Analysis of non-targeted serum metabolomics in patients with chronic kidney disease and hyperuricemia
Bibliographic record
Abstract
Hyperuricemia (HUA) is a common complication of chronic kidney disease (CKD). Conversely, HUA can promote the disease progression of CKD. However, the molecular mechanism of HUA in CKD development remains unclear. In the present study, we applied ultra-performance liquid chromatography-tandem mass spectrometry (UPLC-MS/MS) to analyze the serum metabolite profiling of 47 HUA patients, 41 non-hyperuricemic CKD (NUA-CKD) patients, and 51 CKD and HUA (HUA-CKD) patients, and then subjected to multivariate statistical analysis, metabolic pathway analysis and diagnostic performance evaluation. Metabolic profiling of serums showed that 40 differential metabolites (fold-change threshold (FC) > 1.5 or<2/3, variable importance in projection (VIP) > 1, and p < 0.05) were screened in HUA-CKD and HUA patients, and 24 differential metabolites (FC > 1.2 or<0.83, VIP>1, and p < 0.05) were screened in HUA-CKD and NUA-CKD patients. According to the analysis of metabolic pathways, significant changes existed in three metabolic pathways (compared with the HUA group) and two metabolic pathways (compared with the HUA-CKD group) in HUA-CKD patients. Glycerophospholipid metabolism was a significant pathway in HUA-CKD. Our findings show that the metabolic disorder in HUA-CKD patients was more serious than that in NUA-CKD or HUA patients. A theoretical basis is provided for HUA to accelerate CKD progress.
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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 | 0.001 | 0.001 |
| 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) | 0.000 | 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 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".