We conducted a cross-sectional study among adults who presented for their annual health examinations at the First Affiliated Hospital of Zhejiang University School of Medicine in 2014. The analyses were limited to participants who had full records of anthropometric and biochemical data, as well as results of hepatic ultrasonography examination. Exclusion criteria included: (1) those taking antihypertensive or antidiabetic agents, lipid-lowering agents, or uric-acid-lowing agents; (2) those with alcohol consumption > 140 g/wk for men and 70 g/wk for women; (3) those with a history of other known causes of chronic liver disease such as viral hepatitis or autoimmune hepatitis; and (4) those using hepatotoxic medications (e.g., sulfonamides and azithromycin). A total of 16473 participants (9456 men and 7017 women) were included in the final analysis. All participants were informed verbally about the purpose and design of the study. The personal information of each participant was anonymized both at collection and prior to analysis. The study was approved by the Ethics Committee of the First Affiliated Hospital of Zhejiang University School of Medicine.

Study data included four parts: medical history, questionnaire, and anthropometric and biochemical measurements. All medical histories including previous diseases and drug prescription were assessed by examining physicians. Questions about alcohol intake included the frequency of alcohol consumption per week and the usual amount per day. Persons smoking at that time were considered to be current smokers.

The anthropometric measurements involved height, weight, blood pressure and waist circumference (WC). Height and weight were measured while wearing light clothing without shoes. Body mass index (BMI) was calculated as weight (kg) divided by the square of the height (m). Blood pressure, including systolic blood pressure (SBP) and diastolic blood pressure (DBP), was measured on the right arm with participants in a sitting position after a 5-min rest. WC was measured with the measuring tape positioned midway between the lowest rib and the superior border of the iliac crest as the patient exhaled normally.

Biochemical measurements were performed after participants were instructed to complete an overnight fast. Fasting blood samples were obtained from an antecubital vein, and the samples were used for the analysis of biochemical values. The values included triglyceride (TG), total cholesterol (TC), high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), γ-glutamyltransferase (GGT), AFU, uric acid (UA), and fasting plasma glucose (FPG). All biochemical values were measured using a Hitachi 7600 clinical analyzer (Hitachi, Tokyo, Japan) and Sysmex XE-2100 auto-analyzer (Sysmex, Kobe, Japan) using standard methods.

Abdominal ultrasonographic examinations were carried out by experienced radiologists who were unaware of the aims of the study and were blinded to the laboratory values, using a Toshiba Nemio 20 sonography machine (Toshiba, Tokyo, Japan) with a 3.5-MHz probe. Images were captured in a standard fashion, with the patient in the supine position, with the right arm raised above the head. Fatty liver disease was diagnosed and its degree was assessed according to the criteria described by the Chinese Liver Disease Association[11].

The diagnosis of MetS was based on the definition recommended by the Asia-Pacific Working Party on NAFLD 2006[12]. MetS was diagnosed if any three or more of the following were present: (1) central obesity: WC > 90 cm for men and > 80 cm for women and/or BMI > 25 kg/m² in both genders; (2) hypertriglyceridemia: TGs ≥ 1.7 mmol/L; (3) low HDL-C: HDL-C < 1.03 mmol/L for men and < 1.29 mmol/L for women; (4) elevated blood pressure: blood pressure ≥ 130/85 mmHg; and (5) elevated fasting glucose: FPG ≥ 5.6 mmol/L or previously diagnosed type 2 diabetes.

Statistical analyses were performed using SPSS for Windows version 13.0 (SPSS, Chicago, IL, United States). Continuous variables are presented as the mean ± SD or the median and interquartile range (IQR), as appropriate. The Student’s t test or Mann-Whitney U test was used for comparisons of continuous data, while the χ² test was used for comparisons of categorical variables. Linear regression analysis was used to determine the relationship between AFU level and prevalence of NAFLD and MetS. Stepwise multiple regression analysis (Backward: Wald; Entry: 0.05, Removal: 0.10) was applied to assess the risk factors for NAFLD. P < 0.05 (two-tailed test) was considered statistically significant. The receiver operating characteristic (ROC) curve was used to determine the sensitivity and specificity of AFU in the diagnosis of NAFLD.

Of the 16473 subjects enrolled in this study, 6263 (38.0%) and 4177 (25.4%) fulfilled the diagnostic criteria for NAFLD and MetS, respectively. The prevalence rates of MetS components, including central obesity, hypertriglyceridemia, low HDL-C, elevated blood pressure and elevated FPG, were 46.48%, 29.11%, 36.73%, 33.98% and 12.19%, respectively. Demographic and biochemical characteristics were compared by NAFLD status (Table 1). Patients with NAFLD exhibited higher AFU. Meanwhile, BMI, WC, SBP, DBP, white blood cell count, UA, FBG, TG, TC, LDL, very-low density lipoprotein, alanine aminotransferase (ALT), aspartate aminotransferase (AST), GGT, cholinesterase, alkaline phosphatase, carcinoembryonic antigen and HDL were higher in the NAFLD group.

In order to have a further understanding of the association between AFU and NAFLD, all 16473 subjects were classified into quartiles by their AFU levels (quartile 1 was defined as AFU ≤ 22 U/L, quartile 2 was 22-27 U/L, quartile 3 was UA 27-31 U/L, and quartile 4 was ≥ 31 U/L). As seen in Table 2, the prevalence rate of NAFLD was significantly and positively correlated with AFU levels. The prevalence rate for NAFLD substantially increased with increasing AFU levels. Compared with individuals in the lowest AFU quartile, those in the highest quartile had a prevalence ratio of 1.85.

NAFLD is often considered a hepatic manifestation of MetS. To understand better the role of AFU in increasing incidence of NAFLD, we performed another investigation on the association between AFU and MetS. The results showed a significantly higher prevalence rate of MetS with higher AFU levels. In addition, all the five components (central obesity, hypertriglyceridemia, low HDL-C, elevated blood pressure, and elevated FPG) were also seen to be significantly and positively correlated with AFU (Figure 1). It can be inferred that AFU level may be not only an important factor for NAFLD, but also a significant factor for MetS.

Open in New Tab   Full Size Figure   Download Figure

Figure 1 Prevalence rates of metabolic syndrome and its five components in patients with different quartile levels of α-L-fucosidase.

To explore the independent risk factors associated with the presence of NAFLD, we performed stepwise multiple regression analysis with a logistic regression model. AFU was found to be a significant independent risk factor for NAFLD (OR = 1.009, 95%CI: 1.003-1.014, P < 0.001). The other risk factors are listed in Table 3, including age, gender, height, weight, BMI, WC, DBP, platelet count, white blood cell count, neutrophil, albumin, UA, FBG, TG, HDL, AFU, ALT, AST, cholinesterase, and AFP.

The ROC curve of AFU plotted for the diagnosis of NAFLD is shown in Figure 2. The best cut-off value for AFU was 27.5 U/L, at which the sensitivity was 54.6% and the specificity was 61.8%. The area under the curve (diagnostic efficacy index) was 0.606.

Open in New Tab   Full Size Figure   Download Figure

Figure 2 Receiver operating characteristic curve of α-L-fucosidase plotted for diagnosis of non-alcoholic fatty liver disease. The area under the curve (diagnostic efficacy index) is 0.606.

Non-alcoholic fatty liver disease (NAFLD) has attracted attention in recent years because it may progress to hepatocellular carcinoma (HCC). There is no good biomarker to predict NAFLD and this is the first study to investigate the association between serum α-L-fucosidase (AFU) and NAFLD.

Several biomarkers have been investigated to predict NAFLD, but none seems satisfactory.

This study found that AFU levels were positively associated with NAFLD and may act as an independent risk factor. AFU may be a potential biomarker for the diagnosis of NAFLD.

AFU levels are positively associated with NAFLD, which may provide a new promising biomarker to predict NAFLD. It is necessary to monitor serum AFU levels when people undergo health examination.

Changes in permeability of cell membranes and intracellular organelles such as mitochondria and lysosomes are seen in hepatocytes of patients with NAFLD. AFU comes from lysosomal leakage and may act as a predictor of liver function.

It is a preliminary study, which is the first to show AFU levels are positively associated with NAFLD and may act as an independent risk factor. The design is strict and methods are valid. We are inferred that it is necessary to monitor serum AFU to screen NAFLD and the potential risk of HCC.