Published online Nov 16, 2022. doi: 10.12998/wjcc.v10.i32.11789
Peer-review started: September 5, 2022
First decision: September 26, 2022
Revised: October 10, 2022
Accepted: October 17, 2022
Article in press: October 17, 2022
Published online: November 16, 2022
Processing time: 63 Days and 17.8 Hours
Existing risk scores and screening instruments for hyperglycemia are mainly focused on diabetes. Prior studies on prediabetes assessment are restricted to studies with small sample sizes or low sensitivity or specificity.
To address the problem of scarcity in the robust assessment model of prediabetes in a large sample, we established a prediabetes risk assessment model based on the China National Diabetes and Metabolic Disorders Study (CNDMDS), which was a population-based survey involving nearly 48000 participants across China from 2007 to 2008. External validation was performed in a broad spectrum of populations that have marked racial and demographical differences.
This study aims to establish a robust assessment model for prediabetes and to validate the model in different populations.
A logistic model with stepwise selection was performed to identify significant risk factors for prediabetes and was internally validated by bootstrapping in the China National Diabetes and Metabolic Disorders Study. External validations were performed in diverse populations, including populations of Hispanic (Mexican American, other Hispanic) and non-Hispanic (White, Black and Asian) participants in the National Health and Nutrition Examination Survey (NHANES) in the United States and 66108 participants in the Thyroid Disorders, Iodine Status and Diabetes Epidemiological Survey (TIDE) study in China. C statistics and calibration plots were adopted to evaluate the model’s discrimination and calibration performance.
A set of easily measured indicators (age, education, family history of diabetes, waist circumference, body mass index, and systolic blood pressure) were selected as significant risk factors. A risk assessment model was established for prediabetes with a C statistic of 0.6998 (95%CI: 0.6933 to 0.7063) and a calibration slope of 1.0002. External validation was performed in a broad spectrum of populations that have marked racial and demographical differences, and the satisfactory discrimination and calibration performance enhance the model’s generalizability across nations. A risk score was derived to assess prediabetes. Individuals with scores ≥ 7 points were at high risk of prediabetes, with a sensitivity of 60.19% and specificity of 67.59%.
An easy-to-use assessment model for prediabetes was established and was internally and externally validated in different populations. The model had a satisfactory performance and could screen individuals with a high risk of prediabetes.
Considering the inherent methodological limitation, a cohort study might be needed to further validate the discriminative accuracy of the model. Data on long-term outcomes, e.g., the occurrence of pre