Building a Nomogram for Metabolic Syndrome Using Logistic Regression with a Complex Sample—A Study with 39,991,680 Cases
Metabolic syndrome can cause complications, such as stroke and cardiovascular disease. We aimed to propose a nomogram that visualizes and predicts the probability of metabolic syndrome occurrence after identifying risk factors related to metabolic syndrome for prevention and recognition. We created...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-02-01
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| Series: | Healthcare |
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| Online Access: | https://www.mdpi.com/2227-9032/10/2/372 |
| _version_ | 1797479635851673600 |
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| author | Min-Seok Shin Jea-Young Lee |
| author_facet | Min-Seok Shin Jea-Young Lee |
| author_sort | Min-Seok Shin |
| collection | DOAJ |
| description | Metabolic syndrome can cause complications, such as stroke and cardiovascular disease. We aimed to propose a nomogram that visualizes and predicts the probability of metabolic syndrome occurrence after identifying risk factors related to metabolic syndrome for prevention and recognition. We created a nomogram related to metabolic syndrome in this paper for the first time. We analyzed data from the Korea National Health and Nutrition Examination Survey VII. Total 17,584 participants were included in this study, and the weighted sample population was 39,991,680, which was 98.1% of the actual Korean population in 2018. We identified 14 risk factors affecting metabolic syndrome using the Rao-Scott chi-squared test. Next, logistic regression analysis was performed to build a model for metabolic syndrome and 11 risk factors were finally obtained, including BMI, marriage, employment, education, age, stroke, sex, income, smoking, family history and age* sex. A nomogram was constructed to predict the occurrence of metabolic syndrome using these risk factors. Finally, the nomogram was verified using a receiver operating characteristic curve (ROC) and a calibration plot. |
| first_indexed | 2024-03-09T21:48:38Z |
| format | Article |
| id | doaj.art-17e0e7533937417da2b2a123457ef14b |
| institution | Directory Open Access Journal |
| issn | 2227-9032 |
| language | English |
| last_indexed | 2024-03-09T21:48:38Z |
| publishDate | 2022-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Healthcare |
| spelling | doaj.art-17e0e7533937417da2b2a123457ef14b2023-11-23T20:10:48ZengMDPI AGHealthcare2227-90322022-02-0110237210.3390/healthcare10020372Building a Nomogram for Metabolic Syndrome Using Logistic Regression with a Complex Sample—A Study with 39,991,680 CasesMin-Seok Shin0Jea-Young Lee1Department of Statistics, Yeungnam University, Gyeongsan 38541, KoreaDepartment of Statistics, Yeungnam University, Gyeongsan 38541, KoreaMetabolic syndrome can cause complications, such as stroke and cardiovascular disease. We aimed to propose a nomogram that visualizes and predicts the probability of metabolic syndrome occurrence after identifying risk factors related to metabolic syndrome for prevention and recognition. We created a nomogram related to metabolic syndrome in this paper for the first time. We analyzed data from the Korea National Health and Nutrition Examination Survey VII. Total 17,584 participants were included in this study, and the weighted sample population was 39,991,680, which was 98.1% of the actual Korean population in 2018. We identified 14 risk factors affecting metabolic syndrome using the Rao-Scott chi-squared test. Next, logistic regression analysis was performed to build a model for metabolic syndrome and 11 risk factors were finally obtained, including BMI, marriage, employment, education, age, stroke, sex, income, smoking, family history and age* sex. A nomogram was constructed to predict the occurrence of metabolic syndrome using these risk factors. Finally, the nomogram was verified using a receiver operating characteristic curve (ROC) and a calibration plot.https://www.mdpi.com/2227-9032/10/2/372logistic regressionmetabolic syndromenomogramrisk factor |
| spellingShingle | Min-Seok Shin Jea-Young Lee Building a Nomogram for Metabolic Syndrome Using Logistic Regression with a Complex Sample—A Study with 39,991,680 Cases Healthcare logistic regression metabolic syndrome nomogram risk factor |
| title | Building a Nomogram for Metabolic Syndrome Using Logistic Regression with a Complex Sample—A Study with 39,991,680 Cases |
| title_full | Building a Nomogram for Metabolic Syndrome Using Logistic Regression with a Complex Sample—A Study with 39,991,680 Cases |
| title_fullStr | Building a Nomogram for Metabolic Syndrome Using Logistic Regression with a Complex Sample—A Study with 39,991,680 Cases |
| title_full_unstemmed | Building a Nomogram for Metabolic Syndrome Using Logistic Regression with a Complex Sample—A Study with 39,991,680 Cases |
| title_short | Building a Nomogram for Metabolic Syndrome Using Logistic Regression with a Complex Sample—A Study with 39,991,680 Cases |
| title_sort | building a nomogram for metabolic syndrome using logistic regression with a complex sample a study with 39 991 680 cases |
| topic | logistic regression metabolic syndrome nomogram risk factor |
| url | https://www.mdpi.com/2227-9032/10/2/372 |
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