https://orcid.org/0009-0006-6857-2728
Figures
Abstract
Background
The importance of obesity as a factor that increases the probability of migraine episodes is increasingly acknowledged. Thus, this study aimed to explore the potential correlation between central obesity and migraine, emphasizing the waist-to-height ratio (WHtR) as a key measure in assessing this relationship.
Methods
This cross-sectional analysis included 13,344 individuals who participated in the National Health and Nutrition Examination Survey (NHANES) from 1999–2004. To investigate the association associations between WHtR and migraine, we utilized refined multivariate logistic regression models, smoothing curve fitting methods, subpopulation analysis, and interactive testing.
Results
Of the 13,344 participants, 2,764 (20.72%) had migraines. A significant positive correlation was observed between the WHtR and migraine incidence in both the partially adjusted model (3.08 [95% CI: 1.92–4.94]) and the crude model (1.95 [95% CI: 1.23–3.08]). The participants in the highest quartile of the WHtR had a 13% greater incidence of migraine than those in the lowest quartile [1.13(0.99,1.28)]. The interaction analysis revealed a statistically significant difference (p<0.01) in this relationship among the subgroups. Notably, the correlation between WHtR and migraine risk was not significant and negative in patients ≥60 years, indicating that obesity has a mitigating role in preventing migraine in this elderly population.
Conclusions
The incidence of migraine increased concomitantly with increased WHtR. However, central obesity has a protective effect against migraine occurrence in individuals ≥60 years. Thus, our findings underscore the importance of WHtR in migraine prevention and management strategies and highlight its potential as a critical biomarker for mitigating migraine incidence.
Citation: Jin J, Zheng Y, Gao T, Lin X, Li S, Huang C (2024) Associations between the waist-to-height ratio index and migraine: A cross-section study of the NHANES 1999–2004. PLoS ONE 19(10): e0312321. https://doi.org/10.1371/journal.pone.0312321
Editor: Aleksandra Klisic, University of Montenegro-Faculty of Medicine, MONTENEGRO
Received: June 27, 2024; Accepted: September 13, 2024; Published: October 23, 2024
Copyright: © 2024 Jin et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Data Availability: The data of this study are publicly available for free from the NHANES database(https://www.cdc.gov/nchs/nhanes/).
Funding: The author(s) received no specific funding for this work.
Competing interests: All authors declare that they have no competing financial interests.
Background
A global burden of disease (GBD) study revealed that migraine is a significant global public health concern and one of the most prevalent and debilitating medical conditions [1]. Fifteen percent of adults in the United States endure migraine attacks each year. This disorder is characterized by recurrent intense headache episodes and significantly impacts a person’s overall quality of life [2]. Therefore, identifying modifiable or preventable factors is critical for minimizing the morbidity associated with migraine. A global surge in the prevalence of obesity, encompassing >200 million individuals categorized as overweight and representing about 30% of the world’s population, has been reported [3]. Despite the complexities of migraine pathogenesis, the obesity-related inflammatory milieu significantly exacerbates migraine symptoms [4]. Consequently, a thorough correlation analysis between obesity and migraine incidence could provide crucial insights into the potential interplay between them.
Traditional obesity metrics, such as body mass index and waist circumference (WC) present certain limitations. The utilization of the Body Mass Index (BMI) is inadequate for distinguishing individuals with excessive adipose tissue and those with a higher muscular composition, and it fails to explain the variations in body fat distribution [5]. Although WC is a central obesity indicator, its precision is significantly impacted by variables such as age and height. Thus, stature and the manifestation of central obesity should be considered in individuals with elevated metabolic risk. Given this context, the WHtR can be a potentially advantageous alternative metric that comprehensively assesses obesity-related health risks [6]. A prospective investigation and meta-analyses have shown that the WHtR is either equivalent to marginally better than WC and surpasses BMI in predicting increased cardiometabolic risk [7]. The pathogenesis of migraine is complex, with obesity status considered a significant and critical risk factor [8]. Although the WHtR is well known as an effective tool for predicting the risk of several cardiovascular diseases, to date, there is a paucity of research on the possible association between WHtR and migraine. If this potential association could be scientifically validated, it could provide a novel and powerful tool for healthcare professional teams in dealing with migraine attack management and care strategies. Therefore, the present study aimed to investigate whether there is a significant association between WHtR and migraine headaches through a rigorous cross-sectional analysis based on the rich data resources of the NHANES from 1999–2004, with the aim of providing new perspectives and rationales for the prevention and treatment of migraine headache in the future.
Materials and methods
Study population
Conducted by the Centers for Disease Control and Prevention (CDC), the NHANES is a unique program that uses interviews, physical examinations, and laboratory evaluations to assess the health and nutritional status of American participants [9]. Thus, a stratified multistage probability sampling method was adopted to create a representative sample reflecting the nation’s diverse demographic traits. This methodology guarantees the reliability and validity of the results when they are extrapolated to other American populations [10]. All study participants underwent rigorous home interviews, encompassing comprehensive physical examinations and laboratory assessments. Before the study commenced, these procedures were assessed and approved by the Research Ethics Review Committee of the National Center for Health Statistics (NCHS) in compliance with the strictest ethical norms. Furthermore, all participants provided written informed consent for ethical soundness [11]. A comprehensive synopsis of the NHANES study is available on the official website at https://www.cdc.gov/nchs/nhanes/, providing comprehensive details regarding the program’s range and research approach.
Thus, we used the NHANES dataset from 1999–2004 for analysis. Our study specifically included participants whose comprehensive data concerning migraine episodes and WHtR were available. Initially, we recruited 31,126 individuals. However, we excluded those with missing or incomplete data regarding WC (n = 2,926), height (n = 2,670), and migraine (n = 12,186) to maintain accuracy. Consequently, our final cohort comprised 13,344 participants (Fig 1).
NHANES, National Health and Nutrition Examination Survey.
Definition of WHtR
The WHtR is a popular and novel assessment indicator. It is a reliable marker for evaluating obesity levels, with a higher score indicating greater obesity rates. More specifically, the WHtR is obtained by dividing an individual’s WC in centimeters by their height [7]. Anthropometric assessments, administered by qualified medical staff in mobile examination facilities, were recorded by dedicated professionals for data integrity. A digital scale is employed for accurate weight measurements. Before the weighing procedure, the participants were dressed in clothing suitable for the examination, and then stood without shoes in the middle of the calibrated scale, keeping their arms at their sides and a forward gaze. This standardized protocol guarantees reliable weight measurements for subsequent analysis [12]. For the anthropometric assessments, a tape measure was used to determine WC precisely. To ensure accuracy, the tape was positioned precisely at the intersection of the midline extending from the axillary region and the horizontal line superior to the right kneecap’s outermost border [13]. We also designated WHtR as the primary exposure variable under investigation.
Migraine definition
A comprehensive questionnaire was utilized to evaluate migraine-associated symptoms, emphasizing various manifestations and attributes of pain. The participants were assumed to have experienced migraine if their response to this query, “During the past three months, did you have severe headaches or migraines?” was affirmative. Thus, migraine was designated as a dependent variable, representing the primary focus of our analysis. Although migraine is not a formal diagnosis following the International Classification of Headache Disorders (ICHD) criteria, the findings of the American Migraine Prevalence and Prevention Study indicate that a significant proportion of individuals reporting severe headaches fulfill the ICHD-II criteria for migraine or probable migraine [14]. This classification has also been previously used in multiple migraine-related analyses, thereby providing a unified front for assessing migraine data [15,16].
Covariates
We incorporated several covariates that might be effective in revealing the association between WHtR and migraine. These covariates included demographic factors such as age, sex, race, education level, hypertension, smoking, diabetes, the poverty-to-income ratio (PIR), and alcohol consumption in the past 12 months.
Statistical analysis
The statistical procedures were carried out in strict adherence to the methodological principles and recommendations outlined by the CDC. We evaluated the participants’ distinctive features by segmenting them into WHtR-based quartiles. Furthermore, we applied statistical tests, such as chi-square or t-test, to examine the variations in characteristics across the quartiles. The participants’ demographics were analyzed across WHtR quartiles using the chi-square test. To investigate the linear associations between WHtR and migraine, we utilized multivariate linear regression and logistic regression analyses in three distinct models. Model 1 did not have covariate adjustments. Model 2 involved adjustments for age, sex, and race, while Model 3 was adjusted for age, sex, race, education level, hypertension, PIR, smoking, and alcohol consumption. Following the WHtR score’s categorization from a continuous variable into quartiles, a trend analysis was implemented to scrutinize the potential linear trend between the WHtR and migraine. A sub-stratified analysis was subsequently conducted to explore the potential relationship between WHtR and migraine, considering demographic and clinical variables such as age, sex, race, education level, hypertension, PIR, and smoking and alcohol consumption status. Moreover, interaction tests were conducted to determine the consistency of the observed relationships across various subgroups. Moreover, we employed smooth curve fitting techniques to explore the possible nonlinear correlation between WHtR and migraine [17]. All the statistical analyses were performed via the R package (version 4.2) or EmpowerStats software (version 5.0). Statistical significance was established at a two-tailed p-value <0.05.
Results
Baseline characteristics
The current study incorporated NHANES data from 1999–2004. In accordance with the exclusion criteria, 13,344 participants aged ≥20 years were included in the analysis. Table 1 displays the participants’ baseline demographic and clinical features, which were grouped into quartiles according to their WHtR. The mean age of the participants was 49.17 ±18.81 years. Among them, 7,003 individuals (52.48%) were females, and 6,665 (50.31%) were from the non-Hispanic White ethnic group. The prevalence of migraine was 2,764 participants, accounting for 20.72% of the total sample size. Notably, this prevalence tended to increase as the WHtR quartiles increased. The average WHtR across all participants was 0.58 ±0.09. Specifically, the WHtR values for the distinct quartiles were as follows: quartile 1: 0.35–0.52; quartile 2: 0.52–0.58; quartile 3: 0.58–0.6; and quartile 4: 0.64–1.01. Compared with individuals in the lowest WHtR quartile, those in the lowest WHtR quartile presented a significantly greater tendency to belong to the adult population aged >60 years, especially among non-Hispanic White women. Furthermore, they tended toward lower educational attainment, reduced physical activity levels, and a greater risk of developing diabetes and hypertension. Furthermore, a higher WHtR was consistently associated with corresponding increases in BMI, WC, and body height as well as weight. However, the participants presented lower PIRs despite these elevations, as shown in Table 1.
Association between WHtR and migraines
Table 2 shows the relationship between the WHtR and migraine occurrence. Both the unadjusted [1.95 (1.23, 3.08)] and the partially corrected [3.08 (1.92, 4.94)] models revealed a notable and positive correlation between WHtR and migraine incidence. After adjustments, the aforementioned positive correlation remained statistically significant with an odds ratio of [1.70 (1.04, 2.78)]. Specifically, for every incremental unit in WHtR, the probability of migraine incidence increased by 70%. Following the inclusion of the WHtR quartiles, the positive correlation between WHtR and migraine incidence did not change. Particularly, participants in the highest WHtR quartile presented a 13% greater migraine incidence in comparison to those in the lowest quartile, with an odds ratio of [1.13 (0.99, 1.28)]. Furthermore, the smoothed curve fitting analysis results offered additional verification of the non-linear positive correlation between WHtR and migraine occurrence (Fig 2).
The solid red line indicates a smooth curve fit between the variables. The 95% confidence intervals for the fitted results are indicated by the blue bars.
Subgroup analyses
We performed subgroup analyses and interaction tests, segmented by gender, race, education, hypertension, PIR, alcohol consumption, and smoking to assess the possible correlation between WHtR and migraine and uncover potential disparities among various population subgroups. The analysis outcomes are presented in Table 3. Our results indicated a statistically significant difference in the association between WHtR and migraine occurrence among different age groups (p-value for interaction <0.01). Among individuals <60 years of age, a unit-wise WHtR increase was linked to an 82% increased likelihood of migraine. In contrast, the previous correlation between WHtR and migraine had a nonsignificant negative association, with a 68% decrease in migraine incidence for each unit increase in WHtR in participants >60 years (Fig 3). Moreover, obesity may have a protective effect against migraines in individuals ≥60 years [18].
Discussion
In this extensive cross-sectional study, we utilized 13,344 participants and observed a direct correlation between WHtR and migraine. Furthermore, significant age-related dependency was evident, indicating a complex interplay between WHtR, age, and migraine occurrence. This discovery suggests that higher WHtR values could significantly lead to increased migraine incidence, especially in adults <60 years of age. These observational studies confirmed that an elevated WHtR was an independent and significant risk trigger for migraine occurrence. This important finding further highlights the key role of the WHtR indicator in planning migraine prevention strategies as well as in daily health management.
Our study marks the initial investigation into the relationship between WHtR and migraine, thereby progressing the sphere of inquiry in this area. Many studies have extensively explored the association between WHtR and cardiovascular disease (CVD), highlighting its potential significance in cardiovascular health. In a comprehensive meta-analysis encompassing >300,000 adults, Ashwell et al. established that the WHtR is an exceptional indicator of cardiometabolic risk [7]. Remarkably, the WHtR excelled beyond BMI and WC in precisely recognizing persons at increased risk of diabetes mellitus, hypertension, dyslipidemia, and coronary heart disease, confirming its importance in clinical evaluations [19,20]. According to Romero-Saldaña et al., the WHtR has emerged as the superior indicator for predicting metabolic syndrome (MetS), with a key threshold of 0.54. A NHANES cross-sectional analysis revealed that among U.S. adults with a normal waist circumference but an elevated WHtR, 25.5% were significantly susceptible to diabetes mellitus (OR = 2.06, CI [1.66, 2.55]), hypertension (OR = 1.75, CI [1.58, 1.93]), and CVD (OR = 1.32, CI [1.11, 1.57]) [21]. Furthermore, we revealed a nonlinear positive correlation between WHtR and migraine, aligning with previous research that underscored the adverse impacts of WHtR on cardiovascular health. This finding strengthens the notion that the WHtR can be a crucial indicator for assessing migraine risk and maintaining cardiovascular health. Accumulating research data firmly establishes that obesity is a significant contributing factor to migraine headaches. The WHtR has emerged as a superior indicator for predicting cardiovascular risk, surpassing conventional obesity measures such as BMI or WC when utilized independently. This measurement provides a precise representation of the adverse impacts of central obesity on cardiovascular well-being [22]. Moreover, the WHtR measure is less expensive, easier to use, and has strong academic applicability [23]. An extensive Chinese epidemiological investigation revealed significant variations in the correlation between BMI and migraine occurrence. This dataset underscored the possible impact of BMI fluctuations on the onset and reappearance of migraine attacks [24]. In a comprehensive meta-analysis encompassing 15 observational studies, Ornello et al. revealed that for obese individuals, with every increase of one BMI unit, there was a significant 14% rise in migraine occurrence, along with a considerable 75% surge in the probability of chronic migraine, compared to people with normal weight [25]. Two crucial studies involving NHANES participants have suggested an association between obesity and the frequency of migraine occurrence. Ford et al. evaluated a cohort of 7,601 individuals. Their findings indicated that participants categorized as underweight (BMI<18.5) or obese (BMI≥30) displayed a significantly increased likelihood of enduring severe headaches or migraines, in contrast to those with healthy body weight [26]. In addition, studies have shown that both total body obesity (TBO) and abdominal obesity (AO) are associated with a higher incidence of migraine. These two types of obesity indicators include only BMI and WC and do not consider height or central obesity [27]. Despite a potential link between traditional obesity indicators and migraine, the phenomenon known as the “obesity paradox” continues to persist [19]. Several studies demonstrating the occurrence of the obesity paradox have relied primarily on BMI as the sole obesity assessment measure. However, the relative reduction in central adiposity and lean body mass might be more significant than BMI alone in assessing obesity-related health risks in older adults [28]. The WHtR represents a practical and age-sensitive metric that incorporates height and central adiposity. Thus, as a central obesity measure, the WHtR might offer a more accurate evaluation of migraine-associated risk factors, such as MetS, diabetes, and hypertension [7,29,30].
Numerous investigations have unequivocally demonstrated that diminishing abdominal obesity effectively enhances lipid profiles, reduces hypertension, optimizes glycemic management, and significantly lessens cardiovascular episodes [31,32]. WHtR has become a more precise indicator of obesity, and our study effectively captures the complex link between obesity and migraine, with a 70% increase in migraine incidence for each unit increase in WHtR. Focusing on WHtR values in the clinical setting can help prevent the onset and recurrence of migraine. Existing studies have revealed a possible vicious cycle mechanism between migraine and obesity [33]. Specifically, after a migraine attack, individuals may tend to gain weight due to a variety of factors (e.g., pain-induced activity reduction, medication side effects affecting metabolism, etc.), resulting in obesity [34]. This transition not only exacerbates the physiological burden on the patient but also influences the course of migraine. Obesity, as a systemic metabolic abnormality, is accompanied by pathophysiological changes such as inflammatory responses, hormonal imbalances, and vascular dysfunction, which have been shown to directly or indirectly increase the severity of migraine-related symptoms [8]. For example, obesity may exacerbate neurovascular dysregulation and promote increased sensitivity to migraine triggers, leading to more frequent and uncontrollable migraine attacks [35]. Moreover, migraine-induced obesity may also contribute to the progression of migraine from an episodic, acute state to a chronic state. Chronic migraine not only make patients suffer from headaches for a long time but also significantly reduce their quality of life, increase the consumption of healthcare resources, and may be accompanied by psychological disorders, such as anxiety and depression, which create more complex health challenges. Therefore, an appropriate WHtR is essential for cardiovascular health [36]. This includes maintenance through weight loss diets, ketogenic diets, low-calorie diets, moderate exercise, and weight control [37], which in turn reduce the occurrence and recurrence of migraine [36,38].
Multiple physiological mechanisms may explain the positive association between WHtR and migraine. First, an increased WHtR indicates dysfunctional adipose tissue, marked by a pro-inflammatory condition resulting from increased circulating cytokine concentrations. This inflammation-prone environment likely acts as a catalyst for migraine attacks, further emphasizing the importance of the WHtR as a key indicator for assessing migraine risk [39]. Moreover, p molecules within the adipose tissue help in both the amplification of adipose deposition and the initiation of obesity-induced inflammatory sequences. These proinflammatory states might be interconnected with neurovascular inflammation in migraine sufferers [40]. Third, the increased calcitonin gene-related peptide (CGRP) levels in obese individuals suggest its potential involvement as a significant postsynaptic modulator of trigeminal vascular inflammation in migraine. Moreover, this neuropeptide, a central mediator in the pathological process of chronic migraine, is a highly promising target in the therapeutic strategy of CGRP inhibitors, which, by targeting this neuropeptide, opens new avenues for the relief of chronic migraine symptoms [41]. Finally, adipose tissue secretes a variety of hormones that have a profound effect on the function of the hypothalamus, a key central regulator that is not only responsible for the fine-tuning of body weight and food intake but also involved in triggering migraine attacks, underscoring its central position in the neuroendocrine regulatory network [42]. Thus, obesity-induced alterations in hypothalamic function may alter the incidence of migraines. Hence, weight control is an effective strategy to reduce migraine occurrence; however, psychological and behavioral factors should be considered for preventing migraine.
Moreover, our findings revealed a non-substantial inverse association between WHtR and migraine among individuals aged≥60 years, indicating a lack of significant correlation in this particular cohort. A new study confirms our findings, providing insight into the complex relationship between BMI and WC on mortality in an older age group >80 years, and revealing an inverse correlation between BMI and WC [43]. Additionally, the benefit of decreased all-cause mortality associated with obesity in older adults may be due to protective energy reserves, adipokine concentrations, endotoxin-lipoprotein interactions, or adipotoxin segregation [44].
Our study’s strength lies in the utilization of NHANES data, which were collected via a stratified, multistage probability sampling approach to yield an extensive sample size. This approach ensures the study’s credibility, generalizability, and reliability. Furthermore, subgroup analyses were performed among various populations to obtain more comprehensive insight into the correlation between WHtR and migraine. Nonetheless, our study had a few limitations. Owing to our cross-sectional design, we were unable to conclusively prove a causal relationship between WHtR and migraine. Furthermore, the inclusion criteria in the migraine analysis were based solely on self-reported histories of migraine attacks, and the specific staging phenotype of migraine remained an unknown variable. We could also not explore the potential correlations between distinct migraine subtypes and WHtR, thereby limiting our comprehension of their intricate relationships. Despite several unaccounted confounders, the robust correlation between WHtR and migraine status remained significant without any subsequent alterations.
Conclusion
In conclusion, our comprehensive analysis revealed a nonlinear, positive association between WHtR and migraine. Central obesity also had a protective effect on deterring migraine episodes among adults >60 years of age. This finding provides evidence for the intricate relationship between body composition and migraine pathogenesis, especially among older adults. Thus, this discovery can significantly contribute novel perspectives toward customized migraine management strategies. However, it is imperative to conduct additional high-quality prospective studies to rigorously validate and substantiate our results.
References
- 1. Stovner LJ, Hagen K, Linde M, Steiner TJ. The global prevalence of headache: an update, with analysis of the influences of methodological factors on prevalence estimates. J Headache Pain. 2022;23(1):34. pmid:35410119.
- 2. Zhang N, Robbins MS. Migraine. Ann Intern Med. 2023;176(1):Itc1-itc16. https://doi.org/10.7326/aitc202301170. pmid:36623287.
- 3. Caballero B. Humans against Obesity: Who Will Win? Adv Nutr. 2019;10(suppl_1):S4–s9. pmid:30721956.
- 4. Ramachandran R. Neurogenic inflammation and its role in migraine. Semin Immunopathol. 2018;40(3):301–14. pmid:29568973.
- 5. Moltrer M, Pala L, Cosentino C, Mannucci E, Rotella CM, Cresci B. Body mass index (BMI), waist circumference (WC), waist-to-height ratio (WHtR) e waist body mass index (wBMI): Which is better? Endocrine. 2022;76(3):578–83. pmid:35304685.
- 6. Schneider HJ, Klotsche J, Silber S, Stalla GK, Wittchen HU. Measuring abdominal obesity: effects of height on distribution of cardiometabolic risk factors risk using waist circumference and waist-to-height ratio. Diabetes Care. 2011;34(1):e7. pmid:21193616.
- 7. Ashwell M, Gunn P, Gibson S. Waist-to-height ratio is a better screening tool than waist circumference and BMI for adult cardiometabolic risk factors: systematic review and meta-analysis. Obes Rev. 2012;13(3):275–86. pmid:22106927.
- 8. Jahromi SR, Martami F, Morad Soltani K, Togha M. Migraine and obesity: what is the real direction of their association? Expert Rev Neurother. 2023;23(1):75–84. pmid:36714917.
- 9. Borrud L, Chiappa MM, Burt VL, Gahche J, Zipf G, Johnson CL, et al. National Health and Nutrition Examination Survey: national youth fitness survey plan, operations, and analysis, 2012. Vital Health Stat 2. 2014;(163):1–24. pmid:24709592.
- 10. Curtin LR, Mohadjer LK, Dohrmann SM, Kruszon-Moran D, Mirel LB, Carroll MD, et al. National Health and Nutrition Examination Survey: sample design, 2007–2010. Vital Health Stat 2. 2013;(160):1–23. pmid:25090039.
- 11. Xie R, Zhang Y. Association between 19 dietary fatty acids intake and rheumatoid arthritis: Results of a nationwide survey. Prostaglandins Leukot Essent Fatty Acids. 2023;188:102530. pmid:36586398.
- 12. Xie F, Xiao Y, Li X, Wu Y. Association between the weight-adjusted-waist index and abdominal aortic calcification in United States adults: Results from the national health and nutrition examination survey 2013–2014. Front Cardiovasc Med. 2022;9:948194. pmid:36186965.
- 13. Tao J, Zhang Y, Tan C, Tan W. Associations between weight-adjusted waist index and fractures: a population-based study. J Orthop Surg Res. 2023;18(1):290. pmid:37038167.
- 14. Buse DC, Loder EW, Gorman JA, Stewart WF, Reed ML, Fanning KM, et al. Sex differences in the prevalence, symptoms, and associated features of migraine, probable migraine and other severe headache: results of the American Migraine Prevalence and Prevention (AMPP) Study. Headache. 2013;53(8):1278–99. pmid:23808666.
- 15. Pogoda JM, Gross NB, Arakaki X, Fonteh AN, Cowan RP, Harrington MG. Severe Headache or Migraine History is Inversely Correlated With Dietary Sodium Intake: NHANES 1999–2004. Headache. 2016;56(4):688–98. pmid:27016121.
- 16. Slavin M, Li H, Khatri M, Frankenfeld C. Dietary magnesium and migraine in adults: A cross-sectional analysis of the National Health and Nutrition Examination Survey 2001–2004. Headache. 2021;61(2):276–86. pmid:33503279.
- 17. Xie R, Zhang Y. Is assessing the degree of hepatic steatosis and fibrosis based on index calculations the best choice for epidemiological studies? Environ Pollut. 2023;317:120783. https://doi.org/10.1016/j.envpol.2022.120783. pmid:36460186.
- 18. Donini LM, Pinto A, Giusti AM, Lenzi A, Poggiogalle E. Obesity or BMI Paradox? Beneath the Tip of the Iceberg. Front Nutr. 2020;7:53. pmid:32457915.
- 19. Alshamiri MQ, Mohd AHF, Al-Qahtani SS, Alghalayini KA, Al-Qattan OM, El-Shaer F. Waist-to-Height Ratio (WHtR) in Predicting Coronary Artery Disease Compared to Body Mass Index and Waist Circumference in a Single Center from Saudi Arabia. Cardiol Res Pract. 2020;2020:4250793. pmid:32257425.
- 20. Wu W, Wu Y, Yang J, Sun D, Wang Y, Ni Z, et al. Relationship between obesity indicators and hypertension-diabetes comorbidity among adults: a population study from Central China. BMJ Open. 2022;12(7):e052674. pmid:35858720.
- 21. Yang B, Yang J, Wong MM, Rana J, Yang Q, Chan V, et al. Trends in elevated waist-to-height ratio and waist circumference in U.S. adults and their associations with cardiometabolic diseases and cancer, 1999–2018. Front Nutr. 2023;10:1124468. pmid:37113294.
- 22. Marrodán M, Alvarez JM, de Espinosa MG, Carmenate M, López-Ejeda N, Cabañas M, et al. Predicting percentage body fat through waist-to-height ratio (WtHR) in Spanish schoolchildren. Public Health Nutr. 2014;17(4):870–6. pmid:23537776.
- 23. Kawamoto R, Kikuchi A, Akase T, Ninomiya D, Kumagi T. Usefulness of waist-to-height ratio in screening incident metabolic syndrome among Japanese community-dwelling elderly individuals. PLoS One. 2019;14(4):e0216069. pmid:31034487.
- 24. Huang Q, Liang X, Wang S, Mu X. Association between Body Mass Index and Migraine: A Survey of Adult Population in China. Behav Neurol. 2018;2018:6585734. pmid:29849812.
- 25. Ornello R, Ripa P, Pistoia F, Degan D, Tiseo C, Carolei A, et al. Migraine and body mass index categories: a systematic review and meta-analysis of observational studies. J Headache Pain. 2015;16:27. pmid:25903159.
- 26. Ford ES, Li C, Pearson WS, Zhao G, Strine TW, Mokdad AH. Body mass index and headaches: findings from a national sample of US adults. Cephalalgia. 2008;28(12):1270–6. pmid:18727641.
- 27. Kristoffersen ES, Børte S, Hagen K, Zwart JA, Winsvold BS. Migraine, obesity and body fat distribution—a population-based study. J Headache Pain. 2020;21(1):97. pmid:32762643.
- 28. Bosello O, Vanzo A. Obesity paradox and aging. Eat Weight Disord. 2021;26(1):27–35. pmid:31865598.
- 29. Li Y, Gui J, Zhang X, Wang Y, Mei Y, Yang X, et al. Predicting hypertension by obesity- and lipid-related indices in mid-aged and elderly Chinese: a nationwide cohort study from the China Health and Retirement Longitudinal Study. BMC Cardiovasc Disord. 2023;23(1):201. pmid:37081416.
- 30. Zhang FL, Ren JX, Zhang P, Jin H, Qu Y, Yu Y, et al. Strong Association of Waist Circumference (WC), Body Mass Index (BMI), Waist-to-Height Ratio (WHtR), and Waist-to-Hip Ratio (WHR) with Diabetes: A Population-Based Cross-Sectional Study in Jilin Province, China. J Diabetes Res. 2021;2021:8812431. pmid:34056007.
- 31. Das S, Goswami V, Chandel S. Normal weight central obesity and hypertension in India: Cross-sectional finding from LASI, 2017–19. Nutr Metab Cardiovasc Dis. 2023;33(10):1888–98. pmid:37544873.
- 32. Katta N, Loethen T, Lavie CJ, Alpert MA. Obesity and Coronary Heart Disease: Epidemiology, Pathology, and Coronary Artery Imaging. Curr Probl Cardiol. 2021;46(3):100655. pmid:32843206.
- 33. Westgate CSJ, Israelsen IME, Jensen RH, Eftekhari S. Understanding the link between obesity and headache- with focus on migraine and idiopathic intracranial hypertension. J Headache Pain. 2021;22(1):123. pmid:34629054.
- 34. Pavlovic JM, Vieira JR, Lipton RB, Bond DS. Association Between Obesity and Migraine in Women. Curr Pain Headache Rep. 2017;21(10):41. pmid:28842821.
- 35. May A, Schulte LH. Chronic migraine: risk factors, mechanisms and treatment. Nat Rev Neurol. 2016;12(8):455–64. pmid:27389092.
- 36. Razeghi Jahromi S, Ghorbani Z, Martelletti P, Lampl C, Togha M. Association of diet and headache. J Headache Pain. 2019;20(1):106. pmid:31726975.
- 37. Huai P, Liu J, Ye X, Li WQ. Association of Central Obesity With All-Cause and Cause-Specific Mortality in US Adults: A Prospective Cohort Study. Front Cardiovasc Med. 2022;9:816144. pmid:35155634.
- 38. Cervoni C, Bond DS, Seng EK. Behavioral Weight Loss Treatments for Individuals with Migraine and Obesity. Curr Pain Headache Rep. 2016;20(2):13. pmid:26862055.
- 39. Thuraiaiyah J, Erritzøe-Jervild M, Al-Khazali HM, Schytz HW, Younis S. The role of cytokines in migraine: A systematic review. Cephalalgia. 2022;42(14):1565–88. pmid:35962530.
- 40. Bond DS, Roth J, Nash JM, Wing RR. Migraine and obesity: epidemiology, possible mechanisms and the potential role of weight loss treatment. Obes Rev. 2011;12(5):e362–71. pmid:20673279.
- 41. Blair HA. Rimegepant: A Review in the Acute Treatment and Preventive Treatment of Migraine. CNS Drugs. 2023;37(3):255–65. pmid:36739335.
- 42. Coppola G, Di Renzo A, Petolicchio B, Tinelli E, Di Lorenzo C, Serrao M, et al. Increased neural connectivity between the hypothalamus and cortical resting-state functional networks in chronic migraine. J Neurol. 2020;267(1):185–91. pmid:31606759.
- 43. Lv Y, Zhang Y, Li X, Gao X, Ren Y, Deng L, et al. Body mass index, waist circumference, and mortality in subjects older than 80 years: a Mendelian randomization study. Eur Heart J. 2024;45(24):2145–54. pmid:38626306.
- 44. Lv Y, Mao C, Gao X, Ji JS, Kraus VB, Yin Z, et al. The obesity paradox is mostly driven by decreased noncardiovascular disease mortality in the oldest old in China: a 20-year prospective cohort study. Nat Aging. 2022;2(5):389–96. pmid:37118064.