Adipokines levels are associated with the severity of liver disease in patients with alcoholic cirrhosis

AIM:To investigate the adipokine levels of leptin,adiponectin,resistin,visfatin,retinol-binding protein 4(RBP4),apelin in alcoholic liver cirrhosis(ALC).METHODS:Forty non-diabetic ALC patients[median age:59 years,males:35(87.5%),Child-Pugh(CP)score:median 7(5-12),CP A/B/C:18/10/12,Model for End-stage Liver Disease(MELD):median 10(6-25),follow-up:median 32.5 mo(10-43)]were prospectively included.The serum adipokine levels were estimated in duplicate by ELISA.Somatometric characteristics were assessed with tetrapolar bioelectrical impedance analysis.Pearson’s rank correlation coefficient was used to assess possible associations with adipokine levels.Univariate and multivariate Cox regression analysis was used to determine independent predictors for overallsurvival.RESULTS:Body mass index:median 25.9(range:20.1-39.3),fat:23.4%(7.6-42.1),fat mass:17.8(5.49-45.4),free fat mass:56.1(39.6-74.4),total body water(TBW):40.6(29.8-58.8).Leptin and visfatin levels were positively associated with fat mass(P<0.001/P=0.027,respectively)and RBP4 with TBW(P=0.025).Median adiponectin levels were significantly higher in CPC compared to CPA(CPA:7.99±14.07,CPB:7.66±3.48,CPC:25.73±26.8,P=0.04),whereas median RBP4 and apelin levels decreased across the spectrum of disease severity(P=0.006/P=0.034,respectively).Following adjustment for fat mass,visfatin and adiponectin levels were significantly increased from CPA to CPC(both P<0.001),whereas an inverse correlation was observed for both RBP4 and apelin(both P<0.001).In the multivariate Cox regression analysis,only MELD had an independent association with overall survival(HR=1.53,95%CI:1.05-2.32;P=0.029).CONCLUSION:Adipokines are associated with deteriorating liver function in a complex manner in patients with alcoholic liver cirrhosis.

Development and validation of an artificial intelligence model for predicting post-transplant hepatocellular cancer recurrence

Dear Editor,In recent years,criteria based on the combinationof morphology and biology have been proposed forimproving the selection of hepatocellular cancer(HCC)patients waiting for liver transplantation(LT)[1,2].Since all the proposed models showed suboptimalresults in predicting the risk of postLT recurrence,aprediction model constructed using artificial intelligence(Al)could be an attractive way to surpass this limit[3,4].Therefore,the Time_Radiological-response_Alpha-fetoproteIN_Artificial-Intelligence(TRAIN-AI)modelwas developed,combining morphology and biology tumorvariables.