Associations of Thyroid Hormones and Resting Heart Rate in Patients Referred to Coronary Angiography

Impaired sensitivity to thyroid hormones is associated with lower heart rate in the euthyroid population

BACKGROUND

The association of thyroid hormone sensitivity with heart rate remains unclear.

OBJECTIVE

This study aims to elucidate the relationship between impaired thyroid hormone sensitivity and lower heart rate in the euthyroid population.

METHODS

A total of 550 participants were included. Heart rate and serum biochemicals were measured. Thyroid hormone sensitivity indices were calculated using the thyrotropin index (TSHI), thyrotropin thyroxine resistance index (TT4RI), thyroid feedback quantile-based index (TFQI), Chinese-referenced parametric TFQI (PTFQI), and the ratio of free triiodothyronine to free thyroxine (FT3/FT4 ratio). Logistic regression analyses were applied to explore the relationship between thyroid hormone sensitivity indices and heart rate.

RESULTS

TSHI, TT4RI, TFQI, and PTFQI were higher, and FT3/FT4 ratio was lower in participants with heart rate ≤ 60 beats/min (P < .001 for all). Participants with increased TSHI, TT4RI, TFQI, and PTFQI and reduced FT3/FT4 ratio had lower heart rate (≤60 beats/min) (P for trend < .001 for all). Odds ratios (95% confidence intervals) for TSHI, TT4RI, TFQI, PTFQI, and FT3/FT4 ratio in the highest quartile were 2.090 (1.092-4.000), 2.240 (1.151-4.361), 2.014 (1.043-3.887), 2.163 (1.123-4.166), and 0.498, (0.249-0.996), respectively, as compared with the lowest quartile after adjusting for sex, age, body mass index, smoking, drinking, hypertension, diabetes, coronary artery disease, glycated hemoglobin, total cholesterol, low-density lipoprotein cholesterol, and triglycerides.

CONCLUSION

Impaired sensitivity to thyroid hormones was associated with lower heart rate in euthyroid participants. Future large-scale studies are needed to confirm our findings.

A machine learning-assisted system to predict thyrotoxicosis using patients' heart rate monitoring data: a retrospective cohort study

Previous studies have shown a correlation between resting heart rate (HR) measured by wearable devices and serum free thyroxine concentration in patients with thyroid dysfunction. We have developed a machine learning (ML)-assisted system that uses HR data collected from wearable devices to predict the occurrence of thyrotoxicosis in patients. HR monitoring data were collected using a wearable device for a period of 4 months in 175 patients with thyroid dysfunction. During this period, 3 or 4 thyroid function tests (TFTs) were performed on each patient at intervals of at least one month. The HR data collected during the 10 days prior to each TFT were paired with the corresponding TFT results, resulting in a total of 662 pairs of data. Our ML-assisted system predicted thyrotoxicosis of a patient at a given time point based on HR data and their HR-TFT data pair at another time point. Our ML-assisted system divided the 662 cases into either thyrotoxicosis and non-thyrotoxicosis and the performance was calculated based on the TFT results. The sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) of our system for predicting thyrotoxicosis were 86.14%, 85.92%, 52.41%, and 97.18%, respectively. When subclinical thyrotoxicosis was excluded from the analysis, the sensitivity, specificity, PPV, and NPV of our system for predicting thyrotoxicosis were 86.14%, 98.28%, 94.57%, and 95.32%, respectively. Our ML-assisted system used the change in mean, relative standard deviation, skewness, and kurtosis of HR while sleeping, and the Jensen-Shannon divergence of sleep HR and TFT distribution as major parameters for predicting thyrotoxicosis. Our ML-assisted system has demonstrated reasonably accurate predictions of thyrotoxicosis in patients with thyroid dysfunction, and the accuracy could be further improved by gathering more data. This predictive system has the potential to monitor the thyroid function status of patients with thyroid dysfunction by collecting heart rate data, and to determine the optimal timing for blood tests and treatment intervention.

The Association of Thyroid Hormones with Coronary Atherosclerotic Severity in Euthyroid Patients

The aim of the work was to explore the correlation between thyroid hormones and coronary atherosclerotic severity. This cross-sectional study included 340 euthyroid patients who underwent diagnostic coronary artery angiography (CAG). Gensini Score (GS) was applied to assess the severity of coronary atherosclerosis. Thyroid hormones and routine biochemical parameters were measured. The associations between thyroid hormones and coronary atherosclerosis severity were analyzed. Thyroid hormones levels or parameters were taken as both continuous variables and tertiles into analysis, and the lowest tertile was usually used as the reference (OR=1) for medium and highest tertiles. Free triiodothyronine (FT3) level was associated with GS≥22 (Median GS) in Model I adjusted for age and sex [Continuous: OR=0.46, 95% CI (0.23, 0.92), p=0.029; Tertile 3: OR=0.54, 95% CI (0.30, 0.97), p=0.038], and Model II adjusted for all known risk factors of coronary artery disease (CAD) [Continuous: OR=0.44, 95% CI (0.20, 0.95), p=0.036; Tertile 3: OR=0.49, 95% CI (0.25, 0.96), p=0.039]. Subjects with highest tertile of FT3 to free thyroxine (FT4) ratio (FT3/FT4 ratio) appeared to have the remarkably decreased risk of CAD in both Non-adjusted Model [OR=0.49, 95% CI (0.24, 0.98), p=0.044] and Model I [OR=0.45, 95% CI (0.22, 0.93), p=0.031]. Higher FT3 level within normal range was independently and negatively associated with severity of coronary atherosclerosis. Besides, FT3/FT4 ratio was remarkably correlated with the prevalence of CAD in euthyroid population.

Mathematical Modeling of Free Thyroxine Concentrations During Methimazole Treatment for Graves' Disease: Development and Validation of a Computer-Aided Thyroid Treatment Method

BACKGROUND

Methimazole (MMI) is the first-line treatment for patients with Graves' disease (GD). While there are empirical recommendations for initial MMI doses, there is no clear guidance for subsequent MMI dose titrations. We aimed to (a) develop a mathematical model capturing the dynamics of free thyroxine (FT4) during MMI treatment (b), validate this model by use of numerical simulation in comparison with real-life patient data (c), develop the software application Digital Thyroid (DigiThy) serving either as a practice tool for treating virtual patients or as a decision support system with dosing recommendations for MMI, and (d) validate this software framework by comparing the efficacy of its MMI dosing recommendations with that from clinical endocrinologists.

METHODS

Based on concepts of automatic control and by use of optimization techniques, we developed two first order ordinary differential equations for modeling FT4 dynamics during MMI treatment. Clinical data from patients with GD derived from the outpatient clinic of Endocrinology at the Medical University of Graz, Austria, were used to develop and validate this model. It was subsequently used to create the web-based software application DigiThy as a simulation environment for treating virtual patients and an autonomous computer-aided thyroid treatment (CATT) method providing MMI dosing recommendations.

RESULTS

Based on MMI doses, concentrations of FT4, thyroid-stimulating hormone (TSH), and TSH-receptor antibodies (TRAb), a mathematical model with 8 patient-specific constants was developed. Predicted FT4 concentrations were not significantly different compared to the available consecutively measured FT4 concentrations in 9 patients with GD (52 data pairs, p=0.607). Treatment success of MMI dosing recommendations in 41 virtually generated patients defined by achieved target FT4 concentrations preferably with low required MMI doses was similar between CATT and usual care. Statistically, CATT was significantly superior (p<0.001).

CONCLUSIONS

Our mathematical model produced valid FT4 predictions during MMI treatment in GD and provided the basis for the DigiThy application already serving as a training tool for treating virtual patients. Clinical trial data are required to evaluate whether DigiThy can be approved as a decision support system with automatically generated MMI dosing recommendations.