<i>In vitro</i> studies of the renin-angiotensin system in human adipose tissue/adipocytes and possible relationship to SARS-CoV-2: a scoping review
Bibliographic record
Abstract
The renin-angiotensin system (RAS) operates within adipose tissue. Obesity-related changes can affect adipose RAS, predisposing to hypertension, type 2 diabetes, and possibly severe COVID-19. We evaluated the in vitro research on human adipose RAS and identified gaps in the literature. Medline (Ovid), Embase (Ovid), Web of Science, Scopus, and 1findr were searched to identify relevant studies. Fifty primary studies met our inclusion criteria for analysis. Expression of RAS components (n = 14), role in differentiation (n = 14), association with inflammation (n = 15) or blood pressure (n = 7) were investigated. We found (1) obesity-related changes in RAS were frequently studied (30%); (2) an upswing of articles investigating adipose ACE-2 expression since the COVID-19 pandemic; (3) a paucity of papers on AT2R and Ang (1–7)/MasR which counterbalance Ang II/ART1; (4) weight loss lowered adipose ACE-2 mRNA expression; and (5) angiotensin receptor blockers (ARBs) reduced deleterious effects of angiotensin II. Overall, these studies link Ang II/ATR1 signalling to impaired adipogenesis and a pro-inflammatory dysfunctional adipose tissue, with ATR1 blockade limiting these responses. ACE-2 may mitigate Ang II effects by converting it to Ang(1–7) which binds MasR. More work is needed to understand adipose RAS in various pathologic states such as obesity and COVID-19 infection.T.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.010 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".