The randomized clinical trial evaluated patients seen at the NAFLD outpatient clinic of Shuguang Hospital Affiliated to Shanghai University of Traditional Chinese Medicine from January 2013 to May 2014. Healthy volunteer controls were enrolled from among employees of the medical center. Figure 1 provides an overview of the study. A total of 108 subjects were recruited, 33 in the NAFLD group, which included patients with steatosis and normal liver function; 45 in the NASH group, which included patients with steatohepatitis and abnormal liver function; and 30 healthy controls. All subjects provided written informed consent.

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Figure 1 Flow diagram of the study protocol. NASH: Non-alcoholic steatohepatitis; NAFLD: Non-alcoholic fatty liver disease.

General information recorded at each participant’s first visit to a doctor included age, gender, and medical history. The results of laboratory tests and ultrasound were also recorded. Urine samples for metabolic profiling were collected from participants at their second visit.

The diagnostic criteria for NAFLD included: (1) a history of no or limited daily alcohol intake (< 20 g for women and < 30 g for men); (2) the presence of hepatic steatosis by imaging or histology; and (3) the exclusion of all other liver diseases[11]. The diagnostic criteria for NASH included: (1) a diagnosis of NAFLD, as above; and (2) a significant increase in alanine aminotransferase (ALT) activity or other liver function parameters.

Males and females aged 18-60 years, without medication, were eligible following a screening test to confirm the presence of NAFLD. Based on their symptoms and the results of liver function tests, NAFLD patients were divided into NAFLD and NASH groups, consisting of patients with normal and abnormal liver function, respectively.

Patients were excluded if they: (1) had a history of diabetes mellitus or any metabolic disease; (2) consumed > 20 g alcohol per day; (3) had acute diseases or other untreated illness requiring treatment; (4) had impaired hepatic or renal functions; (5) were female of childbearing age who were pregnant, lactating, or unwilling to use an effective form of birth control; (6) had medication or other treatment before; or (7) had a history or presence of any condition that, in the investigator’s opinion, would endanger the individual’s safety or affect the study results.

Urine samples were collected from each participant during mid-morning and were centrifuged at 4 °C for 15 min at 1509.3 × g. The supernatants were frozen and stored at -80 °C until analysis. If required, urine samples were transported using Drikold.

Pretreatment: 100 μL of urine and 300 μL of acetonitrile were vortexed for 3 min and then centrifuged at 12000 r/min, 4 °C for 10 min. Supernatants were kept as prepared samples for further analysis.

Liquid chromatography (LC) separation was performed on an Agilent 1200 series LC system (Agilent, CA, United States). Aliquots of 2 μL of the prepared samples were injected into a Waters Shield C18 column (3.5 μm, 2.1 mm × 150 mm) maintained at 20 °C, and eluted with a mobile phase of 0.01% formic acid in water-acetonitrile (90:10) at a flow rate of 0.3 mL/min. MS detection was performed on an API 4000 triple quadrupole mass spectrometer (Sciex Applied Biosystems), using positive electronic spray ionization in multiple reaction monitoring mode, and at a source temperature of 700 °C and a voltage of 5500 V. The dwell time for the multiple reaction monitoring mode was 0.08 s. Nitrogen was used as the curtain, nebulizer, and collision gas, at pressures of 50, 60, and 70 psi, respectively. Certain ion transitions for amino acids and their internal standards were monitored, and peak area ratios of amino acids to internal standards were calculated after correcting for transition overlaps of natural leucine and isoleucine[12].

Compounds were identified by comparison with library entries of purified standards and recurrent unknown entities. Known chemical entities were identified based on comparisons with metabolomic library entries of more than 2362 commercially available purified standards and an online database (http://metlin.scripps.edu/). In addition, currently unknown entities were identified by their recurrent nature[13].

Data were analyzed by parametric and nonparametric statistical tests using SPSS (version 16) and Simca-P (version 11.0). Continuous data were compared by one-way ANOVA. Differences in metabolic profiles on LC/MS were determined by principal component analysis (PCA) and partial least squares discriminant analysis (PLS-DA).

To validate the importance of the metabolites, and to further gauge their ability to distinguish among patients with NASH and NAFLD and healthy controls, their potential predictive utility for the process of NAFLD was assessed by receiver operating characteristic (ROC) curve analysis. ROC analysis was performed using MS peak areas corresponding to the metabolite concentrations in each of the three subject groups. Areas under the ROC curve were calculated using the ROCR package (classifier visualization in R).

The measurements from each patient’s laboratory test results were entered into an Excel spreadsheet, followed by re-checking of all data to ensure accuracy.

There were no significant differences among the three groups in terms of patient number, age, and height. Weight and body mass index (BMI) were significantly higher in the NAFLD and NASH groups compared with those in the control group (Table 1).

Compared with the healthy group, patients with NAFLD and NASH had much higher concentrations of glycosylated hemoglobin (HbA1c), low-density lipoprotein-cholesterol (LDL), triglycerides (TG), total cholesterol (TC), ALT, aspartate aminotransferase (AST), gamma-glutamyl transferase (GGT), and total bilirubin (TBiL). Compared with the NAFLD group, patients with NASH had much higher ALT, AST, and GGT concentrations, and significantly different results on ultrasound examinations (Table 2 and Figure 2).

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Figure 2 Characteristics of the study participants. Mean concentrations of (A) glycosylated hemoglobin (HbA1c); (B) fasting blood glucose (FBG), high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), triglycerides (TG), total cholesterol (TC); (C) alanine aminotransferase (ALT), and aspartate aminotransferase (AST), gamma-glutamyl transferase (GGT), and alpha-fetoprotein (AFP); and (D) total bilirubin (TBil) and direct bilirubin (DBil) in the non-alcoholic fatty liver disease (NAFLD), non-alcoholic steatohepatitis (NASH), and healthy control groups. E: Results of ultrasound examination in the three groups. Significant differences among the three groups were assessed by one-way ANOVA in A-D and by t-tests in E. ^aP < 0.05, ^bP < 0.01 vs the control group; ^cP < 0.05, ^dP < 0.01 vs the NAFLD group.

PCA was performed using samples from the three groups. S-plots showed obvious metabolic differences among these three groups (Figure 3). This was followed by pair-wise comparisons.

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Figure 3 S-plots of PCA analysis (A) with electrospray ionization (ESI+) and (B) without electrospray ionization (ESI-) in the non-alcoholic steatohepatitis, non-alcoholic fatty liver disease, no comma symbol and control groups. PCA: Principal component analysis; NASH: Non-alcoholic steatohepatitis; NAFLD: Non-alcoholic fatty liver disease.

NAFLD group vs control group: Urinary metabonomics were used to assess differences between the NAFLD and control groups. PCA showed a spectral separation between these two groups, indicating significant metabolic differences. This was further supported by PLS-DA and orthogonal projections to latent structures-discriminant analysis (OPLS-DA) (Figure 4). After filtering out interference signals, 53 different metabolites were detected; mainly nucleic acids and amino acids (Table 3). The concentrations of the nucleic acid metabolites hypoxanthine, xanthine, and carnitine were lower in the urine of patients with NAFLD than in the control subjects. In addition, the concentrations of the amino acid metabolites, citrulline, arginine, valine, and indole acetic acid, as well as glucose and gluconic acid, were higher in patients with NAFLD than in the controls. ROC analysis, performed to identify the key metabolites that could distinguish NAFLD patients from healthy individuals, found that 7-methylxanthine, 2-methylguanosine, gluconic acid, and indoxylsulfuric acid were markers for NAFLD (Figure 5).

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Figure 4 S-plots following (A) PCA, (B) PLS, and (C) OPLS analyses with (A1, B1 and C1) electrospray ionization (ESI+) and without (A2, B2 and C2) electrospray ionization (ESI-) in the non-alcoholic fatty liver disease and control groups. PCA: Principal component analysis; NASH: Non-alcoholic steatohepatitis; NAFLD: Non-alcoholic fatty liver disease.

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Figure 5 Receiver operating characteristic curves for 2-methylguanosine, 7-methylxanthine, gluconic acid, and indoxylsulfuric acid in the non-alcoholic fatty liver disease and control groups. NAFLD: Non-alcoholic fatty liver disease.

NASH group vs control group: Urinary metabonomics were also used to assess differences between the NASH and control groups. PCA analysis showed obvious spectral separation between the two groups, indicative of significant metabolic differences between NASH patients and healthy controls. This was further supported by PLS-DA and OPLS-DA (Figure 6). After filtering out the interference signals, 88 different metabolites (Table 4) were detected, consisting mainly of amino acids and their metabolic intermediates. Compared with the healthy controls, patients with NASH had much higher urinary levels of lysine, valine, citrulline, arginine, threonine, tyrosine, leucine, hippuric acid, and 3-indoleacetic acid, and lower levels of derivatives of indole acetic acid, such as 5-hydroxy indole acetic acid and indole-3-formic acid. In addition, cortisol levels decreased significantly. ROC analysis showed that 2-methylguanosine, gluconic acid, indoxylsulfuric acid, cAMP, indolelactic acid, and acetyl-DL-leucine could distinguish patients with NASH from healthy individuals (Figure 7).

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Figure 6 S-plots following (A) PCA, (B) PLS, and (C) OPLS analyses with (A1, B1, C1) electrospray ionization (ESI+) and without (A2, B2, C2) electrospray ionization (ESI-) in the non-alcoholic steatohepatitis and control groups. PCA: Principal component analysis; NASH: Non-alcoholic steatohepatitis.

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Figure 7 Receiver operating characteristic curves for indoleacetic acid, gluconic acid, 2-methylguanosine, cAMP, indoxylsulfuric acid, and acetyl-DL-leucine in the non-alcoholic steatohepatitis and control groups. NASH: Non-alcoholic steatohepatitis.

NAFLD group vs NASH group: Metabolic PCA analysis of urine samples from patients with NAFLD and NASH showed spectral separation between the two groups of samples, indicating significant metabolic differences. OPLS-DA was performed to better assess these differences (Figure 8). After filtering out interference signals, 31 different metabolites (Table 5) were detected, mainly nucleic acids and amino acids. Compared with the NAFLD group, patients with NASH had much higher concentrations of methyl xanthine, tryptophan, 3-indole acetic acid, and gluconic acid, and a lower level of proline. ROC analysis showed that 3-indoleacetic acid, L-carnitine, pyroglutamic acid, and indolelactic acid could distinguish NASH from NAFLD samples (Figure 9).

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Figure 8 S-plots following (A) PCA and (B) PLS analyses with (A1, B1) electrospray ionization (ESI+) and without (A2, B2) electrospray ionization (ESI-) in the non-alcoholic steatohepatitis and non-alcoholic fatty liver disease groups. NAFLD: Non-alcoholic fatty liver disease; NASH: Non-alcoholic steatohepatitis.

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Figure 9 Receiver operating characteristic curves for 3-indoleacetic acid, indoleacetic acid, L-carnitine, and pyroglutamic acid in the non-alcoholic steatohepatitis and non-alcoholic fatty liver disease groups. NAFLD: Non-alcoholic fatty liver disease; NASH: Non-alcoholic steatohepatitis.

Key differential metabolites among the NAFLD, NASH, and control groups: The differentially expressed metabolites in the three pairwise comparisons were combined to determine the metabolites that overlapped in the three groups. Seven metabolites were screened through Venn analysis: L-carnitine, acetyl carnitine, gluconic acid, deoxycorticosterone, 2-keto-D-gluconic acid, pyroglutamic acid, and indolelactic acid (Figure 10). Kyoto Encyclopedia of Genes and genomes pathway analysis showed that these seven metabolites were enriched in seven pathways: metabolic pathways, the pentose phosphate pathway, antibiotic biosynthesis pathways antibiotics, steroid hormone biosynthesis, bile secretion, carbon metabolism, and glutathione metabolism. Three of these pathways, the pentose phosphate, carbon metabolism, and glutathione metabolism pathways, might be associated closely with the pathological processes of NAFLD and NASH.

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Figure 10 Venn diagram of metabolites differentially expressed in urinary samples of the non-alcoholic fatty liver disease vs control, non-alcoholic steatohepatitis vs control, and non-alcoholic fatty liver disease vs non-alcoholic steatohepatitis groups. NAFLD: Non-alcoholic fatty liver disease; NASH: Non-alcoholic steatohepatitis.

Nonalcoholic fatty liver disease (NAFLD) comprises a spectrum of pathological conditions, including simple steatosis, nonalcoholic steatohepatitis (NASH), and cirrhosis. The prevalence of NAFLD is linked strongly to obesity, insulin resistance, and a cluster of metabolic disorders, including hypertriglyceridemia and hyperuricemia, which impair health seriously. No standard treatment exists currently to manage NAFLD, or even NASH. Identification of metabolic differences among stages of NAFLD might result in the development of more effective and specific treatments for NAFLD and NASH. This study was designed to investigate correlations between disease stages and urine metabonomics in patients with NAFLD, specifically to determine whether urine metabonomics could be used to distinguish NAFLD from NASH, which would aid the diagnosis and treatment of NAFLD.

A panel of biomarkers that can distinguish between NAFLD and NASH and can help to determine the molecular mechanism involved in the process of development of NASH from NAFLD was developed. Urinary biomarkers may be diagnostic in these patients and might be used to assess responses to therapeutic interventions.

Metabolic changes have been detected in clinical trials. Serum glucose, lactate, glutamate/glutamine, taurine concentrations, bile acids, markers of glutathione, lipids, and amino acid metabolism have been reported to differ between patients with NAFLD and healthy controls. Among those findings, low carnitine concentrations could result in cell oxidative damage, fatty acid synthesis, and energy metabolism disorders, ultimately resulting in NAFLD. Cortisol concentrations have been reported to correlate with the severity of NAFLD and indole acetic acid concentration correlates with liver damage. Differential levels of hormones, cytokines, and neurotransmitters might result in abnormal energy metabolism in patients with NAFLD.

The urinary biomarkers found in this study might be diagnostic in these patients and could be used for diagnose and to evaluate the treatment of NAFLD.

NAFLD means nonalcoholic fatty liver disease. In our study, patients with NAFLD refers to those patients that were diagnosed using B ultrasound and their liver functions were normal. NASH means nonalcoholic steatohepatitis. In our study, patients with NASH refers to those patients that were diagnosed using B ultrasound and their liver functions were abnormal. Principal component analysis means principal component analysis. Partial least squares discriminant analysis refers to partial least squares-discriminate analysis. Orthogonal projections to latent structures-discriminant analysis is orthogonal projections to latent structures discriminant analysis. All these analyses were used to distinguish different groups of patients or controls.

This article investigated urinary biomarkers to distinguish NAFLD from NASH, which could help to determine the molecular mechanism involved in the process of developing NASH from NAFLD and improve the diagnosis and treatment of NAFLD. Not only the results, but also the methods will be attractive for readers.