An integrated proteomics and metabolomics approach to assess graft quality and predict early allograft dysfunction after liver transplantation: a retrospective cohort study
Bibliographic record
Abstract
BACKGROUND: Early allograft dysfunction (EAD) is a common complication after liver transplantation (LT) and is associated with poor prognosis. Graft itself plays a major role in the development of EAD. We aimed to reveal the EAD-specific molecular profiles to assess graft quality and establish EAD predictive models. METHODS: A total of 223 patients who underwent LT were enrolled and divided into training ( n =73) and validation ( n =150) sets. In the training set, proteomics was performed on graft biopsies, together with metabolomics on paired perfusates. Differential expression, enrichment analysis, and protein-protein interaction network were used to identify the key molecules and pathways involved. EAD predictive models were constructed using machine learning and verified in the validation set. RESULTS: A total of 335 proteins were differentially expressed between the EAD and non-EAD groups. These proteins were significantly enriched in triglyceride and glycerophospholipid metabolism, neutrophil degranulation, and the MET-related signaling pathway. The top 12 graft proteins involved in the aforementioned processes were identified, including GPAT1, LPIN3, TGFB1, CD59, and SOS1. Moreover, downstream metabolic products, such as lactate dehydrogenase, interleukin-8, triglycerides, and the phosphatidylcholine/phosphorylethanolamine ratio in the paired perfusate displayed a close relationship with the graft proteins. To predict the occurrence of EAD, an integrated model using perfusate metabolic products and clinical parameters showed areas under the curve of 0.915 and 0.833 for the training and validation sets, respectively. It displayed superior predictive efficacy than that of currently existing models, including donor risk index and D-MELD scores. CONCLUSIONS: We identified novel biomarkers in both grafts and perfusates that could be used to assess graft quality and provide new insights into the etiology of EAD. Herein, we also offer a valid tool for the early prediction of EAD.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.000 | 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".