Angiotensin-Converting Enzyme 2 and Urine Amino Acid Excretion Increase in COVID-19 Patients With AKI
Bibliographic record
Abstract
Background: Angiotensin-converting enzyme 2(ACE2), the receptor for SARS-CoV-2, is highly expressed in the kidneys. ACE2 also possess a unique function to facilitate amino acid absorption. A persistent elevation in plasma ACE2 during COVID-19 is related to increased mortality. The present study sought to explore the relationship between urine ACE2(uACE2) and renal outcomes in COVID-19 patients. Methods: In 104 COVID-19 patients without acute kidney injury(AKI), 43 patients with COVID-19-mediated AKI, and 36 non-COVID-19 controls, uACE2, urine tumor necrosis factor receptors I and II(uTNF-RI and uTNF-RII), neutrophil gelatinaseassociated lipocalin(uNGAL), and urine albumin-creatinine ratio were measured. We also assessed ACE2 staining in autopsy kidney samples and generated a propensityscore matched subgroup to perform a targeted urine metabolomic study to describe the characteristic urine signature of COVID-19. Results: uACE2 was increased in patients with COVID-19, and further increased in those that developed AKI(Figure 1). After adjusting uACE2 levels for age, sex and previous comorbidities, increased uACE2 was independently associated with over 3-fold higher risk(OR 3.05,95%CI:1.23-7.58, p=0.017) of developing AKI. Increased uACE2 corresponded to a tubular loss of ACE2 in kidney sections and strongly correlated with uTNF-RI and uTNF-RII, suggesting that ADAM17 could be responsible for ACE2 shedding. Urine quantitative metabolome analysis revealed an increased excretion of essential amino acids in COVID-19 patients, including leucine, isoleucine, tryptophan and phenylalanine. Additionally, a strong correlation was observed between urine amino acids and uACE2(Figure 1). Conclusions: Elevated uACE2 is related to AKI in patients with COVID-19. The loss of tubular ACE2 during SARS-CoV-2 infection demonstrates a potential link between aminoaciduria and proximal tubular injury. Funding: Government Support - Non-U.S.
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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 | 0.000 |
| 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 | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".