Feng, C., Osgood, N. D., & Dyck, R. F. (2018). Low birth weight, cumulative obesity dose, and the risk of incident type 2 diabetes. Journal of Diabetes Research, 2018, Article 8435762. https://doi.org/10.1155/2018/8435762
Feng, C., Osgood, N. D., & Dyck, R. F. (2018). Low birth weight, cumulative obesity dose, and the risk of incident type 2 diabetes. Journal of Diabetes Research, 2018, Article 8435762. https://doi.org/10.1155/2018/8435762
This study shows the complexity of identifying risk factors and, unlike other studies, closely examines the roles of obesity and birth weight. In this large cohort study of people in Britain, the researchers examined the roles of several demographic factors as well as a few different measures of weight and BMI changes. This study did not see a link between low birth weight and the risk ...
Feng, C., Osgood, N. D., & Dyck, R. F. (2018). Low birth weight, cumulative obesity dose, and the risk of incident type 2 diabetes. Journal of Diabetes Research, 2018, Article 8435762. https://doi.org/10.1155/2018/8435762
This study shows the complexity of identifying risk factors and, unlike other studies, closely examines the roles of obesity and birth weight. . . .
Ivarsdottir, E. V., Steinthorsdottir, V., Daneshpour, M. S., Thorleifsson, G., Sulem, P., Holm, H., Sigurdsson, S., Hreidarsson, A. B., Siggurdson, G., Bjarnason, R., Thorsson, A. V., Benediktsson, R., Eyjolfsson, G., Sigurdardottir, O., Zeinali, S., Azizi, F., Thorsteinsdottier, U., Gudbjartsson, D. F., & Stefansson, K. (2017). Effect of sequence variants on variance in glucose levels predicts type 2 diabetes risk and accounts for heritability. Nature Genetics, 49(9), 1398-1402. https://doi.org/10.1038/ng.3928
The real value of this article is in the supplementary materials the authors shared. The authors included a copy of the code they used to calculate the differences between all of their samples. It can be modified to be used with other genetic traits. In addition, the authors also linked out to a set of open access genetic data sets for type 2 diabetes research. Both sets of information make this article super useful for additional genetic research.
Overall, they found a 2.4% incidence rate for diabetes in their study population . . .
The study’s findings may not be generalizable to other areas of the world due to differences in diabetes’ prevalence and incidence, the health care structures in place, and environmental factors.
. . . no one factor alone can predict if a person will develop diabetes. . .
. . . it shows the complexity of identifying risk factors for developing type 2 diabetes. . .
. . . This reinforces the idea that diabetes has several interacting causes. . .