Introduction: Medication-overuse headache (MOH) is a secondary chronic headache disorder that occurs in individuals with a pre-existing primary headache disorder, particularly migraine disorder. Obesity is often combined with chronic daily headaches and is considered a risk factor for the transformation of episodic headaches into chronic headaches. However, the association between obesity and MOH among individuals with migraine has rarely been studied. The present study explored the association between body mass index (BMI) and MOH in people living with migraine. Methods: This cross-sectional study is a secondary analysis of data from the Survey of Fibromyalgia Comorbidity with Headache study. Migraine and MOH were diagnosed using the criteria of the International Classification of Headache Disorders, 3rd Edition. BMI (kg/m2) is calculated by dividing the weight (kg) by the square of the height (m). Multivariable logistic regression analysis was used to evaluate the association between BMI and MOH. Results: A total of 2,251 individuals with migraine were included, of whom 8.7% (195/2,251) had a concomitant MOH. Multivariable logistic regression analysis, adjusted for age, sex, education level, headache duration, pain intensity, headache family history, chronic migraine, depression, anxiety, insomnia, and fibromyalgia, demonstrated there was an association between BMI (odds ratio [OR], 1.05; 95% confidence interval [CI], 1.01–1.11; p = 0.031) and MOH. The results remained when the BMI was transformed into a category. Compared to individuals with Q2 (18.5 kg/m2 ≤ BMI ≤23.9 kg/m2), those with Q4 (BMI ≥28 kg/m2) had an adjusted OR for MOH of 1.81 (95% CI, 1.04–3.17; p = 0.037). In the subgroup analyses, BMI was associated with MOH among aged more than 50 years (OR, 1.13; 95%, 1.03–1.24), less than high school (OR, 1.08; 95%, 1.01–1.15), without depression (OR, 1.06; 95%, 1.01–1.12), and without anxiety (OR, 1.06; 95%, 1.01–1.12). An association between BMI and MOH was found in a sensitivity analysis that BMI was classified into four categories according to the World Health Organization guidelines. Conclusion: In this cross-sectional study, BMI was associated with MOH in Chinese individuals with migraine.

Medication-overuse headache (MOH) is a secondary headache disorder that occurs in individuals with a preexisting primary headache disorder, particularly migraine disorder [1], because of frequent overuse of pain medication [2]. In the general population, an average prevalence of MOH is about 3.4% (median 2.6%), with range of 0.6–7.1% [3], especially in female sex and in patients under 50 years of age [4‒6]. According to a 2015 Global Burden of Disease (GBD) study, it was estimated that around 59 million people worldwide have MOH, ranking third in the prevalence of neurological disorders [7, 8]. The burden from noncommunicable diseases has significantly surpassed that of communicable diseases as the population ages [9, 10]. Notably, in the 2013 GBD study, MOH, categorized as a noncommunicable disease, ranked 18th among all leading causes of global years lived with disability [11]. Although previous studies confirmed female sex, low educational level, preexisting headache type, headache pain intensity, insomnia, depression, anxiety, and fibromyalgia as risk factors for MOH [3, 5, 12], there is an urgent need to explore other potential modifiable risk factors associated with the prevalence of MOH.

Obesity is often combined with chronic daily headache (CDH) and is considered a risk factor for the transformation of episodic headache into chronic headache [13]. Moreover, previous population-based study in Chinese mainland has found that MOH accounts for 60% of headache patients with CDH [14]. However, the association between obesity and MOH has rarely been studied. Although a population-based survey from Mongolia reported a significant association between possible MOH and obesity (odds ratio [OR], 1.8; 95% confidence interval [CI], 1.1–2.9), other potential confounders were not considered [15]. In addition, a previous study reported that MOH in women with migraine is associated with abdominal obesity, while the association between body mass index (BMI) and MOH in Chinese individuals with migraine remains unclear [16].

To fill this knowledge gap, we used the data from the Survey of Fibromyalgia Comorbidity with Headache (SEARCH) to explore the association between BMI and MOH. We hypothesized that BMI would be positively associated with MOH in individuals with migraine.

Study Population

This cross-sectional study is a secondary analysis of data from the SEARCH study, which included patients with primary headache disorders attending 23 headache centers in China between September 2020 and May 2021 [17]. The detailed design of the study and the baseline characteristics of the participants have been previously reported [17]. The study was approved by the Medical Ethics Committee of the General Hospital of the Chinese People's Liberation Army (approval number: S2020-238-01) and registered with the China Clinical Trials Registry (registration number: ChiCTR2000034894). Informed written agreement was obtained from all attendees.

Participants with a diagnosis of migraine were included in this study and those with a diagnosis of tension-type headache, trigeminal autonomic cephalgias, or other primary headache disorders were excluded. In addition, we excluded participants with missing information about BMI.

BMI and MOH Assessment

BMI (kg/m2) is calculated by dividing the weight (kg) by the square of the height (m). According to the Chinese weight standards for adults, weight is classified into four categories based on BMI: underweight (<18.5 kg/m2), normal weight (18.5–23.9 kg/m2), overweight (24.0–27.9 kg/m2), and obese (≥28.0 kg/m2) [18]. Migraine and MOH were diagnosed by a headache expert using the criteria of the International Classification of Headache Disorders, 3rd Edition [2]. MOH is defined as patients with primary headache disorders that occurred ≥15 days/month for >3 months and are associated with frequent (≥10 days/month or ≥15 days per month, depending on the medication) and long-term (≥3 months) overuse of medication for acute or symptomatic headache [2].

Covariates

Based on previous studies, the following covariates were included: age, sex, education level, depression, anxiety, insomnia, fibromyalgia, headache duration, intensity, chronic migraine, and headache family history [4‒6, 19]. Education level was divided into two categories: high school or lower and junior college or higher. Depression and anxiety were assessed using the Hospital Anxiety and Depression Scale (HADS) [20], with scores of ≥10 on the Hospital Depression Scale and ≥13 on the Hospital Anxiety Scale defining depression and anxiety, respectively [21]. The Insomnia Severity Index is used to determine insomnia, and a score of ≥11 is defined as insomnia [22]. Fibromyalgia was diagnosed based on the modified 2010 American College of Rheumatology criteria [23]. A visual analog scale was used to assess the intensity of pain, with a score of 0 to 10 indicating from no pain to the most intense pain [24].

Statistical Analysis

Categorical data are presented as frequencies (%), while continuous data are presented as means (standard deviation) or medians (interquartile range) accordingly. One-way analyses of variance or Kruskal-Wallis tests were used to compare the differences across groups based on the normality of the continuous distribution. The χ2 test was used to compare categorical variables among groups. To assess the association between BMI and MOH, multivariable logistic regression analysis was performed. BMI was entered as a continuous variable and as a categorical variable. The variables included in the model were since the variable was theoretically important in explaining the exposure-outcome relationship [4‒6, 19]. Model 1 was adjusted for age, sex, and education level. Further analyses cumulatively included adjustment for headache duration, pain intensity, headache family history, chronic migraine (model 2), depression, anxiety, insomnia, and fibromyalgia (model 3).

Furthermore, potential modifiers of the association between BMI and MOH were assessed, including age (<50 vs. ≥ 50 years), sex (male vs. female), education level (≤ high school vs. > high school), depression, anxiety, insomnia, and fibromyalgia [4]. Heterogeneity and interactions between subgroups were examined using multivariable logistic regression analysis and the likelihood ratio testing, respectively.

To robust the results, BMI is classified into four categories according to the World Health Organization guidelines: underweight (<18.5 kg/m2), normal weight (18.5 to <25 kg/m2), overweight (25 to <30 kg/m2), and obese (≥30 kg/m2). In addition, multiple imputations based on five replications were used for individuals with missing data (missing rate was 6.9%) about covariates.

There is no prior calculation of statistical power as the sample size is based entirely on available data. All analyses were performed using R 4.2.3 (http://www.R-project.org; The R Foundation, Vienna, Austria) and the Free Statistics software (version 1.8; Beijing Free Clinical Medical Technology Co., Ltd, Beijing, China) [25]. A two-tailed p value <0.05 was considered significant.

Study Population

Of the 3,044 patients recruited from 23 hospitals, 2,258 patients with a diagnosis of migraine were included (shown in Fig. 1). Seven patients were excluded due to lack of BMI data, and finally 2,251 patients were included in the final analysis.

Fig. 1.

Flowchart.

Baseline Characteristics

The baseline characteristics of participants are shown in Table 1. According to the ICHD-3 criteria, 8.7% (195/2,251) of the individuals with migraine have a combination of MOH. The average age of the patients was 41.0 (12.4) years and 77.5% (1,745/2,251) were female. BMI was generally higher among older participants (40.0 vs. 34.4 years) and had a longer duration of headache (10.0 vs. 7.0 years). They were also more likely to have chronic migraine (20.6% [95/462] vs. 15.8% [25/158]), depression (12.3% [57/462] vs. 9.5% [15/158]), fibromyalgia (5.4% [25/462] vs. 1.9% [3/158]), and MOH (11.0% [51/462] vs. 7.0% [11/158]).

Table 1.

Baseline characteristics of individuals with migraine

VariablesBMI, kg/m2p value
totalQ1, <18.5 kg/m2Q2, 18.5–23.9 kg/m2Q3, 24.0–27.9 kg/m2Q4, ≥28.0 kg/m2
N 2,251 158 1,127 504 462  
Age, years, mean (SD) 41.0 (12.4) 34.4 (11.7) 41.1 (12.5) 43.7 (12.3) 40.0 (11.7) <0.001 
Gender, n (%)      <0.001 
 Man 506 (22.5) 12 (7.6) 222 (19.7) 184 (36.5) 88 (19.0)  
 Woman 1,745 (77.5) 146 (92.4) 905 (80.3) 320 (63.5) 374 (81.0)  
Education levela, n (%)      <0.001 
 ≤ High school 955 (42.4) 59 (37.3) 477 (42.3) 260 (51.6) 159 (34.4)  
 > High school 1,140 (50.6) 92 (58.2) 568 (50.4) 211 (41.9) 269 (58.2)  
Headache duration, years, median (IQR) 10.0 (4.0, 15.0) 7.0 (3.0, 11.8) 10.0 (4.0, 15.4) 10.0 (4.0, 20.0) 10.0 (4.0, 15.0) 0.042 
VAS, mean (SD) 6.7 (1.7) 6.4 (1.8) 6.6 (1.7) 6.7 (1.7) 7.0 (1.6) <0.001 
Chronic migraine, n (%) 448 (19.9) 25 (15.8) 198 (17.6) 130 (25.8) 95 (20.6) <0.001 
Headache family history, n (%) 996 (44.2) 70 (44.3) 498 (44.2) 207 (41.1) 221 (47.8) 0.215 
Depressionb, n (%) 256 (11.4) 15 (9.5) 111 (9.8) 73 (14.5) 57 (12.3) 0.037 
Anxietyb, n (%) 74 (3.3) 5 (3.2) 32 (2.8) 17 (3.4) 20 (4.3) 0.511 
Insomniac, n (%) 445 (19.8) 28 (17.7) 221 (19.6) 110 (21.8) 86 (18.6) 0.539 
Fibromyalgia, n (%) 117 (5.2) 3 (1.9) 59 (5.2) 30 (6.0) 25 (5.4) 0.249 
MOH, n (%) 195 (8.7) 11 (7.0) 73 (6.5) 60 (11.9) 51 (11.0) <0.001 
VariablesBMI, kg/m2p value
totalQ1, <18.5 kg/m2Q2, 18.5–23.9 kg/m2Q3, 24.0–27.9 kg/m2Q4, ≥28.0 kg/m2
N 2,251 158 1,127 504 462  
Age, years, mean (SD) 41.0 (12.4) 34.4 (11.7) 41.1 (12.5) 43.7 (12.3) 40.0 (11.7) <0.001 
Gender, n (%)      <0.001 
 Man 506 (22.5) 12 (7.6) 222 (19.7) 184 (36.5) 88 (19.0)  
 Woman 1,745 (77.5) 146 (92.4) 905 (80.3) 320 (63.5) 374 (81.0)  
Education levela, n (%)      <0.001 
 ≤ High school 955 (42.4) 59 (37.3) 477 (42.3) 260 (51.6) 159 (34.4)  
 > High school 1,140 (50.6) 92 (58.2) 568 (50.4) 211 (41.9) 269 (58.2)  
Headache duration, years, median (IQR) 10.0 (4.0, 15.0) 7.0 (3.0, 11.8) 10.0 (4.0, 15.4) 10.0 (4.0, 20.0) 10.0 (4.0, 15.0) 0.042 
VAS, mean (SD) 6.7 (1.7) 6.4 (1.8) 6.6 (1.7) 6.7 (1.7) 7.0 (1.6) <0.001 
Chronic migraine, n (%) 448 (19.9) 25 (15.8) 198 (17.6) 130 (25.8) 95 (20.6) <0.001 
Headache family history, n (%) 996 (44.2) 70 (44.3) 498 (44.2) 207 (41.1) 221 (47.8) 0.215 
Depressionb, n (%) 256 (11.4) 15 (9.5) 111 (9.8) 73 (14.5) 57 (12.3) 0.037 
Anxietyb, n (%) 74 (3.3) 5 (3.2) 32 (2.8) 17 (3.4) 20 (4.3) 0.511 
Insomniac, n (%) 445 (19.8) 28 (17.7) 221 (19.6) 110 (21.8) 86 (18.6) 0.539 
Fibromyalgia, n (%) 117 (5.2) 3 (1.9) 59 (5.2) 30 (6.0) 25 (5.4) 0.249 
MOH, n (%) 195 (8.7) 11 (7.0) 73 (6.5) 60 (11.9) 51 (11.0) <0.001 

Depression defined as Hospital Depression Scale ≥10.

Anxiety defined as Hospital Anxiety Scale ≥13.

Insomnia defined as Insomnia Severity Index ≥11.

BMI, body mass index; VAS, visual analog scale; MOH, medication-overuse headache; SD, standard deviation; IQR, interquartile range.

aData missing for 156 (7.0%) patients.

bData missing for 11 (0.5%) patients.

cData missing for 13 (0.9%) patients.

Association between BMI and MOH

A high BMI was associated with an increased prevalence of MOH (OR, 1.05; 95% CI, 1.01–1.11; p = 0.031), after adjusting for age, sex, education level, headache duration, pain intensity, headache family history, chronic migraine, depression, anxiety, insomnia, and fibromyalgia. The association was maintained when the BMI was transformed into a categorical variable. Compared to individuals with Q2 (18.5 kg/m2 ≤ BMI ≤23.9 kg/m2), those with Q4 (BMI ≥28 kg/m2) had an adjusted OR for MOH of 1.81 (95% CI, 1.04–3.17; p = 0.037; Table 2).

Table 2.

Association between BMI and MOH in individuals with migraine

Total, NNo. event (%)Model 1, OR (95% CI)p valueModel 2, OR (95% CI)p valueModel 3, OR (95% CI)p value
BMI, kg/m2 2,095 177 (8.4) 1.04 (1.01–1.08) 0.012 1.06 (1.01–1.11) 0.021 1.05 (1.01–1.11) 0.031 
BMI groups 
 Q1, <18.5 kg/m2 151 11 (7.3) 1.58 (0.80–3.11) 0.190 1.61 (0.65–3.97) 0.299 1.68 (0.68–4.19) 0.262 
 Q2, 18.5–23.9 kg/m2 1,045 69 (6.6) 1 (ref)  1 (ref)  1 (ref)  
 Q3, 24.0–27.9 kg/m2 471 55 (11.7) 1.61 (1.10–2.36) 0.014 1.44 (0.88–2.36) 0.146 1.45 (0.88–2.38) 0.147 
 Q4, ≥28.0 kg/m2 428 42 (9.8) 1.74 (1.15–2.62) 0.008 1.84 (1.06–3.21) 0.031 1.81 (1.04–3.17) 0.037 
Total, NNo. event (%)Model 1, OR (95% CI)p valueModel 2, OR (95% CI)p valueModel 3, OR (95% CI)p value
BMI, kg/m2 2,095 177 (8.4) 1.04 (1.01–1.08) 0.012 1.06 (1.01–1.11) 0.021 1.05 (1.01–1.11) 0.031 
BMI groups 
 Q1, <18.5 kg/m2 151 11 (7.3) 1.58 (0.80–3.11) 0.190 1.61 (0.65–3.97) 0.299 1.68 (0.68–4.19) 0.262 
 Q2, 18.5–23.9 kg/m2 1,045 69 (6.6) 1 (ref)  1 (ref)  1 (ref)  
 Q3, 24.0–27.9 kg/m2 471 55 (11.7) 1.61 (1.10–2.36) 0.014 1.44 (0.88–2.36) 0.146 1.45 (0.88–2.38) 0.147 
 Q4, ≥28.0 kg/m2 428 42 (9.8) 1.74 (1.15–2.62) 0.008 1.84 (1.06–3.21) 0.031 1.81 (1.04–3.17) 0.037 

Model 1 was adjusted for age, sex, and education level.

Model 2 was adjusted for model 1 + headache duration, pain intensity, headache family history, and chronic migraine.

Model 3 was adjusted for model 2 + depression, anxiety, insomnia, and fibromyalgia.

BMI, body mass index; MOH, medication-overuse headache; OR, odds ratio; CI, confidence interval.

Subgroup Analyses

The subgroup analyses of the roles of age, sex, education level, depression, anxiety, insomnia, and fibromyalgia are presented in Figure 2. BMI was associated with MOH among aged more than 50 years (OR, 1.13; 95%, 1.03–1.24), less than high school (OR, 1.08; 95%, 1.01–1.15), without depression (OR, 1.06; 95%, 1.01–1.12), and without anxiety (OR, 1.06; 95%, 1.01–1.12). A p value of less than 0.05 for the interaction of age may not be statistically significant considering multiple testing.

Fig. 2.

Association between BMI and MOH. Except for the stratification component itself, each stratification factor was adjusted for all other variables (age, sex, education level, headache duration, pain intensity, headache family history, chronic migraine, depression, anxiety, insomnia, and fibromyalgia). The purple diamonds represent the overall odds ratio (OR), while the outer dots on the diamonds represent the 95% confidence intervals (CI). The blue diamonds represent the OR, and the horizontal line represents the 95% CI.

Fig. 2.

Association between BMI and MOH. Except for the stratification component itself, each stratification factor was adjusted for all other variables (age, sex, education level, headache duration, pain intensity, headache family history, chronic migraine, depression, anxiety, insomnia, and fibromyalgia). The purple diamonds represent the overall odds ratio (OR), while the outer dots on the diamonds represent the 95% confidence intervals (CI). The blue diamonds represent the OR, and the horizontal line represents the 95% CI.

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Sensitivity Analysis

When we classified weight according to WHO guidelines, compared to patients in the Q2 (18.5 kg/m2 ≤ BMI <25 kg/m2) of BMI, the adjusted OR for MOH in patients in Q4 (BMI ≥30 kg/m2) was 2.42 (95% CI, 1.23–4.77; p = 0.010; Table 3). We performed multiple imputations of missing covariates, and the association between BMI and MOH remained stable. Higher BMI was associated with an increased MOH prevalence (OR, 1.06; 95% CI, 1.01–1.11; p = 0.015). Compared to individuals with Q2 (18.5 kg/m2 ≤ BMI ≤23.9 kg/m2), those with Q4 (BMI ≥28 kg/m2) had an adjusted OR of 2.00 (95% CI, 1.19–3.37; p = 0.009; Table 3) for MOH.

Table 3.

Sensitivity analysis

Total, NNo. event (%)Adjusted OR (95% CI)p value
Classification according to WHO criteria 
BMI groups 
 Q1, <18.5 kg/m2 151 11 (7.3) 1.73 (0.70–4.26) 0.236 
 Q2, 18.5 to <25 kg/m2 1,127 84 (6.9) 1 (ref)  
 Q3, 25 to <30 kg/m2 425 54 (12.7) 1.71 (1.05–2.80) 0.032 
 Q4, ≥30 kg/m2 302 28 (9.3) 2.42 (1.23–4.77) 0.010 
Multiple imputation of missing data 
BMI, kg/m2 2,251 195 (8.7) 1.06 (1.01–1.11) 0.015 
BMI groups 
 Q1, <18.5 kg/m2 158 11 (7.0) 1.64 (0.67–4.01) 0.281 
 Q2, 18.5–23.9 kg/m2 1,127 73 (6.5) 1 (ref)  
 Q3, 24–27.9 kg/m2 504 60 (11.9) 1.38 (0.86–2.23) 0.186 
 Q4, ≥28 kg/m2 462 51 (11.0) 2.00 (1.19–3.37) 0.009 
Total, NNo. event (%)Adjusted OR (95% CI)p value
Classification according to WHO criteria 
BMI groups 
 Q1, <18.5 kg/m2 151 11 (7.3) 1.73 (0.70–4.26) 0.236 
 Q2, 18.5 to <25 kg/m2 1,127 84 (6.9) 1 (ref)  
 Q3, 25 to <30 kg/m2 425 54 (12.7) 1.71 (1.05–2.80) 0.032 
 Q4, ≥30 kg/m2 302 28 (9.3) 2.42 (1.23–4.77) 0.010 
Multiple imputation of missing data 
BMI, kg/m2 2,251 195 (8.7) 1.06 (1.01–1.11) 0.015 
BMI groups 
 Q1, <18.5 kg/m2 158 11 (7.0) 1.64 (0.67–4.01) 0.281 
 Q2, 18.5–23.9 kg/m2 1,127 73 (6.5) 1 (ref)  
 Q3, 24–27.9 kg/m2 504 60 (11.9) 1.38 (0.86–2.23) 0.186 
 Q4, ≥28 kg/m2 462 51 (11.0) 2.00 (1.19–3.37) 0.009 

Adjusted for age, sex, education level, headache duration, pain intensity, headache family history, chronic migraine, depression, anxiety, insomnia, and fibromyalgia.

BMI, body mass index; MOH, medication-overuse headache; OR, odds ratio; CI, confidence interval.

In this cross-sectional study, BMI was found to be associated with MOH in individuals with migraine from the Chinese mainland. Subgroup analyses and sensitivity analysis demonstrated that the associations between BMI and MOH were robust.

A previous population-based survey from Mongolia showed that obesity was associated with possible MOH without adjusting potential confounders [15]. In addition, a previous clinic-based study from China reported that MOH was associated with abdominal obesity in female individuals with migraine. The German DMKG Headache Study reported that MOH patients typically have a BMI ≥30 kg/m2 compared with episodic migraine [26]. However, few studies have evaluated the association between BMI and MOH among Chinese population. In our study, individuals with migraine with higher BMI had a higher risk of MOH after adjusting for age, sex, education level, headache duration, pain intensity, headache family history, chronic migraine, depression, anxiety, insomnia, and fibromyalgia, which was consistent with the previous studies. Moreover, our study focused on the participants with migraine from Chinese population, which may complement previous studies. Therefore, we need to pay attention to the fact that there was an association between BMI and MOH. However, whether weight control can reduce the prevalence of MOH needs further confirmation in a future cohort study.

Our findings suggest that in China, the higher the BMI, the higher the prevalence of MOH. As one of the BRICS countries, we should manage our healthcare system more effectively to reduce the burden of disease caused by MOH [27]. We must devote greater attention to the obese population and ensure a commensurate enhancement of medical resources. However, the varying economic landscapes across countries and regions present a significant hurdle in strategizing the allocation of these resources [27]. In the future, we can conduct further research on health economics for the MOH population, which will provide a basis for rational allocation of medical resources in the future.

The mechanisms underlying the associations between obesity and MOH are not fully understood, but our findings are biologically plausible based on the available evidence. First, addiction is involved in the development of obesity and MOH. Previous findings demonstrated that there was an association between overweight/obesity, food, and addiction [28], proposing an addiction-like eating concept as a potential interpretation of overweight and obesity [29, 30]. Some clinical studies reported that MOH patients have compulsive medication-taking behaviors or withdrawal symptoms after stopping medication [31], suggesting that addiction is involved in the pathogenesis of MOH [32]. Moreover, imaging studies suggest that dopaminergic pathways, which are implicated in substance addiction, may be impaired in obese individuals and those with MOH [32‒34]. The preclinical and clinical studies conducted thus far have presented robust evidence that obese individuals demonstrate reduced metabolic activity within the orbitofrontal cortex (OFC) [35]. Given the association between OFC dysfunction and the onset of obsessive-compulsive disorder [36], it is reasonable to hypothesize that this decrease in metabolic activity acts as a catalyst for hyperphagia. Furthermore, the OFC is a critical component in complex decision-making processes [37], and any disturbances in its function can increase an individual’s vulnerability to medication misuse. Additionally, the OFC plays a vital role in nociceptive modulation, interfacing with the periaqueductal gray, a primary center for pain modulation and relief [38]. Based on these findings, we propose that obesity is associated with compromised OFC function, leading to weakened nociceptive inhibition and compromised decision-making abilities. As a result, obese individuals may be more prone to experiencing headaches and developing a reliance on analgesics. Secondly, obesity is considered a pro-inflammatory state [39]. A variety of inflammatory factors, including interleukin-6, tumor necrosis factor-alpha, and C-reactive protein are elevated in individuals with obese [40]. Additionally, previous study demonstrated that plasma calcitonin gene-related peptide (CGRP) levels are elevated in obese individuals, especially in female [41]. CGRP is thought to have a vital role in the development of MOH [3]. In animals, prolonged exposure to analgesics upregulates CGRP in the dorsal root ganglia [42‒45]. The pro-inflammatory states and elevated CGRP levels in obese patients make individuals with migraine more susceptible to central sensitization, which is associated with increased frequency of migraine attacks, leading to overuse of medication and increased risk of MOH. In conclusion, we hypothesized that addiction, pro-inflammatory states, and elevated CGRP levels in obese patients contribute to a greater susceptibility to MOH in individual with migraine. However, the association between obesity and MOH needs to be further confirmed in future cohort studies.

Few previous studies have investigated the association and interaction between BMI and MOH in different subgroups. Even if some subgroups had a large OR span, which was thought to be related to sample size. However, the overall effect size suggested that BMI was positively associated with MOH. In addition, the interaction between subgroups was insignificant, indicating that the differences were not statistically significant. Given the complexity of this association, there is a need to explore the underlying mechanisms and conduct longitudinal studies in larger populations to clarify further the potential role and means by which BMI was positively associated with MOH.

The main strengths of this study are its multicenter design and the large sample size. However, there were some limitations to be aware of. First, this study included patients attending hospitals, and therefore, the results need to be cautiously generalized to general population. Second, as the diagnosis of migraine was determined on the predominant headache characteristics at the time of presentation, a combination of multiple headache types in the same patient cannot be absolutely ruled out. Third, due to the limitations of the cross-sectional study design itself, we were only able to conclude that there was an association between obesity and MOH, and could not draw causal inferences. Future prospective cohort studies are required to investigate the cause-and-effect association between BMI and MOH. Fourth, because of the limitations of BMI in diagnosing obesity, we need to be cautious in concluding that there is an association between obesity and MOH.

The present cross-sectional study demonstrated that BMI is positively associated with MOH in individuals with migraine. Therefore, the potential association between obesity and MOH should be considered in the treatment and prevention of these conditions.

We thank all the patients who took part in the study.

The study was approved by the Medical Ethics Committee of the General Hospital of the Chinese People's Liberation Army (approval number: S2020-238-01) and registered with the China Clinical Trials Registry (registration number: ChiCTR2000034894). Informed written agreement was obtained from all attendees in compliance with the Helsinki.

The authors have no conflicts of interest to declare.

This study was not supported by any sponsor or funder.

H.L., M.D., K.L., Z.J., W.G., Y.C., Y.L., K.Q., H.R.Z., J.C., D.Z., Z.F., X.Y., D.H., H.X., M.L., B.W., S.C., P.X., Q.R., Q.H., Z.R., F.Y., H.Z., M.C., T.Y., H.Q., X.A., H.G., X.Z., X.P., X.W., S.Q., L.Z., H.Z., X.P., Q.W., L.Y., J.L., Z.Y., M.Z., Y.R., and X.H. analyzed and interpreted the data and results and drafted the manuscript. H.L., M.D., Z.D., and S.Y. revised the manuscript. S.Y. and Z.D. proposed the concept and design of the study and revised the manuscript for critical intellectual content. H.L. and Z.D. revised the manuscript for critical intellectual content. All authors had access to the data and read and approved the final manuscript.

Additional Information

Huanxian Liu and Hongru Zhao contributed equally to this work.

Due to ethical restrictions, the analyzed data in this study are not available to the public but can be requested from Prof. Dong Zhao. For further inquiries, please contact Prof. Dong Zhao.

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