A Glycomarker for Short-term Prediction of Hepatocellular Carcinoma: A Longitudinal Study With Serial Measurements
Yu-Ju Lin1, Chia-Ling Chang2, Liang-Chun Chen3
1Institute of Clinical Medicine, National Yang-Ming University, Taipei, Taiwan.
Objectives:
Wisteria floribunda agglutinin-positive human Mac-2-binding protein (WFA+-M2BP) is a glycomarker. The present community-based long-term follow-up study repeatedly determined the serum WFA+-M2BP level and examined its short- and long-term associations with hepatitis C virus (HCV)-related hepatocellular carcinoma (HCC).
Methods:
A total of 921 participants with antibodies against HCV seropositive, but seronegative for hepatitis B surface antigen were enrolled from seven townships in Taiwan during 1991-1992. The participants were regularly followed and their serum WFA+-M2BP levels were measured at baseline and follow-up. HCC was ascertained through active follow-up and computerized data linkage with the National Cancer Registration System until December 31, 2013. Cox proportional hazards and logistic regression models were applied to estimate the magnitude of associations between serum WFA+-M2BP levels and HCC.
Results:
During a median follow-up of 21.7 years, 122 new-onset HCC cases were identified. Elevated serum WFA+-M2BP levels were associated with an increased risk of HCC (p < 0.001). Patients with increasing changes in serum WFA+-M2BP levels, relative to their baseline levels, had a 4.36-fold risk of HCC. The areas under receiver operating curves (AUROCs) of WFA+-M2BP for predicting HCC showed that the prediction efficacy was significantly higher while closer to HCC diagnosis (p = 0.024). The AUROC was 0.91 for predicting HCC within 1 year by including the predictors of age, sex, alanine aminotransferase, alpha-fetoprotein (AFP) and WFA+-M2BP.
Conclusions:
Serum WFA+-M2BP level may elevate before HCC onset and is a short-term predictor of HCC among patients infected with HCV.
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However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y.


