Hypertension and Obesity: Risk Factors for Thyroid Disease


Globally, the prevalence of thyroid disorders is rising quickly, and among American women, thyroid cancer is the fifth most frequently diagnosed disease. Understanding the pathophysiology of thyroid illness could help people avoid risk factors as diagnostic rates for the condition increase, potentially lowering morbidity and mortality rates. Researchers are now having trouble identifying the primary etiology of thyroid illness. A small number of research used systematic literature analysis to find potential risk factors linked to the illness. Radiation, depression, obesity, hormone variables, and gene heredity are some of the factors that have been found. Numerous elements remain debatable and cannot be exclusively proven by depending on qualitative investigation. Medical datasets have many dimensions, and because various elements interact with one another, it might be difficult to pinpoint the pathophysiology of thyroid disease. The goal of association rule mining (ARM) is to uncover and define undiscovered relationships between various variables. While ARM approaches have been shown to be effective at extracting information from medical records, their use in determining the pathophysiology of thyroid disease is still in its infancy.

In a new study published in the Journal Frontiers in Endocrinology, Feng Liu from Sichuan University and Xinyu Zhang from Monash University addressed the clinical challenge of uncovering the pathogenesis of thyroid disease with the help of ARM techniques. The dependability of the risk indicators found by data mining approaches were compared to those observed through qualitative analysis. Additionally, the new work analyzed two open-access datasets using two ARM knowledge discovery algorithms in order to acquire objective results.

While ARM approaches have been shown to be successful in extracting knowledge for probable comorbidity, their application to thyroid disease etiology is still lacking. The authors used two ARM algorithms to identify risk factors associated with thyroid disease and fills in a gap in the literature. They found that gender and age are two parameters that are substantially connected to thyroid disease based on their examination of the derived rules from two different datasets. It is well known that girls have higher diagnostic chances for thyroid disease than males do. This may be because of the hormonal changes brought on by pregnancy or pubertal development, which were thought to be more susceptible in young females. The study also found obesity and hypertension as two major risk factors, indicating that the presence of these underlying conditions may raise the likelihood of developing thyroid disease given that hypertension is strongly associated with overt hypothyroidism. However, research on the relationship between thyroid nodules and hypertension is still lacking. New research should be given to fill this gap. As a result, practical applications in the therapeutic setting are possible: assessing thyroid functions in cases of elevated blood pressure is advised. By modulating thyroid hormones, this aids in reducing the negative health impacts of improper thyroid functioning, such as issues with high blood pressure. The study demonstrated that people who are obese are more prone to develop thyroid disease. According to the authors, additional clinical evaluations should be used to routinely check people with hypertension and obesity for thyroid function. The results supported the notion that thyroid disease development is positively correlated with gender, hypertension, and obesity. Triiodothyronine levels and the history of I131 treatment can be considered when assessing thyroid illness in the future.

In conclusion, two association rule mining algorithms are used in this study to extract knowledge and identify potential risk variables associated with thyroid illness. The results by Feng Liu and Xinyu Zhang reveal that key risk factors for the development of thyroid illness include gender, a history of I131 treatment, T3 level, hypertension, and obesity. The study stresses the benefits to society; thyroid disease’s mortality and morbidity rates can be greatly reduced by recognizing the associated risk factors.


Liu F, Zhang X. Hypertension and Obesity: Risk Factors for Thyroid Disease. Frontiers in Endocrinology. 2022;13.

Go To Journal of Frontiers in Endocrinology.