Nutritional Strategies to Manage Malnutrition and Sarcopenia following Liver Transplantation: A Narrative Review
Bibliographic record
Abstract
Persisting or newly developed malnutrition and sarcopenia after liver transplant (LT) are correlated with adverse health outcomes. This narrative review aims to examine the literature regarding nutrition strategies to manage malnutrition and sarcopenia after LT. The secondary aims are to provide an overview of the effect of nutrition strategies on the incidence of infections, hospital length of stay (LOS), acute cellular rejection (ACR), and mortality after LT. Four databases were searched. A total of 25 studies, mostly of mid-high quality, were included. Six studies found a beneficial effect on nutritional parameters using branched-chain amino acids (BCAA), immunomodulating diet (IMD), or enteral nutrition (EN) whereas two studies using beta-hydroxy-beta-methylbutyrate (HMB) found a beneficial effect on muscle mass and function. Fourteen studies using pre- or pro-biotics, IMD, and EN were effective in lowering infection and six studies using IMD, BCAA or HMB reported reduced hospital LOS. Finally, four studies using HMB and vitamin D were effective in reducing ACR and one study reported reduced mortality using vitamin D after LT. In conclusion, nutritional intervention after LT has different beneficial effects on malnutrition, sarcopenia, and other advert outcomes. Additional large and well-constructed RCTs using validated tools to assess nutritional status and sarcopenia are warranted to ensure more robust conclusions.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".