Caspian Journal of Internal Medicine
Babol University of Medical Sciences
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Volume 9, Issue 2 (1-2018)
Caspian J Intern Med 2018, 9(2): 144-150 |
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Wu H, Ouyang P, Sun W. High -density lipoprotein cholesterol as a predictor for diabetes mellitus . Caspian J Intern Med 2018; 9 (2) :144-150
URL: http://caspjim.com/article-1-946-en.html
URL: http://caspjim.com/article-1-946-en.html
School of Management, Harbin Institute of Technology, Harbin, China , ouyp@hit.edu.cn
Abstract: (6905 Views)
Background: Diabetes is a prevalent chronic disease around the world. To evaluate risk of diabetes comprehensively, we developed a score model for the risk prediction with HDL-C as a protective factor.
Methods: We extracted physical examination data of 2728 individuals. The data contain 18 demographic and clinical variables. To identify the statistical significant feature variables, the backward stepwise logistic regression was used based on the data of the “exploratory population”. To ascertain the cutoff value of the selected variables, we used the Youden index. Then we assigned each variable level a score according to the estimated regression model coefficients and then calculated the individual’s total score. We gained the cutoff value for the total score through the Youden Index and stratified the total score into four levels. We employed the data of “validation population” to test the performance of the score model based on the area under the ROC curve.
Results: Age, LDL-C, HDL-C, BMI, family history of diabetes, diastolic blood pressure and TCHO were selected as statistically significant variables. The diabetes risk score range varied from 0 to 17. The risk level categorized by the total score was low, middle, high and extremely high, with a score range of 0-2, 3-7, 8-12 and 13-17, respectively.
Conclusions: The score model based on physical examination data is an efficient and valuable tool to evaluate and monitor the potential diabetes risk for both healthy and unhealthy people at an individual level.
Methods: We extracted physical examination data of 2728 individuals. The data contain 18 demographic and clinical variables. To identify the statistical significant feature variables, the backward stepwise logistic regression was used based on the data of the “exploratory population”. To ascertain the cutoff value of the selected variables, we used the Youden index. Then we assigned each variable level a score according to the estimated regression model coefficients and then calculated the individual’s total score. We gained the cutoff value for the total score through the Youden Index and stratified the total score into four levels. We employed the data of “validation population” to test the performance of the score model based on the area under the ROC curve.
Results: Age, LDL-C, HDL-C, BMI, family history of diabetes, diastolic blood pressure and TCHO were selected as statistically significant variables. The diabetes risk score range varied from 0 to 17. The risk level categorized by the total score was low, middle, high and extremely high, with a score range of 0-2, 3-7, 8-12 and 13-17, respectively.
Conclusions: The score model based on physical examination data is an efficient and valuable tool to evaluate and monitor the potential diabetes risk for both healthy and unhealthy people at an individual level.
Type of Study: Original Article |
Subject:
Health
Received: 2017/01/3 | Accepted: 2017/07/9 | Published: 2018/01/9
Received: 2017/01/3 | Accepted: 2017/07/9 | Published: 2018/01/9
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