AzoresDiab model: the risk prediction of type 2 diabetes in the Azores
Introduction: The incidence of type 2 diabetes mellitus is increasing in the Azores. The Azorean population has unique health-related characteristics that emphasize the necessity of developing a predictive model, namely the double insularity phenomena and consanguinity marriages. Therefore, the aut...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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James Cook University
2021-10-01
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| Series: | Rural and Remote Health |
| Subjects: | |
| Online Access: | https://www.rrh.org.au/journal/article/5967/ |
| _version_ | 1819000488342847488 |
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| author | Duarte de Sousa Tavares Ana Jorge |
| author_facet | Duarte de Sousa Tavares Ana Jorge |
| author_sort | Duarte de Sousa Tavares |
| collection | DOAJ |
| description | Introduction: The incidence of type 2 diabetes mellitus is increasing in the Azores. The Azorean population has unique health-related characteristics that emphasize the necessity of developing a predictive model, namely the double insularity phenomena and consanguinity marriages. Therefore, the authors aimed to develop a model, the AzoresDiab model, that assesses the risk of type 2 diabetes for residents of the Azores.
Methods: The variables used for developing the model included the history of cardiovascular disease, hypertension, sex, body mass index, triacylglycerol level, glucose level, and age. This model was developed using binary logistic regression wherein the dependent variable was considered 0 if the patient had type 2 diabetes and 1 if the patient did not. The sample comprised 6834 individuals who were Azores residents, aged over 18 years and who were not missing values for the covariates under study; individuals were included regardless of whether they had a previous diagnosis of type 2 diabetes. Participants were considered to have type 2 diabetes if they had been previously diagnosed with type 2 diabetes or had been prescribed at least one antidiabetic drug listed in the norms of the Portuguese General Directorate of Health and the Portuguese Medical Association.
Results: This model showed an area under the receiver operating characteristic curve of 0.863 based on internal validation performed with bootstrapping.
Conclusion: The AzoresDiab model exhibited excellent discrimination of patients with and without type 2 diabetes. The use of predictive risk models will enable the early implementation of disease prevention programs in medium- and high-risk individuals, and public health policies to prevent the onset of the disease in these populations. |
| first_indexed | 2024-12-20T22:34:07Z |
| format | Article |
| id | doaj.art-cf064fb772b3458ca383a442e5c1294a |
| institution | Directory Open Access Journal |
| issn | 1445-6354 |
| language | English |
| last_indexed | 2024-12-20T22:34:07Z |
| publishDate | 2021-10-01 |
| publisher | James Cook University |
| record_format | Article |
| series | Rural and Remote Health |
| spelling | doaj.art-cf064fb772b3458ca383a442e5c1294a2022-12-21T19:24:39ZengJames Cook UniversityRural and Remote Health1445-63542021-10-012110.22605/RRH5967AzoresDiab model: the risk prediction of type 2 diabetes in the AzoresDuarte de Sousa Tavares0Ana Jorge1Faculdade de Economia, Universidade do Algarve, Faro, Portugal; and Egas Moniz – Cooperativa de Ensino Superior, CRL, Almada, PortugalFaculdade de Medicina, Universidade de Lisboa, Lisbon, PortugalIntroduction: The incidence of type 2 diabetes mellitus is increasing in the Azores. The Azorean population has unique health-related characteristics that emphasize the necessity of developing a predictive model, namely the double insularity phenomena and consanguinity marriages. Therefore, the authors aimed to develop a model, the AzoresDiab model, that assesses the risk of type 2 diabetes for residents of the Azores. Methods: The variables used for developing the model included the history of cardiovascular disease, hypertension, sex, body mass index, triacylglycerol level, glucose level, and age. This model was developed using binary logistic regression wherein the dependent variable was considered 0 if the patient had type 2 diabetes and 1 if the patient did not. The sample comprised 6834 individuals who were Azores residents, aged over 18 years and who were not missing values for the covariates under study; individuals were included regardless of whether they had a previous diagnosis of type 2 diabetes. Participants were considered to have type 2 diabetes if they had been previously diagnosed with type 2 diabetes or had been prescribed at least one antidiabetic drug listed in the norms of the Portuguese General Directorate of Health and the Portuguese Medical Association. Results: This model showed an area under the receiver operating characteristic curve of 0.863 based on internal validation performed with bootstrapping. Conclusion: The AzoresDiab model exhibited excellent discrimination of patients with and without type 2 diabetes. The use of predictive risk models will enable the early implementation of disease prevention programs in medium- and high-risk individuals, and public health policies to prevent the onset of the disease in these populations.https://www.rrh.org.au/journal/article/5967/Azorespersonalized medicinepreventive medicinerisk predictor modeltype 2 diabetes. |
| spellingShingle | Duarte de Sousa Tavares Ana Jorge AzoresDiab model: the risk prediction of type 2 diabetes in the Azores Rural and Remote Health Azores personalized medicine preventive medicine risk predictor model type 2 diabetes. |
| title | AzoresDiab model: the risk prediction of type 2 diabetes in the Azores |
| title_full | AzoresDiab model: the risk prediction of type 2 diabetes in the Azores |
| title_fullStr | AzoresDiab model: the risk prediction of type 2 diabetes in the Azores |
| title_full_unstemmed | AzoresDiab model: the risk prediction of type 2 diabetes in the Azores |
| title_short | AzoresDiab model: the risk prediction of type 2 diabetes in the Azores |
| title_sort | azoresdiab model the risk prediction of type 2 diabetes in the azores |
| topic | Azores personalized medicine preventive medicine risk predictor model type 2 diabetes. |
| url | https://www.rrh.org.au/journal/article/5967/ |
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