Machine learning-based exploration of the associations between multiple minerals' intake and thyroid dysfunction: data from the National Health and Nutrition Examination Survey

Abstract

Objectives: The associations between various minerals' intake and thyroid dysfunction (TD), including hyperthyroidism and hypothyroidism, are still inconclusive, which may be attributed to the potential synergistic effects among various minerals.

Methods: The data were obtained from the National Health and Nutrition Examination Survey (NHANES) 2001-2002 and 2007-2012 databases. Dietary interviews were conducted to collect the consumption of multiple minerals. Blood samples were collected to measure concentrations of free triiodothyronine, free thyroxine, and thyroid-stimulating hormone. A total of 7,779 participants with aged over 20 years were effectively enrolled in this study and categorized into hyperthyroidism or hypothyroidism groups. Weighted multivariate logistic regression model along with three machine learning models WQS, qg-comp, and BKMR were employed to investigate the individual and joint effect of multiple minerals' consumption on TD.

Results: Among 7,779 subjects, 134 participants were diagnosed as hyperthyroidism and 184 participants were diagnosed as hypothyroidism, with prevalence of 1.6 and 2.4%, respectively. The results from logistic regression model showed that the higher the intakes of calcium, magnesium and potassium, the lower the prevalence of hyperthyroidism, with OR values of 0.591, 0.472, and 0.436, respectively (all P < 0.05); while the higher the intake of iodine, the higher the prevalence of hyperthyroidism, with OR and 95%CI values of 1.262 (1.028, 1.550). Three machine learning models were employed to evaluate the joint effect of nine minerals' consumption on TD, revealing a negative correlation with both hyperthyroidism and hypothyroidism. Of them, the potential minerals associated with TD were calcium, zinc, copper, and magnesium.

Conclusion: In short, the maintenance of a well-balanced consumption of multiple minerals is considered crucial in the prevention and treatment of TD, and the intakes of various minerals exhibit varying degrees of association with TD.

Keywords: dietary health; hyperthyroidism; hypothyroidism; machine learning model; minerals.

Figure 1

Flow chart for study participants selection.

Figure 2

WQS model regression index weights for hypothyroidism (figure left) and hyperthyroidism (figure right). The WQS models were adjusted by age, gender, energy intake, serum cotinine, BMI, PIR, marital status, alcohol consumption, educational level, and race/ethnicity. WQS, weighted quantile sum.

Figure 3

Qg-comp model regression index weights for hypothyroidism (figure left) and hyperthyroidism (figure right). The qg-comp models were adjusted by age, gender, energy intake, serum cotinine, BMI, PIR, marital status, alcohol consumption, educational level, and race/ethnicity. qg-comp, quantile g-computation.

Figure 4

The overall effects of mixed minerals' intake on hypothyroidism (figure left) and hyperthyroidism (figure right) obtained by BKMR model. The BKMR models were adjusted by age, gender, energy intake, serum cotinine, BMI, PIR, marital status, alcohol consumption, educational level, and race/ethnicity. BKMR, bayesian kernel machine regression.