Nomogram Model for Screening the Risk of Type II Diabetes in Western Xinjiang, China

Area of Science:

• Endocrinology and Metabolism
• Public Health
• Biostatistics

Background:

• Type 2 Diabetes Mellitus (T2DM) is a growing global health concern.
• Early identification of high-risk individuals is crucial for effective prevention and management.
• Existing screening methods may not always be accessible or cost-effective in all populations.

Purpose of the Study:

• To develop and validate a simple screening model for identifying individuals at high risk of T2DM.
• To utilize easily available variables for a practical T2DM risk assessment tool.
• To establish a nomogram for predicting T2DM risk in the western Xinjiang, China population.

Main Methods:

• Recruited 458,153 participants from a national health examination.
• Employed logistic regression and LASSO models for variable selection and model building.
• Evaluated model performance using ROC curves, Hosmer-Lemeshow tests, and clinical decision analysis.

Main Results:

• A nomogram was developed incorporating factors like age, gender, family history, waist circumference, cholesterol, triglycerides, BMI, and HDLc.
• The model demonstrated strong discrimination with an Area Under the ROC Curve of 0.864 for men and 0.816 for women in the development group.
• Calibration and clinical decision curve analyses confirmed the nomogram's accuracy and clinical utility.

Conclusions:

• The developed nomogram is a simple, affordable, and credible tool for identifying adults at high risk for T2DM.
• The model shows potential for wide implementation in public health settings.
• Further research is recommended to assess the model's feasibility across diverse settings.