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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".