Observational Study
Copyright ©The Author(s) 2022. Published by Baishideng Publishing Group Inc. All rights reserved.
World J Clin Cases. Nov 16, 2022; 10(32): 11789-11803
Published online Nov 16, 2022. doi: 10.12998/wjcc.v10.i32.11789
Development and validation of a risk assessment model for prediabetes in China national diabetes survey
Li-Ping Yu, Fen Dong, Yong-Ze Li, Wen-Ying Yang, Si-Nan Wu, Zhong-Yan Shan, Wei-Ping Teng, Bo Zhang
Li-Ping Yu, Wen-Ying Yang, Bo Zhang, Department of Endocrinology, China-Japan Friendship Hospital, Beijing 100029, China
Fen Dong, Si-Nan Wu, Institute of Clinical Medical Sciences, China-Japan Friendship Hospital, Beijing 100029, China
Yong-Ze Li, Zhong-Yan Shan, Wei-Ping Teng, Department of Endocrinology and Metabolism, First Hospital of China Medical University, Shenyang 110001, Liaoning Province, China
Author contributions: Yu LP and Dong F contributed equally to this study as co-first authors; Yu LP, Dong F, Zhang B and Teng WP analyzed and interpreted the data; Dong F and Li YZ conducted the statistical analysis; Yu LP and Dong F wrote the draft of the manuscript; Zhang B, Teng WP, Shan ZY and Wu SN revised the manuscript; Yang WY designed and led the China National Diabetes and Metabolic Disorders Study; Teng WP and Shan ZY designed and led the TIDE study.
Supported by the National Key Research and Development Program of China, No. 2018YFC1313902.
Institutional review board statement: The CNDMDS was approved by the Ethics Review Board of China-Japan Friendship Hospital and the ethics committees of local institutions (No. 2007-026). The TIDE study was approved by the medical ethics committee of China Medical University (No. 2014-103-2).
Informed consent statement: All participants in the CNDMDS and the TIDE study provided informed consent and signed written informed consent.
Conflict-of-interest statement: All the authors declare that they have no conflict of interest.
Data sharing statement: The datasets of CNDMDS and TIDE are available from the corresponding authors upon reasonable request. The NHANES study design and data were accessed via the website https://www.cdc.gov/nchs/nhanes/index.htm.
STROBE statement: The authors have read the STROBE Statement—checklist of items, and the manuscript was prepared and revised according to the STROBE Statement—checklist of items.
Open-Access: This article is an open-access article that was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution NonCommercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: https://creativecommons.org/Licenses/by-nc/4.0/
Corresponding author: Bo Zhang, Doctor, Professor, Department of Endocrinology, China-Japan Friendship Hospital, No. 2 East Yinghua Road, Chaoyang District, Beijing 100029, China. prediabetes@sina.com
Received: September 5, 2022
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
Abstract
BACKGROUND

Prediabetes risk assessment models derived from large sample sizes are scarce.

AIM

To establish a robust assessment model for prediabetes and to validate the model in different populations.

METHODS

The China National Diabetes and Metabolic Disorders Study (CNDMDS) collected information from 47325 participants aged at least 20 years across China from 2007 to 2008. The Thyroid Disorders, Iodine Status and Diabetes Epidemiological Survey (TIDE) study collected data from 66108 participants aged at least 18 years across China from 2015 to 2017. A logistic model with stepwise selection was performed to identify significant risk factors for prediabetes and was internally validated by bootstrapping in the CNDMDS. 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 TIDE study in China. C statistics and calibration plots were adopted to evaluate the model’s discrimination and calibration performance.

RESULTS

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. When externally validated in the NHANES and TIDE studies, the model showed increased C statistics in Mexican American, other Hispanic, Non-Hispanic Black, Asian and Chinese populations but a slightly decreased C statistic in non-Hispanic White individuals. Applying the risk assessment model to the TIDE population, we obtained a C statistic of 0.7308 (95%CI: 0.7260 to 0.7357) and a calibration slope of 1.1137. 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%.

CONCLUSION

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.

Keywords: Hyperglycemia, Prediabetes, Risk assessment model, Risk scores

Core Tip: This was the first study to utilize easily-measured metrics to develop prediabetes assessment model in a large population and validated the model in different populations. Data of the China National Diabetes and Metabolic Disorders Study survey with 47325 participants was used to establish the risk assessment model for prediabetes. 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. Risk score was derived to assess prediabetes. Stratified individuals at ≥ 7 points were at high risk of prediabetes, with sensitivity of 60.19% and specificity of 67.59%.