A Prediction Model of the Incidence of Type 2 Diabetes in Individuals with Abdominal Obesity: Insights from the General Population
Caixia Tan,1 Bo Li,2 Lingzhi Xiao,1 Yun Zhang,1 Yingjie Su,3 Ning Ding3 1The Second Affiliated Hospital, Department of Emergency Medicine, Hengyang Medical School, University of South China, Hengyang, Hunan, 421001, People’s Republic of China; 2The Second Affiliated Hospital, Department of Critical...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Dove Medical Press
2022-11-01
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| Series: | Diabetes, Metabolic Syndrome and Obesity |
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| Online Access: | https://www.dovepress.com/a-prediction-model-of-the-incidence-of-type-2-diabetes-in-individuals--peer-reviewed-fulltext-article-DMSO |
| _version_ | 1797710297090228224 |
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| author | Tan C Li B Xiao L Zhang Y Su Y Ding N |
| author_facet | Tan C Li B Xiao L Zhang Y Su Y Ding N |
| author_sort | Tan C |
| collection | DOAJ |
| description | Caixia Tan,1 Bo Li,2 Lingzhi Xiao,1 Yun Zhang,1 Yingjie Su,3 Ning Ding3 1The Second Affiliated Hospital, Department of Emergency Medicine, Hengyang Medical School, University of South China, Hengyang, Hunan, 421001, People’s Republic of China; 2The Second Affiliated Hospital, Department of Critical Care Medicine, Hengyang Medical School, University of South China, Hengyang, People’s Republic of China; 3Department of Emergency Medicine, The Affiliated Changsha Central Hospital, Hengyang Medical School, University of South China, Changsha, People’s Republic of ChinaCorrespondence: Ning Ding, Department of Emergency Medicine, The Affiliated Changsha Central Hospital, Hengyang Medical School, University of South China, No. 161 Shaoshan South Road, Changsha, People’s Republic of China, Email doctordingning@sina.comBackground: 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 |
| first_indexed | 2024-03-12T06:50:08Z |
| format | Article |
| id | doaj.art-5dde82b3468a482da087d58d30f7690a |
| institution | Directory Open Access Journal |
| issn | 1178-7007 |
| language | English |
| last_indexed | 2024-03-12T06:50:08Z |
| publishDate | 2022-11-01 |
| publisher | Dove Medical Press |
| record_format | Article |
| series | Diabetes, Metabolic Syndrome and Obesity |
| spelling | doaj.art-5dde82b3468a482da087d58d30f7690a2023-09-03T00:21:27ZengDove Medical PressDiabetes, Metabolic Syndrome and Obesity1178-70072022-11-01Volume 153555356479669A Prediction Model of the Incidence of Type 2 Diabetes in Individuals with Abdominal Obesity: Insights from the General PopulationTan CLi BXiao LZhang YSu YDing NCaixia Tan,1 Bo Li,2 Lingzhi Xiao,1 Yun Zhang,1 Yingjie Su,3 Ning Ding3 1The Second Affiliated Hospital, Department of Emergency Medicine, Hengyang Medical School, University of South China, Hengyang, Hunan, 421001, People’s Republic of China; 2The Second Affiliated Hospital, Department of Critical Care Medicine, Hengyang Medical School, University of South China, Hengyang, People’s Republic of China; 3Department of Emergency Medicine, The Affiliated Changsha Central Hospital, Hengyang Medical School, University of South China, Changsha, People’s Republic of ChinaCorrespondence: Ning Ding, Department of Emergency Medicine, The Affiliated Changsha Central Hospital, Hengyang Medical School, University of South China, No. 161 Shaoshan South Road, Changsha, People’s Republic of China, Email doctordingning@sina.comBackground: 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 modelhttps://www.dovepress.com/a-prediction-model-of-the-incidence-of-type-2-diabetes-in-individuals--peer-reviewed-fulltext-article-DMSOtype 2 diabetest2dmabdominal obesitynomogramprediction model |
| spellingShingle | Tan C Li B Xiao L Zhang Y Su Y Ding N A Prediction Model of the Incidence of Type 2 Diabetes in Individuals with Abdominal Obesity: Insights from the General Population Diabetes, Metabolic Syndrome and Obesity type 2 diabetes t2dm abdominal obesity nomogram prediction model |
| title | A Prediction Model of the Incidence of Type 2 Diabetes in Individuals with Abdominal Obesity: Insights from the General Population |
| title_full | A Prediction Model of the Incidence of Type 2 Diabetes in Individuals with Abdominal Obesity: Insights from the General Population |
| title_fullStr | A Prediction Model of the Incidence of Type 2 Diabetes in Individuals with Abdominal Obesity: Insights from the General Population |
| title_full_unstemmed | A Prediction Model of the Incidence of Type 2 Diabetes in Individuals with Abdominal Obesity: Insights from the General Population |
| title_short | A Prediction Model of the Incidence of Type 2 Diabetes in Individuals with Abdominal Obesity: Insights from the General Population |
| title_sort | prediction model of the incidence of type 2 diabetes in individuals with abdominal obesity insights from the general population |
| topic | type 2 diabetes t2dm abdominal obesity nomogram prediction model |
| url | https://www.dovepress.com/a-prediction-model-of-the-incidence-of-type-2-diabetes-in-individuals--peer-reviewed-fulltext-article-DMSO |
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