroni correction (alpha ≈ 0.0025 for ~ 20 primary tests) confirming that key findings remained directionally consistent or significant [52]. Reporting follows the STROBE guidelines for cross-sectional studies. Additionally, sensitivity analyses were conducted by including smoking status and family history of migraine in the adjusted models to assess robustness; results were largely unchanged (see Supplementary Table S1).

Results

Participant characteristics

A total of 390 individuals were initially assessed for eligibility. Of these, 92 were excluded due to not meeting inclusion criteria (n = 75), declining participation (n = 14), or other reasons (n = 3). Ultimately, 298 participants were enrolled in the study. During data cleaning, 18 participants were excluded due to incomplete FFQ responses (n = 13) or implausible energy intake (< 800 or > 4500 kcal/day) (n = 5). Thus, 280 participants were included in the final analysis (Fig. 1).

Fig. 1.

Flowchart of participant’s enrollment

A total of 280 women with migraine, aged 18–50 years, were included in this cross-sectional study conducted between August 2024 and June 2025 at specialized neurology clinics in Zanjan, Iran. Table 1 summarizes the demographic, anthropometric, and clinical characteristics of the study population. The mean age was 35.62 ± 7.65 years, with a mean body mass index (BMI) of 27.15 ± 4.65 kg/m². Most participants (63.22%) were single or divorced, 46.41% held an associate or bachelor’s degree, and 40% were housewives. Smoking status revealed 60% never smoked, 21% were current smokers, and 19% were former smokers. A family history of migraine was reported by 26.79% of participants, and 45.71% used prophylactic medications for migraine management. Physical activity levels varied, with 52.50% classified as low, 27.85% as moderate, and 19.65% as high (mean: 119.15 ± 25.47 MET-min/day). The mean socioeconomic status score was 15.26 ± 1.65.

Table 1.

Characteristics of the study subjects

Variable (qualitative)	Number	Percentage (%)
Education Status
High school and below	65	23.22
Associate degree and Bachelor’s degree	113	46.41
Master’s degree and Doctorate	85	30.45
Employment Status
Housewife	112	40
Self-employed	62	22.14
Employee or Student	106	37.86
Marital Status
Married	103	36.78
Single or divorced	177	63.22
Smoking Status
Never smoked	168	60
Currently smoking	59	21
Former smoker	53	19
Family members or close relatives with migraine
No	205	73.21
Yes	75	26.79
Medication Use
Yes	128	45.71
No	152	54.29
Physical Activity
Low	147	52.50
Moderate	78	27.85
High	55	19.65

Variable (quantitative)	Mean	Standard Deviation
Age (years)	35.62	7.65
Height (cm)	167.29	5.22
Weight (kg)	66.44	10.74
Waist circumference (cm)	82.43	8.55
Hip circumference (cm)	99.22	10.13
BMI(kg/m2)	27.15	4.65
WHR	0.80	0.04
Daily water intake (cups)	4.23	2.16
Physical Activity (MET/Min/day)	119.15	25.47
Social status score	15.26	1.65

BMI Body Mass Index, WHR Waist-to-Hip Ratio

Univariate analyses across dietary index tertiles showed associations (p < 0.20) for age, BMI, physical activity, medication use, and socioeconomic status with at least one exposure or outcome, justifying their inclusion as confounders. Smoking status and family history of migraine did not meet this threshold and were not included in primary models. Sensitivity analyses incorporating these variables yielded similar results to the primary adjusted models (see Supplementary Table S1).

Dietary intake and indices

Table 2 presents the dietary intake and scores for the dietary indices. The mean daily energy intake was 2164.14 ± 315.33 kcal, with carbohydrates contributing 255.24 ± 39.44 g, protein 64.52 ± 9.26 g, and fat 79.15 ± 6.92 g. Micronutrient intakes included calcium (844.22 ± 43.88 mg), vitamin E (13.49 ± 5.66 mg), zinc (9.43 ± 2.77 mg), and selenium (29.67 ± 4.73 mg). Dietary fiber intake averaged 31.84 ± 12.44 g/day, and sodium intake was 2691.24 ± 575.33 mg/day. Food group intakes included 362.74 ± 98.25 g/day of fruits, 284.31 ± 94.37 g/day of vegetables, and 133.46 ± 69.72 g/day of whole grains. The mean AHEI score was 59.44 ± 13.48, indicating moderate dietary quality. The mean NEAP score was 52.17 ± 25.17, and the mean DAI score was 4.15 ± 1.33.

Table 2.

Distribution of the level of energy intake, macro- and micronutrients, mean score of dietary indices

Variable	Total(n = 280)
Daily energy intake (kcal)	2164.14 ± 315.33
Carbohydrate (g)	255.24 ± 39.44
Protein (g)	64.52 ± 9.26
Fat (g)	79.15 ± 6.92
Monounsaturated fatty acid (g)	21.28 ± 2.19
Polyunsaturated fatty acid (g)	15.24 ± 4.22
Saturated fatty acid (g)	23.64 ± 3.63
Cholesterol (mg)	198.44 ± 8.92
Fiber (g)	31.84 ± 12.44
Calcium (mg)	844.22 ± 43.88
Vitamin D (IU)	44.16 ± 3.96
Vitamin E (mg)	13.49 ± 5.66
Iron (mg)	12.65 ± 4.19
Zinc (mg)	9.43 ± 2.77
Magnesium (mg)	264.85 ± 37.90
Selenium (mg)	29.67 ± 4.73
Sodium (mg)	2691.24 ± 575.33
Whole grain (g)	133.46 ± 69.72
Refined grain(g)	348.27 ± 155.82
Fruits(g)	362.74 ± 98.25
Vegetables(g)	284.31 ± 94.37
Dairy (g)	482.29 ± 72.99
Meat & alternative (g)	94.73 ± 25.84
Beans & nuts (g)	39.88 ± 16.22
AHEI score	59.44 ± 13.48
NEAP score	52.17 ± 25.17
DAI score	4.15 ± 1.33

AHEI Alternative Healthy Eating Index, DAI dietary antioxidant index, NEAP Net Endogenous Acid Production

Distribution of characteristics across dietary index tertiles

Table 3 shows the distribution of demographic and dietary variables across tertiles of NEAP and DAI scores. No significant differences were observed across tertiles for key covariates, including age, marital status, smoking, family history of migraine, education, anthropometric measures, physical activity, or socioeconomic status (all p > 0.23). Dietary macronutrient intakes also showed no significant differences across tertiles (all p > 0.18). These findings indicate balanced distribution of potential confounders across exposure categories.

Table 3.

General, demographic and dietary variables of participants based on tertiles of NEAP and DAI¹

Characteristics	NEAP				DAI
	T1(n = 94)	T2(n = 93)	T3(n = 93)	P	T1(n = 94)	T2(n = 93)	T3(n = 93)	P 2
Age, years (mean ± SD)	35.14 ± 7.49	35.88 ± 8.13	35.60 ± 7.72	0.37	35.49 ± 7.22	35.79 ± 7.66	35.40 ± 7.91	0.44
Married, n (%)	32 (31)	40 (43)	31 (33.4)	0.26	34(36.17)	37 (39.8)	32 (34.4)	0.37
Current smoker, n (%)	19 (20)	17 (18.27)	23 (24.73)	0.33	21(22.34)	18 (19.35)	20 (21.5)	0.48
Family member with migraine, n (%)	23(24.5)	26 (27.95)	26 (27.95)	0.29	25 (26.6)	26 (27.95)	24 (25.80)	0.59
Education, n (%)				0.37				0.43
High school and below,	22 (23.4)	25(26.9)	18 (19.35)		22 (23.4)	22 (23.65)	21 (22.58)
Associate degree and Bachelor’s degree	37 (39.36)	35 (37.64)	41 (44.08)		39 (41.48)	35 (37.64)	39 (41.48)
Master’s degree and Doctorate	24 (25.53)	26 (27.95)	25(26.88)		26 (27.65)	24 (25.80)	25 (26.88)
Weight (kg)	65.19 ± 9.52	66.24 ± 10.37	67.23 ± 9.86	0.55	65.72 ± 10.04	66.74 ± 9.84	66.17 ± 10.11	0.48
Waist circumference (cm)	81.55 ± 8.32	81.93 ± 8.55	82.13 ± 8.76	0.41	82.04 ± 7.94	81.74 ± 8.10	81.55 ± 8.23	0.44
Physical Activity (MET/Min/day)	116.74 ± 23.76	119.22 ± 18.90	122.40 ± 24.18	0.23	118.57 ± 19.86	122.03 ± 21.50	119.11 ± 19.37	0.31
Social status score	14.85 ± 1.33	15.55 ± 1.60	15.14 ± 1.45	0.52	14.91 ± 1.48	15.22 ± 1.76	14.64 ± 1.70	0.48
Dietary Intake
Calorie (kcal/day)	2189.33 ± 376.60	2063.77 ± 325.11	2204.15 ± 355.90	0.18	2198.31 ± 340.21	2175.42 ± 370.63	2104.80 ± 341.52	0.36
Carbohydrate (gr/day)	250.52 ± 37.11	246.64 ± 35.32	258.36 ± 39.62	0.26	255.66 ± 35.18	252.14 ± 33.90	247.78 ± 35.66	0.41
Protein (gr/day)	65.72 ± 8.92	60.47 ± 8.65	68.36 ± 9.12	0.52	63.41 ± 8.65	65.72 ± 9.15	62.91 ± 8.17	0.61
Fat (gr/day)	80.47 ± 7.36	75.37 ± 6.18	82.30 ± 7.27	0.25	86.31 ± 7.14	82.14 ± 7.20	78.40 ± 7.34	0.34

DAI dietary antioxidant index, NEAP Net Endogenous Acid Production

¹Values are means ± SDs for continuous variables and n (%) for categorical variables

²Pearson’s χ2 test was performed for categorical variables and ANOVA test for continuous variables

Association of AHEI score with migraine outcomes

Multivariable-adjusted associations across tertiles of each dietary index with migraine pain intensity (VAS), disability (MIDAS), and headache duration are presented in Tables 4, 5 and 6. Results focus on adjusted models; crude models showed similar directional patterns. Higher AHEI tertiles were associated with progressively lower odds of severe pain (T2: OR 0.84, 95% CI 0.61–1.15, p = 0.271; T3: OR 0.69, 95% CI 0.51–0.89, p = 0.010; P-for-trend = 0.009), with a non-significant attenuation for moderate pain (T3: OR 0.75, 95% CI 0.55–1.04, p = 0.082; P-for-trend = 0.079). No significant associations were observed with MIDAS disability categories (all P-for-trend > 0.198). Higher AHEI tertiles were associated with shorter headache duration, primarily driven by the highest tertile (T3: β − 1.58, 95% CI − 2.75 to − 0.41, p = 0.009; P-for-trend = 0.008) (Table 4).

Table 4.

Multivariable-adjusted associations of migraine outcomes across tertiles of AHEI score

Outcome	Tertile 1 (Ref)	Tertile 2 OR/β (95% CI)	p-value	Tertile 3 OR/β (95% CI)	p-value	P-for-trend
VAS pain intensity
Moderate pain	1.00	0.87 (0.63–1.20)	0.392	0.75 (0.55–1.04)	0.082	0.079
Severe pain	1.00	0.84 (0.61–1.15)	0.271	0.69 (0.51–0.89)	0.010	0.009
MIDAS disability
Mild disability	1.00	0.96 (0.70–1.31)	0.787	0.92 (0.68–1.25)	0.589	0.512
Moderate disability	1.00	0.94 (0.69–1.29)	0.709	0.86 (0.63–1.17)	0.336	0.289
Severe disability	1.00	0.92 (0.67–1.26)	0.592	0.83 (0.61–1.13)	0.237	0.198
Mean headache duration (hours)	0	–0.82 (–1.82 to 0.18)	0.107	–1.58 (–2.75 to − 0.41)	0.009	0.008

Values are odds ratios (ORs) with 95% confidence intervals (CIs) for categorical outcomes (multinomial logistic regression) or β coefficients with 95% CIs for continuous outcome (linear regression) across tertiles of Alternative Healthy Eating Index (AHEI) score. Tertile 1 served as the reference category. Models were adjusted for age (years), body mass index (BMI, kg/m²), physical activity (MET-min/day), prophylactic medication use (yes/no), and socioeconomic status (score). The Visual Analog Scale (VAS) categories were mild pain (1–3; reference within pain outcomes), moderate pain (4-7), and severe pain (8-10). The Migraine Disability Assessment Scale (MIDAS) categories were none (0–5; reference within disability outcomes), mild (6–10), moderate (11–20), and severe (> 20). P-for-trend was calculated by assigning the median value of each tertile as a continuous variable in the adjusted regression models

Table 6.

Multivariable-adjusted associations of migraine outcomes across tertiles of NEAP score

Outcome	Tertile 1 (Ref)	Tertile 2 OR/β (95% CI)	p-value	Tertile 3 OR/β (95% CI)	p-value	P-for-trend
VAS pain intensity
Moderate pain	1.00	1.05 (0.81–1.36)	0.712	1.12 (0.89–1.41)	0.335	0.289
Severe pain	1.00	1.17 (0.91–1.50)	0.224	1.38 (1.08–1.66)	0.021	0.018
MIDAS disability
Mild disability	1.00	1.07 (0.79–1.45)	0.658	1.15 (0.83–1.59)	0.401	0.312
Moderate disability	1.00	1.03 (0.76–1.40)	0.847	1.10 (0.81–1.49)	0.542	0.468
Severe disability	1.00	1.13 (0.88–1.45)	0.342	1.29 (1.02–1.56)	0.044	0.039
Mean headache duration (hours)	Ref	0.62 (–0.42 to 1.66)	0.240	1.28 (0.25 to 2.31)	0.015	0.013

Values are odds ratios (ORs) with 95% confidence intervals (CIs) for categorical outcomes (multinomial logistic regression) or β coefficients with 95% CIs for continuous outcome (linear regression) across tertiles of Net Endogenous Acid Production (NEAP) score. Tertile 1 served as the reference category. Models were adjusted for age (years), body mass index (BMI, kg/m²), physical activity (MET-min/day), prophylactic medication use (yes/no), and socioeconomic status (score). The Visual Analog Scale (VAS) categories were mild pain (1–3; reference within pain outcomes), moderate pain (4–7), and severe pain (8–10). The Migraine Disability Assessment Scale (MIDAS) categories were none (0–5; reference within disability outcomes), mild (6–10), moderate (11–20), and severe (> 20). P-for-trend was calculated by assigning the median value of each tertile as a continuous variable in the adjusted regression models

Table 5.

Multivariable-adjusted associations of migraine outcomes across tertiles of DAI score

Outcome	Tertile 1 (Ref)	Tertile 2 OR/β (95% CI)	p-value	Tertile 3 OR/β (95% CI)	p-value	P-for-trend
VAS pain intensity
Moderate pain	1.00	0.73 (0.54–0.99)	0.042	0.59 (0.41–0.83)	0.035	0.012
Severe pain	1.00	0.68 (0.49–0.94)	0.019	0.47 (0.33–0.74)	0.024	0.007
MIDAS disability
Mild disability	1.00	0.85 (0.62–1.16)	0.305	0.78 (0.55–1.08)	0.134	0.089
Moderate disability	1.00	0.81 (0.59–1.11)	0.186	0.73 (0.52–0.97)	0.048	0.031
Severe disability	1.00	0.79 (0.57–1.09)	0.148	0.69 (0.47–0.91)	0.038	0.022
Mean headache duration (hours)	0	–0.70 (–1.70 to 0.30)	0.168	–1.34 (–2.33 to − 0.35)	0.008	0.006

Values are odds ratios (ORs) with 95% confidence intervals (CIs) for categorical outcomes (multinomial logistic regression) or β coefficients with 95% CIs for continuous outcome (linear regression) across tertiles of Dietary Antioxidant Index (DAI) score. Tertile 1 served as the reference category. Models were adjusted for age (years), body mass index (BMI, kg/m²), physical activity (MET-min/day), prophylactic medication use (yes/no), and socioeconomic status (score). The Visual Analog Scale (VAS) categories were mild pain (1–3; reference within pain outcomes), moderate pain (4–7), and severe pain (8–10). The Migraine Disability Assessment Scale (MIDAS) categories were none (0–5; reference within disability outcomes), mild (6–10), moderate (11–20), and severe (> 20). P-for-trend was calculated by assigning the median value of each tertile as a continuous variable in the adjusted regression models

Association of NEAP score with migraine outcomes

Higher NEAP tertiles were associated with progressively greater odds of severe pain (T2: OR 1.17, 95% CI 0.91–1.50, p = 0.224; T3: OR 1.38, 95% CI 1.08–1.66, p = 0.021; P-for-trend = 0.018) and severe disability (T3: OR 1.29, 95% CI 1.02–1.56, p = 0.044; P-for-trend = 0.039), with non-significant trends for moderate pain and other disability categories (all P-for-trend > 0.289). Higher NEAP was also linked to longer headache duration (T3: β 1.28, 95% CI 0.25–2.31, p = 0.015; P-for-trend = 0.013) (Table 5).

Association of DAI score with migraine outcomes

Higher DAI tertiles showed the strongest graded protective associations, with reduced odds of moderate pain (T2: OR 0.73, 95% CI 0.54–0.99, p = 0.042; T3: OR 0.59, 95% CI 0.41–0.83, p = 0.035; P-for-trend = 0.012) and severe pain (T2: OR 0.68, 95% CI 0.49–0.94, p = 0.019; T3: OR 0.47, 95% CI 0.33–0.74, p = 0.024; P-for-trend = 0.007). Similar patterns were observed for moderate (T3: OR 0.73, 95% CI 0.52–0.97, p = 0.048; P-for-trend = 0.031) and severe disability (T3: OR 0.69, 95% CI 0.47–0.91, p = 0.038; P-for-trend = 0.022), with a non-significant trend for mild disability (P-for-trend = 0.089). Higher DAI was associated with shorter headache duration (T3: β − 1.34, 95% CI − 2.33 to − 0.35, p = 0.008; P-for-trend = 0.006) (Table 6). Post-hoc power analyses for the observed effect sizes confirmed adequate power (> 80%) for most significant tertile comparisons and trends, particularly for DAI and severe outcomes (Supplementary Table S3).

Discussion

The present cross-sectional study investigated the associations between dietary quality (assessed via AHEI), NEAP, and DAI with migraine severity, disability, and duration among 280 Iranian women with migraine. Our findings showed that higher AHEI and DAI scores were inversely associated with migraine pain intensity and headache duration, while higher NEAP scores were positively linked to severe pain, disability, and longer headache episodes. These associations persisted after adjusting for potential confounders such as age, BMI, physical activity, medication use, and socioeconomic status, underscoring the potential role of dietary patterns in modulating migraine characteristics.

Our observation that higher adherence to the AHEI was associated with reduced odds of severe migraine pain aligns with emerging evidence linking overall diet quality to migraine outcomes [10, 22]. For instance, a cross-sectional study among Iranian women reported that greater AHEI adherence correlated with lower migraine severity, disability, and frequency, potentially due to the index’s emphasis on anti-inflammatory components like fruits, vegetables, and whole grains [42]. Similarly, adherence to high-quality diets such as the Mediterranean or DASH patterns sharing components with AHEI has been inversely associated with migraine frequency and severity in women [53, 54]. However, unlike our study, which found no significant link with MIDAS-assessed disability, some investigations using the Healthy Eating Index (HEI) have reported broader protective effects on disability metrics [55]. This discrepancy may stem from differences in population demographics or the specific components scored in each index, highlighting the need for standardized dietary quality assessments in migraine research.

While the AHEI assumes linear associations between its nine components (vegetables, fruits, whole grains, nuts and legumes, long-chain n-3 fatty acids, polyunsaturated fatty acids, sugar-sweetened beverages and fruit juices, and red/processed meat) and health outcomes like migraine burden, this may not fully reflect non-linear relationships in real-world nutrition [56]. For detrimental components, such as red/processed meat and sugar-sweetened beverages, minimum nutritional requirements should be considered; for example, red meat provides essential nutrients like heme iron, vitamin B12, and high-quality protein, with recommended intakes limited to 350–500 g/week (cooked weight) to balance benefits against risks like inflammation or colorectal cancer risk, as per guidelines from the World Cancer Research Fund (WCRF) [57]. Similarly, sugar-sweetened beverages offer negligible nutritional value but may contribute to hydration in minimal amounts, though guidelines like those from the World Health Organization (WHO) recommend limiting free sugars to < 10% of total energy intake to prevent metabolic disturbances. For beneficial components, excessive intake carries risks: high consumption of nuts and legumes (> 100 g/day) could lead to caloric surplus, weight gain, or digestive issues due to high fiber and phytates [58]; overconsumption of polyunsaturated fatty acids (PUFAs) and long-chain n-3 fatty acids (> 5 g/day EPA/DHA combined supplemental) might increase lipid peroxidation or bleeding risk if not balanced with antioxidants^ [64, 65]; and excessive fruits and vegetables (> 800 g/day) could result in gastrointestinal discomfort or nutrient imbalances (e.g., hyperkalemia) [59]. These thresholds, drawn from established nutritional guidelines (e.g., USDA Dietary Guidelines 2020–2025 and EFSA recommendations) [60], suggest that while higher AHEI scores were protective in our cohort, optimal migraine management may require personalized moderation to avoid potential adverse effects from extremes in intake.

The lack of significant association between AHEI and MIDAS disability grades, despite links with VAS pain intensity, may reflect limited statistical power rather than a true absence of effect. The wide confidence intervals for MIDAS outcomes suggest imprecision, potentially due to the smaller sample size per category and the ordinal nature of MIDAS, which could mask subtle associations. This nuance underscores the need for larger studies to confirm whether diet quality influences functional disability independently of pain severity.

Regarding dietary acid load, higher NEAP scores were associated with increased odds of severe pain and severe disability, consistent with prior studies indicating a deleterious role of acidogenic diets in migraine [16, 49]. A case-control survey among migraine patients found that elevated dietary acid load increased the odds of migraine diagnosis, possibly through mechanisms involving metabolic acidosis and heightened cortical excitability [15]. Another cross-sectional analysis in Iranian adults linked higher DAL to greater headache intensity and duration, mirroring our findings on NEAP and prolonged attacks [16, 18]. These associations may be mediated by acid load’s impact on systemic inflammation and endothelial dysfunction, which exacerbate neurogenic inflammation in migraine pathogenesis [11]. Notably, the scarcity of studies directly examining NEAP in migraine underscores our contribution, though the positive correlation observed here warrants caution, as alkaline diets (low NEAP) could offer therapeutic potential.

The strongest protective associations were observed with DAI, where higher scores correlated with reduced odds of moderate and severe pain and disability. This supports recent evidence on antioxidant-rich diets mitigating migraine burden [19, 20]. For example, higher Composite Dietary Antioxidant Index (CDAI) values—a metric akin to DAI—were inversely related to severe headache or migraine risk in large cohort studies, attributed to antioxidants’ role in countering oxidative stress, a key trigger in migraine [19, 20]. Cross-sectional data from women with migraine similarly showed that elevated dietary antioxidant quality scores reduced headache intensity and frequency, aligning with our results [21, 61].

Mechanistically, antioxidants like vitamins C and E, selenium, and β-carotene in DAI may attenuate reactive oxygen species, thereby reducing trigeminovascular activation and neuroinflammation [62, 63]. Our findings extend this by demonstrating DAI’s broad impact on VAS, MIDAS, and duration, suggesting antioxidant capacity as a modifiable factor in migraine management.

The underlying mechanisms linking these dietary indices to migraine likely involve overlapping pathways. High-quality diets (AHEI) may promote anti-inflammatory profiles, while low acid load (NEAP) maintains acid-base homeostasis, and antioxidants (DAI) combat oxidative damage—all implicated in migraine’s multifactorial etiology [7]. Inflammation, as measured by dietary inflammatory indices, has been inversely associated with migraine frequency in women, supporting our integrated findings [64, 65].

This study has several strengths, including a well-powered sample size calculated a priori, use of validated tools (e.g., FFQ, VAS, MIDAS), consecutive recruitment to minimize selection bias, and multivariable adjustments for confounders. The focus on women addresses sex-specific migraine prevalence, and the Iranian context adds diversity to the literature, often dominated by Western populations.

Limitations must be acknowledged. First, the cross-sectional design precludes causal inference, and reverse causation (e.g., migraine influencing dietary choices) cannot be ruled out. Second, dietary data relied on self-reported FFQ assessing intake over the previous year, which is susceptible to recall bias. Additionally, the AHEI scoring used sample-dependent deciles based on the study participants’ energy-adjusted intakes, rather than the absolute cut-off points from the original AHEI-2010 guidelines or a general reference population.

This relative method, while minimizing misclassification in our Iranian cohort with unique dietary norms, may limit external validity and comparability to studies using standardized absolute criteria. Moreover, this long-term exposure assessment introduces a temporal mismatch with migraine outcomes: VAS captures pain intensity over the past month, while MIDAS evaluates disability over the past three months. This discrepancy may lead to measurement error, as the FFQ assumes stable dietary patterns over a year influence shorter-term symptoms, potentially overlooking recent dietary fluctuations or acute triggers that could independently affect migraine severity, disability, or duration. Although we mitigated this by recruiting participants with recent migraine episodes (at least three in the preceding month) and excluding implausible intakes, the mismatch remains a major limitation that could attenuate or bias the observed associations. Third, selection bias may arise from recruiting only women at their first evaluation in specialized neurology clinics, requiring at least three migraine attacks in the preceding month. This likely selects for individuals with more severe or clinically managed migraine, potentially overestimating associations in milder or community-based cases. To mitigate, we used consecutive sampling from multiple clinics serving diverse socioeconomic groups, but the findings may not generalize to less severe or undiagnosed migraineurs, men, older adults, or those with complex health profiles.

Another limitation of this study is the modification of the AHEI-2010 by excluding alcohol and trans fatty acids, which may limit direct comparability with studies using the full 11-component index (scored 0-110). While this adaptation is necessary due to cultural, legal, and data availability constraints in the Iranian context and has been used in other Iranian studies without formal external validation [33,34], it could affect absolute score interpretations. However, it does not compromise relative associations within our cohort, as the modified index maintains internal consistency for the population studied. Future research could validate this version against biomarkers or compare it to full-index applications in diverse settings.

Moreover, the temporal mismatch between annual dietary assessment (FFQ) and shorter-term migraine outcomes (VAS: 1 month; MIDAS: 3 months) represents a significant limitation. This discrepancy likely introduced non-differential misclassification bias, potentially attenuating observed associations toward the null. The FFQ assumes that stable dietary patterns over one year influence recent symptoms but cannot capture acute dietary triggers or short-term fluctuations that may independently affect migraine severity. This fundamental mismatch may explain why some hypothesized associations (e.g., AHEI with MIDAS) were not statistically significant despite plausible biological mechanisms. Although we mitigated this by recruiting participants who had experienced recent attacks (≥ 3 in the preceding month) and excluding implausible intake data, the temporal discordance remains a core validity concern [66, 67]. Finally, while NEAP and DAI are established, their global means for normalization may not fully reflect Iranian dietary norms.

Conclusion

In conclusion, our findings suggest that higher diet quality, greater dietary antioxidant capacity, and lower dietary acid load are associated with reduced migraine severity, disability, and duration in women. These results highlight potential associations between dietary factors and migraine outcomes, but do not imply causality. Future prospective cohort studies and randomized controlled trials are needed to establish causality and evaluate the potential efficacy of dietary modifications in diverse populations.

Authors’ contributions

HG conceived the study, SKK, RH, SA, MMHMR, SSG and MF collected and analyzed the data, HG interpreted the statistical analyses and wrote the first draft of the manuscript. SKK contributed to the manuscript writing. MMHMR and HKh prepared the revision file. All of the authors critically revised the manuscript. The author(s) read and approved the final manuscript.

Funding

This research was financially supported by Zanjan University of Medical Sciences.

Data availability

The data underlying the findings of this study can be obtained from the corresponding author upon reasonable request. However, due to ethical and privacy concerns, the data are not publicly available.

Declarations

Ethics approval and consent to participate

All methods were performed in accordance with the Declaration of Helsinki guidelines and regulations. The study protocol received approval from the Ethics Committee of Zanjan University of Medical Sciences (IR.ZUMS.REC.1403.041). Prior to participation, all individuals provided written informed consent, affirming their voluntary participation after being fully informed about the study’s purpose, methodology, potential risks, and benefits.

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.