The utility of syndecan-1 circulating levels as a biomarker in patients with previous or active COVID-19: a systematic review and meta-analysis
Bibliographic record
Abstract
BACKGROUND: With the emergence of coronavirus disease of 2019 (COVID-19), several blood biomarkers have been identified, including the endothelial biomarker syndecan-1, a surface proteoglycan. In the current systematic review and meta-analysis, we aimed to assess the diagnostic and prognostic role of syndecan-1 in COVID-19. METHODS: PubMed, Embase, Scopus, and Web of Science, as international databases, were searched for relevant studies measuring blood syndecan-1 levels in COVID-19 patients, COVID-19 convalescents, and healthy control subjects, in patients with different COVID-19 severities and/or in COVID-19 patients with poor outcomes. Random-effect meta-analysis was performed using STATA to calculate the standardized mean difference (SMD) and 95% confidence interval (CI) for the comparison between COVID-19 patients and healthy control subjects or COVID-19 convalescents and controls. RESULTS: After screening by title/abstract and full text, 17 studies were included in the final review. Meta-analysis of syndecan-1 levels in COVID-19 compared with healthy control subjects revealed that patients with COVID-19 had significantly higher syndecan-1 levels (SMD 1.53, 95% CI 0.66 to 2.41, P < 0.01). In contrast, COVID-19 convalescent patients did not show significant difference with non-convalescents (SMD 0.08, 95% CI -0.63 to 0.78, P = 0.83). Regarding disease severity, two studies reported that more severe forms of the disease were associated with increased syndecan-1 levels. Moreover, patients who died from COVID-19 had higher syndecan-1 levels compared with survivors (SMD 1.22, 95% CI 0.10 to 2.33, P = 0.03). CONCLUSION: Circulating syndecan-1 level can be used as a biomarker of endothelial dysfunction in COVID-19, as it was increased in COVID-19 patients and was higher in more severe instances of the disease. Further larger studies are needed to confirm these findings and further enlighten the role of syndecan-1 in clinical settings.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.012 | 0.029 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.019 | 0.037 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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 source (direct Gemma or distilled Codex), 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".