Thyroid disorders, including hypothyroidism, hyperthyroidism, and thyroid malignancies, are among the most common metabolic and endocrine conditions worldwide. Traditional diagnosis relies on clinical symptoms, physical evaluation, and biochemical assays. These assays measure levels of thyroid hormones, such as thyroid-stimulating hormone (TSH), Free T3 and Free T4, and thyroid antibodies (TPOAb). In addition, diagnosis uses ultrasound examinations and fine-needle aspiration biopsies. While established pathways are helpful, standard clinical cutoffs can sometimes miss subtle dysfunctions or fail to capture complex individual physiological variations. Machine learning (ML) has become increasingly essential in thyroid disease prediction by handling complex diagnostic variables and reducing diagnostic errors.

Supervised and unsupervised learning models

All machine learning models can be divided into two categories: supervised and unsupervised learning models, depending on the data they work with. Supervised models, such as logistic regression, decision trees, and neural networks, work with labeled data featuring explicitly defined outcomes. For example, each sample has a pre-defined target label like “hypothyroid” or “euthyroid”. These models generate predictions on new unlabeled data, usually referred to as validation samples. For this purpose, they use trained prediction values from the training set of labeled data.

Unsupervised models, such as K-means clustering, work with unlabeled data to discover hidden patterns or underlying structural distributions without pre-defined target labels.

Logistic regression models

Logistic Regression (LR) is a foundational statistical and machine learning model. It maps input features to a probability score using the sigmoid function. For thyroid disease prediction, it frequently serves as a powerful baseline model, achieving high classification accuracies on structured thyroid datasets. Because the model outputs explicit regression coefficients, medical researchers can directly translate them into odds ratios. These ratios quantify how much a single unit change in a biomarker, such as TSH, alters thyroid disorder risk.

However, LR relies on manual pre-modeled outcome-predictor relationships, often requiring manual feature engineering and complex data normalization. Oftentimes, a patient record has missing laboratory values, such as an unmeasured Free T3 test. Therefore, the missing data must be handled beforehand through imputation techniques.

Decision trees and extreme gradient boosting

Decision Trees are non-parametric supervised learning models that split data recursively based on feature thresholds. For example, these thresholds can be specific cutoff values for TSH or Free T4 levels. These models effectively capture non-linear relationships and interactions between various clinical parameters, such as age, sex, and hormone profiles. Unlike LR models, they do not require complex data normalization.

XGBoost is an advanced gradient-boosting ensemble framework that builds sequential decision trees, where each new tree corrects the residual errors of previous ones. XGBoost consistently ranks among the top-performing algorithms for tabular medical datasets by minimizing bias and variance, preventing overfitting on noisy clinical datasets, and handling missing values effectively.

Neural networks for thyroid disease prediction

Recent advancements in deep learning have dramatically enhanced how neural networks process data for thyroid disease prediction and classification. For example, they process image data, such as ultrasound scans, and numerical data, such as age, gender and TSH levels.

Medical imaging, particularly thyroid ultrasonography, relies on Convolutional Neural Networks (CNNs, shown in Figure 1) and specialized deep learning architectures to distinguish between benign and malignant thyroid nodules. Researchers frequently employ standard or retrained backbone CNNs, such as ResNet, via transfer learning to evaluate radiomics features like nodule shape, margins, echogenicity, and calcifications.

Joint predictive models, such as hybrid and radiomics-clinical fusion models, merge numerical patient metrics with quantitative image radiomics features. Fusing these data streams helps optimize diagnostic workflows and minimizes the necessity for invasive fine-needle aspiration biopsies on benign nodules.

Figure 1. The workflow for thyroid nodule classification using convolutional neural networks with ResNet18 and feature extraction. Evaluation indicators are used to evaluate the effect of model classification. In addition, a heatmap was employed to visualize the prediction results, by which authors extracted and analyzed the highlighted areas. Adapted from J. Yang et al 2022 [5].

How ML can change clinical practice

In thyroid disease prediction, performance evaluation ensures high validation standards and reliability before deployment. For example, evaluation uses metrics like AUC, F1 score, precision, and recall. In addition, feature extraction isolates essential clinical variables and radiomic biomarkers to catch subtle physiological variations early.

Bias and explainability in thyroid disease prediction

Key potential risks associated with almost every advanced ML model include algorithm bias from non-representative training datasets. In addition, the “black-box” nature makes clinical interpretability difficult. To fix bias, we need to build training sets that genuinely represent diverse patient groups. We also need to use smart data adjustments to stop health disparities from happening.

Tackling the “black-box” problem involves integrating explainable AI frameworks, such as SHapley Additive exPlanations (SHAP, shown in Figure 2). These frameworks, in turn, mathematically expose how specific features drive a prediction. Keeping these tools safe, fair and reliable comes down to regular check-ups on how models perform. In addition, teams with varied perspectives need to guide their use. Finally, doctors need to stay directly involved to oversee every decision before AI enters day-to-day clinical work.

Figure 2: Example of SHAP values of important features which impact the model output. Large positive SHAP values indicate that the feature’s specific value pushes the model’s prediction toward a positive outcome (such as a higher risk of thyroid disease), while large negative SHAP values pushes the prediction toward a negative outcome (such as a lower risk of thyroid disease, or a normal euthyroid state). Adopted from C. J. Ejiyi et al 2026 [6]

Author: Lucia Buljac (University of Split, Faculty of Science)

Key references

1. Alkhazzar, H., & Moslem, B. (2024). Thyroid Disease Prediction Using Machine Learning Model: Decision Tree. EDRAAK, 2024, 84–100. https://doi.org/10.70470/edraak/2024/011
2. Chaganti, R., Rustam, F., De La Torre Díez, I., Mazón, J. L. V., Rodríguez, C. L., & Ashraf, I. (2022). Thyroid Disease Prediction Using Selective Features and Machine Learning Techniques. Cancers, 14(16), 3914. https://doi.org/10.3390/cancers14163914
3. Hu, M., Asami, C., Iwakura, H., Nakajima, Y., Sema, R., Kikuchi, T., Miyata, T., Sakamaki, K., Kudo, T., Yamada, M., Akamizu, T., & Sakakibara, Y. (2022). Development and preliminary validation of a machine learning system for thyroid dysfunction diagnosis based on routine laboratory tests. Communications Medicine, 2, Article 71. https://doi.org/10.1038/s43856-022-00071-1
4. Chan, W.-K., Sun, J.-H., Liou, M.-J., Li, Y.-R., Chou, W.-Y., Liu, F.-H., Chen, S.-T., & Peng, S.-J. (2021). Using Deep Convolutional Neural Networks for Enhanced Ultrasonographic Image Diagnosis of Differentiated Thyroid Cancer. Biomedicines, 9(12), 1771. https://doi.org/10.3390/biomedicines9121771 Cited by: 38
5. Yang, J., Shi, X., Wang, B., Qiu, W., Tian, G., Wang, X., Wang, P., & Yang, J. (2022). Ultrasound Image Classification of Thyroid Nodules Based on Deep Learning. Frontiers in Oncology, 12, 905955. https://doi.org/10.3389/fonc.2022.905955
6. Ejiyi, C.J., Eze, F.O., Hossin, M.A. et al. A robust framework for clinically interpretable hypothyroidism diagnosis with Polynomial-SHAP and DeepSeqNet. J Big Data 13, 63 (2026). https://doi.org/10.1186/s40537-025-01332-1

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When researchers discover that two things are associated, an important question always follows: does one actually cause the other?

For example, people with low vitamin D levels may have a higher risk of developing certain diseases. Individuals with obesity often have altered thyroid hormone levels. Patients with autoimmune thyroid diseases frequently have other immune-related conditions.

But are these relationships truly causal, or are they simply correlations influenced by other factors such as lifestyle, age, diet, or underlying health conditions?

Distinguishing correlation from causation is one of the greatest challenges in medical research. This is where Mendelian randomization (MR) has emerged as a powerful tool in modern genetic epidemiology.

Why Observational Studies Are Not Enough

Traditional observational studies can identify associations between a potential risk factor (called an exposure) and a disease (the outcome). However, these associations may be affected by confounding factors or reverse causation.

Imagine that researchers observe people with lower thyroid hormone levels also have a higher risk of cardiovascular disease. Does reduced thyroid function increase cardiovascular risk? Or do other factors, such as obesity, physical inactivity, medication use, or chronic illness, influence both thyroid function and heart disease?

Similarly, if patients with a particular disease have lower vitamin D concentrations, we cannot immediately conclude that vitamin D deficiency caused the disease. The disease itself may reduce vitamin D levels, or both may be influenced by completely different factors.

Randomized controlled trials (RCTs) are considered the gold standard for establishing causality because participants are randomly assigned to different interventions. Unfortunately, RCTs are often expensive, time-consuming, and sometimes impossible or unethical to perform.

Mendelian randomization offers an elegant alternative.

What Is Mendelian Randomization?

Mendelian randomization is a statistical method that uses naturally occurring genetic variation to investigate whether an observed association between a risk factor and a disease is likely to be causal.

The method is based on a simple biological principle first described by Gregor Mendel: genetic variants are randomly inherited from parents at conception. This natural random allocation resembles the randomization process used in clinical trials.

This concept is illustrated in Figure 1. While participants in randomized controlled trials are randomly assigned to intervention or control groups by the investigator, individuals in Mendelian randomization studies are naturally “allocated” to different levels of an exposure through the random inheritance of genetic variants at conception.

This natural randomization helps minimise confounding and enables researchers to investigate causal relationships using observational data.

Because genetic variants are assigned before birth, they are generally not influenced by lifestyle, environmental exposures, disease progression, or most confounding factors encountered in observational studies.

In Mendelian randomization, genetic variants associated with an exposure serve as instrumental variables (or genetic instruments) that allow researchers to estimate the causal effect of that exposure on a disease outcome.

Figure 1. Mendelian randomization as a natural analogue of a randomized controlled trial. In randomized controlled trials, participants are randomly assigned to intervention or control groups by the investigator. In Mendelian randomization, the random allocation of genetic variants at conception creates groups that are generally balanced with respect to confounding factors, allowing causal relationships to be investigated using observational data. Adapted from Davey Smith & Ebrahim (2005).

How Does Mendelian Randomization Work?

The basic concept is straightforward.

First, researchers identify genetic variants that are robustly associated with a specific exposure, such as:

• thyroid-stimulating hormone (TSH)
• free thyroxine (fT4)
• vitamin D levels
• body mass index
• cholesterol concentrations
• inflammatory biomarkers

These variants are identified through large genome-wide association studies (GWAS), often involving hundreds of thousands of participants.

Next, researchers examine whether the same genetic variants are also associated with a disease outcome.

If genetic variants that increase (or decrease) the exposure consistently lead to a higher (or lower) disease risk, this provides evidence supporting a causal relationship.

In other words, instead of asking:

“Do people with higher TSH develop disease more often?”

MR asks:

“Do people who are genetically predisposed to have higher TSH throughout their lives also have a different risk of developing disease?”

Why Is Mendelian Randomization So Powerful?

One of the major strengths of MR is its ability to reduce bias.

Because genetic variants are inherited before birth:

• they usually cannot be changed by disease,
• they are independent of lifestyle factors,
• they are less affected by confounding than conventional observational studies.

As a result, MR often provides stronger evidence about causality than observational research alone.

Importantly, MR does not completely replace randomized clinical trials. Instead, it complements them by helping researchers prioritize which biological pathways are most promising for further investigation and potential therapeutic intervention.

The Three Core Assumptions

For Mendelian randomization to provide reliable causal estimates, three key assumptions must be satisfied.

1. Relevance

The genetic variants must be strongly associated with the exposure of interest.

2. Independence

The genetic variants should not be associated with confounding factors that influence both the exposure and the disease.

3. Exclusion Restriction

The genetic variants should influence the disease only through the exposure being studied, and not through alternative biological pathways.

Researchers routinely evaluate these assumptions using a range of statistical sensitivity analyses, helping ensure that the conclusions are as robust as possible.

Mendelian Randomization in Thyroid Research

Mendelian randomization has become an increasingly valuable tool for understanding thyroid biology.

Researchers have applied MR to investigate questions such as:

• Do thyroid hormone levels causally influence cardiovascular disease?
• Does thyroid function affect obesity or metabolic syndrome?
• Is vitamin D involved in autoimmune thyroid diseases?
• Does thyroid function influence bone health or cognitive decline?
• Are circulating biomarkers involved in thyroid cancer development?

Rather than relying solely on observational associations, MR allows researchers to determine whether these relationships are likely to reflect genuine biological mechanisms.

Such evidence is particularly valuable when identifying potential therapeutic targets or deciding whether an intervention is likely to improve patient outcomes.

Mendelian Randomization in the InnoThyroGen Project

Within the InnoThyroGen project, Mendelian randomization is used to identify biomarkers and biological factors that are likely to play a causal role in thyroid diseases.

By integrating large-scale genome-wide association study (GWAS) data with advanced statistical methods, MR enables us to distinguish variables that are merely associated with thyroid disease from those that are more likely to contribute directly to disease development.

This information is particularly valuable for the development of the integrated thyroid disease risk prediction tool being developed within InnoThyroGen.

Rather than selecting biomarkers based solely on observational associations, MR helps prioritise variables with evidence of causality for inclusion alongside polygenic risk scores (PRS) and clinical characteristics.

Combining genetic predisposition captured by PRS with causally relevant biomarkers has the potential to improve risk prediction, support earlier identification of individuals at increased risk, and contribute to more personalised approaches to thyroid disease prevention and management.

Together, Mendelian randomization and polygenic risk scores form complementary components of the InnoThyroGen analytical pipeline, providing the evidence needed to develop a robust and clinically relevant integrated risk prediction tool.

Looking Ahead

The rapid growth of publicly available genetic datasets has transformed Mendelian randomization into one of the most widely used approaches in genetic epidemiology.

Today, researchers can investigate thousands of potential causal relationships using data from international biobanks and collaborative genome-wide association studies involving hundreds of thousands—or even millions—of participants.

As these resources continue to expand, Mendelian randomization will play an increasingly important role in identifying disease mechanisms, guiding drug development, and advancing precision medicine.

For projects such as InnoThyroGen, this means moving beyond identifying simple associations towards understanding why diseases develop and which biological pathways offer the greatest potential for prevention, diagnosis, and treatment.

Ultimately, Mendelian randomization is helping researchers answer one of the most fundamental questions in medicine: what truly causes disease?

Author: Nikolina Pleić (University of Split, School of Medicine)

Key References

1. Davey Smith G, Ebrahim S. ‘Mendelian randomization’: can genetic epidemiology contribute to understanding environmental determinants of disease? International Journal of Epidemiology. 2003;32(1):1–22. https://doi.org/10.1093/ije/dyg070
2. Davey Smith G, Hemani G. Mendelian randomization: genetic anchors for causal inference in epidemiological studies. Human Molecular Genetics. 2014;23(R1):R89–R98. https://doi.org/10.1093/hmg/ddu328
3. Davies NM, Holmes MV, Davey Smith G. Reading Mendelian randomisation studies: a guide, glossary, and checklist for clinicians. BMJ. 2018;362:k601. https://doi.org/10.1136/bmj.k601
4. Lawlor DA, Harbord RM, Sterne JAC, Timpson N, Davey Smith G. Mendelian randomization: using genes as instruments for making causal inferences in epidemiology. Statistics in Medicine. 2008;27:1133–1163. https://doi.org/10.1002/sim.3034

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Thyroid gland is an organ located in the lower front part of the neck. The main function of the thyroid gland is to produce triiodothyronine (T3) and thyroxine (T4). Thyroid hormones are key regulators of basal energy expenditure and metabolism, regular cell development and differentiation, neurodevelopmental processes in the first years of life, linear growth, body composition, and weight. Normal thyroid function is regulated by the hypothalamic thyrotropin-releasing hormone (TRH) that stimulates the release of the thyroid-stimulating hormone (TSH) from the pituitary gland. In turn, TSH drives the synthesis and secretion of thyroid hormones from the thyroid gland. TSH and TRH levels increase when circulating thyroid hormone levels are low, and vice versa. Serum TSH and free thyroxine (FT4) concentrations are routinely used to assess thyroid function of an individual in everyday clinical practice. Free T3 or free T4 measures an active thyroid hormone which is not bound to protein, and is able to affect body tissues. Total T3 or total T4 measures the bound and free hormone, which can be influenced by protein levels in the blood. Tests measuring free hormones more accurately reflect how the thyroid gland is functioning.

Common Thyroid Disorders: Hypothyroidism and Hyperthyroidism

Hypothyroidism: Causes, Symptoms and Treatment

Hypothyroidism is a condition characterized by reduced production and secretion of thyroid hormones. In primary hypothyroidism, the thyroid gland cannot produce adequate thyroid hormones. The less commonly seen secondary hypothyroidism results from abnormal pituitary gland function. The main indicator of the hypothyroid condition is a low level of thyroid hormones and an elevated TSH level. The most common cause of hypothyroidism is Hashimoto’s thyroiditis. Patients with hypothyroidism require lifelong thyroid hormone replacement treatment with levothyroxine. The primary goal of treatment is to provide the appropriate daily dose of thyroid hormone to restore normal thyroid function for each individual patient.

Hashimoto’s Thyroiditis as a Cause of Hypothyroidism

Hashimoto’s thyroiditis is an autoimmune disease in which a dysfunctional immune system leads to chronic damage of thyroid tissue, followed by replacement of the damaged areas with scar tissue and lymphocytes. This diagnosis is more common in women, and its prevalence increases with age. The disease is characterized by positive antibodies against thyroglobulin and thyroid peroxidase. Metabolism slows down due to a decline in hormone levels. Patients most often complain of fatigue, poor concentration and memory, sensitivity to cold, constipation, weight gain, hair loss, low mood, a sensation of choking or tightness in the neck, and lack of motivation.

Hyperthyroidism: Symptoms, Risks and Management

Hyperthyroidism is a condition of excessive production and secretion of thyroid hormones into the blood. The main indicators are elevated levels of thyroid hormones and a decreased level of TSH. An accelerated metabolism of substances and nutrients in the body may lead to the development of other health problems after a prolonged period of hyperthyroidism. In most cases, the cause of hyperthyroidism is Basedow–Graves disease, but other possible causes include multinodular toxic goiter, toxic adenoma, and thyroid inflammation. Untreated hyperthyroidism can cause cardiac arrhythmias, congestive heart failure, osteoporosis, adverse obstetric outcomes, and metabolic derangements such as increased resting energy expenditure and gluconeogenesis. Treatment should be patient-centered and individualized, taking into account age, comorbidities, severity of hyperthyroidism, likelihood of remission, plans for pregnancy, available surgical expertise, and patient preferences.

Basedow–Graves Disease and Autoimmune Hyperthyroidism

Basedow–Graves disease is an autoimmune disorder that occurs more frequently in younger women. It is manifested by diffuse enlargement of the thyroid gland, which can be accompanied by bulging eyes. A hallmark of the disease is the presence of antibodies against thyroglobulin, thyroid peroxidase, and TSH receptor. Patients most commonly complain of palpitations, accelerated digestion, weight loss despite increased appetite, feeling hot and sweating, insomnia, changes in the menstrual cycle and sexual desire, nervousness, and trembling hands. In patients with Graves’ disease, treatment should focus on controlling hyperthyroidism with the anticipated eventual remission of Graves’ disease with antithyroid drugs. Antithyroid drugs decrease thyroid hormone synthesis and secretion. In patients who do not achieve remission, or in those whose hyperthyroidism remits and then recurs, definitive therapy with radioactive iodine or thyroidectomy should be considered.

Multinodular Toxic Goiter and Toxic Adenoma

Multinodular toxic goiter is a disease characterized by multiple nodules in the thyroid gland that secrete thyroid hormones excessively without regulation by the pituitary gland. A toxic adenoma is a type of benign tumor that similarly excessively secretes thyroid hormones. Toxic nodular goitre and toxic adenoma are mostly treated with radioactive iodine or thyroidectomy.

Thyroid Tumors and Thyroid Cancer

A tumor is an abnormal accumulation of tissue. Tumors can be benign or malignant. The etiology of thyroid carcinoma is not fully understood. However, it is known that a higher risk is present in females, individuals with a family history of cancer, those who have been exposed to radiation, and that it occurs more frequently after the age of thirty.

Benign Thyroid Tumors: Follicular Adenoma

Follicular adenoma is the most common epithelial benign tumor. It occurs in all age groups, but more often in young adults. These are solitary, spherical formations with a diameter of less than 3 cm. Adenomas may be functional (toxic) or non-functional. Functional adenomas produce thyroid hormones uncontrollably, without activation of the pituitary gland.

Malignant Thyroid Tumors: Types and Treatment Options

Malignant tumors of the thyroid gland are very rare and account for about 1% of all malignant tumors. They can be differentiated (papillary and follicular) or undifferentiated (medullary and anaplastic). Differentiated tumor cells resemble normal tissue cells. Tumors made up of such cells respond better to treatment. Undifferentiated tumors are made up of cells that do not resemble normal tissue, so they behave more aggressively and require a more aggressive approach to treatment. Malignant tumors occur somewhat more frequently in women, typically between 30 and 45 years of age. Treatment options are determined by the type and stage of thyroid cancer. Surgically removing the thyroid gland is a common approach. Any remaining cancer cells are then treated with radioactive iodine therapy. After surgical removal of the thyroid gland, patients need to take thyroid hormone replacement therapy for the rest of their lives to maintain normal physiological processes. External beam radiation therapy may be an option for individuals whose aggressive or advanced thyroid tumors do not respond to standard care.

Papillary Thyroid Carcinoma

Papillary carcinoma is the most common thyroid cancer. This type of cancer has the best prognosis. It is more often diagnosed before the age of 40. It can remain localized in the gland for years, but in some patients, metastases to the cervical lymph nodes are already present at the time of diagnosis.

Follicular Thyroid Carcinoma

Follicular carcinoma is the second most common type of thyroid cancer. It occurs mainly in middle-aged and older individuals. This type of cancer is more aggressive than papillary thyroid carcinoma, but likewise has a good prognosis for recovery.

Medullary Thyroid Carcinoma

Medullary carcinoma is a rare type of thyroid cancer. It occurs in middle and late adulthood, usually sporadically as a solitary thyroid nodule. The prognosis of medullary thyroid cancer is usually not as favorable as that of differentiated thyroid cancers.

Anaplastic Thyroid Carcinoma

Anaplastic carcinoma is the rarest histological type of thyroid cancer, but it is extremely aggressive. Compared with other types of thyroid carcinoma, it unfortunately has the worst prognosis.

Current Research and Advances in Thyroid Disease Treatment

Since thyroid disorders are largely a consequence of a hectic lifestyle, it is important to introduce some changes in an individual’s daily life. In the treatment of thyroid disease, in addition to medication, the importance of a proper diet is also emphasized. The diet should be varied and balanced, with frequent fluid intake. There is no specific diet therapy for thyroid problems, but the Mediterranean diet is recommended, as it has proven to be extremely beneficial for a wide range of health problems. It is important to emphasize the importance of consuming minimally processed foods or avoiding industrially prepared foods that are abundant in stores. Selenium and selenoproteins are important in the creation and metabolism of thyroid hormones. They have an anti-inflammatory effect, can reduce the titer of thyroid peroxidase antibodies, have a positive effect on hypothyroidism, and result in a reduction in the required dose of levothyroxine.

In addition to existing diagnostic methods, additional research is being conducted for the purpose of earlier detection of diseases, personalized medicine, and better treatment methods. The association of various inflammatory parameters with thyroid disorders is being investigated to serve as additional diagnostic tools. The involvement of various genes in thyroid diseases is also being examined, which could be useful in a personalized approach to patients in the future. Several international ultrasound-based thyroid nodule risk stratification systems have been developed with the goal of reducing unnecessary biopsies. Less invasive alternatives to surgery for low-risk thyroid cancer, such as active surveillance and minimally invasive interventions, are being explored. New systemic therapies are now available for patients with advanced thyroid cancer. The progress of targeted therapy and biomarker research in thyroid cancer represents a possible change in therapeutic methods aimed at precision and individualization.

Author: Marija Banović (Polyclinic Leptir)

References

1. Patil N, Rehman A, Anastasopoulou C, et al. Hypothyroidism. [Updated 2024 Feb 18]. In: StatPearls [Internet]. Treasure Island (FL): StatPearls Publishing; 2025 Jan-. Available from: https://www.ncbi.nlm.nih.gov/books/NBK519536/
2. Mathew P, Kaur J, Rawla P. Hyperthyroidism. [Updated 2023 Mar 19]. In: StatPearls [Internet]. Treasure Island (FL): StatPearls Publishing; 2025 Jan-. Available from: https://www.ncbi.nlm.nih.gov/books/NBK537053/
3. Guo M, Sun Y, Wei Y, Xu J and Zhang C. (2024) Advances in targeted therapy and biomarker research in thyroid cancer. Front. Endocrinol. 15:1372553. doi: 10.3389/fendo.2024.1372553
4. Brčić L, Barić A, Gračan S, Brdar D, Torlak Lovrić V, Vidan N, Zemunik T, Polašek O, Barbalić M, Punda A, Boraska Perica V. (2016): Association of established thyroid peroxidase autoantibody (TPOAb) genetic variants with Hashimoto’s thyroiditis, Autoimmunity, DOI: 10.1080/08916934.2016.1191475

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Based on all this information, the doctor will then follow medical guidelines to decide which tests you may need, what should be examined, and what the possible diagnosis could be. Each of these steps is based on previous knowledge gained by studying diseases and how patients respond to different treatments.

Why standard medical approaches are not enough for every patient

However, every patient experiences a disease in their own unique way. A symptom that is typical for a certain condition may not appear in every person. Some patients might have an unusual course of the disease. A treatment that works well for most people may be ineffective for someone else or even cause side effects that make the condition worse.

These differences cannot be fully explained only by what we know about the disease itself; the individual patient needs to be understood and taken into account as well.

Evidence-based medicine and its limitations

Currently, the standard medical approach relies on knowledge gathered from many previous cases, and this approach is known as evidence-based medicine. It assumes that when diseases are studied in large groups of patients, they behave in a similar way: most people respond to the same procedures, and treatments usually lead to predictable results. In other words, disease courses are expected to follow the same general patterns across large patient populations.

What is personalised medicine?

Personalised medicine is an approach that focuses on the individual patient and their unique characteristics in the context of a disease and its treatment. Hippocrates, the father of medicine, famously said that “it is more important to know what sort of person has a disease than to know what sort of disease a person has.”

This idea lies at the heart of personalised medicine: to understand the patient as a whole. This includes factors such as age, sex, weight, ethnicity, psychological and social characteristics, physiological and biochemical traits, environment and, most importantly, the patient’s genome.

Why personalised medicine is especially important for thyroid disorders

Consequently, personalised medicine is especially valuable in treating thyroid dysfunction, a group of conditions that can be complex and highly variable. By taking individual differences into account, personalised medicine can help improve the understanding of risk factors, enhance diagnostic accuracy and support more effective treatment. This approach is important for all patients with thyroid dysfunction, including pregnant women who require additional care and attention.There are two main factors that interact in the development of any disorder: a person’s genes and their environment. Around 70% of the risk for developing autoimmune thyroid disease is linked to genetics. This is why autoimmune diseases often appear repeatedly within the same families.

However, the exact genetic factors that increase the risk of autoimmune disease are still largely unknown. This means that both the external environment (such as exposure to viruses or harmful substances) and the internal environment (such as pregnancy or specific gene changes) also play an important role in triggering the disease in individuals who are genetically predisposed.

Personalised diagnosis in thyroid disorders

Diagnosis cannot rely solely on standard reference ranges. Thyroid hormone levels naturally vary from person to person depending on age, sex, weight, ethnicity, and even the time of day or season of the year.

In addition, it is important to understand each patient’s genetic background to determine whether their predisposition comes from the immune system, the thyroid gland itself, or inherited molecular mechanisms.

Patients also respond differently to environmental influences, so information about lifestyle, psychosocial factors and exposure to toxins can provide valuable insight. Finally, personalised diagnostic approaches can even help predict whether a patient is likely to experience side effects from thyroid medications.

Personalised treatment approaches

Personalised medicine relies on tailoring treatment to each individual patient. For example, in cases of Hashimoto’s thyroiditis or after thyroidectomy, researchers are exploring whether new thyroid tissue could one day be created from stem cells. Small-molecule therapies are being developed to specifically target thyroid autoantibodies.

Thyroid hormone replacement can be adjusted based on a patient’s gastrointestinal absorption, deiodinase activity, hormone metabolism and the way their tissues respond. Some patients may require calcitonin replacement after thyroidectomy, while others – especially those with thyrotoxic heart disease — may need real-time monitoring of blood pressure and heart rhythm.

Personalised medicine during pregnancy

Pregnancy is a sensitive period in a woman’s life, and the body goes through many changes. Because of this, personalised medicine would adjust how often thyroid function is checked depending on the trimester. This is important because problems with the mother’s thyroid function or autoimmunity can influence how the baby’s brain develops. Another aim of personalised medicine is to create new treatments that are safe for pregnant women with thyroid problems and do not harm the developing baby.

Benefits of personalised medicine compared to traditional approaches

Compared with traditional evidence-based medicine, personalised medicine looks at the whole person, not just the disease. It tries to understand all factors that may influence a patient’s health, so that treatment can be tailored as precisely as possible. The goal is to prevent disease whenever possible, choose the best therapy from the start instead of trying different options randomly, reduce unwanted side effects and improve the patient’s overall quality of life. In the end, this approach is more focused on the individual and supports better long-term wellbeing.

Author: Vladimir Gašić

References:

1. Sackett D L, Rosenberg W M C, Gray J A M, Haynes R B, Richardson W S. Evidence based medicine: what it is and what it isn’t BMJ 1996; 312 :71 doi:10.1136/bmj.312.7023.71
2. Ferrari SM, Ragusa F, Elia G, Paparo SR, Mazzi V, Baldini E, Benvenga S, Antonelli A, Fallahi P. Precision Medicine in Autoimmune Thyroiditis and Hypothyroidism. Front Pharmacol. 2021 Nov 17;12:750380. doi: 10.3389/fphar.2021.750380. PMID: 34867359; PMCID: PMC8635786.
3. Galofré JC, Díez JJ, Cooper DS. Thyroid dysfunction in the era of precision medicine. Endocrinol Nutr. 2016 Aug-Sep;63(7):354-63. English, Spanish. doi: 10.1016/j.endonu.2016.04.009. Epub 2016 Jun 3. PMID: 27267315.
4. Abrahams E, Silver M. The case for personalized medicine. J Diabetes Sci Technol. 2009 Jul 1;3(4):680-4. doi: 10.1177/193229680900300411. PMID: 20144313; PMCID: PMC2769975.

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