Prediction models constructed for Hashimoto’s thyroiditis risk based on clinical and laboratory factors

Abstract

BackgroundHashimoto’s thyroiditis (HT) frequently occurs among autoimmune diseases and may simultaneously appear with thyroid cancer. However, it is difficult to diagnose HT at an early stage just by clinical symptoms. Thus, it is urgent to integrate multiple clinical and laboratory factors for the early diagnosis and risk prediction of HT.MethodsWe recruited 1,303 participants, including 866 non-HT controls and 437 diagnosed HT patients. 44 HT patients also had thyroid cancer. Firstly, we compared the difference in thyroid goiter degrees between controls and patients. Secondly, we collected 15 factors and analyzed their significant differences between controls and HT patients, including age, body mass index, gender, history of diabetes, degrees of thyroid goiter, UIC, 25-(OH)D, FT3, FT4, TSH, TAG, TC, FPG, low-density lipoprotein cholesterol, and high-density lipoprotein cholesterol. Thirdly, logistic regression analysis demonstrated the risk factors for HT. For machine learning modeling of HT and thyroid cancer, we conducted the establishment and evaluation of six models in training and test sets.ResultsThe degrees of thyroid goiter were significantly different among controls, HT patients without cancer (HT-C), and HT patients with thyroid cancer (HT+C). Most factors had significant differences between controls and patients. Logistic regression analysis confirmed diabetes, UIC, FT3, and TSH as important risk factors for HT. The AUC scores of XGBoost, LR, SVM, and MLP models indicated appropriate predictive power for HT. The features were arranged by their importance, among which, 25-(OH)D, FT4, and TSH were the top three high-ranking factors.ConclusionsWe firstly analyzed comprehensive factors of HT patients. The proposed machine learning modeling, combined with multiple factors, are efficient for thyroid diagnosis. These discoveries will extensively promote precise diagnosis, personalized therapies, and reduce unnecessary cost for thyroid diseases.

Impact of Vitamin D on Immunopathology of Hashimoto’s Thyroiditis: From Theory to Practice

“…Despite many studies leading to a better understanding of autoimmune processes at the cellular and molecular level, the exact causes and triggers that initiate the thyroid destruction process in HT remain unknown. Multiple factors have been proven to impact the risk of HT, including exposure to environmental components (e.g., infections, irradiation, selenium), the presence of different diseases (e.g., diabetes, allergic rhinitis), and differentially expressed genes [ 54 , 55 , 56 ]. Genetic predisposition includes MHC genes (e.g., HLA-DR3) and genes regulating immune responses like IL-7R (Interleukin 7 receptor) gene, OR2J3 (olfactory receptor 2J3) gene, or CTLA4 (cytotoxic T-lymphocyte-associated protein 4) gene.…”

Machine Learning-Based Prediction of Hashimoto’s Thyroiditis Development Risk

“…In recent years, there has been a growing interest in using ML for the diagnosis and prognosis of HT. 8,9 This study aims to develop a ML model to predict the risk of HT development with high accuracy. To achieve this, different ML algorithms will be employed, and their hyperparameters will be optimized.…”

“…In recent years, there has been a growing interest in using ML for the diagnosis and prognosis of HT. 8,9…”

Clinical Efficacy and Mechanism of Vitamin D2 in Treating Hashimoto’s Thyroiditis

“…In China, the prevalence of HT ranges from 0.2% to 1.3%. 2 , 3 HT pathology mainly includes diffuse lymphocyte and plasma cell infiltration, interstitial fibrosis, glandular atrophy, and acidic degeneration of follicles. 4 The onset of HT is indistinct; many patients are asymptomatic during the early stages, and the cardinal clinical feature is painless, diffuse goiter.…”