A nomogram model for screening the risk of diabetes in a large-scale Chinese population: an observational study from 345,718 participants

• Published in: Scientific reports 2020-07, Vol.10 (1), p.11600-11600, Article 11600
• Main Authors: Xue, Mingyue, Su, Yinxia, Feng, Zhiwei, Wang, Shuxia, Zhang, Mingchen, Wang, Kai, Yao, Hua
• Format: Article
• Language: English
• Subjects: 631/114; 692/163; Adult; Blood Pressure; Body Mass Index; China - epidemiology; Cross-Sectional Studies; Diabetes; Diabetes mellitus; Diabetes Mellitus, Type 2 - diagnostic imaging; Diabetes Mellitus, Type 2 - epidemiology; Diabetes Mellitus, Type 2 - pathology; Fatty liver; Female; Gallbladder; Health risk assessment; Heart rate; Humanities and Social Sciences; Humans; Liver diseases; Male; Middle Aged; multidisciplinary; Nomograms; Observational studies; Population - genetics; Population studies; Population Surveillance; Reclassification; Risk Factors; Science; Science (multidisciplinary); Waist-Height Ratio
• ISSN: 2045-2322; 2045-2322
• DOI: 10.1038/s41598-020-68383-7

Our study is major to establish and validate a simple type||diabetes mellitus (T2DM) screening model for identifying high-risk individuals among Chinese adults. A total of 643,439 subjects who participated in the national health examination had been enrolled in this cross-sectional study. After excluding subjects with missing data or previous medical history, 345,718 adults was included in the final analysis. We used the least absolute shrinkage and selection operator models to optimize feature selection, and used multivariable logistic regression analysis to build a predicting model. The results showed that the major risk factors of T2DM were age, gender, no drinking or drinking/time > 25 g, no exercise, smoking, waist-to-height ratio, heart rate, systolic blood pressure, fatty liver and gallbladder disease. The area under ROC was 0.811 for development group and 0.814 for validation group, and the p values of the two calibration curves were 0.053 and 0.438, the improvement of net reclassification and integrated discrimination are significant in our model. Our results give a clue that the screening models we conducted may be useful for identifying Chinses adults at high risk for diabetes. Further studies are needed to evaluate the utility and feasibility of this model in various settings.