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

Background: Hashimoto'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. Methods: We 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. Results: The 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. Conclusions: We 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.

Association of MTHFR C677T polymorphism and type 2 diabetes mellitus (T2DM) susceptibility.

INTRODUCTION: Methylenetetrahydrofolate reductase (MTHFR) is essential in mediating folate metabolism, and thus plays an important role in diabetes and diabetic complications. MTHFR C677T (rs1801133 C>T) polymorphism has been proposed to be linked with type 2 diabetes mellitus (T2DM) susceptibility. However, the conclusions are inconsistent. Therefore, we rechecked their linkage aiming to obtain a more reliable estimation by performing an updated meta-analysis. METHODS: We searched electronic databases PubMed, EMBASE, CNKI, and Wanfang to obtain studies updated to October 2019. RESULTS: After carefully screening, we finally incorporated 68 studies with 10,812 cases and 8,745 controls. The genotype frequency of C677T polymorphism was analyzed pooled to generate odds ratios (ORs) and 95% confidence intervals (CIs). Pooled results presented that MTHFR C677T polymorphism was significantly associated with T2DM under homozygous (OR = 1.64, 95% CI = 1.39-1.94), heterozygous (OR = 1.38, 95% CI = 1.20-1.59), recessive (OR = 1.41, 95% CI = 1.23-1.61), dominant (OR = 1.47, 95% CI = 1.27-1.70), and allele (OR = 1.37, 95% CI = 1.23-1.52) genetic models. Stratified analysis demonstrated that C677T genotype was associated with T2DM in Asian populations, but not Caucasian and African populations. CONCLUSION: Our results indicated that MTHFR C677T polymorphism confers to T2DM, especially in Asian populations. Much more large-scale case-control studies are needed to strengthen such conclusion in the future.