Original Article | Published:

Epidemiology and Population Health

Association of elevation, urbanization and ambient temperature with obesity prevalence in the United States

International Journal of Obesity volume 37, pages 14071412 (2013) | Download Citation

Abstract

Background:

The macrogeographic distribution of obesity in the United States, including the association between elevation and body mass index (BMI), is largely unexplained. This study examines the relationship between obesity and elevation, ambient temperature and urbanization.

Methods and Findings:

Data from a cross-sectional, nationally representative sample of 422 603 US adults containing BMI, behavioral (diet, physical activity, smoking) and demographic (age, sex, race/ethnicity, education, employment, income) variables from the 2011 Behavioral Risk Factor Surveillance System were merged with elevation and temperature data from WorldClim and with urbanization data from the US Department of Agriculture. There was an approximately parabolic relationship between mean annual temperature and obesity, with maximum prevalence in counties with average temperatures near 18 °C. Urbanization and obesity prevalence exhibited an inverse relationship (30.9% in rural or nonmetro counties, 29.2% in metro counties with <250 000 people, 28.1% in counties with population from 250 000 to 1 million and 26.2% in counties with >1 million). After controlling for urbanization, temperature category and behavioral and demographic factors, male and female Americans living <500 m above sea level had 5.1 (95% confidence interval (CI) 2.7–9.5) and 3.9 (95% CI 1.6–9.3) times the odds of obesity, respectively, as compared with counterparts living 3000 m above sea level.

Conclusions:

Obesity prevalence in the United States is inversely associated with elevation and urbanization, after adjusting for temperature, diet, physical activity, smoking and demographic factors.

Introduction

Although prevalence has stabilized in recent years,1, 2 obesity remains a top public health concern in the United States. Regional differences in body mass index (BMI) become evident upon cursory examination of state-level US maps published by the Centers for Disease Control and Prevention (CDC).3 Obesity appears most prevalent in the Southeast and Midwest states and less prevalent in the Mountain West. Despite significant research into the environmental determinants of obesity, including the built environment, the explanation for these macrogeographic differences is unclear.

Differences in elevation provide a biologically plausible explanation for regional variation.4 Potential mechanisms include increased metabolic demands, altered leptin signaling secondary to hypoxia,5, 6, 7, 8, 9, 10, 11, 12, 13, 14 reduced birth weight,15, 16, 17 reduced childhood growth18, 19 and increased sympathetic tone.20 Cross-sectional studies of the relationship between BMI and elevation, however, have produced conflicting results. Among 617 Tibetans, waist circumference, waist-to-hip ratio and BMI were inversely related to elevation, in a range from 1200 to 3700 m above sea level.21 Similarly, in an endogamous Indian population, women living in the plains were overweight, whereas those living at elevations above 2400 m were of normal weight.22 Similarly, dogs were found to have lower rates of obesity in the Mountain West than in lower elevation areas of the United States.23 An opposite pattern, however, was observed for childhood overweight and obesity in Saudi Arabia24 and for metabolic syndrome in Peru, although the latter did not achieve statistical significance.25

Urbanization and mean annual temperature also demonstrate regional variation. Rural residence is a known risk factor for poor diet,26 and cold ambient temperature has been described as catabolic.21 In this study we assessed the geographic distribution of obesity in the United States as it relates to elevation, temperature and urbanization, after correcting for known behavioral and demographic covariates.

Materials and methods

Data sources

The Behavioral Risk Factor Surveillance System (BRFSS) is a nationwide telephone health survey with a well-defined sampling strategy that permits extrapolation to the noninstitutionalized US adult population using sampling weights provided in the data set. Methods of data collection and limitations are described elsewhere.27 Unlike 2010, the 2011 data set includes information on diet and physical activity recommendation compliance, in addition to the demographic questions (age, sex, race/ethnicity, education and income) collected annually.

In brief, we evaluated obesity as a function of macrogeographic independent variables (elevation, mean annual temperature and urbanization) after adjusting for known demographic and lifestyle predictors. For this analysis, education was dichotomized at the college degree level (<college degree, college degree), annual income was trichotomized at <$20 000 and $75 000 levels and self-reported race/ethnicity was re-categorized as white, black, Hispanic, Asian, other and missing. Degree of urbanization was categorized using modified Beale codes (BC) as follows: counties with ≥1 million residents (BC=1); 250 000 to 1 million (BC=2); those in metro areas, but with <250 000 (BC=3); and those in nonmetro or rural areas (BC=4–9).28 Obesity was defined as BMI 30 kg m−2 and was classified as missing for pregnant women and for those with a BMI value 99.99 or 12.00 kg m−2. In order to account for the sampling strategy employed by CDC, the final weighting variable (_LLCPWT) was used as an inverse probability weight. Complex survey design can reduce precision due to stratification, but analysis of this data set demonstrated that the effect was negligible for our study.

Elevation above sea level, mean annual temperature (degrees centigrade) and urbanization for subjects were based on county of residence reported in the 2011 survey. Mean elevation and annual temperature for 3134 administrative areas (counties) in the United States were obtained through publicly available data sets. Elevation and temperature data were obtained from WorldClim (www.worldclim.org) and were processed using ArcGIS version 10.0 (ESRI, Redlands, CA, USA). WorldClim provides weather data that are interpolated from average monthly weather station data to 1 km resolution grids using well-described methodology.29 WorldClim elevation data are resampled to 1 km resolution from the Shuttle Radar Topography Mission altitude data. County administrative boundaries were downloaded from the Global Administrative Areas website (www.gadm.org). The ArcGIS program, Zonal Statistics as Table, was used to calculate mean annual temperature and mean elevation by county (Supplementary Figure 1). The merged ArcGIS outputs combined via Microsoft Access and Excel with the federal information processing standards (FIPS) codes and county typology codes (such as urbanization) obtained from US Department of Agriculture (USDA) Economic Research Service were matched using state and county name.28 The combined data set was then merged by state and county FIPS codes with the 2011 BRFSS data. All subsequent analyses were performed using SAS version 9.3 (SAS Institute Inc., Cary, NC, USA) and STATA version 12.0 (StataCorp, College Station, TX, USA). The CDC used a modified sampling strategy to include cell phones where the place of residence for each cell phone observation was based on self-report rather than location of telephone service, as 5090 individuals reported living in a different state. For 31 of these (<1%), the recorded county FIPS code did not exist within the reported state of residence; these 31 observations were excluded.

Population

There were 504 408 observations in the data set. Aside from those in Puerto Rico (n=6613), missing county codes (n=52 972, including 888 codes n=1489) and the 31 observations reported above, every unique FIPS code (n=2231) contained within the BRFSS data set for the remaining 444 792 observations matched with a corresponding USDA FIPS code. Elevation and temperature data derived from WorldClim for three cities (Baltimore, MD, St Louis, MO, and Fairfax, VA) did not merge with the USDA file because of FIPS code discrepancies between them and their surrounding counties of the same name. The FIPS codes corresponding to these cities were manually assigned the temperature and elevation derived from their respective counties. Excluding invalid BMI as explained above, the final data set included 422 603 subjects representative of 207 million Americans.

Statistical analyses

We considered whether elevation, urbanization and ambient temperature were associated with obesity (generalized estimating equation (GEE)) or median BMI (quantile regression) after adjusting for lifestyle (smoking, physical activity and diet) and demographics (age, sex, race/ethnicity, education, employment status and income). A hierarchal analysis was necessary to account for the differing unit of observation from the county level (elevation, urbanization and temperature) to individual observations (from BRFSS). Thus, GEE was employed with an exchangeable correlation matrix, logit link and the repeating unit defined as the FIPS code. Results reported separately by sex were analyzed with stratification by sex. To calculate the number of Americans represented, frequency weighting was used by rounding the final weight to the nearest integer.

Results

Elevation

Compared with the lowest elevation category of <500 m above sea level (322 681 observations representing 170 million individuals), subjects in the highest elevation category of 3000 ms above sea level (236 observations representing 41 271 individuals) included lower proportions of smokers and higher proportions of diet and physical activity recommendation compliance with other protective demographic characteristics (Table 1).

Table 1: Prevalence of obesity-related risk factors by lowest vs highest elevation regions; odds ratio (OR) for association of risk factor with obesity in multilevel analysis controlling for elevation category

Obesity prevalence decreased with increasing elevation by 200 m increments (Figure 1). Variance in BMI was wider at lower elevations (Figure 2), particularly in rural areas. Multiple measures of the overall distribution of the weighted crude BMI data (outside values, upper and lower adjacent, upper and lower hinge), displayed by box plot (Figure 2), demonstrated a progressive decrement in BMI with increasing elevation category.

Figure 1
Figure 1

Proportion obese by elevation in 200 m increments (unweighted).

Figure 2
Figure 2

Box plots of BMI distribution by elevation category using inverse probability weight (0=<0.50 km of elevation above sea level; 1=0.50–0.99 km; 2=1.00–1.49 km; 3=1.50–1.99 km; 4=2.00–2.49 km; 5=2.50–2.99; 6=3.00 km).

After controlling for urbanization, temperature category and behavioral and demographic factors, male and female Americans living <500 m above sea level had 5.1 (95% confidence interval (CI) 2.7–9.5) and 3.9 (95% CI 1.6–9.3) times the odds of obesity, respectively, as compared with counterparts living 3000 m above sea level (Table 2). The data are also presented using <500 m as the referent group, as this is a more common exposure (Table 2). When modeling elevation as a continuous variable with nonhierarchical first-degree fractional polynomial regression, after controlling for demographics, lifestyle, temperature and urbanization, the odds of obesity were 7.5% lower for Americans at 1000 m of elevation as compared with their counterparts at sea level. Although the reduced odds of obesity were modest at the low dose of 1000 m, the coefficient of reduced odds increased to the squared power with increasing elevation beyond 1000 m. This can be expressed by the formula pOe=0.925 × (E20.2) × pOc, where pOe is predicted odds of obesity, E is elevation in km and pOc is predicted odds of obesity based on other covariates.

Table 2: Adjusted odds of obesity by elevation category, stratified by sex

Using quantile regression, there was an inverse dose response between median BMI and elevation, after controlling for demographics, lifestyle and urbanization; those living 3000 m above sea level had a median BMI 2.4 BMI units lower than those living <500 m above sea level (Table 3). This nonparametric, nonhierarchical analysis is a fully adjusted point estimate with similar results as the unadjusted graphical representation of the distribution of BMI by altitude category seen in Figure 3. The relationship between elevation and BMI was not substantially altered when adjusted for diet and physical activity.

Table 3: Adjusted median body mass index (BMI) by elevation, urbanization and temperature category
Figure 3
Figure 3

Relationship between temperature and proportion of population obese.

Urbanization

The crude prevalence of obesity decreased with increasing urbanization: 30.9% in rural and nonmetro counties; 29.2% in metro counties with <250 000 people; 28.1% in counties with population between 250 000 and 1 million; and 26.2% in population of >1 million. Compared with metro counties with >1 million residents, obesity was more prevalent in metro counties with populations of 250 000 to 1 million residents (odds ratio (OR) 1.08; 95% CI 1.02–1.15), metro counties with <250 000 (OR 1.11; 95% CI 1.05–1.18) and nonmetro or rural counties (OR 1.17; 95% CI 1.12–1.23), after controlling for demographics, lifestyle, elevation and temperature category using GEE (Table 1). Median BMI showed the same trend according to urbanization (Table 3).

Ambient temperature

The relationship between temperature and obesity prevalence was approximately parabolic when plotting mean annual temperature in 1/4 °C increments (Figure 3). Florida accounted for over 80% of lean inhabitants residing at a mean annual temperature of >22 °C. Among lean Floridians living at >22 °C, 25% were 65 years old. In the fully adjusted GEE model controlling for elevation, urbanization, demographics and lifestyle, all temperature categories (5 °C increments) were not statistically significantly different than the highest temperature category, but extremes of temperature category trended to the lowest odds (Table 1). Median BMI by quantile regression was similar across temperature categories with suggestion of lower median BMIs at the extremes of temperature category (Table 3).

Discussion

Based on a cross-sectional and nationally representative data set of 422 603 observations, representing 207 million Americans, an inverse dose-response relationship between elevation and prevalent obesity was evident in both unadjusted and fully adjusted models. In other words, Americans living at high elevation are less likely to be obese. Moreover, variance in BMI narrows with increasing elevation. This is consistent with findings from an interventional study, in which amount of weight loss during a 33-day period on Kunlun Mountain (elevation: 4678 m, location: China) was correlated to baseline body weight (r=0.677, P<0.01).30 Others have demonstrated that sensitivity to known obesity risk factors is ‘more potent with increasing adiposity.’31 This study identifies low elevation and rural residence as additional risk factors subject to this phenomenon.

There are several plausible mechanisms relating elevation and obesity, including hypoxia, leptin signaling, metabolic demands, norepinephrine levels and fetal/childhood growth. Relative hypoxia is an attractive mechanism as it could link decreased odds of obesity found in smoking (chronic carbon monoxide exposure), urbanization (chronic air pollution exposure) and higher elevation (hypobaric atmospheric conditions). Hypoxia also presents a solution that is consistent with the exponential dose response between elevation and obesity prevalence identified in this study. Additionally, this has implications for therapy as artificial hypoxia can be obtained at any elevation using an altitude chamber. Prior studies have evaluated the way oxygen saturation varies by elevation.32, 33 In the future, use of oxygen saturation prediction models or pressure altitude could be used rather than topographical elevation to better model the underlying constructs.

Protection against obesity in hypoxic environments may be biologically adaptive, whereby hypoxia alters the near term survival tradeoff between the benefit of increased energy storage and the cost of excess body weight. Numerous observational studies have reported that lowlanders traveling to various elevations above 2650 m experience acute anorexia, decreased caloric intake and weight loss.4, 5, 6, 7, 30, 34, 35 In a clinical trial, obese subjects randomized to low-intensity physical activity in hypoxic conditions achieved greater weight loss than those randomized to the same activity in sham hypoxia.36 Given that many human studies have demonstrated that elevation causes anorexia and weight loss in the short term, our study is significant because it also suggests a role for elevation in weight homeostasis over the long term.

The impact of hypoxia on concentration of plasma leptin—a hormone secreted by adipose tissue that produces negative feedback on appetite—is complex and controversial.10, 11 Some studies have found that plasma leptin concentrations rise at elevation5, 7 or in hypoxic conditions6, 14 in both traveling and acclimatized subjects, whereas others have found unchanged or decreased levels.8, 13 Hypoxia may modulate leptin levels through hypoxia-inducible transcription factor, which regulates both iron metabolism and leptin gene expression.10, 12 Regardless of serum leptin levels, hypoxia may also improve leptin signaling through increased production of leptin receptor. In mouse hepatocytes exposed to chronic hypoxia, the leptin receptor gene is upregulated more than any other gene.9 This may explain why hypoxic conditions induce anorexia in rats, including polycythemic rats, while other environmental conditions are held constant.37 Obese rats exposed to artificial pulsatile hypoxia (8% O2), moreover, decrease ad libitum food consumption for 1 month, signifying perhaps a therapeutic potential of hypoxia for human obesity.38

Previous literature has suggested that reduced temperature at increased elevation may lead to weight loss through catabolic effects.21 This is biologically plausible based on animal39, 40 and human41 studies; a theoretical thermodynamic model predicted annual caloric expenditure equivalent to 5.1–7.3 kg of adipose tissue to maintain core temperature during a 5 °C reduction in ambient temperature.42 There is also evidence of decreased energy balance beyond either side of a ‘thermoneutral zone,’ at which increased metabolic expenditures are required to cope with either very hot or very cold temperatures.43 Temperature, however, did not account for the effect of elevation on obesity seen in this study.

Norepinephrine has also been implicated in the relationship between elevation and weight loss.20 Among six subjects who endured a simulated 40-day ascent, plasma norepinephrine concentrations tripled, whereas epinephrine concentrations remained stable.44 This increased sympathetic tone during ascent alters blood flow to the gut and thereby diminishes appetite.45 Similarly, depending on ancestry, blood flow is also altered during pregnancy at high elevation,46 and birth weight is reduced by a curvilinear dose response.15, 16, 47 The lower birth weight is attributed to reduced subcutaneous tissue.47 Decreased childhood growth and stunting are more common at elevation.18, 19 Furthermore, birth weight is correlated with BMI in adulthood.17 In multiple experimental animal models, however, reducing prenatal nutrition increases adiposity.43 Therefore, although the relationship between elevation and birth weight may be mechanistic, an alternative explanation is that the relationship between elevation and BMI exists throughout all stages of human life. The complex relationship between elevation, hypoxia, leptin, thermoregulation and norepinephrine underscores the need for further research and the potential for new obesity treatments.

The inverse dose-response relationship between urbanization and obesity was also a notable finding. This may represent increased food security, enhanced walkability or better diet. Objective calculation of walkability is a function of population density,48 and rurality has been associated with poor diet.26 Although urbanization is associated with increased obesity in India,49 our findings are consistent with data from the National Health and Nutrition Examination Survey showing higher prevalence of obesity in rural US residents.50 Our study adds to this research by showing a dose-response relationship while correcting for other geographic factors.

The major strength of this study is the combination of precise geographic information with a large data set representative of 207 million Americans. Extensive demographic and behavioral data, furthermore, allow for evaluation of potential confounders for obesity. However, the findings should be interpreted in light of their limitations. Although the geographic pattern of elevation existed before the development of macrogeographic disparities in obesity, this study is best characterized as a cross-sectional analysis. Although sensitivity analyses removing either USDA-defined retirement or population loss FIPS codes from a fully adjusted logistic model did not meaningfully decrease the effect of elevation, person-specific duration of residence would be preferable; however, this was not feasible with the available data. Furthermore, the observations regarding elevation should be interpreted with caution as the sampling and weighting methodology was not designed to detect differences in elevation. Observations in the highest elevation category (>3000 m) were limited to counties in only one state (Colorado) and despite including over 500 000 observations in this data set, only 236 observations in this analysis came from the counties 3000 m in elevation. Finally, self-reporting of body weight in BRFSS is likely underestimated among some individuals.51

In summary, this study demonstrates that obesity prevalence in the United States is inversely associated with elevation and urbanization after controlling for temperature, behavioral factors and demographic factors.

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Acknowledgements

Matthew Gilbert provided help with Microsoft Access. Robert Smalley provided help with Microsoft Excel and suggested quantile regression. Daniel Burnett first presented and discussed BRFSS obesity maps with principal investigator and discussed the referent group. Roger Gibson reviewed the project and discussed with Principal Investigator. Cara Olsen provided biostatistical support and Tzucheg Kao provided base code for GEE in SAS. The USUHS School of Medicine Office of Research approved this study as non-human subjects research. The project was completed while the principal investigator was in training within the USUHS Public Health and General Preventive Medicine Residency.

Author contributions

Study concept and design: J Voss and P Masuoka; acquisition of data: J Voss, P Masuoka and AI Scher; analysis and interpretation of data: J Voss, P Masuoka, BJ Webber, AI Scher and RL Atkinson; drafting of the manuscript: J Voss, P Masuoka and BJ Webber; critical revision of the manuscript for important intellectual content: P Masuoka, AI Scher and RL Atkinson; statistical analysis: J Voss, P Masuoka, BJ Webber, AI Scher and RL Atkinson; administrative, technical or material support: P Masuoka and AI Scher; study supervision: AI Scher and RL Atkinson.

Disclaimer

This work is the sole responsibility of the authors and does not represent the official views of the Uniformed Services University of the Health Sciences, Department of Defense or Virginia Commonwealth University.

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Author notes

    • R L Atkinson

    Editor at the International Journal of Obesity.

Affiliations

  1. Department of Preventive Medicine and Biometrics, Uniformed Services University of the Health Sciences, Bethesda, MD, USA

    • J D Voss
    • , P Masuoka
    • , B J Webber
    •  & A I Scher
  2. Virginia Commonwealth University, Richmond, VA, USA

    • R L Atkinson
  3. Obetech Obesity Research Center, Richmond, VA, USA

    • R L Atkinson

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Correspondence to J D Voss.

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DOI

https://doi.org/10.1038/ijo.2013.5

Supplementary Information accompanies the paper on International Journal of Obesity website (http://www.nature.com/ijo)

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