- author = "Enrico Buccheri and Daniele Dell'Aquila and Marco Russo",
- title = "Stratified analysis of the age-related waist circumference cut-off model for the screening of dysglycemia at zero-cost",
- journal = "Obesity Medicine",
- year = "2022",
- volume = "31",
- pages = "100398",
- month = may,
- keywords = "genetic algorithms, genetic programming, type 2 diabetes, Dysglycemia screening tool, Stratified analysis, Artificial intelligence, AI",
- ISSN = "2451-8476",
-
URL = "
https://www.sciencedirect.com/science/article/pii/S2451847622000100",
-
DOI = "
doi:10.1016/j.obmed.2022.100398",
-
abstract = "Aims
We perform a stratified analysis of the recently published age-related waist circumference cut-off model to validate its performance in the screening of dysglycemia in the US population.
Methods
We use NHANES data as representative of the US population. Data were subdivided into sex, ethnic and glycemia groups. We evaluate the performance of the model separately in each group through the Wilcox statistic area under the (ROC) curve, AUC. We also discuss the calibration of the model.
Results
For the sex-stratified analysis, we obtain AUC = 0.69--0.71 (95percent confidence interval) for male individuals and AUC = 0.75--0.78 (95percent C.I.) for female individuals. The stratified analysis is performed in different ethnic groups, namely Mexican American, Other Hispanic, Non-Hispanic White, Non-Hispanic Black and Other Race -- Including Multi-Racial. We obtain, respectively, AUC = 0.74--0.75, AUC = 0.76--0.78, AUC = 0.73--0.75, AUC = 0.74--0.77 and AUC = 0.71--0.73 (95percent C.I.). The model achieves AUC = 0.70--0.73 (95percent C.I.) in the identification of individuals with prediabetes and AUC = 0.70--0.80 (95percent C.I.) in the identification of individuals with diabetes.
Conclusions
The accuracy of the model turns out to be similar in each group considered in the stratified analysis, indicating that the model is suitable to be used as a screening tool for dysglycemia in the US population",
