High triiodothyronine levels induce myocardial hypertrophy via <scp>BAFF</scp> overexpression

Li, Guoqing; Liu, Xiaomei; Liu, Bingli; Zhong, Yisheng; Gu, Qing-Wei; Miao, Jing‐Jing; Wang, Jie; Liu, Shu; Mao, Xiao‐Ming

doi:10.1111/jcmm.17470

Cited by 2 publications

(1 citation statement)

References 39 publications

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“…However, our AI-ECG system did not identify approximately 20% of overt HT patients. Given that functional and structural heart changes may be related to the cumulative duration of high circulating thyroid hormone exposure 20 , 21 , these patients may comprise less severity or shorter duration of overt HT. Like most tests, AI-ECG may have less difficulty identifying patients with more severe HT than subclinical HT.…”

Section: Discussionmentioning

confidence: 99%

Artificial intelligence-enabled electrocardiography contributes to hyperthyroidism detection and outcome prediction

Lin,

Kuo,

Chau

et al. 2024

Commun Med

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Background Hyperthyroidism is frequently under-recognized and leads to heart failure and mortality. Timely identification of high-risk patients is a prerequisite to effective antithyroid therapy. Since the heart is very sensitive to hyperthyroidism and its electrical signature can be demonstrated by electrocardiography, we developed an artificial intelligence model to detect hyperthyroidism by electrocardiography and examined its potential for outcome prediction. Methods The deep learning model was trained using a large dataset of 47,245 electrocardiograms from 33,246 patients at an academic medical center. Patients were included if electrocardiograms and measurements of serum thyroid-stimulating hormone were available that had been obtained within a three day period. Serum thyroid-stimulating hormone and free thyroxine were used to define overt and subclinical hyperthyroidism. We tested the model internally using 14,420 patients and externally using two additional test sets comprising 11,498 and 596 patients, respectively. Results The performance of the deep learning model achieves areas under the receiver operating characteristic curves (AUCs) of 0.725–0.761 for hyperthyroidism detection, AUCs of 0.867–0.876 for overt hyperthyroidism, and AUC of 0.631–0.701 for subclinical hyperthyroidism, superior to a traditional features-based machine learning model. Patients identified as hyperthyroidism-positive by the deep learning model have a significantly higher risk (1.97–2.94 fold) of all-cause mortality and new-onset heart failure compared to hyperthyroidism-negative patients. This cardiovascular disease stratification is particularly pronounced in subclinical hyperthyroidism, surpassing that observed in overt hyperthyroidism. Conclusions An innovative algorithm effectively identifies overt and subclinical hyperthyroidism and contributes to cardiovascular risk assessment.

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Section: Discussionmentioning

confidence: 99%

Artificial intelligence-enabled electrocardiography contributes to hyperthyroidism detection and outcome prediction

Lin,

Kuo,

Chau

et al. 2024

Commun Med

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High triiodothyronine levels induce myocardial hypertrophy via BAFF overexpression

Liu

et al. 2022

J Cellular Molecular Medi

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Activated B cells contribute to heart diseases, and inhibition of B‐cell activating factor (BAFF) expression is an effective therapeutic target for heart diseases. Whether activated B cells participate in the development and progression of hyperthyroid heart disease, and what induces B cells activation in hyperthyroidism are unknown. The present study aimed to determine the roles of BAFF overexpression induced by high concentrations of triiodothyronine (T3) in the pathogenesis of hyperthyroid heart disease. Female C57BL/6J mice were subcutaneously injected with T3 for 6 weeks, and BAFF expression was inhibited using shRNA. Protein and mRNA expression of BAFF in mouse heart tissues evaluated via immunohistochemistry, western blotting and polymerase chain reaction (PCR). Proportions of B cells in mouse cardiac tissue lymphocytes were quantified via flow cytometry. Morphology and left ventricle function were assessed using pathological sections and echocardiography, respectively. Here, we demonstrate that compared with the control group, the proportion of myocardial B cells was larger in the T3 group; immunohistochemistry, western blotting and PCR analyses revealed increased protein and mRNA expression levels of TNF‐α and BAFF in heart tissues of the T3 group. Compared with the normal controls group, in the T3 group, the diameter of myocardial cells and some echocardiographic values significantly increased and hypertrophy and structural disorder were noticeable. Our results revealed that elevated levels of circulating T3 can promote the expression of BAFF in myocardial cells and can lead to B‐cell activation, an elevated inflammatory response and ventricular remodelling.

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High triiodothyronine levels induce myocardial hypertrophy via BAFF overexpression

Cited by 2 publications

References 39 publications

Artificial intelligence-enabled electrocardiography contributes to hyperthyroidism detection and outcome prediction

Artificial intelligence-enabled electrocardiography contributes to hyperthyroidism detection and outcome prediction

High triiodothyronine levels induce myocardial hypertrophy via BAFF overexpression

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