Non-targeted Serum Metabolomics Identifies Candidate Biomarkers Panels Associated with Nonalcoholic Fatty Liver Disease: A Pilot Study in Russian Male Patients

1. INTRODUCTION

Chronic Liver Disease (CLD), and especially Non-Alcoholic Fatty Liver Disease (NAFLD), is becoming an increasing burden worldwide, both medically and financially. The global prevalence of NAFLD is ~25% [1]. Since 60-75% of CLD patients are predicted to suffer from NAFLD, the total number of patients with CLD may be in the range of 2.5-3 billion people worldwide.

NAFLD is associated with obesity, insulin resistance, type 2 diabetes mellitus, arterial hypertension, dyslipidaemia, and metabolic syndrome. NAFLD stages range from Simple Steatosis (SS) to Non-Alcoholic Steatohepatitis (NASH), which can progress to fibrosis and cirrhosis, Hepatocellular Carcinoma (HCC), and liver transplantation. NASH is an important cause of liver cirrhosis. From 1990 to 2017, the prevalence of compensated cirrhosis due to NASH more than doubled, and cases of decompensated cirrhosis more than tripled due to NASH [2].

Until recently, the ‘two hits’ theory was routinely applied to NAFLD pathogenesis, in which the first hit is steatosis development, and the second hit is steatohepatitis. This concept is now outdated and has been replaced by the ‘multiple hits’ hypothesis, which more accurately reflects the complex mechanisms triggering the onset and progression of NAFLD. This concept includes pathogenetic factors such as insulin resistance, adipose tissue hormones, obesity, diet, genetic and epigenetic factors, as well as the gut-liver axis, which appears to play a key role in the development and progression of NAFLD. The leading players in this axis are the gut microbiota, bacterial metabolites and intestinal barrier [3].

Early diagnosis of NAFLD and monitoring of disease progression via metabolomics is extremely important. Metabolites determine the molecular phenotype of an organism since they are the substrates, intermediates and products of biochemical reactions. Therefore, changes are reflected in the metabolome, including those associated with the progression of pathological processes. In particular, serum metabolome analysis provides an opportunity for efficient diagnosis of various diseases [4]. Technological advances in recent years make it possible to identify hundreds or even thousands of metabolites in a single sample in just a few minutes, which is ideal for the diagnosis of multifactorial diseases such as NAFLD [5, 6].

In the present study, we compared the serum metabolomes of individuals with simple steatosis or NASH with those of controls. Together with the accumulating literature, our results indicate that markers may not only reflect dysregulation of metabolic pathways, but also adaptive responses to disease, and they may indicate regulatory effects on organisms [7-11].

2. MATERIALS AND METHODS

3. RESULTS

The study involved male patients with NAFLD, and controls with non-pathological liver ultrasound patterns, normal non-invasive blood test (FibroMax) results, and normal Alanine Transaminase (ALT) and Aspartate Transaminase (AST) levels (Table 1). Body Mass Index (BMI) was 25-30 kg/m² for controls, and 30-35 kg/m² (class I obesity) for patients with NAFLD. Patients were characterised by increased ALT and AST levels and significant steatosis (progressive stages of fibrosis were observed in some patients with NASH).

We identified 319 metabolites in patients with NAFLD and controls. To visualise the differences between the metabolomes of patients and controls, PCA was employed. Control samples were grouped on the left side of the plane relative to the axis of Principal Component 1 (PC1), and SS samples are distributed at different distances from each other, mainly along the right side of the plane (Fig. 1A). The seven NASH samples are located at a distance from controls, and the four samples overlap (Fig. 1B). The SS and NASH samples are spread out over the plane, and the steatosis group is different from the NASH group (Fig. 1C).

Compared with controls, the metabolomes of patients with SS were characterised by a significant increase in the amount of hydroxy acids: 2-hydroxybutyric acid (8.1-fold), 3-hydroxybutyric acid (β-OHB, a ketone body, 5.8-fold), 2-hydroxy-3-methyl butyric acid (3.4-fold); amino acids: threonine (7.5-fold), proline (5.3-fold), pyroglutamic acid (4.9-fold), and isoleucine, amino acid with a branched side chain (6.0-fold); sugar D-xylose (14.7-fold) and sugar alcohols arabitol (12.3-fold); and a number of unidentified compounds (Supplementary Material-1). Patients with NASH showed significantly higher serum levels of 3-methyl-2-oxovaleric acid (6.8-fold increase) and another four unidentified compounds than those with SS. A significant increase in levels of 2,3-dihydroxybutyric acid (7.3-fold), arabitol (17.9-fold), and another five compounds was observed in serum from NASH patients relative to controls.

Candidate biomarkers were identified using multivariate analysis (SVM, PLS-DA and Naïve Bayes). The classifier was chosen based on the largest area under the ROC curve of sensitivity and specificity. When comparing patients with SS and controls, the classifier was SVM (AUC = 0.961). The average accuracy of the classifier was 0.87, the variance of the accuracy was 0.026, the sensitivity was 88.2%, and the specificity was 83.2%. Based on SVM analysis, all compounds were sorted in decreasing order of importance. The first 30 compounds were selected for further statistical analysis, and nine of these compounds differed significantly (p < 0.05) between patient and control groups (Table 2).

Some compounds were not annotated, but their retention time (RT), a set of characteristic ions, and their mass spectra could still be used. In patients with SS, levels of eight compounds were increased, while 15.399_compound was decreased approximately five-fold compared with controls. 3-hydroxybutyric acid (β-OHB), 2-hydroxybutyric acid, and arabitol were identified as candidate biomarkers. The normalised levels of candidate biomarkers in serum are shown in (Fig. 2). Notably, levels of these compounds differ more between patients in the SS group than in controls.

Next, was identified candidate biomarkers that could distinguish patients with SS from those with NASH. Analysis of ROC curves for three classifiers showed that the largest area under the curve corresponded to PLS-DA (AUC = 0.992). The average accuracy of the classifier was 0.985, the variance of the accuracy was 0.0035, and sensitivity and specificity were 98.5%. It was shown levels of 3-methyl-2-oxovaleric acid, 21.229_compound, and 15.399_compound differed significantly between the two groups (Table 3).

Levels of all compounds increased by more than two-fold in the serum of patients with NASH compared with the SS group and varied greatly (Fig. 3).

Multivariate analysis of the metabolomes of NASH patients and controls based on three classifiers showed that the largest area under the ROC curve corresponded to SVM (AUC = 0.788). The average accuracy of the classifier was 0.81, the variance of the accuracy was 0.027, the sensitivity was 100%, and the specificity was 63.7%. Following SVM analysis, 2,3-dihydroxybutyric acid, arabitol, 13.309_compound, 21.229 _compound, and 19.355_compound differed significantly between the groups, and were selected. Levels of all these compounds were increased in patients with NASH (Table 4).

The level of all compounds more than 50% increased in serum of the patients with NASH. As in previous comparisons, the normalized level of the candidate biomarkers varied greatly in NASH group (Fig. 4).

ROC curves were constructed, and the Area Under the Curve (AUC) was calculated for each candidate biomarker. The area under the ROC curve ranged from 0.782 to 0.971 (Table 5).

Finally, we analysed combinations of biomarkers. In most cases, panels yielded higher AUROC (0.998), sensitivity (1), and specificity (1) values than individual biomarkers. Paired combinations with candidate biomarkers 19.355_compound and 20.523_compound achieved the highest prognostic indicator values for SS and Control groups. The predictive value of panels of three, four or more candidate biomarkers also yielded the highest values (AUROC = 0.998, sensitivity = 1, specificity = 1). The panel including 3-methyl-2-oxovaleric acid, 15.399_compound, and 21.229_compound achieved the highest prognostic value for distinguishing SS and NASH patients. The panel with 21.229_compound and arabitol, as well as panels of three, four and five candidate biomarkers achieved the highest AUROC (0.998), sensitivity (1), and specificity (1) values for distinguishing NASH patients from controls.

4. DISCUSSION

Pathological processes lead to changes in specific metabolic pathways, which is in turn reflected by changes in serum metabolites (i.e. the serum metabolome). Thus, metabolites are not only indicators of the dysregulation of metabolic pathways but factors of pathogenesis and/or the responses to a pathological state.

In the present study, we compared the serum metabolomes of patients with NAFLD and controls with non-pathological liver. The software packages used for data processing identified 319 compounds and 108 compounds were previously annotated. Non-annotated compounds were characterised by retention time and characteristic ions. The distribution of samples from patients and controls obtained by PCA revealed differences in metabolomic profiles between patients with SS and NASH, and between both patient and control groups. Conversely, similarities between groups may indicate similarity between metabolomic profiles.

The metabolome analysis results revealed significant changes in certain key pathways, specifically glutathione metabolism, and lipid and amino acid metabolism (Supplementary Material 1). Some of the identified metabolites for which levels varied significantly between groups (isoleucine, proline, 3-hydroxybutiric acid, arabitol, 3-methyl-2-oxovaleric acid, 2-hydroxy-3-methylbutyric acid) were related to endogenous and/or microbial production. For example, the increase in 2-hydroxy-3-methylbutyric acid (2-hydroxyisovaleric acid) in patients with SS could be the result of increased production by Proteus mirabilis, Eggerthella lenta, or Listeria spp., as well as chronic intestinal inflammation [15]. Among the amino acids was identified isoleucine (6-fold increased in patients with SS compared to controls, p = 0.025), a member of the branched-chain amino acid (BCAA) group, which has been associated with an increased risk of metabolic disease, including insulin resistance (IR) and NAFLD [16, 17]. The level of pyroglutamic acid (or 5-oxoproline) in patients with SS was 5-fold higher than controls (p = 0.01). Another previous study demonstrated the high diagnostic value of pyroglutamic acid, for separating patients with steatosis from patients with NASH [18]. The concentration of pyroglutamate in serum of patients with steatosis was increased 1.56-fold compared with the control group, and 2.26-fold in patients with NASH compared with steatosis. In our current work, the results of multivariate analysis indicated that this compound was not a biomarker. In addition, an increase in the level D-xylose (14.7-fold) was observed in serum, which is presumably associated with increased intestinal permeability in disease patients. A similar explanation appears to be applicable for the increased serum level 1-kestose in serum from NASH patients.

Multivariate analysis of the metabolomes of patients with SS and controls showed that the levels of nine candidate biomarkers differed significantly between SS and control groups. The SS patients displayed a 5.8-fold increase in 3-hydroxybutyric acid (β-hydroxybutyrate, β-OHB), a major ketone body. Most ketone bodies are produced in the liver [19], although small amounts can be produced in other tissues through the aberrant expression of ketogenic enzymes or alteration of the ketolysis pathway. The observed increase in 3-hydroxybutyric acid is probably associated with an increase in beta-oxidation, as well as an increase in oxidative metabolism in the liver in general. Increased levels of this metabolite in patients with SS were reported previously. Interestingly, the authors reported a decrease in 3-hydroxybutyric acid with the progression from SS to NASH [20]. Presumably, an increase in β-OHB is an adaptive response that protects the liver against NAFLD progression during the early stages of SS. Subsequently, the progression of NAFLD leads to impaired ketogenesis and the development of maladaptive ketogenic insufficiency, contributing to NASH and hyperglyacemia. Therefore, levels of β-OHB in NASH may decrease [8, 9].

The second most important biomarker was 2-hydroxybutyric acid, level of which was increased 8.1-fold in patients. This compound is derived from α-ketobutyric acid, which is formed mainly in the liver during the catabolism of L-threonine, and the synthesis of glutathione [21]. Oxidative stress or enhanced detoxification of xenobiotics in the liver stimulates a sharp increase in the rate of glutathione synthesis, which can lead to increased production of 2-hydroxybutyric acid as an intermediate metabolic product [22, 23]. Recent studies have shown that an increased concentration of 2-hydroxybutyric acid may reflect early signs of IR, and serve as an independent predictor of the development of impaired glucose tolerance (prediabetes) and early-stage type 2 diabetes [22, 24-27].

In the SS group, the sugar alcohol arabitol was increased 12.3-fold relative to controls. Sugar alcohols are hydrogenated forms of carbohydrates in which the carbonyl group (aldehyde or ketone-reducing sugar) has been reduced to a primary or secondary hydroxyl group. Increased levels of arabitol in plasma and urine have been reported earlier for patients with congenital cirrhosis of the liver due to transaldolase deficiency [28].

It should be emphasised that we observed a high degree of variability in the amounts of the identified markers in patient serum. Since the serum metabolome reflects all changes in tissues and organs, and not just any individual organ, this may be especially important for NAFLD. Changes in hormones, cytokines, enzymes and other metabolic alterations can affect not only the liver, but also adipose tissue, skeletal muscle, and other systems. Thus, the observed variability may be due to differences in the state of organs and systems in different patients or to changes in metabolite levels over time.

Three candidate biomarkers distinguishing the SS and NASH groups were identified: 3-methyl-2-oxovaleric acid, 21.229_compound, and 15.399_compound. The most significant among them was 3-methyl-2-oxovaleric acid, which increased 6.8-fold with the progression of NAFLD. This metabolite is the first product of isoleucine degradation, and an increase in its concentration in serum indicates an increase in BCAA degradation. Another study demonstrated a correlation between the level of 3-methyl-2-oxovaleric acid and the development of type 2 diabetes mellitus [29]. A recent pilot study of the effects of curcumin on the serum metabolomic profile of patients with NAFLD showed that certain BCAA degradation products, such as 3-methyl-2-oxovaleric acid and 3-hydroxyisobutyrate, could consider both biomarkers and therapeutic targets for NAFLD [30]. It is possible that an increase in the level of this acid is associated not only with the increased degradation of BCAAs, but also with their production by the microbiota. Thus, in NAFLD, the amount of isoleucine produced by bacteria (Bacteroides vulgatus, Prevotella copri, Streptococcus sp., Clostridium sp., Eubacterium rectale) is increased [16, 31, 32]. Additionally, the body’s responses to a pathological process can alter metabolite levels. For example, when steatosis progresses to steatohepatitis, levels of BCAA: leucine (127%), isoleucine (139%) and valine (147%) are increased, while leucine supplementation activates the target of rapamycin (mTOR), which is a critical mediator regulating protein synthesis, cell proliferation and insulin sensitivity [33, 34]. In addition, BCAAs exert protective inhibition in cancer development. Thus, they may be increased under certain conditions, and their degradation products can be considered an adaptive response of the liver to oxidative stress during the NASH stage [35].

Multivariate analysis of NASH patient and control metabolomes allowed us to identify five candidate biomarkers. Their levels were increased from 1.9- (compound 13.309_ compound) to 99-fold (compound 19.355_compound) in the serum of patients with NASH. The second and third most important biomarkers were 2,3-dihydroxybutyric acid and arabitol; their levels were increased by 7.3- and 17.3-fold, respectively. 2,3-Dihydroxybutyric acid has two enantiomers: 4-deoxyerythronic acid ((2R,3R)-2,3-dihydroxybutanoic acid) and 4-deoxythreonic acid ((2S,3R)-2,3-dihydroxybutanoic acid). Currently, very little is known about the pathways of the formation of these entiomers. It is assumed that the main source of 4-deoxyerythronic acid is threonine. It has been found to be inversely associated with age in adults [36], and higher levels of 4-deoxyerythronic acid, 4-deoxythreonic acid, and 2-hydroxybutyric acid have been observed in children with type I diabetes [37]. 4-Deoxyerythronic acid is presumably formed from threonine by the action of threonine dehydrogenase, which is a relatively minor contributor to threonine oxidation in humans (about 10%) [38]. Lau C.E.
et al. suggest that an increased concentration of 4-deoxyerythronic acid may result not only from endogenous catabolism of threonine but also from exogenous sources or microbial metabolism [39]. Since it is assumed that 2,3-dihydroxybutyric acid may contribute to the pathophysiology of metabolic disorders such as obesity and diabetes [37, 39], we hypothesize its possible involvement in the pathogenesis of NAFLD.

All candidate biomarkers yielded good or excellent test results, but did not always have high sensitivity and specificity (Table 5). Panels of three to eight biomarkers yielded excellent test results with high sensitivity and specificity, thus it is preferable for diagnosis over the use of individual biomarkers. Panels of biomarkers have been successfully applied for the diagnosis of cancer, Parkinson’s disease, type 2 diabetes, and other multifactorial diseases [40-42]. NAFLD is a multifactorial disease, and given the various pathophysiological processes involved in the progression of NAFLD, it is doubtful whether a single marker could reflect all pathological changes. By contrast, a panel of markers can reflect the actual pathophysiological status of a patient, resulting in a more accurate diagnosis. In addition, the use of a panel of biomarkers obviously reduces the risk of misdiagnosis due to incorrect identification of a marker, or an incorrect measurement of its concentration in blood and also allows the inclusion of non-annotated compounds.

CONCLUSION

In conclusion, we identified nine biomarkers of SS, five biomarkers of NASH, and three biomarkers that distinguished SS from NASH patients. Since NAFLD is a multifactorial disease, we suggest that the use of a panel of markers is preferred over individual metabolites. We believe that markers may not only be the result of dysregulation of metabolic pathways in patients with NAFLD, but may also play a role in adaptive responses to disease and may therefore reflect functional changes in the intestinal microbiota. Further studies with a larger population are needed to confirm our hypotheses, and identify non-annotated biomarkers.

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