While experimental and observational studies suggest that sugar intake is associated with the development of type 2 diabetes, independent of its role in obesity, it is unclear whether alterations in sugar intake can account for differences in diabetes prevalence among overall populations. Using econometric models of repeated cross-sectional data on diabetes and nutritional components of food from 175 countries, we found that every 150 kcal/person/day increase in sugar availability (about one can of soda/day) was associated with increased diabetes prevalence by 1.1% (p <0 .001="" after="" and="" biases="" cereals="" controlling="" fibers="" food="" for="" fruits="" including="" meats="" oils="" other="" potential="" selection="" testing="" types="" u="">, total calories, overweight and obesity, period-effects, and several socioeconomic variables such as aging, urbanization and income. No other food types yielded significant individual associations with diabetes prevalence after controlling for obesity and other confounders. The impact of sugar on diabetes was independent of sedentary behavior and alcohol use, and the effect was modified but not confounded by obesity or overweight. Duration and degree of sugar exposure correlated significantly with diabetes prevalence in a dose-dependent manner, while declines in sugar exposure correlated with significant subsequent declines in diabetes rates independently of other socioeconomic, dietary and obesity prevalence changes. Differences in sugar availability statistically explain variations in diabetes prevalence rates at a population level that are not explained by physical activity, overweight or obesity.0>
There's no doubt in my tiny bean that sugar holds a causal relationship with diabetes, and this study would only reinforce such belief. But the larger point is that this study, and others like it, should never be used to assume causal relationships.
The language tells the tale, even for those who may not be tuned into the philosophy of science. They tried to account for all the variables, but of course, they don't know all the variables, and there's no guarantee they have accurately weighted the variables they know of.
For example - how do they possibly know how to control for "fiber" and fiber's so called effects on blood glucose and other issues related to diabetes? Most of what they think they know about fiber is just a derivative of other observational studies.