Construction and Validation of a Model for Predicting Impaired Fasting Glucose Based on More Than 4000 General Population

Abstract

Purpose

Impaired fasting glucose (IFG) is associated with an increased risk of multiple diseases. Therefore, the early identification and intervention of IFG are particularly significant. Our study aims to construct and validate a clinical and laboratory-based nomogram (CLN) model for predicting IFG risk.

Patients and Methods

This cross-sectional study collected information on health check-up subjects. Risk predictors were screened mainly by the LASSO regression analysis and were applied to construct the CLN model. Furthermore, we showed examples of applications. Then, the accuracy of the CLN model was evaluated by the receiver operating characteristic (ROC) curve, the area under the ROC curve (AUC) values, and the calibration curve of the CLN model in the training set and validation set, respectively. The decision curve analysis (DCA) was used to estimate the level of clinical benefit. Furthermore, the performance of the CLN model was evaluated in the independent validation dataset.

Results

In the model development dataset, 2340 subjects were randomly assigned to the training set (N = 1638) and validation set (N = 702). Six predictors significantly associated with IFG were screened and used in the construction of the CLN model, a subject was randomly selected, and the risk of developing IFG was predicted to be 83.6% by using the CLN model. The AUC values of the CLN model were 0.783 in the training set and 0.789 in the validation set. The calibration curve demonstrated good concordance. DCA showed that the CLN model has good clinical application. We further performed independent validation (N = 1875), showed an AUC of 0.801, with the good agreement and clinical diagnostic value.

Conclusion

We developed and validated the CLN model that could predict the risk of IFG in the general population. It not only facilitates the diagnosis and treatment of IFG but also helps to reduce the medical and economic burdens of IFG-related diseases.

Keywords:

• glucose metabolism
• nomogram model
• risk prediction
• disease prevention

Data Sharing Statement

The datasets generated for this study are available on request to the corresponding author.

Acknowledgments

We gratefully acknowledge all the subjects who enabled this study. We sincerely thank all authors for their hard work and dedication.

Author Contributions

All authors made a significant contribution to the work reported, whether that is in the conception, study design, execution, acquisition of data, analysis and interpretation, or in all these areas; took part in drafting, revising or critically reviewing the article; gave final approval of the version to be published; have agreed on the journal to which the article has been submitted; and agree to be accountable for all aspects of the work.

Disclosure

The authors declare that they have no competing interests.

Additional information

Funding

This work was supported by the United Fund of the Second Hospital of Dalian Medical University, Dalian Institute of Chemical Physics, Chinese Academy of Sciences (UF-ZD-202011), Project of Education Department of Liaoning Province (LZ2020009), and Liaoning Provincial Natural Science Foundation (2023-MS-272).