Iron related biomarkers predict disease severity in a cohort of Portuguese adult patients during COVID-19 acute infection
Bibliographic record
Abstract
ABSTRACT BACKGROUND Growing evidence indicates a link between iron metabolism and COVID-19 clinical progression, supporting the use of iron and inflammatory parameters as relevant biomarkers to predict patients’ outcomes. METHODS We evaluated iron metabolism and immune response in 303 patients admitted to the main hospital of the northern region of Portugal with variable clinical pictures, from September to November 2020. Of these, 127 tested positive for SARS-CoV-2 and 176 tested negative. Iron-related laboratory parameters and cytokines were determined in blood samples collected soon after admission and, in a subgroup of patients, throughout hospitalization. Demographic data, comorbidities and clinical outcomes were recorded. Patients were assigned into 5 groups according to disease severity. RESULTS Serum iron and transferrin levels at admission were lower in COVID-19-positive than in COVID-19-negative patients. Conversely, the levels of interleukin(IL)-6 and monocyte chemoattractant protein 1 (MCP1) were increased in COVID-19-positive patients. The lowest serum iron and transferrin levels at diagnosis were associated with the worst outcomes. Iron levels negatively correlated with IL-6 and higher levels of this cytokine were associated with a worse prognosis. Serum ferritin levels at diagnosis were higher in COVID-19-positive than in COVID-19-negative patients but did not correlate with disease severity. Longitudinal determinations of iron and ferritin made in a subgroup of patients (n=23) revealed highly variable results. CONCLUSIONS Serum iron is the simplest laboratory test to be implemented as a predictor of disease progression in hospitalized acute COVID-19-positive patients. Variation of ferritin with time should be revisited in larger cohorts. Key points COVID-19-positive patients have lower serum iron and higher ferritin than COVID-19-negative patients in variable clinical contexts. Lowest serum iron and highest IL-6 levels at hospital admission associate with the poorest outcomes. Association of serum ferritin with disease progression is debatable.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".