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  • Original Article
  • OPEN ACCESS

Liver Fat Scores for Noninvasive Diagnosis and Monitoring of Nonalcoholic Fatty Liver Disease in Epidemiological and Clinical Studies

  • Mona Reinshagen1,2,
  • Stefan Kabisch1,2,* ,
  • Andreas F.H. Pfeiffer1,2 and
  • Joachim Spranger1,2
 Author information
Journal of Clinical and Translational Hepatology 2023;11(5):1212-1227

DOI: 10.14218/JCTH.2022.00019

Abstract

Nonalcoholic fatty liver disease (NAFLD) is strongly associated with the metabolic syndrome and type 2 diabetes and independently contributes to long-term complications. Being often asymptomatic but reversible, it would require population-wide screening, but direct diagnostics are either too invasive (liver biopsy), costly (MRI) or depending on the examiner’s expertise (ultrasonography). Hepatosteatosis is usually accommodated by features of the metabolic syndrome (e.g. obesity, disturbances in triglyceride and glucose metabolism), and signs of hepatocellular damage, all of which are reflected by biomarkers, which poorly predict NAFLD as single item, but provide a cheap diagnostic alternative when integrated into composite liver fat indices. Fatty liver index, NAFLD LFS, and hepatic steatosis index are common and accurate indices for NAFLD prediction, but show limited accuracy for liver fat quantification. Other indices are rarely used. Hepatic fibrosis scores are commonly used in clinical practice, but their mandatory reflection of fibrotic reorganization, hepatic injury or systemic sequelae reduces sensitivity for the diagnosis of simple steatosis. Diet-induced liver fat changes are poorly reflected by liver fat indices, depending on the intervention and its specific impact of weight loss on NAFLD. This limited validity in longitudinal settings stimulates research for new equations. Adipokines, hepatokines, markers of cellular integrity, genetic variants but also simple and inexpensive routine parameters might be potential components. Currently, liver fat indices lack precision for NAFLD prediction or monitoring in individual patients, but in large cohorts they may substitute nonexistent imaging data and serve as a compound biomarker of metabolic syndrome and its cardiometabolic sequelae.

Keywords

NAFLD, Liver fat indices, Fatty liver index, Prediction, Dietary intervention

Nonalcoholic fatty liver disease (NAFLD)

NAFLD is defined when hepatic steatosis is present in at least 5% of hepatocytes in liver histology, provided that there are no secondary causes of fatty liver (FL), such as excessive alcohol abuse, steatogenic medications, or specific chronic liver disease (e.g., chronic viral hepatitis C). NAFLD can be histologically classified into nonalcoholic fatty liver (NAFL) without evidence of hepatocellular injury and nonalcoholic steatohepatitis (NASH) with inflammation and ballooning of hepatocytes, which can finally lead to liver fibrosis, cirrhosis, or hepatocellular carcinoma. Environmental factors, such as unhealthy diet or lack of exercise, contribute to the progression of NAFLD, providing linkage to obesity, type 2 diabetes mellitus (DM), dyslipidemia, hypertension, or the entire metabolic syndrome (MetS).1,2 In general, lipids that cannot be stored in adipocytes are deposited in ectopic tissue, such as liver, muscle, or pancreas.2 Fatty liver itself results in hepatic insulin resistance (HIR), disinhibition of hepatic glucose production and dysglycemia. Prevalence in the general population has increased to about 25%3 and there is also a high prevalence in obese children (about 7–8% population-wide and above 30% in obesity-enriched studies).4 The global presence of NAFLD in patients with DM is 55.5% and attains 68% in Europe.5 The presence of NAFLD and increasing liver enzyme levels are predictors for DM development6,7 while the reduction of liver fat reduces the DM risk irrespective of weight loss.7 Although obesity is the most commonly reported cause of NAFLD, there is a high number of nonobese or lean persons with NAFLD8,9 with comparable long-term risks as obese NAFLD patients.10 The prevalence of NAFLD in lean persons is estimated by around 10%, the global prevalence of lean NAFLD reaches 4% and within all NAFLD cases, around 20% are neither overweight nor obese.8,9

The pathogenesis of NAFLD is influenced by several factors. Genome-wide association studies (GWAS) have found several single nucleotide polymorphisms (SNPs) associated with an increased risk of liver diseases. Loss of function mutations in the genes for the patatin-like phospholipase domain-containing protein 3 (PNPLA3),11–14 transmembrane 6 superfamily member 2 (TM6SF2),11–15 and glucokinase regulator (GCKR) increase liver fat.11 Other SNPs in the membrane bound O-acyltransferase domain-containing 7 (MBOAT7) gene11,16,17,18 and the 17β-hydroxysteroid dehydrogenase type 13 gene (HSD17B13) have an impact on the risk of NAFLD onset and severity.19–22

In the general setting of the metabolic syndrome (MetS), overconsumption or unbalanced distribution of carbohydrates and fats leads to accumulation of lipids in the liver. In interaction with nutrient-specific transcription factors (such as ChREBP and SREPB1), lipogenesis is promoted over lipolysis and ß-oxidation.23,24 Biochemical alteration of those lipids (e.g. peroxidation) augments the burden to liver function beyond simple storage, inducing oxidative and metabolic stress which progresses from simple NAFLD to NASH, fibrosis, cirrhosis and hepatic cancer. The progression from low-grade steatosis to severe hepatic damage is promoted by several independent factors including diet, gut microbiome, genetic background, metabolic comorbidities, and their endocrine sequelae.25

The strong correlation between NAFLD and the development of DM underlines the importance of early diagnosis of NAFLD regarding the prevention and dietary intervention in patients with prediabetes or DM. Improvement in hepatic (e.g., HCC) and extrahepatic outcomes can also be achieved by early detection of mild steatosis. In recent studies, the incidence of HCC in patients with NASH cirrhosis was 0.5–2.6% and 0.1–1.3/1,000 patient-years in NAFLD patients without cirrhosis. Early diagnosis of the highly common NAFLD is crucial,26–28 especially as it is fully treatable at that point. Genetic variations, alterations of lipid and glucose metabolism, clinical features of MetS and the biochemical fingerprint of hepatic damage can be detected in the blood, allowing prediction and monitoring of NAFLD with minimal invasiveness.

Diagnosis of NAFLD

Liver biopsy is the gold standard for the detection of intrahepatic lipids (IHL) and liver fibrosis. It is also the only direct measure of tissue inflammation. The NAFLD activity score evaluates steatosis, inflammation, and hepatocyte ballooning, while the fibrosis activity score (FAS) provides a semiquantitative estimation of the grade of fibrosis.29 Nevertheless, biopsies have limited application in clinical routine due to high costs, sampling errors, and risk to patients. NAFLD can occur as nonhomogeneous patchy steatosis, therefore biopsies can miss highly affected areas. Biopsies have risks of internal bleeding or biliary leakage, especially in patients with liver fibrosis. These patients are at higher risk for progressing liver disease and require a bioptic diagnosis in order to quantify liver damage and to rule out specific (e.g. monogenetic) causes of liver disease. Biopsies should therefore preferably be used on patients with assumed liver fibrosis or steatohepatitis, as the highly prevalent, simple, often asymptomatic NAFLD should not be routinely diagnosed by invasive liver biopsies.30

Another option for diagnosing NAFLD that can be used quickly and cost-effectively in the clinical setting is the use of ultrasound-based techniques. This method allows a noninvasive examination of the liver. By observing parameters, such as liver size and shape, parenchyma echogenicity, and imaging of hepatic vessels, steatosis can be staged using various scores and indices. There are also quantitative methods including the controlled attenuation parameter (CAP) based on the Fibro Scan system and the speed of sound estimation that can be used for the evaluation of hepatic fat content. The disadvantages of the conventional ultrasound technique are the low sensitivity of detecting mild steatosis, the dependence of the examination on the observer, and the difficulty using in obese patients, which is often associated with NAFLD.31 CAP has 87% sensitivity and 91% specificity for mild steatosis, 85% sensitivity and 74% specificity for moderate steatosis, and 76% sensitivity and 58% specificity for severe steatosis.32 In order to use the Fibro Scan system in obese patients, an XL probe has been developed, detecting the degree of steatosis and fibrosis with higher accuracy than the initial M-probe.33 As in 20–50% of all HCC cases, not pre-existing cirrhosis is found, monitoring of NAFLD before severe fibrosis is necessary. Also, a rather common scenario of NAFLD monitoring affects patients with pronounced obesity, in which ultrasound examinations are quite complicated. For the sake of HCC prevention, steatosis measurements independent of high-grade fibrosis and obesity are warranted.

Magnetic resonance imaging (MRI) techniques, such as proton magnetic resonance spectroscopy (1H-MRS) or MRI-estimated proton density fat fraction (MRI-PDFF) are other noninvasive methods to determine liver fat content.34 MRI-PDFF has also been shown to provide information about the histological progress accompanying steatosis, which ultrasound-based techniques and computed tomography cannot provide.35,36 Although MRI-based techniques are highly sensitive and involve no radiation exposure, they have some limitations in clinical practice such as high costs or clinical contraindications (severe obesity, claustrophobia, or metal implants). For those reasons, methods are needed to detect NAFLD rapidly, cost-effectively, noninvasively, and without clinical contraindications. Some biomarkers have already been developed for that purpose, even for the estimation of the amount of hepatic steatosis.

Liver fat scores

Owing to the increasing prevalence of NAFLD,3 NASH-related liver transplantation,37 and the risk of liver, metabolic, and cardiovascular malignancies,1,2 early diagnosis of NAFLD is important. Reduction of liver fat through lifestyle interventions (e.g. exercise or dietary intervention)38 can lead to improvement of cardiovascular risk and metabolic status7 and might also prevent progression to NASH and fibrosis. Liver fibrosis scores including FIB-4, the NAFLD fibrosis score (NFS), BMI-ALT/AST-Ratio-DM (BARD) score, and the enhanced liver fibrosis score (ELF) are capable of differentiating liver fibrosis from nonfibrotic NAFLD. However, they are limited in their prediction of simple steatosis and early stage NAFLD,39–41 as they require markers reflecting fibrotic reorganization (Hyaluronic acid, tissue inhibitor of matrix metalloproteinase (TIMP)-1, procollagen III amino-terminal propeptide (PIIINP); ELF score), hepatic injury (AST/ALT ratio; FIB-4 and APRI) or extrahepatic sequelae (thrombocyte count; FIB-4, APRI), which are not elevated in nonfibrotic NAFLD. Scores specifically designed to detect simple steatosis are reviewed below. The most relevant simple tools using parameters of routine blood samples to detect hepatic steatosis are the fatty liver index (FLI),42 the NAFLD liver fat score (NAFLD-LFS)43 and the hepatic steatosis index (HSI).44 The parameters relevant for NAFLD are summarized in Table 1.42–44

Table 1

Calculation of fatty liver index (FLI), liver fat score (LFS) and hepatic steatosis index (HSI)

PublicationIndexCalculationCutoff valueGold standard in primary publicationAUROC, sensitivity, specificity
Bedogni et al.42Fatty liver index (FLI)FLI=(e0.953*loge (TG)+0.139*BMI+0.718*loge(GGT)+0.053*WC−15.745)/(1+e0.953*loge(TG)+0.139*BMI+0.718*loge(GGT)+0.053*WC−15.745)*100FLI<30 rules out, FLI≥60 rules inUltrasonographySe.: 87% (lower cutoff)/Sp.: 86% (upper cutoff)
Kotronen et al.43NAFLD liver fat score (LFS)NAFLD-LFS=−2.89+1.18*MetS (yes=1/no=0)+0.45*DM (yes=2/no=0)+0.15*FI (mU/L)+0.04*fS-AST (U/L) – 0.94*AST/ALTLFS>−0.640 rules in, LFS<−0.640 rules out1H-MRSAUROC=0.88; Se.: 86%/Sp.: 71%
Liver fat (%)Liver fat (%)=10(−0,805+0,282*MetS(yes=1/no=0)+0,078*DM(yes=2/no=0)+0,525*log(FI[mU/L])+0,521*LOG(AST[U/L])–0,454*LOG(AST/ALT))1H-MRS-
Lee et al.44Hepatic steatosis index (HSI)HSI=8*ALT/AST+BMI (+2, if DM; +2, if female)HSI<30.0 rules out, HSI>36.0 rules inUltrasonographySe.: 92.5%/Sp.: 92.4%

ALT, alanine amino transferase; AST, aspartate amino transferase; BMI, body mass index; DM, type 2 diabetes mellitus; FI, fasting insulin; GGT, gamma-glutamyl transferase; MetS, metabolic syndrome; TG, triglycerides; WC, waist circumference.

FLI

Bedogni et al.42 developed the FLI, which is based on data from the Dionysos Nutrition and Liver Study. The study population included 497 (males, n=305) Italian subjects, 18 to 75 years of age, with suspected liver disease (SLD, n=224) compared with a population without SLD (n=287). Alanine aminotransferase (ALT) levels >30 U/L or gamma-glutamyl transferase (GGT) >35 U/L were necessary to define SLD, and subjects with viral hepatitis (HBV, HCV) were excluded from the study. NAFLD was diagnosed by ultrasonography. After identifying independent predictors of FL, the FLI was calculated by the level of TG, GGT, body mass index (BMI), and waist circumference (WC). Hepatic steatosis was ruled out with an FLI of <30 (sensitivity 87%) and ruled in with an FLI≥60 (specificity 86%). The main limitation of this study was the selection of ultrasonography as the diagnostic gold standard method for the detection of NAFLD, which has a poor significance and cannot precisely detect hepatic steatosis.42

NAFLD-LFS

Kotronen et al.43 was the first study to introduce the NAFLD liver fat score (NAFLD-LFS). It included 470 (males, n=221) Finnish individuals, 18–75 years of age with (n=111) or without DM (n=359) who were divided into an estimation group and a validation group. Except for obesity or DM, no known acute or chronic disease and alcohol consumption less than 20 g per day were criteria to be included in the study. Subjects using antihypertensives influencing glucose metabolism (ß-blockers and thiazides), thiazolidinediones or those being currently pregnant were excluded. Liver fat was measured by 1H-MRS. The presence of the MetS and DM, fasting serum insulin, aspartate aminotransferase (AST) and the AST/alanine aminotransferase (ALT) ratio were the strongest predictors of FL and were used for the calculation. Firstly, the liver fat risk score was developed predicting NAFLD starting from values greater than −0.640 (sensitivity 86%, specificity 71%). Secondly, an algorithm was generated that calculated the estimated percentage liver fat content, cross-validated by 1H-MRS measurement of the liver fat content. The calculated liver fat percentage was strongly correlated with the liver fat measured in the MRS (r=0.70) both in the validation group (R2=0.45) and in all subjects (R2=0.49). Additional genotyping was performed for SNPs in the PNPLA3 gene (rs738409, adiponutrin gene; see above). Although this was a strong predictor of NAFLD, integrating the SNP as cofactor of the prediction equation provided no significant improvement over other indices.43

HSI

Lee et al.44 based their calculation of the HSI on data from Korean subjects who underwent routine health checkups. Of 21,130 participants, 3,591 were excluded because of chronic liver diseases (hepatitis B, C, excessive alcohol consumption, certain medications). After adjusting for sex (70% male) and age (mean=52.2 years), 10,724 subjects (5,362 with NAFLD) were randomly assigned to derivation and validation cohorts. The HSI algorithm considers the presence of DM, female sex, BMI, and the AST/ALT ratio as predictors of NAFLD. NAFLD is ruled out at an HSI value of <30.0 (sensitivity 92.5%) and ruled in at a value of >36.0 (specificity 92.4%). As with the FLI, the validation study for HSI is limited by the diagnostic ambiguity of ultrasound analysis, which was used as gold standard. Moreover, the development in an Asian population questions the significance of HSI in Caucasians44 as there is evidence for ethnic differences in the accuracy of liver fat scores45 and in the appearance of NAFLD.46

Other indices

A variety of other indices were established in the past decades, but were rarely used in other studies. This is due to their complex algorithm structure, specific and rather expensive score elements and/or their nonsuperiority over the previously mentioned indices (Table 2).47–65 Based on the FLI, Kantartzis et al.47 developed an extended FLI which could predict NAFLD more accurately by adding stimulated TG and glucose levels from an oral glucose tolerance test and including rs738409 (PNPLA3) in the equation.47 The extended FLI was capable of predicting changes in liver fat with higher accuracy than the original FLI (n=213; standardized beta coefficient: 0.23–0.29). External validation of the extended FLI was not found in the literature. Because of ethnic differences in the accuracy of liver fat scores, other indices have been designed for various ethnic groups.

Table 2

Calculation of less common liver fat indices

PublicationIndexCalculationCutoff valuesGold standard in primary publicationAUROC, sensitivity, specificity
Kantartzis et al.47Extended FLIExtended FLI: (x/1+x)×100; ×=e0,4508×loge(TG)+0,0621×BMI+0,4022×loge(GGT)+0,0454×(WC)+4,8874×(fold-change TGOGTT)+2,4134×loge(2-h glucose)–1,1143×(rs738409 C>G variant of PNPLA3; C=1 and XG=0)–19,1367FLI<30 rules out, FLI≥60 rules in1H-MRSAUROC=0.86; Se.: 78% (lower cutoff)/Sp.: 92% (upper cutoff)
Wang et al.48ZJU indexZJU: BMI (kg/m2)+FPG (mmol/L)+TG (mmol/L)+3×ALT (IU/L)/AST (IU/L) ratio (+2, if female)ZJU<32.0 rules out, ZJU>38.0 rules inUltrasound and liver biopsyAUROC=0.822; Se.: 92.2% (lower cutoff)/Sp.: 93.4% (upper cutoff)
Bhatt et al.49Indian Fatty Liver Index (IFLI-C)IFLI-C: 1 (double chin)+15.5 (SBP)+13.8 (buffalo hump); SBP: 1 if >120/80); 0 if otherwiseIFLI-C≥1 rules inUltrasoundAUROC=0.650, Se.: 65%/Sp.: 62%
Indian Fatty Liver Index (IFLI-CB)IFLI-CB: TG (1 if ≥150 mg/dL; 0 if otherwise)+12×(FI; 1 if >2.7 µU/mL; 0 if otherwise)+16×(SBP; 1 if >120/80 mmHg; 0 if otherwise)+18×(buffalo hump)IFLI-CB≥28 rules inUltrasoundAUROC=0.719, Se.: 64%/Sp.: 67%
Birjandi et al.50Iranian classification tree methodclassification tree using BMI, WHR, TG, FPG, SPB, and ALTUltrasoundAUROC=0.78, Se.: 74%/Sp.: 83%
Jamali et al.51NAFLD discriminant scoreNAFLD discriminant score: [(−0.298×adiponectin)+(0.022×TNF-α)+(1.021×Log visfatin)+(0.709×Log IL-6)+1.154]Score>−0,29 rules inUltrasoundAUROC=0.94, Se.: 91%/Sp.: 83%
Yip et al.52NAFLD Ridge ScoreNAFLD ridge score: −0.614+0.007×ALT −0.214×HDL+0.053×TG+0.144×HbA1c+0.032×WBC+0.132×hypertension (yes=1; no=0)Score<0.24 rules out, Score>0.44 rules in1H-MRSAUROC=0.87; Se.: 92% (lower cutoff)/Sp.: 90% (upper cutoff)
NAFLD logit scoreNAFLD logit score: e7.338+0.046×ALT−1.277×HDL+0.486×TG+0.911×HbA1c+0.207×WBC+0.589×hypertension (yes=1; no=0)/(1+e7.338+0.046×ALT−1.277×HDL+0.486×TG+0.911×HbA1c+0.207×WBC+0.589×hypertension (yes=1; no=0))Score<0.19 rules out, Score>0.45 rules in1H-MRSAUROC=0.87; Se.: 90% (lower cutoff)/Sp.: 90% (upper cutoff)
NAFLD AdaBoost scoreCombination of decision tree and weighted scores for ALT, HDL, HbA1c, WBC, and TG in one sumScore<−0.76 rules out, Score>0.05 rules in1H-MRSAUROC=0.88; Se.: 91% (lower cutoff)/Sp.: 90% (upper cutoff)
NAFLD decision tree scoreclassification tree using ALT, TG, WBC, ALT and HbA1cScore<0.27 rules out, Score>0.57 rules in1H-MRSAUROC=0.89; Se.: 95% (lower cutoff)/Sp.: 92% (upper cutoff)
Kahn et al.,53 Bedogni et al.54Lipid accumulation product (LAP)LAP: (WC−65)×TG for men; (WC−58)×TG for women; interpretation based on logarithmic derivationultrasoundAUROC=0.78−0.80, Se. and Sp. not given
Poynard et al.55SteatoTesttotal bilirubin, GGT, α2m, haptoglobin, ALT, apolipoprotein AI, BMI, total cholesterol, TG, FPG, age and sex for adjustmentLiver biopsyAUROC=0.79; several reports on Se. and Sp.
Poynard et al.56SteatoTest-2GGT, α2m, haptoglobin, ALT, apolipoprotein AI, total cholesterol, TG, FPG; age and sex for adjustment0,40Liver biopsyAUROC=0.77−0.87; Se.: 79%; Sp.: 50%
Long et al.57Framingham Steatosis Index (FSI)FSI: −7.981+0.011×age−0.146×sex (female=1, male=0)+0.173×BMI+0.007×TG+0.593×hypertension (yes=1, no=0)+0.789×diabetes (yes=1, no=0)+1.1×ALT/AST ratio≥1.33 (yes=1, no=0).CTAUROC=0.845; several reports on Se. and Sp.
Jeong et al.58K-NAFLD scoreK-NAFLD-S.: 0.913×sex (female=2, male=1)+0.089×WC+0.032×(SBP+FPG)+0.007×TG+0.105×ALT − 20.929Score<−3.285 rules out, Score>0.884 rules inNAFLD-LFSAUROC=0.929; Se. and Sp. not given
Zhou et al.59NAFL screening score (NSS)age, BMI, TG, ALT, AST, FPG, uric acidScore<29 (female) or <33 (male) rules outUltrasoundAUROC=0.825−0.861
Zhou et al.60NAFL risk scoreBMI, TG, GGT, ALT, AST, LDL, HDL, uric acidCutoff of 7 (male) and 13 (female) for high 4-year riskUltrasoundAUROC=0.739–0.823
Pan et al.61Not namedBMI, WC, TG, HDL, ALT, diabetes, hyperuricemia, intake of tubers, and fried foodSpecific nomogram; score<180 rules out, Score>340 rules inUltrasoundAUROC=0.834; Se: 99%; Sp. 94%
Meffert et al.62Study of health in pomerania scoreAge, AST, ALT, WC, BMI, TG, ferritin, history of goutUltrasoundAUROC=0.860
Ruhl et al.63USA fatty liver indexAge, WC, FI, FPG, GGT, ethnicityUS FLI<10 rules out, US FLI>30 rules inUltrasoundAUROC=0.80, Se.: 86% (lower cutoff)/Sp.: 88% (upper cutoff)
McHenry et al.64Dallas steatosis index (DSI)ALT, BMI, age, sex, TG, FPG, diabetes, hypertension, ethnicity1H-MRSAUROC=0,824
Rodriguez et al.65NAFLD-MESA indexage, sex, ethnicity, diabetes, smoking history, BMI, GGT, TG>21 rules inCTAUROC=0.83; Se. 75%; Sp. 72%
NAFLD clinical indexage, sex, ethnicity, diabetes, smoking history, BMI>19 rules inCTAUROC=0.78; Se. 80%; Sp. 60%

BMI, body mass index; FI, fasting insulin; FPG, fasting plasma glucose; GGT, gamma-glutamyl transferase; HbA1c, hemoglobin A1c; IL6/8, interleukin 6/8; SBP, systolic blood pressure; TG, triglycerides; TNFα, tumor necrosis factor α; WBC, white blood cell count, α2m, α2-macroglobulin; WC, waist circumference.

Wang et al.48 developed the Zhejiang University (ZJU) index for Chinese subjects, which was validated against ultrasound and biopsy diagnoses. The ZJU index also might be a tool to classify NAFLD into different stages, including NASH. It was externally validated in some Asian populations, predicting NAFLD with moderate-to-high AUROCs (0.69–0.96),66–71 and equal or better predictive power than scores developed in Western populations.72,73

Bhatt et al.49 developed two Indian fatty liver indices (IFLIs) calculated by either clinical variables (IFLI-C) or by both clinical and biochemical variables (IFLI-CB), and validated by ultrasonography49 IFLI-C and IFLI-CB performed better than the FLI and liver fat (%), but similar to NAFLD-LFS. To our knowledge, the IFLI indices have not been replicated or used in later studies.

Brandi et al.50 proposed an index derived from a classification tree, which was validated by ultrasonography-based NAFLD diagnosis. Following the method, NAFLD can be ruled in or ruled out. The particular algorithm has never been replicated or even used in clinical trials.

Jamali et al.51 developed the NAFLD/NASH discriminating score based on histological data from liver biopsies. Given the rather specific and expensive parameters, the usability in clinical routine is very limited.

The indices by Yip et al.52 were tested against 1H-MRS measurements as gold standard. The simplest NAFLD ridge score was proposed as the best choice, but has not been frequently used in other studies.

The lipid accumulation product (LAP) was introduced as new parameter to reflect obesity-related (e.g., cardiovascular) risks by integrating both WC and triglycerides (TG), including consideration of sex differences,53 and testing the index against ultrasound sonography. Lacking clear cut-offs54 and being outperformed by NAFLD-LFS and HSI in high-risk settings and by FLI in population-based settings,74 the LAP has limited usability for NAFLD prediction.

The original SteatoTest includes costly parameters that limit routine use.55 The simpler version (SteatoTest-2) neglects BMI and bilirubin as typical sources of real-world confounding and performs comparably well.56

The Framingham Steatosis Index (FSI) by Long et al.,57 the Korean K-NAFLD score,58 and the NAFL screening score (NSS)59 use parameters similar to the FLI, NAFLD-LFS, and HSI.

Uric acid is a component found in the NAFL risk score,60 and the score by Pan et al.,61 which integrate data on anthropometric characteristics, metabolism, and diet. History of gout and ferritin levels are novel parameters used in a score based on the Study of Health in Pomerania (SHIP). It also integrates more common items (age, AST, ALT, WC, BMI, TG) and performed well in comparison to HSI and FLI.62

Ethnicity can be an important confounder of score reliability. Several studies reported a poorer performance of the HSI in non-Asian cohorts. In order to address this issue for multiethnic populations the score by Ruhl et al.,63 the Dallas Steatosis Index (DSI),64 the NAFLD-MESA index and the NAFLD clinical index were developed and outperformed the original FLI.65

External validation of common liver fat scores for prediction of NAFLD and liver fat content

A number of recent studies have evaluated available liver fat scores for clinical practice. A summary of predictive values for the indices from original and external validation studies is shown in Table 3.36,42–44,75–81 Koot et al.75 found that these scores are poor predictors of NAFLD in obese children.30 The cohort (119 severely obese children (14.3±2.1 years of age, BMI z-score 3.35±0.35; 47% NAFLD cases) was investigated with MR spectroscopy as gold standard; FLI, NAFLD-LFS, and HSI were assessed. As these scores were developed for adult populations, their poor performance in pediatric patients is not entirely unexpected. However, the pediatric prediction score did not outperform the others either.

Table 3

Area under the receiver operating characteristic curve (AUROC), 95% confidence interval (CI)), sensitivity (%) and specificity (%) for fatty liver index (FLI), NAFLD liver fat score LFS and hepatic steatosis index (HSI) for prediction of NAFLD in original and external validation studies

IndexStudyAUROC (CI)Sensitivity, %Specificity, %
Fatty liver index (FLI)42Original study8786
Validation studyKoot et al.750.71 (0.61–0.80)7060
Cuthbertson et al.760.79 (0.74–0.84)9591
Kabisch et al.770.786
Kabisch et al.780.731
Kahl et al.790.72 (0.59–0.85)7683
Motamed et al.800.86 (0.85–0.88)
Fedchuk et al.810.83 (0.72–0.91)7687
NAFLD liver fat score (LFS)43Original study0.88 (0.84–0.92)8671
Validation studyKoot et al.750.75 (0.66–0.84)7771
Kabisch et al.770.77
Kabisch et al.780.75
Kahl et al.790.70 (0.53–0.87)3591
Fedchuk et al.810.80 (0.69–0.88)6587
Hepatic steatosis index (HSI)44Original study0.81 (0.80–0.82)92.592.4
Validation studyKoot et al.750.68 (0.59–0.78)6762
Kabisch et al.780.77
Kahl et al.790.79 (0.68–0.90)10075
Fedchuk et al.810.81 (0.71–0.88)6193

Another study76 aimed at validating the FLI in 168 healthy adults and another 168 adults with components of the MetS by measuring IHL by 1H-MRS. This study showed that FLI can detect presence of NAFLD in the individual but doubted its accuracy in predicting the degree of hepatic steatosis/actual liver fat content. Thus, in accord with its original designation, the FLI can primarily be used to identify patients with possible steatosis in order to perform further diagnostics or to roughly classify groups of patients.

Kabisch et al.77,78 showed that the FLI and NAFLD-LFS had a highly significant correlation with IHL at the baseline visit of their intervention studies, replicating their capability to predict NAFLD and to mirror actual liver fat content with good precision (AUROCs around 0.73; r-values around 0.5).77,78 Both cohorts included participants with prediabetes or overt DM and a considerable proportion of NAFLD patients, allowing for plausible correlations.

A similar approach was adopted by Kahl et al.,79 who reported only moderate accuracy of the scores that did not allow quantification of liver fat content. However, in that study, predominantly nonobese persons with low liver fat content were included, limiting the replication of the index performances.

Unlike other studies, Motamed et al.80 reported a very high predictive power of FLI, with NAFLD diagnosed by ultrasound rather than 1H-MRS. The study did not find significant differences between the FLI and WC as a single factor for the diagnosis of NAFLD. In the development of the FLI,42 WC was already identified as the strongest predictor of NAFLD, along with BMI. The excellent replication of the FLI performance in this study can be attributed to the large cohort of more than 5,000 middle-aged patients with high prevalence of NAFLD.

A retrospective analysis of 324 liver biopsies from middle-aged, overweight-to-obese patients (64% males) that categorized NAFLD as mild (≥5−33%), moderate (>33–66%), and severe (>66%) steatosis, showed that all indices were capable of accurately ruling NAFLD in or out and correlated strongly with insulin resistance. However, they were limited in the quantification of steatosis, which makes the markers questionable for describing changes in liver fat. On the other hand, liver biopsies can fail to reflect the average steatosis in the entire organ.81

Liver fat scores in prediction models for non-NAFLD outcomes

Elevated liver fat content and NAFLD are strongly associated with hepatic insulin resistance, hypertriglyceridemia, and chronic cardiometabolic complications such as T2DM and CVD.7,82–84 In the TULIP study, NAFLD predicted failure in even compliant dietary intervention in patients with prediabetes.85 Other diabetes prevention studies indicated that prediabetes patients with NAFLD had better responses to treatment that improving insulin resistance.77,86–89 NAFLD predicts CVD and mortality independently of glycemic metabolism.90 Therefore, it is of interest if liver fat scores have the same predictive potential for metabolic sequelae. In a study by Kahl et al.,79 FLI and NAFLD-LFS were also inversely correlated with parameters of insulin sensitivity such as the quantitative insulin sensitivity check index (QUICKI), describing fasting insulin sensitivity as well as the oral glucose insulin sensitivity index (OGIS), and Matsuda’s index (ISIcomp) for dynamic insulin sensitivity. Fasting ß-cell function and parameters describing post-load insulin secretion such as the disposition index (DI), adaptation index (AI), and insulinogenic indices (IGI_Ins) positively correlated with fatty liver indices (Table 4).79 However, this positive correlation may be a spurious relationship as the study included participants without T2DM. In such a healthy cohort, low insulin secretion is mainly triggered secondarily by fatty liver and hepatic insulin resistance. The study population did not include patients with advanced insulin-deficient diabetes. The correlation of fatty liver scores, especially FLI, with DM and insulin resistance (Table 4) has already been shown in a number of studies, and emphasizes the strong association between DM and NAFLD.90–93

Table 4

Correlation of liver fat scores and type 2 diabetes-related outcomes in external validation studies

StudyOutcomeFLINAFLD-LFSHSI
Kahl et al.79ISIcomp_Inr=−0.62***r=−0.71***r=−0.53***
OGISr=−0.62***r=−0.51***r=−0.50***
QUICKIr=−0.55***r=−0.68***r=−0.42***
DI_Inr=0.47***r=0.57***r=0.48***
B-cell func_Inr=0.57***r=0.57***r=0.47***
AIr=0.34***r=0.35***r=0.33**
IGI_CP_Inr=−0.02r=0.05r=0.02
IGI_Ins_Inr=0.16r=0.26*r=0.19
Hep_Extr_Inr=−0.39***r=−0.55***r=−0.42***
Gastaldelli et al.91Glucose concentrationR=0.34****
Ln (Insulin concentration)R=0.62****
Ln (Clamp FFA concentration)R=0.41****
Ln (Peripheral IS)R=−0.43****
Bozkurt et al.92PGDM – IS vs. NGTp=0.104
PGDM – IR vs. NGTp<0.001
PGDM–IS vs. PGDM−IRp=0.006
Balkau et al.93Incident diabetes (men)≤ 57.67: OR=1; >57.67: OR=4.46≤−1.15: OR=1; >−1.15: OR=4.88
Incident diabetes (women)≤ 21.64: OR=1; >21.64: OR=11.58≤−1.82: OR=1; >−1.82: OR=12.48
Unalp-Arida et al.83DiabetesL: HR=0.5; I: HR=2.1; H: HR=7.4L: HR=0.6; I: HR=5.2; H: HR=16.8L: HR=0.6; I: HR=2.0; H: HR=6.9

*p<0.05; **p<0.01; ***p<0.001; ****p<0.0005. r, correlation coefficient; R, correlation coefficients of univariate analysis. AI, adaptation index; B-cell func_In, fasting ß-cell function; DI, disposition index; Hep_Extr_In, hepatic insulin extraction; IGI_CP, insulinogenic C-peptide index; IGI_Ins, insulinogenic indices; IR, insulin resistance; IS, insulin sensitivity; ISIcomp, Matsuda’s index; NGT, control group (p-values from Fisher protected least significant difference test); OGIS, oral glucose insulin sensitivity index; OR, odds ratio; PGDM, previous gestational diabetes; QUICKI, quantitative insulin sensitivity check index.

Other studies have found an association of FLI with hepatic and cardiovascular diseases and cancer.82,91,94 A recent study in an US population reported that none of the scores correlated with increased cardiovascular mortality.83 NAFLD-LFS and FLI were associated only with increased liver disease mortality and NAFLD-LFS was also associated with increased diabetes mortality. Correlations of liver fat scores with hepatic, cardiometabolic, cardiovascular, and cancer outcomes are shown in Table 5.82,83,91,94

Table 5

Correlation between liver fat scores and risk parameters for cardiovascular and hepatic diseases and cancer from external validation studies

StudyOutcomeFLINAFLD-LFSHSI
Gastaldelli et al.91Systolic blood pressureR=0.39****
Diastolic blood pressureR=0.35****
CCA IMTR=0.30****
Framingham scoreR=0.34****
Ln (LDL cholesterol)R=0.33****
Ln (HDL cholesterol)R=−0.50****
Calori et al.9415-year hepatic-related mortalityHR=1.036****
15-year CVD mortalityHR=1.007**
15-year cancer mortalityHR=1.006*
15-year all-cause mortalityHR=1.006**
Lerchbaum et al.82All-cause mortalityQ1: HR=1.0; Q2: HR=1.14; Q3: HR=1.11; Q4: HR=1.26
Cardiovascular mortalityQ1: HR=1.0; Q2: HR=1.28; Q3: HR=1.35; Q4: HR=1.32
Cancer mortalityQ1: HR=1.0; Q2: HR=1.10; Q3: HR=0.75; Q4: HR=1.01
Non-cardiovascular mortalityQ1: HR=1.0; Q2: HR=0.98; Q3: HR=0.88; Q4: HR=1.27
Unalp-Arida et al.83All-cause mortalityL: HR=19.0; I: HR=32.1; H: HR=42.2L: HR=23.8; I: HR=41.3; H: HR=48.9L: HR=28.5; I: HR=29.2; H: HR=35.1
Cardiovascular diseaseL: HR=6.0; I: HR=13.1; H: HR=19.0L: HR=9.2; I: HR=18.2; H: HR=27.2L: HR=11.3; I: HR=12.5; H: HR=16.0
NeoplasmsL: HR=5.2; I: HR=11.9; H: HR=13.5L: HR=7.5; I: HR=13.4; H: HR=12.4L: HR=8.7; I: HR=9.5; H: HR=10.3
Liver diseaseL: HR=0.4; I: HR=0.6; H: HR=2.5L: HR=0.5; I: HR=1.6; H: HR=5.5L: HR=0.8; I: HR=0.8; H: HR=1.7

*p<0.05, **p<0.01, ***p<0.001, ****p<0.0005. Fat probability (low, L; intermediate, I; High, H); Framingham score, prediction score for coronary heart disease (CHD); r, correlation coefficient; Quartiles for FLI levels: Q1 (< 31.0); Q2 (31.0–53.7); Q3 (53.8–75.6); Q4 (>75.6); R, regression coefficients of univariate analysis. CCA, common carotid artery; CVD, cardiovascular disease; HR, Cox proportional hazard ratio (without adjustment); IMT, carotid intima media thickness.

Usability of liver fat scores as monitoring tools for interventional changes in liver fat

The predictive power of the indices is also of interest for longitudinal studies in which IHL is reduced, e.g., by lifestyle interventions (Table 6).47,77,78,95–97 Keating et al.95 performed a lifestyle intervention trial with either an 8-week exercise or a 12-week nutraceutical intervention. They evaluated whether some surrogate markers, including FLI, HSI, NAFLD-LFS and WC, indicated changes in liver fat measured by 1H-MRS. At baseline, the scores correlated weakly or moderately with measured liver fat content. During intervention, only changes of FLI, HSI, and in particular the single parameter WC correlated with changes in IHL.95

Table 6

Correlation of changes in liver fat scores with changes in liver fat content after lifestyle interventions

StudyFLINAFLD-LFSHSI
Keating et al.95r=0.466**r=0.117r=0.245*
Kantartzis et al.47BL: 70.39±3.01; FU: 63.35±3.44. p=0.002BL: 41.62±0.65; FU: 41.45±0.83. p=0.059
Kabisch et al.77Low fat: r=0.499***; Low carb: r=0.075Low fat: r=0.438**; Low carb: r=0.257
Kabisch et al.78r=0.342r=−0.058r=−0.049
Vilar-Gomez et al.96ΔCCI=−1.95±0.22***; ΔUC=0.47±0.41
Arslanow et al.97Δ%=−21.3 (−74.0–0.0)***

*p<0.05; **p<0.01; ***p<0.001. ΔCCI/UC, change in liver fat after 1 year of continuous care intervention or standard diabetes care; Δ%, percentage change in liver fat after 2 weeks hypocaloric high-fiber and high-protein diet; r=Spearman correlation coefficient. BL, baseline; FU, follow-up; Low fat/carb, changes in liver fat after 3 weeks of low fat or low carb dietary intervention.

Another trial found that FLI values were significantly lower after 9 months of a low-fat, high-fiber lifestyle intervention. In contrast, HSI did not detect changes in liver fat.47 Other predictors of change in liver fat were 2-h TG and TG fold-change measured at 2 h during OGTT (TGOGTT) as the strongest predictor. Kabisch et al.77 evaluated the power of FLI and NAFLD-LFS comparing two randomized lifestyle interventions (low-carb and low-fat diets) in patients with prediabetes. IHL were measured by 1H-MRS. The scores strongly correlated with IHL at baseline, however both scores only correlated moderately with liver fat with a low-fat but not with a low-carb diet. Liver fat content decreased significantly in both diet groups without a significant difference between the groups. However, in contrast to the low-fat group, changes of IHL in the low-carb group only correlated with changes of two parameters (fasting insulin, ALT) used to calculate the indices. In the low-fat group, changes in body weight correlated with changes of liver fat. As reduction of liver fat under low-carb conditions is mainly independent of body weight reduction and correlates with different parameters than in the low-fat group, the mechanisms of IHL reduction might vary with different diets or other treatments. Similar results were reported in DM patients on a high-protein diet, in which the reduction in liver fat was also found to be independent of weight reduction. Only FLI correlated weakly with changes in liver fat, but at baseline all indices were significantly correlated with IHL. Apart from WC, none of the index parameters (body weight, WC, fasting insulin, TG, AST, ALT, AST/ALT ratio, GGT) were correlated with changes in liver fat. Correlation of WC with IHL can be explained by the simultaneous reduction of visceral and hepatic fat.78 As anthropometric parameters of obesity such as BMI or body weight are important index components, the lack of correlation of weight reduction with liver fat reduction is probably the crucial reason for the poor performance of the indices. As the intervention was performed in subjects with T2DM and reduced insulin secretion, insulin levels do not necessarily reflect improvement in fatty liver. Although TG and GGT were among the most representative parameters for changes in liver fat, there was also no significant correlation with liver fat reduction. The high-protein diet improved lipid and aminotransferase levels, which is why their changes may have appeared beyond steatosis and therefore lacked correlation with liver fat reduction.78 High aminotransferase levels may also have been influenced by pre-existing hepatic inflammation, NASH, or fibrosis.

In a study comparing a comprehensive continuous care intervention (CCI) with a nutritional ketosis diet and standard diabetes care treatment (UC), NAFLD-LFS was significantly improved in patients undergoing CCI in comparison to the UC patients.96 Similar to the results of Kabisch et al.,77 NAFLD-LFS changed progressively with higher weight reduction in the diet (≥10%). The study was limited by the lack of IHL data from imaging or biopsy.96 During a short-term hypocaloric high-fiber and high-protein diet, liver fat measured by CAP and FLI significantly decreased in the study cohort, with the reduction being stronger in patients with weight loss ≥5%.97 The correlation between CAP and FLI was not determined. However, CAP at baseline correlated with some parameters of the FLI (BMI, WC), but not TG or GGT.

The limited accuracy of liver fat indices in general can also be explained by treatment effects. Even in cross-sectional designs, antidiabetic treatment, lipid-lowering drugs, antihypertensive medication, inflammatory disorders, specific liver conditions without linkage to NAFLD (Morbus Meulengracht), and of course unreported excess alcohol intake may confound parameters of NAFLD scores.98

Potential biomarkers of novel liver fat indices

The accuracy of indices in cross-sectional studies is sometimes considerably lower than the reported accuracy in the original studies. Even at moderate or high precision, their usefulness for indicating liver fat content has been questioned. Therefore, they should be used only for predicting the possible presence of NAFLD, assessment of the risk of liver malignancy or cardiovascular disease and selection of patients for detailed examination.

In longitudinal studies, the indices correlated with the measured liver fat before the intervention, while changes in IHL not clearly indicated by the indices during and after the interventions. Even moderate correlations appeared only in the case of strong changes in body weight, as all common indices are mainly based on BMI and/or WC. As liver fat reduction can be achieved independent of body weight reduction especially in low-carb or high-protein diets or due to medication, the indices are presumably useless as monitoring tools when used with these treatments.

The most important parameters included in the equations were BMI, TG, WC, GGT, FPG, fasting insulin, AST, ALT, AST/ALT and SBP. Although fasting insulin and parameters of insulin sensitivity (QUICKI, OGIS, ISIcomp), ß-cell function and post-load insulin resistance (DI, AI, IGI) were associated with liver fat scores and IHL in recent studies,77,79,81,91–93,99 fasting insulin is only included in the NAFLD-LFS.43 High variability among insulin test kits complicates the utility of insulin as a parameter for liver fat scores. Moreover, the pulsatile insulin release requires multiple, i.e. costly blood draws in short intervals.100 FPG is used as a parameter in some more recently introduced scores50,55,56,58,59,63,64 for being rarely reported as a possible predictor for NAFLD in the literature. A few studies found a significant positive association between the triglyceride and glucose index (TyG), calculated as Ln [TG (mg/dL)×FPG (mg/dL)], and NAFLD.101–103 Moreover, according to the literature, TyG-related parameters, such as TyG-BMI (=TyG×BMI), TyG-WC (=TyG×WC), have been more reliable predictors than the classical TyG and former parameters.103,104 Abnormal glucose tolerance, defined as either impaired glucose tolerance (2 h glucose between 7.8 and 11.0 mmol/L) or DM (2h glucose≥11.1 mmol/L or FPG≥7.0 mmol/L) was reported as a significant predictor for steatohepatitis and fibrosis in NAFLD patients.105 Another study showed significantly increased fasting glucose and HbA1c levels in subjects with steatosis compared to subjects without steatosis.106

In addition to the above parameters, other predictors associated with NAFLD have been discussed. The role of adipokines (especially adiponectin) in the pathogenesis of NAFLD has been investigated in many studies. In rat models107,108 as well as in human subjects with NAFLD,51,109,110 the concentration of adiponectin was significantly decreased and adiponectin gene polymorphisms associated with NAFLD were found.111,112 One publication investigated the association between serum retinol-binding protein 4 (RBP4) and NAFLD.113 In this study, they found lower RBP4 levels in patients with NASH than in patients with simple steatosis. Nevertheless, the differences found were not significant and there was no correlation of RBP4 with BMI, HOMA, FPG, or fasting insulin. A similar lack of correlation was reported in subjects with DM,114 probably owing to medication effects.98

In a meta-analysis, fetuin-A and fetuin-B concentrations were significantly higher in subjects with NAFLD and fetuin-A played a role in the process of simple steatosis to NASH.115 Another approach found a significant association between elevated fetuin-A concentrations and increased FLI, ALT and AST116 and a prospective cross-sectional study found an independent correlation of increased fetuin-A concentrations with NAFLD.117 One study discussed progranulin as a potential predictor for NAFLD, as progranulin was significantly higher in patients with NAFLD and positively correlated with total cholesterol and LDL cholesterol.41 Although these markers could be used to predict NAFLD, their measurement is too expensive for routine clinical use. Less expensive parameters are needed.

FIB-4, NFS, BARD, or ELF calculate fibrosis risk in patients with NAFLD using specific markers of extracellular matrix production.118 Typically, they cannot be used as early predictors for simple nonprogressed steatosis. However, serum cytokeratin-18, as one of those matrix markers, is also strongly related to simple NAFLD and ALT levels in adults119,120 and children,121 providing sensitivity and specificity of over 97% for detection of moderate-to-high steatosis.119 A parameter correlated with histological classifications of hepatic steatosis, is plasma cathepsin D (CatD), which was found to be significantly decreased after gastric bypass surgery in subjects with NASH.122 Another study reported a positive association of insulin resistance (HOMA-IR and plasma insulin levels) with CatD in subjects with NAFLD.123 In contrast, CatD had only weak effectiveness in indicating changes in NAFLD and NASH in an Asian population, again underscoring ethnic differences in the parameters of NAFLD.124 Again, quantification of cathepsin D is expensive and possibly not cost-effective for NAFLD screening.

Recent studies have reported a relationship between iron metabolism and body composition and NAFLD.125–135 A prospective study reported an association between a high serum iron-to-ferritin ratio with healthy body composition and reduced risk of fatty liver progression in young adult women <45 years of age, but not in middle-aged women ≥45 years of age.126 A study of obese male pediatric patients found that serum ferritin was more strongly linked to liver fat content and inflammation than body iron status was.127 Furthermore, increased serum ferritin levels predicted the risk of NASH and fibrosis development in adult patients with NAFLD.128 A similar association of serum ferritin with parameters of liver health (liver fat content, ALT, hepatic iron) and with glucose and lipid metabolism also found this parameter might be a predictor of NAFLD.130 The best area under the curve (AUC) for the prediction of hepatic steatosis was found by combining blood ferritin, FPG, and ALT. The SHIP NAFLD score is the only index using ferritin.62

Another possible predictor of NAFLD is alkaline phosphatase (ALP), which has been reported as an independent predictor of DM.6 An experimental study investigated the role of tissue nonspecific alkaline phosphatase (TNAP) in mice with TNAP+/− haplodeficiency (absence of an allele of TNAP) and found that the mice developed hepatic steatosis similar to that induced by a diet deficient in methionine and choline (MCD).136 Acetylcholine (ACh) might also participate in hepatic steatosis and fibrosis progression as it induced fibrogenesis in hepatic stellate cells in vitro as well as in human whole-liver samples of NASH fibrosis via muscarinic ACh receptors.137 Furthermore, serum cholinesterase activity was significant higher in patients with NAFLD138,139 and DM140 in previous studies. In contrast, a study of DM patients with or without NAFLD found decreased cholinesterase activity in those with hepatic steatosis.141 Cholinesterase activity may have been elevated at earlier stages of the disease and only had decreased activity in cases with advanced liver cell damage, which may explain the controversial results. This fact indicates that cholinesterase activity may also serve as an indicator of the severity of steatosis. To investigate the predictive power of ALP and cholinesterase for NAFLD, further studies are needed.

Uric acid has been considered as a possible predictor of NAFLD,142 and was reported to be positively correlated with NAFLD risk143,144 and to induce hepatic fat accumulation and insulin resistance.145 NSS and NAFL risk scores are the first indices implementing that parameter as a continuous parameter.59,60 The German SHIP NAFLD score integrates history of gout,62 and another score developed in China uses a binary expression for hyperuricemia.61 As none of those scores are widely used, uric acid is still a parameter of interest for novel indices and may help to develop scores that outperform others with respect to longitudinal reflection of liver fat changes.

As mentioned above, SNPs influence the risk of chronic liver and heart diseases, the factors PNPLA3, TM6SF2, MBOAT7, GCKR, and HSD17B13 have key involvement.11–15,17–22 In addition to those genetic variants, GWAS found several further genetic factors that were significantly associated with NAFLD.146 PYGO1 is a protein contributing to the Wnt signaling pathway. Absence in a homologue PYGO2 in mice resulted in increased adiposity and impaired glucose tolerance.147 The rs429358-C variant defines the main three alleles of apolipoprotein E (ApoE) in combination with rs7412. Although all three alleles appeared to be protective against NAFLD, one simultaneously increased cardiovascular and metabolic risk via enhanced hepatic VLDL secretion. Furthermore, increased ApoE serum levels correlated with higher FLI.148 Mitochondrial amidoxime reducing component 1 (MARC1) and sorting and assembly machinery component (SAMM50) encode for proteins located in the outer mitochondrial membrane.149,150 Missense variants in the MARC 1 protein protected against all-cause cirrhosis and were associated with lower levels of hepatic fat, NAFLD risk and aminotransferase levels.149,151 SAMM50 SNPs were associated with NAFLD and might be involved in the progression of NAFLD.150 The neurocan core protein (NCAN), a proteoglycan involved in remodeling central nervous system, is also expressed in the liver. NCAN function is linked to hepatic steatosis, lobular inflammation, and fibrosis. protein phosphatase 1 regulatory subunit 3B (PPP1R3B) has a variant associated with CT-diagnosed but not histologically diagnosed hepatic steatosis (rs4240624) and one predicting severe NAFLD on ultrasound (rs61756425). Tribbles pseudokinase 1 (TRIB1) was associated with increased ALT and NAFLD diagnosed histologically or by ultrasound.148

Other genetic factors were not identified by GWAS, but in other studies or meta-analyses increased risk of NAFLD was linked to a loss of function in the phosphatidylethanolamine N-methyltransferase (PEMT), nine ERLIN1-CHUK-CWF19L1 variants, a mitochondrial transport protein (MTTP) polymorphism, and a superoxide dismutase 2 (SOD2) variant. A polymorphism in the uncoupling protein 2 (UCP) was associated with reduced risk of NASH and higher hepatic protein levels.152

To improve the prognostic accuracy of genetic risk factors, they are often combined to polygenetic risk scores (PRS). Di Costanzo et al.153 developed a 4-SNP-PRS (TM6SF2, GCKR, PNPLA3, MBOAT7) with a high predictive value of NAFLD and found that PPP1R3B and MBOAT7 could have an impact on the severity of NAFLD. The same SNPs were combined with clinical fibrosis scores (NFS, Fib-4, aspartate aminotransferase-to-platelet ratio, BARD, and the Forns score) and improved the prediction of severe liver disease in subjects with metabolic risk factors.154 An 11-SNP risk score (PNPLA3, HSD17B13, TM6SF2, GATAD2A, GCKR, SUGP1, SAMM50, ERLIN1-CHUK-CWF191, MBOAT7, TRIB1) was developed using data from multiple ethnic groups. The resulting GRS was significantly associated with NAFLD in several ethnic groups (Latinos, Japanese Americans, Native Hawaiians, Whites, African Americans) and had higher accuracy in patients with NAFLD cirrhosis. The impact of PPP1R3B and MBOAT7 on the severity of NAFLD was also found in that study.155 Gao et al.156 combined PNPLA3 and HSD17B13 with sex, MetS, HOMA-IR, and serum AST levels to predict NASH. The developed nomogram could be used in both groups with or without prediabetes or MetS. The identified SNPs and the PRS show that NAFLD is a polygenic condition whose risk is best assessed by combining different genetic variants, especially PNPLA3, TM6SF2, MBOAT7, GCKR, and HSD17B13, with metabolic and clinical factors. SNPs should be further considered as accurate markers of NAFLD and could be used in new liver fat scores.

Conclusions

Existing liver fat scores can be used as biomarkers to capture the probable presence or absence of NAFLD at baseline. Given the limited reliability and precision, a confirmed diagnosis for individuals in clinical practice or clinical research as the basis for therapeutic decisions or study inclusion is not possible. Liver fat indices are not capable of replacing imaging as a more accurate method, and they cannot clearly quantify the grade of NAFLD. However, they may serve as useful tools for larger cohorts, such as in epidemiological studies where imaging data is not available owing to high cost or the historic nature of certain cohorts. In those settings, FLI, NAFLD-LFS, and HSI are most common scores and are a practical, cheap and post-hoc available approach for risk stratification of metabolic, cardiovascular diseases, or liver malignancy. Their performance for the prediction of NAFLD is consistently replicated. Noninvasive indices for NASH may be even more warranted, as noninflammatory NAFLD has a rather low risk for long-term complications, while identifying the smaller portion of NASH patients among all NAFLD cases would improve their disease management.

The power of the indices as monitoring tools for interventions has rarely been validated. However, the existing longitudinal studies show that even the well-established indices have poor power in representing changes in liver fat in the context of lifestyle interventions, especially when interventional benefits are mainly independent of weight loss. That awaits development of new indices predicting liver fat changes irrespective of the performed diet. Ideally, these should be able to predict liver fat in both cross-sectional and longitudinal settings.

Moreover, it would be useful to have an index that would allow quantification of liver fat and thus help to categorize NAFLD into mild, moderate, and severe types. In addition to the parameters that have been used in the indices, other parameters may be considered as candidates for the diagnosis of NAFLD. While parameters such as adipokines, RBP-4, fetuin-A/B, progranulin are too expensive, and liver fibrosis variables are not considered for early diagnosis of NAFLD, parameters such as FPG, ferritin, ALP, cholinesterase, or uric acid could be other favorable predictors for the diagnosis of NAFLD and should be further investigated. Genetic variants and polygenic risk scores are potential tools to stratify the NAFLD risk and should also be considered for new liver fat scores.

Abbreviations

1H-MRS: 

proton magnetic resonance spectroscopy

AI: 

adaptation index

ALP: 

alkaline phosphatase

BMI: 

body mass index

CAP: 

controlled attenuation parameter

DI: 

disposition index

DM: 

type 2 diabetes mellitus

FI: 

fasting insulin

FL: 

fatty liver

FLI: 

fatty liver index

FPG: 

fasting plasma glucose

GGT: 

gamma-glutamyl transferase

GWAS: 

genome-wide association studies

HBV: 

hepatitis B virus

HCV: 

hepatitis C virus

HIR: 

hepatic insulin resistance

HSI: 

hepatic steatosis index

IGI: 

insulinogenic index

IHL: 

intrahepatic lipids

IL6: 

interleukin 6

IL8: 

interleukin 8

MetS: 

metabolic syndrome

MRE: 

magnetic resonance elastography

MRI: 

magnetic resonance imaging

MRI-PDFF: 

MRI-estimated proton density fat fraction

MS: 

metabolic syndrome

NAFL: 

nonalcoholic fatty liver

NAFLD: 

nonalcoholic fatty liver disease

NAFLD-LFS: 

NAFLD liver fat score

NASH: 

nonalcoholic steatohepatitis

OGIS: 

oral glucose insulin sensitivity index

PRS: 

polygenetic risk scores

QUICKI: 

quantitative insulin sensitivity check index

ROC: 

receiver operating characteristic

SBP: 

systolic blood pressure

SLD: 

suspected liver disease

TG: 

triglycerides

TNFα: 

tumor necrosis factor alpha

WC: 

waist circumference

WHR: 

waist to hip ratio

Declarations

Funding

None to declare.

Conflict of interest

The authors have no conflict of interests related to this publication.

Authors’ contributions

Wrote the paper and are guarantors of this work (MR, SK), conceptualized the publication (SK), provided supervision, reviewed the manuscript and, acquired financial support by public funding (AFHP, JS).

References

  1. Chalasani N, Younossi Z, Lavine JE, Diehl AM, Brunt EM, Cusi K, et al. The diagnosis and management of non-alcoholic fatty liver disease: practice Guideline by the American Association for the Study of Liver Diseases, American College of Gastroenterology, and the American Gastroenterological Association. Hepatology 2012;55(6):2005-2023 View Article PubMed/NCBI
  2. Godoy-Matos AF, Silva Júnior WS, Valerio CM. NAFLD as a continuum: from obesity to metabolic syndrome and diabetes. Diabetol Metab Syndr 2020;12:60 View Article PubMed/NCBI
  3. Younossi ZM, Koenig AB, Abdelatif D, Fazel Y, Henry L, Wymer M. Global epidemiology of nonalcoholic fatty liver disease-Meta-analytic assessment of prevalence, incidence, and outcomes. Hepatology 2016;64(1):73-84 View Article PubMed/NCBI
  4. Anderson EL, Howe LD, Jones HE, Higgins JP, Lawlor DA, Fraser A. The Prevalence of Non-Alcoholic Fatty Liver Disease in Children and Adolescents: A Systematic Review and Meta-Analysis. PLoS One 2015;10(10):e0140908 View Article PubMed/NCBI
  5. Younossi ZM, Golabi P, de Avila L, Paik JM, Srishord M, Fukui N, et al. The global epidemiology of NAFLD and NASH in patients with type 2 diabetes: A systematic review and meta-analysis. J Hepatol 2019;71(4):793-801 View Article PubMed/NCBI
  6. Chen SC, Tsai SP, Jhao JY, Jiang WK, Tsao CK, Chang LY. Liver Fat, Hepatic Enzymes, Alkaline Phosphatase and the Risk of Incident Type 2 Diabetes: A Prospective Study of 132,377 Adults. Sci Rep 2017;7(1):4649 View Article PubMed/NCBI
  7. Cho HJ, Hwang S, Park JI, Yang MJ, Hwang JC, Yoo BM, et al. Improvement of Nonalcoholic Fatty Liver Disease Reduces the Risk of Type 2 Diabetes Mellitus. Gut Liver 2019;13(4):440-449 View Article PubMed/NCBI
  8. Ye Q, Zou B, Yeo YH, Li J, Huang DQ, Wu Y, et al. Global prevalence, incidence, and outcomes of non-obese or lean non-alcoholic fatty liver disease: a systematic review and meta-analysis. Lancet Gastroenterol Hepatol 2020;5(8):739-752 View Article PubMed/NCBI
  9. Lu FB, Zheng KI, Rios RS, Targher G, Byrne CD, Zheng MH. Global epidemiology of lean non-alcoholic fatty liver disease: A systematic review and meta-analysis. J Gastroenterol Hepatol 2020;35(12):2041-2050 View Article PubMed/NCBI
  10. Kim D, Kim WR. Nonobese Fatty Liver Disease. Clin Gastroenterol Hepatol 2017;15(4):474-485 View Article PubMed/NCBI
  11. Eslam M, Valenti L, Romeo S. Genetics and epigenetics of NAFLD and NASH: Clinical impact. J Hepatol 2018;68(2):268-279 View Article PubMed/NCBI
  12. Huang Y, Cohen JC, Hobbs HH. Expression and characterization of a PNPLA3 protein isoform (I148M) associated with nonalcoholic fatty liver disease. J Biol Chem 2011;286(43):37085-37093 View Article PubMed/NCBI
  13. Romeo S, Kozlitina J, Xing C, Pertsemlidis A, Cox D, Pennacchio LA, et al. Genetic variation in PNPLA3 confers susceptibility to nonalcoholic fatty liver disease. Nat Genet 2008;40(12):1461-1465 View Article PubMed/NCBI
  14. Pingitore P, Pirazzi C, Mancina RM, Motta BM, Indiveri C, Pujia A, et al. Recombinant PNPLA3 protein shows triglyceride hydrolase activity and its I148M mutation results in loss of function. Biochim Biophys Acta 2014;1841(4):574-580 View Article PubMed/NCBI
  15. Luo F, Oldoni F, Das A. TM6SF2: A Novel Genetic Player in Nonalcoholic Fatty Liver and Cardiovascular Disease. Hepatol Commun 2022;6(3):448-460 View Article PubMed/NCBI
  16. Teo K, Abeysekera KWM, Adams L, Aigner E, Anstee QM, Banales JM, et al. rs641738C>T near MBOAT7 is associated with liver fat, ALT and fibrosis in NAFLD: A meta-analysis. J Hepatol 2021;74(1):20-30 View Article PubMed/NCBI
  17. Xia M, Chandrasekaran P, Rong S, Fu X, Mitsche MA. Hepatic deletion of Mboat7 (LPIAT1) causes activation of SREBP-1c and fatty liver. J Lipid Res 2021;62:100031 View Article PubMed/NCBI
  18. Gijón MA, Riekhof WR, Zarini S, Murphy RC, Voelker DR. Lysophospholipid acyltransferases and arachidonate recycling in human neutrophils. J Biol Chem 2008;283(44):30235-30245 View Article PubMed/NCBI
  19. Martin K, Hatab A, Athwal VS, Jokl E, Piper Hanley K. Genetic Contribution to Non-alcoholic Fatty Liver Disease and Prognostic Implications. Curr Diab Rep 2021;21(3):8 View Article PubMed/NCBI
  20. Kim DY, Park JY. Genetic risk factors associated with NAFLD. Hepatoma Res 2020;6:85 View Article PubMed/NCBI
  21. Ma Y, Belyaeva OV, Brown PM, Fujita K, Valles K, Karki S, et al. 17-Beta Hydroxysteroid Dehydrogenase 13 Is a Hepatic Retinol Dehydrogenase Associated With Histological Features of Nonalcoholic Fatty Liver Disease. Hepatology 2019;69(4):1504-1519 View Article PubMed/NCBI
  22. Abul-Husn NS, Cheng X, Li AH, Xin Y, Schurmann C, Stevis P, et al. A Protein-Truncating HSD17B13 Variant and Protection from Chronic Liver Disease. N Engl J Med 2018;378(12):1096-1106 View Article PubMed/NCBI
  23. Ortega-Prieto P, Postic C. Carbohydrate Sensing Through the Transcription Factor ChREBP. Front Genet 2019;10:472 View Article PubMed/NCBI
  24. Shimano H, Yahagi N, Amemiya-Kudo M, Hasty AH, Osuga J, Tamura Y, et al. Sterol regulatory element-binding protein-1 as a key transcription factor for nutritional induction of lipogenic enzyme genes. J Biol Chem 1999;274(50):35832-35839 View Article PubMed/NCBI
  25. Buzzetti E, Pinzani M, Tsochatzis EA. The multiple-hit pathogenesis of non-alcoholic fatty liver disease (NAFLD). Metabolism 2016;65(8):1038-1048 View Article PubMed/NCBI
  26. Ioannou GN. Epidemiology and risk-stratification of NAFLD-associated HCC. J Hepatol 2021;75(6):1476-1484 View Article PubMed/NCBI
  27. Huang DQ, El-Serag HB, Loomba R. Global epidemiology of NAFLD-related HCC: trends, predictions, risk factors and prevention. Nat Rev Gastroenterol Hepatol 2021;18(4):223-238 View Article PubMed/NCBI
  28. Cheemerla S, Balakrishnan M. Global Epidemiology of Chronic Liver Disease. Clin Liver Dis (Hoboken) 2021;17(5):365-370 View Article PubMed/NCBI
  29. Kleiner DE, Brunt EM, Van Natta M, Behling C, Contos MJ, Cummings OW, et al. Design and validation of a histological scoring system for nonalcoholic fatty liver disease. Hepatology 2005;41(6):1313-1321 View Article PubMed/NCBI
  30. Papatheodoridi M, Cholongitas E. Diagnosis of Non-alcoholic Fatty Liver Disease (NAFLD): Current Concepts. Curr Pharm Des 2018;24(38):4574-4586 View Article PubMed/NCBI
  31. Ferraioli G, Soares Monteiro LB. Ultrasound-based techniques for the diagnosis of liver steatosis. World J Gastroenterol 2019;25(40):6053-6062 View Article PubMed/NCBI
  32. Pu K, Wang Y, Bai S, Wei H, Zhou Y, Fan J, et al. Diagnostic accuracy of controlled attenuation parameter (CAP) as a non-invasive test for steatosis in suspected non-alcoholic fatty liver disease: a systematic review and meta-analysis. BMC Gastroenterol 2019;19(1):51 View Article PubMed/NCBI
  33. Tavaglione F, De Vincentis A, Bruni V, Gallo IF, Carotti S, Tuccinardi D, et al. Accuracy of controlled attenuation parameter for assessing liver steatosis in individuals with morbid obesity before bariatric surgery. Liver Int 2022;42(2):374-383 View Article PubMed/NCBI
  34. Choi SJ, Kim SM, Kim YS, Kwon OS, Shin SK, Kim KK, et al. Magnetic Resonance-Based Assessments Better Capture Pathophysiologic Profiles and Progression in Nonalcoholic Fatty Liver Disease. Diabetes Metab J 2021;45(5):739-752 View Article PubMed/NCBI
  35. Jia S, Zhao Y, Liu J, Guo X, Chen M, Zhou S, et al. Magnetic Resonance Imaging-Proton Density Fat Fraction vs. Transient Elastography-Controlled Attenuation Parameter in Diagnosing Non-alcoholic Fatty Liver Disease in Children and Adolescents: A Meta-Analysis of Diagnostic Accuracy. Front Pediatr 2021;9:784221 View Article PubMed/NCBI
  36. Beyer C, Hutton C, Andersson A, Imajo K, Nakajima A, Kiker D, et al. Comparison between magnetic resonance and ultrasound-derived indicators of hepatic steatosis in a pooled NAFLD cohort. PLoS One 2021;16(4):e0249491 View Article PubMed/NCBI
  37. Agopian VG, Kaldas FM, Hong JC, Whittaker M, Holt C, Rana A, et al. Liver transplantation for nonalcoholic steatohepatitis: the new epidemic. Ann Surg 2012;256(4):624-633 View Article PubMed/NCBI
  38. Katsagoni CN, Georgoulis M, Papatheodoridis GV, Panagiotakos DB, Kontogianni MD. Effects of lifestyle interventions on clinical characteristics of patients with non-alcoholic fatty liver disease: A meta-analysis. Metabolism 2017;68:119-132 View Article PubMed/NCBI
  39. Sun W, Cui H, Li N, Wei Y, Lai S, Yang Y, et al. Comparison of FIB-4 index, NAFLD fibrosis score and BARD score for prediction of advanced fibrosis in adult patients with non-alcoholic fatty liver disease: A meta-analysis study. Hepatol Res 2016;46(9):862-870 View Article PubMed/NCBI
  40. Vallet-Pichard A, Mallet V, Nalpas B, Verkarre V, Nalpas A, Dhalluin-Venier V, et al. FIB-4: an inexpensive and accurate marker of fibrosis in HCV infection. comparison with liver biopsy and fibrotest. Hepatology 2007;46(1):32-36 View Article PubMed/NCBI
  41. Yilmaz Y, Eren F, Yonal O, Polat Z, Bacha M, Kurt R, et al. Serum progranulin as an independent marker of liver fibrosis in patients with biopsy-proven nonalcoholic fatty liver disease. Dis Markers 2011;31(4):205-210 View Article PubMed/NCBI
  42. Bedogni G, Bellentani S, Miglioli L, Masutti F, Passalacqua M, Castiglione A, et al. The Fatty Liver Index: a simple and accurate predictor of hepatic steatosis in the general population. BMC Gastroenterol 2006;6:33 View Article PubMed/NCBI
  43. Kotronen A, Peltonen M, Hakkarainen A, Sevastianova K, Bergholm R, Johansson LM, et al. Prediction of non-alcoholic fatty liver disease and liver fat using metabolic and genetic factors. Gastroenterology 2009;137(3):865-872 View Article PubMed/NCBI
  44. Lee JH, Kim D, Kim HJ, Lee CH, Yang JI, Kim W, et al. Hepatic steatosis index: a simple screening tool reflecting nonalcoholic fatty liver disease. Dig Liver Dis 2010;42(7):503-508 View Article PubMed/NCBI
  45. Xia MF, Yki-Järvinen H, Bian H, Lin HD, Yan HM, Chang XX, et al. Influence of Ethnicity on the Accuracy of Non-Invasive Scores Predicting Non-Alcoholic Fatty Liver Disease. PLoS One 2016;11(8):e0160526 View Article PubMed/NCBI
  46. Weston SR, Leyden W, Murphy R, Bass NM, Bell BP, Manos MM, et al. Racial and ethnic distribution of nonalcoholic fatty liver in persons with newly diagnosed chronic liver disease. Hepatology 2005;41(2):372-379 View Article PubMed/NCBI
  47. Kantartzis K, Rettig I, Staiger H, Machann J, Schick F, Scheja L, et al. An extended fatty liver index to predict non-alcoholic fatty liver disease. Diabetes Metab 2017;43(3):229-239 View Article PubMed/NCBI
  48. Wang J, Xu C, Xun Y, Lu Z, Shi J, Yu C, et al. ZJU index: a novel model for predicting nonalcoholic fatty liver disease in a Chinese population. Sci Rep 2015;5:16494 View Article PubMed/NCBI
  49. Bhatt SP, Misra A, Nigam P, Guleria R, Pasha MA. Phenotype, Body Composition, and Prediction Equations (Indian Fatty Liver Index) for Non-Alcoholic Fatty Liver Disease in Non-Diabetic Asian Indians: A Case-Control Study. PLoS One 2015;10(11):e0142260 View Article PubMed/NCBI
  50. Birjandi M, Ayatollahi SM, Pourahmad S, Safarpour AR. Prediction and Diagnosis of Non-Alcoholic Fatty Liver Disease (NAFLD) and Identification of Its Associated Factors Using the Classification Tree Method. Iran Red Crescent Med J 2016;18(11):e32858 View Article PubMed/NCBI
  51. Jamali R, Arj A, Razavizade M, Aarabi MH. Prediction of Nonalcoholic Fatty Liver Disease Via a Novel Panel of Serum Adipokines. Medicine (Baltimore) 2016;95(5):e2630 View Article PubMed/NCBI
  52. Yip TC, Ma AJ, Wong VW, Tse YK, Chan HL, Yuen PC, et al. Laboratory parameter-based machine learning model for excluding non-alcoholic fatty liver disease (NAFLD) in the general population. Aliment Pharmacol Ther 2017;46(4):447-456 View Article PubMed/NCBI
  53. Kahn HS. The “lipid accumulation product” performs better than the body mass index for recognizing cardiovascular risk: a population-based comparison. BMC Cardiovasc Disord 2005;5:26 View Article PubMed/NCBI
  54. Bedogni G, Kahn HS, Bellentani S, Tiribelli C. A simple index of lipid overaccumulation is a good marker of liver steatosis. BMC Gastroenterol 2010;10:98 View Article PubMed/NCBI
  55. Poynard T, Ratziu V, Naveau S, Thabut D, Charlotte F, Messous D, et al. The diagnostic value of biomarkers (SteatoTest) for the prediction of liver steatosis. Comp Hepatol 2005;4:10 View Article PubMed/NCBI
  56. Poynard T, Peta V, Munteanu M, Charlotte F, Ngo Y, Ngo A, et al. The diagnostic performance of a simplified blood test (SteatoTest-2) for the prediction of liver steatosis. Eur J Gastroenterol Hepatol 2019;31(3):393-402 View Article PubMed/NCBI
  57. Long MT, Pedley A, Colantonio LD, Massaro JM, Hoffmann U, Muntner P, et al. Development and Validation of the Framingham Steatosis Index to Identify Persons With Hepatic Steatosis. Clin Gastroenterol Hepatol 2016;14(8):1172-1180.e2 View Article PubMed/NCBI
  58. Jeong S, Kim K, Chang J, Choi S, Kim SM, Son JS, et al. Development of a simple nonalcoholic fatty liver disease scoring system indicative of metabolic risks and insulin resistance. Ann Transl Med 2020;8(21):1414 View Article PubMed/NCBI
  59. Zhou YJ, Zhou YF, Zheng JN, Liu WY, Van Poucke S, Zou TT, et al. NAFL screening score: A basic score identifying ultrasound-diagnosed non-alcoholic fatty liver. Clin Chim Acta 2017;475:44-50 View Article PubMed/NCBI
  60. Zhou YJ, Zheng JN, Liu WY, Miele L, Vitale A, Van Poucke S, et al. The NAFL Risk Score: A simple scoring model to predict 4-y risk for non-alcoholic fatty liver. Clin Chim Acta 2017;468:17-24 View Article PubMed/NCBI
  61. Pan X, Xie X, Peng H, Cai X, Li H, Hong Q, et al. Risk Prediction for Non-alcoholic Fatty Liver Disease Based on Biochemical and Dietary Variables in a Chinese Han Population. Front Public Health 2020;8:220 View Article PubMed/NCBI
  62. Meffert PJ, Baumeister SE, Lerch MM, Mayerle J, Kratzer W, Völzke H. Development, external validation, and comparative assessment of a new diagnostic score for hepatic steatosis. Am J Gastroenterol 2014;109(9):1404-1414 View Article PubMed/NCBI
  63. Ruhl CE, Everhart JE. Fatty liver indices in the multiethnic United States National Health and Nutrition Examination Survey. Aliment Pharmacol Ther 2015;41(1):65-76 View Article PubMed/NCBI
  64. McHenry S, Park Y, Browning JD, Sayuk G, Davidson NO. Dallas Steatosis Index Identifies Patients With Nonalcoholic Fatty Liver Disease. Clin Gastroenterol Hepatol 2020;18(9):2073-2080.e7 View Article PubMed/NCBI
  65. Rodriguez LA, Shiboski SC, Bradshaw PT, Fernandez A, Herrington D, Ding J, et al. Predicting Non-Alcoholic Fatty Liver Disease for Adults Using Practical Clinical Measures: Evidence from the Multi-ethnic Study of Atherosclerosis. J Gen Intern Med 2021;36(9):2648-2655 View Article PubMed/NCBI
  66. Jung TY, Kim MS, Hong HP, Kang KA, Jun DW. Comparative Assessment and External Validation of Hepatic Steatosis Formulae in a Community-Based Setting. J Clin Med 2020;9(9):2851 View Article PubMed/NCBI
  67. Li L, You W, Ren W. The ZJU index is a powerful index for identifying NAFLD in the general Chinese population. Acta Diabetol 2017;54(10):905-911 View Article PubMed/NCBI
  68. Li T, Zhu J, Zhang Y, Liu Y, Shuai P. Validation of the ZJU index for nonalcoholic fatty liver disease in West China: a cross-sectional study. Int J Clin Exp Med 2016;9(9):18395-18399 View Article PubMed/NCBI
  69. Shen YN, Yu MX, Gao Q, Li YY, Huang JJ, Sun CM, et al. External validation of non-invasive prediction models for identifying ultrasonography-diagnosed fatty liver disease in a Chinese population. Medicine (Baltimore) 2017;96(30):e7610 View Article PubMed/NCBI
  70. Shi M, Liu P, Li J, Su Y, Zhou X, Wu C, et al. The performance of noninvasive indexes of adults in identification of nonalcoholic fatty liver disease in children. J Diabetes 2021;13(9):744-753 View Article PubMed/NCBI
  71. Zhang L, Zhang M, Wang M, Wang M, Zhang R, Wang H, et al. External validation and comparison of simple tools to screen for nonalcoholic fatty liver disease in Chinese community population. Eur J Gastroenterol Hepatol 2022;34(8):865-872 View Article PubMed/NCBI
  72. Foschi FG, Conti F, Domenicali M, Giacomoni P, Borghi A, Bevilacqua V, et al. External Validation of Surrogate Indices of Fatty Liver in the General Population: the Bagnacavallo Study. J Clin Med 2021;10(3):520 View Article PubMed/NCBI
  73. Fu CP, Ali H, Rachakonda VP, Oczypok EA, DeLany JP, Kershaw EE. The ZJU index is a powerful surrogate marker for NAFLD in severely obese North American women. PLoS One 2019;14(11):e0224942 View Article PubMed/NCBI
  74. Lind L, Johansson L, Ahlström H, Eriksson JW, Larsson A, Risérus U, et al. Comparison of four non-alcoholic fatty liver disease detection scores in a Caucasian population. World J Hepatol 2020;12(4):149-159 View Article PubMed/NCBI
  75. Koot BG, van der Baan-Slootweg OH, Bohte AE, Nederveen AJ, van Werven JR, Tamminga-Smeulders CL, et al. Accuracy of prediction scores and novel biomarkers for predicting nonalcoholic fatty liver disease in obese children. Obesity (Silver Spring) 2013;21(3):583-590 View Article PubMed/NCBI
  76. Cuthbertson DJ, Weickert MO, Lythgoe D, Sprung VS, Dobson R, Shoajee-Moradie F, et al. External validation of the fatty liver index and lipid accumulation product indices, using 1H-magnetic resonance spectroscopy, to identify hepatic steatosis in healthy controls and obese, insulin-resistant individuals. Eur J Endocrinol 2014;171(5):561-569 View Article PubMed/NCBI
  77. Kabisch S, Bäther S, Dambeck U, Kemper M, Gerbracht C, Honsek C, et al. Liver Fat Scores Moderately Reflect Interventional Changes in Liver Fat Content by a Low-Fat Diet but Not by a Low-Carb Diet. Nutrients 2018;10(2):157 View Article PubMed/NCBI
  78. Kabisch S, Markova M, Hornemann S, Sucher S, Pivovarova-Ramich O, Machann J, et al. Liver fat scores do not reflect interventional changes in liver fat content induced by high-protein diets. Sci Rep 2021;11(1):8843 View Article PubMed/NCBI
  79. Kahl S, Straßburger K, Nowotny B, Livingstone R, Klüppelholz B, Keßel K, et al. Comparison of liver fat indices for the diagnosis of hepatic steatosis and insulin resistance. PLoS One 2014;9(4):e94059 View Article PubMed/NCBI
  80. Motamed N, Sohrabi M, Ajdarkosh H, Hemmasi G, Maadi M, Sayeedian FS, et al. Fatty liver index vs waist circumference for predicting non-alcoholic fatty liver disease. World J Gastroenterol 2016;22(10):3023-3030 View Article PubMed/NCBI
  81. Fedchuk L, Nascimbeni F, Pais R, Charlotte F, Housset C, Ratziu V, LIDO Study Group. Performance and limitations of steatosis biomarkers in patients with nonalcoholic fatty liver disease. Aliment Pharmacol Ther 2014;40(10):1209-1222 View Article PubMed/NCBI
  82. Lerchbaum E, Pilz S, Grammer TB, Boehm BO, Stojakovic T, Obermayer-Pietsch B, et al. The fatty liver index is associated with increased mortality in subjects referred to coronary angiography. Nutr Metab Cardiovasc Dis 2013;23(12):1231-1238 View Article PubMed/NCBI
  83. Unalp-Arida A, Ruhl CE. Liver fat scores predict liver disease mortality in the United States population. Aliment Pharmacol Ther 2018;48(9):1003-1016 View Article PubMed/NCBI
  84. Ruissen MM, Mak AL, Beuers U, Tushuizen ME, Holleboom AG. Non-alcoholic fatty liver disease: a multidisciplinary approach towards a cardiometabolic liver disease. Eur J Endocrinol 2020;183(3):R57-R73 View Article PubMed/NCBI
  85. Stefan N, Staiger H, Wagner R, Machann J, Schick F, Häring HU, et al. A high-risk phenotype associates with reduced improvement in glycaemia during a lifestyle intervention in prediabetes. Diabetologia 2015;58(12):2877-2884 View Article PubMed/NCBI
  86. Hjorth MF, Astrup A, Zohar Y, Urban LE, Sayer RD, Patterson BW, et al. Personalized nutrition: pretreatment glucose metabolism determines individual long-term weight loss responsiveness in individuals with obesity on low-carbohydrate versus low-fat diet. Int J Obes (Lond) 2019;43(10):2037-2044 View Article PubMed/NCBI
  87. Hjorth MF, Bray GA, Zohar Y, Urban L, Miketinas DC, Williamson DA, et al. Pretreatment Fasting Glucose and Insulin as Determinants of Weight Loss on Diets Varying in Macronutrients and Dietary Fibers-The POUNDS LOST Study. Nutrients 2019;11(3):586 View Article PubMed/NCBI
  88. Knowler WC, Fowler SE, Hamman RF, Christophi CA, Hoffman HJ, Brenneman AT, et al. 10-year follow-up of diabetes incidence and weight loss in the Diabetes Prevention Program Outcomes Study. Lancet 2009;374(9702):1677-1686 View Article PubMed/NCBI
  89. Gerstein HC, Mohan V, Avezum A, Bergenstal RM, Chiasson JL, Garrido M, et al. Long-term effect of rosiglitazone and/or ramipril on the incidence of diabetes. Diabetologia 2011;54(3):487-495 View Article PubMed/NCBI
  90. Targher G. Non-alcoholic fatty liver disease, the metabolic syndrome and the risk of cardiovascular disease: the plot thickens. Diabet Med 2007;24(1):1-6 View Article PubMed/NCBI
  91. Gastaldelli A, Kozakova M, Højlund K, Flyvbjerg A, Favuzzi A, Mitrakou A, et al. Fatty liver is associated with insulin resistance, risk of coronary heart disease, and early atherosclerosis in a large European population. Hepatology 2009;49(5):1537-1544 View Article PubMed/NCBI
  92. Bozkurt L, Göbl CS, Tura A, Chmelik M, Prikoszovich T, Kosi L, et al. Fatty liver index predicts further metabolic deteriorations in women with previous gestational diabetes. PLoS One 2012;7(2):e32710 View Article PubMed/NCBI
  93. Balkau B, Lange C, Vol S, Fumeron F, Bonnet F, Group Study D.E.S.I.R. BMC Gastroenterology . E.S.I.R. Nine-year incident diabetes is predicted by fatty liver indices: the French D.E.S.I.R. study. BMC Gastroenterol 2010;10:56 View Article PubMed/NCBI
  94. Calori G, Lattuada G, Ragogna F, Garancini MP, Crosignani P, Villa M, et al. Fatty liver index and mortality: the Cremona study in the 15th year of follow-up. Hepatology 2011;54(1):145-152 View Article PubMed/NCBI
  95. Keating SE, Parker HM, Hickman IJ, Gomersall SR, Wallen MP, Coombes JS, et al. NAFLD in clinical practice: Can simple blood and anthropometric markers be used to detect change in liver fat measured by (1) H-MRS?. Liver Int 2017;37(12):1907-1915 View Article PubMed/NCBI
  96. Vilar-Gomez E, Athinarayanan SJ, Adams RN, Hallberg SJ, Bhanpuri NH, McKenzie AL, et al. Post hoc analyses of surrogate markers of non-alcoholic fatty liver disease (NAFLD) and liver fibrosis in patients with type 2 diabetes in a digitally supported continuous care intervention: an open-label, non-randomised controlled study. BMJ Open 2019;9(2):e023597 View Article PubMed/NCBI
  97. Arslanow A, Teutsch M, Walle H, Grünhage F, Lammert F, Stokes CS. Short-Term Hypocaloric High-Fiber and High-Protein Diet Improves Hepatic Steatosis Assessed by Controlled Attenuation Parameter. Clin Transl Gastroenterol 2016;7(6):e176 View Article PubMed/NCBI
  98. Wai JW, Fu C, Wong VW. Confounding factors of non-invasive tests for nonalcoholic fatty liver disease. J Gastroenterol 2020;55(8):731-741 View Article PubMed/NCBI
  99. Amirkalali B, Poustchi H, Keyvani H, Khansari MR, Ajdarkosh H, Maadi M, et al. Prevalence of Non-Alcoholic Fatty Liver Disease and Its Predictors in North of Iran. Iran J Public Health 2014;43(9):1275-1283 View Article PubMed/NCBI
  100. Laurenti MC, Matveyenko A, Vella A. Measurement of Pulsatile Insulin Secretion: Rationale and Methodology. Metabolites 2021;11(7):409 View Article PubMed/NCBI
  101. Zheng R, Du Z, Wang M, Mao Y, Mao W. A longitudinal epidemiological study on the triglyceride and glucose index and the incident nonalcoholic fatty liver disease. Lipids Health Dis 2018;17(1):262 View Article PubMed/NCBI
  102. Kitae A, Hashimoto Y, Hamaguchi M, Obora A, Kojima T, Fukui M. The Triglyceride and Glucose Index Is a Predictor of Incident Nonalcoholic Fatty Liver Disease: A Population-Based Cohort Study. Can J Gastroenterol Hepatol 2019;2019:5121574 View Article PubMed/NCBI
  103. Khamseh ME, Malek M, Abbasi R, Taheri H, Lahouti M, Alaei-Shahmiri F. Triglyceride Glucose Index and Related Parameters (Triglyceride Glucose-Body Mass Index and Triglyceride Glucose-Waist Circumference) Identify Nonalcoholic Fatty Liver and Liver Fibrosis in Individuals with Overweight/Obesity. Metab Syndr Relat Disord 2021;19(3):167-173 View Article PubMed/NCBI
  104. Wang R, Dai L, Zhong Y, Xie G. Usefulness of the triglyceride glucose-body mass index in evaluating nonalcoholic fatty liver disease: insights from a general population. Lipids Health Dis 2021;20(1):77 View Article PubMed/NCBI
  105. Haukeland JW, Konopski Z, Linnestad P, Azimy S, Marit Løberg E, Haaland T, et al. Abnormal glucose tolerance is a predictor of steatohepatitis and fibrosis in patients with non-alcoholic fatty liver disease. Scand J Gastroenterol 2005;40(12):1469-1477 View Article PubMed/NCBI
  106. Cazzo E, Jimenez LS, Gestic MA, Utrini MP, Chaim FHM, Chaim FDM, et al. Type 2 Diabetes Mellitus and Simple Glucose Metabolism Parameters may Reliably Predict Nonalcoholic Fatty Liver Disease Features. Obes Surg 2018;28(1):187-194 View Article PubMed/NCBI
  107. Neumeier M, Hellerbrand C, Gäbele E, Buettner R, Bollheimer C, Weigert J, et al. Adiponectin and its receptors in rodent models of fatty liver disease and liver cirrhosis. World J Gastroenterol 2006;12(34):5490-5494 View Article PubMed/NCBI
  108. Wanninger J, Bauer S, Eisinger K, Weiss TS, Walter R, Hellerbrand C, et al. Adiponectin upregulates hepatocyte CMKLR1 which is reduced in human fatty liver. Mol Cell Endocrinol 2012;349(2):248-254 View Article PubMed/NCBI
  109. Baranova A, Gowder SJ, Schlauch K, Elariny H, Collantes R, Afendy A, et al. Gene expression of leptin, resistin, and adiponectin in the white adipose tissue of obese patients with non-alcoholic fatty liver disease and insulin resistance. Obes Surg 2006;16(9):1118-1125 View Article PubMed/NCBI
  110. Zhang H, Niu Y, Gu H, Lu S, Zhang W, Li X, et al. Low serum adiponectin is a predictor of progressing to nonalcoholic fatty liver disease. J Clin Lab Anal 2019;33(3):e22709 View Article PubMed/NCBI
  111. Hsieh CJ, Wang PW, Hu TH. Association of adiponectin gene polymorphism with nonalcoholic fatty liver disease in Taiwanese patients with type 2 diabetes. PLoS One 2015;10(6):e0127521 View Article PubMed/NCBI
  112. Liu J, Xing J, Wang B, Wei C, Yang R, Zhu Y, et al. Correlation Between Adiponectin Gene rs1501299 Polymorphism and Nonalcoholic Fatty Liver Disease Susceptibility: A Systematic Review and Meta-Analysis. Med Sci Monit 2019;25:1078-1086 View Article PubMed/NCBI
  113. Alkhouri N, Lopez R, Berk M, Feldstein AE. Serum retinol-binding protein 4 levels in patients with nonalcoholic fatty liver disease. J Clin Gastroenterol 2009;43(10):985-989 View Article PubMed/NCBI
  114. Wang YS, Ye J, Yang X, Zhang GP, Cao YH, Zhang R, et al. Association of retinol binding protein-4, cystatin C, homocysteine and high-sensitivity C-reactive protein levels in patients with newly diagnosed type 2 diabetes mellitus. Arch Med Sci 2019;15(5):1203-1216 View Article PubMed/NCBI
  115. Pan X, Kaminga AC, Chen J, Luo M, Luo J. Fetuin-A and Fetuin-B in Non-Alcoholic Fatty Liver Disease: A Meta-Analysis and Meta-Regression. Int J Environ Res Public Health 2020;17(8):2735 View Article PubMed/NCBI
  116. Huang Y, Huang X, Ding L, Wang P, Peng K, Chen Y, et al. Serum Fetuin-A Associated With Fatty Liver Index, Early Indicator of Nonalcoholic Fatty Liver Disease: A Strobe-Compliant Article. Medicine (Baltimore) 2015;94(39):e1517 View Article PubMed/NCBI
  117. Ballestri S, Meschiari E, Baldelli E, Musumeci FE, Romagnoli D, Trenti T, et al. Relationship of serum fetuin-A levels with coronary atherosclerotic burden and NAFLD in patients undergoing elective coronary angiography. Metab Syndr Relat Disord 2013;11(4):289-295 View Article PubMed/NCBI
  118. European Association for Study of Liver, Asociacion Latinoamericana para el Estudio del Higado. EASL-ALEH Clinical Practice Guidelines: Non-invasive tests for evaluation of liver disease severity and prognosis. J Hepatol 2015;63(1):237-264 View Article PubMed/NCBI
  119. Darweesh SK, AbdElAziz RA, Abd-ElFatah DS, AbdElazim NA, Fathi SA, Attia D, et al. Serum cytokeratin-18 and its relation to liver fibrosis and steatosis diagnosed by FibroScan and controlled attenuation parameter in nonalcoholic fatty liver disease and hepatitis C virus patients. Eur J Gastroenterol Hepatol 2019;31(5):633-641 View Article PubMed/NCBI
  120. Altaf B, Rehman A, Jawed S, Raouf A. Association of liver biomarkers and cytokeratin-18 in Nonalcoholic fatty liver disease patients. Pak J Med Sci 2020;36(3):387-390 View Article PubMed/NCBI
  121. Lebensztejn DM, Wierzbicka A, Socha P, Pronicki M, Skiba E, Werpachowska I, et al. Cytokeratin-18 and hyaluronic acid levels predict liver fibrosis in children with non-alcoholic fatty liver disease. Acta Biochim Pol 2011;58(4):563-566 View Article PubMed/NCBI
  122. Walenbergh SM, Houben T, Rensen SS, Bieghs V, Hendrikx T, van Gorp PJ, et al. Plasma cathepsin D correlates with histological classifications of fatty liver disease in adults and responds to intervention. Sci Rep 2016;6:38278 View Article PubMed/NCBI
  123. Ding L, De Munck TJI, Oligschlaeger Y, Verbeek J, Koek GH, Houben T, et al. Insulin resistance is positively associated with plasma cathepsin D activity in NAFLD patients. Biomol Concepts 2021;12(1):110-115 View Article PubMed/NCBI
  124. Kamarajah SK, Khoo S, Chan WK, Sthaneshwar P, Nik Mustapha NR, Mahadeva S. Limited applicability of cathepsin D for the diagnosis and monitoring of non-alcoholic steatohepatitis. JGH Open 2019;3(5):417-424 View Article PubMed/NCBI
  125. Pisano G, Lombardi R, Fracanzani AL. Vascular Damage in Patients with Nonalcoholic Fatty Liver Disease: Possible Role of Iron and Ferritin. Int J Mol Sci 2016;17(5):675 View Article PubMed/NCBI
  126. Sabrina N, Bai CH, Chang CC, Chien YW, Chen JR, Chang JS. Serum Iron:Ferritin Ratio Predicts Healthy Body Composition and Reduced Risk of Severe Fatty Liver in Young Adult Women. Nutrients 2017;9(8):833 View Article PubMed/NCBI
  127. Mörwald K, Aigner E, Bergsten P, Brunner SM, Forslund A, Kullberg J, et al. Serum Ferritin Correlates With Liver Fat in Male Adolescents With Obesity. Front Endocrinol (Lausanne) 2020;11:340 View Article PubMed/NCBI
  128. Kowdley KV, Belt P, Wilson LA, Yeh MM, Neuschwander-Tetri BA, Chalasani N, et al. Serum ferritin is an independent predictor of histologic severity and advanced fibrosis in patients with nonalcoholic fatty liver disease. Hepatology 2012;55(1):77-85 View Article PubMed/NCBI
  129. Lombardi R, Pisano G, Fargion S. Role of Serum Uric Acid and Ferritin in the Development and Progression of NAFLD. Int J Mol Sci 2016;17(4):548 View Article PubMed/NCBI
  130. Galarregui C, Marin-Alejandre BA, Perez-Diaz-Del-Campo N, Cantero I, Monreal JI, Elorz M, et al. Predictive Value of Serum Ferritin in Combination with Alanine Aminotransferase and Glucose Levels for Noninvasive Assessment of NAFLD: Fatty Liver in Obesity (FLiO) Study. Diagnostics (Basel) 2020;10(11):917 View Article PubMed/NCBI
  131. Britton L, Bridle K, Reiling J, Santrampurwala N, Wockner L, Ching H, et al. Hepatic iron concentration correlates with insulin sensitivity in nonalcoholic fatty liver disease. Hepatol Commun 2018;2(6):644-653 View Article PubMed/NCBI
  132. Valenti L, Fracanzani AL, Dongiovanni P, Bugianesi E, Marchesini G, Manzini P, et al. Iron depletion by phlebotomy improves insulin resistance in patients with nonalcoholic fatty liver disease and hyperferritinemia: evidence from a case-control study. Am J Gastroenterol 2007;102(6):1251-1258 View Article PubMed/NCBI
  133. Valenti L, Dongiovanni P, Fracanzani AL, Santorelli G, Fatta E, Bertelli C, et al. Increased susceptibility to nonalcoholic fatty liver disease in heterozygotes for the mutation responsible for hereditary hemochromatosis. Dig Liver Dis 2003;35(3):172-178 View Article PubMed/NCBI
  134. Britton LJ, Subramaniam VN, Crawford DH. Iron and non-alcoholic fatty liver disease. World J Gastroenterol 2016;22(36):8112-8122 View Article PubMed/NCBI
  135. Du SX, Lu LL, Geng N, Victor DW, Chen LZ, Wang C, et al. Association of serum ferritin with non-alcoholic fatty liver disease: a meta-analysis. Lipids Health Dis 2017;16(1):228 View Article PubMed/NCBI
  136. Gámez-Belmonte R, Tena-Garitaonaindia M, Hernández-Chirlaque C, Córdova S, Ceacero-Heras D, de Medina FS, et al. Deficiency in Tissue Non-Specific Alkaline Phosphatase Leads to Steatohepatitis in Mice Fed a High Fat Diet Similar to That Produced by a Methionine and Choline Deficient Diet. Int J Mol Sci 2020;22(1):51 View Article PubMed/NCBI
  137. Morgan ML, Sigala B, Soeda J, Cordero P, Nguyen V, McKee C, et al. Acetylcholine induces fibrogenic effects via M2/M3 acetylcholine receptors in non-alcoholic steatohepatitis and in primary human hepatic stellate cells. J Gastroenterol Hepatol 2016;31(2):475-483 View Article PubMed/NCBI
  138. Nomura F, Ohnishi K, Koen H, Hiyama Y, Nakayama T, Itoh Y, et al. Serum cholinesterase in patients with fatty liver. J Clin Gastroenterol 1986;8(5):599-602 View Article PubMed/NCBI
  139. Turecky L, Kupcova V, Mojto V, Smutny M, Uhlikova E, Vozar I. Serum cholinesterase activity and proteosynthetic function of liver in patients with diabetes mellitus. Bratisl Lek Listy 2005;106(8-9):266-269 View Article PubMed/NCBI
  140. Inácio Lunkes G, Stefanello F, Sausen Lunkes D, Maria Morsch V, Schetinger MR, Gonçalves JF. Serum cholinesterase activity in diabetes and associated pathologies. Diabetes Res Clin Pract 2006;72(1):28-32 View Article PubMed/NCBI
  141. Ogunkeye OO, Chuhwak EK, Otokwula AA. Serum cholinesterase activity in the diagnosis of nonalcoholic fatty liver disease in type 2 diabetic patients. Pathophysiology 2010;17(1):29-32 View Article PubMed/NCBI
  142. Brennan P, Clare K, George J, Dillon JF. Determining the role for uric acid in non-alcoholic steatohepatitis development and the utility of urate metabolites in diagnosis: An opinion review. World J Gastroenterol 2020;26(15):1683-1690 View Article PubMed/NCBI
  143. Lonardo A, Loria P, Leonardi F, Borsatti A, Neri P, Pulvirenti M, et al. ST.E.N.A. Study Group. Policentrica Steatosi Epatica Non Alcolica. Fasting insulin and uric acid levels but not indices of iron metabolism are independent predictors of non-alcoholic fatty liver disease. A case-control study. Dig Liver Dis 2002;34(3):204-211 View Article PubMed/NCBI
  144. Darmawan G, Hamijoyo L, Hasan I. Association between Serum Uric Acid and Non-Alcoholic Fatty Liver Disease: A Meta-Analysis. Acta Med Indones 2017;49(2):136-147 View Article PubMed/NCBI
  145. Wan X, Xu C, Lin Y, Lu C, Li D, Sang J, et al. Uric acid regulates hepatic steatosis and insulin resistance through the NLRP3 inflammasome-dependent mechanism. J Hepatol 2016;64(4):925-932 View Article PubMed/NCBI
  146. Anstee QM, Darlay R, Cockell S, Meroni M, Govaere O, Tiniakos D, et al. Genome-wide association study of non-alcoholic fatty liver and steatohepatitis in a histologically characterised cohort. J Hepatol 2020;73(3):505-515 View Article PubMed/NCBI
  147. Xie YY, Mo CL, Cai YH, Wang WJ, Hong XX, Zhang KK, et al. Pygo2 Regulates Adiposity and Glucose Homeostasis via β-Catenin-Axin2-GSK3β Signaling Pathway. Diabetes 2018;67(12):2569-2584 View Article PubMed/NCBI
  148. Fairfield CJ, Drake TM, Pius R, Bretherick AD, Campbell A, Clark DW, et al. Genome-Wide Association Study of NAFLD Using Electronic Health Records. Hepatol Commun 2022;6(2):297-308 View Article PubMed/NCBI
  149. Klein JM, Busch JD, Potting C, Baker MJ, Langer T, Schwarz G. The mitochondrial amidoxime-reducing component (mARC1) is a novel signal-anchored protein of the outer mitochondrial membrane. J Biol Chem 2012;287(51):42795-42803 View Article PubMed/NCBI
  150. Kitamoto T, Kitamoto A, Yoneda M, Hyogo H, Ochi H, Nakamura T, et al. Genome-wide scan revealed that polymorphisms in the PNPLA3, SAMM50, and PARVB genes are associated with development and progression of nonalcoholic fatty liver disease in Japan. Hum Genet 2013;132(7):783-792 View Article PubMed/NCBI
  151. Emdin CA, Haas ME, Khera AV, Aragam K, Chaffin M, Klarin D, et al. A missense variant in Mitochondrial Amidoxime Reducing Component 1 gene and protection against liver disease. PLoS Genet 2020;16(4):e1008629 View Article PubMed/NCBI
  152. Chandrasekharan K, Alazawi W. Genetics of Non-Alcoholic Fatty Liver and Cardiovascular Disease: Implications for Therapy?. Front Pharmacol 2019;10:1413 View Article PubMed/NCBI
  153. Di Costanzo A, Belardinilli F, Bailetti D, Sponziello M, D’Erasmo L, Polimeni L, et al. Evaluation of Polygenic Determinants of Non-Alcoholic Fatty Liver Disease (NAFLD) By a Candidate Genes Resequencing Strategy. Sci Rep 2018;8(1):3702 View Article PubMed/NCBI
  154. De Vincentis A, Tavaglione F, Jamialahmadi O, Picardi A, Antonelli Incalzi R, Valenti L, et al. A Polygenic Risk Score to Refine Risk Stratification and Prediction for Severe Liver Disease by Clinical Fibrosis Scores. Clin Gastroenterol Hepatol 2022;20(3):658-673 View Article PubMed/NCBI
  155. Wang J, Conti DV, Bogumil D, Sheng X, Noureddin M, Wilkens LR, et al. Association of Genetic Risk Score With NAFLD in An Ethnically Diverse Cohort. Hepatol Commun 2021;5(10):1689-1703 View Article PubMed/NCBI
  156. Gao F, Zheng KI, Chen SD, Lee DH, Wu XX, Wang XD, et al. Individualized Polygenic Risk Score Identifies NASH in the Eastern Asia Region: A Derivation and Validation Study. Clin Transl Gastroenterol 2021;12(3):e00321 View Article PubMed/NCBI

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