Immune determinants of cardiometabolic risk in pre-existing type-2 diabetes (T2D) severe COVID-19 patients
Bibliographic record
Abstract
Abstract COVID-19, the disease caused by SARS-CoV-2, has led to a global public health emergency. Severity of disease course may be related to a dysregulated immune response and pre-existing health conditions. Recent studies have demonstrated that SARS-CoV-2 infection may directly or indirectly lead to an increase in cardiometabolic complications in patients with pre-existing type-2 diabetes mellites (T2DM) when compare to non-DM patients. A lack of mechanistic and systematic studies on how SARS-CoV-2 infection related immune responses may contribute to increase risk of cardiometabolic complications in pre-existing T2DM patients, hinder early risk identification and therapeutic interventions. Thus, in this study we investigate the biomarkers of cardiometabolic risk in non-DM and T2DM, severe COVID-19 patients admitted to the Intensive care unit. Using high-dimensional flowcytometry and immune biomarker assays, we investigated functional and phenotypic changes in immune subsets in whole blood and plasma biomarkers of cardiovascular disease in healthy donors (n=17), T2DM severe-COVID-19 patients (n=10), non-diabetic severe-COVID-19 patients (n=10) admitted to the OSU medical center’s intensive care unit. We found neutrophils and Intermediate monocytes (ITM) were significantly higher in the T2DM group compared to non T2DM patients. However, activated (HLA-DR+) NKT-like cells and NKG2A+ CD56 Dim CD16+ NK cells, were significantly lower in the T2DM-COVID-19+ group. Interestingly, LBP, FABP4, sCD14, IL-1b, RANTES and MIP-1a were significantly higher in the COVID-19 T2DM patients. In this study, we identify core immune signatures that may predict increased cardiovascular disease risk in T2DM patients who had severe COVID-19. Supported by PI’s startup funding
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".