Early Prediction for Prediabetes and Type 2 Diabetes Using the Genetic Risk Score and Oxidative Stress Score
We aimed to use a genetic risk score (GRS) constructed with prediabetes and type 2 diabetes-related single nucleotide polymorphisms (SNPs) and an oxidative stress score (OSS) to construct an early-prediction model for prediabetes and type 2 diabetes (T2DM) incidence in a Korean population. The study...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-06-01
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| Series: | Antioxidants |
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| Online Access: | https://www.mdpi.com/2076-3921/11/6/1196 |
| _version_ | 1797490452828520448 |
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| author | Ximei Huang Youngmin Han Kyunghye Jang Minjoo Kim |
| author_facet | Ximei Huang Youngmin Han Kyunghye Jang Minjoo Kim |
| author_sort | Ximei Huang |
| collection | DOAJ |
| description | We aimed to use a genetic risk score (GRS) constructed with prediabetes and type 2 diabetes-related single nucleotide polymorphisms (SNPs) and an oxidative stress score (OSS) to construct an early-prediction model for prediabetes and type 2 diabetes (T2DM) incidence in a Korean population. The study population included 549 prediabetes and T2DM patients and 1036 normal subjects. The GRS was constructed using six prediabetes and T2DM-related SNPs, and the OSS was composed of three recognized oxidative stress biomarkers. Among the nine SNPs, six showed significant associations with the incidence of prediabetes and T2DM. The GRS was profoundly associated with increased prediabetes and T2DM (OR = 1.946) compared with individual SNPs after adjusting for age, sex, and BMI. Each of the three oxidative stress biomarkers was markedly higher in the prediabetes and T2DM group than in the normal group, and the OSS was significantly associated with increased prediabetes and T2DM (OR = 2.270). When BMI was introduced to the model with the OSS and GRS, the area under the ROC curve improved (from 69.3% to 70.5%). We found that the prediction model composed of the OSS, GRS, and BMI showed a significant prediction ability for the incidence of prediabetes and T2DM. |
| first_indexed | 2024-03-10T00:33:11Z |
| format | Article |
| id | doaj.art-2947d552dab34b13a613829a65f75c6d |
| institution | Directory Open Access Journal |
| issn | 2076-3921 |
| language | English |
| last_indexed | 2024-03-10T00:33:11Z |
| publishDate | 2022-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Antioxidants |
| spelling | doaj.art-2947d552dab34b13a613829a65f75c6d2023-11-23T15:21:15ZengMDPI AGAntioxidants2076-39212022-06-01116119610.3390/antiox11061196Early Prediction for Prediabetes and Type 2 Diabetes Using the Genetic Risk Score and Oxidative Stress ScoreXimei Huang0Youngmin Han1Kyunghye Jang2Minjoo Kim3Department of Food and Nutrition, College of Life Science and Nano Technology, Hannam University, Daejeon 34054, KoreaInstitute for Health Promotion, Graduate School of Public Health, Yonsei University, Seoul 03722, KoreaNakdonggang National Institute of Biological Resources, Sangju, Gyeongsangbuk-do 37242, KoreaDepartment of Food and Nutrition, College of Life Science and Nano Technology, Hannam University, Daejeon 34054, KoreaWe aimed to use a genetic risk score (GRS) constructed with prediabetes and type 2 diabetes-related single nucleotide polymorphisms (SNPs) and an oxidative stress score (OSS) to construct an early-prediction model for prediabetes and type 2 diabetes (T2DM) incidence in a Korean population. The study population included 549 prediabetes and T2DM patients and 1036 normal subjects. The GRS was constructed using six prediabetes and T2DM-related SNPs, and the OSS was composed of three recognized oxidative stress biomarkers. Among the nine SNPs, six showed significant associations with the incidence of prediabetes and T2DM. The GRS was profoundly associated with increased prediabetes and T2DM (OR = 1.946) compared with individual SNPs after adjusting for age, sex, and BMI. Each of the three oxidative stress biomarkers was markedly higher in the prediabetes and T2DM group than in the normal group, and the OSS was significantly associated with increased prediabetes and T2DM (OR = 2.270). When BMI was introduced to the model with the OSS and GRS, the area under the ROC curve improved (from 69.3% to 70.5%). We found that the prediction model composed of the OSS, GRS, and BMI showed a significant prediction ability for the incidence of prediabetes and T2DM.https://www.mdpi.com/2076-3921/11/6/1196genetic risk scoreoxidative stress scoreprediabetestype 2 diabetes |
| spellingShingle | Ximei Huang Youngmin Han Kyunghye Jang Minjoo Kim Early Prediction for Prediabetes and Type 2 Diabetes Using the Genetic Risk Score and Oxidative Stress Score Antioxidants genetic risk score oxidative stress score prediabetes type 2 diabetes |
| title | Early Prediction for Prediabetes and Type 2 Diabetes Using the Genetic Risk Score and Oxidative Stress Score |
| title_full | Early Prediction for Prediabetes and Type 2 Diabetes Using the Genetic Risk Score and Oxidative Stress Score |
| title_fullStr | Early Prediction for Prediabetes and Type 2 Diabetes Using the Genetic Risk Score and Oxidative Stress Score |
| title_full_unstemmed | Early Prediction for Prediabetes and Type 2 Diabetes Using the Genetic Risk Score and Oxidative Stress Score |
| title_short | Early Prediction for Prediabetes and Type 2 Diabetes Using the Genetic Risk Score and Oxidative Stress Score |
| title_sort | early prediction for prediabetes and type 2 diabetes using the genetic risk score and oxidative stress score |
| topic | genetic risk score oxidative stress score prediabetes type 2 diabetes |
| url | https://www.mdpi.com/2076-3921/11/6/1196 |
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