Skip to main content

Thank you for visiting You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

Epidemiology and Population Health

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



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.


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


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), 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 ( 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 ( 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.


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.



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

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

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

Relationship between temperature and proportion of population obese.


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).


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.


  1. 1

    Flegal KM, Carroll MD, Kit BK, Ogden CL . Prevalence of obesity and trends in the distribution of body mass index among US adults, 1999–2010. JAMA 2012; 307: 491–497.

    Article  Google Scholar 

  2. 2

    Ogden CL, Carroll MD, Kit BK, Flegal KM . Prevalence of obesity and trends in body mass index among US children and adolescents, 1999–2010. JAMA 2012; 307: 483–490.

    Article  Google Scholar 

  3. 3

    Centers for Disease Control and Prevention. Overweight and obesity, 2012. [cited 2012 June 15]; Available from

  4. 4

    Hamad N, Travis SP . Weight loss at high altitude: pathophysiology and practical implications. Eur J Gastroenterol Hepatol 2006; 18: 5–10.

    Article  Google Scholar 

  5. 5

    Shukla V, Singh SN, Vats P, Singh VK, Singh SB, Banerjee PK . Ghrelin and leptin levels of sojourners and acclimatized lowlanders at high altitude. Nutr Neurosci 2005; 8: 161–165.

    CAS  Article  Google Scholar 

  6. 6

    Lippl FJ, Neubauer S, Schipfer S, Lichter N, Tufman A, Otto B et al. Hypobaric hypoxia causes body weight reduction in obese subjects. Obesity (Silver Spring) 2010; 18: 675–681.

    Article  Google Scholar 

  7. 7

    Tschop M, Strasburger CJ, Hartmann G, Biollaz J, Bartsch P . Raised leptin concentrations at high altitude associated with loss of appetite. Lancet 1998; 352: 1119–1120.

    CAS  Article  Google Scholar 

  8. 8

    Vats P, Singh VK, Singh SN, Singh SB . High altitude induced anorexia: effect of changes in leptin and oxidative stress levels. Nutr Neurosci 2007; 10: 243–249.

    CAS  Article  Google Scholar 

  9. 9

    Baze MM, Schlauch K, Hayes JP . Gene expression of the liver in response to chronic hypoxia. Physiol Genomics 2010; 41: 275–288.

    CAS  Article  Google Scholar 

  10. 10

    Yingzhong Y, Droma Y, Rili G, Kubo K . Regulation of body weight by leptin, with special reference to hypoxia-induced regulation. Intern Med 2006; 45: 941–946.

    Article  Google Scholar 

  11. 11

    Sierra-Johnson J, Romero-Corral A, Somers VK, Johnson BD . Effect of altitude on leptin levels, does it go up or down? J Appl Physiol 2008; 105: 1684–1685.

    Article  Google Scholar 

  12. 12

    Ambrosini G, Nath AK, Sierra-Honigmann MR, Flores-Riveros J . Transcriptional activation of the human leptin gene in response to hypoxia. Involvement of hypoxia-inducible factor 1. J Biol Chem 2002; 277: 34601–34609.

    CAS  Article  Google Scholar 

  13. 13

    Vats P, Singh SN, Shyam R, Singh VK, Singh SB, Banerjee PK et al. Leptin may not be responsible for high altitude anorexia. High Alt Med Biol 2004; 5: 90–92.

    Article  Google Scholar 

  14. 14

    Snyder EM, Carr RD, Deacon CF, Johnson BD . Overnight hypoxic exposure and glucagon-like peptide-1 and leptin levels in humans. Appl Physiol Nutr Metab 2008; 33: 929–935.

    CAS  Article  Google Scholar 

  15. 15

    Gonzales GF, Tapia V . Birth weight charts for gestational age in 63,620 healthy infants born in Peruvian public hospitals at low and at high altitude. Acta Paediatr 2009; 98: 454–458.

    Article  Google Scholar 

  16. 16

    Moore LG, Shriver M, Bemis L, Vargas E . An evolutionary model for identifying genetic adaptation to high altitude. Adv Exp Med Biol 2006; 588: 101–118.

    Article  Google Scholar 

  17. 17

    Sorensen HT, Sabroe S, Rothman KJ, Gillman M, Fischer P, Sorensen TI . Relation between weight and length at birth and body mass index in young adulthood: cohort study. BMJ 1997; 315: 1137.

    CAS  Article  Google Scholar 

  18. 18

    Yip R, Binkin NJ, Trowbridge FL . Altitude and childhood growth. J Pediatr 1988; 113: 486–489.

    CAS  Article  Google Scholar 

  19. 19

    Dang S, Yan H, Yamamoto S . High altitude and early childhood growth retardation: new evidence from Tibet. Eur J Clin Nutr 2008; 62: 342–348.

    CAS  Article  Google Scholar 

  20. 20

    Barnholt KE, Hoffman AR, Rock PB, Muza SR, Fulco CS, Braun B et al. Endocrine responses to acute and chronic high-altitude exposure (4300 meters): modulating effects of caloric restriction. Am J Physiol Endocrinol Metab 2006; 290: E1078–E1088.

    CAS  Article  Google Scholar 

  21. 21

    Sherpa LY, Deji, Stigum H, Chongsuvivatwong V, Thelle DS, Bjertness E . Obesity in Tibetans aged 30-70 living at different altitudes under the north and south faces of Mt. Everest. Int J Environ Res Public Health 2010; 7: 1670–1680.

    Article  Google Scholar 

  22. 22

    Tyagi R, Tungdim MG, Bhardwaj S, Kapoor S . Age, altitude and gender differences in body dimensions. Anthropol Anz 2008; 66: 419–434.

    PubMed  Google Scholar 

  23. 23

    Lund EAP, Kirk C, Klausner J . Obesity in adult dogs from private US veterinary practices. Intern J Appl Res Vet Med 2006; 4: 10.

    Google Scholar 

  24. 24

    Khalid Mel H . Is high-altitude environment a risk factor for childhood overweight and obesity in Saudi Arabia? Wilderness Environ Med 2008; 19: 157–163.

    Article  Google Scholar 

  25. 25

    Baracco R, Mohanna S, Seclen S . A comparison of the prevalence of metabolic syndrome and its components in high and low altitude populations in peru. Metab Syndr Relat Disord 2007; 5: 55–62.

    Article  Google Scholar 

  26. 26

    Lutfiyya MN, Chang LF, Lipsky MS . A cross-sectional study of US rural adults’ consumption of fruits and vegetables: do they consume at least five servings daily? BMC public health 2012; 12: 280.

    Article  Google Scholar 

  27. 27

    Centers for Disease Control and Prevention (CDC). Behavioral Risk Factor Surveillance System Survey Data. Atlanta, Georgia: US Department of Health and Human Services, Centers for Disease Control and Prevention, 2011.

  28. 28

    Parker T . 2004 ERS County Typology Codes. In USDAUSDA, Economic Research Service 2009.

  29. 29

    Hijmans R, Cameron S, Parra J, Jones P, Jarvis A . Very high resolution interpolated climate surfaces for global land areas. Int J Climatol 2005; 25: 1965–1978.

    Article  Google Scholar 

  30. 30

    Ge RL, Wood H, Yang HH, Liu YN, Wang XJ, Babb T . The body weight loss during acute exposure to high-altitude hypoxia in sea level residents. Sheng Li Xue Bao 2010; 62: 541–546.

    PubMed  Google Scholar 

  31. 31

    Williams PT . Evidence that obesity risk factor potencies are weight dependent, a phenomenon that may explain accelerated weight gain in western societies. PLoS One 2011; 6: e27657.

    CAS  Article  Google Scholar 

  32. 32

    Goldberg S, Buhbut E, Mimouni FB, Joseph L, Picard E . Effect of moderate elevation above sea level on blood oxygen saturation in healthy young adults. Respiration 2012; 84: 207–211.

    CAS  Article  Google Scholar 

  33. 33

    Tannheimer M, Thomas A, Gerngross H . Oxygen saturation course and altitude symptomatology during an expedition to broad peak (8047 m). Int J Sports Med 2002; 23: 329–335.

    CAS  Article  Google Scholar 

  34. 34

    Wagner PD . Operation Everest II. High Alt Med Biol 2010; 11: 111–119.

    Article  Google Scholar 

  35. 35

    Aeberli I, Erb A, Spliethoff K, Meier D, Gotze O, Fruhauf H et al. Disturbed eating at high altitude: influence of food preferences, acute mountain sickness and satiation hormones. Eur J Nutr 2012. e-pub ahead of print 11 May 2012.

  36. 36

    Netzer NC, Chytra R, Kupper T . Low intense physical exercise in normobaric hypoxia leads to more weight loss in obese people than low intense physical exercise in normobaric sham hypoxia. Sleep Breath 2008; 12: 129–134.

    Article  Google Scholar 

  37. 37

    Norese MF, Lezon CE, Alippi RM, Martinez MP, Conti MI, Bozzini CE . Failure of polycythemia-induced increase in arterial oxygen content to suppress the anorexic effect of simulated high altitude in the adult rat. High Alt Med Biol 2002; 3: 49–57.

    Article  Google Scholar 

  38. 38

    Quintero P, Milagro FI, Campion J, Martinez JA . Impact of oxygen availability on body weight management. Med Hypotheses 2010; 74: 901–907.

    CAS  Article  Google Scholar 

  39. 39

    Vaanholt LM, Daan S, Schubert KA, Visser GH . Metabolism and aging: effects of cold exposure on metabolic rate, body composition, and longevity in mice. Physiol Biochem Zool 2009; 82: 314–324.

    CAS  Article  Google Scholar 

  40. 40

    Zhao ZJ, Chi QS, Cao J, Han YD . The energy budget, thermogenic capacity and behavior in Swiss mice exposed to a consecutive decrease in temperatures. J Exp Biol 2010; 213 (Pt 23): 3988–3997.

    Article  Google Scholar 

  41. 41

    Wijers SL, Schrauwen P, Saris WH, van Marken Lichtenbelt WD . Human skeletal muscle mitochondrial uncoupling is associated with cold induced adaptive thermogenesis. PLoS One 2008; 3: e1777.

    Article  Google Scholar 

  42. 42

    Hansen JC, Gilman AP, Odland JO . Is thermogenesis a significant causal factor in preventing the "globesity" epidemic? Med Hypotheses 2010; 75: 250–256.

    Article  Google Scholar 

  43. 43

    McAllister EJ, Dhurandhar NV, Keith SW, Aronne LJ, Barger J, Baskin M et al. Ten putative contributors to the obesity epidemic. Crit Rev Food Sci Nutr 2009; 49: 868–913.

    Article  Google Scholar 

  44. 44

    Young PM, Rose MS, Sutton JR, Green HJ, Cymerman A, Houston CS . Operation Everest II: plasma lipid and hormonal responses during a simulated ascent of Mt. Everest. J Appl Physiol 1989; 66: 1430–1435.

    CAS  Article  Google Scholar 

  45. 45

    Loshbaugh JE, Loeppky JA, Greene ER . Effects of acute hypobaric hypoxia on resting and postprandial superior mesenteric artery blood flow. High Alt Med Biol 2006; 7: 47–53.

    Article  Google Scholar 

  46. 46

    Wilson MJ, Lopez M, Vargas M, Julian C, Tellez W, Rodriguez A et al. Greater uterine artery blood flow during pregnancy in multigenerational (Andean) than shorter-term (European) high-altitude residents. Am J Physiol Regul Integr Comp Physiol 2007; 293: R1313–R1324.

    CAS  Article  Google Scholar 

  47. 47

    Galan HL, Rigano S, Radaelli T, Cetin I, Bozzo M, Chyu J et al. Reduction of subcutaneous mass, but not lean mass, in normal fetuses in Denver, Colorado. Am J Obstet Gynecol 2001; 185: 839–844.

    CAS  Article  Google Scholar 

  48. 48

    Hankey S, Marshall JD, Brauer M . Health impacts of the built environment: within-urban variability in physical inactivity, air pollution, and ischemic heart disease mortality. Environ Health Perspect 2012; 120: 247–253.

    CAS  Article  Google Scholar 

  49. 49

    Ebrahim S, Kinra S, Bowen L, Andersen E, Ben-Shlomo Y, Lyngdoh T et al. The effect of rural-to-urban migration on obesity and diabetes in India: a cross-sectional study. Plos Med 2010; 7: e1000268.

    Article  Google Scholar 

  50. 50

    Befort CA, Nazir N, Perri MG . Prevalence of obesity among adults from rural and urban areas of the United States: findings from NHANES (2005-2008). J Rural Health 2012; 28: 392–397.

    Article  Google Scholar 

  51. 51

    Ezzati M, Martin H, Skjold S, Vander Hoorn S, Murray CJ . Trends in national and state-level obesity in the USA after correction for self-report bias: analysis of health surveys. J R Soc Med 2006; 99: 250–257.

    Article  Google Scholar 

Download references


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.


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.

Author information



Corresponding author

Correspondence to J D Voss.

Ethics declarations

Competing interests

The authors declare no conflict of interest.

Additional information

Supplementary Information accompanies the paper on International Journal of Obesity website

Supplementary information

Rights and permissions

Reprints and Permissions

About this article

Cite this article

Voss, J., Masuoka, P., Webber, B. et al. Association of elevation, urbanization and ambient temperature with obesity prevalence in the United States. Int J Obes 37, 1407–1412 (2013).

Download citation


  • altitude
  • geographic
  • GIS
  • urbanization
  • hypoxia

Further reading


Quick links