Establishment and validation of a multivariate logistic model for risk factors of thyroid nodules using lasso regression screening

Jianning Liu; Zhuoying Feng; Ru Gao; Peng Liu; Fangang Meng; Lijun Fan; Lixiang Liu; Yang Du

doi:10.3389/fendo.2024.1346284

Establishment and validation of a multivariate logistic model for risk factors of thyroid nodules using lasso regression screening

Front Endocrinol (Lausanne). 2024 Apr 2:15:1346284. doi: 10.3389/fendo.2024.1346284. eCollection 2024.

Authors

Jianning Liu^#^{1

2}, Zhuoying Feng^#³, Ru Gao^{1

2}, Peng Liu^{1

2}, Fangang Meng^{1

2}, Lijun Fan^{1

2}, Lixiang Liu^{1

2}, Yang Du^{1

2}

Affiliations

¹ Center for Endemic Disease Control, Chinese Center for Disease Control and Prevention, Harbin Medical University, Harbin, Heilongjiang, China.
² Key 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, China.
³ Department of Physical Diagnostics, Beidahuang Industry Group General Hospital, Harbin, Heilongjiang, China.

^# Contributed equally.

Abstract

Objective: This 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.

Methods: The 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.

Results: Based 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).

Conclusions: advanced age, females, overweight individuals, those with impaired fasting glucose, and individuals with dyslipidemia are potential risk factors for thyroid nodule disease.

Keywords: lasso regression; logistic regression; metabolic syndrome; risk factor; thyroid nodule.

MeSH terms

Dyslipidemias*
Female
Glucose
Humans
Logistic Models
Overweight / complications
Risk Factors
Thyroid Nodule* / diagnosis
Thyroid Nodule* / epidemiology

Substances

Glucose

Grants and funding

The author(s) declare financial support was received for the research, authorship, and/or publication of this article. This study was supported by grants from the National Key Research and Development Program of China (2023YFC2508301).