Exploring the risk factors of impaired fasting glucose in middle-aged population living in South Korean communities by using categorical boosting machine
ObjectiveThis epidemiological study (1) identified factors associated with impaired fasting glucose using 3,019 subjects (≥30 years old and <60 years old) without diabetes mellitus from national survey data and (2) developed a nomogram that could predict groups vulnerable to impaired fasting...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2022-09-01
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| Series: | Frontiers in Endocrinology |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fendo.2022.1013162/full |
| _version_ | 1811208120374919168 |
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| author | Haewon Byeon Haewon Byeon |
| author_facet | Haewon Byeon Haewon Byeon |
| author_sort | Haewon Byeon |
| collection | DOAJ |
| description | ObjectiveThis epidemiological study (1) identified factors associated with impaired fasting glucose using 3,019 subjects (≥30 years old and <60 years old) without diabetes mellitus from national survey data and (2) developed a nomogram that could predict groups vulnerable to impaired fasting glucose by using machine learning.MethodsThis study analyzed 3,019 adults between 30 and 65 years old who completed blood tests, physical measurements, blood pressure measurements, and health surveys. Impaired fasting glucose, a dependent variable, was classified into normal blood glucose (glycated hemoglobin<5.7% and fasting blood glucose ≤ 100mg/dl) and impaired fasting glucose (glycated hemoglobin is 5.7-6.4% and fasting blood glucose is 100-125mg/dl). Explanatory variables included socio-demographic factors, health habit factors, anthropometric factors, dietary habit factors, and cardiovascular disease risk factors. This study developed a model for predicting impaired fasting glucose by using logistic nomogram and categorical boosting (CatBoost).ResultsIn this study, the top eight variables with a high impact on CatBoost model output were age, high cholesterol, WHtR, BMI, drinking more than one shot per month for the past year, marital status, hypertension, and smoking.ConclusionIt is necessary to improve lifestyle and continuously monitor subjects at the primary medical care level so that we can detect non-diabetics vulnerable to impaired fasting glucose living in the community at an early stage and manage their blood glucose. |
| first_indexed | 2024-04-12T04:16:25Z |
| format | Article |
| id | doaj.art-3cd2724c056c4feaaa310f24bed51089 |
| institution | Directory Open Access Journal |
| issn | 1664-2392 |
| language | English |
| last_indexed | 2024-04-12T04:16:25Z |
| publishDate | 2022-09-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Endocrinology |
| spelling | doaj.art-3cd2724c056c4feaaa310f24bed510892022-12-22T03:48:23ZengFrontiers Media S.A.Frontiers in Endocrinology1664-23922022-09-011310.3389/fendo.2022.10131621013162Exploring the risk factors of impaired fasting glucose in middle-aged population living in South Korean communities by using categorical boosting machineHaewon Byeon0Haewon Byeon1Department of Digital Anti-aging Healthcare (BK21), Graduate School of Inje University, Gimhae, South KoreaDepartment of Medical Big Data, College of AI Convergence, Inje University, Gimhae, South KoreaObjectiveThis epidemiological study (1) identified factors associated with impaired fasting glucose using 3,019 subjects (≥30 years old and <60 years old) without diabetes mellitus from national survey data and (2) developed a nomogram that could predict groups vulnerable to impaired fasting glucose by using machine learning.MethodsThis study analyzed 3,019 adults between 30 and 65 years old who completed blood tests, physical measurements, blood pressure measurements, and health surveys. Impaired fasting glucose, a dependent variable, was classified into normal blood glucose (glycated hemoglobin<5.7% and fasting blood glucose ≤ 100mg/dl) and impaired fasting glucose (glycated hemoglobin is 5.7-6.4% and fasting blood glucose is 100-125mg/dl). Explanatory variables included socio-demographic factors, health habit factors, anthropometric factors, dietary habit factors, and cardiovascular disease risk factors. This study developed a model for predicting impaired fasting glucose by using logistic nomogram and categorical boosting (CatBoost).ResultsIn this study, the top eight variables with a high impact on CatBoost model output were age, high cholesterol, WHtR, BMI, drinking more than one shot per month for the past year, marital status, hypertension, and smoking.ConclusionIt is necessary to improve lifestyle and continuously monitor subjects at the primary medical care level so that we can detect non-diabetics vulnerable to impaired fasting glucose living in the community at an early stage and manage their blood glucose.https://www.frontiersin.org/articles/10.3389/fendo.2022.1013162/fullimpaired fasting glucoserisk factorCatBoostmachine learningmiddle-aged population |
| spellingShingle | Haewon Byeon Haewon Byeon Exploring the risk factors of impaired fasting glucose in middle-aged population living in South Korean communities by using categorical boosting machine Frontiers in Endocrinology impaired fasting glucose risk factor CatBoost machine learning middle-aged population |
| title | Exploring the risk factors of impaired fasting glucose in middle-aged population living in South Korean communities by using categorical boosting machine |
| title_full | Exploring the risk factors of impaired fasting glucose in middle-aged population living in South Korean communities by using categorical boosting machine |
| title_fullStr | Exploring the risk factors of impaired fasting glucose in middle-aged population living in South Korean communities by using categorical boosting machine |
| title_full_unstemmed | Exploring the risk factors of impaired fasting glucose in middle-aged population living in South Korean communities by using categorical boosting machine |
| title_short | Exploring the risk factors of impaired fasting glucose in middle-aged population living in South Korean communities by using categorical boosting machine |
| title_sort | exploring the risk factors of impaired fasting glucose in middle aged population living in south korean communities by using categorical boosting machine |
| topic | impaired fasting glucose risk factor CatBoost machine learning middle-aged population |
| url | https://www.frontiersin.org/articles/10.3389/fendo.2022.1013162/full |
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