Establishment and validation of a multivariate logistic model for risk factors of thyroid nodules using lasso regression screening
ObjectiveThis study aims to analyze the association between the occurrence of thyroid nodules and various factors and to establish a risk factor model for thyroid nodules.MethodsThe study population was divided into two groups: a group with thyroid nodules and a group without thyroid nodules. Regres...
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Frontiers Media S.A.
2024-04-01
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| Series: | Frontiers in Endocrinology |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fendo.2024.1346284/full |
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| author | Jianning Liu Jianning Liu Zhuoying Feng Ru Gao Ru Gao Peng Liu Peng Liu Fangang Meng Fangang Meng Lijun Fan Lijun Fan Lixiang Liu Lixiang Liu Yang Du Yang Du |
| author_facet | Jianning Liu Jianning Liu Zhuoying Feng Ru Gao Ru Gao Peng Liu Peng Liu Fangang Meng Fangang Meng Lijun Fan Lijun Fan Lixiang Liu Lixiang Liu Yang Du Yang Du |
| author_sort | Jianning Liu |
| collection | DOAJ |
| description | ObjectiveThis study aims to analyze the association between the occurrence of thyroid nodules and various factors and to establish a risk factor model for thyroid nodules.MethodsThe study population was divided into two groups: a group with thyroid nodules and a group without thyroid nodules. Regression with the least absolute shrinkage and selection operator (Lasso) was applied to the complete dataset for variable selection. Binary logistic regression was used to analyze the relationship between various influencing factors and the prevalence of thyroid nodules.ResultsBased on the screening results of Lasso regression and the subsequent establishment of the Binary Logistic Regression Model on the training dataset, it was found that advanced age (OR=1.046, 95% CI: 1.033-1.060), females (OR = 1.709, 95% CI: 1.342-2.181), overweight individuals (OR = 1.546, 95% CI: 1.165-2.058), individuals with impaired fasting glucose (OR = 1.590, 95% CI: 1.193-2.122), and those with dyslipidemia (OR = 1.588, 95% CI: 1.197-2.112) were potential risk factors for thyroid nodule disease (p<0.05). The area under the curve (AUC) of the receiver operating characteristic (ROC) curve for the Binary Logistic Regression Model is 0.68 (95% CI: 0.64-0.72).Conclusionsadvanced age, females, overweight individuals, those with impaired fasting glucose, and individuals with dyslipidemia are potential risk factors for thyroid nodule disease. |
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| language | English |
| last_indexed | 2024-04-24T15:28:59Z |
| publishDate | 2024-04-01 |
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| series | Frontiers in Endocrinology |
| spelling | doaj.art-976cfd22d78e4c8182ae0a29b1ede85d2024-04-02T05:05:26ZengFrontiers Media S.A.Frontiers in Endocrinology1664-23922024-04-011510.3389/fendo.2024.13462841346284Establishment and validation of a multivariate logistic model for risk factors of thyroid nodules using lasso regression screeningJianning Liu0Jianning Liu1Zhuoying Feng2Ru Gao3Ru Gao4Peng Liu5Peng Liu6Fangang Meng7Fangang Meng8Lijun Fan9Lijun Fan10Lixiang Liu11Lixiang Liu12Yang Du13Yang Du14Center for Endemic Disease Control, Chinese Center for Disease Control and Prevention, Harbin Medical University, Harbin, Heilongjiang, ChinaKey Lab of Etiology and Epidemiology, Education Bureau of Heilongjiang Province & Ministry of Health (23618504), Heilongjiang Provincial Key Lab of Trace Elements and Human Health, Harbin Medical University, Harbin, Heilongjiang, ChinaDepartment of Physical Diagnostics, Beidahuang Industry Group General Hospital, Harbin, Heilongjiang, ChinaCenter for Endemic Disease Control, Chinese Center for Disease Control and Prevention, Harbin Medical University, Harbin, Heilongjiang, ChinaKey Lab of Etiology and Epidemiology, Education Bureau of Heilongjiang Province & Ministry of Health (23618504), Heilongjiang Provincial Key Lab of Trace Elements and Human Health, Harbin Medical University, Harbin, Heilongjiang, ChinaCenter for Endemic Disease Control, Chinese Center for Disease Control and Prevention, Harbin Medical University, Harbin, Heilongjiang, ChinaKey Lab of Etiology and Epidemiology, Education Bureau of Heilongjiang Province & Ministry of Health (23618504), Heilongjiang Provincial Key Lab of Trace Elements and Human Health, Harbin Medical University, Harbin, Heilongjiang, ChinaCenter for Endemic Disease Control, Chinese Center for Disease Control and Prevention, Harbin Medical University, Harbin, Heilongjiang, ChinaKey Lab of Etiology and Epidemiology, Education Bureau of Heilongjiang Province & Ministry of Health (23618504), Heilongjiang Provincial Key Lab of Trace Elements and Human Health, Harbin Medical University, Harbin, Heilongjiang, ChinaCenter for Endemic Disease Control, Chinese Center for Disease Control and Prevention, Harbin Medical University, Harbin, Heilongjiang, ChinaKey Lab of Etiology and Epidemiology, Education Bureau of Heilongjiang Province & Ministry of Health (23618504), Heilongjiang Provincial Key Lab of Trace Elements and Human Health, Harbin Medical University, Harbin, Heilongjiang, ChinaCenter for Endemic Disease Control, Chinese Center for Disease Control and Prevention, Harbin Medical University, Harbin, Heilongjiang, ChinaKey Lab of Etiology and Epidemiology, Education Bureau of Heilongjiang Province & Ministry of Health (23618504), Heilongjiang Provincial Key Lab of Trace Elements and Human Health, Harbin Medical University, Harbin, Heilongjiang, ChinaCenter for Endemic Disease Control, Chinese Center for Disease Control and Prevention, Harbin Medical University, Harbin, Heilongjiang, ChinaKey Lab of Etiology and Epidemiology, Education Bureau of Heilongjiang Province & Ministry of Health (23618504), Heilongjiang Provincial Key Lab of Trace Elements and Human Health, Harbin Medical University, Harbin, Heilongjiang, ChinaObjectiveThis study aims to analyze the association between the occurrence of thyroid nodules and various factors and to establish a risk factor model for thyroid nodules.MethodsThe study population was divided into two groups: a group with thyroid nodules and a group without thyroid nodules. Regression with the least absolute shrinkage and selection operator (Lasso) was applied to the complete dataset for variable selection. Binary logistic regression was used to analyze the relationship between various influencing factors and the prevalence of thyroid nodules.ResultsBased on the screening results of Lasso regression and the subsequent establishment of the Binary Logistic Regression Model on the training dataset, it was found that advanced age (OR=1.046, 95% CI: 1.033-1.060), females (OR = 1.709, 95% CI: 1.342-2.181), overweight individuals (OR = 1.546, 95% CI: 1.165-2.058), individuals with impaired fasting glucose (OR = 1.590, 95% CI: 1.193-2.122), and those with dyslipidemia (OR = 1.588, 95% CI: 1.197-2.112) were potential risk factors for thyroid nodule disease (p<0.05). The area under the curve (AUC) of the receiver operating characteristic (ROC) curve for the Binary Logistic Regression Model is 0.68 (95% CI: 0.64-0.72).Conclusionsadvanced age, females, overweight individuals, those with impaired fasting glucose, and individuals with dyslipidemia are potential risk factors for thyroid nodule disease.https://www.frontiersin.org/articles/10.3389/fendo.2024.1346284/fullthyroid nodulelasso regressionmetabolic syndromerisk factorlogistic regression |
| spellingShingle | Jianning Liu Jianning Liu Zhuoying Feng Ru Gao Ru Gao Peng Liu Peng Liu Fangang Meng Fangang Meng Lijun Fan Lijun Fan Lixiang Liu Lixiang Liu Yang Du Yang Du Establishment and validation of a multivariate logistic model for risk factors of thyroid nodules using lasso regression screening Frontiers in Endocrinology thyroid nodule lasso regression metabolic syndrome risk factor logistic regression |
| title | Establishment and validation of a multivariate logistic model for risk factors of thyroid nodules using lasso regression screening |
| title_full | Establishment and validation of a multivariate logistic model for risk factors of thyroid nodules using lasso regression screening |
| title_fullStr | Establishment and validation of a multivariate logistic model for risk factors of thyroid nodules using lasso regression screening |
| title_full_unstemmed | Establishment and validation of a multivariate logistic model for risk factors of thyroid nodules using lasso regression screening |
| title_short | Establishment and validation of a multivariate logistic model for risk factors of thyroid nodules using lasso regression screening |
| title_sort | establishment and validation of a multivariate logistic model for risk factors of thyroid nodules using lasso regression screening |
| topic | thyroid nodule lasso regression metabolic syndrome risk factor logistic regression |
| url | https://www.frontiersin.org/articles/10.3389/fendo.2024.1346284/full |
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