Hypercoagulopathy and Adipose Tissue Exacerbated Inflammation May Explain Higher Mortality in COVID-19 Patients With Obesity
Bibliographic record
Abstract
COVID-19, caused by SARS-CoV-2, is characterized by pneumonia, lymphopenia, exhausted lymphocytes and a cytokine storm. Several reports from around the world have identified obesity and severe obesity as one of the strongest risk factors for COVID-19 hospitalization and mechanical ventilation. Moreover, countries with greater obesity prevalence have a higher morbidity and mortality risk of developing serious outcomes from COVID-19. The understanding of how this increased susceptibility of the people with obesity to develop severe forms of the SARS-CoV-2 infection occurs is crucial for implementing appropriate public health and therapeutic strategies to avoid COVID-19 severe symptoms and complications in people living with obesity. We hypothesize here that increased ACE2 expression in adipose tissue displayed by people with obesity may increase SARS-CoV-2 infection and accessibility to this tissue. Individuals with obesity have increased white adipose tissue, which may act as a reservoir for a more extensive viral spread with increased shedding, immune activation and pro-inflammatory cytokine amplification. Here we discuss how obesity is related to a pro-inflammatory and metabolic dysregulation, increased SARS-CoV-2 host cell entry in adipose tissue and induction of hypercoagulopathy, leading people with obesity to develop severe forms of COVID-19 and also death. Taken together, it may be crucial to better explore the role of visceral adipose tissue in the inflammatory response to SARS-CoV-2 infection and investigate the potential therapeutic effect of using specific target anti-inflammatories (canakinumab or anakinra for IL-1β inhibition; anti-IL-6 antibodies for IL-6 inhibition), anticoagulant or anti-diabetic drugs in COVID-19 treatment of people with obesity. Defining the immunopathological changes in COVID-19 patients with obesity can provide prominent targets for drug discovery and clinical management improvement.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".