A Prediction Model of the Incidence of Type 2 Diabetes in Individuals with Abdominal Obesity: Insights from the General Population

Abstract

Background

This study aimed to distinguish the risk factors for type 2 diabetes mellitus (T2DM) and construct a predictive model of T2DM in Japanese adults with abdominal obesity.

Methods

This study was a post hoc analysis. A total of 2012 individuals with abdominal obesity were included and randomly divided into training and validation groups at 70% (n = 1518) and 30% (n = 494), respectively. The LASSO method was used to screen for risk variables for T2DM, and to construct a nomogram incorporating the selected risk factors in the training group. We used the C-index, calibration plot, decision curve analysis, and cumulative hazard analysis to test the discrimination, calibration and clinical significance of the nomogram.

Results

In the training cohort, the C-index and receiver operating characteristic were 0.819 and the 95% CI was 0.776–0.858, with a specificity and sensitivity of 77% and 74.68%, respectively. In the validation cohort, the C-index was 0.853; sensitivity and specificity were 77.6% and 88.1%, respectively. The decision curve analysis showed that the model’s prediction was effective and cumulative hazard analysis demonstrated that the high-risk score group was more likely to develop T2DM than the low-risk score group.

Conclusion

This nomogram may help clinicians screen abdominal obesity at a high risk for T2DM.

Keywords:

• type 2 diabetes
• T2DM
• abdominal obesity
• nomogram
• prediction model

Abbreviations

BMI, body mass index; T2DM, type 2 diabetes mellitus; SBP, systolic blood pressure; DBP, diastolic blood pressure; WC, waist circumference; TG, triglyceride; HbA1c, glycated hemoglobin A1c; FPG, fasting blood glucose; TC, total cholesterol; HDL-C, high-density lipoprotein cholesterol; ALT, alanine aminotransferase; AST, aspartate aminotransferase; GGT, gamma-glutamyl transferase; WHO, World Health Organization; ER, endoplasmic reticulum; DCA, decision curve analysis; LASSO, the least absolute shrinkage age and selection operator; ROC, receiver operating characteristic.

Data Sharing Statement

The datasets that support the conclusions of this article can be found in the Dryad repository.

Ethics Approval and Consent to Participate

As the authorization of the ethics committee of Murakami Memorial Hospital has been obtained in the previous study, this study does not need to be submitted for ethical approval again.

Acknowledgments

We thank all participants in this study.

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 report no conflicts of interest in this work.

Additional information

Funding

The study was supported by Changsha and Technology Bureau (kzd21084).