The association of uric acid with the development of thyroid nodules: a retrospective cohort study

Machine learning model for differentiating malignant from benign thyroid nodules based on the thyroid function data

OBJECTIVES

To develop and validate a machine learning (ML) model to differentiate malignant from benign thyroid nodules (TNs) based on the routine data and provide diagnostic assistance for medical professionals.

SETTING

A qualified panel of 1649 patients with TNs from one hospital were stratified by gender, age, free triiodothyronine (FT3), free thyroxine (FT4) and thyroid peroxidase antibody (TPOAB).

PARTICIPANTS

Thyroid function (TF) data of 1649 patients with TNs were collected in a single centre from January 2018 to June 2022, with a total of 273 males and 1376 females, respectively.

MEASURES

Seven popular ML models (Random Forest, Decision Tree, Logistic Regression (LR), K-Neighbours, Gaussian Naive Bayes, Multilayer Perception and Gradient Boosting) were developed to predict malignant and benign TNs, whose performance indicators included area under the curve (AUC), accuracy, recall, precision and F1 score.

RESULTS

A total of 1649 patients were enrolled in this study, with the median age of 45.15±13.41 years, and the male to female ratio was 1:5.055. In the multivariate LR analysis, statistically significant differences existed between the TNs group and thyroid cancer group in gender, age, free triiodothyronine (FT3), free thyroxine (FT4) and TPOAB. Among the seven tested ML models, the best performance was achieved in the Gradient Boosting model in terms of precision, AUC, accuracy, recall and F1 score, with the AUC of 0.82, accuracy of 79.4% and precision of 0.814 after experimental verification. FT4, TPOAB and FT3 were validated as the top three features in the Gradient Boosting model.

CONCLUSIONS

This study innovatively developed a predictive model for benign and malignant TNs based on the Gradient Boosting Decision Tree algorithm. For the first time, it validated the clinical predictive value of TF parameters (FT4, FT3) and TPOAB as key biomarkers.

Pituitary-Thyroid Hormones and Related Indices in Euthyroid Type 2 Diabetes: Association With Thyroid Nodules

Aim

Diabetes is an independent risk factor for thyroid nodules (TNs), however, the influencing factors of TNs have not been fully clarified under the condition of diabetes. Previous studies have shown a connection between pituitary-thyroid hormone (TH) functions and TNs in different populations. However, the potential link in euthyroid patients with diabetes is unclear. We aimed to explore the correlates of TNs in euthyroid type 2 diabetes (T2D) patients.

Methods

This cross-sectional study included 1965 euthyroid adults with T2D. Clinical and biochemical data, including TH and relevant covariates were collected. TNs were evaluated using ultrasound. Univariate and multivariate logistic regression analyses were performed to identify factors associated with TNs, with analyses stratified by sex.

Results

The overall prevalence of TNs was 51.9%. The prevalence of TNs was higher in females. Stratified by sex, the results from univariate and further logistic regression analyses showed that free triiodothyronine (FT3) (OR: 1.381; 95% CI: 1.066-1.790) and thyroid-stimulating hormone index (TSHI) (OR: 0.740; 95% CI: 0.584-0.937) were independently correlated with TNs only in females, while no TH-related indicators entered the regression model in males.

Conclusion

The relationship between TH and related indices with TNs exhibited sex differences. Specifically, FT3 and TSHI were independently associated with TNs in females. These findings underscore the importance of evaluating TH and related indices for early monitoring and management of TNs, particularly in euthyroid female T2D patients.

Thyroid disease as a comorbidity in inflammatory rheumatic diseases

Thyroid disease is a common comorbidity in inflammatory rheumatic diseases (IRDs). It complicates disease management and treatment of IRDs. The interplay between thyroid disfunction and IRDs is confounded by shared autoimmune mechanisms and systemic inflammation. Available evidence suggests that rheumatoid arthritis (RA), systemic lupus erythematosus (SLE), and systemic sclerosis (SSc) are associated with an elevated risk of hypothyroidism, hyperthyroidism, and autoimmune thyroid disease (AITD). This review explores mechanisms of thyroid disease in IRDs, overviews implications for disease management, and highlights approaches to optimal patient outcomes. Recent studies point to the need for routine thyroid screening in high-risk IRD populations and justify therapies with anti-TNF-α and anti-IL-17 agents, targeting both thyroid disease and IRDs. This integrated management strategy is crucial for optimizing therapeutic approaches in overlapping autoimmune conditions.

Analysis of risk factors for papillary thyroid carcinoma and the association with thyroid function indicators

Objective

This study aims to analyze the relationship between papillary thyroid carcinoma (PTC) and various factors.

Methods

The study involved two groups-PTC patients and non-PTC controls. We utilized binary logistic regression and Least Absolute Shrinkage and Selection Operator (Lasso) regression for variable selection and risk factor analysis. Correlation analysis was performed using Spearman's rank correlation. The diagnostic value of thyroid stimulating hormone (TSH) levels for PTC was assessed using Receiver Operating Characteristic (ROC) curves.

Results

PTC patients exhibited higher body mass index (BMI) (23.71 vs. 22.66, p<0.05) and TSH levels (3.38 vs. 1.59, p<0.05). Urinary iodine concentration (UIC) was an independent predictor of PTC (OR=1.005, p<0.05). The optimal TSH threshold for PTC diagnosis was 2.4 mIU/L [The Area Under the Curve (AUC)=67.3%, specificity=71.4%, sensitivity=70.1%]. TSH levels positively correlated with BMI (r=0.593, p<0.05) and UIC (r=0.737, p<0.05).

Conclusions

UIC may be an independent predictor of PTC, and TSH levels have some diagnostic value for identifying PTC.

Uric Acid Status in Subclinical Hypothyroidism

Overt hypothyroidism is associated with high levels of serum uric acid (UA) however, the association between UA and thyroid function in patients with subclinical thyroid dysfunction remains unclear. Subclinical hypothyroidism (SCH) is a common endocrine disorder characterized by normal thyroxine (T4) and triiodothyronine (T3), and elevated thyroid stimulating hormone (TSH) levels, usually without clinical manifestations. Therefore, we carried out a study of patients with subclinical thyroid dysfunction to assess the relationship between thyroid function and UA. This lead us to review the literature to find to what extent subclinical hypothyroidism is associated with uric acid. This study adopts the method of retrospective analysis to collect general information and laboratory results aimed at assessing the correlation between uric acid and thyroid hormone levels. We searched 3 databases using different keywords. Literature search was done for articles published in the last ten years, between 2013-2023. All relevant studies were screened. A total of eighteen articles were finalized for the review. Some studies supported T3 supplementation resulting in SCH correction. Our study indicates that it is important to screen for serum uric acid levels routinely in patients with subclinical hypothyroidism.

Associations of Combined Exposure to Metabolic and Inflammatory Indicators with Thyroid Nodules in Adults: A Nested Case-Control Study

Objective

To explore associations of combined exposure to metabolic/inflammatory indicators with thyroid nodules.

Methods

We reviewed personal data for health screenings from 2020 to 2021. A propensity score matching method was used to match 931 adults recently diagnosed with thyroid nodules in a 1 : 4 ratio based on age and gender. Conditional logistic regression and Bayesian kernel machine regression (BKMR) were used to explore the associations of single metabolic/inflammatory indicators and the mixture with thyroid nodules, respectively.

Results

In the adjusted models, five indicators (ORQ4 vs. Q1: 1.30, 95% CI: 1.07-1.58 for fasting blood glucose; ORQ4 vs. Q1: 1.30, 95% CI: 1.08-1.57 for systolic blood pressure; ORQ4 vs. Q1: 1.26, 95% CI: 1.04-1.53 for diastolic blood pressure; ORQ4 vs. Q1: 1.23, 95% CI: 1.02-1.48 for white blood cell; ORQ4 vs. Q1: 1.28, 95% CI: 1.07-1.55 for neutrophil) were positively associated with the risk of thyroid nodules, while high-density lipoproteins (ORQ3 vs. Q1: 0.75, 95% CI: 0.61-0.91) were negatively associated with the risk of thyroid nodules. Univariate exposure-response functions from BKMR models showed similar results. Moreover, the metabolic and inflammatory mixture exhibited a significant positive association with thyroid nodules in a dose-response pattern, with systolic blood pressure being the greatest contributor within the mixture (conditional posterior inclusion probability of 0.82). No interaction effects were found among the five indicators. These associations were more prominent in males, participants with higher age (≥40 years old), and individuals with abnormal body mass index status.

Conclusions

Levels of the metabolic and inflammatory mixture have a linear dose-response relationship with the risk of developing thyroid nodules, with systolic blood pressure levels being the most important contributor.

Do metabolic factors increase the risk of thyroid cancer? a Mendelian randomization study

Background

Epidemiological studies emphasize the link between metabolic factors and thyroid cancer. Using Mendelian randomization (MR), we assessed the possible causal impact of metabolic factors on thyroid cancer for the first time.

Methods

Summary statistics for metabolic factors and thyroid cancer were obtained from published Genome-wide association studies. The causal relationships were assessed using the inverse-variance weighted (IVW) method as the primary method through a two-sample Mendelian Randomization (MR) analysis. To account for the potential existence of horizontal pleiotropy, four additional methods were employed, including Mendelian Randomization-Egger (MR-Egger), weighted median method (WM), simple mode, and weighted mode method. Given the presence of interactions between metabolic factors, a multivariable MR analysis was subsequently conducted.

Results

The results showed there was a genetic link between HDL level and protection effect of thyroid cancer using IVW (OR= 0.75, 95% confidence intervals [CIs] 0.60-0.93, p=0.01) and MR-Egger method (OR= 0.70, 95% confidence intervals [CIs] 0.50- 0.97, p=0.03). The results remained robust in multivariable MR analysis for the genetic link between HDL level and protection effect of thyroid cancer (OR= 0.74, 95% confidence intervals [CIs] 0.55-0.99, p=0.04).

Conclusions

This study suggests a protection role for HDL on thyroid cancer. The study findings provide evidence for the public health suggestion for thyroid cancer prevention. HDL's potential as a pharmacological target needs further validation.

Helicobacter pylori infection increases the risk of thyroid nodules in adults of Northwest China

Background

Thyroid nodules (TNs) are very common in the adults of Northwest China. The role of Helicobacter pylori (H. pylori) infection in TNs is poorly investigated and even with controversial conclusions. Our study aimed at highlighting the relationship between H. pylori infection and the risk of TNs.

Methods

9,042 individuals were enrolled with thyroid ultrasonography and 14C-urea breath test (14C-UBT). Baseline characteristics and relevant covariates were obtained, including basic and laboratory indicators. After applying the exclusion criteria, 8,839 patients were included and divided into 2 groups: a cross-sectional study of single follow-up (n=8,711) and a retrospective cohort study of multiple follow-ups for 5 years (n=139).

Results

The prevalence of H. pylori infection and TNs was 39.58% and 47.94% in the adults of Northwest China, respectively. The prevalence of TNs was significantly higher among H. pylori-positive individuals than those without infection (52.55% vs. 44.92%, p<0.01). The result of binary logistic regression revealed that the crude odds ratio (OR) was 1.624 (95% CI 1.242~2.123) in Model 1 without adjustment compared to H. pylori-negative group, and was also positive in Model 2, 3, and 4 (Model 2: OR=1.731, 95% CI 1.294~2.316; Model 3: OR=2.287, 95% CI 1.633~3.205; Model 4: OR=2.016, 95% CI 1.390~2.922) after the adjustment. The data of 5-year follow-up showed that the annual incidence of TNs was significantly higher in individuals with persistent H. pylori infection than non-infected counterparts (all p<0.05).

Conclusions

H. pylori is an independent risk factor for TNs in the adults of Northwest China.

Detection and Correlation Analysis of Serum Uric Acid in Patients with Thyroid-Associated Ophthalmopathy

Purpose

To probe the property of serum uric acid in evaluating the activity of patients with thyroid-associated ophthalmopathy.

Methods

A total of 443 patients with TAO admitted to our hospital from March 2016 to February 2021 were selected for the observation group. Simultaneously, 443 healthy subjects were selected for the control group. The observation group was divided into the active group (n = 254) and the inactive group (n = 189) according to the clinical activity score (CAS). Besides, the patients were divided into mild group (n = 201), moderate severe group (n = 133) and extremely severe group (n = 109) based on the severity of TAO. Serum uric acid, free triiodothyronine (FT3), free thyroid hormone (FT4), thyrotropin stimulating hormone (TSH) and glycosylated hemoglobin (HbA1c) levels were detected and analyzed in each group.

Results

Serum UA, FT3, FT4, TSH and HbA1c in the active group were significantly enhanced than those in the other two groups (P < 0.05), and there was no significant difference between the inactive group and the control group (P > 0.05). In different disease severity groups, the serum UA level of patients in the active group was significantly promoted than that in the inactive group and control group (P < 0.05) and was decreased successively in extremely severe group, moderate severe group and mild group, with statistical significance (P < 0.05). Pearson's analysis showed that UA was positively correlated with FT3, FT4, and HbA1c (r = 0.652, P = 0.031; r = 0.571, P = 0.042; r = 0.737, P = 0.024), while was reversely correlated with TSH level (r = -0.137, P = 0.262). There was no correlation between UA and FT3, FT4, and HbA1c levels in the inactive group. UA detection showed the average sensitivity and specificity of TAO activity were 94.3% and 85.2%, respectively. There was no significant correlation between the severity of disease and serum UA in inactive patients (P = 0.135). There was a positive correlation between the severity of disease and serum UA in active patients (P = 0.005).

Conclusion

UA may be used as a laboratory indicator for quantitative clinical diagnosis of thyroid-associated ophthalmopathy (TAO) and as a parameter for the presence of TAO activity.