Metabolomics in Acute Kidney Injury: The Experimental Perspective
Bibliographic record
Abstract
Acute kidney injury (AKI) affects increasing numbers of in-hospital patients in Central Europe and the USA, the prognosis remains poor. Although substantial progress has been achieved in the identification of molecular/cellular processes that induce and perpetuate AKI, more integrated pathophysiological perspectives are missing. Metabolomics enables the identification of low-molecular-weight (< 1.5 kD) substances from biological specimens such as certain types of fluid or tissue. The aim of the article was to review the literature on metabolic profiling in experimental AKI and to answer the question if metabolomics allows the integration of distinct pathophysiological events such as tubulopathy and microvasculopathy in ischemic and toxic AKI. The following databases were searched for references: PubMed, Web of Science, Cochrane Library, Scopus. The period lasted from 1940 until 2022. The following terms were utilized: "acute kidney injury" OR "acute renal failure" OR "AKI" AND "metabolomics" OR "metabolic profiling" OR "omics" AND "ischemic" OR "toxic" OR "drug-induced" OR "sepsis" OR "LPS" OR "cisplatin" OR "cardiorenal" OR "CRS" AND "mouse" OR "mice" OR "murine" OR "rats" OR "rat". Additional search terms were "cardiac surgery", "cardiopulmonary bypass", "pig", "dog", and "swine". In total, 13 studies were identified. Five studies were related to ischemic, seven studies to toxic (lipopolysaccharide (LPS), cisplatin), and one study to heat shock-associated AKI. Only one study, related to cisplatin-induced AKI, was performed as a targeted analysis. The majority of the studies identified multiple metabolic deteriorations upon ischemia/the administration of LPS or cisplatin (e.g., amino acid, glucose, lipid metabolism). Particularly, abnormalities in the lipid homeostasis were shown under almost all experimental conditions. LPS-induced AKI most likely depends on the alterations in the tryptophan metabolism. Metabolomics studies provide a deeper understanding of pathophysiological links between distinct processes that are responsible for functional impairment/structural damage in ischemic or toxic or other types of AKI.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.009 | 0.013 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".