What caused the obesity epidemic? As I've noted in my writing and talks, the obesity epidemic was paralleled by an increase in daily calorie intake that was sufficiently large to fully account for it. There are two main sources of data for US calorie intake. The first is NHANES surveys conducted by the Centers for Disease Control. They periodically collect data on food intake using questionnaires, and these surveys confirm that calorie intake has increased. The problem with the NHANES food intake data is that they're self-reported and therefore subject to major reporting errors. However, NHANES surveys provide the best quality (objectively measured) data on obesity prevalence since 1960, which we'll be using in this post.
The second source of data on calorie intake is the USDA Economic Research Service (1). The ERS estimates food consumption based on production. The data are freely available and some of them go all the way back to 1909. Despite the limitations of this method, I've come to believe that the ERS database is the most accurate and complete record of US food intake available. After appropriate adjustments*, ERS data show that on average, US adults consumed 363 more calories per day in 2009 than we did in 1960.
If we plot calorie intake and obesity prevalence on the same graph, the correspondence is striking, but it only occurred to me recently to try to put them both into a scatterplot. Scatterplots directly plot one variable vs. another (i.e., one on the horizontal axis and one on the vertical axis), and they are useful for determining how tightly two variables are correlated. The more tightly the two variables are correlated, the better the points approximate a diagonal line. When I plot calorie intake vs. obesity prevalence between 1961 and 2006, the correlation is striking:
The R-squared value quantifies the strength of the correlation, with a value ranging between 0 and 1. The R-squared value of 0.93 indicates an extremely strong correlation between calorie intake and the prevalence of obesity, and this correlation is highly statistically significant. The slope of the "best fit line" also allows us to draw another conclusion: each 100-calorie increment corresponds to a 4.2 percent increase in the prevalence of obesity.
When we consider extreme obesity (BMI greater than 40), we see a similar correlation:
The ERS data also allow us to look at the macronutrients carbohydrate, fat, and protein. Let's see how they correlate with the prevalence of obesity, starting with carbohydrate:
The correlation with carbohydrate isn't quite as strong as with calories, but it's still extremely strong. How about fat?
Again, the correlation is slightly weaker than with total calories, but still extremely strong. How about protein?
Surprisingly, this was the strongest correlation of all-- at an R-squared value of 0.94, it slightly surpasses the correlation strength of total calories.
Here's what the graphs show:
- We're eating more calories than we used to.
- There is a very strong relationship between the number of calories we eat and the prevalence of obesity in the US.
- The extra calories are coming from carbohydrate, fat, and protein, and increased intake of all three tightly correlate with increased obesity prevalence.
In other words, we're eating more of everything than we did 50 years ago.
This begs the question, why are we eating more of everything? There are multiple reasons for it, but I described some of the most compelling explanations in my talk Why Do We Overeat? These same concepts form the basis of the Ideal Weight Program.
* Gross values reduced by 28.8% to account for waste between production and consumption (adjustment determined by the ERS). Also adjusted for an artifact in 2000 that results from a change in the liquid oils assessment method and artificially inflates fat intake.