High glycosylated serum protein to high density lipoprotein cholesterol ratios are predictive of worse acute on chronic liver failure prognoses

Abstract

Hepatitis B-related acute-on-chronic liver failure (HBV-ACLF) could result in disrupted glucose and lipid homeostasis, but its associations with ACLF is not fully defined. Here, we incorporated biomarkers associated with HBV-ACLF prognoses into a predictive nomogram, and examined its short- and long-term predictive capabilities. Eight hundred sixty-one HBV-ACLF, 20 healthy, and 54 chronic hepatitis B (CH) patients were recruited; the 4 characteristics most strongly associated with HBV-ACLF prognoses (age, glycosylated serum protein [GSP], high-density lipoprotein cholesterol [HDL-c], international normalized ratio), identified by logistic regression (uni-, multivariate) and machine-learning based analyses, were incorporated into the predictive nomogram. The nomogram was, under receiver operating characteristic and calibration curve analyses, highly accurate in identifying ACLF patients with worse prognoses after 28- and 90-days; it also demonstrated good clinical utility under decision curve analysis. Furthermore, higher GSP/HDL-c (GHR) was associated with worse ACLF prognoses, plus higher 28- and 90-day cumulative risk of death under Kaplan-Meier analysis. Therefore, the nomogram was able to accurately identify ACLF patients, who also had high GHR, at high risk for adverse prognosis; consequently, both glucose and lipid metabolism indicators are equally important for determining ACLF prognoses, and could serve as valuable early diagnostic tools for tailored ACLF interventions.

Keywords: Acute-on-chronic liver failure; Glycosylated serum protein; High-density lipoprotein cholesterol; Prognosis.

Fig. 1

Applying the 3 machine-learning analyses to identify the predictive metabolic indicators associated with Hepatitis B-related acute-on-chronic liver failure (ACLF) prognoses. (A) Least absolute shrinkage and selection operator (LASSO) regression with 13-fold cross-validation, corresponding to the 13 statistically significant ACLF patient indicators found from univariate logistic regression analysis, was used to reduce the dimension of the grouping characteristics. (B) 6 indicators corresponded to the minimum error: age, glycosylated serum protein (GSP), high-density lipoprotein cholesterol (HDL-c), Na⁺, prothrombin time (PT), and international normalized ratio (INR). (C) 13-fold cross-validation error plot from random forest (RF) analysis. (D) 9 indicators were identified from RF, based on a cut-off threshold of 20: prothrombin activity (PTA), PT, INR, Na⁺, Model for End-Stage Liver Disease (MELD), HDL-c, age, white blood cell (WBC), and GSP. (E) Support vector machine (SVM) analysis identified 12 indicators: age, creatinine, GSP, HDL-c, Na⁺, PT, PTA, INR, WBC, MELD, infection, and artificial liver treatment. (F) Venn diagram showing the number of shared indicators between multivariate logistic regression, LASSO, RF, and SVM.

Fig. 2

Predictive nomogram for 28- and 90-day HBV-ACLF prognoses.

Fig. 3

Validation of the predictive nomogram for 28- and 90-day HBV-ACLF prognoses. (A) Receiver operating characteristic (ROC), (B) Decision (DCA), and (C) Calibration curve analyses of the predictive nomogram for 28-day HBV-ACLF prognoses, based on the training set. (D) ROC, (E) DCA, and (F) Calibration curve analyses of the nomogram for 90-day ACLF prognoses, based on the training set. (G) ROC, (H) DCA, and (I) Calibration curve analyses of the nomogram for the validation set.

Fig. 4

Kaplan-Meier analyses for (A) 28- and (B) 90-day examination periods.