Nutritional intake of micronutrient and macronutrient and type 2 diabetes: machine learning schemes
Abstract Background Diabetes mellitus, an endocrine system disease, is a common disease involving many patients worldwide. Many studies are performed to evaluate the correlation between micronutrients/macronutrients on diabetes but few of them have a high statistical population and a long follow-up...
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| Format: | Article |
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BMC
2025-02-01
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| Series: | Journal of Health, Population and Nutrition |
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| Online Access: | https://doi.org/10.1186/s41043-024-00712-2 |
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| author | Mohammad Rashidmayvan Amin Mansoori Elahe Derakhshan-Nezhad Davoud Tanbakuchi Fatemeh Sangin Maryam Mohammadi-Bajgiran Malihehsadat Abedsaeidi Sara Ghazizadeh MohammadReza Mohammad Taghizadeh Sarabi Ali Rezaee Gordon Ferns Habibollah Esmaily Majid Ghayour-Mobarhan |
| author_facet | Mohammad Rashidmayvan Amin Mansoori Elahe Derakhshan-Nezhad Davoud Tanbakuchi Fatemeh Sangin Maryam Mohammadi-Bajgiran Malihehsadat Abedsaeidi Sara Ghazizadeh MohammadReza Mohammad Taghizadeh Sarabi Ali Rezaee Gordon Ferns Habibollah Esmaily Majid Ghayour-Mobarhan |
| author_sort | Mohammad Rashidmayvan |
| collection | DOAJ |
| description | Abstract Background Diabetes mellitus, an endocrine system disease, is a common disease involving many patients worldwide. Many studies are performed to evaluate the correlation between micronutrients/macronutrients on diabetes but few of them have a high statistical population and a long follow-up period. We aimed to investigate the relationship between intake of macro/micronutrients and the incidence of type 2 diabetes (T2D) using logistic regression (LR) and a decision tree (DT) algorithm for machine learning. Method Our research explores supervised machine learning models to identify T2D patients using the Mashhad Cohort Study dataset. The study population comprised 9704 individuals aged 35–65 years were enrolled regarding their T2D status, and those with T2D history. 15% of individuals are diabetic and 85% of them are non-diabetic. For ten years (until 2020), the participants in the study were monitored to determine the incidence of T2D. LR is a statistical model applied in dichotomous response variable modeling. All data were analyzed by SPSS (Version 22) and SAS JMP software. Result Nutritional intake in the T2D group showed that potassium, calcium, magnesium, zinc, iodine, carotene, vitamin D, tryptophan, and vitamin B12 had an inverse correlation with the incidence of diabetes (p < 0.05). While phosphate, iron, and chloride had a positive relationship with the risk of T2D (p < 0.05). Also, the T2D group significantly had higher carbohydrate and protein intake (p-value < 0.05). Conclusion Machine learning models can identify T2D risk using questionnaires and blood samples. These have implications for electronic health records that can be explored further. |
| format | Article |
| id | doaj-art-62e874c01b7e48de83d3f6a85cb175c9 |
| institution | Kabale University |
| issn | 2072-1315 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | BMC |
| record_format | Article |
| series | Journal of Health, Population and Nutrition |
| spelling | doaj-art-62e874c01b7e48de83d3f6a85cb175c92025-02-09T12:42:54ZengBMCJournal of Health, Population and Nutrition2072-13152025-02-0144111110.1186/s41043-024-00712-2Nutritional intake of micronutrient and macronutrient and type 2 diabetes: machine learning schemesMohammad Rashidmayvan0Amin Mansoori1Elahe Derakhshan-Nezhad2Davoud Tanbakuchi3Fatemeh Sangin4Maryam Mohammadi-Bajgiran5Malihehsadat Abedsaeidi6Sara Ghazizadeh7MohammadReza Mohammad Taghizadeh Sarabi8Ali Rezaee9Gordon Ferns10Habibollah Esmaily11Majid Ghayour-Mobarhan12Department of Nutrition, Food Sciences and Clinical Biochemistry, School of Medicine, Social Determinants of Health Research Center, Gonabad University of Medical SciencesDepartment of Applied Mathematics, School of Mathematical Sciences, Ferdowsi University of MashhadFaculty of Medicine, Islamic Azad University of MashhadDepartment of Biostatistics, School of Health, Mashhad University of Medical SciencesDepartment of Computer Engineering, Center of Excellence on Soft Computing and Intelligent Information, Processing Ferdowsi University of MashhadInternational UNESCO center for Health-Related Basic Sciences and Human Nutrition, Mashhad University of Medical SciencesDepartment of Basic Sciences, Faculty of Veterinary Medicine, Ferdowsi University of MashhadDepartment of Biology, Mashhad Branch, Islamic Azad UniversityDepartment of Biology, Mashhad Branch, Islamic Azad UniversityDepartment of Biology, Mashhad Branch, Islamic Azad UniversityBrighton and Sussex Medical School, Division of Medical EducationDepartment of Biostatistics, School of Health, Mashhad University of Medical SciencesInternational UNESCO center for Health-Related Basic Sciences and Human Nutrition, Mashhad University of Medical SciencesAbstract Background Diabetes mellitus, an endocrine system disease, is a common disease involving many patients worldwide. Many studies are performed to evaluate the correlation between micronutrients/macronutrients on diabetes but few of them have a high statistical population and a long follow-up period. We aimed to investigate the relationship between intake of macro/micronutrients and the incidence of type 2 diabetes (T2D) using logistic regression (LR) and a decision tree (DT) algorithm for machine learning. Method Our research explores supervised machine learning models to identify T2D patients using the Mashhad Cohort Study dataset. The study population comprised 9704 individuals aged 35–65 years were enrolled regarding their T2D status, and those with T2D history. 15% of individuals are diabetic and 85% of them are non-diabetic. For ten years (until 2020), the participants in the study were monitored to determine the incidence of T2D. LR is a statistical model applied in dichotomous response variable modeling. All data were analyzed by SPSS (Version 22) and SAS JMP software. Result Nutritional intake in the T2D group showed that potassium, calcium, magnesium, zinc, iodine, carotene, vitamin D, tryptophan, and vitamin B12 had an inverse correlation with the incidence of diabetes (p < 0.05). While phosphate, iron, and chloride had a positive relationship with the risk of T2D (p < 0.05). Also, the T2D group significantly had higher carbohydrate and protein intake (p-value < 0.05). Conclusion Machine learning models can identify T2D risk using questionnaires and blood samples. These have implications for electronic health records that can be explored further.https://doi.org/10.1186/s41043-024-00712-2Data miningDiabetesMacro/MicronutrientsDecision tree |
| spellingShingle | Mohammad Rashidmayvan Amin Mansoori Elahe Derakhshan-Nezhad Davoud Tanbakuchi Fatemeh Sangin Maryam Mohammadi-Bajgiran Malihehsadat Abedsaeidi Sara Ghazizadeh MohammadReza Mohammad Taghizadeh Sarabi Ali Rezaee Gordon Ferns Habibollah Esmaily Majid Ghayour-Mobarhan Nutritional intake of micronutrient and macronutrient and type 2 diabetes: machine learning schemes Journal of Health, Population and Nutrition Data mining Diabetes Macro/Micronutrients Decision tree |
| title | Nutritional intake of micronutrient and macronutrient and type 2 diabetes: machine learning schemes |
| title_full | Nutritional intake of micronutrient and macronutrient and type 2 diabetes: machine learning schemes |
| title_fullStr | Nutritional intake of micronutrient and macronutrient and type 2 diabetes: machine learning schemes |
| title_full_unstemmed | Nutritional intake of micronutrient and macronutrient and type 2 diabetes: machine learning schemes |
| title_short | Nutritional intake of micronutrient and macronutrient and type 2 diabetes: machine learning schemes |
| title_sort | nutritional intake of micronutrient and macronutrient and type 2 diabetes machine learning schemes |
| topic | Data mining Diabetes Macro/Micronutrients Decision tree |
| url | https://doi.org/10.1186/s41043-024-00712-2 |
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