Metabolic Complications in Liver Transplantation Recipients: How We Can Optimize Long‐Term Survival
Bibliographic record
Abstract
Liver transplantation (LT) recipients have experienced a significant improvement in short-term survival during the past 3 decades attributed to advancements in surgical techniques, perioperative management, and effective immunosuppressive regimens. However, long-term survival is affected by a high incidence of metabolic disorders and their consequences, including cardiovascular disease (CVD) and malignancies. Pretransplant metabolic impairments especially in those with nonalcoholic steatohepatitis cirrhosis are aggravated by the addition of posttransplant weight gain, physical inactivity, and reversal from catabolic to anabolic state. Moreover, although immunosuppressants are vital to avoid graft rejection, long-term exposure to these medications is implicated in metabolic impairments after LT. In this review, we summarize the molecular pathogenesis of different metabolic disorders after LT, including diabetes mellitus, dyslipidemia, and nonalcoholic fatty liver disease. Furthermore, CVD, malignancies, and graft rejections were provided as significant complications of post-LT metabolic conditions threatening both the patient and graft survival. Ultimately, emerging preventive and treatment strategies for posttransplant diabetes mellitus are summarized. This review highlights the significant need for more clinical trials of antihyperglycemic agents in LT recipients. Also, translational studies will help us to better understand the molecular and genetic factors underlying these metabolic complications and could lead to more personalized management in this high-risk population.
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 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| 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 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".