Unveiling the Hidden Burden: Estimating All-Cause Mortality Risk in Older Individuals with Type 2 Diabetes
Background. The mortality rate among older persons with diabetes has been steadily increasing, resulting in significant health and economic burdens on both society and individuals. The objective of this study is to develop and validate a predictive nomogram for estimating the 5-year all-cause mortal...
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| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2024-01-01
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| Series: | Journal of Diabetes Research |
| Online Access: | http://dx.doi.org/10.1155/2024/1741878 |
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| _version_ | 1832553071026110464 |
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| author | Dikang Pan Hui Wang Sensen Wu Jingyu Wang Yachan Ning Jianming Guo Cong Wang Yongquan Gu |
| author_facet | Dikang Pan Hui Wang Sensen Wu Jingyu Wang Yachan Ning Jianming Guo Cong Wang Yongquan Gu |
| author_sort | Dikang Pan |
| collection | DOAJ |
| description | Background. The mortality rate among older persons with diabetes has been steadily increasing, resulting in significant health and economic burdens on both society and individuals. The objective of this study is to develop and validate a predictive nomogram for estimating the 5-year all-cause mortality risk in older persons with T2D (T2D). Methods. We obtained data from the National Health and Nutrition Survey (NHANES). A random 7 : 3 split was made between the training and validation sets. By linking the national mortality index up until December 31, 2019, we ensured a minimum of 5 years of follow-up to assess all-cause mortality. A nomogram was developed in the training cohort using a logistic regression model as well as a least absolute shrinkage and selection operator (LASSO) regression model for predicting the 5-year risk of all-cause mortality. Finally, the prediction performance of the nomogram is evaluated using several validation methods. Results. We constructed a comprehensive prediction model based on the results of multivariate analysis and LASSO binomial regression. These models were then validated using data from the validation cohort. The final model includes four independent predictors: age, gender, estimated glomerular filtration rate, and white blood cell count. The C-index values for the training and validation cohorts were 0.748 and 0.762, respectively. The calibration curve demonstrates satisfactory consistency between the two cohorts. Conclusions. The newly developed nomogram proves to be a valuable tool in accurately predicting the 5-year all-cause mortality risk among older persons with diabetes, providing crucial information for tailored interventions. |
| format | Article |
| id | doaj-art-89bb59de4a1345d1a9114e5e5ab4075c |
| institution | Kabale University |
| issn | 2314-6753 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Diabetes Research |
| spelling | doaj-art-89bb59de4a1345d1a9114e5e5ab4075c2025-02-03T05:57:02ZengWileyJournal of Diabetes Research2314-67532024-01-01202410.1155/2024/1741878Unveiling the Hidden Burden: Estimating All-Cause Mortality Risk in Older Individuals with Type 2 DiabetesDikang Pan0Hui Wang1Sensen Wu2Jingyu Wang3Yachan Ning4Jianming Guo5Cong Wang6Yongquan Gu7Xuanwu HospitalXuanwu HospitalXuanwu HospitalRenal DivisionXuanwu HospitalXuanwu HospitalXuanwu HospitalXuanwu HospitalBackground. The mortality rate among older persons with diabetes has been steadily increasing, resulting in significant health and economic burdens on both society and individuals. The objective of this study is to develop and validate a predictive nomogram for estimating the 5-year all-cause mortality risk in older persons with T2D (T2D). Methods. We obtained data from the National Health and Nutrition Survey (NHANES). A random 7 : 3 split was made between the training and validation sets. By linking the national mortality index up until December 31, 2019, we ensured a minimum of 5 years of follow-up to assess all-cause mortality. A nomogram was developed in the training cohort using a logistic regression model as well as a least absolute shrinkage and selection operator (LASSO) regression model for predicting the 5-year risk of all-cause mortality. Finally, the prediction performance of the nomogram is evaluated using several validation methods. Results. We constructed a comprehensive prediction model based on the results of multivariate analysis and LASSO binomial regression. These models were then validated using data from the validation cohort. The final model includes four independent predictors: age, gender, estimated glomerular filtration rate, and white blood cell count. The C-index values for the training and validation cohorts were 0.748 and 0.762, respectively. The calibration curve demonstrates satisfactory consistency between the two cohorts. Conclusions. The newly developed nomogram proves to be a valuable tool in accurately predicting the 5-year all-cause mortality risk among older persons with diabetes, providing crucial information for tailored interventions.http://dx.doi.org/10.1155/2024/1741878 |
| spellingShingle | Dikang Pan Hui Wang Sensen Wu Jingyu Wang Yachan Ning Jianming Guo Cong Wang Yongquan Gu Unveiling the Hidden Burden: Estimating All-Cause Mortality Risk in Older Individuals with Type 2 Diabetes Journal of Diabetes Research |
| title | Unveiling the Hidden Burden: Estimating All-Cause Mortality Risk in Older Individuals with Type 2 Diabetes |
| title_full | Unveiling the Hidden Burden: Estimating All-Cause Mortality Risk in Older Individuals with Type 2 Diabetes |
| title_fullStr | Unveiling the Hidden Burden: Estimating All-Cause Mortality Risk in Older Individuals with Type 2 Diabetes |
| title_full_unstemmed | Unveiling the Hidden Burden: Estimating All-Cause Mortality Risk in Older Individuals with Type 2 Diabetes |
| title_short | Unveiling the Hidden Burden: Estimating All-Cause Mortality Risk in Older Individuals with Type 2 Diabetes |
| title_sort | unveiling the hidden burden estimating all cause mortality risk in older individuals with type 2 diabetes |
| url | http://dx.doi.org/10.1155/2024/1741878 |
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