Abstract
BACKGROUND: Thyroid nodules (TNs) are common in adults and have been linked to various metabolic and demographic factors. This study aims to explore the associations between metabolic indicators and TNs in a Chinese health examination population, and to develop a simplified predictive model based on independent risk factors. METHODS: We conducted a cross-sectional analysis of 23,305 adults (12,977 men, 10,328 women; aged 18-90 years) who underwent health examinations at the Second Hospital of Hebei Medical University between January 2021 and December 2022. Exclusion criteria included prior thyroid surgery, endocrine or systemic disorders, pregnancy, and incomplete data. Demographic, lifestyle, and biochemical parameters were collected. Group differences were assessed using chi-square tests for categorical variables and t-tests or Mann-Whitney U tests for continuous variables. Univariate and multivariate logistic regression analyses were performed to identify independent risk factors, with model performance evaluated by the area under the receiver operating characteristic curve (AUC). RESULTS: The overall prevalence of TNs was 64.7% (n=15,085). The prevalence increased from 38.8% in those aged 30 years or younger to 87.8% in those older than 70 years (P for trend <0.01), and was higher in women (70.8%) compared to men (59.9%) (χ²=509.8, P<0.01). In multivariate analysis, older age (OR = 1.06 per year, 95% CI: 1.06-1.06, P<0.01), female sex (OR = 2.12, 95% CI: 1.93-2.32, P<0.01), and higher body mass index (OR = 1.04 per unit, 95% CI: 1.03-1.05, P<0.01) were identified as independent risk factors. The three-variable model yielded an AUC of 0.706. CONCLUSIONS: Thyroid nodules are highly prevalent in this health examination population. Age, female sex, and higher body mass index are independent risk factors. Other metabolic disturbances were more common in individuals with TNs, but they were not independent predictors. A simplified model based on age, sex, and body mass index may help identify high-risk individuals in large-scale screenings.