Type 2 diabetes, but not obesity, prevalence is positively associated with ambient temperature

John R Speakman, Sahar Heidari-Bakavoli, John R Speakman, Sahar Heidari-Bakavoli

Abstract

Cold exposure stimulates energy expenditure and glucose disposal. If these factors play a significant role in whole body energy balance, and glucose homeostasis, it is predicted that both obesity and type 2 diabetes prevalence would be lower where it is colder. Previous studies have noted connections between ambient temperature and obesity, but the direction of the effect is confused. No previous studies have explored the link of type 2 diabetes to ambient temperature. We used county level data for obesity and diabetes prevalence across the mainland USA and matched this to county level ambient temperature data. Average ambient temperature explained 5.7% of the spatial variation in obesity and 29.6% of the spatial variation in type 2 diabetes prevalence. Correcting the type 2 diabetes data for the effect of obesity reduced the explained variation to 26.8%. Even when correcting for obesity, poverty and race, ambient temperature explained 12.4% of the variation in the prevalence of type 2 diabetes, and this significant effect remained when latitude was entered into the model as a predictor. When obesity prevalence was corrected for poverty and race the significant effect of temperature disappeared. Enhancing energy expenditure by cold exposure will likely not impact obesity significantly, but may be useful to combat type 2 diabetes.

Figures

Figure 1. Levels of obesity and type…
Figure 1. Levels of obesity and type 2 diabetes prevalence across the mainland USA.
Plots show the county level data (n = 2655 counties) for (A) obesity prevalence (proportion of population with BMI >30) and average annual temperature (°C), (B) type 2 diabetes prevalence and average annual temperature (°C), (C) the association between obesity and type 2 diabetes prevalence and (D) the association between type 2 diabetes prevalence corrected for obesity levels and average annual temperature (°C). Fitted lines show the least squares fit regression equations with associated equations and r2 values.
Figure 2. Associations between obesity and type…
Figure 2. Associations between obesity and type 2 diabetes with poverty and race.
Plots show the county level data across the USA (n = 2655) for (A) obesity prevalence (proportion of population with BMI >30) and poverty (% of population below poverty line), (B) type 2 diabetes prevalence and poverty, (C) obesity and the proportion of the population that are African American (arcsin transformed) and (D) the association between type 2 diabetes prevalence and the proportion of the population that are African American (arcsin transformed). Fitted lines show the least squares fit regression equations with associated equations and r2 values.
Figure 3. Temperature effect on Type 2…
Figure 3. Temperature effect on Type 2 diabetes prevalence across the mainland USA corrected for levels of poverty, obesity and population racial make-up.
Plot shows the county level data (n = 2651 counties) for type 2 diabetes corrected for levels of obesity, poverty and race against average annual temperature (°C). The curve shows the best fit polynomial regression.
Figure 4. Effects of temperatures in different…
Figure 4. Effects of temperatures in different months on type 2 diabetes prevalence.
(A) Histogram showing the percentage variation in type 2 diabetes prevalence (corrected for obesity, poverty and race) explained by ambient temperature and temperature squared (gray bars) in each month of the year and the average monthly temperature across all sites (n = 2651) (open bars). The explained variation was greater in months when it was colder. (B,C) Example relationships between type 2 diabetes prevalence (corrected for obesity, poverty and race) and ambient temperature in July and January. The curves show the best fit relationships and the associated equations.
Figure 5. Illustration of selection process for…
Figure 5. Illustration of selection process for variogram analysis.
Shaded map of Georgia state to illustrate the variogram analysis. (A) shows the counties surrounding Greene county up to 6 steps away and Fig. 1B shows the same for Irwin county. Original county map purchased from www.mapresources.com.
Figure 6. Variogram analysis.
Figure 6. Variogram analysis.
The plot shows the correlation in relation to the step away from the focal county for each of the 6 states that were analysed (see Fig. 5). The approximate limits for the 95% significance levels (Bonferoni corrected) are also shown as dashed lines (p = 0.05). Values falling outside these two lines were significant.

References

    1. World Health organisation. Obesity: preventing and managing a global epidemic. WHO Geneva (1997).
    1. Wang Y. et al.. Will all Americans become overweight or obese? Estimating the progression and cost of the US obesity epidemic. Obesity 16, 2323–2330 (2008).
    1. Hall K. H. et al.. Energy balance and body weight regulation: a useful concept for understanding the obesity epidemic. American Journal of Clinical Nutrition 95, 989–994 (2012).
    1. Daly M. Association of ambient indoor temperature with body mass index in England. Obesity 22, 626–629 (2014).
    1. Cannon B. & Nedergaard J. Brown adipose tissue: function and physiological significance. Physiological Rev. 84, 277–359 (2004).
    1. Nedergaard J. et al.. UCP1: the only protein able to mediate adaptive non-shivering thermogenesis and metabolic inefficiency. Biochimica et biophysica acta 1504, 82–106 (2001).
    1. Nedergaard J., Bengtsson T. & Cannon B. Unexpected evidence for active brown adipose tissue in adult humans. Am. J. Physiol. 293, E444–E452 (2007).
    1. Cypress A. M. et al.. Identification and importance of brown adipose tissue in adult humans. N. Eng. J. Med. 360, 1509–1517 (2009).
    1. van Marken Lichtenbelt W. D. et al.. Cold-activated brown adipose tissue in healthy men. N. Eng. J. Med 360, 1500–1508 (2009).
    1. Virtanen K. A. et al.. Functional brown adipose tissue in healthy adults. N Engl J Med. 360, 1518–1525 (2009).
    1. Saito M. et al.. High incidence of metabolically active brown adipose tissue in healthy adult humans: effects of cold exposure and adiposity. Diabetes 58, 1526–1531 (2009).
    1. Matsushita M. et al.. Impact of brown adipose tissue on body fatness and glucose metabolism in healthy humans Int. J. Obesity 38, 812–817 (2014).
    1. Yoneshiro T. & Saito M. Activation and recruitment of brown adipose tissue as anti-obesity regimens in humans Annals of Medicine 47, 133–141 (2015).
    1. Quellett V. et al.. Brown adipose tissue oxidative metabolism contributes to energy expenditure during acute cold exposure in humans. J. Clin. Endo. Met. 96, 192–199 (2011).
    1. Orava J. N. et al.. Different metabolic responses of human brown adipose tissue to activation by cold and insulin. Cell metabolism 14, 272–279 (2011).
    1. Chen K. Y. et al.. Brown fat activation mediates cold-induced thermogenesis in adult humans in response to a mild decrease in ambient temperature J. Clin. Endo Met. 98, E1218–1223 (2013).
    1. Au-Yong I. T. H. et al.. Brown adipose tissue and seasonal variation in humans. Diabetes 58, 2583–2587 (2009).
    1. Vosselman M. J. et al.. Low brown adipose tissue activity in endurance-trained compared with lean sedentary men. International Journal of obesity 39, 1696–1702 (2013).
    1. Johnson F. et al.. Could increased time spent in a thermal comfort zone contribute to population increases in obesity? Obes Rev 12, 543–551 (2011).
    1. Biddle J. Explaining the spread of residential air conditioning, 1955–1980. Expl. Econ. Hist. 45, 402–423 (2008).
    1. Valdes S. et al.. Ambient temperature and prevalence of obesity in the Spanish population: the Study. Obesity 22, 2328–2332 (2014).
    1. Voss J. D. et al.. Association of elevation, urbanisation and ambient temperature with obesity prevalence in the united states. Int. J. Obesity 37, 1407–1412 (2013).
    1. Drewnowski A. & Specter S. E. Poverty and obesity: the role of energy density and energy costs. Am. J. Clin. Nutr. 79, 6–16 (2004).
    1. Connolly V. et al.. Diabetes prevalence and socioeconomic status: a population based study showing increased prevalence of type 2 diabetes mellitus in deprived areas. J. Epidemiol and community health 54, 173–177 (2000).
    1. Ogden C. L. et al.. Prevalence of overweight and obesity in the United States 1999–2004. JAMA 295, 1549–1555 (2006).
    1. Goran M. I. Ethnic specific pathways to obesity related disease: the Hispanic vs. African-American Paradox. Obesity 16, 2561–2565 (2008).
    1. Holick M. F. Vitamin D: A millennium perspective. J. Cell. Biochem. 88, 296–307 (2003).
    1. Pittas A. G., Lau J., Hu F. & Dawson-Hughes B. The role of vitamin D and calcium in type 2 diabetes: a systematic review and meta-analysis. J. Clin. Endo. Met. 92, 2017–2029 (2007).
    1. Mathieu C. Vitamin D and diabetes: where do we stand? Diabetes research and clinical practice 108, 201–209 (2015).
    1. Mitri J. et al.. Vitamin D and type 2 diabetes: a systematic review. Eur J. Clin Nut. 65, 1005–1015 (2011).
    1. Billings L. K. & Florez J. C. The genetics of type 2 diabetes: what have we learned from GWAS? Annals of NY acad. sci. 1212, 59–77 (2010).
    1. Diabetes Epidemiology Research International Group. Geographic patterns of childhood insulin-dependent diabetes mellitus. Diabetes 37, 1113–9 (1988).
    1. Waermbaum I. & Dahlquist G. Low mean temperature rather than few sunshine hours are associated with an increased incidence of type 1 diabetes in children. Eur. J. epidemiology 31, 61–65 (2016).
    1. Kinney D. K. et al.. Relation of schizophrenia prevalence to latitude, climate, fish consumption, infant mortality and skin color: a role for pre-natal vitamin D deficiency and infections? Schizophrenia bull. 35, 582–595 (2009).
    1. Woolcott O. O. et al.. Inverse association between diabetes and altitude: a cross-sectional study in the adult population of the United States. Obesity 22, 2080–2090 (2014).
    1. van Marken Lichtenbelt W. et al.. Individual variation in body temperature and energy expenditure in response to mild cold. Am. J. physiol. 282, E1007–1083 (2002).
    1. Kingma B. et al.. The thermoneutral zone: implications for metabolic studies. Frontiers in bioscience 4, 1975–85 (2012).
    1. Westerterp-Plantenga M. S. et al.. Energy metabolism in humans at lowered ambient temperature. Eur. J. Clin. Nut. 56, 288–296 (2002).
    1. Bartelt A. et al.. Brown adipose tissue activity controls triglyceride clearance. Nature Med. 17, 200–205 (2011).
    1. Hanssen et al.. Short-term cold acclimationimproves insulin sensitivity in patients with type 2 diabetes mellitus. Nature medicine 21, 863–865 (2015).
    1. Okura Y. et al.. Agreement between self-report questionnaires and medical record data was substantial for diabetes, hypertension, myocardial infarction and stroke but not for heart failure. J Clin. Epidemiol 57, 1096–110 (2004).
    1. Rowland M. L. Self-reported weight and height. Am J Clin Nutr. 52, 1125–1133 (1990).
    1. Villanueva E. V. The validity of self-reported weight in US adults: a population based cross-sectional study. BMC Public Health. 1, 1–11 (2001).

Source: PubMed

赞助者和合作者

医疗条件

药物干预

3
订阅