59

J Pharm Chem Biol Sci , August 2014; 2(2):59-76

Journal of Pharmaceutical, Chemical and Biological Sciences ISSN: 2348-7658

Jun-August 2014; 2(2):59-76

Available online at http://www.jpcbs.info

Non-invasive Assessment of Liver Fibrosis Using Serum Markers

Entsar A. Saad*

Chemistry Department, Faculty of Science, Damietta University, Damietta, Egypt, Tel.: +2057 2403866; Fax: +2057 2403868; E-mail: entsarsaad@gmail.com

*Corresponding Author Received: 13 June 2014 Revised: 21 June 2014 Accepted: 23 June 2014

INTRODUCTION

More than 2,000 studies in the last five years have

employed serological markers to assess liver fibrosis

[1]. Several non-invasive markers are currently

being validated as potential tools to determine liver

damage and some of them are now commercially

available but they have not been yet incorporated in

clinical practice in most countries [2]. An exception

to this rule is France, where three well-validated

non-invasive methods (Fibrotest, Fibrometer, and

FibroScan) have been approved by the public health

system and are routinely used in clinical practice [3].

Review Article

ABSTRACT

Fibrosis is an intrinsic response to chronic injury, that elicits excess deposition of matrix compounds and

scarring, which ultimately alters liver structure and function. Liver biopsy has long been the gold standard for

assessing the degree of liver fibrosis. Due to liver biopsy invasiveness, specialists and patients favor alternative

non-invasive tests for fibrosis staging. In recent years, many non-invasive tests based on measurement of

direct or indirect serum markers have been developed. Direct and indirect markers may be used alone or, more

commonly, in combination with each other, to produce composite liver fibrosis scores/panels. Almost all of

them have the ability to identify significant fibrosis and cirrhosis in particular. However, only those tests with

the highest diagnostic accuracy and availability should be introduced. Apart from their diagnostic accuracy, the

potential ability of these tests to predict disease outcomes should be compared with that of liver biopsy.

Continued research in this area will give the opportunity to offer patients with liver diseases more precise and

non-invasive diagnostic tools. Until now liver biopsy still a part of clinical practice but we hope progress in

biomedicine can change the current situation in the next few decades. Consequently, liver biopsy will be a

history.

Keywords: Non-invasive; liver fibrosis; serum markers; scores; panels

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Serum markers that reflect liver function (indirect

markers) or that related to matrix metabolism

(direct markers) or combinations of both are

currently used. Recent studies have proposed

multiple indices based on the combination of: (1)

indirect and direct serum markers (e.g. SHASTA

index), (2) serum markers with other different non-

invasive methods (e.g. PLF score), (3) serum

biochemical markers with clinical data (e.g. Fib-4) or

(4) serum biochemical and variable clinical markers

with fibrosis parameters (e.g. Fibrometer) to

increase the diagnostic accuracy [1, 4].

The best marker panels show the area under

receiver operating characteristic curves (AUROCs)

around 0.8–0.85 to differentiate between no/mild

fibrosis (Metavir F0–F1) and moderate/severe

fibrosis (F2–F4). Fibrosis markers have almost

exclusively been validated as predictors of fibrosis

stage, while especially the direct parameters may

rather reflect the dynamics of fibrogenesis and/or

fibrolysis [1].

1. Fibrosis

Fibrosis is the excess accumulation of extracellular

matrix proteins (ECMP), which results from chronic

inflammation. This inflammation triggers a wound-

healing process that mitigates inflammatory tissue

destruction but also leads to scar tissue formation.

2. Liver fibrosis history

Liver fibrosis results from chronic damage to the

liver in conjunction with the accumulation of ECMP,

which is a characteristic of most types of chronic

liver diseases. The accumulation of ECMP distorts

the hepatic architecture by forming a fibrous scar,

and the subsequent development of nodules of

regenerating hepatocytes defines cirrhosis. Cirrhosis

produces hepatocellular dysfunction and increased

intrahepatic resistance to blood flow, which result in

hepatic insufficiency and portal hypertension.

Hepatic fibrosis was historically thought to be a

passive and irreversible process. Currently, it is

considered a model of the wound-healing response

to chronic liver injury. Since the demonstration, in

the 1990s, that even advanced liver fibrosis is

reversible, researchers have been stimulated to

identify antifibrotic therapies [5].

3. Liver fibrosis etiology and consequences

3.1 Etiology

Liver fibrosis is mainly due to chronic viral hepatitis

B or C, autoimmune and biliary diseases, alcoholic

steatohepatitis (ASH) and nonalcoholic

steatohepatitis (NASH) (Figure 1). While mild fibrosis

remains largely asymptomatic, its progression

toward cirrhosis is the major cause of liver-related

morbidity and mortality [1].

Fig. 1: The main liver fibrosis etiologies

NASH, Nonalcoholic steatohepatitis; HCC, Hepatocellular

carcinoma.

3.2 Clinical consequences

(a) Fibrosis progression toward cirrhosis;

(b) Liver functional failure, including failing

hemostatic, nitrogen handling, and detoxification

systems;

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(c) Portal hypertension with consequent formation

of ascites and bleeding esophageal or gastric

varices;

(d) High susceptibility to infection; and

(e) High risk to develop hepatocellular carcinoma

(HCC)

4. Pathogenesis of liver fibrosis

Hepatic fibrosis is the result of the wound-healing

response of the liver to repeated injury (Figure 2)

[6]. After an acute liver injury, parenchymal cells

regenerate and replace the necrotic or apoptotic

cells. This process is associated with an

inflammatory response and a limited deposition of

ECMP. If the hepatic injury persists, then eventually

the liver regeneration fails, and hepatocytes are

substituted with abundant ECMP. As fibrotic liver

diseases advance, disease progression from collagen

bands to bridging fibrosis to frank cirrhosis occurs

[7]. In advanced stages, the liver contains

approximately 6 times more ECM components than

normal, including collagens (I, III, and IV),

fibronectin, undulin, elastin, laminin, hyaluronan,

and proteoglycans. Accumulation of ECM

components results from both increased synthesis

and decreased degradation. Decreased activity of

ECM-removing matrix metalloproteinases (MMPs) is

mainly due to an overexpression of their specific

tissue inhibitors (TIMPs) [8].

Fig. 2: Changes in the hepatic architecture (A) associated with advanced hepatic fibrosis (B).

Following chronic liver injury, inflammatory lymphocytes infiltrate the hepatic parenchyma. Some hepatocytes undergo

apoptosis, and Kupffer cells activate, releasing fibrogenic mediators. Hepatic stellate cells (HSCs) proliferate and undergo

a dramatic phenotypical activation, secreting large amounts of extracellular matrix proteins. Sinusoidal endothelial cells

lose their fenestrations, and the tonic contraction of HSCs causes increased resistance to blood flow in the hepatic

sinusoid. Figure modified with permission from Science & Medicine (S28).

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5. Liver fibrosis scoring/staging

5.1 Importance of liver fibrosis staging

Fibrosis stages assessment is important to

determine the prognosis of chronic liver disease,

select patients for specific treatment, and to

monitor the success of treatment [9].

The history of the fibrosis scoring systems dates

back to 1981 when the histological features of

chronic hepatitis were evaluated for potential

importance in determining its prognosis by Knodell

and colleagues [10]. The Ishak score, or revised

Knodell system, considers grading and staging as

two separate items; liver fibrosis is classified as: 0 =

absent, 1-2 = mild, 3-4 = moderate and 5-6 =

severe/cirrhosis. The grading system has been

subsequently modified by other pathologists. The

Metavir classification system includes two separate

scores, one for necroinflammatory grade (A) and

another for the fibrosis stage (F). The grade is

usually scored from 0-4, with A0 being no activity

and A3 to A4 considered severe activity. The stage

scored from F0 to F4 (Figure 3): F0 is the absence of

fibrosis, F1 is portal fibrosis without septa, F2 is

portal fibrosis with rare septa, F3 is numerous septa

without cirrhosis, and F4 is cirrhosis [11].

The current modalities used for quantifying and

staging hepatic fibrosis are summarized in Figure 4.

5.2 Liver fibrosis evaluation methods

They can be divided into:

(1) Invasive: For the past 50 years liver biopsy has

been considered to be the gold standard for staging

of liver fibrosis. This technique allows physicians to

obtain diagnostic information not only on fibrosis,

but also on many other liver injuring processes, such

as inflammation, necrosis, steatosis, hepatic

deposits of iron or copper [12].

However, many studies clearly highlight several

crucial drawbacks of liver biopsy, including variable

accessibility, high cost, sampling errors and

Fig. 3: Liver fibrosis progression stages [3].

inaccuracy due to inter- and intra-observer

variability of pathologic interpretations [12].

In addition, there is a small but important risk of

liver biopsy-associated morbidity and mortality;

pain, hypotension, intraperitoneal bleeding and

injury to the biliary system. The risk for

hospitalization after liver biopsy is 1-5%, the risk for

severe complications is 0.57%, and mortality rate is

0.009-0.12% [14, 15]. Because of these reasons,

some patients may choose to go without liver

biopsy.

(2) Non-invasive: It include: (1) serum tests, (2)

proteomic profiles/genetic tests, and (3) physical

(imaging) techniques. In addition to being less

invasive, there is a low risk of sampling error and

small observer-related variability. Moreover,

measurements may be performed repeatedly over

time, allowing for ongoing monitoring of fibrosis

[16]. Most serum biomarkers have only been used

as investigative, rather than diagnostic, parameters

in the clinic [17].

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Fig. 4: A scheme depicting various means of liver fibrosis diagnostics.

Liver biopsy is an invasive method that remains an imperfect golden standard. Proteomics based profiles are unlikely to

be introduced to routine clinical care anytime soon, but are valuable from the research point of view. Imaging

techniques, serum biomarkers and biomarker panels are advancing along the route to the clinic stage, but require

extensive validation [13].

5.3 Classification of liver fibrosis serum markers

[18]

Direct serum markers based on ECMP reflecting the

activity of the fibrotic process, and are thought to

indicate the extent of connective tissue deposition.

Indirect serum markers based on routine liver

function parameters that have been used in clinical

practice.

Direct and indirect markers may be used alone or,

more commonly, in combination with each other, to

produce composite scores/panels. The calculation of

such scores can be relatively simple or can be based

on complicated formulas [13].

6. Criteria for ideal non-invasive serum markers

The ideal non-invasive marker would have the

following characteristics [13, 19]: simple, safe,

readily available, inexpensive, accurate,

reproducible, and sensitive to the effects of

treatment, specific to identify different stages of

fibrosis, useful in tracking disease progression or

regression and not be susceptible to false positive

results.

The diagnostic performances of fibrosis markers are

assessed by receiver operating characteristic (ROC)

curves. The most commonly used index of accuracy

is the area under the ROC (AUROC) curve with

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values close to 1 indicating a high diagnostic

accuracy [20].

7. Direct serum fibrosis markers (Table 1)

Several fibrosis panels have been developed and

some of them are now available for commercial use

[9].

Table 1. Direct serum markers of liver fibrosis

No. Serum marker Reference

1 Transforming growth factor-β1 (TGF- β 1)

13

2 Procollagen type I carboxy-terminal peptide (PICP)

21

3 Procollagen type III amino-terminal peptide (PIIINP)

22

4 Hyaluronic acid (HA) 24 5 Matrix metalloproteinases

(MMPs) [MMP-2, MMP-3 and MMP-9 ]

25-28

6 Tissue inhibitors of metalloproteinases (TIMPs) [TIMP-1 TIMP-2 ]

26, 28

7 YKL-40 (chondrex) 30 8 Connective tissue growth

factor (CTGF) 18

9 Paraoxonase 1 (PON-1) 31 10 Laminin 33 11 Microfibril-associated

glycoprotein 4 (MFAP-4) 34

7.1 Transforming growth factor-β1 (TGF- β1): It is a

cytokine involved in tissue growth, differentiation,

ECM components production and the immune

response. TGF-β1 is commonly accepted as a central

component of fibrogenic response to wounding and

is up-regulated in a variety of different diseases. A

correlation between TGF-β1 levels and the rate of

fibrosis progression is widely accepted [13].

7.2 Extracellular matrix components

7.2.1 Procollagen peptides

Procollagen type I carboxy-terminal peptide (PICP)

levels are normal in patients with mild chronic

hepatitis C (CHC) and elevated in 50% of patients

with moderately advanced or advanced chronic

hepatitis C, including patients with liver cirrhosis of

this etiology [21].

Procollagen type III amino-terminal peptide (PIIINP)

is a product of cleavage of procollagen and has been

proposed as a serum marker of hepatic fibrosis

more than two decades ago [22]. Its relative

concentration in the basement membrane is higher

in hepatic fibrogenesis [23]. In several studies in

patients with chronic HCV infection, this biomarker

showed only moderate diagnostic accuracy [9].

Unfortunately, PIIINP is not specific for the fibrosis

of the liver as it is also elevated in acromegaly, lung

fibrosis, chronic pancreatitis, and rheumatologic

disease [18].

7.2.2 Hyaluronic acid (HA): HA is a polysaccharide

present in ECM and elevated in serum in patients

with hepatic fibrosis. Commercial test kits are

available from Corgenix (Westminster, Colorado).

The diagnostic accuracy of HA was found to be

superior to that of PIIINP [24].

7.2.3 Matrix metalloproteinases and their

inhibitors

Matrix metalloproteinases (MMPs): Excess ECMP

are degraded by matrix metalloproteinases which

are in turn inhibited by tissue inhibitors of

metalloproteinases (TIMPs). The three most

commonly studied MMPs are MMP-2, MMP-3, and

MMP-9. MMP-2 is secreted by activated HSCs;

elevated levels of MMP-2 and its proenzyme have

been observed in various liver diseases [25]. During

hepatic fibrogenesis, the expression of MMP-2 is

markedly increased. The potential for MMP-2 for

predicting liver fibrosis remains unclear as some

contradictory data have been reported by studies

performed so far [26, 27]. In one study, MMP-9

levels were negatively correlated to the histological

severity of the liver disease in patients with CHC

[28].

Tissue inhibitors of metalloproteinases: TIMP-1

controls activity of most MMPs, whereas TIMP-2

specifically inhibits MMP-2. TIMPs-dependent

inhibition of ECMP degradation may promote liver

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fibrosis; elevation of TIMPs’ levels has been

observed in chronic liver disease. For example, CHC

causes the elevation of both TIMP-1 and TIMP-2 in

corollary with fibrosis progression [26]. A recent

study showed that serum levels of MMP-9 in chronic

hepatitis patients were low as compared to the

controls. Moreover, serum MMP-9 levels decrease

as chronic hepatitis progresses to cirrhosis, while

TIMP-1 levels increase along with an increase of the

degree of fibrosis. These findings prompt using

serum TIMP-1 as a non-invasive assay in liver

fibrosis [28]. Thus, TIMP-1 is a component of several

composite fibrosis panels.

7.2.4 YKL-40 (chondrex): YKL-40 is a mammalian

glycoprotein homologue of the bacterial chitinases

involved in remodeling or degradation of the

extracellular matrix [29]. In liver diseases, serum

levels of YKL-40 are closely related to the degree of

histologically documented fibrosis [30].

7.2.5 Connective tissue growth factor (CTGF): It is

synthesized in response to profibrogenic factor TGF-

β by both activated HSC and hepatocytes. However,

serum CTGF levels decrease in the end-stage

cirrhosis [18].

7.2.6 Paraoxonase 1 (PON-1): It is an enzyme that

hydrolyzes lipid peroxides, has antioxidant

properties and influences hepatic cell apoptosis.

Measurement of serum PON-1 activity has been

proposed as a potential test for the evaluation of

liver function, however, its clinical acceptance is

limited due to instability and toxicity of its substrate,

paraoxon [31].

7.2.7 Laminin: It is a major non-collagenous

glycoprotein synthesized by the HSC and deposited

in the basement membrane of the liver. During

fibrosis, laminin accumulates around the vessels, in

the perisinusoidal spaces and near the portal tract

[32]. Elevated levels of laminin and pepsin resistant

laminin (laminin P1) were found to correlate with

the degree of perisinusoidal fibrosis [33].

7.2.8 Microfibril-associated glycoprotein-4 (MFAP-

4): It is a ligand for integrins. Quantitative analysis of

MFAP-4 serum levels showed high diagnostic

accuracy for the prediction of non-diseased liver

versus cirrhosis (AUROC = 0.97) as well as stage 0

versus stage 4 fibrosis (AUROC = 0.84), and stages 0

to 3 versus stage 4 fibrosis (AUROC = 0.76) [34].

8. Indirect serum fibrosis markers (Table 2)

Most serum indirect indices composed of routine laboratory parameters that reflect changes in liver function are readily available at no additional cost.

Table 2. Fibrosis scores and indices based on indirect serum markers of liver fibrosis

No. Score/Index (Reference)

Calculation/Variables

1 AST/ALT ratio (AAR score) [35] AST (IU/L)/ALT (IU/L)

2 Age-AST model [37] Age-AST model = Age (year)/AST (IU/L)

3 Age- platelet index (API) [40] API = Age (year)/PLT (× 109/L)

4 Cirrhosis discriminant score (CDS) [41] AST/ALT(IU/L), INR and platelet

5 AST to platelet ratio index (APRI) [42] [(AST/Upper limit of normal)/platelet count (109/L)]

× 100

6 Globulin/platelet (GP) model [44] GP model = GLOB (g/mL) × 100/PLT (× 109/L)

7 Fibro-quotient (FibroQ) index [45] FibroQ = 10 × (age × AST × INR)/(ALT × platelet

count)

8 PGA index [46, 47] Prothrombin Index, γ- glutamyl transferase levels

and apolipoprotein A1

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9 Göteborg University Cirrhosis Index (GUCI)

[48]

(ASTxINRx100)/platelet count (109/L)

10 King's score [49] King's score = age (years)xAST (IU/L)xINR/platelet

count (109/L)

11 SHASTA index [50] Serum hyaluronic acid (HA), AST, and albumin

12 Pohl score [51] AST/ALT(IU/L) and platelet number/µL

13 Fibrosis 4 score (FIB-4) [43] FIB-4 = age (years)xAST (IU/L)/platelet count

(109/L)xALT (IU/L)1/2

14 Fibrosis-cirrhosis index (FCI) [53] FCI = (ALPxbilirubin)/(albuminxplatelet count)

15 Forns index (FORNS) [54] 7.811 - 3.131 × ln (platelet count(109/L)) + 0.781 × ln

(GGT) + 3.467 × ln (age) - 0.014 × (cholesterol

[mg/dL])

16 LOK (Model 3) [56] Log odds = -5.56 - 0.0089 × platelet (× 109/L) + 1.26 ×

AST/ALT ratio + 5.27 × INR

17 Fibrotest (Fibrosure) [57] α2-macroglobulin, haptoglobin, apolipoprotein A1,

GGT, and total bilirubin

18 FibroIndex [58] 1.738 - 0.064 (platelets [× 104/mm3]) + 0.005 (AST

[IU/L]) + 0.463 (gamma globulin [g/dL])

19 Fibrometer test* [59] α2-macroglobulin, HA, AST, platelet count,

prothrombin index, urea, and age

20 Fibrospect II assay* [61] HA, TIMP-1 and α2-macroglobulin

21 Enhanced liver fibrosis (ELF) (ELFTM test)*

[62]

Age, HA, PIIINP, and TIMP-1

22 Hepascore [63] Bilirubin, GGT, HA, α2-macroglobulin, age and sex

23 TGF-ß1, HA, PIIINPand TIMP-1panel [64] TGF-ß1, HA, PIIINP and TIMP-1

24 Fibroscan/Fibrotest [65] Fibroscan and Fibrotest

25 Sequential algorithms [66] APRI, Fibrotest and/or Forns index

26 Predicted liver fibrosis score (PLF score) [67] Transient elastography (TE) (Fibroscan) and multiple

serological tests [PLF score = 0.956 + 0.084 × TE -

0.004 × King score + 0.124 × Forns score + 0.202 ×

APRI score]

INR, prothrombin time expressed as international normalized ratio; PLT, platelet; TIMP-1, tissue inhibitor of

metalloprotienase-1; PIIINP, procollagen type IIIaminoterminal peptide; TGF-ß1, transforming growth factor ß1. *Means

the test is commercially available

8.1 Aspartate aminotransferase/Alanine amino-

transferase ratio (AAR score) [35]: It is calculated

as: AAR score = AST (IU/L)/ALT (IU/L). In a study of

Ansar et al. (2009) the mean AAR value was found

to be higher for each increasing stage of fibrosis.

This finding is related to an increased release of

mitochondrial AST, decreased AST clearance and/or

impaired synthesis of ALT in advanced liver disease

[36].

8.2 Age-AST model [37]: It is calculated as: Age-AST

model = Age (year)/AST (IU/L).

8.3 Platelet count: Hepatic fibrosis may lead to

thrombocytopenia as a consequence of impaired

synthesis of thrombopoietin and/or sequestering of

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platelets in an enlarged spleen. Surprisingly, few

data exist on the diagnostic value of platelet count

per se although the platelet count has been

included in several composite fibrosis scores. Ono et

al. (1999) reported the use of platelet count could

discriminate F4 from F1-F3 in 75%-80% of patients

with CHC [38]. In Lackner et al. (2005) study, a

platelet count of < 150 × 109/L had a positive

predictive value (PPV) > 90% for significant fibrosis,

whereas at a cut-off of ≥ 150 × 109/L it had a

negative predictive value (NPV) > 90% for cirrhosis

[39].

8.4 Age- platelet index (API) [40]: It is calculated as

API = Age (year)/PLT (× 109/L) [37].

8.5 Cirrhosis discriminant score (CDS): Platelet

count has been also combined with AAR and

prothrombin time in the CDS [41] but the diagnostic

accuracy of these composite scores was not

superior to platelet count per se [39].

8.6 AST to platelet ratio index (APRI) [42]: It is

calculated as: APRI = [(AST/Upper limit of

normal)/platelet count (109/L)] × 100. Its diagnostic

accuracy for both significant fibrosis and cirrhosis

has been confirmed. Using the cut-offs proposed by

Wai et al. [42], approximately 50% of the patients

can be correctly classified without a liver biopsy. In

recent study, Mean APRI values significantly

increased with successive fibrosis levels and the

AUROC distinguishing severe (F3-F4) from mild-to-

moderate fibrosis (F0-F2) was 0.8 [43].

8.7 Globulin/platelet (GP) model: It is calculated as:

GP model = GLOB (g/mL) × 100/PLT (× 109/L). Using

this model chronic HBV-infected patients with

minimal fibrosis and cirrhosis can be diagnosed

accurately, and the clinical application of this model

may reduce the need for liver biopsy in HBVinfected

patients [44].

8.8 Fibro-quotient (FibroQ) index: It includes

prothrombin time international normalized ratio

(INR), platelet count, AST, ALT, and age, and it is

calculated as FibroQ = 10 × (age × AST × INR)/(ALT ×

platelet count) to predict significant fibrosis [45].

8.9 PGA index: It combines the measurement of the

prothrombin index, γ- glutamyl transferase levels

and apolipoprotein A1. It was subsequently

modified to the PGAA index by the addition of α2-

macroglobulin, which resulted in marginal

improvement in its performance. However, overall

accuracy of this index is relatively low [46, 47].

8.10 Göteborg University Cirrhosis Index (GUCI):

GUCI = (ASTxINRx100)/platelet count (109/L). It can

with a high degree of accuracy discriminate patients

with from those without hepatitis C-related

significant fibrosis and cirrhosis [48].

8.11 King's score: It is calculated as: King's score =

age (years)xAST (IU/L)xINR/platelet count (109/L) . It

is a simple and accurate index for predicting

cirrhosis in chronic hepatitis C. The AUROC for

predicting cirrhosis and significant fibrosis (F3-6)

were 0.91 and 0.79, respectively [49].

8.12 SHASTA index: It consists of serum HA, AST,

and albumin was evaluated in 95 patients with

HIV/HCV co-infection. It has performed significantly

better than the APRI test [50].

8.13 Pohl score: It includes AST/ALT(IU/L) and

platelet number/µL. In patients with hepatitis C with

AST/ALT less than 1 and platelets less than 150,000

are excluded from marked fibrosis [51].

8.14 Fibrosis 4 score (FIB-4): It is calculated as: FIB-4

= age (years)xAST (IU/L)/platelet count (109/L)xALT

(IU/L)1/2. Holmberg et al. (2013) reported that, mean

FIB-4 values significantly increased with successive

fibrosis levels and the AUROC analysis distinguishing

severe (F3-F4) from mild-to-moderate fibrosis (F0-

F2) was 0.83 [43]. Recently, Li et al. (2014) expanded

and validated serum marker indices of fibrosis

assessment in 284 chronic hepatitis B (CHB) and

2304 CHC patients. For the prediction of advanced

fibrosis and cirrhosis, the FIB-4 score outperformed

the other serum marker indices in the CHC cohort

and was similar to APRI in the CHB cohort. The

AUROC for FIB-4 in differentiating F3-F4 from F0-F2

was 0.86 for CHB and 0.83 for CHC [52].

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8.15 Fibrosis-cirrhosis index (FCI): FCI =

(ALPxbilirubin)/(albuminxplatelet count). The FCI

accurately predicted fibrosis stages in HCV infected

patients and seems more efficient than AAR, APRI,

FI and FIB-4 used serum indexes [53].

8.16 Forns index (FORNS): Forns et al. (2002)

developed an index derived from age, γ-glutamyl

transferase (GGT), cholesterol, and platelet count in

a study of 476 untreated HCV patients, which is

calculated as follows: 7.811 - 3.131 × ln (platelet

count(109/L)) + 0.781 × ln (GGT) + 3.467 × ln (age) -

0.014 × (cholesterol [mg/dL]). In their study, the

AUROC for prediction of significant fibrosis (F2-F4

according to the Scheuer classification) was 0.86 in

the test set and 0.81 in the validation set [54]. The

diagnostic accuracy of this index has been

confirmed in patients with HIV-HCV co-infection

[55].

8.17 LOK (Model 3): Lok et al. (2005) proposed

another prognostic model for prediction/exclusion

of cirrhosis in patients with CHC [56]. This index can

be derived from the regression formula: log odds = -

5.56 - 0.0089 × platelet (× 109/L) + 1.26 × AST/ALT

ratio + 5.27 × INR. Using model 3 at a cut-off of <

0.20, cirrhosis could be excluded with an NPV of

99%.

8.18 Fibrotest (Fibrosure): French investigators

analyzed an extensive array of biochemical tests in

339 patients with CHC and identified a panel of 5

markers which could best predict the stage of

fibrosis: α2-macroglobulin, haptoglobin,

apolipoprotein A1, GGT, and total bilirubin [57]. This

test has been marketed as FibrotestTM in Europe

and as FibrosureTM in the United States. The

Fibrotest has been validated in several studies in

patients with CHC and its diagnostic accuracy is

limited by hemolysis (leading to a reduction in

haptoglobin), Gilbert’s syndrome (increasing the

bilirubin level), and recent or ongoing infection

(leading to elevations of α2-macroglobulin and

haptoglobin) [9].

8.19 FibroIndex: Japanese investigators proposed

another index: 1.738 - 0.064 (platelets [× 104/mm3])

+ 0.005 (AST [IU/L]) + 0.463 (gamma globulin [g/dL]).

The AUROC for prediction of significant fibrosis was

0.83 (0.82 in the validation set) which was slightly

superior to APRI or the Forns index assessed in the

same study [58].

8.20 Fibrometer: The Fibrometer test incorporates

α2-macroglobulin, HA, AST, platelet count,

prothrombin index, urea, and age. Cales et al. (2005)

reported superior diagnostic accuracy of Fibrometer

to Forns index, Fibrotest, and APRI [59]. However,

this finding was not confirmed in an external

validation study [60]. Several Fibrometers are now

commercially available at BioLiveScale (Angers,

France) for assessment of fibrosis in chronic viral

hepatitis (Fibrometer V), alcoholic liver disease

(Fibrometer A), and metabolic steatopathy

(Fibrometer S).

8.21 Fibrospect II: The Fibrospect II assay

(Prometheus Laboratories Inc., San Diego, CA) uses

HA, TIMP-1 and α2-macroglobulin. This test was

found to accurately predict significant fibrosis in a

study of 294 HCV patients, validated in 402 HCV

patients [61] and further validated in another study

[59].

8.22 Enhanced liver fibrosis (ELF): In a study of 1021

patients with chronic liver disease of different

etiologies, an algorithm consisting of age, HA,

PIIINP, and TIMP-1 was developed [62]. Using the

Scheuer fibrosis score as a reference test, its overall

diagnostic accuracy was similar to that of other non-

invasive fibrosis tests (AUROC 0.78 for significant

fibrosis, 0.89 for cirrhosis). Performance of the

algorithm was slightly lower in a subgroup of

patients with CHC (n = 325, AUROC 0.77 for

prediction of F3-F4). This test is being marketed as

Enhanced Liver Fibrosis (ELFTM) test by Siemens

Medical Solutions Diagnostics (Tarrytown, NY).

8.23 Hepascore: It was developed by Australian

investigators and is derived from bilirubin, GGT, HA,

α2-macroglobulin, age and sex. High diagnostic

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performance was reported for both significant

fibrosis and cirrhosis [63], but external validation

yielded somewhat lower diagnostic accuracies [60].

8.24 TGF-ß1, HA, PIIINP and TIMP-1panel: Valva et

al. (2011) studied the presence of a pro-fibrogenic

cytokine (TGF-ß1) as well as different matrix

deposition markers [HA, PIIINP and TIMP-1] related

to liver injury during CHC. They demonstrated that

given the diagnostic accuracy of HA, PIIINP, TGF-ß1,

their combination may provide a potential useful

tool to assess liver fibrosis in adults [64].

8.25 Fibroscan/Fibrotest: Castera et al. (2005)

investigated APRI, Fibrotest, Fibroscan and

combinations thereof in 183 patients with chronic

hepatitis C. For significant fibrosis, the combination

of Fibroscan and Fibrotest had superior diagnostic

accuracy (AUROC 0.88) to that of respective

individual tests [65].

8.26 Sequential algorithms: The sequential use of

several markers may overcome some of the

limitations of individual markers. Sebastiani et al.

(2006) developed three different sequential

algorithms (including APRI, Fibrotest and/or Forns

index) for the diagnosis of significant fibrosis with

elevated ALT, significant fibrosis with persistently

normal ALT, and cirrhosis, respectively, which

allowed classifying 100% of the patients for each

entity [66].

8.27 Predicted liver fibrosis score (PLF score): It is

derived from Fibroscan and multiple serological

tests. It is calculated as: PLF score = 0.956 + 0.084 ×

TE - 0.004 × King score + 0.124 × Forns score + 0.202

× APRI score. A direct correlation was found to exist

between PLF scoring system and the Metavir scoring

system (P < 0.0001). The correlation of this scoring

system with the severity of fibrosis was better than

that of the other methods alone. The mean PLF

scores for different stages of fibrosis ranged from

1.93 ± 0.45 for F0 to 3.64 ± 0.55 for F4. While there

was no significant difference between mean PLF

scores for F0 and F1 stages of fibrosis [67].

Table 3. Recent serum markers of liver fibrosis

No. Serum marker Calculation/Variables

1 SBA model [68] Serum bile acids (SBAs), age, BMI, serum AST, glucose and cholesterol levels

2 Wu index [69] HA and INR[Wu index = (INR×HA/100)] 3 AngioScore (AS) [70]

Serum levels of angiopoietin-2 [AS = -6.634 + (1.083xlog Ang2) + (1.792xlog Age) + (3.782xlog INR) + (1.052xlog AST) – (0.005xplatelets) + (0.653xlog GGT) ]

4 Carbohydrate19.9 antigen (CA19.9) [71]

CA19.9

5 Combination of sFas with TGF-ß1, HA, PIIINP [4]

sFas, TGF-ß1, HA and PIIINP

6 Soluble CD163 (sCD163) and soluble mannose receptor (sMR, sCD206) [73]

sCD163 and sMR, sCD206

7 CD163-HCV-FS and CD163-HBV-FS fibrosis scores [74]

CD163

8 WFA+-M2BP [75] Hyperglycosylated Wisteria floribunda agglutinin-positive Mac-2 binding protein (WFA+-M2BP)

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9 Plasma caspase-generated cytokeratin-18 fragments (CK-18) [76]

CK-18

10 Fibroscan/Fibrotest [77] FibroTest™ (FT) and Transient Elastography (TE) (FibroScan®)

BMI, body mass index; HA, hyaluronic acid; INR, prothrombin time expressed as international normalized ratio; GGT,

gamma glutamyl transferase; sFas, soluble Fas; TGF-ß1, transforming growth factor ß1; PIIINP, procollagen type III

aminoterminal peptide.

9. Recent serum fibrosis markers (Table 3)

9.1 Serum bile acid (SBA) model: Halfon et al.

(2013) analyzed SBA levels of 135 patients with CHC

infection and correlated these levels with the

degree of liver fibrosis determined by liver biopsy.

They assessed the accuracy of SBA levels as

predictor of liver fibrosis by comparison to patients’

Fibrotest scores. The SBA levels were significantly

higher in patients with severe Fibrosis (Metavir F3-

F4) compared to non-severe fibrosis (Metavir F0-

F2). Furthermore, the ROC curve based on a new

model that included serum bile acids, age, BMI,

serum AST, glucose and cholesterol levels suggested

that this combination reliably predicts the degree of

liver fibrosis with high degree of accuracy, and is not

inferior to the Fibrotest score [68].

9.2 Wu index based on prothrombin time as INR

and HA: Wu et al. (2013) introduced a new simple

index can easily predict significant liver fibrosis with

a high degree of accuracy. It is calculated as Wu

index = (INR×HA/100) to discriminate between F2-

F4 patients and F0-F1 patients. It showed AUROC

value of 0.921, and was better than APRI and FIB-4

[69].

9.3 AngioScore (AS): Herna´ndez-Bartolome´et al.

(2013) developed a novel index based on serum

levels of angiopoietin-2 measured in 108 CHC

patients and validated in 71 CHC patients.

AngioScore is calculated as follows: AS = -6.634 +

(1.083xlog Ang2) + (1.792xlog Age) + (3.782xlog INR)

+ (1.052xlog AST) – (0.005xplatelets) + (0.653xlog

GGT). The accuracy of this model was compared

with APRI, FIB4, King, AAR, GUCI, Lok, Forns, FI, and

FCI. The model classified CHC patients by discarding

significant fibrosis and diagnosing moderate and

severe fibrosis (AUROC 0.886, 0.920, 0.923,

respectively) with greater accuracy, sensitivity, and

specificity. Therefore, it outperforms other indices

and should help substantially in managing CHC and

monitoring long-term follow-up prognosis [70].

9.4 Carbohydrate 19.9 antigen (CA19.9): CA19.9 is a

glycoprotein expressed by several epithelial cancers,

as well as in normal pancreatic and biliary duct

epithelia, and it is used currently in the diagnosis

and follow-up of gastrointestinal tumors. In a study

of Bertino et al. (2013) on 180 patients, 116 with

HCV-related chronic liver disease and 64 with HBV-

related chronic liver disease, they suggested that

elevation of CA 19.9 is related to the severity of

fibrosis and to the viral etiology of hepatitis. They

proposed that CA19.9 could be used in the

combinations with the other markers already in use,

in order to increase the diagnostic accuracy of the

available tests, rising both PPV and NPV predictive

values [71].

9.5 Combination of sFas with TGF-ß1, HA, PIIINP:

Valva et al. (2013) proposed the addition of

apoptosis markers, particularly sFas combined with

TGF-ß1, HA, PIIINP in adult patients to more

accurately assess liver fibrosis severity. The

diagnostic accuracy evaluation demonstrated a

good performance for sFas to evaluate advanced

fibrosis in adults (AUROC: 0.8) [4].

9.6 Soluble CD163 (sCD163) and soluble mannose

receptor (sMR, sCD206): Macrophages regulate the

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fibrotic process in chronic liver disease. CD163 is an

endocytic receptor for haptoglobin-hemoglobin

complexes and is expressed solely on macrophages

and monocytes. As a result of ectodomain shedding,

the extracellular portion of CD163 circulates in

blood as a soluble protein (sCD163) [72]. In a pilot

study of Andersen et al. (2014), two new

macrophage-specific serum biomarkers [sCD163 and

sMR, sCD206] were evaluated as potential fibrosis

markers in 40 CHC patients. The plasma

concentrations of both biomarkers were

significantly higher in infected patients and in

cirrhosis compared to those with no/mild liver

fibrosis (5.77 mg/l vs. 2.49 mg/l and 0.44 mg/l vs.

0.30 mg/l for sCD163 and sMR, respectively). The

best separation between groups was obtained by

sCD163 (AUC 0.89) as compared to sMR (AUC 0.75).

sCD163 and sMR correlated significantly (r (2) =

0.53, p<0.0001). Interestingly, sCD163 was also

correlated significantly with TNF-α. Thus, sCD163 is

a promising new fibrosis marker in patients infected

with HCV [73].

9.6 CD163-HCV-FS and CD163-HBV-FS fibrosis

scores: Recently, Kazankov et al., investigated

sCD163 in 551 patients with CHC and 203 patients

with CHB before anti-viral treatment. sCD163 was

associated with fibrosis stages for both HCV and

HBV patients, with highest levels in patients with

advanced fibrosis and cirrhosis. sCD163 was a

marker of fibrosis independent of other biochemical

parameters and known risk factors. They created

two novel sCD163-based fibrosis scores, CD163-

HCV-FS and CD163-HBV-FS, which showed AUROC

of 0.79 and 0.71, respectively, for significant

fibrosis. Compared to existing scores, CD163-HCV-FS

was significantly superior to the APRI for all fibrosis

stages and to FIB-4 for significant fibrosis, but

CD163-HBV-FS was not [74].

9.7 Hyperglycosylated Wisteria floribunda

agglutinin-positive Mac-2 binding protein (WFA+-

M2BP): Serum WFA+-M2BP values were evaluated

in 200 patients with chronic liver diseases. There

were significant differences between fibrosis stages

F1 and F2, and between F2 and F3. AUROC for the

diagnosis of fibrosis (F ≥ 3) using serum WFA+-M2BP

value (0.812) was superior to the other surrogate

markers, including APRI (0.694), HA (0.683), and

type 4 collagen (0.625) [75].

9.8 Plasma caspase-generated cytokeratin-18

fragments (CK-18): Cusi et al. in a study on 424

patients, proposed CK-18 as a non-invasive

alternative for liver biopsy in diagnosing and staging

fibrosis. The CK-18 AUROC to predict fibrosis was

0.68 and the overall sensitivity/specificity was 85%

[76].

9.9 Fibroscan/Fibrotest: FibroTest™ (FT) and

Transient Elastography (TE) (FibroScan®) have been

validated as non-invasive markers of METAVIR

fibrosis stages from F0 to F4 using biopsy, and as

prognostic markers of liver related mortality in

patients with CHC. Poynard et al. extended the

validation of FT and TE as markers of critical steps

defined by occurrence of cirrhosis without

complications (F4.1), esophageal varices (F4.2), and

severe complications (F4.3): primary liver cancer,

variceal bleeding, or decompensation (ascites,

encephalopathy, or jaundice). At 5 years, among

501 patients without varices at baseline (F4.1)

varices occurred in 19 patients [F4.2]. The predictive

performance (AUROC) of FT was 0.77. At 10 years

severe complications occurred in 203 patients,

[F4.3], including primary liver cancer in 84 patients.

FT was predictive of severe complications [AUROC

0.79], including primary liver cancer [AUROC 0.84].

Similarly TE was predictive of severe complications

[AUROC 0.77], including primary liver cancer

[AUROC 0.86] [77].

10. Limitations of fibrosis serum biomarkers

1. They have a tendency to be more elevated in the

presence of inflammation; they are not liver-specific

where they can detect fibrogenesis in organs other

than the liver [17].

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2. Serum marker readings may be falsely high due to

their low clearance rates [17].

3. Some of these markers are not routinely available

in most clinical laboratories [3].

Based on the above mentioned data in this review

about different serum markers and their panels

used for assessment of liver fibrosis we can become

aware of some important facts. Firstly, the

performance of simple tests derived from routine

laboratory parameters appears to be similar to that

of more complex and expensive panels. Almost all

tests showed a better performance for diagnosing

cirrhosis (AUROC 0.97-0.80) than for significant

fibrosis (AUROC 0.76-0.89). Among non-invasive

methods, based on serum markers, some gained a

strong clinical foothold such as serum-based APRI,

Fibrometer, Fibrotest and Fibroscan/Fibrotest. Some

tests have limited clinical acceptance such as that

based on serum PON-1 or PGAA. There are many

other tests that are not yet widely validated such as

TGF-β1, HA, PIIINP and TIMP-1 panel, SBA model,

AngioSore, sCD163, and WFA+-M2BP, but remain

promising. Most non-invasive tests fail to

differentiate between early stages of fibrosis (F0

and F1-2) such as that based on serum CTGF,

FibroQ, FIB-4, and PLF score. In fact, most of these

tests can primarily distinguish cirrhosis from no or

minimal fibrosis. Moreover, validity of some non-

invasive tests (e.g. Fibrotest and Fibroscan) is

extended recently to make them predictive markers

for different cirrhosis complications too. Searching

for tests able to differentiate accurately between F1

and F2 fibrosis stages is still needed.

CONCLUSION

The current utility of non-invasive diagnostics

remains limited to pre-screening allowing physician

to narrow the population of patient before

definitive testing of liver fibrosis by biopsy of the

liver. Thus, more studies on serum fibrosis markers

and more validation efforts on large cohorts of

patients with chronic liver diseases are needed. In

addition, the use of a standardized system to

evaluate the utility of serum markers would

facilitate their introduction in clinical practice.

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Cite this article as: Entsar A. Saad. Non-invasive assessment of liver fibrosis using serum markers. J Pharm Chem Biol Sci 2014; 2(2):59-76.