Yu LP, Dong F, Li YZ, Yang WY, Wu SN, Shan ZY, Teng WP, Zhang B. Development and validation of a risk assessment model for prediabetes in China national diabetes survey. World J Clin Cases 2022; 10(32): 11789-11803 [PMID: 36405266 DOI: 10.12998/wjcc.v10.i32.11789]
Corresponding Author of This Article
Bo Zhang, Doctor, Professor, Department of Endocrinology, China-Japan Friendship Hospital, No. 2 East Yinghua Road, Chaoyang District, Beijing 100029, China. prediabetes@sina.com
Research Domain of This Article
Endocrinology & Metabolism
Article-Type of This Article
Observational Study
Open-Access Policy of This Article
This article is an open-access article which was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution Non Commercial (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: http://creativecommons.org/licenses/by-nc/4.0/
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 bythe 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
ARTICLE HIGHLIGHTS
Research background
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.
Research motivation
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.
Research objectives
This study aims to establish a robust assessment model for prediabetes and to validate the model in different populations.
Research methods
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.
Research 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. 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%.
Research conclusions
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.
Research perspectives
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 prediabetes during follow-up, need to be collected longitudinally to evaluate the accuracy of assessment.
