Document Type
Article
Publication Date
6-14-2011
Abstract
Objective
The lack of standardized reference range for the homeostasis model assessment-estimated insulin resistance (HOMA-IR) index has limited its clinical application. This study defines the reference range of HOMA-IR index in an adult Hispanic population based with machine learning methods.
Methods
This study investigated a Hispanic population of 1854 adults, randomly selected on the basis of 2000 Census tract data in the city of Brownsville, Cameron County. Machine learning methods, support vector machine (SVM) and Bayesian Logistic Regression (BLR), were used to automatically identify measurable variables using standardized values that correlate with HOMA-IR; K-means clustering was then used to classify the individuals by insulin resistance.
Results
Our study showed that the best cutoff of HOMA-IR for identifying those with insulin resistance is 3.80. There are 39.1% individuals in this Hispanic population with HOMA-IR>3.80.
Conclusions
Our results are dramatically different using the popular clinical cutoff of 2.60. The high sensitivity and specificity of HOMA-IR>3.80 for insulin resistance provide a critical fundamental for our further efforts to improve the public health of this Hispanic population.
Recommended Citation
Qu, H. Q., Li, Q., Rentfro, A. R., Fisher-Hoch, S. P., & McCormick, J. B. (2011). The definition of insulin resistance using HOMA-IR for Americans of Mexican descent using machine learning. PloS one, 6(6), e21041. https://doi.org/10.1371/journal.pone.0021041
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.
Publication Title
PLoS One
DOI
https://doi.org/10.1371/journal.pone.0021041
Comments
Copyright Qu et al.