A nomogram model for predicting 5-year risk of prediabetes in Chinese adults
Abstract Early identification is crucial to effectively intervene in individuals at high risk of developing pre-diabetes. This study aimed to create a personalized nomogram to determine the 5-year risk of pre-diabetes among Chinese adults. This retrospective cohort study included 184,188 participant...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2023-12-01
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| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-023-50122-3 |
| _version_ | 1797377065272475648 |
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| author | Yanhua Hu Yong Han Yufei Liu Yanan Cui Zhiping Ni Ling Wei Changchun Cao Haofei Hu Yongcheng He |
| author_facet | Yanhua Hu Yong Han Yufei Liu Yanan Cui Zhiping Ni Ling Wei Changchun Cao Haofei Hu Yongcheng He |
| author_sort | Yanhua Hu |
| collection | DOAJ |
| description | Abstract Early identification is crucial to effectively intervene in individuals at high risk of developing pre-diabetes. This study aimed to create a personalized nomogram to determine the 5-year risk of pre-diabetes among Chinese adults. This retrospective cohort study included 184,188 participants without prediabetes at baseline. Training cohorts (92,177) and validation cohorts (92,011) were randomly assigned (92,011). We compared five prediction models on the training cohorts: full cox proportional hazards model, stepwise cox proportional hazards model, multivariable fractional polynomials (MFP), machine learning, and least absolute shrinkage and selection operator (LASSO) models. At the same time, we validated the above five models on the validation set. And we chose the LASSO model as the final risk prediction model for prediabetes. We presented the model with a nomogram. The model's performance was evaluated in terms of its discriminative ability, clinical utility, and calibration using the area under the receiver operating characteristic (ROC) curve, decision curve analysis, and calibration analysis on the training cohorts. Simultaneously, we also evaluated the above nomogram on the validation set. The 5-year incidence of prediabetes was 10.70% and 10.69% in the training and validation cohort, respectively. We developed a simple nomogram that predicted the risk of prediabetes by using the parameters of age, body mass index (BMI), fasting plasma glucose (FBG), triglycerides (TG), systolic blood pressure (SBP), and serum creatinine (Scr). The nomogram's area under the receiver operating characteristic curve (AUC) was 0.7341 (95% CI 0.7290–0.7392) for the training cohort and 0.7336 (95% CI 0.7285–0.7387) for the validation cohort, indicating good discriminative ability. The calibration curve showed a perfect fit between the predicted prediabetes risk and the observed prediabetes risk. An analysis of the decision curve presented the clinical application of the nomogram, with alternative threshold probability spectrums being presented as well. A personalized prediabetes prediction nomogram was developed and validated among Chinese adults, identifying high-risk individuals. Doctors and others can easily and efficiently use our prediabetes prediction model when assessing prediabetes risk. |
| first_indexed | 2024-03-08T19:47:39Z |
| format | Article |
| id | doaj.art-aabeffff4e404dfa89eda381ab2bc102 |
| institution | Directory Open Access Journal |
| issn | 2045-2322 |
| language | English |
| last_indexed | 2024-03-08T19:47:39Z |
| publishDate | 2023-12-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj.art-aabeffff4e404dfa89eda381ab2bc1022023-12-24T12:16:21ZengNature PortfolioScientific Reports2045-23222023-12-0113111610.1038/s41598-023-50122-3A nomogram model for predicting 5-year risk of prediabetes in Chinese adultsYanhua Hu0Yong Han1Yufei Liu2Yanan Cui3Zhiping Ni4Ling Wei5Changchun Cao6Haofei Hu7Yongcheng He8College of Information Science and Engineering, Liuzhou Institute of TechnologyDepartment of Emergency, Shenzhen Second People’s HospitalDepartment of Neurosurgery, Shenzhen Second People’s HospitalCollege of Information Science and Engineering, Liuzhou Institute of TechnologyCollege of Information Science and Engineering, Liuzhou Institute of TechnologyCollege of Information Science and Engineering, Liuzhou Institute of TechnologyDepartment of Rehabilitation, Shenzhen Dapeng New District Nan’ao People’s HospitalDepartment of Nephrology, Shenzhen Second People’s HospitalDepartment of Nephrology, Shenzhen Hengsheng HospitalAbstract Early identification is crucial to effectively intervene in individuals at high risk of developing pre-diabetes. This study aimed to create a personalized nomogram to determine the 5-year risk of pre-diabetes among Chinese adults. This retrospective cohort study included 184,188 participants without prediabetes at baseline. Training cohorts (92,177) and validation cohorts (92,011) were randomly assigned (92,011). We compared five prediction models on the training cohorts: full cox proportional hazards model, stepwise cox proportional hazards model, multivariable fractional polynomials (MFP), machine learning, and least absolute shrinkage and selection operator (LASSO) models. At the same time, we validated the above five models on the validation set. And we chose the LASSO model as the final risk prediction model for prediabetes. We presented the model with a nomogram. The model's performance was evaluated in terms of its discriminative ability, clinical utility, and calibration using the area under the receiver operating characteristic (ROC) curve, decision curve analysis, and calibration analysis on the training cohorts. Simultaneously, we also evaluated the above nomogram on the validation set. The 5-year incidence of prediabetes was 10.70% and 10.69% in the training and validation cohort, respectively. We developed a simple nomogram that predicted the risk of prediabetes by using the parameters of age, body mass index (BMI), fasting plasma glucose (FBG), triglycerides (TG), systolic blood pressure (SBP), and serum creatinine (Scr). The nomogram's area under the receiver operating characteristic curve (AUC) was 0.7341 (95% CI 0.7290–0.7392) for the training cohort and 0.7336 (95% CI 0.7285–0.7387) for the validation cohort, indicating good discriminative ability. The calibration curve showed a perfect fit between the predicted prediabetes risk and the observed prediabetes risk. An analysis of the decision curve presented the clinical application of the nomogram, with alternative threshold probability spectrums being presented as well. A personalized prediabetes prediction nomogram was developed and validated among Chinese adults, identifying high-risk individuals. Doctors and others can easily and efficiently use our prediabetes prediction model when assessing prediabetes risk.https://doi.org/10.1038/s41598-023-50122-3 |
| spellingShingle | Yanhua Hu Yong Han Yufei Liu Yanan Cui Zhiping Ni Ling Wei Changchun Cao Haofei Hu Yongcheng He A nomogram model for predicting 5-year risk of prediabetes in Chinese adults Scientific Reports |
| title | A nomogram model for predicting 5-year risk of prediabetes in Chinese adults |
| title_full | A nomogram model for predicting 5-year risk of prediabetes in Chinese adults |
| title_fullStr | A nomogram model for predicting 5-year risk of prediabetes in Chinese adults |
| title_full_unstemmed | A nomogram model for predicting 5-year risk of prediabetes in Chinese adults |
| title_short | A nomogram model for predicting 5-year risk of prediabetes in Chinese adults |
| title_sort | nomogram model for predicting 5 year risk of prediabetes in chinese adults |
| url | https://doi.org/10.1038/s41598-023-50122-3 |
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