Nowadays, obesity is becoming more and more common in every country, it is a concerned health problem in the world. There are some factors cause obesity epidemics, such as over consuming high fat foods, lack of physical activities which leads to calories intake excess and heredity. Regarding the food types consumption correlates to obesity, many people will think these kinds of food must include cheese, meat, nut and cooking oil etc. Surprisingly, the primary research article “Survey of American food trends and the growing obesity epidemic” written by Qin Shao and Khew-Voon Chin found out that the crucial factor of rising obesity is the consumption of corn product.
(This picture is referencing from Public Health & Social Justice)
(This picture is referencing from Public Health & Social Justice)
The authors surveyed and obtained the Loss-Adjusted Food Availability Data (1970-2008) from the Food Availability Data System at the United States Department of Agriculture (USDA) Economic Research Services (ERS) in this research. In order to analyze the relationship between obesity prevalence and calories consumption for various food types, they used Pearson’s correlation and statistical analysis by fitting a multiple linear regression and using both full and reduced model functions. The analysis of Loss-Adjusted Food Availability Data “which include seven major aggregated food groups including 1, meat, eggs, and nuts; 2, dairy; 3, fruit; 4, vegetables; 5, flour and cereal products; 6, added fats and oils, and dairy fats; and 7, caloric sweeteners” (Shao & Qin, 2011) demonstrated that these food types showed either negative trends or no change in trends of consumption and did not lead to the rising trends in obesity. In addition, they also analyzed the relationship between rising obesity and total cheese intake, but the result also revealed that there is no correlation with the two.
(This picture is referencing from PubMed Central)
However, in the further test the authors “performed a fitting by multiple linear regression analysis with food types that showed correlation coefficients > 0.95, which included chicken, corn products, dairy fats, salad and cooking oils, and total cheese, in a full model function” (Shao & Qin, 2011). This analysis showed that “only corn products had p-values smaller than 0.05” (Shao & Qin, 2011), which means that consumption of corn products had a significant effect on rising obesity trends. On the other hand, they found out corn products and total cheese both have p-values closest to 0.05 in the reduced model, but their results confirmed a correlation between corn products, but not total cheese, and obesity trends.
(This picture is referencing from PubMed Central)
Further more, the authors asked whether genetically modified corn might be associated with the rising obesity because genetically modified corn has been planted in the U.S. since 1996. To investigate this relationship, they obtained data between 2000 and 2008 on the adoption of GM corn from the USDA. The result showed that the trends of obesity and adoption of GM corn were similar which proved that the hypothesis is true.
Further more, the authors asked whether genetically modified corn might be associated with the rising obesity because genetically modified corn has been planted in the U.S. since 1996. To investigate this relationship, they obtained data between 2000 and 2008 on the adoption of GM corn from the USDA. The result showed that the trends of obesity and adoption of GM corn were similar which proved that the hypothesis is true.
In conclusion, the research article “Survey of American food trends and the growing obesity epidemic” proved the novel correlation between corn product consumption and obesity prevalence. The research applied the knowledge of math and statistic to investigate and analyze the factor which lead to rising obesity, this method is not only accurate but also credible.
Work Cited
Shao, S., & Chin, K.V. (2011 June 21). Survey of American food trends and the growing obesity epidemic. Retrieved from http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3133759/?tool=pmcentrez. (2011, July 23 )