This dataset is a cleaned, transformed, and research-informed version of the original Thyroid Disease records compiled by the Garavan Institute and J. Ross Quinlan (1984–1987). It contains 9,000+ patient records with lab values, clinical history, and diagnosis outcomes. All missing values (?) were removed, and redundant or conflicting entries were dropped.

The dataset is valuable for Healthcare and Medical Research focused on thyroid-related Health Conditions. It supports Multiclass Classification problems in Artificial Intelligence and Data Analytics, particularly within Computer Science and Healthcare domains.

Missing values have been removed and categorical flags (t/f) converted to boolean (True/False). Meaningful multiclass target labels were created based on clinical diagnosis codes, including handling ambiguous cases. Data types were adjusted for compatibility with machine learning workflows using tools like pandas, NumPy, and scikit-learn.

This dataset is suitable for users of all skill levels—Beginner, Intermediate, and Advanced—interested in exploring Classification, Feature Extraction, Data Cleaning, and Exploratory Data Analysis in healthcare datasets. Visualizations can be developed using Matplotlib and Seaborn to better understand thyroid disease patterns.

Boolean fields (t/f) were converted to True/False, and a meaningful target variable was created based on clinically grounded groupings described in the research article “Thyroid Disease Prediction Using Selective Features and Machine Learning Techniques” (PMCID: PMC9405591).

The original diagnosis strings (e.g., A|B) were resolved to reflect the most likely class, and diagnoses were mapped into simplified, interpretable categories suitable for classification tasks (e.g., hypothyroid, hyperthyroid, normal, etc.).

This version is optimized for machine learning applications such as:

• Classification of thyroid status (e.g., binary or multi-class)

• Feature selection and model evaluation

• Clinical decision support research

• Ideal for healthcare ML projects, educational purposes, and benchmarking thyroid disorder prediction models.

Thyroid Risk Assessment Dataset

Overview

This dataset is designed for assessing the risk of thyroid disorders based on various clinical and demographic features. It can serve as a valuable resource for researchers and practitioners in healthcare, data science, and machine learning who aim to develop models for thyroid disorder prediction or risk stratification.

Dataset Structure

The dataset consists of 17 columns:

1. Age: Age of the individual (in years). 2. Gender: Gender of the individual (1 for male, 0 for female). 3. Pregnancy: Pregnancy status (1 if pregnant, 0 otherwise). 4. Family_History_of_Thyroid: Indicates whether there is a family history of thyroid disorders (1 for yes, 0 for no). 5. Goiter: Presence of goiter (1 for yes, 0 for no). 6. Fatigue: Presence of fatigue symptoms (1 for yes, 0 for no). 7. Weight_Change: History of weight changes (1 for yes, 0 for no). 8. Hair_Loss:Presence of hair loss (1 for yes, 0 for no). 9. Heart_Rate_Changes: Changes in heart rate (1 for yes, 0 for no). 10. Sensitivity_to_Cold_or_Heat:Sensitivity to cold or heat (1 for yes, 0 for no). 11. Increased_Sweating: Increased sweating (1 for yes, 0 for no). 12. Muscle_Weakness:Presence of muscle weakness (1 for yes, 0 for no). 13. Constipation_or_More_Bowel_Movements: Changes in bowel movement patterns (1 for yes, 0 for no). 14. Depression_or_Anxiety: Presence of depression or anxiety (1 for yes, 0 for no). 15. Difficulty_Concentrating_or_Memory_Problems:Difficulty concentrating or memory problems (1 for yes, 0 for no). 16. Dry_or_Itchy_Skin:Presence of dry or itchy skin (1 for yes, 0 for no). 17. Thyroid_Risk_Level:Risk level of thyroid disorders (0: No risk, 1: Moderate risk, 2: High risk).

Dataset Format

• The dataset is provided in a tabular format with binary (0/1) and integer values.
• Each row represents an individual patient record.
• The target variable is Thyroid_Risk_Level, which can be used for classification tasks. ### Use Cases Predictive Modeling: Train machine learning models to classify thyroid risk levels. Feature Analysis: Perform statistical analysis to identify the most significant factors contributing to thyroid disorders. Healthcare Insights: Develop tools for early detection of thyroid-related health risks. Educational Purposes: Utilize the dataset for teaching and training purposes in medical and data science courses.

Potential Applications

• Development of a clinical decision support system for thyroid risk assessment.
• Research on the correlation between demographic, genetic, and clinical factors with thyroid disorders.
• Building predictive algorithms for personalized healthcare. ### Limitations
• The dataset may require preprocessing and normalization depending on the machine learning model used.
• Limited to binary and integer features, which may not capture the full complexity of thyroid disorders.
• Additional medical context might be needed for interpretation.

this dataset 0 raw and 0 features

Background: Primary biliary cholangitis (PBC) is an autoimmune disease and is often accompanied by thyroid dysfunction. Understanding the potential causal relationship between PBC and thyroid dysfunction is helpful to explore the pathogenesis of PBC and to develop strategies for the prevention and treatment of PBC and its complications.Methods: We used a two-sample Mendelian randomization (MR) method to estimate the potential causal effect of PBC on the risk of autoimmune thyroid disease (AITD), thyroid-stimulating hormone (TSH) and free thyroxine (FT4), hyperthyroidism, hypothyroidism, and thyroid cancer (TC) in the European population. We collected seven datasets of PBC and related traits to perform a series MR analysis and performed extensive sensitivity analyses to ensure the reliability of our results.Results: Using a sensitivity analysis, we found that PBC was a risk factor for AITD, TSH, hypothyroidism, and TC with odds ratio (OR) of 1.002 (95% CI: 1.000–1.005, p = 0.042), 1.016 (95% CI: 1.006–1.027, p = 0.002), 1.068 (95% CI: 1.022–1.115, p = 0.003), and 1.106 (95% CI: 1.019–1.120, p = 0.042), respectively. Interestingly, using reverse-direction MR analysis, we also found that AITD had a significant potential causal association with PBC with an OR of 0.021 (p = 5.10E−4) and that the other two had no significant causal relation on PBC.Conclusion: PBC causes thyroid dysfunction, specifically as AITD, mild hypothyroidism, and TC. The potential causal relationship between PBC and thyroid dysfunction provides a new direction for the etiology of PBC.

IntroductionOvulatory dysfunction is usually caused by an endocrine disorder, of which polycystic ovary syndrome (PCOS) is the most common cause. PCOS is usually associated with estrogen levels within the normal range and can be characterized by oligo-/anovulation resulting in decreased progesterone levels. It is suggested that decreased progesterone levels may lead to more autoimmune diseases in women with PCOS. In addition, it is often claimed that there is an association between hyperprolactinemia and PCOS. In this large well-phenotyped cohort of women with PCOS, we have studied the prevalence of thyroid dysfunction and hyperprolactinemia compared to controls, and compared this between the four PCOS phenotypes.MethodsThis retrospective cross-sectional study contains data of 1429 women with PCOS and 299 women without PCOS. Main outcome measures included thyroid stimulating hormone (TSH), Free Thyroxine (FT4), and anti-thyroid peroxidase antibodies (TPOab) levels in serum, the prevalence of thyroid diseases and hyperprolactinemia.ResultsThe prevalence of thyroid disease in PCOS women was similar to that of controls (1.9% versus 2.7%; P = 0.39 for hypothyroidism and 0.5% versus 0%; P = 0.99 for hyperthyroidism). TSH levels were also similar (1.55 mIU/L versus 1.48 mIU/L; P = 0.54). FT4 levels were slightly elevated in the PCOS group, although within the normal range (18.1 pmol/L versus 17.7 pmol/L; P ConclusionWomen with PCOS do not suffer from thyroid dysfunction more often than controls. Also, the prevalence of positive TPOab, being a marker for future risk of thyroid pathology, was similar in both groups. Furthermore, the prevalence of hyperprolactinemia was similar in women with PCOS compared to controls.

BackgroundObservational studies have reported an association between coronavirus disease 2019 (COVID-19) risk and thyroid dysfunction, but without a clear causal relationship. We attempted to evaluate the association between thyroid function and COVID-19 risk using a bidirectional two-sample Mendelian randomization (MR) analysis.MethodsSummary statistics on the characteristics of thyroid dysfunction (hypothyroidism and hyperthyroidism) were obtained from the ThyroidOmics Consortium. Genome-wide association study statistics for COVID-19 susceptibility and its severity were obtained from the COVID-19 Host Genetics Initiative, and severity phenotypes included hospitalization and very severe disease in COVID-19 participants. The inverse variance-weighted (IVW) method was used as the primary analysis method, supplemented by the weighted-median (WM), MR-Egger, and MR-PRESSO methods. Results were adjusted for Bonferroni correction thresholds.ResultsThe forward MR estimates show no effect of thyroid dysfunction on COVID-19 susceptibility and severity. The reverse MR found that COVID-19 susceptibility was the suggestive risk factor for hypothyroidism (IVW: OR = 1.577, 95% CI = 1.065–2.333, P = 0.022; WM: OR = 1.527, 95% CI = 1.042–2.240, P = 0.029), and there was lightly association between COVID-19 hospitalized and hypothyroidism (IVW: OR = 1.151, 95% CI = 1.004–1.319, P = 0.042; WM: OR = 1.197, 95% CI = 1.023-1.401, P = 0.023). There was no evidence supporting the association between any phenotype of COVID-19 and hyperthyroidism.ConclusionOur results identified that COVID-19 might be the potential risk factor for hypothyroidism. Therefore, patients infected with SARS-CoV-2 should strengthen the monitoring of thyroid function.

Background: In adults, a significant impact of thyroid dysfunction and autoimmunity on health-related quality of life (HRQoL) and mental health is described. However, studies in children and adolescents are sparse, underpowered, and findings are ambiguous.Methods: Data from 759 German children and adolescents affected by thyroid disease [subclinical hypothyroidism: 331; subclinical hyperthyroidism: 276; overt hypothyroidism: 20; overt hyperthyroidism: 28; Hashimoto's thyroiditis (HT): 68; thyroid-peroxidase antibody (TPO)-AB positivity without apparent thyroid dysfunction: 61] and 7,293 healthy controls from a nationwide cross-sectional study (“The German Health Interview and Examination Survey for Children and Adolescents”) were available. Self-assessed HRQoL (KINDL-R) and mental health (SDQ) were compared for each subgroup with healthy controls by analysis of covariance considering questionnaire-specific confounding factors. Thyroid parameters (TSH, fT4, fT3, TPO-AB levels, thyroid volume as well as urinary iodine excretion) were correlated with KINDL-R and SDQ scores employing multiple regression, likewise accounting for confounding factors.Results: The subsample of participants affected by overt hypothyroidism evidenced impaired mental health in comparison to healthy controls, but SDQ scores were within the normal range of normative data. Moreover, in no other subgroup, HRQoL or mental health were affected by thyroid disorders. Also, there was neither a significant relationship between any single biochemical parameter of thyroid function and HRQoL or mental health, nor did the combined thyroid parameters account for a significant proportion of variance in either outcome measure. Importantly, the present study was sufficiently powered to identify even small effects in children and adolescents affected by HT, subclinical hypothyroidism, and hyperthyroidism.Conclusions: In contrast to findings in adults, and especially in HT, there was no significant impairment of HRQoL or mental health in children and adolescents from the general pediatric population affected by thyroid disease. Moreover, mechanisms proposed to explain impaired mental health in thyroid dysfunction in adults do not pertain to children and adolescents in the present study.

Dataset

This dataset was created by Amir Mohammad Parvizi

Released under MIT

Contents

https://cdn.prod.website-files.com/5c17fc782f30f90cd15c25b4/63189857cdf8f072fcccfd5e_Thyroid.gif" alt="">

Source:

Ross Quinlan

Data Set Information:

From Garavan Institute Documentation: as given by Ross Quinlan 6 databases from the Garavan Institute in Sydney, Australia Approximately the following for each database:

2800 training (data) instances and 972 test instances Plenty of missing data 29 or so attributes, either Boolean or continuously-valued

2 additional databases, also from Ross Quinlan, are also here

Hypothyroid.data and sick-euthyroid.data Quinlan believes that these databases have been corrupted Their format is highly similar to the other databases

1 more database of 9172 instances that cover 20 classes, and a related domain theory

Another thyroid database from Stefan Aeberhard

3 classes, 215 instances, 5 attributes No missing values

A Thyroid database suited for training ANNs

3 classes 3772 training instances, 3428 testing instances Includes cost data (donated by Peter Turney)

Attribute Information:

N/A

:Link

https://archive.ics.uci.edu/ml/datasets/Thyroid+Disease

R. Quinlan. "Thyroid Disease," UCI Machine Learning Repository, 1986. [Online]. Available: https://doi.org/10.24432/C5D010.

Complete dataset included in the full report. Detailed tables, regional splits, forecasts, and methodologies are available with purchase.

this dataset content 10 raw and 30 featurs

ABSTRACT Objective In this study, we aimed to describe the prevalence and distribution of positive antithyroperoxidase antibodies (TPOAb) according to sex, age strata, and presence of thyroid dysfunction using baseline data from the Brazilian Longitudinal Study of Adult Health (ELSA-Brasil). Materials and methods Thyroid hormone tests were obtained from each study participant at baseline. Levels of thyroid-stimulating hormone (TSH) and free thyroxine (FT4) were measured using a third-generation immunoenzymatic assay. Antithyroperoxidase antibodies were measured by electrochemiluminescence and were considered to be positive when ≥ 34 IU/mL. Results The prevalence of TPOAb among 13,503 study participants was 12%. Of participants with positive TPOAb, 69% were women. Almost 60% of the individuals with positive TPOAb were white. The presence of positive TPOAb was associated with the entire spectrum of thyroid diseases among women, but only with overt hyperthyroidism and overt hypothyroidism in men. Conclusion The distribution of positive TPOAb across sex, race, age, and thyroid function in the ELSA-Brasil study is aligned with the worldwide prevalence of positive TPOAb reported in iodine-sufficient areas. In women, the presence of TPOAb was related to the entire spectrum of thyroid dysfunction, while in men, it was only related to the occurrence of overt thyroid disease.

As per the results of a large scale survey conducted across India in 2021, about *** percent of the respondents suffered from thyroid related problems. This was a slight fall in the share of people with thyroid issues when compared to the previous year of the survey.

Additional file 1. It consist of all data generated during this study, which includes age, gender, duration of RA, history of thyroid disease and its subtypes, tender and swollen joint count, VAS, History of medication used for RA treatment, lab studies such as TSH, FT3, FT4, anti-CCP, anti-MCV, anti-TPO, RF, ESR, CRP and DAS-28.

Thyroid Support Complex Market Outlook

According to our latest research, the global Thyroid Support Complex market size reached USD 2.14 billion in 2024, reflecting robust demand for dietary supplements and nutraceuticals targeting thyroid health. The market is experiencing a healthy growth trajectory, with a projected CAGR of 7.2% from 2025 to 2033. By the end of 2033, the market is forecasted to reach USD 4.08 billion. The primary growth factor driving this expansion is the increasing prevalence of thyroid-related disorders, coupled with heightened consumer awareness regarding preventive healthcare and the benefits of natural supplements for thyroid function.

The growing incidence of thyroid diseases, such as hypothyroidism and hyperthyroidism, is a significant catalyst for the expanding Thyroid Support Complex market. As per global health statistics, thyroid disorders affect millions of individuals, particularly women, and are increasingly being detected due to improved diagnostic practices and greater public awareness. This surge in diagnosis has led to a higher demand for both prescription and over-the-counter thyroid support products, especially those formulated with natural ingredients. Consumers are seeking solutions that can help maintain hormonal balance, support metabolic health, and alleviate symptoms associated with thyroid dysfunction, which has prompted manufacturers to innovate and diversify their product offerings.

In addition to the rising prevalence of thyroid disorders, the market is benefiting from the broader trend toward preventive healthcare and wellness. Consumers are proactively seeking nutritional supplements that offer holistic health benefits, including those targeting endocrine and metabolic health. The Thyroid Support Complex market is capitalizing on this shift, with products that combine herbal extracts, vitamins, minerals, and amino acids to address thyroid health comprehensively. Furthermore, the integration of scientific research and clinical validation has enhanced consumer confidence in these products, further propelling market growth. The increasing popularity of e-commerce and digital health platforms has also made thyroid support complexes more accessible, contributing to the expansion of the market across both developed and emerging regions.

Another key driver is the innovation in product formulations and delivery formats. Manufacturers are investing in research and development to introduce advanced formulations, such as gummies, liquids, and powders, alongside traditional capsules and tablets. These new formats cater to diverse consumer preferences, making thyroid support supplements more appealing to a broader demographic, including younger consumers and those with specific dietary requirements. Additionally, the clean label trend, emphasizing natural, non-GMO, and allergen-free ingredients, is shaping product development strategies, as consumers increasingly scrutinize ingredient lists and demand transparency from brands.

Regionally, North America holds the largest share of the Thyroid Support Complex market, owing to high healthcare awareness, significant purchasing power, and a well-established dietary supplement industry. However, the Asia Pacific region is poised for the fastest growth, driven by rising health consciousness, increasing disposable incomes, and a growing burden of lifestyle-related diseases. Europe also represents a substantial market, supported by favorable regulatory frameworks and a strong tradition of herbal and natural health products. Latin America and the Middle East & Africa are emerging as promising markets, fueled by improving healthcare infrastructure and expanding retail channels. The global landscape is thus characterized by diverse growth dynamics, with each region presenting unique opportunities and challenges for market participants.

Product Type Analysis

The Thyroid Support Complex market is segmented by product type into capsules, tablets, gummies, powders, liquids, and others. Capsules contin

Thyroid disease presents a significant health risk, lowering the quality of life and increasing treatment costs. The diagnosis of thyroid disease can be challenging, especially for inexperienced practitioners. Machine learning has been established as one of the methods for disease diagnosis based on previous studies. This research introduces a novel and more effective technique for predicting thyroid disease by utilizing machine learning methodologies, surpassing the performance of previous studies in this field. This study utilizes the UCI thyroid disease dataset, which consists of 9172 samples and 30 features, and exhibits a highly imbalanced target class distribution. However, machine learning algorithms trained on imbalanced thyroid disease data face challenges in reliably detecting minority data and disease. To address this issue, re-sampling is employed, which modifies the ratio between target classes to balance the data. In this study, the down-sampling approach is utilized to achieve a balanced distribution of target classes. A novel RF-based self-stacking classifier is presented in this research for efficient thyroid disease detection. The proposed approach demonstrates the ability to diagnose primary hypothyroidism, increased binding protein, compensated hypothyroidism, and concurrent non-thyroidal illness with an accuracy of 99.5%. The recommended model exhibits state-of-the-art performance, achieving 100% macro precision, 100% macro recall, and 100% macro F1-score. A thorough comparative assessment is conducted to demonstrate the viability of the proposed approach, including several machine learning classifiers, deep neural networks, and ensemble voting classifiers. The results of K-fold cross-validation provide further support for the efficacy of the proposed self-stacking classifier.

According to Cognitive Market Research, the global Thyroid Functioning Tests market size will be USD 4968.5 million in 2025. It will expand at a compound annual growth rate (CAGR) of 7.00% from 2025 to 2033.

North America held the major market share for more than 40% of the global revenue with a market size of USD 1838.35 million in 2025 and will grow at a compound annual growth rate (CAGR) of 5.8% from 2025 to 2033.
Europe accounted for a market share of over 30% of the global revenue with a market size of USD 1440.8 million.
APAC held a market share of around 23% of the global revenue with a market size of USD 1192.4 million in 2025 and will grow at a compound annual growth rate (CAGR) of 9.6% from 2025 to 2033.
South America has a market share of more than 5% of the global revenue with a market size of USD 188.80 million in 2025 and will grow at a compound annual growth rate (CAGR) of 7.3% from 2025 to 2033.
Middle East had a market share of around 2% of the global revenue and was estimated at a market size of USD 198.74 million in 2025 and will grow at a compound annual growth rate (CAGR) of 7.5% from 2025 to 2033.
Africa had a market share of around 1% of the global revenue and was estimated at a market size of USD 109.31 million in 2025 and will grow at a compound annual growth rate (CAGR) of 6.7% from 2025 to 2033.
Power Filters category is the fastest growing segment of the Thyroid Functioning Tests industry

Market Dynamics of Thyroid Functioning Tests Market

Key Drivers for Thyroid Functioning Tests Market

Rising Prevalence of Thyroid Disorders to Boost Market Growth

Hypothyroidism and hyperthyroidism are becoming increasingly prevalent worldwide due to factors such as genetics, lifestyle changes, and environmental influences. Over 12 percent of the U.S. population will develop a thyroid condition at some point in their lives, and an estimated 20 million Americans currently have some form of thyroid disease. However, up to 60 percent of those with thyroid conditions are unaware of their diagnosis. Women are more likely to experience thyroid issues, with the risk being five to eight times higher than that of men. In fact, one in eight women will develop a thyroid disorder during their lifetime. These conditions can have a significant impact on metabolism and overall health, contributing to a growing demand for thyroid function tests. Additionally, autoimmune diseases, such as Hashimoto's thyroiditis (a common cause of hypothyroidism) and Graves' disease (which causes hyperthyroidism), are becoming more prevalent, further increasing the need for regular screening. As the global population ages, the likelihood of developing thyroid disorders, particularly hypothyroidism, also rises. This aging demographic requires more routine testing to monitor and manage thyroid health.

https://www.thyroid.org/media-main/press-room//./

Rise in Lifestyle-related Risk Factors to Boost Market Growth

Increasing stress, poor diet, lack of physical activity, and environmental pollution are major risk factors for thyroid disorders. Approximately 284 million people worldwide suffer from anxiety disorders, and nearly 90% of U.S. adults report losing sleep due to concerns about health and the economy. Additionally, about 75% of Americans experience physical or mental symptoms of stress, with more than three-quarters of adults reporting issues such as headaches, fatigue, and depression. These lifestyle changes are contributing to a higher incidence of thyroid-related problems, which is driving the growing demand for thyroid function tests. Furthermore, exposure to chemicals and toxins in the environment, including those found in certain pesticides, has been linked to thyroid dysfunction, further increasing the need for regular monitoring.

https://www.singlecare.com/blog/news/stress-statistics/./

Restraint Factor for the Thyroid Functioning Tests Market

High Cost of Advanced Diagnostic Tests and Challenges in Test Accuracy and Standardization, Will Limit Market Growth

The cost of diagnostic equipment for thyroid function tests, such as automated analyzers and high-quality reagents, can be prohibitively expensive, especially for small clinics and healthcare facilities in emerging markets. Additionally, testing at high-end laboratories or through advanced diagnostic platforms often comes with higher costs, which may discourage patients from undergoing regular testing. Th...

The coronavirus disease 2019 (COVID-19) pandemic, caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus, has presented multifaceted health challenges. COVID-19 primarily targets the respiratory system but also affects multiple organ systems, including the endocrine system. Emerging evidence suggests interactions between thyroid function, the acute phase of COVID-19, and the prolonged symptoms known as post-COVID sequalae or long COVID. Several studies have reported that COVID-19 can induce thyroid dysfunction, leading to conditions such as thyroiditis and alterations in thyroid hormone levels. The mechanisms through which SARS-CoV-2 affects the thyroid include direct viral infection of thyroid cells, leading to viral thyroiditis, which causes inflammation and transient or sustained thyroid dysfunction, as well as an excessive systemic immune response (cytokine storm). This is associated with elevated levels of cytokines, such as IL-6, that disrupt thyroid function and lead to nonthyroidal illness syndrome (NTIS). Medications administered during the acute illness phase, such as corticosteroids and antiviral drugs, can also impact thyroid hormone actions. The involvement of the thyroid gland in long COVID, or postacute sequelae of SARS-CoV-2 infection, is an area not well defined, with potential implications for understanding and managing this condition. Persistent low-grade inflammation affecting thyroid function over time can lead to ongoing thyroiditis or exacerbate pre-existing thyroid conditions. Viral infections, including SARS-CoV-2, can trigger or worsen autoimmune thyroid diseases, such as Hashimoto’s thyroiditis and Graves’ disease. Long COVID may disrupt the hypothalamic–pituitary–adrenal (HPA) axis, which can, in turn, affect the hypothalamic-pituitary-thyroid (HPT) axis, leading to abnormal thyroid function. This review was designed to systematically capture recent literature on COVID-19-related thyroid dysfunction in the adult population, the prognostic consequences of thyroid dysfunction during COVID-19, and the effects of thyroid dysfunction on patients with long COVID. A comprehensive search of PubMed and EMBASE databases was conducted. The systematic review was performed based on the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement. Study quality was assessed using the Critical Appraisal Skills Programme (CASP). A total of 53 studies met the inclusion criteria. The review summarises recent findings and provides an update of the current understanding of thyroid dysfunction in COVID-19-related spectrum of disorders, underscoring the complex nature of SARS-CoV-2 infection and its far-reaching impacts on human health.

Background: The relationship between thyroid function and cardiac disease is complex. Both hypothyroidism and thyrotoxicosis can predispose to ventricular arrhythmia and other major adverse cardiovascular events (MACE), so that a U-shaped relationship between thyroid signaling and the incidence of MACE has been postulated. Moreover, recently published data suggest an association between thyroid hormone concentration and the risk of sudden cardiac death (SCD) even in euthyroid populations with high-normal FT4 levels. In this study, we investigated markers of repolarization in ECGs, as predictors of cardiovascular events, in patients with a spectrum of subclinical and overt thyroid dysfunction.Methods: Resting ECGs of 100 subjects, 90 patients (LV-EF > 45%) with thyroid disease (60 overt hyperthyroid, 11 overt hypothyroid and 19 L-T4-treated and biochemically euthyroid patients after thyroidectomy or with autoimmune thyroiditis) and 10 healthy volunteers were analyzed for Tp-e interval. The Tp-e interval was measured manually and was correlated to serum concentrations of thyroid stimulating hormone (TSH), free triiodothyronine (FT3) and thyroxine (FT4).Results: The Tp-e interval significantly correlated to log-transformed concentrations of TSH (Spearman's rho = 0.30, p Conclusion: We observed significant inverse correlations of Tp-e and JT intervals with FT4 and FT3 over the whole spectrum of thyroid function. Our data suggest a possible mechanism of SCD in hypothyroid state by prolongation of repolarization. We do not observe a U-shaped relationship, so that the mechanism of SCD in patients with high FT4 or hyperthyroidism seems not to be driven by abnormalities in repolarization.