Somayeh Ghiasi Hafezi , Maryam Saberi-Karimian , Morteza Ghasemi , Mark Ghamsary , Mohsen Moohebati , Habibollah Esmaily , Saba Maleki , Gordon A. Ferns , Maryam Alinezhad-Namaghi , Majid Ghayour-Mobarhan
{"title":"利用机器学习方法，根据伊朗人口的高级人体测量指数预测 2 型糖尿病的 10 年发病率。","authors":"Somayeh Ghiasi Hafezi , Maryam Saberi-Karimian , Morteza Ghasemi , Mark Ghamsary , Mohsen Moohebati , Habibollah Esmaily , Saba Maleki , Gordon A. Ferns , Maryam Alinezhad-Namaghi , Majid Ghayour-Mobarhan","doi":"10.1016/j.diabres.2024.111755","DOIUrl":null,"url":null,"abstract":"<div><h3>Background</h3><p>Type 2 diabetes mellitus (T2DM) is a growing chronic disease that can lead to disability and early death. This study aimed to establish a predictive model for the 10-year incidence of T2DM based on novel anthropometric indices.</p></div><div><h3>Methods</h3><p>This was a prospective cohort study comparing people with (n = 1256) and without (n = 5193) diabetes mellitus in phase II of the Mashhad Stroke and Heart Atherosclerotic Disorder (MASHAD) study.</p><p>The association of several anthropometric indices in phase I, including Body Mass Index (BMI), Body Adiposity Index (BAI), Abdominal Volume Index (AVI), Visceral Adiposity Index (VAI), Weight-Adjusted-Waist Index (WWI), Body Roundness Index (BRI), Body Surface Area (BSA), Conicity Index (C-Index) and Lipid Accumulation Product (LAP) with T2DM incidence (in phase II) were examined; using Logistic Regression (LR) and Decision Tree (DT) analysis.</p></div><div><h3>Results</h3><p>BMI followed by VAI and LAP were the best predictors of T2DM incidence. Participants with BMI < 21.25 kg/m<sup>2</sup> and VAI <span><math><mrow><mo>≤</mo></mrow></math></span> 5.9 had a lower chance of diabetes than those with higher BMI and VAI levels (0.033 vs. 0.967 incident rate). For BMI > 25 kg/m<sup>2</sup>, the chance of diabetes rapidly increased (OR = 2.27).</p></div><div><h3>Conclusions</h3><p>BMI, VAI, and LAP were the best predictors of T2DM incidence.</p></div>","PeriodicalId":11249,"journal":{"name":"Diabetes research and clinical practice","volume":null,"pages":null},"PeriodicalIF":6.1000,"publicationDate":"2024-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Prediction of the 10-year incidence of type 2 diabetes mellitus based on advanced anthropometric indices using machine learning methods in the Iranian population\",\"authors\":\"Somayeh Ghiasi Hafezi , Maryam Saberi-Karimian , Morteza Ghasemi , Mark Ghamsary , Mohsen Moohebati , Habibollah Esmaily , Saba Maleki , Gordon A. Ferns , Maryam Alinezhad-Namaghi , Majid Ghayour-Mobarhan\",\"doi\":\"10.1016/j.diabres.2024.111755\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><h3>Background</h3><p>Type 2 diabetes mellitus (T2DM) is a growing chronic disease that can lead to disability and early death. This study aimed to establish a predictive model for the 10-year incidence of T2DM based on novel anthropometric indices.</p></div><div><h3>Methods</h3><p>This was a prospective cohort study comparing people with (n = 1256) and without (n = 5193) diabetes mellitus in phase II of the Mashhad Stroke and Heart Atherosclerotic Disorder (MASHAD) study.</p><p>The association of several anthropometric indices in phase I, including Body Mass Index (BMI), Body Adiposity Index (BAI), Abdominal Volume Index (AVI), Visceral Adiposity Index (VAI), Weight-Adjusted-Waist Index (WWI), Body Roundness Index (BRI), Body Surface Area (BSA), Conicity Index (C-Index) and Lipid Accumulation Product (LAP) with T2DM incidence (in phase II) were examined; using Logistic Regression (LR) and Decision Tree (DT) analysis.</p></div><div><h3>Results</h3><p>BMI followed by VAI and LAP were the best predictors of T2DM incidence. Participants with BMI < 21.25 kg/m<sup>2</sup> and VAI <span><math><mrow><mo>≤</mo></mrow></math></span> 5.9 had a lower chance of diabetes than those with higher BMI and VAI levels (0.033 vs. 0.967 incident rate). For BMI > 25 kg/m<sup>2</sup>, the chance of diabetes rapidly increased (OR = 2.27).</p></div><div><h3>Conclusions</h3><p>BMI, VAI, and LAP were the best predictors of T2DM incidence.</p></div>\",\"PeriodicalId\":11249,\"journal\":{\"name\":\"Diabetes research and clinical practice\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":6.1000,\"publicationDate\":\"2024-06-25\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Diabetes research and clinical practice\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S016882272400665X\",\"RegionNum\":3,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"ENDOCRINOLOGY & METABOLISM\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Diabetes research and clinical practice","FirstCategoryId":"3","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S016882272400665X","RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENDOCRINOLOGY & METABOLISM","Score":null,"Total":0}
Prediction of the 10-year incidence of type 2 diabetes mellitus based on advanced anthropometric indices using machine learning methods in the Iranian population
Background
Type 2 diabetes mellitus (T2DM) is a growing chronic disease that can lead to disability and early death. This study aimed to establish a predictive model for the 10-year incidence of T2DM based on novel anthropometric indices.
Methods
This was a prospective cohort study comparing people with (n = 1256) and without (n = 5193) diabetes mellitus in phase II of the Mashhad Stroke and Heart Atherosclerotic Disorder (MASHAD) study.
The association of several anthropometric indices in phase I, including Body Mass Index (BMI), Body Adiposity Index (BAI), Abdominal Volume Index (AVI), Visceral Adiposity Index (VAI), Weight-Adjusted-Waist Index (WWI), Body Roundness Index (BRI), Body Surface Area (BSA), Conicity Index (C-Index) and Lipid Accumulation Product (LAP) with T2DM incidence (in phase II) were examined; using Logistic Regression (LR) and Decision Tree (DT) analysis.
Results
BMI followed by VAI and LAP were the best predictors of T2DM incidence. Participants with BMI < 21.25 kg/m2 and VAI 5.9 had a lower chance of diabetes than those with higher BMI and VAI levels (0.033 vs. 0.967 incident rate). For BMI > 25 kg/m2, the chance of diabetes rapidly increased (OR = 2.27).
Conclusions
BMI, VAI, and LAP were the best predictors of T2DM incidence.
期刊介绍：
Diabetes Research and Clinical Practice is an international journal for health-care providers and clinically oriented researchers that publishes high-quality original research articles and expert reviews in diabetes and related areas. The role of the journal is to provide a venue for dissemination of knowledge and discussion of topics related to diabetes clinical research and patient care. Topics of focus include translational science, genetics, immunology, nutrition, psychosocial research, epidemiology, prevention, socio-economic research, complications, new treatments, technologies and therapy.
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