Submitted Successfully!
To reward your contribution, here is a gift for you: A free trial for our video production service.
Thank you for your contribution! You can also upload a video entry or images related to this topic.
Version Summary Created by Modification Content Size Created at Operation
1
Yoshio Sumida
 + 4649 word(s) 4649 2021-05-03 11:08:24 |
2 format correct
Conner Chen
Meta information modification 4649 2021-05-25 06:14:04 |

Video Upload Options

Do you have a full video?
Send video materials Upload full video

Confirm

Are you sure to Delete?
Yes No
Cite
If you have any further questions, please contact Encyclopedia Editorial Office.
Sumida, Y. The Fibrosis-4 (FIB-4) Index. Encyclopedia. Available online: https://encyclopedia.pub/entry/9721 (accessed on 04 May 2024).
Sumida Y. The Fibrosis-4 (FIB-4) Index. Encyclopedia. Available at: https://encyclopedia.pub/entry/9721. Accessed May 04, 2024.
Sumida, Yoshio. "The Fibrosis-4 (FIB-4) Index" Encyclopedia, https://encyclopedia.pub/entry/9721 (accessed May 04, 2024).
Sumida, Y. (2021, May 17). The Fibrosis-4 (FIB-4) Index. In Encyclopedia. https://encyclopedia.pub/entry/9721
Sumida, Yoshio. "The Fibrosis-4 (FIB-4) Index." Encyclopedia. Web. 17 May, 2021.
The Fibrosis-4 (FIB-4) Index
Edit
This entry is adapted from the peer-reviewed paper 10.3390/life11020143

The fibrosis-4 (FIB-4) index is the first triaging tool for excluding advanced fibrosis because of its accuracy, simplicity, and cheapness, especially for general physicians or endocrinologists, although the FIB-4 index has several drawbacks. Accumulating evidence has suggested that vibration-controlled transient elastography (VCTE) and the enhanced liver fibrosis (ELF) test may become useful as the second step after triaging by the FIB-4 index. The leading cause of mortality in MAFLD is cardiovascular disease (CVD), extrahepatic malignancy, and liver-related diseases. MAFLD often complicates chronic kidney disease (CKD), resulting in increased simultaneous liver kidney transplantation. The FIB-4 index could be a predictor of not only liver-related mortality and incident hepatocellular carcinoma, but also prevalent and incident CKD, CVD, and extrahepatic malignancy.

hepatic fibrosis hepatocellular carcinoma cardiovascular disease FIB-4

1. Background

Obesity-associated disease is the most serious health problem worldwide (so-called metabodemic) [1]. In the adult population, 25% of the general population is estimated to be suffering from nonalcoholic fatty liver disease (NAFLD) [2]. Lifestyle-related diseases, such as obesity, type 2 diabetes (T2D), dyslipidemia, and hypertension, are closely associated with NAFLD, and patients who are not obese can also present with NAFLD; this is known as lean NAFLD. Lean NAFLD is defined as NAFLD that develops in patients with a body mass index (BMI) of <25 kg/m2 [3]. The prevalence of lean NAFLD varies from 7% in the US [4] to as high as 19% in Asia [5][6]. The pathogenesis of lean NAFLD is not well understood. Lean NAFLD patients demonstrate early alterations in the bile acid and gut microbiota profile [7]. The possession of the patatin-like phospholipase domain containing 3 (PNPLA3) polymorphism has been shown to be an independent factor associated with lean NAFLD patients [8][9].
The nomenclature of NAFLD should be updated to metabolic-dysfunction-associated fatty liver disease (MAFLD) [10]. Global experts suggest that the term MAFLD is more appropriate than NAFLD [11]. NAFLD has been diagnosed after exclusion of other liver diseases, while MAFLD can coexist with other liver diseases [10]. Therefore, MAFLD plus the hepatitis B virus (HBV) inactive carrier, MAFLD plus alcoholic liver disease (ALD), MAFLD plus autoimmune hepatitis (AIH), or MAFLD plus drug-induced liver injury (DILI) are plausible as a final diagnosis in clinical practice. Hepatic fibrosis is the most important risk factor for not only incident HCC, but also liver-related mortality in MAFLD [12]. Liver biopsy is now the gold standard for evaluating hepatic fibrosis, but it has several drawbacks such as hemorrhage risk, invasiveness, cost, observers’ variability, and patients’ unwillingness. Considering a large population of MAFLD patients, non-invasive tests (NITs) without performing liver biopsy are urgently required [13]. The American Association for the Study of Liver Disease (AASLD) practice guidance 2018 recommends the use of an NAFLD fibrosis score (NFS), the fibrosis-4 (FIB-4) index, vibration-controlled transient elastography (VCTE), and magnetic resonance elastography (MRE) [14]. However, all institutions do not have these innovative imaging modalities such as VCTE or MRE. The FIB-4 index, consisting of four parameters (age, aspartate aminotransferase (AST), alanine aminotransferase (ALT), and platelets), is a simple, cheap, and accurate tool [15][16]. We here review the role of the FIB-4 index for evaluation of hepatic fibrosis, incident comorbidities, carcinogenesis (hepatocellular carcinoma (HCC) and extrahepatic malignancy), overall/liver-related mortality or morbidity, and treatment efficacy in the management of NAFLD.

2. Which Fibrosis Stage Should We Pick up in MAFLD?

For a long time, a variety of NITs have been proposed to differentiate steatohepatitis from simple steatosis: cytokerarin-18, The hypertension, ALT levels, and insulin resistance (HAIR) score, and the NAFIC score (NASH, ferritin, insulin, and type IV collagen 7s). None of the NITs were globally accepted, because histological diagnosis of steatohepatitis has several limitations such as existence of borderline steatohepatitis, observers’ variability, and sampling error.
Fibrosis stages in MAFLD can be classified into F0, F1, F2, F3, and F4 [17][18]. F3 or F4 were defined as advanced fibrosis. Currently, MAFLD patients with advanced fibrosis should be examined for HCC surveillance considering cost-benefit balance [19][20]. Considering exponential increase in liver-related mortality in MAFLD patients with ≥ F2 compared with those with F0/1 (hazard ratio [HR] 9.57, 95% confidence interval [CI] 1.67–54.93) [12], we wonder which fibrosis stage (F2, F3, or F4) we should mine among a huge population of MAFLD. A variety of NITs for identifying advanced fibrosis in MAFLD have been established (Table 1). Vilar-Gomez et al. reported that NFS and the FIB-4 index are useful screening tools for determining the stage of liver fibrosis to be routinely applied in clinical practice [21]. Thus, the FIB-4 index and NFS are now recommended for excluding advanced fibrosis in the AASLD practice guidance 2018 [14].
The enhanced liver fibrosis (ELF) test is a non-invasive blood test that measures three direct markers of fibrosis: Hyaluronic acid (HA), procollagen III amino-terminal peptide (PIIINP), and tissue inhibitor of matrix metalloproteinase 1 (TIMP-1) [22]. According to a two-step algorithm from EU [23], ELF test can be applied to the intermediate group of FIB-4 index (1.3–3.25). If NAFLD patients have an ELF score of 10.35 or above, they are likely to have advanced fibrosis. ELF can reduce unnecessary liver biopsies. Recently, the usefulness of the ELF test was also validated in the Japanese NAFLD population [24]. Combinations or sequential procedures using VCTE complement the diagnostic performance of the ELF test for the identification of advanced fibrosis. From the view of economic cost, the combination of FIB-4 index plus ELF test is superior to the combination of FIB-4 index plus VCTE [25]. In the two-step algorithm for identifying severe fibrosis in MAFLD, FIB-4 index has been established as the 1st step, while ELF score, VCTE, or MRE may be diagnostic modalities as the 2nd step.
Table 1. A variety of non-invasive tests (NITs) for identifying severe fibrosis (F3/4) in MAFLD.
Index Formula Strengths Weaknesses
FIB-4 index
[15][16]		 (age [years] × AST [U/L]/(platelet count [109/L] × √ALT [U/L])
https://www.eapharma.co.jp/medicalexpert/product/livact/fib-4/calculator.html (accessed on 25 January 2021)
  • Simple (only four parameters)
  • Accurate
  • Validated globally
  • Requires an intermediate group
  • Overpredict in old patients
  • Inferior in patients with T2D?
NAFLD fibrosis score
[26]		 −1.675 + 0.037 × age (years) + 0.094 × BMI (kg/m2) + 1.13 × impaired fasting glucose/diabetes (yes = 1, no = 0) + 0.99 × AST/ALT ratio–0.013 × platelet count (×109/L) − 0.66 × albumin (g/dL)
http://nafldscore.com/ (accessed on 25 January 2021)
  • Validated globally
  • Accurate
  • Complex
  • (six parameters)
  • Requires an intermediate group
  • Overpredict in old patients
APRI
[27]		 AST to platelet ratio index
  • Simple (only two parameters)
  • Conflicting results
BARD
[28]		 BMI > 28 kg/m2 = 1 point
AST/ALT ratio > 0.8 = 2 points
Diabetes = 1 point
  • Very simple
  • Conflicting results
CA-fibrosis index
[29]		 1.5 × type IV collagen 7S (ng/mL) + 0.0264 × AST (IU/l)
  • Simple (only two parameters)
  • Only available in Japan
  • No external validation studies
ELF test
[22]		 −7.412 + (In [HA] × 0.681) + (In [P3NP] × 0.775) + (In [TIMP1] × 0.494)
  • Accurate
  • Validated globally
  • High cost? (three parameters)
MAFLD: Metabolic dysfunction-associated fatty liver disease, APRI: AST to platelet ratio index, BARD: BMI, AST/ALT ratio, and diabetes, ELF: Enhanced liver fibrosis, FIB-4: Fibrosis-4, AST: Aspartate aminotransferase, ALT: Alanine aminotransferase, BMI: Body mass index, HA: Hyaluronic acid, PIIINP: Aminoterminal propeptide of type III procollagen. TIMP-1: Tissue inhibitor of matrix metalloproteinase type 1, CA: Type IV collagen 7s and AST.

3. The Usefulness of FIB-4 Index to Evaluating Severe Fibrosis in MAFLD

The FIB-4 index is a score based on readily available blood tests that are routinely measured (age, AST, ALT, and platelet count). FIB-4 index is originally developed for evaluating hepatic fibrosis in patients with HIV/HCV co-infection [30]. At first, multiple regression analysis identified four variables as independent predictors of fibrosis: Age, AST, PT-INR, and platelet count in 505 patients with HIV/HCV co-infection. The second model that was investigated was applicable to 553 patients, and considered age, AST, platelet count, and ALT instead of PT-INR [30]. FIB-4 index enabled the correct identification of patients with severe fibrosis (F3/4) in HCV-monoinfected patients [31]. HCV eradicated patients without cirrhosis, but those with FIB-4 scores ≥ 3.25 have a high enough risk to merit HCC surveillance [32]. In noncirrhotic patients with chronic HBV infection, low FIB-4 index is useful for the prediction of the lowest risks of liver related events (carcinogenesis, cirrhosis progression, and mortality) [33][34][35]. Taken together, FIB-4 index has been established as NIT for identifying severe fibrosis or high risk of liver-related event in patients with chronic viral hepatitis.
In MAFLD, the first report by Shah and colleagues in a study of 541 MAFLD patients found that FIB-4 index had better diagnostic accuracy for estimation of liver fibrosis among various serum markers [15]. FIB-4 index has been suggested as a prescreening strategy to improve the efficiency of referral for specialized liver care, prioritizing patients who are at higher risk of significant liver disease. First of all, diagnostic accuracy is superior to other simple NITs such as NFS, AST to platelet ratio index (APRI), and BARD (BMI, AST/ALT ratio, diabetes) score [15][16][26][36][37][38][39][40] (Table 2). The NPV values of all methods (APRI, FIB-4 index, BARD score and NFS) were greater than 75% for the diagnosis of severe fibrosis. The summary specificities of the four models (APRI, FIB-4 index, and NFS) were greater than 85% for predicting severe fibrosis. The BARD score was inferior to other parameters. When APRI and FIB-4 index were used to detect severe fibrosis, their corresponding summary specificities were greater than 95%. The summary specificities of APRI (cutoff of 1.5), FIB-4 index (cutoff of 2.67), BARD score (cutoff of 2), and NFS (cutoff of 0.67–0.676) were 96.1%, 96.5%, 61.3%, and 94.6%, respectively. Only FIB-4 and NFS had a summary PPV greater than 70% [36].
Table 2. Summary sensitivities, specificities, PPV, and NPV of APRI, FIB-4, BARD score, and NAFLD Score, at various diagnostic thresholds for prediction of severe fibrosis [36].
Cutoff Values No. of Studies (No. of Patients) Summary Sensitivity, %, Mean (Range) Summary Specificity, %, Mean (Range) Summary PPV, %, Mean (Range) Summary NPV, %, Mean (Range)
APRI
0.452–0.50 5 (729) 72.9
(50.0–87.4)		 67.7
(43.1–91.0)		 44.8
(22.9–71.0)		 89.4
(84.9–95.0)
0.54–0.98 7 (1,351) 68.6
(61.0–76.2)		 72.7
(59.4–86.0)		 61.4
(46.9–76.2)		 77.6
(59.4–94.0)
1.00 4 (1101) 43.2
(27.0–67.0)		 86.1
(81.0–89.0)		 33.5
(26.0–40.0)		 89.8
(84.0–95.0)
1.50 4 (682) 32.9
(6.3–70.0)		 90.5
(74.5–97.0)		 55.5
(40.0–72.1)		 79.1
(73.2–87.2)
FIB-4 index
1.24–1.45 10 (2759) 77.8
(63.0–90.0)		 71.2
(55.5–88.0)		 40.3
(24.0–50.6)		 92.7
(88.0–98.0)
1.51–2.24 8 (1533) 77.0
(70.6–89.5)		 79.2
(67.1–93.6)		 66.4
(37.4–85.7)		 83.9
(58.6–97.2)
2.67 6 (1910) 31.9
(12.0–63.2)		 95.7
(88.3–98.7)		 66.0
(51.1–80.0)		 85.0
(79.4–92.6)
3.25 6 (1890) 37.3
(5.0–56.0)		 95.8
(89.0–100)		 72.5
(37.0–100)		 87.3
(78.5–94.0)
5.31–10.62 4 (543) 67.5
(50.0–100)		 80.8
(54.0–100)		 90.0
(80.0–100)		 85.1
(80.0–90.2)
BARD
1.5 1 (242) 83.0 59.0 34.0 93.0
2 14 (3057) 75.2
(41.7–100)		 61.6
(32.5–88.9)		 38.3
(15.0–79.8)		 88.7
(49.6–100)
3–4 5 (736) 59.4
(33.3–85.2)		 75.1
(59.9–91.8)		 55.2
(24.0–69.2)		 81.0
(71.4–90.1)
NFS
(−26.93)–(−2.16) 2 (106) 80.5
(78.0–83.0)		 69.5
(69.0–70.0)		 None None
−1.455 10 (3057) 72.9
(22.7–96.0)		 73.8
(42.9–100)		 50.4
(24.0–100)		 91.8
(81.3–98.1)
(−1.31)–(0.156) 5 (963) 78.2
(69.0–86.4)		 71.7
(60.0–83.0)		 58.4
(34.0–80.8)		 82.1
(54.1–95.0)
0.67–0.676 14 (3896) 43.1
(8.3–100)		 88.4
(25.0–100)		 66.9
(26.0–100)		 88.5
(78.6–100)
0.735 1 (235) 68.4 88.3 53.0 93.5
FIB-4 index could differentiate between steatohepatitis and non-steatohepatitis, even with steatohepatitis patients with mild or no fibrosis [41]. FIB-4 index has several advantages. First, calculation of FIB-4 index requires only four parameters, age, AST, ALT, and platelet count, while calculating the formula of NFS is slightly more complex [26] (Table 1). Second, FIB-4 index is available even in MAFLD patients with normal ALT levels [42][43][44]. A meta-analysis proved that 25% MAFLD patients and 19% NASH patients possess the normal ALT value [43]. Another strength of FIB-4 index is the availability of free online calculators (https://www.eapharma.co.jp/medicalexpert/product/livact/fib-4/calculator.html) (accessed on 25 January 2021).

4. The Compassion between FIB-4 Index and VCTE

To assess liver fibrosis, several non-invasive US-based elastography techniques have been developed. These methods include VCTE (FibroScan; Echosens, Paris, France), acoustic radiation force impulse (ARFI) imaging, and shear wave elastography (SWE) [45]. US-based VCTE performed with the FibroScan (Echosens) is the most thoroughly validated and commonly used elastography method worldwide. A systematic review and meta-analysis of VCTE in patients with NAFLD by Kwok et al. indicated that VCTE is good for the diagnosis of F3 (85% sensitivity and 82% specificity) and excellent for F4 (92% sensitivity and 92% sensitivity). However, it has a slightly lower accuracy for diagnosing F2 (79% sensitivity and 75% specificity) [46]. VCTE has several limitations. VCTE is limited to referral centers due to high equipment cost and had substantial failure rate, especially in obese patients. VCTE has a better diagnostic accuracy for advanced fibrosis than both FIB-4 index and NFS only in nonobese and/or low ALT patients [47]. However, liver stiffness measurement (LSM) by VCTE is influenced by not only hepatic fibrosis, but also a various factors, including steatosis, inflammation, congestion, and cholestasis. LSM has also intra- or inter-observers’ variability. The two-step algorithm, using FIB-4 index as the first step followed by VCTE as the second step, has been proposed in the US, Canada, and Asia [48][49][50][51][52]. The optimal cutoff value of LSM for identifying advanced fibrosis should be discussed.

5. FIB-4 Index and Carcinogenesis

In HCV, increased risk for HCC persists up to 10 years after HCV eradication in patients with baseline cirrhosis or high FIB-4 index [53]. In hepatis virus infected patients, a meta-analysis confirmed prognostic values of the FIB-4 index for overall survival and recurrence-free survival in HCC [54]. In NAFLD, Kanwal et al. showed that an FIB-4 index > 2.67 is associated with an increased risk of HCC not only in those with known cirrhosis, but also in those without a prior diagnosis of cirrhosis [55]. It is noteworthy whether FIB-4 index can be a predictor of incident malignancy in NAFLD, including HCC. NAFLD patients had a higher risk of HCC, colon cancer, and breast cancer compared with the non-NAFLD population [56]. NAFLD patients with FIB-4 index > 1.45 had higher risk of all cancer incidence compared to those with FIB-4 index < 1.45 (HR: 13.99, 95% CI: 3.00–65.23) [56]. In another study, FIB-4 index and NFS can predict HCC development and extra-cancer incidence, although the number of NAFLD patients involved in this study is small (n = 123) [57]. In Japan, the FIB-4 index was useful for predicting liver-related diseases but had limitations in predicting extrahepatic malignancies [58]. The relationship between NITs and extrahepatic cancer should be explored further.
It remains to be solved whether hepatic fibrosis could accelerate carcinogenesis in extrahepatic organs.

6. FIB-4 Index and Mortality

NAFLD patients with higher FIB-4 index are associated with increased liver disease and overall mortality [59][60][61][62] (Table 3). When NITs are applied to the general population, NITs did not become better predictor of severe liver disease than expected [57]. In NAFLD with diabetes, FIB-4 index, NFS, and APRI cannot predict liver-related mortality and morbidity [63]. In Japan, liver related mortality is extremely low in US-diagnosed NAFLD patients (9/4073) [64]. The main cause of mortality in that study is cardiovascular events and extrahepatic malignancies. NFS can stratify risk of cardiovascular events and extrahepatic malignancies [64]. FIB-4 index is also associated with all-cause mortality of systemic chronic diseases such as rheumatoid arthritis [65], microscopic polyangiitis, granulomatosis with polyangiitis [66], and chronic obstructive pulmonary disease [67]. The underlying mechanisms of these relationships remain unknown.
Table 3. NITs predicting for over-all mortality/morbidity, liver-related mortality/morbidity, liver related event, CVD, mortality, and extrahepatic cancer incidence in NAFLD.
Subjects N Nation Dx Observation Period Over-all Mortality
/Morbidity		 Liver-Related Mortality/Morbidity Liver Event HCC CVD Mortality Extrahepatic Cancer
NAFLD [62] 646 Sweden Biopsy 19.9
±8.7 years		 FIB-4 ○   FIB-4 ○      
NFS ○ NFS ○
Viral hepatitis-negative adults
[61]		 14,841 USA General population Median 19.3 years (IRQ, 17.5–21.1) years APRI ○ APRI ○     FIB-4 ○ APRI ○
FIB-4 ○ FIB-4 ○
NFS ○ NFS ○
Forns score ○ Forns score○
NAFLD [57] 153 Israel Biopsy 100 months
(mean)		 FIB-4 ○     FIB-4 ○   FIB-4 ○
NFS ○ NFS ○ NFS ○
APRI × APRI ○ APRI ○
NAFLD [68] 180 China US 6.6 (range 0.5–14.8)
years		 NFS ◎          
FIB-4 ○
APRI×
BARD×
NAFLD [58] 646 Japan Biopsy   FIB-4 ○     FIB-4 ○   FIB-4 ×
NAFLD [64] 4073 Japan US           NFS ○ NFS ○
NAFLD with diabetes
[63]		 284 Australia US 51.4 (range 6.1–146).
months		   NFS ×        
FIB-4 ×
APRI ×
NAFLD [60] 11,154 US US 14.5 years FIB-4 ○       FIB-4 ○  
NFS ○ NFS ○
APRI ○ APRI ○
NASH
[69]		 148 Canada biopsy Median: 5 years (IQR: 3–8) FIB-4 ○          
NFS ○
APRI ○
NAFLD
[59]		 153 US biopsy Median 104.8 (range, 3–317) months   NFS ◎        
FIB-4 ○
APRI ○
HCC: Hepatocellular carcinoma, CVD: Cardiovascular disease, FIB-4: Fibrosis-4, NFS: NAFLD fibrosis score, APRI: AST to platelet ratio index, IQR: Interquartile range. Forns score = 7.811−3.131 log(platelet count [109/L]) + 0.781 log(GGT [IU/L]) + 3.467 log(age [years]) − 0.014 total cholesterol (mg/dL) ◎ can predict very well, ○ can predict, × cannot predict.

7. FIB-4 Index and Risk of Cardiovascular Disease

The leading cause of mortality in MAFLD patients is cardiovascular disease (CVD), followed by extrahepatic cancer and liver related diseases [70]. MAFLD is an independent risk factor of coronary sclerosis [71], atrial fibrillation (AF) [72], coronary artery disease (CAD), and left ventricular dysfunction [73][74]. In daily clinical practice, we should pay attention to CVD event and control other risk factors, such as hypertension, dyslipidemia, and type 2 diabetes (T2D). FIB-4 index appears to be associated with high risk of CVD mortality [60]. Over a median follow-up time of 41.4 months (3044.4 patient-years) in 898 consecutive outpatients (mean age, 56.4 ± 12.7 years; 37.5% women), 58 cardiovascular events (1.9%/year) were registered. The rate of cardiovascular events was higher in patients with (n = 643, 2.1%/year) vs. without MAFLD (n = 255, 1.0%/year) (p = 0.066). In multivariable Cox proportional regression analysis, MAFLD increased risk for cardiovascular events (HR, 2.41; 95% CI, 1.06–5.47; p = 0.036) after adjustment for metabolic syndrome. Among patients with MAFLD, male sex, previous cardiovascular events, metabolic syndrome, and FIB-4 index ≥ 2.67 (HR, 4.02; 95% CI, 1.21–13.38; p = 0.023) were independently associated with risk of incident cardiovascular events [75]. A post hoc analysis of SAKURA AF Registry study showed that higher FIB-4 index ≥ 2.51 is independently associated with risks of CVD events and all-cause mortality in patients with AF [76]. The highest levels of NIT such as NFS, FIB-4 index, APRI, gamma-glutamyltransferase (GGT) to platelet ratio (GPR), and Forns score were associated with all-cause mortality and cardiovascular mortality [77]. In Japan, FIB-4 index is well correlated with coronary atherosclerosis (coronary artery calcium [CAC] score > 100), and subjects with higher FIB-4 index were prone to receive percutaneous coronary intervention [78]. In 665 Korean NAFLD subjects, the NFS and FIB-4 index were associated with coronary atherosclerosis (CAC score > 100) [79]. In patients with CAD, the highest NITs of hepatic fibrosis are associated with increased risks of all-cause and cardiovascular mortality [80]. FIB-4 index is also associated with all-cause mortality in patients with heart failure (HF) [81]. Among 96,373 participants over 6.9 years, 3844 incident congestive heart failure (CHF) events occurred. FIB-4 between 1.45 and 3.25 and FIB-4 > 3.25 were associated with incident CHF (HR [95% CI], 1.17 [1.07–1.27], and 1.65 [1.43–1.92], respectively) [82]. These results suggest that hepatic fibrosis (mild to severe) is associated with incident HF in the general population.

8. FIB-4 Index and Risk of Chronic Kidney Disease

MAFLD often complicates chronic kidney disease (CKD), resulting in growing indication for simultaneous liver kidney transplantation (SLKT) [83]. Risk of kidney graft loss was over 1.5-fold higher in recipients with MAFLD-cirrhosis than those with other etiologies [83]. A meta-analysis by Musso from Italy showed that MAFLD was associated with an increased risk of prevalent (OR 2.12, 95% CI 1.69–2.66) and incident (HR 1.79, 95% CI 1.65–1.95) CKD. Advanced fibrosis was associated with a higher prevalence (OR 5.20, 95% CI 3.14–8.61) and incidence (HR 3.29, 95% CI 2.30–4.71) of CKD than non-advanced fibrosis [84]. A variety of common drug pipelines exists for MAFLD and CKD [85][86]. In a cross sectional study based on 755 patients with USA-based diagnosed MAFLD, high FIB-4 index (≥1.10) is associated with an increased risk of prevalent CKD. The area under the receiver operating characteristic curve (AUROC) was the greatest for FIB-4 index (0.750), followed by NFS (0.710), AAR (0.594), APRI (0.587), and BARD score (0.561). In an analysis of the National Health and Nutrition Examination Survey (NHANES) conducted in the USA between 1988 and 1994, FIB-4 index is the better predictor of an increased risk of prevalent CKD compared with NFS, BARD, and APRI score [87].
The annual rate of incident CKD in MAFLD patients is estimated to be about 1.2% [88]. Five factors of baseline low eGFR level (60–75 mL/min), aging, T2D, hypertension, and elevated GGT, increase the risk of the development of CKD [88]. High FIB-4 index is a significant risk factor for incident CVD, and patients with increased FIB-4 index showed larger reduction in eGFR compared with those with decreased FIB-4 index [89]. The association of PNPLA3 genotype with incident CVD is conflicting [89][90][91].

9. Distribution of FIB-4 Index in MAFLD Population

The distribution of FIB-4 index in a healthy general population remains unknown, while some reports showed the distribution of FIB-4 index in MAFLD population. A total of 1370 MAFLD patients (78.5%) exhibited a low cut-off index (COI) (<1.30), 357 (20.5%), exhibited an indeterminate COI (1.30–2.67), and 18 (1.0%) exhibited a high COI (>2.67) [92]. Among 5410 Japanese MAFLD patients who were diagnosed by health checkups, 87.4% exhibited low COI (<1.45), 12.1% exhibited an indeterminate COI (1.45–3.26), and 0.5% exhibited a high COI (>3.26) [93]. On data of 576 MAFLD with biopsy proven MAFLD from JSG-NAFLD, 336 (58.3%) exhibited low COI (<1.45), 31.4% exhibited an indeterminate COI (1.45–3.26), and 59 (10.2%) exhibited a high COI (>3.26) [16]. Distribution of FIB-4 index in MAFLD depends on population age, ethnics, and selection bias (population-based, hospital-based, or biopsy proven). We are now planning to clarify the distribution of FIB-4 index in a healthy general population undergoing health checkups or non-biased MAFLD population.

10. Drawbacks of FIB-4 Index

FIB-4 index is a simple, reliable, and cheap parameter. Because FIB-4 index shows a high negative positive value (NPV) for detecting advanced fibrosis, FIB-4 index is useful to exclude advanced hepatic fibrosis. However, the FIB-4 index has also several drawbacks [94].
First, FIB-4 index requires an intermediate group. NAFLD patients classified into that group have to receive other NITs or liver biopsies. After exclusion of no or mild fibrosis, 2nd step diagnosis should be applied to the intermediate group. In Europe, the ELF test is usually applied to this intermediate group [23]. In the US or Asia, VCTE has been inducted as the second step.
Second, the positive predictive value (PPV) for identifying advanced fibrosis is not so high, so the FIB-4 index cannot help us to pick up advanced fibrosis.
Third, there is a concern that FIB-4 index may overpredict fibrosis in older patients [95][96], because its formula includes age. On the basis of data in JSG-NAFLD including 1050 biopsy-proven MAFLD patients, the box plot of the FIB-4 index according to each age group was shown in Figure 1. The FIB-4 index increases with age. Using conventional COI, the exclusion of advanced fibrosis is decreasing as the age becoming higher, and the detection of advanced fibrosis is decreasing as the age become lower. The new proposed low COI are 1.88 in 60–69 years, and 1.95 in ≥70 years [96] (Figure 2). McPherson and colleagues also suggested 2.0 of low COI in 65 years or older [95]. On data of 1008 patients with MAFLD from nine centers across eight countries (The Gut and Obesity in Asia (GOASIA) Workgroup), NITs such as APRI, NFS, and FIB-4 index had a lower specificity in elderly (AUROC 0.62–0.65) [97]. Female (OR: 3.21; 95% CI 1.37–7.54] and hypertension (OR 3.68; 95%CI 1.11–12.23) were predicting factors for advanced fibrosis in the elderly [97].
Life 11 00143 g001 550
Figure 1. Box plot of the FIB-4 index in each age group according to the presence (F = 3, 4) or absence (F = 0–2) of advanced fibrosis [96]. cHCO: Conventional high cutoff, cLCO: Conventional low cutoff.
Life 11 00143 g002 550
Figure 2. Modified cutoff values of FIB-4 index according to age group [96].
Fourth, low COI of FIB-4 index are variable according to ethnics. Low COI of FIB-4 index was generally accepted as 1.3 in western countries [15][98], while 1.45 in Asia [16][50][52]. Over-referral and under-referral are tradeoff relationships (Table 4). The problem of over-referral includes increased unnecessary liver biopsies, overwork of hepatologists, and high healthcare costs [99]. Over-referral has merits, such as decrease in burden for general physician and early identification of HCC, resulting in improving overall survival. The selection of over-referral or under-referral depends on hospital human resources, and physicians‘ or hepatologists’ commitment for MAFLD.
Table 4. The tradeoff relationship between over-referral and under-referral for MAFLD.
Over-Referral Under-Referral
FIB-4 index low COI 1.3 1.45
GP Work ↓ Work ↑
Hepatologists Work ↑ Work ↓
Unnecessary liver biopsy May increase May reduce
HCC early detection Possible? May delay diagnosis?
Heath economic costs High? Low?
COI: Low cutoff index, GP: General physician, HCC: Hepatocellular carcinoma.
Fifth, the FIB-4 index has limitations in a certain population of MAFLD patients. FIB-4 index showed significantly lower AUROCs for advanced fibrosis in obese MAFLD than in non-obese NAFLD [100]. Moreover, we found that FIB-4 index might be inferior in MAFLD patients with T2D compared to those without T2D [101]. In a study from Australia, NITs such as FIB-4 index, NFS, and APRI did not predict liver related events in 284 patients with MAFLD and diabetes [102]. Although its precise mechanism underlying inferiority of these NITs in T2D patients remains unknown, platelet count tends to be higher in MAFLD patients with T2D compared to those without T2D [101]. FIB-4 index in MAFLD patients with T2D is also lower than in those without T2D at the same fibrosis stages. FIB-4 index had reasonable specificity (69.9%), but poor sensitivity for detecting advanced fibrosis (72.6%) in T2D [103]. Type IV collagen 7S is the best predictor in Japanese MAFLD patients with T2D [101]. The combination of type IV collagen 7S and AST (CA index) may be more useful than type IV collagen 7S alone for detecting severe fibrosis [29].
Sixth, Shah S and colleagues feel that a low cut-off of 1.3 may be inappropriate, as it would include patients with F2 fibrosis [104]. They propose lowering COI of FIB-4 index to 1.0 in order to capture F2 patients. F2 fibrosis confers an increased mortality of liver-related diseases compared with no fibrosis (F0) (HR: 2.52) [12]. “Active fibrotic NASH” which requires intensive treatment is defined as NASH with NAFLD activity score (NAS) ≥ 4 and ≥ F2. Inclusion criteria in a variety of drug pipelines include NASH with NAS ≥ 4 and ≥ F2 [105][106]. FAST (FibroScan–AST) score, consisting of three parameters, including FibroScan-based controlled attenuation parameter (CAP), FibroScan-based LSM, and AST, can predict “active fibrotic NASH” [107][108][109]. “Active fibrotic NASH” patients had better receive intensive treatments for preventing progression to advanced stage. FAST score was designed to isolate “active fibrotic NASH” patients with elevated NAS ≥ 4 and significant fibrosis (≥F2) who could benefit from early interventions with anti-steatohepatitis and/or antifibrotic agents.
Although several problems of FIB-4 index remain to be solved, FIB-4 index is believed to be enough as the first triaging tool to exclude hepatic fibrosis, especially for general physicians or endocrinologists. However, limitations of FIB-4 index were kept in mind. As mentioned above, the MAFLD population with obesity or T2D might be inferior to that without obesity or T2D. It is plausible that heterogeneity of MAFLD has some impact on the performance of NIT.

FAST score =e−1.65+1.07×In(LSM)+2.66×10−8×CAP863.3×AST−11+e−1.65+1.07×In(LSM)+2.66×10−8×CAP3−63.3×AST−1

11. Two-Step Diagnostic Algorithm Using FIB-4 Index as the First Step

Globally, two-step diagnostic algorithms using FIB-4 index as the first step are generally accepted. Assessment of the potential impact of implementing a FIB-4 first strategy to triage patients using a clinical referral pathway for suspected NAFLD was performed at a tertiary liver center in Canada [98]. FIB-4 first strategy would decrease costs and decrease unnecessary referrals, as well as increase access to screening in non-specialized facilities. It remains unknown which parameters are the most appropriate as the second step among a variety of NITs, including ELF test [22], Mac-2 binding protein glycated isomer (M2BPGi) [110][111][112], type IV collagen 7S [29][111], ProC3 [113], and autotaxin [114][115] (Figure 3).
Life 11 00143 g003 550
Figure 3. The two-step algorithm in MAFLD. * Higher low cutoff point (FIB-4 index < 2.0) can be applied to patients aged over 65 years. ELF: Enhanced liver fibrosis, M2BPGi: Mac-2 binding protein glycosylation isomer, VCTE: Vibration-controlled transient elastography, MRE: Magnetic resonance elastography.

12. FIB-4 Index as Milestones of Treatment in MAFLD

Hard endpoints of treatments such as overall or liver-related mortality are difficult to evaluate. The gold standard to evaluate steatohepatitis treatment efficacy is now histological finding by liver biopsy. The primary endpoints are (1) steatohepatitis resolution without worsening fibrosis, or (2) fibrosis improvement of more than 1 stage without worsening steatohepatitis. However, repeated biopsies are also difficult to perform, because of risk, patients’ unwillingness, cost, and diagnostic variability. NITs monitoring treatment efficacy are urgently needed to avoid repeated liver biopsies for evaluation of treatment efficacy. Hepatic steatosis has been evaluated by innovative imaging modalities such as VCTE-based CAP, magnetic resonance imaging-proton density fat fraction (MRI-PDFF), or ultrasound-guided attenuation parameter (UGAP) [116][117][118]. However, it remains unknown that reduction in hepatic fat content can really result in amelioration of hepatic fibrosis in MAFLD. It also remains unknown whether NITs evaluating hepatic fibrosis in cross-sectional studies can also reflect hepatic fibrosis in longitudinal studies. Accumulating evidence has suggested that improvement in ABC (ALT, body weight, and A1c) is related to ameliorating hepatic fibrosis [106]. It is expected that FIB-4 index can become alternative to liver biopsies for evaluating treatment efficacy [119][120]. Finally, reduction in ALT, body weight, HbA1c, APRI, and FIB-4 index may become milestones for ameliorate hepatic fibrosis in these longitudinal studies (Table 5).
Table 5. NITs or parameters for monitoring treatment efficacy in MAFLD.
Author Subjects Outcomes Parameter Correlated with Pathological Improvement
Hamaguchi
[121]		 MAFLD (n = 39) Hepatic fibrosis ⊿HbA1c reduction
Seko
[122]		 Steatohepatitis (n = 52) NAS
Hepatic fibrosis		 ⊿ALT reduction ≥ 30% from baseline
Hoofnagle
[123]		 Steatohepatitis (n = 139) without DM
PIVENS trial		 NAS
Hepatic fibrosis		 ⊿ALT reduction ≥ 30% from baseline or post-treatment ALT ≤ 40 IU/L
Vilar-Gomez
[124]		 Steatohepatitis (n = 261) NASH resolution w/o worsening fibrosis ⊿BW reduction, absence of T2D ALT normalization, younger age, NAS < 5
Vuppalanchi Adult steatohepatitis (n = 231)
Pediatric MAFLD (n = 152)		 Histological improvement ⊿CK18 reduction
(inferior to ⊿ALT reduction)
Siddiqui
[119]		 MAFLD (n = 292) Hepatic fibrosis ⊿FIB-4 index, ⊿NFS, ⊿APRI
Jayakumar
[111]		 Steatohepatitis, stage 2–3 (n = 54)
Selonsertib (Phase 2)		 Hepatic fibrosis ⊿MRE
Hepatic steatosis MRI-PDFF > 25% reduction
Chalasani
[120]		 Steatohepatitis (n = 200)
FLINT trial (Phase 2)
Placebo vs. OCA 72wk		 Hepatic fibrosis ⊿FIB-4 index
⊿APRI
(⊿NFS: no correlation)
Loomba NAS ≥ 2 points reduction without worsening fibrosis OCA(+), pretreatment NAS > 5, TG ≤ 154 mg/dL, INR < 1, AST < 49 IU/L, ⊿ALT at 24wk (>17 IU/L)
MAFLD: Metabolic dysfunction associated fatty liver disease, DM: Diabetes mellitus, HbA1c: Glycated hemoglobin, NAS: NAFLD activity score, ALT: Alanine aminotransferase, BW: Body weight, CK18: Cytokeratin 18, FIB-4: Fibrosis-4, NFS: NAFLD fibrosis score, APRI: AST to platelet ration, MRE: Magnetic resonance elastography, MRI-PDFF: Magnetic resonance imaging-proton density fat fraction, OCA: Obeticholic acid, NAS: NAFLD activity score, TG: Triglyceride, INR: International normalized ratio, AST: Aspartate aminotransferase, The ⊿ symbol represents the change in various laboratory values between the first and second liver biopsy.

References

  1. GBD 2015 Obesity Collaborators; Afshin, A.; Forouzanfar, M.H.; Reitsma, M.B.; Sur, P.; Estep, K.; Lee, A.; Marczak, L.; Mokdad, A.H.; Moradi-Lakeh, M.; et al. Health Effects of Overweight and Obesity in 195 Countries over 25 Years. N. Engl. J. Med. 2017, 377, 13–27.
  2. Younossi, Z.; Anstee, Q.M.; Marietti, M.; Hardy, T.; Henry, L.; Eslam, M.; George, J.; Bugianesi, E. Global burden of NAFLD and NASH: Trends, predictions, risk factors and prevention. Nat. Rev. Gastroenterol. Hepatol. 2018, 15, 11–20.
  3. VanWagner, L.B.; Armstrong, M.J. Lean NAFLD: A not so benign condition? Hepatol. Commun. 2018, 2, 5–8.
  4. Younossi, Z.M.; Stepanova, M.; Negro, F.; Hallaji, S.; Younossi, Y.; Lam, B.; Srishord, M. Nonalcoholic fatty liver disease in lean individuals in the United States. Medicine 2012, 91, 319–327.
  5. Fan, J.G.; Kim, S.U.; Wong, V.W. New trends on obesity and NAFLD in Asia. J. Hepatol. 2017, 67, 862–873.
  6. Wei, J.L.; Leung, J.C.; Loong, T.C.; Wong, G.L.; Yeung, D.K.; Chan, R.S.; Chan, H.L.; Chim, A.M.; Woo, J.; Chu, W.C.; et al. Prevalence and Severity of Nonalcoholic Fatty Liver Disease in Non-Obese Patients: A Population Study Using Proton-Magnetic Resonance Spectroscopy. Am. J. Gastroenterol. 2015, 110, 1306–1315.
  7. Chen, F.; Esmaili, S.; Rogers, G.B.; Bugianesi, E.; Petta, S.; Marchesini, G.; Bayoumi, A.; Metwally, M.; Azardaryany, M.K.; Coulter, S.; et al. Lean NAFLD: A Distinct Entity Shaped by Differential Metabolic Adaptation. Hepatology 2020, 71, 1213–1227.
  8. Nishioji, K.; Mochizuki, N.; Kobayashi, M.; Kamaguchi, M.; Sumida, Y.; Nishimura, T.; Yamaguchi, K.; Kadotani, H.; Itoh, Y. The Impact of PNPLA3 rs738409 Genetic Polymorphism and Weight Gain ≥10 kg after Age 20 on Non-Alcoholic Fatty Liver Disease in Non-Obese Japanese Individuals. PLoS ONE 2015, 10, e0140427.
  9. Fracanzani, A.L.; Petta, S.; Lombardi, R.; Pisano, G.; Russello, M.; Consonni, D.; Di Marco, V.; Cammà, C.; Mensi, L.; Dongiovanni, P.; et al. Liver and Cardiovascular Damage in Patients With Lean Nonalcoholic Fatty Liver Disease, and Association with Visceral Obesity. Clin. Gastroenterol. Hepatol. 2017, 15, 1604–1611.
  10. Eslam, M.; Newsome, P.N.; Sarin, S.K.; Anstee, Q.M.; Targher, G.; Romero-Gomez, M.; Zelber-Sagi, S.; Wai-Sun Wong, V.; Dufour, J.F.; Schattenberg, J.M.; et al. A new definition for metabolic dysfunction-associated fatty liver disease: An international expert consensus statement. J. Hepatol. 2020, 73, 202.
  11. Eslam, M.; Sanyal, A.J.; George, J.; International Consensus Panel. MAFLD: A Consensus-Driven Proposed Nomenclature for Metabolic Associated Fatty Liver Disease. Gastroenterology 2020, 158, 1999–2014.
  12. Dulai, P.S.; Singh, S.; Patel, J.; Soni, M.; Prokop, L.J.; Younossi, Z.; Sebastiani, G.; Ekstedt, M.; Hagstrom, H.; Nasr, P.; et al. Increased risk of mortality by fibrosis stage in nonalcoholic fatty liver disease: Systematic review and meta-analysis. Hepatology 2017, 65, 1557–1565.
  13. Eguchi, Y.; Wong, G.; Akhtar, O.; Sumida, Y. Non-invasive diagnosis of nonalcoholic steatohepatitis (NASH) and advanced fibrosis in Japan: A targeted literature review. Hepatol. Res. 2020, in press.
  14. Chalasani, N.; Younossi, Z.; Lavine, J.E.; Charlton, M.; Cusi, K.; Rinella, M.; Harrison, S.A.; Brunt, E.M.; Sanyal, A.J. The diagnosis and management of nonalcoholic fatty liver disease: Practice guidance from the American Association for the Study of Liver Diseases. Hepatology 2018, 67, 328–357.
  15. Shah, A.G.; Lydecker, A.; Murray, K.; Tetri, B.N.; Contos, M.J.; Sanyal, A.J.; Nash Clinical Research Network. Comparison of noninvasive markers of fibrosis in patients with nonalcoholic fatty liver disease. Clin. Gastroenterol. Hepatol. 2009, 7, 1104–1112.
  16. Sumida, Y.; Yoneda, M.; Hyogo, H.; Itoh, Y.; Ono, M.; Fujii, H.; Eguchi, Y.; Suzuki, Y.; Aoki, N.; Japan Study Group of Nonalcoholic Fatty Liver Disease (JSG-NAFLD); et al. Validation of the FIB4 index in a Japanese nonalcoholic fatty liver disease population. BMC Gastroenterol. 2012, 12, 2.
  17. Brunt, E.M.; Janney, C.G.; Di Bisceglie, A.M.; Neuschwander-Tetri, B.A.; Bacon, B.R. Nonalcoholic steatohepatitis: A proposal for grading and staging the histological lesions. Am. J. Gastroenterol. 1999, 94, 2467–2474.
  18. Kleiner, D.E.; Brunt, E.M.; Van Natta, M.; Behling, C.; Contos, M.J.; Cummings, O.W.; Ferrell, L.D.; Liu, Y.C.; Torbenson, M.S.; Unalp-Arida, A.; et al. Design and validation of a histological scoring system for nonalcoholic fatty liver disease. Design and validation of a histological scoring system for nonalcoholic fatty liver disease. Hepatology 2005, 41, 1313–1321.
  19. Loomba, R.; Lim, J.K.; Patton, H.; El-Serag, H.B. AGA Clinical Practice Update on Screening and Surveillance for Hepatocellular Carcinoma in Patients with Nonalcoholic Fatty Liver Disease: Expert Review. Gastroenterology 2020, 158, 1822.
  20. Sumida, Y.; Yoneda, M.; Seko, Y.; Ishiba, H.; Hara, T.; Toyoda, H.; Yasuda, S.; Kumada, T.; Hayashi, H.; Kobayashi, T.; et al. Japan Study Group of NAFLD (JSG-NAFLD). Surveillance of Hepatocellular Carcinoma in Nonalcoholic Fatty Liver Disease. Diagnostics 2020, 10, 579.
  21. Vilar-Gomez, E.; Chalasani, N. Non-invasive assessment of non-alcoholic fatty liver disease: Clinical prediction rules and blood-based biomarkers. J Hepatol. 2018, 68, 305–315.
  22. Vali, Y.; Lee, J.; Boursier, J.; Spijker, R.; Löffler, J.; Verheij, J.; Brosnan, M.J.; Böcskei, Z.; Anstee, Q.M.; LITMUS Systematic Review Team; et al. Enhanced liver fibrosis test for the non-invasive diagnosis of fibrosis in patients with NAFLD: A systematic review and meta-analysis. J. Hepatol. 2020, 73, 252–262.
  23. Srivastava, A.; Gailer, R.; Tanwar, S.; Trembling, P.; Parkes, J.; Rodger, A.; Suri, D.; Thorburn, D.; Sennett, K.; Morgan, S.; et al. Prospective evaluation of a primary care referral pathway for patients with non-alcoholic fatty liver disease. J. Hepatol. 2019, 71, 371–378.
  24. Inadomi, C.; Takahashi, H.; Ogawa, Y.; Inadomi, C.; Takahashi, H.; Ogawa, Y.; Oeda, S.; Imajo, K.; Kubotsu, Y.; Tanaka, K.; et al. Accuracy of the Enhanced Liver Fibrosis test, and combination of the Enhanced Liver Fibrosis and non-invasive tests for the diagnosis of advanced liver fibrosis in patients with non-alcoholic fatty liver disease. Hepatol. Res. 2020, 50, 682–692.
  25. Crossan, C.; Majumdar, A.; Srivastava, A.; Thorburn, D.; Rosenberg, W.; Pinzani, M.; Longworth, L.; Tsochatzis, E.A. Referral pathways for patients with NAFLD based on non-invasive fibrosis tests: Diagnostic accuracy and cost analysis. Liver Int. 2019, 39, 2052–2060.
  26. Angulo, P.; Hui, J.M.; Marchesini, G.; Bugianesi, E.; George, J.; Farrell, G.C.; Enders, F.; Saksena, S.; Burt, A.D.; Bida, J.P.; et al. The NAFLD fibrosis score: A noninvasive system that identifies liver fibrosis in patients with NAFLD. Hepatology 2007, 45, 846–854.
  27. Peleg, N.; Issachar, A.; Sneh-Arbib, O.; Shlomai, A. AST to platelet ratio index and fibrosis 4 calculator scores for non-invasive assessment of hepatic fibrosis in patients with non-alcoholic fatty liver disease. Dig. Liver Dis. 2017, 49, 1133–1138.
  28. Harrison, S.A.; Oliver, D.; Arnold, H.L.; Gogia, S.; Neuschwander-Tetri, B.A. Development and validation of a simple NAFLD clinical scoring system for identifying patients without advanced disease. Gut 2008, 57, 1441–1447.
  29. Okanoue, T.; Ebise, H.; Kai, T.; Mizuno, M.; Shima, T.; Ichihara, J.; Aoki, M. A simple scoring system using type IV collagen 7S and aspartate aminotransferase for diagnosing nonalcoholic steatohepatitis and related fibrosis. J. Gastroenterol. 2018, 53, 129–139.
  30. Sterling, R.K.; Lissen, E.; Clumeck, N.; Sola, R.; Correa, M.C.; Montaner, J.; Sulkowski, M.S.; Torriani, F.J.; Dieterich, D.T.; Thomas, D.L.; et al. Development of a simple noninvasive index to predict significant fibrosis in patients with HIV/HCV coinfection. Hepatology 2006, 43, 1317–1325.
  31. Vallet-Pichard, A.; Mallet, V.; Nalpas, B.; Verkarre, V.; Nalpas, A.; Dhalluin-Venier, V.; Fontaine, H.; Pol, S. FIB-4: An inexpensive and accurate marker of fibrosis in HCV infection. comparison with liver biopsy and fibrotest. Hepatology 2017, 46, 32–36.
  32. Ioannou, G.N.; Beste, L.A.; Green, P.K.; Singal, A.G.; Tapper, E.B.; Waljee, A.K.; Sterling, R.K.; Feld, J.J.; Kaplan, D.E.; Taddei, T.H.; et al. Increased Risk for Hepatocellular Carcinoma Persists Up to 10 Years After HCV Eradication in Patients With Baseline Cirrhosis or High FIB-4 Scores. Gastroenterology 2019, 157, 1264–1278.
  33. Tseng, T.C.; Liu, C.J.; Su, T.H.; Yang, W.T.; Chen, C.L.; Yang, H.C.; Kuo, S.F.; Liu, C.H.; Chen, P.J.; Chen, D.S.; et al. Fibrosis-4 index predicts cirrhosis risk and liver-related mortality in 2075 patients with chronic HBV infection. Aliment. Pharmacol. Ther. 2018, 47, 1480–1489.
  34. Tseng, T.C.; Liu, C.J.; Su, T.H.; Yang, W.T.; Chen, C.L.; Yang, H.C.; Wang, C.C.; Kuo, S.F.; Liu, C.H.; Chen, P.J.; et al. Fibrosis-4 Index Helps Identify HBV Carriers With the Lowest Risk of Hepatocellular Carcinoma. Am. J. Gastroenterol. 2017, 112, 1564–1574.
  35. Nishikawa, H.; Nishijima, N.; Enomoto, H.; Sakamoto, A.; Nasu, A.; Komekado, H.; Nishimura, T.; Kita, R.; Kimura, T.; Iijima, H.; et al. Comparison of FIB-4 index and aspartate aminotransferase to platelet ratio index on carcinogenesis in chronic hepatitis B treated with entecavir. J. Cancer 2017, 8, 152–161.
  36. Xiao, G.; Zhu, S.; Xiao, X.; Yan, L.; Yang, J.; Wu, G. Comparison of laboratory tests, ultrasound, or magnetic resonance elastography to detect fibrosis in patients with nonalcoholic fatty liver disease: A meta-analysis. Hepatology 2017, 66, 1486–1501.
  37. Patel, Y.A.; Gifford, E.J.; Glass, L.M.; Turner, M.J.; Han, B.; Moylan, C.A.; Choi, S.; Suzuki, A.; Provenzale, D.; Hunt, C.M. Identifying Nonalcoholic Fatty Liver Disease Advanced Fibrosis in the Veterans Health Administration. Dig. Dis. Sci. 2018, 63, 2259–2266.
  38. McPherson, S.; Stewart, S.F.; Henderson, E.; Burt, A.D.; Day, C.P. Simple non-invasive fibrosis scoring systems can reliably exclude advanced fibrosis in patients with non-alcoholic fatty liver disease. Gut 2010, 59, 1265–1269.
  39. De Carli, M.A.; de Carli, L.A.; Correa, M.B.; Junqueira GJr Tovo, C.V.; Coral, G.P. Performance of noninvasive scores for the diagnosis of advanced liver fibrosis in morbidly obese with nonalcoholic fatty liver disease. Eur. J. Gastroenterol. Hepatol. 2020, 32, 420–425.
  40. Kobayashi, N.; Kumada, T.; Toyoda, H.; Tada, T.; Ito, T.; Kage, M.; Okanoue, T.; Kudo, M. Ability of Cytokeratin-18 Fragments and FIB-4 Index to Diagnose Overall and Mild Fibrosis Nonalcoholic Steatohepatitis in Japanese Nonalcoholic Fatty Liver Disease Patients. Dig. Dis. 2017, 35, 521–530.
  41. McPherson, S.; Anstee, Q.M.; Henderson, E.; Day, C.P.; Burt, A.D. Are simple noninvasive scoring systems for fibrosis reliable in patients with NAFLD and normal ALT levels? Eur. J. Gastroenterol. Hepatol. 2013, 25, 652–658.
  42. Ma, X.; Liu, S.; Zhang, J.; Dong, M.; Wang, Y.; Wang, M.; Xin, Y. Proportion of NAFLD patients with normal ALT value in overall NAFLD patients: A systematic review and meta-analysis. BMC Gastroenterol. 2020, 20, 10.
  43. Yoneda, M.; Imajo, K.; Eguchi, Y.; Fujii, H.; Sumida, Y.; Hyogo, H.; Ono, M.; Suzuki, Y.; Kawaguchi, T.; Japan Study Group of Nonalcoholic Fatty Liver Disease (JSG-NAFLD); et al. Noninvasive scoring systems in patients with nonalcoholic fatty liver disease with normal alanine aminotransferase levels. J. Gastroenterol. 2013, 48, 1051–1060.
  44. Honda, Y.; Yoneda, M.; Imajo, K.; Nakajima, A. Elastography Techniques for the Assessment of Liver Fibrosis in Non-Alcoholic Fatty Liver Disease. Int. J. Mol. Sci. 2020, 21, 4039.
  45. Kwok, R.; Tse, Y.K.; Wong, G.L.; Ha, Y.; Lee, A.U.; Ngu, M.C.; Chan, H.L.; Wong, V.W. Systematic review with meta-analysis: Non-invasive assessment of non-alcoholic fatty liver disease--the role of transient elastography and plasma cytokeratin-18 fragments. Aliment. Pharmacol. Ther. 2014, 39, 254–269.
  46. Petta, S.; Wai-Sun Wong, V.; Bugianesi, E.; Fracanzani, A.L.; Cammà, C.; Hiriart, J.B.; Lai-Hung Wong, G.; Vergniol, J.; Wing-Hung Chan, A.; Giannetti, A.; et al. Impact of Obesity and Alanine Aminotransferase Levels on the Diagnostic Accuracy for Advanced Liver Fibrosis of Noninvasive Tools in Patients With Nonalcoholic Fatty Liver Disease. Am. J. Gastroenterol. 2019, 114, 916–928.
  47. Castera, L.; Friedrich-Rust, M.; Loomba, R. Noninvasive Assessment of Liver Disease in Patients with Nonalcoholic Fatty Liver Disease. Gastroenterology 2019, 156, 1264–1281.
  48. Davyduke, T.; Tandon, P.; Al-Karaghouli, M.; Abraldes, J.G.; Ma, M.M. Impact of Implementing a “FIB-4 First” Strategy on a Pathway for Patients with NAFLD Referred From Primary Care. Hepatol. Commun. 2019, 3, 1322–1333.
  49. Sumida, Y.; Shima, T.; Mitsumoto, Y.; Katayama, T.; Umemura, A.; Yamaguchi, K.; Itoh, Y.; Yoneda, M.; Okanoue, T. Epidemiology, Pathogenesis, and Diagnostic Strategy of Diabetic Liver Disease in Japan. Int. J. Mol. Sci. 2020, 21, 4337.
  50. Yoneda, M.; Imajo, K.; Takahashi, H.; Ogawa, Y.; Eguchi, Y.; Sumida, Y.; Yoneda, M.; Kawanaka, M.; Saito, S.; Tokushige, K.; et al. Clinical strategy of diagnosing and following patients with nonalcoholic fatty liver disease based on invasive and noninvasive methods. J. Gastroenterol. 2018, 53, 181.
  51. Chan, W.K.; Treeprasertsuk, S.; Goh, G.B.; Fan, J.G.; Song, M.J.; Charatcharoenwitthaya, P.; Duseja, A.; Dan, Y.Y.; Imajo, K.; Nakajima, A.; et al. Optimizing Use of Nonalcoholic Fatty Liver Disease Fibrosis Score, Fibrosis-4 Score, and Liver Stiffness Measurement to Identify Patients with Advanced Fibrosis. Clin. Gastroenterol. Hepatol. 2019, 17, 2570–2580.
  52. Zhang, Y.; Wang, R.; Yang, X. FIB-4 index serves as a noninvasive prognostic biomarker in patients with hepatocellular carcinoma: A meta-analysis. Medicine (Baltimore) 2018, 97, e13696.
  53. Kanwal, F.; Kramer, J.R.; Mapakshi, S.; Natarajan, Y.; Chayanupatkul, M.; Richardson, P.A.; Li, L.; Desiderio, R.; Thrift, A.P.; Asch, S.M.; et al. Risk of Hepatocellular Cancer in Patients with Non-Alcoholic Fatty Liver Disease. Gastroenterology 2018, 155, 1828.
  54. Kim, G.A.; Lee, H.C.; Choe, J.; Kim, M.J.; Lee, M.J.; Chang, H.S.; Bae, I.Y.; Kim, H.K.; An, J.; Shim, J.H.; et al. Association between non-alcoholic fatty liver disease and cancer incidence rate. J. Hepatol. 2018, 68, 104–146.
  55. Peleg, N.; Sneh Arbib, O.; Issachar, A.; Cohen-Naftaly, M.; Braun, M.; Shlomai, A. Noninvasive scoring systems predict hepatic and extra-hepatic cancers in patients with nonalcoholic fatty liver disease. PLoS ONE 2018, 13, e0202393.
  56. Ito, T.; Ishigami, M.; Ishizu, Y.; Kuzuya, T.; Honda, T.; Hayashi, K.; Nishimura, D.; Toyoda, H.; Kumada, T.; Goto, H.; et al. Utility and limitations of noninvasive fibrosis markers for predicting prognosis in biopsy-proven Japanese non-alcoholic fatty liver disease patients. J. Gastroenterol. Hepatol. 2019, 34, 207–214.
  57. Angulo, P.; Bugianesi, E.; Bjornsson, E.S.; Charatcharoenwitthaya, P.; Mills, P.R.; Barrera, F.; Haflidadottir, S.; Day, C.P.; George, J. Simple noninvasive systems predict long-term outcomes of patients with nonalcoholic fatty liver disease. Gastroenterology 2013, 145, 782–789.
  58. Kim, D.; Kim, W.R.; Kim, H.J.; Therneau, T.M. Association between noninvasive fibrosis markers and mortality among adults with nonalcoholic fatty liver disease in the United States. Hepatology 2013, 57, 1357–1365.
  59. Unalp-Arida, A.; Ruhl, C.E. Liver fibrosis scores predict liver disease mortality in the United States population. Hepatology 2017, 66, 84–95.
  60. Hagström, H.; Nasr, P.; Ekstedt, M.; Stål, P.; Hultcrantz, R.; Kechagias, S. Accuracy of Noninvasive Scoring Systems in Assessing Risk of Death and Liver-Related Endpoints in Patients With Nonalcoholic Fatty Liver Disease. Clin. Gastroenterol. Hepatol. 2019, 17, 1148–1156.
  61. Bertot, L.C.; Jeffrey, G.P.; de Boer, B.; MacQuillan, G.; Garas, G.; Chin, J.; Huang, Y.; Adams, L.A. Diabetes impacts prediction of cirrhosis and prognosis by non-invasive fibrosis models in non-alcoholic fatty liver disease. Liver Int. 2018, 38, 1793–1802.
  62. Tada, T.; Kumada, T.; Toyoda, H.; Mizuno, K.; Sone, Y.; Akita, T.; Tanaka, J. Progression of liver fibrosis is associated with non-liver-related mortality in patients with nonalcoholic fatty liver disease. Hepatol. Commun. 2017, 1, 899–910.
  63. Kim, S.U.; Kim, B.K.; Park, J.Y.; Kim, D.Y.; Ahn, S.H.; Park, Y.B.; Han, K.H.; Lee, S.W. Fibrosis-4 index at diagnosis can predict all-cause mortality in patients with rheumatoid arthritis: A retrospective monocentric study. Mod. Rheumatol. 2020, 30, 70–77.
  64. Park, H.J.; Park, J.Y.; Jung, S.M.; Song, J.J.; Park, Y.B.; Lee, S.W. Fibrosis-4 index at diagnosis is associated with all-cause mortality in patients with microscopic polyangiitis and granulomatosis with polyangiitis. BMC Gastroenterol. 2019, 19, 90.
  65. Yong, S.H.; Leem, A.Y.; Kim, Y.S.; Park, M.S.; Chang, J.; Kim, S.U.; Jung, J.Y. Hepatic Fibrosis Assessed Using Fibrosis-4 Index Is Predictive of All-Cause Mortality in Patients with Chronic Obstructive Pulmonary Disease. Int. J. Chronic Obstr. Pulm. Dis. 2020, 15, 831–839.
  66. Xun, Y.H.; Guo, J.C.; Lou, G.Q.; Jiang, Y.M.; Zhuang, Z.J.; Zhu, M.F.; Luo, Y.; Ma, X.J.; Liu, J.; Bian, D.X.; et al. Non-alcoholic fatty liver disease (NAFLD) fibrosis score predicts 6.6-year overall mortality of Chinese patients with NAFLD. Clin. Exp. Pharmacol. Physiol. 2014, 41, 643–649.
  67. Sebastiani, G.; Alshaalan, R.; Wong, P.; Rubino, M.; Salman, A.; Metrakos, P.; Deschenes, M.; Ghali, P. Prognostic Value of Non-Invasive Fibrosis and Steatosis Tools, Hepatic Venous Pressure Gradient (HVPG) and Histology in Nonalcoholic Steatohepatitis. PLoS ONE 2015, 10, e0128774.
  68. Ong, J.P.; Pitts, A.; Younossi, Z.M. Increased overall mortality and liver-related mortality in non-alcoholic fatty liver disease. J. Hepatol. 2008, 49, 608–612.
  69. Lee, S.B.; Park, G.M.; Lee, J.Y.; Lee, B.U.; Park, J.H.; Kim, B.G.; Jung, S.W.; Jeong, I.D.; Bang, S.J.; Shin, J.W.; et al. Association between non-alcoholic fatty liver disease and subclinical coronary atherosclerosis: An observational cohort study. J. Hepatol. 2018, 68, 1018–1024.
  70. Käräjämäki, A.J.; Pätsi, O.P.; Savolainen, M.; Kesäniemi, Y.A.; Huikuri, H.; Ukkola, O. Non-Alcoholic Fatty Liver Disease as a Predictor of Atrial Fibrillation in Middle-Aged Population (OPERA Study). PLoS ONE 2015, 10, e0142937.
  71. Mantovani, A.; Pernigo, M.; Bergamini, C.; Bonapace, S.; Lipari, P.; Pichiri, I.; Bertolini, L.; Valbusa, F.; Barbieri, E.; Zoppini, G.; et al. Nonalcoholic Fatty Liver Disease Is Independently Associated with Early Left Ventricular Diastolic Dysfunction in Patients with Type 2 Diabetes. PLoS ONE 2015, 10, e0135329.
  72. Chung, G.E.; Lee, J.H.; Lee, H.; Kim, M.K.; Yim, J.Y.; Choi, S.Y.; Kim, Y.J.; Yoon, J.H.; Kim, D. Nonalcoholic fatty liver disease and advanced fibrosis are associated with left ventricular diastolic dysfunction. Atherosclerosis 2018, 272, 137–144.
  73. Baratta, F.; Pastori, D.; Angelico, F.; Balla, A.; Paganini, A.M.; Cocomello, N.; Ferro, D.; Violi, F.; Sanyal, A.J.; Del Ben, M. Nonalcoholic Fatty Liver Disease and Fibrosis Associated With Increased Risk of Cardiovascular Events in a Prospective Study. Clin. Gastroenterol. Hepatol. 2020, 18, 2324–2331.
  74. Saito, Y.; Okumura, Y.; Nagashima, K.; Fukamachi, D.; Yokoyama, K.; Matsumoto, N.; Tachibana, E.; Kuronuma, K.; Oiwa, K.; Matsumoto, M.; et al. Impact of the Fibrosis-4 Index on Risk Stratification of Cardiovascular Events and Mortality in Patients with Atrial Fibrillation: Findings from a Japanese Multicenter Registry. J. Clin. Med. 2020, 9, 584.
  75. Chen, Q.; Li, Q.; Li, D.; Chen, X.; Liu, Z.; Hu, G.; Wang, J.; Ling, W. Association between liver fibrosis scores and the risk of mortality among patients with coronary artery disease. Atherosclerosis 2020, 299, 45–52.
  76. Ishiba, H.; Sumida, Y.; Kataoka, S.; Kuroda, M.; Akabame, S.; Tomiyasu, K.; Tanaka, M.; Arai, M.; Taketani, H.; Seko, Y.; et al. Association of coronary artery calcification with liver fibrosis in Japanese patients with non-alcoholic fatty liver disease. Hepatol. Res. 2016, 46, 1107–1117.
  77. Song, D.S.; Chang, U.I.; Kang, S.G.; Song, S.W.; Yang, J.M. Noninvasive Serum Fibrosis Markers are Associated with Coronary Artery Calcification in Patients with Nonalcoholic Fatty Liver Disease. Gut Liver 2019, 13, 658–668.
  78. Sato, Y.; Yoshihisa, A.; Kanno, Y.; Watanabe, S.; Yokokawa, T.; Abe, S.; Misaka, T.; Sato, T.; Suzuki, S.; Oikawa, M.; et al. Liver stiffness assessed by Fibrosis-4 index predicts mortality in patients with heart failure. Open Heart 2017, 4, e000598.
  79. So-Armah, K.A.; Lim, J.K.; Lo Re, V.; Tate, J.P.; Chang, C.H.; Butt, A.A.; Gibert, C.L.; Rimland, D.; Marconi, V.C.; Goetz, M.B.; et al. Veterans Aging Cohort Study Project Team. FIB-4 stage of liver fibrosis predicts incident heart failure among HIV-infected and uninfected patients. Hepatology 2017, 66, 1286–1295.
  80. Singal, A.K.; Hasanin, M.; Kaif, M.; Wiesner, R.; Kuo, Y.F. Nonalcoholic Steatohepatitis is the Most Rapidly Growing Indication for Simultaneous Liver Kidney Transplantation in the United States. Transplantation 2016, 100, 607–612.
  81. Musso, G.; Gambino, R.; Tabibian, J.H.; Ekstedt, M.; Kechagias, S.; Hamaguchi, M.; Hultcrantz, R.; Hagström, H.; Yoon, S.K.; Charatcharoenwitthaya, P.; et al. Association of non-alcoholic fatty liver disease with chronic kidney disease: A systematic review and meta-analysis. PLoS Med. 2014, 11, e1001680.
  82. Sumida, Y.; Yoneda, M.; Toyoda, H.; Yasuda, S.; Tada, T.; Hayashi, H.; Nishigaki, Y.; Suzuki, Y.; Naiki, T.; Morishita, A.; et al. Japan Study Group of NAFLD (JSG-NAFLD). Common Drug Pipelines for the Treatment of Diabetic Nephropathy and Hepatopathy: Can We Kill Two Birds with One Stone? Int. J. Mol. Sci. 2020, 21, E4939.
  83. Musso, G.; Cassader, M.; Cohney, S.; De Michieli, F.; Pinach, S.; Saba, F.; Gambino, R. Fatty Liver and Chronic Kidney Disease: Novel Mechanistic Insights and Therapeutic Opportunities. Diabetes Care 2016, 39, 1830–1845.
  84. Wijarnpreecha, K.; Thongprayoon, C.; Scribani, M.; Ungprasert, P.; Cheungpasitporn, W. Noninvasive fibrosis markers and chronic kidney disease among adults with nonalcoholic fatty liver in USA. Eur. J. Gastroenterol. Hepatol. 2018, 30, 404–410.
  85. Arase, Y.; Suzuki, F.; Kobayashi, M.; Suzuki, Y.; Kawamura, Y.; Matsumoto, N.; Akuta, N.; Kobayashi, M.; Sezaki, H.; Saito, S.; et al. The development of chronic kidney disease in Japanese patients with non-alcoholic fatty liver disease. Intern. Med. 2011, 50, 1081–1087.
  86. Seko, Y.; Yano, K.; Takahashi, A.; Okishio, S.; Kataoka, S.; Okuda, K.; Mizuno, N.; Takemura, M.; Taketani, H.; Umemura, A.; et al. FIB-4 Index and Diabetes Mellitus Are Associated with Chronic Kidney Disease in Japanese Patients with Non-Alcoholic Fatty Liver Disease. Int. J. Mol. Sci. 2019, 21, 171.
  87. Mantovani, A.; Taliento, A.; Zusi, C.; Baselli, G.; Prati, D.; Granata, S.; Zaza, G.; Colecchia, A.; Maffeis, C.; Byrne, C.D.; et al. PNPLA3 I148M gene variant and chronic kidney disease in type 2 diabetic patients with NAFLD: Clinical and experimental findings. Liver Int. 2020, 40, 1130–1141.
  88. Sun, D.Q.; Zheng, K.I.; Xu, G.; Ma, H.L.; Zhang, H.Y.; Pan, X.Y.; Zhu, P.W.; Wang, X.D.; Targher, G.; Byrne, C.D.; et al. PNPLA3 rs738409 is associated with renal glomerular and tubular injury in NAFLD patients with persistently normal ALT levels. Liver Int. 2020, 40, 107–119.
  89. Wada, T.; Zeniya, M. Background of the FIB-4 index in Japanese non-alcoholic fatty liver disease. Intern. Med. 2015, 54, 127–132.
  90. Sumida, Y.; Sakuragi, S.; Hibino, S.; Furutani, M. Diatribution of FIB4 index in Japanese Nonalcoholic fatty liver disease population: A multi-center study. Ningen Dock Int. 2015, 2, 32–34.
  91. McPherson, S.; Hardy, T.; Dufour, J.F.; Petta, S.; Romero-Gomez, M.; Allison, M.; Oliveira, C.P.; Francque, S.; Van Gaal, L.; Schattenberg, J.; et al. Age as a Confounding Factor for the Accurate Non-Invasive Diagnosis of Advanced NAFLD Fibrosis. Am J. Gastroenterol. 2017, 112, 740–751.
  92. Ishiba, H.; Sumida, Y.; Tanaka, S.; Yoneda, M.; Hyogo, H.; Ono, M.; Fujii, H.; Eguchi, Y.; Suzuki, Y.; Yoneda, M.; et al. Japan Study Group of Non-Alcoholic Fatty Liver Disease (JSG-NAFLD). The novel cutoff points for the FIB4 index categorized by age increase the diagnostic accuracy in NAFLD: A multi-center study. J. Gastroenterol. 2018, 53, 1216–1224.
  93. Pitisuttithum, P.; Chan, W.K.; Piyachaturawat, P.; Imajo, K.; Nakajima, A.; Seki, Y.; Kasama, K.; Kakizaki, S.; Fan, J.G.; Song, M.J.; et al. Predictors of advanced fibrosis in elderly patients with biopsy-confirmed nonalcoholic fatty liver disease: The GOASIA study. BMC Gastroenterol. 2020, 20, 88.
  94. Blond, E.; Disse, E.; Cuerq, C.; Drai, J.; Valette, P.J.; Laville, M.; Thivolet, C.; Simon, C.; Caussy, C. EASL-EASD-EASO clinical practice guidelines for the management of non-alcoholic fatty liver disease in severely obese people: Do they lead to over-referral? Diabetologia 2017, 60, 1218–1222.
  95. Joo, S.K.; Kim, W.; Kim, D.; Kim, J.H.; Oh, S.; Lee, K.L.; Chang, M.S.; Jung, Y.J.; So, Y.H.; Lee, M.S.; et al. Steatosis severity affects the diagnostic performances of noninvasive fibrosis tests in nonalcoholic fatty liver disease. Liver Int. 2018, 38, 331–341.
  96. Ishiba, Y.; Sumida, Y.; Tanaka, S.; Yoneda, M.; Hyogo, H.; Ono, M.; Fujii, H.; Eguchi, Y.; Suzuki, Y.; Japan Study Group of Non-Alcoholic Fatty Liver Disease (JSG-NAFLD); et al. Type IV collagen 7S is the most accurate test for identifying advanced fibrosis in non-alcoholic fatty liver disease with type 2 diabetes. Hepatol. Commun. 2020, in press.
  97. Singh, A.; Gosai, F.; Siddiqui, M.T.; Gupta, M.; Lopez, R.; Lawitz, E.; Poordad, F.; Carey, W.; McCullough, A.; Alkhouri, N. Accuracy of Noninvasive Fibrosis Scores to Detect Advanced Fibrosis in Patients With Type-2 Diabetes With Biopsy-proven Nonalcoholic Fatty Liver Disease. J. Clin. Gastroenterol. 2020, in press.
  98. Shah, S.; Dhami-Shah, H.; Kamble, S.; Shukla, A. FIB-4 cut-off of 1.3 may be inappropriate in a primary care referral pathway for patients with non-alcoholic fatty liver disease. J. Hepatol. 2020, 73, 216–217.
  99. Sumida, Y.; Yoneda, M.; Ogawa, Y.; Yoneda, M.; Okanoue, T.; Nakajima, A.; Japan Study Group of NAFLD (JSG-NAFLD). Current and new pharmacotherapy options for non-alcoholic steatohepatitis. Expert Opin. Pharmacother. 2020, 21, 953–967.
  100. Sumida, Y.; Yoneda, M. Current and future pharmacological therapies for NAFLD/NASH. J. Gastroenterol. 2018, 53, 362–376.
  101. Newsome, P.N.; Sasso, M.; Deeks, J.J.; Paredes, A.; Boursier, J.; Chan, W.K.; Yilmaz, Y.; Czernichow, S.; Zheng, M.H.; Wong, V.W.; et al. FibroScan-AST (FAST) score for the non-invasive identification of patients with non-alcoholic steatohepatitis with significant activity and fibrosis: A prospective derivation and global validation study. Lancet Gastroenterol. Hepatol. 2020, 5, 362–373.
  102. Oeda, S.; Takahashi, H.; Imajo, K.; Seko, Y.; Kobayashi, T.; Ogawa, Y.; Moriguchi, M.; Yoneda, M.; Anzai, K.; Irie, H.; et al. Diagnostic accuracy of FibroScan-AST score to identify non-alcoholic steatohepatitis with significant activity and fibrosis in Japanese patients with non-alcoholic fatty liver disease: Comparison between M and XL probes. Hepatol. Res. 2020, 50, 831–839.
  103. Noureddin, N.; Alkhouri, N.; Brown, K.A.; Noureddin, M. Driving NASH forward using the FAST score but obey the traffic lights. Hepatology 2020, in press.
  104. Abe, M.; Miyake, T.; Kuno, A.; Imai, Y.; Sawai, Y.; Hino, K.; Hara, Y.; Hige, S.; Sakamoto, M.; Yamada, G.; et al. Association between Wisteria floribunda agglutinin-positive Mac-2 binding protein and the fibrosis stage of non-alcoholic fatty liver disease. J. Gastroenterol. 2015, 50, 776–784.
  105. Ogawa, Y.; Honda, Y.; Kessoku, T.; Tomeno, W.; Imajo, K.; Yoneda, M.; Kawanaka, M.; Kirikoshi, H.; Ono, M.; Taguri, M.; et al. Wisteria floribunda agglutinin-positive Mac-2-binding protein and type 4 collagen 7S: Useful markers for the diagnosis of significant fibrosis in patients with non-alcoholic fatty liver disease. J. Gastroenterol. Hepatol. 2018, 33, 1795–1803.
  106. Kim, M.; Jun, D.W.; Park, H.; Kang, B.K.; Sumida, Y. Sequential Combination of FIB-4 Followed by M2BPGi Enhanced Diagnostic Performance for Advanced Hepatic Fibrosis in an Average Risk Population. J. Clin. Med. 2020, 9, 1119.
  107. Daniels, S.J.; Leeming, D.J.; Eslam, M.; Hashem, A.M.; Nielsen, M.J.; Krag, A.; Karsdal, M.A.; Grove, J.I.; Neil Guha, I.; Kawaguchi, T.; et al. ADAPT: An Algorithm Incorporating PRO-C3 Accurately Identifies Patients With NAFLD and Advanced Fibrosis. Hepatology 2019, 69, 1075–1086.
  108. Fujimori, N.; Umemura, T.; Kimura, T.; Tanaka, N.; Sugiura, A.; Yamazaki, T.; Joshita, S.; Komatsu, M.; Usami, Y.; Sano, K.; et al. Serum autotaxin levels are correlated with hepatic fibrosis and ballooning in patients with non-alcoholic fatty liver disease. World J. Gastroenterol. 2018, 24, 1239–1249.
  109. Honda, Y.; Imajo, K.; Kobayashi, T.; Kessoku, T.; Ogawa, Y.; Tomeno, W.; Yoneda, M.; Kobayashi, N.; Saito, S.; Nakajima, A. Autotaxin is a valuable biomarker for the prediction of liver fibrosis in patients with non-alcoholic fatty liver disease. Hepatol. Res. 2019, 49, 1136–1146.
  110. Eddowes, P.J.; Sasso, M.; Allison, M.; Tsochatzis, E.; Anstee, Q.M.; Sheridan, D.; Guha, I.N.; Cobbold, J.F.; Deeks, J.J.; Paradis, V.; et al. Accuracy of FibroScan Controlled Attenuation Parameter and Liver Stiffness Measurement in Assessing Steatosis and Fibrosis in Patients With Nonalcoholic Fatty Liver Disease. Gastroenterology 2019, 156, 1717–1730.
  111. Gu, J.; Liu, S.; Du, S.; Zhang, Q.; Xiao, J.; Dong, Q.; Xin, Y. Diagnostic value of MRI-PDFF for hepatic steatosis in patients with non-alcoholic fatty liver disease: A meta-analysis. Eur. Radiol. 2019, 29, 3564–3573.
  112. Tada, T.; Kumada, T.; Toyoda, H.; Kobayashi, N.; Sone, Y.; Oguri, T.; Kamiyama, N. Utility of Attenuation Coefficient Measurement Using an Ultrasound-Guided Attenuation Parameter for Evaluation of Hepatic Steatosis: Comparison With MRI-Determined Proton Density Fat Fraction. AJR Am. J. Roentgenol. 2019, 212, 332–341.
  113. Siddiqui, M.S.; Yamada, G.; Vuppalanchi, R.; Van Natta, M.; Loomba, R.; Guy, C.; Brandman, D.; Tonascia, J.; Chalasani, N.; Neuschwander-Tetri, B.; et al. Diagnostic Accuracy of Noninvasive Fibrosis Models to Detect Change in Fibrosis Stage. Clin. Gastroenterol. Hepatol. 2019, 17, 1877–1885.
  114. Chalasani, N.; Abdelmalek, M.F.; Loomba, R.; Kowdley, K.V.; McCullough, A.J.; Dasarathy, S.; Neuschwander-Tetri, B.A.; Terrault, N.; Ferguson, B.; Shringarpure, R.; et al. Relationship between three commonly used non-invasive fibrosis biomarkers and improvement in fibrosis stage in patients with non-alcoholic steatohepatitis. Liver Int. 2019, 39, 924–932.
  115. Hamaguchi, E.; Takamura, T.; Sakurai, M.; Mizukoshi, E.; Zen, Y.; Takeshita, Y.; Kurita, S.; Arai, K.; Yamashita, T.; Sasaki, M.; et al. Histological course of nonalcoholic fatty liver disease in Japanese patients: Tight glycemic control, rather than weight reduction, ameliorates liver fibrosis. Diabetes Care 2010, 33, 284–286.
  116. Seko, Y.; Sumida, Y.; Tanaka, S.; Mori, K.; Taketani, H.; Ishiba, H.; Hara, T.; Okajima, A.; Yamaguchi, K.; Moriguchi, M.; et al. Serum alanine aminotransferase predicts the histological course of non-alcoholic steatohepatitis in Japanese patients. Hepatol. Res. 2015, 45, E53-61.
  117. Hoofnagle, J.H.; Van Natta, M.L.; Kleiner, D.E.; Clark, J.M.; Kowdley, K.V.; Loomba, R.; Neuschwander-Tetri, B.A.; Sanyal, A.J.; Tonascia, J.; Non-alcoholic Steatohepatitis Clinical Research Network (NASH CRN). Vitamin E and changes in serum alanine aminotransferase levels in patients with non-alcoholic steatohepatitis. Aliment. Pharmacol. Ther. 2013, 38, 134–143.
  118. Vilar-Gomez, E.; Yasells-Garcia, A.; Martinez-Perez, Y.; Calzadilla-Bertot, L.; Torres-Gonzalez, A.; Gra-Oramas, B.; Gonzalez-Fabian, L.; Villa-Jimenez, O.; Friedman, S.L.; Diago, M.; et al. Development and validation of a noninvasive prediction model for nonalcoholic steatohepatitis resolution after lifestyle intervention. Hepatology 2016, 63, 1875–1887.
  119. Vuppalanchi, R.; Jain, A.K.; Deppe, R.; Yates, K.; Comerford, M.; Masuoka, H.C.; Neuschwander-Tetri, B.A.; Loomba, R.; Brunt, E.M.; Kleiner, D.E.; et al. Relationship between changes in serum levels of keratin 18 and changes in liver histology in children and adults with nonalcoholic fatty liver disease. Clin. Gastroenterol. Hepatol. 2014, 12, e1–e2.
  120. Jayakumar, S.; Middleton, M.S.; Lawitz, E.J.; Mantry, P.S.; Caldwell, S.H.; Arnold, H.; Mae Diehl, A.; Ghalib, R.; Elkhashab, M.; Abdelmalek, M.F.; et al. Longitudinal correlations between MRE, MRI-PDFF, and liver histology in patients with non-alcoholic steatohepatitis: Analysis of data from a phase II trial of selonsertib. J. Hepatol. 2019, 70, 133–141.
  121. Loomba, R.; Sanyal, A.J.; Kowdley, K.V.; Terrault, N.; Chalasani, N.P.; Abdelmalek, M.F.; McCullough, A.J.; Shringarpure, R.; Ferguson, B.; Lee, L.; et al. Factors Associated With Histologic Response in Adult Patients With Nonalcoholic Steatohepatitis. Gastroenterology 2019, 156, 88–95.
  122. Estes, C.; Anstee, Q.M.; Arias-Loste, M.T.; Bantel, H.; Bellentani, S.; Caballeria, J.; Colombo, M.; Craxi, A.; Crespo, J.; Day, C.P.; et al. Modeling NAFLD disease burden in China, France, Germany, Italy, Japan, Spain, United Kingdom, and United States for the period 2016–2030. J. Hepatol. 2018, 69, 896–904.
  123. Srivastava, A.; Jong, S.; Gola, A.; Srivastava, A.; Jong, S.; Gola, A.; Gailer, R.; Morgan, S.; Sennett, K.; Tanwar, S.; et al. Cost-comparison analysis of FIB-4, ELF and fibroscan in community pathways for non-alcoholic fatty liver disease. BMC Gastroenterol. 2019, 19, 122.
  124. Patel, P.J.; Banh, X.; Horsfall, L.U.; Hayward, K.L.; Hossain, F.; Johnson, T.; Stuart, K.A.; Brown, N.N.; Saad, N.; Clouston, A.; et al. Underappreciation of non-alcoholic fatty liver disease by primary care clinicians: Limited awareness of surrogate markers of fibrosis. Intern. Med. J. 2018, 48, 144–151.
More
© Text is available under the terms and conditions of the Creative Commons Attribution (CC BY) license; additional terms may apply. By using this site, you agree to the Terms and Conditions and Privacy Policy.
Upload a video for this entry
Information
Subjects: Pathology
Contributor MDPI registered users' name will be linked to their SciProfiles pages. To register with us, please refer to https://encyclopedia.pub/register :
Yoshio Sumida
View Times: 3.8K
Revisions: 2 times (View History)
Update Date: 25 May 2021
Table of Contents
    1000/1000
    Hot Most Recent
    Feedback