SAT-391 Thyroid Adaptive Response to COVID-19 Disease Severity as Evidenced Through Routine Biomarkers
Bibliographic record
Abstract
Abstract Disclosure: A. Aimaganova: None. N. Khovanova: None. E. Braybrook: None. D. Grammatopoulos: None. Thyroid dysfunction appears to be a common endocrinopathy in hospitalized COVID-19 patients. The most common pattern of deranged thyroid function tests (TFTs) reported is reminiscent of non-thyroidal illness (NTIS) or sick-euthyroid syndrome. NTIS is often observed in the critical care unit (CCU) setting, with a clear correlation to poor outcomes. The interdependence between the thyroid and immune system although recognized, is not fully understood. Decoding associations between biomarkers of inflammation and thyroid hormones in the context of disease severity may help to elucidate the adaptive mechanisms of the pituitary-thyroid (PT) axis and identify patterns of severity-specific thyroid dysfunction. In this single-center retrospective observational study, routine biomarker data incorporating TFTs were obtained from COVID patients, hospitalized between April 2020 and September 2022. Patients were classified as having severe COVID disease based on admission to CCU or general ward. Statistical tests based on the distribution of biomarkers were used to examine differences, Spearman rank correlations evaluated the strength of the associations, and correlation-based biomarker networks were developed. 237 data sets from 184 patients were extracted. 59% of data were from patients in CCU and 41% from wards. The mean age was 57 and 63 years respectively. Spearman correlations identified a negative relationship between markers of inflammation, red blood cell (RBC) parameters and iron metabolism, and a positive association between thyroid function, RBC parameters and iron metabolism. A deduced network, built around the correlation of TFTs, exhibited severity-specific characteristics. 11 biomarkers exhibited significant group-specific differences, including free triiodothyronine (fT3) and free thyroxine (fT4). An increased number of patients with fT3 below the reference range, in the context of normal TSH and fT4, were observed in those with severe disease. Correlations between components of the PT axis identified a unique interleukin-6 (IL-6) dependent enhancement of correlations between TSH and fT3 or fT3 to fT4 ratio, present mainly in CCU patients. Routine biomarkers have allowed the investigation of thyroid function dynamics and responses to disease signals, which can arise from distinct pathogenic patterns such as hyperinflammation. In place of singular correlation, we explored patterns of biomarker network correlations, which can provide unique information about an organism’s systemic responses. An increased number of correlations in CCU patients suggests activation of coordinated responses associated with enhanced disease severity. Our study has uncovered important differences in biomarker networks and thyroid hormone adaptive responses orchestrated by inflammatory signals such as IL-6, especially in critically ill patients. Presentation: Saturday, July 12, 2025
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".