Thyroid disease datasets typically contain information about patients, including various attributes such as age, sex, thyroid hormone levels (TSH, T3, T4), medical history, and possibly symptoms or other relevant factors. These datasets are valuable for research purposes, particularly in the fields of endocrinology, machine learning, and healthcare analytics.

Several datasets are available for research purposes, often sourced from hospitals, research institutions, or public health agencies. Here are some common sources where you might find thyroid disease datasets:

1. UCI Machine Learning Repository: This repository hosts various datasets for machine learning research, and it includes some datasets related to thyroid disease.

2. Kaggle: Kaggle is a platform for data science and machine learning competitions, and it also hosts datasets for various purposes. You might find thyroid disease datasets shared by users or organizations.

3. Healthcare Databases: Some hospitals or healthcare institutions maintain databases with anonymized patient data, including information about thyroid diseases. Access to these datasets may require appropriate permissions and approvals due to privacy concerns.

4. Research Publications: Researchers often publish datasets along with their research papers. Searching through academic journals and repositories may lead you to relevant datasets related to thyroid diseases.

When working with any healthcare-related dataset, it's crucial to handle the data with care, ensuring patient privacy and adhering to ethical guidelines and regulations such as HIPAA (in the United States) or GDPR (in the European Union), depending on the jurisdiction.

If you need assistance finding a specific dataset or have other questions about thyroid disease datasets, feel free to ask!

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.

As per the results of a large scale survey conducted across India in 2021, about ** percent of the respondents above 60 years of age suffered from thyroid problems. Whereas around **** percent of the respondents below 19 years of age reported to have thyroid issues.

ABSTRACT Background: Primary headaches, and particularly migraine and tension-type headache (TTH) as well as hypothyroidism are common medical conditions. To date, numerous studies have suggested a possible bidirectional relationship between migraine and hypothyroidism, although certain studies had contradictory results. Objective: To investigate whether there is any association between primary headache subtypes and thyroid disorders. Methods: A retrospective study of consecutive patients aged ≥18 years referred to the Headache Outpatient Clinic of Aeginition Hospital and diagnosed with primary headache and any thyroid disorder. Results: Out of 427 patients (males/females=76/351), 253 (59.3%) were diagnosed with migraine without aura, 53 (12.4%) with TTH, 49 (11.5%) with migraine with aura, 29 (6.8%) with medication-overuse headache, 23 (5.4%) with mixed-type headache (migraine with/without aura and TTH), nine (2.1%) with cluster headache, and 11 (2.6%) with other types of primary headaches. The prevalence of any type of thyroid disorder was 20.8% (89/427 patients). In the total sample, 27 patients (6.3%) reported hypothyroidism, 18 (4.2%) unspecified thyroidopathy, 14 (3.3%) thyroid nodules, 12 (2.8%) Hashimoto thyroiditis, 12 (2.8%) thyroidectomy, three (0.7%) thyroid goiter, and three (0.7%) hyperthyroidism. Further statistical analysis between categorical variables did not reveal any significant association between headache subtypes and thyroid dysfunction. Conclusions: No specific association was found between primary headache subtypes and specific thyroid disorder. However, a high prevalence of thyroid dysfunction in general and specifically hypothyroidism was demonstrated among patients with primary headaches, which lays the foundation for further clarification in prospective longitudinal studies.

BackgroundsCo-occurrence of thyroid dysfunction, metabolic disturbances, and worsening clinical symptoms in major depressive disorder (MDD) patients with suicidal attempts (SA) are common. However, their relationship in SA patients remains unexplored. We aimed to (1) determine the independent association of thyroid dysfunction, clinical symptoms, and metabolic disturbances with SA; and (2) identify their interactions in SA patients via the network approach.Methods1718 FEDN MDD patients were recruited. Depressive, anxiety, and psychotic symptoms were assessed by the Hamilton Rating Scale for Depression (HAMD), the Hamilton Rating Scale for Anxiety (HAMA), and the Positive and Negative Syndrome Subscale positive subscale, respectively. The serum levels of thyroid hormones and other metabolic parameters were assessed. Logistic regression model was applied to determine the correlates of SA. Network analysis was applied to determine the interaction between thyroid dysfunction, clinical symptoms, and metabolic disturbances.ResultsSA patients had significant worse metabolic disturbances, thyroid dysfunction, and clinical symptoms than non-SA patients. Thyroid peroxidases antibody, thyroid stimulating hormone (TSH), HAMD scores, HAMA scores, and systolic blood pressure was independently associated with SA. Network analysis suggested that TSH was the hub of the network, exhibiting substantial associations with metabolic disturbances, anxiety, and psychotic symptoms in SA patients.ConclusionsOur work highlights the predominant role of serum TSH levels in the pathophysiology of SA. Regular thyroid function tests might help early detect SA. Targeting increased TSH levels may help reduce metabolic disturbances and clinical symptoms in SA patients.

Thyroid Disorder Market size was valued at USD 2.15 Billion in 2025 and is expected to reach USD 4.43 Billion by 2035, expanding at a CAGR of 7.5% during the forecast period.

BackgroundThyroid dysfunction significantly affects the health and development of adolescents. However, comprehensive studies on its prevalence and characteristics in US adolescents are lacking.MethodsWe investigated the prevalence of thyroid dysfunction in US adolescents aged 12–18 years using data from the National Health and Nutrition Examination Survey (NHANES) 2001–2002 and 2007–2012 cycles. Thyroid dysfunction was assessed using serum thyroid-stimulating hormone (TSH) and free thyroxine (fT4) measurements. We analyzed the prevalence across demographic subgroups and identified associated risk factors.ResultsThe study included 2,182 participants, representing an estimated 12.97 million adolescents. The group had a weighted mean age of 15.1 ± 0.06 years, with males constituting 51.4%. Subclinical hyperthyroidism emerged as the most prevalent thyroid dysfunction, affecting 4.4% of the population. From 2001–2002 to 2011–2012, subclinical hyperthyroidism remained consistent at 4.99% vs. 5.13% in the overall cohort. Subclinical and overt hypothyroidism was found in 0.41 and 1.03% of adolescents respectively, and overt hyperthyroidism was rare (0.04%). The prevalence of thyroid peroxidase antibody (TPOAb) and thyroglobulin antibody (TgAb) positivity in the overall population were 5.8 and 9.8%, respectively. Positivity for TgAb was risk factors for hypothyroidism, while older age, female and Black Americans were risk factors for hyperthyroidism. Female adolescents and adolescents with an older age were more likely to be positive for TPOAb and TgAb, while Black and Mexican Americans had a lower risk of TPOAb and TgAb positivity.ConclusionSubclinical hyperthyroidism was the most common form of thyroid dysfunction, and its prevalence remained stable from 2001–2002 to 2011–2012. Notable disparities in the prevalence of hyperthyroidism and antibody positivity were observed among different age, sex and racial/ethnic groups.

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.

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.

ObjectiveGuidelines for thyroid function evaluation recommend testing TSH first, then assessing fT4 only if TSH is out of the reference range (two-step), but many clinicians initially request both TSH and fT4 (one-step). Given limitations of previous studies, we aimed to compare the two-step with the one-step approach in an unselected community-dwelling study population, and develop a prediction score based on clinical parameters that could identify at-risk patients for thyroid dysfunction.DesignCross-sectional analysis of the population-based Busselton Health Study.MethodsWe compared the two-step with the one-step approach, focusing on cases that would be missed by the two-step approach, i.e. those with normal TSH, but out-of-range fT4. We used likelihood ratio tests to identify demographic and clinical parameters associated with thyroid dysfunction and developed a clinical prediction score by using a beta-coefficient based scoring method.ResultsFollowing the two-step approach, 93.0% of all 4471 participants had normal TSH and would not need further testing. The two-step approach would have missed 3.8% of all participants (169 of 4471) with a normal TSH, but a fT4 outside the reference range. In 85% (144 of 169) of these cases, fT4 fell within 2 pmol/l of fT4 reference range limits, consistent with healthy outliers. The clinical prediction score that performed best excluded only 22.5% of participants from TSH testing.ConclusionThe two-step approach may avoid measuring fT4 in as many as 93% of individuals with a very small risk of missing thyroid dysfunction. Our findings do not support the simultaneous initial measurement of both TSH and fT4.

This dataset captures various attributes related to thyroid conditions for medical diagnosis. It includes demographic information such as age and sex. Medical history features encompass intake of thyroxine, antithyroid medications, and past surgeries. Patients' current health status regarding sickness, pregnancy, presence of goiter, tumor, or hypopituitary conditions is recorded. Additionally, it notes whether patients suspect hypothyroidism or hyperthyroidism. Laboratory results like TSH, T3, TT4, T4U, and FTI levels are included if measured. Binary classification denotes the presence or absence of hyperthyroidism. Referral sources are indicated as well.

Abstract:

Objectives: Thyroid dysfunction such as hypothyroidism (THO) and hyperthyroidism (THE) are the disease caused by pathological processes in the thyroid. The current diagnosis of thyroid dysfunction is variable because of ages and genders. The aim of this study was to explore the novel candidate biomarker panels for hypothyroidism and hyperthyroidism screening with mass spectrometry and bioinformatics.

Methods: Plasma samples were collected from 15 THE patients, 9 THO patients, and 15 healthy controls. DIA-based proteomic and untargeted metabolomic analyses were performed to identify the novel biomarker panels for THO and THE. Finally, three candidate biomarkers were verified by ELISA in 34 samples.

Results: A total of 2738 proteins and 6103 metabolites were identified, and 173 proteins and 2487 metabolites were found to be differentially expressed among THE, THO and control groups. The results of the ensemble feature selection, K-means clustering and the least absolute shrinkage and selection operator (LASSO) regression model showed that four proteins (C4A, C3/C5 convertase, APOL1, and ITIH4) and four metabolites (L-arginine, L-proline, cortisol, and cortisone) identified by plasma proteomics and metabolomics could help distinguish THO and THE patients from healthy controls.

Conclusions: This study identified and verified two pairs of biomarker panels that can distinguish the THE and THO patients regardless of ages and genders. Consequently, our findings represent a comprehensive analyses of thyroid dysfunction plasma, which is significant for the clinical diagnosis.

The Thyroid Gland Disorders Treatment Market report segments the industry into By Type Of Disorder (Hypothyroidism, Hyperthyroidism, Other Types Of Disorder), By Route Of Administration (Oral, Parenteral, Other Routes Of Administration), By Drug Class (Thioamides, Ionic Inhibitors, Hormone-release Inhibitors, Other Drug Classes), By Distribution Channel (Wholesale Distribution, Retail Stores, and more), and Geography.

PurposeThe novel coronavirus COVID-19, has caused a worldwide pandemic, impairing several human organs and systems. Whether COVID-19 affects human thyroid function remains unknown.MethodsEighty-four hospitalized COVID-19 patients in the First Affiliated Hospital, Zhejiang University School of Medicine (Hangzhou, China) were retrospectively enrolled in this study, among which 22 cases had complete records of thyroid hormones. In addition, 91 other patients with pneumonia and 807 healthy subjects were included as controls.ResultsWe found that levels of total triiodothyronine (TT3) and thyroid stimulating hormone (TSH) were lower in COVID-19 patients than healthy group (p < 0.001). Besides, TSH level in COVID-19 patients was obviously lower than non-COVID-19 patients (p < 0.001). Within the group of COVID-19, 61.9% (52/84) patients presented with thyroid function abnormalities and the proportion of thyroid dysfunction was higher in severe cases than mild/moderate cases (74.6 vs. 23.8%, p < 0.001). Patients with thyroid dysfunction tended to have longer viral nucleic acid cleaning time (14.1 ± 9.4 vs. 10.6 ± 8.3 days, p = 0.088). To note, thyroid dysfunction was also associated with decreased lymphocytes (p < 0.001) and increased CRP (p = 0.002). The correlation between TT3 and TSH level seemed to be positive rather than negative in the early stage, and gradually turned to be negatively related over time.ConclusionThyroid function abnormalities are common in COVID-19 patients, especially in severe cases. This might be partially explained by nonthyroidal illness syndrome.

ObjectiveTo analyze the related factors of the postpartum thyroid function in women with overt hypothyroidism (OH)/subclinical hypothyroidism (SCH) and explore the effects of vitamin D categories.MethodsThyroid hormones, thyroid autoantibody, and serum 25OHD levels were continuously recorded from the first trimester of pregnancy (T1) to the 12th postpartum month. Logistic regression analysis and Cox regression analysis were used to screen the related factors of postpartum thyroid function, and the Latent Class Growth Model was performed to analyze the trajectory characteristics of serum 25OHD levels.ResultsTotally, 252 pregnant women with OH/SCH were enrolled in the study. In the 12th month postpartum, 36.5% of the patients improved thyroid function, 37.3% continued hypothyroidism, and 26.2% developed thyroid dysfunction. Vitamin D sufficiency, positive TPOAb, and positive TgAb in T1 were independent prognostic factors of postpartum thyroid function. Vitamin D sufficiency in T1 was illustrated as an independent factor of the improved postpartum thyroid function, but the protective effect for the developed postpartum thyroid dysfunction was only confirmed in TPOAb-positive patients. Cox regression analysis further confirmed the effects of vitamin D categories. Notably, the high-level 25OHD trajectory during pregnancy and postpartum could predict improved postpartum thyroid function and decrease the risk of developed postpartum thyroid dysfunction.ConclusionAppropriate vitamin D nutrition during pregnancy and postpartum may be beneficial to postpartum thyroid function.

The Thyroid Gland Dataset typically used in machine learning and data science projects is designed to analyze and predict thyroid-related conditions. Here’s an overview of what you might find in such a dataset:

Dataset Overview

1. Purpose:

   • To predict thyroid conditions based on various features such as age, symptoms, and test results.
   • To explore relationships between different features and thyroid disease.

2. Common Columns:

   • Age: Age of the patient.
   • Lithium: Indicates if the patient is taking lithium (often used as a mood stabilizer).
   • Goitre: Presence of goitre (an enlarged thyroid gland).
   • Tumor: Presence of a thyroid tumor.
   • Hypopituitary: Indicates if there is hypopituitarism (a condition where the pituitary gland is underactive).
   • Psych: Psychological condition of the patient.
   • TSH (Thyroid Stimulating Hormone): A hormone that stimulates thyroid function.
   • T3 (Triiodothyronine): A thyroid hormone.
   • TT4 (Total Thyroxine): A measure of the total thyroxine level in the blood.
   • T4U (Thyroxine Uptake): A measure of how well the thyroid is functioning.
   • FTI (Free Thyroxine Index): An index used to assess thyroid function.

3. Target Variable:

   • Target: Indicates the presence or absence of thyroid disease. This could be binary (e.g., 0 for no disease, 1 for disease) or categorical.

4. Possible Features and Analysis:

   • Exploratory Data Analysis (EDA): Understand the distribution of features, check for missing values, outliers, and correlations.
   • Feature Encoding: Convert categorical features into numerical values if necessary for machine learning models.
   • Data Visualization: Create histograms, heatmaps, and scatter plots to visualize relationships between features.

Example Dataset Information

Usage

• Predictive Modeling: Train models to predict thyroid conditions based on the features.
• Feature Importance: Determine which features are most important for predicting the target variable.
• Data Cleaning and Preparation: Handle missing values, encode categorical variables, and normalize data if required.

Example Code to Display Dataset Info

import pandas as pd

# Load the dataset
df = pd.read_csv('/kaggle/input/thyroid-gland-dataset/hypothyroid.csv')

# Display dataset information
print("Dataset Info:")
print(df.info())

# Display the first few rows
print("
First few rows of the dataset:")
print(df.head())

# Describe the dataset
print("
Dataset Description:")
print(df.describe())

This code will give you an overview of the dataset, including data types, missing values, and basic statistics.

Ovarian function measures in normogonadotropic anovulation and subclinical hypothyroidism or thyroid autoimmunity

As per the results of a large scale survey conducted across India in 2019, about ** percent of the severely obese respondents suffered from thyroid problems. Whereas only ***** percent of the respondents in the normal to overweight weight range reported to have thyroid problems.

Thyroid Function Testing Market Outlook

According to our latest research, the global thyroid function testing market size reached USD 1.89 billion in 2024, reflecting robust growth driven by increasing awareness and prevalence of thyroid disorders worldwide. The market is expected to expand at a compound annual growth rate (CAGR) of 5.7% from 2025 to 2033, reaching a projected value of USD 3.13 billion by 2033. This upward trajectory is primarily fueled by the rising incidence of thyroid-related diseases, technological advancements in diagnostic techniques, and the expanding geriatric population susceptible to thyroid dysfunction.

One of the primary growth factors propelling the thyroid function testing market is the increasing prevalence of thyroid disorders such as hypothyroidism, hyperthyroidism, thyroid cancer, and autoimmune thyroid diseases. According to the American Thyroid Association, over 20 million Americans have some form of thyroid disease, and similar trends are observed globally, particularly in regions with iodine deficiency. Early diagnosis and regular screening have become critical, especially as thyroid dysfunction can significantly impact metabolic health, cardiovascular function, and overall quality of life. This growing burden of thyroid diseases has led to a surge in demand for reliable and accurate thyroid function tests, including TSH, T3, and T4 assays, thus driving market expansion.

Technological advancements in diagnostic techniques represent another significant driver for the thyroid function testing market. The emergence of sensitive immunoassays, automated analyzers, and point-of-care testing solutions has revolutionized the detection and monitoring of thyroid disorders. These innovations have improved assay specificity, reduced turnaround times, and minimized manual errors, thereby enhancing the overall efficiency and reliability of thyroid function testing. Additionally, the integration of digital health platforms and electronic medical records has enabled seamless data management and remote monitoring, further supporting market growth by facilitating timely diagnosis and personalized patient care.

Another crucial growth factor is the increasing awareness among healthcare professionals and the general population regarding the importance of thyroid health. Government initiatives, public health campaigns, and educational programs have played a vital role in promoting early screening and diagnosis, particularly in high-risk populations such as pregnant women and the elderly. The rising accessibility of diagnostic laboratories and the expansion of healthcare infrastructure in emerging economies have also contributed to the growing uptake of thyroid function tests. Furthermore, collaborations between diagnostic companies, research institutions, and healthcare providers have accelerated the development and adoption of advanced testing methodologies, thereby strengthening the market outlook.

From a regional perspective, North America continues to dominate the thyroid function testing market, accounting for the largest share in 2024, followed by Europe and Asia Pacific. The United States, in particular, benefits from a well-established healthcare system, high awareness levels, and significant investments in diagnostic technologies. Europe is witnessing steady growth due to increasing healthcare expenditure and the rising prevalence of thyroid disorders, especially in countries with aging populations. Meanwhile, Asia Pacific is emerging as a lucrative market, driven by the rapid expansion of healthcare infrastructure, growing awareness, and a large patient pool. The Middle East & Africa and Latin America are also showing promising growth, albeit from a smaller base, as healthcare access and diagnostic capabilities continue to improve in these regions.

Product Type Analysis

The thyroid function testing market is segmented by product type into TSH Test, T3 Test, T4 Test, and Others. The TSH (thyroid-stimulating hormone) test segment commands the largest market share, owing to its critical role as the primary screening tool for thyroid dysfunction. TSH testing is widely regarded as the most sensitive and specific initial test for evaluating thyroid status, making it indispensable in clinical practice. The high prevalence of hypothyroidism and the routine use of TSH testing in annual health check-ups further bolster segment growth. Moreover, advancements in immunoassay technology ha