ACE2 polymorphisms impact COVID-19 severity in obese patients
Bibliographic record
Abstract
A strong association between obesity and COVID-19 complications and a lack of prognostic factors that explain the unpredictable severity among these patients still exist despite the various vaccination programs. The expression of angiotensin converting enzyme 2 (ACE2), the main receptor for severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2), is enhanced in obese individuals. The occurrence of frequent genetic single nucleotide polymorphisms (SNPs) in ACE2 is suggested to increase COVID-19 severity. Accordingly, we hypothesize that obesity-associated ACE2 polymorphisms increase the severity of COVID-19. In this study, we profiled eight frequently reported ACE2 SNPs in a cohort of lean and obese COVID-19 patients (n = 82). We highlight the significant association of rs2285666, rs2048683, rs879922, and rs4240157 with increased severity in obese COVID-19 patients as compared to lean counterparts. These co-morbid-associated SNPs tend to positively correlate, hence proposing possible functional cooperation to ACE2 regulation. In obese COVID-19 patients, rs2285666, rs879922, and rs4240157 are significantly associated with increased blood nitrogen urea and creatinine levels. In conclusion, we highlight the contribution of ACE2 SNPs in enhancing COVID-19 severity in obese individuals. The results from this study provide a basis for further investigations required to shed light on the underlying mechanisms of COVID-19 associated SNPs in COVID-19 obese patients.
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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) | 0.000 | 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".