Hashimoto Thyroiditis as a Cause or Consequence of Obstructive Sleep Apnea

Local symptoms of Hashimoto's thyroiditis: A systematic review

OBJECTIVE

Hashimoto's thyroiditis (HT) is the most common type of thyroid disease and can cause many different manifestations. The local symptoms of HT are an under-studied area of research. Therefore, the purpose of this study was to investigate the local symptoms of HT and their prevalence.

METHODS

A systematic review was performed to find articles in PubMed that discuss the local symptoms of HT. Relevant vocabulary terms and key terms included: autoimmune thyroid disease (AITD), hyperthyroidism, hypothyroidism, neck, throat, pharynx, airway, esophagus, breathe, swallow, globus, sleep apnea, symptoms, and quality of life. Two investigators independently screened the eligible studies.

RESULTS

A total of 54 articles fulfilled the inclusion criteria. Of these, 25 were clinical studies, 24 were case reports, and five were reviews. These clinical studies and case reports included a total of 2660 HT patients. There were eight local symptoms related to HT: neck pain (0.02%~16%), voice changes (7%~30%), throat discomfort (20%~43.7%), shortness of breath (28%~50%), dysphagia (29%), goiter-related symptoms (69.44%), sleep apnea, and generally defined compressive symptoms. Due to the use of different outcome measures among all the studies, a meta-analysis of the data could not be performed.

CONCLUSION

Goiter symptoms, which are an item on the ThyPRO scales, are the most frequent local symptoms in HT patients, and include neck pain, voice changes, throat discomfort, and dysphagia. These local symptoms should be identified in the clinic and included in the early diagnosis and management of HT, as well as evaluated further to understand their relevance in the pathogenesis of HT.

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

BACKGROUND

Hashimoto's thyroiditis (HT) frequently occurs among autoimmune diseases and may simultaneously appear with thyroid cancer. However, it is difficult to diagnose HT at an early stage just by clinical symptoms. Thus, it is urgent to integrate multiple clinical and laboratory factors for the early diagnosis and risk prediction of HT.

METHODS

We recruited 1,303 participants, including 866 non-HT controls and 437 diagnosed HT patients. 44 HT patients also had thyroid cancer. Firstly, we compared the difference in thyroid goiter degrees between controls and patients. Secondly, we collected 15 factors and analyzed their significant differences between controls and HT patients, including age, body mass index, gender, history of diabetes, degrees of thyroid goiter, UIC, 25-(OH)D, FT3, FT4, TSH, TAG, TC, FPG, low-density lipoprotein cholesterol, and high-density lipoprotein cholesterol. Thirdly, logistic regression analysis demonstrated the risk factors for HT. For machine learning modeling of HT and thyroid cancer, we conducted the establishment and evaluation of six models in training and test sets.

RESULTS

The degrees of thyroid goiter were significantly different among controls, HT patients without cancer (HT-C), and HT patients with thyroid cancer (HT+C). Most factors had significant differences between controls and patients. Logistic regression analysis confirmed diabetes, UIC, FT3, and TSH as important risk factors for HT. The AUC scores of XGBoost, LR, SVM, and MLP models indicated appropriate predictive power for HT. The features were arranged by their importance, among which, 25-(OH)D, FT4, and TSH were the top three high-ranking factors.

CONCLUSIONS

We firstly analyzed comprehensive factors of HT patients. The proposed machine learning modeling, combined with multiple factors, are efficient for thyroid diagnosis. These discoveries will extensively promote precise diagnosis, personalized therapies, and reduce unnecessary cost for thyroid diseases.

Potential effects of shift work on skin autoimmune diseases

Shift work is associated with systemic chronic inflammation, impaired host and tumor defense and dysregulated immune responses to harmless antigens such as allergens or auto-antigens. Thus, shift workers are at higher risk to develop a systemic autoimmune disease and circadian disruption with sleep impairment seem to be the key underlying mechanisms. Presumably, disturbances of the sleep-wake cycle also drive skin-specific autoimmune diseases, but epidemiological and experimental evidence so far is scarce. This review summarizes the effects of shift work, circadian misalignment, poor sleep, and the effect of potential hormonal mediators such as stress mediators or melatonin on skin barrier functions and on innate and adaptive skin immunity. Human studies as well as animal models were considered. We will also address advantages and potential pitfalls in animal models of shift work, and possible confounders that could drive skin autoimmune diseases in shift workers such as adverse lifestyle habits and psychosocial influences. Finally, we will outline feasible countermeasures that may reduce the risk of systemic and skin autoimmunity in shift workers, as well as treatment options and highlight outstanding questions that should be addressed in future studies.