Background: Sleep duration is an identified risk factor for adverse health outcomes. As the endocrine system is closely intertwined with sleep duration and quality, the association between endocrine dysfunction and sleep has been evaluated. Thyroid function, particularly that related to thyrotropin (TSH), is also known to be influenced by the sleep/awake status and circadian rhythm. Additionally, a link between sleep duration and autoimmunity, which is a common cause of thyroid dysfunction, has been suggested; however, depending on the sleep deprivation method used in studies, the effects of sleep on thyroid function vary. The relationship between subclinical thyroid dysfunction and sleep duration is poorly documented. Thus, to elucidate the impact of sleep on thyroid function, we investigated the association of subclinical thyroid dysfunction with sleep duration using representative data from the sixth Korea National Health and Nutrition Examination Survey, conducted from 2013 to 2015. Methods: In all, 4945 participants (2543 male and 2402 female) were included after excluding subjects using the following criteria: <19 years of age, free T4 level outside the normal range, history of thyroid disease, or incomplete data. The population was classified into three groups: short sleeper (8 h/day). The odds ratio (OR) for subclinical hypothyroidism or hyperthyroidism according to sleep duration was evaluated. Results: The short, normal, and long sleeper groups consisted of 2097, 2514, and 334 subjects, respectively. On multiple logistic regression analysis, compared to normal sleepers, short sleepers showed a significantly increased risk of subclinical hyperthyroidism (OR 1.37, 95% confidential interval (CI) 1.02–1.84, p = 0.036), while the risk of subclinical hypothyroidism in short sleepers was not elevated. Comparing long sleepers to normal sleepers, the OR for subclinical hyperthyroidism and hypothyroidism was 1.79 (95% CI 1.12–2.86, p = 0.015) and 1.91 (95% CI 1.03–3.53, p = 0.039), respectively. Conclusions: Both shorter and longer sleep durations were associated with an increase in the risk of subclinical thyroid dysfunction compared to the optimal sleep duration. This analysis of representative population data shows that sleep duration could intertwine with thyroid function resulting in increased risk of subclinical thyroid dysfunction.

Recent studies suggested that a lower serum thyroid hormone level is associated with more vascular calcification. However, it has been rarely evaluated whether lower thyroid hormone levels affect the calcification of thyroid cancer and there is a relationship between calcification patterns of papillary thyroid carcinoma (PTC) and coronary artery calcification (CAC). The study was divided into two groups: First, we retrospectively reviewed 182 PTC patients and examined the correlation between PTC calcification patterns and CAC by coronary computed tomography (CT). Second, the correlation between the calcification pattern of PTC and thyroid hormone concentration was investigated (n = 354). The calcification pattern of PTC was evaluated by thyroid ultrasonography and classified into four groups: no-calcification, microcalcification, macrocalcification, and mixed-calcification. In PTC patients with microcalcification and mixed calcification, more CAC was observed and coronary calcium score (CCS) was higher. Lower free T4 and higher thyroid-stimulating hormone (TSH) levels were associated with microcalcification and mixed calcification, not with macrocalcification and no calcification. PTC with microcalcification and mixed calcification showed more aggressive phenotypes like lymph node metastasis and more advanced TNM (tumor, node, and metastasis) stage than those with no calcification and macrocalcification. Calcification patterns of PTC showed close association with thyroid hormone levels and CAC. Further research is needed to determine how these findings are related to cardiovascular risk and disease-specific mortality.

Young Joo Park, Eun Kyung Lee, Young Shin Song, Bon Seok Koo, Hyungju Kwon, Keunyoung Kim, Mijin Kim, Bo Hyun Kim, Won Gu Kim, Won Bae Kim, Won Woong Kim, Jung-Han Kim, Hee Kyung Kim, Hee Young Na, Shin Je Moon, Jung-Eun Moon, Sohyun Park, Jun-Ook Park, Ji-In Bang, Kyorim Back, Youngduk Seo, Dong Yeob Shin, Su-Jin Shin, Hwa Young Ahn, So Won Oh, Seung Hoon Woo, Ho-Ryun Won, Chang Hwan Ryu, Jee Hee Yoon, Ka Hee Yi, Min Kyoung Lee, Sang-Woo Lee, Seung Eun Lee, Sihoon Lee, Young Ah Lee, Joon-Hyop Lee, Ji Ye Lee, Jieun Lee, Cho Rok Lee, Dong-Jun Lim, Jae-Yol Lim, Yun Kyung Jeon, Kyong Yeun Jung, Ari Chong, Yun Jae Chung, Chan Kwon Jung, Kwanhoon Jo, Yoon Young Cho, A Ram Hong, Chae Moon Hong, Ho-Cheol Kang, Sun Wook Kim, Woong Youn Chung, Do Joon Park, Dong Gyu Na, Korean Thyroid Association Guideline Committee on the Managements of Thyroid Nodule and Cancer. Int J Thyroidol 2024;17:1-20. https://doi.org/10.11106/ijt.2024.17.1.1

Objective Thyroid-stimulating immunoglobulin (TSI) bioassay has a better ability to predict the relapse rate of Graves’ disease (GD) than the thyroid-stimulating hormone (TSH)-binding inhibitory immunoglobulin method in terms of measuring the TSH receptor antibody. However, the optimal TSI bioassay cutoff for predicting relapse after antithyroid drug (ATD) withdrawal is not well evaluated. Methods This retrospective study enrolled GD patients who had been treated with ATD and obtained their TSI bioassay <140% from January 2010 to December 2019 in a referral hospital. Results Among 219 study subjects, 86 patients (39.3%) experienced relapse. The TSI bioassay value of 66.5% significantly predicted the relapse of GD (P = 0.049). The group with a TSI bioassay value > 66.5% were expected to show a 23.8% relapse rate at 2 from ATD withdrawal, and the group with a TSI < 66.5% had a 12.7% relapse rate based on Kaplan-Meier curves analysis. The TSI bioassay showed a good ability to predict relapse GD in the female group (P = 0.041) but did not in the male group (P = 0.573). The risk scoring based on the nomogram with risk factors for GD relapse, which was constructed to overcome the limitation, increased the predictive ability of GD relapse by 11.5% compared to the use of the TSI bioassay alone. Conclusions The cutoff value of the TSI bioassay to predict GD relapse should be lower than that for diagnosing GD. However, as the single use of the TSI bioassay has limitations, a nomogram with multiple risk factors including TSI bioassay could be helpful to predict GD relapse.