Abstract
Aims
To develop and validate sex-specific risk prediction models based on easily obtainable
clinical data for predicting 5-year risk of type 2 diabetes (T2D) among individuals
with impaired fasting glucose (IFG), and generate practical tools for public use.
Methods
The data used for model training and internal validation came from a large prospective
cohort (N = 18,384). Two independent cohorts were used for external validation. A two-step
approach was applied to screen variables. Coefficient-based models were constructed
by multivariate Cox regression analyses, and score-based models were subsequently
generated. The predictive power was evaluated by the area under the curve (AUC).
Results
During a median follow-up of 7.55 years, 5697 new-onset T2D cases were identified.
Predictor variables included age, body mass index, waist circumference, diastolic
blood pressure, triglycerides, fasting plasma glucose, and fatty liver. The proposed
models outperformed five existing models. In internal validation, the AUCs of the
coefficient-based models were 0.741 (95% CI 0.723–0.760) for men and 0.762 (95% CI
0.720–0.802) for women. External validation yielded comparable prediction performance.
We finally constructed a risk scoring system and a web calculator.
Conclusions
The risk prediction models and derived tools had well-validated performance to predict
the 5-year risk of T2D in IFG adults.
Keywords
Abbreviations:
ADA (American Diabetes Association), DBP (diastolic blood pressure), FPG (fasting plasma glucose), IFG (impaired fasting glucose), LASSO (least absolute shrinkage and selection operator), ROC (receiver operating characteristic), RP-IFG (type 2 diabetes Risk Prediction in a Chinese population with IFG), RS-IFG (type 2 diabetes Risk Score in a Chinese population with IFG), SBP (systolic blood pressure), WC (waist circumference)To read this article in full you will need to make a payment
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Article info
Publication history
Published online: February 06, 2023
Accepted:
February 1,
2023
Received in revised form:
January 14,
2023
Received:
August 30,
2022
Identification
Copyright
© 2023 Elsevier B.V. All rights reserved.
