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Logo of nihpaAbout Author manuscriptsSubmit a manuscriptHHS Public Access; Author Manuscript; Accepted for publication in peer reviewed journal;
Obesity (Silver Spring). Author manuscript; available in PMC 2011 November 4.
Published in final edited form as:
PMCID: PMC3208164

BMI and Headache Among Women: Results From 11 Epidemiologic Datasets



To evaluate the association between BMI (kg/m2) and headaches among women.

Methods and Procedures

Cross-sectional analysis of 11 datasets identified after searching for all large publicly available epidemiologic cohort study datasets containing relevant variables. Datasets included National Health Interview Survey (NHIS): 1997–2003, the first National Health Examination and Nutrition Survey, Alameda County Health Study (ACHS), Tecumseh Community Health Study (TCHS), and Women’s Health Initiative (WHI). The women (220,370 in total) were aged 18 years or older and had reported their headache or migraine status.


Using nonlinear regression techniques and models adjusted for age, race, and smoking, we found that increased BMI was generally associated with increased likelihood of headache or severe headache among women. A BMI of ~20 was associated with the lowest risk of headache. Relative to a BMI of 20, mild obesity (BMI of 30) was associated with roughly a 35% increase in the odds for experiencing headache whereas severe obesity (BMI of 40) was associated with roughly an 80% increase in odds. Results were essentially unchanged when models were further adjusted for socioeconomic variables, alcohol consumption, and hypertension. Being diagnosed with migraine showed no association with BMI.


Among US women, a BMI of ~20 (about the 5th percentile) was associated with the lowest likelihood of headache. Consistently across studies, obese women had significantly increased risk for headache. By contrast, the risk for diagnosed migraine headache per se was not obviously related to BMI. The direction of causation and mechanisms of action remain to be determined.


Various forms of headache (e.g., chronic daily headache, tension-type headache, migraine headache) are disabling conditions (13) that, compared to other common pain conditions, produce the greatest loss of productive time in the US workforce (2). Because the prevalence of the different forms of headache varies widely in published studies (e.g., 1.3–86% for tension-type headache (1)) it is difficult both to derive a definitive estimate and to assess whether the headache prevalence has changed over time (4,5). Headache has been shown to be associated with breathing disorders, caffeine consumption, alcohol consumption, hypertension, anxiety, and depressive disorders (6). Emerging evidence from case–control (6,7) and observational studies (810) suggests that increased BMI (kg/m2) might be a risk factor for headache.

In this study we estimate the association between BMI and headache among adult women using data from several large publicly available epidemiologic datasets. We restricted our analyses to women because it has been established that headache prevalence is much higher among women (3), and preliminary unpublished data suggested that obesity’s association with headache varied substantially depending on gender. This is consistent with the different associations observed for men and women between obesity and a variety of health issues (1114). Rather than analyzing a single dataset and issuing the near ubiquitous call for replication in the discussion, to evaluate the reproducibility of results and show how results might change as a function of study-related factors, we opted to analyze multiple data sets using identical statistical procedures. This allowed us to derive estimates of the magnitude of the BMI-headache association across all publicly available epidemiologic datasets meeting a set of inclusion criteria.


Inclusion criteria for datasets

To rigorously evaluate the association between BMI and headache among women, we used cross-sectional epidemiologic datasets that met the following requirements: (i) they must be large enough (i.e., ≥500 women) to allow us to generate reasonably precise estimates across a broad range of BMI; (ii) they must contain the height and weight of respondents (measured or self-reported) allowing calculation of BMI; (iii) they must contain respondents’ age, race, and other variables of interest (i.e., smoking status, socioeconomic status, and hypertension); and (iv) they must contain information on the presence/absence of headache.

Dataset search procedures

To obtain epidemiologic datasets that met the aforementioned criteria, we searched among the following electronic resources: Inter-University Consortium for Political and Social Research (, the National Center for Health Statistics (, the National Heart, Lung, and Blood Institute (NHLBI) Population Studies Dataset (, the North Carolina Center for Population Studies (, the Economic and Social Data Service, United Kingdom (, and the National Library of Medicine’s Medline and pre-Medline dataset ( The search among these resources yielded 11 datasets that met the criteria for inclusion in our analyses.

Overview of datasets used

We briefly describe here and in Table 1 the characteristics of the 11 datasets used in our analysis.

Table 1
Description of epidemiologic datasets used

The Alameda County Health Study (ACHS) followed adults selected in 1965 to represent the non-institutionalized population of Alameda County California (15). Data collected included self-reported demographic information, as well as physical, cognitive, psychological, and social functioning.

The Tecumseh Community Health Study (TCHS), initiated in 1959, investigates health and disease determinants in the rural community of Tecumseh, Michigan. Participants completed extensive questionnaires and medical examinations (16).

The National Health Interview Survey (NHIS: 1997–2003), begun in 1969, is a continuing nationwide survey of the US civilian non-institutionalized population conducted in households on a yearly basis (17). A probability sample of households is interviewed each year. Detailed information on the health of each living member of the sample household is obtained.

The First National Health and Nutrition Examination Survey (NHANES I) was conducted from 1971 to 1975 on a nationwide probability sample of individuals aged 1–74 years. We analyzed the data from women aged 18 and over. NHANES I collected data via questionnaire as well as through comprehensive medical and dental examinations. NHANES I design and sampling methods have been reported previously (18).

The Women’s Health Initiative (WHI) is a 40-center, national US study of risk factors and the prevention of common causes of mortality, morbidity, and impaired quality of life in women. Postmenopausal women, aged 50–79 years, completed health forms and attended a clinic visit at baseline and 3 years later. Details of the sampling design, protocol sampling procedures, and selection criteria have been previously published (19).

Study variables


BMI (kg/m2) was the predictor variable of primary interest and was calculated from either measured or self-reported (NHIS only) weight and height. Self-reported weight has been shown to correlate very highly with measured weight (20). BMI is largely independent of height (r ≈ −0.03), strongly related to weight (r ≈ 0.86), and reasonably correlated with body fatness (21).

Outcomes variables

The datasets varied somewhat with regard to how headache was assessed (see Table 2). We recoded and dichotomized headache outcomes so that 0 = absence of an indicator of severe or frequent headache or migraine versus 1 = presence of an indicator of severe or frequent headache or migraine.

Table 2
The coding of headache among the 11 datasets


Data on age, race, and smoking status were included in the primary analyses (i.e., the primary models) as covariates. We also included socioeconomic status variables (income, education, and employment status), alcohol consumption, and hypertension as covariates in our secondary analyses (i.e., the extended models).

Missing data

Missing data were handled using list-wise deletion (22) because more complex missing data management procedures would impose a significantly greater computational demand on an already computationally demanding set of analyses. Furthermore, the complex sampling designs of datasets, most notably the NHIS, would create additional statistical issues related to imputation. Although there was no reason to hypothesize that “missingness” was systematically related to the study variables, we noted two datasets in which some study variables were missing information in at least 5% of records. In these datasets, we fitted logistic regression models for each variable that had such “missingness” (>5%) to test for a relationship between missingness, coded as a binary dependent variable and the other study variables as possible predictors.

Statistical analysis

Traditionally, analyses of the association between BMI and dichotomous outcomes such as mortality or the presence/absence of a given medical condition have been estimated by treating BMI as either a continuous or categorical variable (23). Each approach has advantages and disadvantages. Advantages of treating BMI as a continuous variable include the fact that it does not degrade the data, tends to preserve power, and does not impose arbitrary cutoff points. Rather, one can adjust for curvature in data via the incorporation of polynomials of BMI into the model. The major advantage of treating BMI as a categorical variable, with the categories chosen a priori, is the (seeming) ease of communication of the results and the allowance for marked nonlinearity that may not be easily captured by polynomials. The nonlinear regression we used offers an alternative that captures the advantages of treating BMI as both a continuous and categorical variable (24). Specifically, we applied piecewise linear free knot spline logistic regression models that do not assume a linear relationship between BMI and headache and allow for fitting “breakpoints” in the logit function at so-called knots that may be interpreted to define categories (24). Thus, these data-driven models can take into account potential nonlinearity by determining BMI categories for contiguous BMI groupings of individuals with like patterns of risk while, at the same time, allowing individuals with different BMI in a category to have different levels of risk estimated as a function of their BMI. In brief, we fitted nonlinear models to each dataset, used a parametric bootstrap procedure to select the optimal spline model for BMI, and then used a non-parametric bootstrap procedure to calculate accurate s.e. estimates and confidence intervals that adjusted for complex sample design features. More details on the nonlinear statistical modeling are contained in Supplementary Data 1.

Two analyses were conducted on each dataset: primary and secondary. In the primary analyses, we adjusted for age, race, and smoking status. In secondary analyses, we assessed an extended model that adjusted for the aforementioned covariates as well as socioeconomic variables, alcohol consumption, and hypertension. For the purposes of comparing models within each dataset, the number of knot parameters fitted in the extended model was fixed at the number of knots found in the primary model.

We excluded BMI values <14 and >90 to avoid possible outlier effects and data recording errors. After exclusions, a total of 220,370 respondents from the 11 datasets were available for statistical analyses. Results were presented as parameter estimates with bootstrapped s.e. and 95% confidence intervals. We also plotted odds ratios (ORs) for graphically demonstrating the shapes of relationships we found and calculated ORs and confidence intervals at selected BMI values (i.e., 18, 25, 30, 35, and 40) as compared to a reference BMI value for each of the 11 datasets. Supplementary Data 2 provides the details for calculating ORs from our results.


Table 3 and Figure 1 present the piecewise logistic regression results for the primary model (i.e., adjusted for age, race, and smoking) for each of the 11 datasets. Increased BMI was generally associated with increased risk for headache or severe headache among women. Moreover, results from the NHIS 1997, NHIS 1999, NHIS 2003, and ACHS datasets located “breakpoints” (knots) in the logit function around a BMI of 20 suggesting that the relationship between increased BMI and the risk of headache may change significantly at this point. As shown in Figure 1, our models often predicted that a BMI of ~20 was associated with the lowest risk for severe and/or frequent headaches. The NHIS 1997 data also produced a second knot at a BMI of ~35. At this point, the increased risk for headache with increased BMI significantly decelerated suggesting that people with a BMI >35 may share the same level of headache risk with respect to BMI. The risk associated with diagnosed migraine headache (as assessed in the WHI) or with taking medication for headache (as assessed in NHANES I) were not significantly related to BMI.

Figure 1
Odds ratios for headaches in women by BMI (reference BMI = 20).
Table 3
Piecewise logistic regression primary model resultsa

Table 4 and Figure 1 present the results for the extended models (i.e., the primary models extended to include socioeconomic status variables, alcohol consumption, and hypertension as covariates). As can be seen, these estimates were generally in accord with those derived from the primary models. The results from the NHANES I data were also not materially altered when headache was coded either as: (No = 0; occasionally and regularly = 1 OR no and occasionally = 0; regularly = 1).

Table 4
Piecewise logistic regression extended model resultsa

Table 5 presents the ORs and confidence intervals derived from the primary and extended models for selected BMI values (i.e., 18, 25, 30, 35, and 40) compared with the reference BMI of 20. We chose a BMI of 20 as the reference level because it was the most common nadir (i.e., the value most often associated with the lowest probability of reporting headache) across the datasets, thus making the computed ORs >1 in most cases. This allowed us to present results on a consistent scale for visually comparing ORs across the datasets we examined. You can see that among women with BMI >20 the ORs were mostly statistically significant and exhibited a similar increasing pattern in headache risk across 9 of our 11 datasets. For example, among the NHIS results: as compared to a BMI of 20, we estimated that mild obesity (BMI of 30) was associated with an increase in the odds of reporting the presence of headache ranging from 31 to 65%, whereas severe obesity (BMI of 40) was associated with an increase in the odds that ranged from 49 to 118%.

Table 5
Odds ratiosa and 95% confidence intervals across selected BMI values for the primary and extended models

We found that only NHANES I and TCHS had variables that were missing information from at least 5% of records. Smoking information was available in only ~40% of the NHANES I women participants and was removed from the analyses presented here. Analyses including smokers in NHANES I produced the same nonsignificant results (data not shown). The missing data regarding smoking was associated with decreased BMI, decreased age, income below $20,000, not being a current drinker, and hypertension. Hypertension was missing in 23% of women in NHANES I and this “missingness” was associated with taking headache medication, increased age, being white, having attended graduate school, earning over $20,000, having ever been an alcohol drinker, and increased BMI (data not shown). In the TCHS data, missing information on income (22%) was associated with increased age; missing information on hypertension (9%) was associated with income over $20,000 and decreased BMI; and missing information on BMI (5%) was associated with having headaches (data not shown).


In this set of analyses of 11 different, large datasets collectively containing >200,000 US women, we found that increased BMI was generally associated with significantly increased risk of headache, but not diagnosed migraines. We note that our results across all datasets, with the exceptions of WHI (diagnosed migraines) and NHANES I (taking headache medication), suggested that, as compared to a BMI of 20, mild obesity (BMI of 30) was associated with an ~35% increase in the odds of reporting headache whereas severe obesity (BMI of 40) was associated with an ~80% increase in the odds of reporting headache. Across the databases, a BMI of ~20 was commonly associated with the lowest risk for headache. These results were not materially altered when socioeconomic variables, alcohol consumption, and hypertension were also included in the model.

With regard to migraine headache, the results from our primary model of the WHI data, the only dataset that explicitly assessed migraine headache diagnosis, suggested that BMI may not be associated with migraine, but our extended model revealed a slight negative association. It is noteworthy that many people with migraines go undiagnosed. Therefore, the relationship between BMI and diagnosed migraines is not likely to reflect the BMI relationship with all migraines (both diagnosed and undiagnosed). Our main conclusion that BMI was associated with an increased likelihood of headaches is based on the following logic: (i) the WHI analysis showed no positive correlation between BMI and diagnosed migraines, and this is a finding of consequence because of the large WHI sample; (ii) the NHIS analyses showed a positive association between BMI and “headaches or migraines”; (iii) the ACHS and TCHS showed a positive association between BMI and “headaches” which were probably interpreted by most participants to include migraines; (iv) finding (i) suggested that there was no association between BMI and diagnosed migraines in our NHIS, ACHS and TCHS analyses; and (v) so we concluded that the findings in NHIS, ACHS and TCHS suggest that BMI was associated with non-migraine headaches and possibly undiagnosed migraine.

Most of the databases we analyzed individually provided ample statistical power to detect the estimated effect sizes we observed. That, along with the consistency of the results, obviated the need to conduct a formal meta-analysis. Our findings clarify and accord with previous studies. Specifically, after adjusting for age, gender, race, and education, Scher and colleagues (6) found that obesity was associated with prevalent chronic daily headache (OR = 1.34). Similarly, Ohayon (10) and colleagues found that overweight/obese (BMI > 27) respondents were more likely to report morning headache than were adults with BMIs 20–25 and among a sample of ~15,000 Australian women, Brown (9) and colleagues found that obese persons were more likely to report headache (OR = 1.47). Also, consistent with our primary model analysis of WHI data, Bigal and colleagues (8) using data from over 30,000 participants, found that BMI was not associated with migraine prevalence.

Interestingly, we observed some evidence in four datasets (NHIS 1997, 1999, 2003; and ACHS) that unusually low BMI may be associated with increased risk for headache. These results suggest that increased BMI may be associated with decreased risk of headache among the category of women with a BMI <20 and increased risk of headache among those with a BMI >20. This finding should be interpreted with caution since the association was statistically significant at the 0.05 level only in the NHIS 1997 and 1999 datasets. It was noteworthy that, since only ~5% of all study participants had BMI values <20, we may have lacked sufficient power to reliably detect the elevated risk levels estimated to be associated with low BMI across studies. To our knowledge, low BMI, in the absence of major illness (e.g., cancer), has not been previously associated with reports of headache. Nonetheless, this finding merits further investigation before definitive conclusions can be drawn.

Nine out of the eleven datasets we examined had no study variables missing >3% data, so any resulting effects from missingness were likely to be minimal in these cases. In NHANES I and TCHS where we saw higher levels of missing data for some variables it was less clear what, if any, effects missing values might have had on our results. Interestingly, in TCHS, those reporting headache were ~80% more likely to be missing BMI data than those not reporting headache, but we cannot know how this would influence the significant linear relationship we detected between BMI and headache. Considering the results available from the more complete datasets (i.e., NHIS 1997–2003 and ACHS) and the similarity of those results to those from TCHS, missingness may not have significantly affected the TCHS results.

The mechanisms that might be responsible for the obesity–headache association are unclear. However, obesity is associated with the metabolic syndrome, a pro-inflammatory, pro-thrombotic state that may contribute to headache development and progression (25,26). Headache is also related to sleep apnea, a condition highly associated with obesity (27). Hypertension is also associated with headache (28) and obesity is a major risk factor for hypertension (29). Moreover, headache is one of the side effects of many medications, including sibutramine, a medication to treat obesity (30). Each of these offers a hypothesis meriting further study.

This study has limitations. First, the headache-related questions in the datasets differed, in some cases substantially. For example, the WHI headache question focused on migraine headache and asked, “Has a doctor told you that you have “migraine”?” By contrast, the NHANES headache question did not ask whether the respondent suffered from headache but, rather, whether they used medication for headache (“During the past 6 months have you used any medicine, drugs or pills for headache?”). Although we coded the headache variables in the datasets to create uniformity in outcome variables (see Table 2), these two datasets (WHI and NHANES I) which asked about headache in a way related to diagnosis or treatment are the only two that did not detect clear and statistically significant associations. The assessment of headache in the other datasets focused primarily on the presence/frequency/severity of headaches. Second, we only considered cross-sectional datasets as they were more widely available and different statistical methodology would be required to analyze longitudinal data. Hence, our analyses were restricted to headache or migraine status concurrent with BMI status. We did not look at data on subjects who were free of headache at baseline, that were followed prospectively to see if BMI or changes in BMI would predict headache or migraine occurrence over time. Follow-up data were available in only the two smallest studies (ACHS and TCHS). In the future, we recommend analyzing any available longitudinal data on headache and BMI by using nonlinear methodology, similar to that which we have applied to these cross-sectional databases.

In conclusion, the results of estimating the association between BMI and headache in large, nationally representative samples of women indicated that obese women have significantly higher risk for headache. Further research is warranted to study the direction and mechanisms of causation as well as to investigate the possible BMI–headache relationship among men. The possibility that weight loss may alleviate severe or chronic headache problems among obese people also warrants investigation.

Supplementary Material

Supplement #1


This research was supported by Ortho-McNeil Pharmaceuticals and National Institutes of Health grants P30DK056336, T32HL079888, K23MH066381, and AR49720-01A1.



The corresponding author, D.B.A., had full access to all the data in the study and had final responsibility for the decision to submit for publication. The investigators have no financial and personal relationships with other people or organizations that could inappropriately influence (bias) their work. They do wish to disclose that the work was funded by Ortho-McNeil Pharmaceuticals.


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