[HTML][HTML] Predicting dyslipidemia after liver transplantation: a significant role of recipient metabolic inflammation profile

HT Huang, XY Zhang, C Zhang, Q Ling… - World journal of …, 2020 - ncbi.nlm.nih.gov
HT Huang, XY Zhang, C Zhang, Q Ling, SS Zheng
World journal of gastroenterology, 2020ncbi.nlm.nih.gov
BACKGROUND Post-transplant dyslipidemia (PTDL) is a common complication in liver
recipients and can cause morbidity and threaten graft function. The crosstalk between
metabolic inflammation and dyslipidemia has been recently revealed. However, the role of
grafts' and recipients' metabolic status in the development of PTDL has not been evaluated.
AIM To investigate the association of recipients' metabolic inflammation status with PTDL
and construct a predictive model. METHODS A total of 396 adult patients who received …
Abstract
BACKGROUND
Post-transplant dyslipidemia (PTDL) is a common complication in liver recipients and can cause morbidity and threaten graft function. The crosstalk between metabolic inflammation and dyslipidemia has been recently revealed. However, the role of grafts’ and recipients’ metabolic status in the development of PTDL has not been evaluated.
AIM
To investigate the association of recipients’ metabolic inflammation status with PTDL and construct a predictive model.
METHODS
A total of 396 adult patients who received primary liver transplantation between 2015 and 2017 were enrolled. Metabolomics and cytokines were analyzed using recipients’ pre-transplant peripheral blood in a training set (n= 72). An integrated prediction model was established according to the clinical risk factors and metabolic inflammation compounds and further verified in a validation set (n= 144).
RESULTS
The serum lipid profile took 3 mo to reach homeostasis after liver transplantation. A total of 278 (70.2%) liver recipients developed PTDL during a follow-up period of 1.78 (1.00, 2.97) years. The PTDL group showed a significantly lower tumor-free survival and overall survival than the non-PTDL group in patients with hepatocellular carcinoma (n= 169). The metabolomic analysis showed that metabolic features discriminating between the PTDL and non-PTDL groups were associated with lipid and glucose metabolism-associated pathways. Among metabolites and cytokines differentially expressed between the two groups, interleukin-12 (p70) showed the best diagnostic accuracy and significantly increased the predictive value when it was incorporated into the clinical model in both training and validation sets.
CONCLUSION
Recipients’ pre-transplant serum interleukin-12 (p70) level is associated with the risk of PTDL and has potential clinical value for predicting PTDL.
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