Transient low T3 syndrome in patients with COVID-19: a new window for prediction of disease severity

Front Endocrinol (Lausanne). 2023 Jul 13:14:1154007. doi: 10.3389/fendo.2023.1154007. eCollection 2023.

Abstract

Objective: To investigate the relationship of low T3 syndrome with disease severity in patients with COVID-19.

Methods: The clinical data of 145 patients with COVID-19 were retrospectively collected, and patients were divided into a low T3 group and a normal T3 group. Logistic regression models were used to assess predictive performance of FT3. Receiver operating characteristic (ROC) analysis was used to evaluate the use of low T3 syndrome in predicting critical disease. Kaplan-Meier analysis was used to analyze the impact of low T3 syndrome on mortality.

Results: The prevalence of low T3 level among COVID-19 patients was 34.48%. The low T3 group was older, and had lower levels of hemoglobin, lymphocytes, prealbumin, and albumin, but higher levels of white blood cells, neutrophils, CRP, ESR, and D-dimer (all p<0.05). The low T3 group had greater prevalences of critical disease and mortality (all p <0.05). Multivariate logistic regression analysis showed that the Lymphocytes, free T3 (FT3), and D-dimer were independent risk factors for disease severity in patients with COVID-19. ROC analysis showed that FT3, lymphocyte count, and D-dimer, and all three parameters together provided reliable predictions of critical disease. Kaplan-Meier analysis showed the low T3 group had increased mortality (p<0.001). Six patients in the low T3 group and one patient in the normal T3 group died. All 42 patients whose T3 levels were measured after recovery had normal levels after discharge.

Conclusion: Patients with COVID-19 may have transient low T3 syndrome at admission, and this may be useful for predicting critical illness.

Keywords: COVID-19; critical illness; low T3 syndrome; prediction; thyroid.

Grants and funding

This study was supported by Youth Talents Project of Joint Fund of Hubei Health Commission (WJ2019H170) and Xiaogan Natural Science Project (XGKJ2020010033).