v
Search
Advanced Search

Publications > Journals > Journal of Clinical and Translational Hepatology > Article Full Text

  • Original Article
  • OPEN ACCESS

Interaction of SAMM50-rs738491, PARVB-rs5764455 and PNPLA3-rs738409 Increases Susceptibility to Nonalcoholic Steatohepatitis

  • Ke Xu1,2,
  • Kenneth I. Zheng1,
  • Pei-Wu Zhu3,
  • Wen-Yue Liu4,
  • Hong-Lei Ma1,
  • Gang Li1,
  • Liang-Jie Tang1,
  • Rafael S. Rios1,
  • Giovanni Targher5,
  • Christopher D. Byrne6,
  • Xiao-Dong Wang1,7,8,
  • Yong-Ping Chen1,7,8 and
  • Ming-Hua Zheng1,7,8,* 
 Author information
Journal of Clinical and Translational Hepatology   2022;10(2):219-229

doi: 10.14218/JCTH.2021.00067

Abstract

Background and Aims

Previous studies have reported that the single nucleotide polymorphisms (SNPs) of SAMM50-rs738491, PARVB-rs5764455 and PNPLA3-rs738409 are associated with nonalcoholic fatty liver disease (NAFLD). However, no studies have examined the effect of interactions between these three genotypes to affect liver disease severity. We assessed the effect of these three SNPs on nonalcoholic steatohepatitis (NASH) and also examined the gene-gene interactions in a Chinese population with biopsy-confirmed NAFLD.

Methods

We enrolled 415 consecutive adult individuals with biopsy-proven NAFLD. Multivariable logistic regression analysis was undertaken to test associations between NASH and SNPs in SAMM50-rs738491, PARVB-rs5764455 and PNPLA3-rs738409. Gene-gene interactions were analyzed by performing a generalized multifactor dimensionality reduction (GMDR) analysis.

Results

The mean ± standard deviation age of these 415 patients was 41.3±12.5 years, and 75.9% were men. Patients with SAMM50-rs738491 TT, PARVB-rs5764455 AA or PNPLA3-rs738409 GG genotypes had a higher risk of NASH, even after adjustment for age, sex and body mass index. GMDR analysis showed that the combination of all three SNPs was the best model for predicting NASH. Additionally, the odds ratio of the haplotype T-A-G for predicting the risk of NASH was nearly three times higher than that of the haplotype G-C-C.

Conclusions

NAFLD patients carrying the SAMM50-rs738491 TT, PARVB-rs5764455 AA or PNPLA3-rs738409 GG genotypes are at greater risk of NASH. These three SNPs may synergistically interact to increase susceptibility to NASH.

Graphical Abstract

Keywords

Gene polymorphisms, Gene-gene interaction, SAMM50, PARVB, PNPLA3, NASH

Introduction

Nonalcoholic steatohepatitis (NASH), i.e. the histological subtype of nonalcoholic fatty liver disease (NAFLD) with lobular inflammation and hepatocyte injury, frequently progresses to advanced fibrosis, cirrhosis and hepatocellular carcinoma.1 With rapid growth of the global prevalence of NAFLD, which has occurred over the past two decades, the prevalence of NASH has also increased rapidly worldwide.2 NAFLD is also associated with an increased risk of developing extra-hepatic diseases, including cardiovascular disease, type 2 diabetes mellitus (T2DM) and some extra-hepatic cancers.3–7 The latest epidemiological predictive models have estimated that China will undergo the highest growth of NAFLD and NASH in the near future due to urbanization, which will cause considerable clinical and economic burdens.8 As the diagnosis of NASH still requires liver biopsy,9 there is currently an unmet need for accurate noninvasive tests to diagnose and monitor the disease condition.

Recent studies have shown that NAFLD is not only highly prevalent among overweight or obese individuals but it may also occur in lean individuals, in who genetic factors play an important role.10 Strong evidence indicates that genetic and epigenetic factors may affect the development and progression of NAFLD.3 In recent years, genome-wide association studies (GWAS) have investigated the genetic background of NAFLD in different ethnic populations.11–13 The rs738409 polymorphism in the patatin-like phospholipase containing protein-3 (PNPLA3) gene has the strongest effect on the entire histopathological spectrum of NAFLD across different countries and ethnicities.14 However, besides the rs738409 polymorphism in the PNPLA3 gene, other genetic variants have been implicated in the development and progression of NAFLD. In a GWAS study, Kitamoto et al.13 showed that polymorphisms in the SAMM50 and PARVB genes are also associated with the risk of NAFLD in a Japanese population. However, the contribution of the SAMM50 polymorphism to the progression of NAFLD from simple steatosis to NASH remains controversial.13,15

To date, to the best of our knowledge, no study has tested the association of SAMM50 and PARVB genes with the presence of NASH in Chinese individuals with biopsy-proven NAFLD. It is important to note that the PNPLA3, SAMM50 and PARVB genes are all located on chromosome 22q13.13 In addition, the rs738491, rs5764455 and rs738409 genetic variants are in the same linkage disequilibrium block, and are closely associated with the histological severity of steatosis and NAFLD activity score (NAS).13 Based on the theory that complex disease traits are affected by the inheritance of different numbers of variants and also gene-gene interactions,16 we speculated that there is an interaction between these three polymorphisms to affect liver disease severity in NASH. Currently, there are no studies that have tested the effect of interactions among these three genes on disease severity in NAFLD.

Thus, our aim was to evaluate whether rs738491, rs5764455 and rs738409 genetic variants are associated with NASH, and to investigate gene-gene interactions and combination effects of these three genetic variants on the susceptibility to NASH in a Chinese population with biopsy-confirmed NAFLD.

Methods

Study population

This study involved analysis of data from the well-characterized Prospective Epidemic Research Specifically Of NASH (known as ‘PERSONS’).17,18 Individuals between 18 and 75 years-old with suspected NAFLD (i.e. defined as evidence of hepatic steatosis on imaging techniques, and/or persistently elevated serum liver enzyme levels) were consecutively recruited for liver biopsy examination at the First Affiliated Hospital of Wenzhou Medical University from December 2016 to November 2018. Participants were excluded for the following reasons: (1) excessive alcohol consumption (>140 g/week for men and >70 g/week for women); (2) viral hepatitis (based on serum viral B or C markers) and autoimmune hepatitis (based on serum autoantibodies and histology); (3) chronic use of potentially hepatotoxic drugs; (4) liver cancer (based on imaging and/or pathological data according to the Clinical Practice Guidelines for hepatocellular carcinoma);19 (5) missing data on genetic polymorphisms or other important laboratory parameters; and (6) fatty liver infiltration <5% on histology. Informed consent was obtained from all participants. The study was approved by the internal review board for ethics of the First Affiliated Hospital of Wenzhou Medical University; the study protocol was registered in the Chinese Clinical Trial Registry (ChiCTR-EOC-17013562).

Clinical and laboratory data

We measured baseline characteristics, including demographics, anthropometry, clinical parameters and comorbidities, in all participants. Body mass index (BMI) was calculated as weight in kilograms divided by height in meters squared. Blood pressure was measured by a standardized method.18 Hypertension was defined as blood pressure ≥140/90 mmHg and/or use of any antihypertensive drugs. Diabetes was diagnosed by fasting glucose levels ≥7.0 mmol/L and or use of any antihyperglycemic agents. Blood biochemical parameters, including alanine aminotransferase, aspartate aminotransferase, gamma-glutamyltransferase, triglycerides, total cholesterol, high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), glucose and uric acid concentrations, were assessed by an automated analyzer (Abbott AxSYM, Park, IL, USA) using standard laboratory methods.

Liver histology

Detailed methods for liver biopsy examination were described in our previous study.18 Liver biopsy specimens were reviewed by a single liver pathologist (X.D. Wang), who was blinded to participants’ clinical data. Histological parameters of NAFLD were scored based on the NASH Clinical Research Network classification.20 NASH was diagnosed as the presence of NAS ≥4, with ≥1 point for each of the three individual histologic components of NASH, including steatosis, lobular inflammation, and ballooning.21–23 Significant liver fibrosis was defined by fibrosis ≥F2 on histology, according to Brunt’s diagnostic criteria.24Supplementary Figure 1 shows the representative hematoxylin-eosin staining images of liver biopsy in patients with NASH and non-NASH.

Genetic analysis

More details about the genetic analysis have been published elsewhere.18 Briefly, blood samples were collected from participants and approximately 20 ng of genomic DNA from white blood cells from each participant was extracted for the genetic analysis of polymorphisms in SAMM50-rs738491, PARVB-rs5764455 and PNPLA3-rs738409 genes. Genotyping of these three single nucleotide polymorphisms (SNPs) was evaluated by using the MassARRAY System (Agena Bioscience, San Diego, CA, USA). DNA samples were first amplified through locus-specific polymerase chain reaction (PCR), according to Assay Design Suite software (Version 3.1). Detection of rs738491, rs5764455 and rs738409 genotypes was performed by Matrix-Assisted Laser Desorption/Ionization-Time of Flight (MALDI-TOF) mass spectrometry.

Statistical analysis

Continuous variables were compared by the unpaired Student’s t-test, one-way analysis of variance or the Mann-Whitney U test (if not normally distributed) and expressed as either mean±standard deviation (SD) or medians (25th, 75th percentiles), respectively. Categorical variables were analyzed by the χ2 test or the Fisher’s exact test (as appropriate) and expressed as numbers and percentages. Hardy-Weinberg equilibrium for the three aforementioned SNPs in the non-NASH and NASH groups was tested by the χ2 test. The distribution of genotypes and alleles between the two patient groups were compared by the χ2 test or the Fisher’s exact test. Logistic and linear regression analyses were performed to test the associations between these genetic variants and individual histological features of NASH, after adjustment for age, sex and BMI. The SHEsis online haplotype analysis software (http://analysis.bio-x.cn/ ) was employed for haplotype analyses.25,26 The strength of the associations is presented as odds ratio (OR) with 95% confidence interval (CI). Generalized multifactor dimensionality reduction (GMDR) analysis27 was used to evaluate the gene-gene interactions, which provided cross-validation consistency, training accuracy, testing accuracy, and the sign test (p). The detailed analytical procedure and the definition of each output parameter have been described elsewhere.28 The effect of interactions of genotypes on the risk of NASH was assessed by logistic regression. Power calculations were completed using the CATS Genetic Power Calculator,29 with settings of a multiplicative genetic model. Prevalence of NASH was approximately 0.025, as estimated in previous studies.30 Assuming a minor allele frequency for the rs738491 of 0.5566 (i.e. the frequency found in our population), and an OR of 1.6, the expected power for a one-stage study is 89.2% in our cohort. Using the same assumption for the rs5764455 and rs738409, with minor allele frequency of 0.4590 and 0.4675 separately, the expected power was 91.0% for both SNPs. All statistical analyses were undertaken with SPSS/PC version 25.0 (IBM Corp., Armonk, NY, USA) software. A two-sided level of p <0.05 was considered statistically significant.

Results

Patient characteristics

A total of 591 Chinese individuals with histologically-proven NAFLD were initially included in the study. After exclusion of those with alcoholic fatty liver (n=79), autoimmune liver diseases (n=1), drug-induced hepatitis (n=1), hepatitis B or C virus (n=45) or those with missing records of laboratory parameters (n=50), 415 patients with biopsy-proven NAFLD were identified for the final analysis. Subjects had a mean±SD age of 41.3±12.5 years (range: 18–72 years), and 75.9% were men. Amongst these patients, 246 (59.3%) had definite NASH. Table 1 shows the demographic and clinical characteristics of participants stratified by presence or absence of NASH. Patients with NASH were younger and had higher values of BMI, HOMA-estimated insulin resistance, serum liver enzymes and uric acid compared to the non-NASH group. As also shown in Table 1, NAS also differed significantly between the two patient groups.

Table 1

Baseline characteristics of participants stratified by nonalcoholic steatohepatitis (NASH) status

Non-NASH n=169 (40.7%)NASH n=246 (59.3%)p
Female sex, n (%)35 (20.7)65 (26.4)0.181
Age, years45 (37–52)39 (29–48)<0.0001
BMI, kg/m225.4 (23.4–27.3)27.36 (25–29.2)<0.0001
Hypertension, n (%)0.727
  Yes59 (34.9)90 (36.6)
  No110 (65.1)156 (63.4)
Type 2 diabetes, n (%)0.610
  Yes40 (23.7)53 (21.5)
  No129 (76.3)193 (78.5)
HOMA-IR2.73 (1.79–4.44)3.99 (2.83–6.35)<0.0001
ALT, U/L37.0 (23.0–54.0)68.0 (41.0–124.0)<0.0001
AST, U/L27.0 (22.0–36.5)43.0 (30.0–66.2)<0.0001
GGT, U/L42.0 (26.0–69.5)56.0 (38.0–91.2)<0.0001
TG, mmol/L1.81 (1.30–2.59)1.98 (1.45–2.92)0.107
TC, mmol/L4.91±1.125.10±1.120.094
HDL-C, mmol/L0.97 (0.85–1.14)0.98 (0.87–1.14)0.320
LDL-C, mmol/L2.95±0.883.11±0.910.088
UA, mmol/L364 (310–438)401 (340.7–481.2)0.001
Liver histology features
Steatosis grade<0.0001
  1156 (92.3)50 (20.3)
  211 (6.5)82 (33.3)
  32 (1.2)114 (46.3)
Lobular inflammation grade<0.0001
  028 (16.6)0
  1138 (81.7)172 (69.9)
  23 (1.8)69 (28.0)
  305 (2.0)
Ballooning grade<0.0001
  020 (11.8)0
  1127 (75.1)102 (41.5)
  222 (13.0)144 (58.5)
Fibrosis grade0.001
  080 (47.3)67 (27.2)
  162 (36.7)117 (47.6)
  220 (11.8)50 (20.3)
  36 (3.6)10 (4.1)
  41 (0.6)2 (0.8)

Sample size n=415. Data are expressed as numbers (percentages) for categorical variables, as mean±SD for normally distributed continuous variables and median (interquartile range) for skewed distributed continuous variables. Differences between the groups were determined using the Student’s t-test or the Mann-Whitney U test for continuous variables, and the Pearson χ2 test or the Fisher’s exact test for categorical variables as appropriate. ALT, alanine aminotransferase; AST, aspartate aminotransferase; GGT, gamma-glutamyltransferase; HOMA-IR, homeostasis model assessment of insulin resistance; TC, total cholesterol; TG, triglycerides; UA, uric acid.

Association between genotypes and alleles of rs738491, rs5764455 and rs738409 and NASH

Genotypes of these three SNPs were in Hardy-Weinberg equilibrium for both patient groups (pnon-NASH=0.582, 0.542, 0.745; pNASH=0.204, 0.885, 0.171). Table 2 shows that the distribution of genotypes and allele frequencies of SAMM50-rs738491 PARVB-rs5764455 and PNPLA3-rs738409 were significantly different between patients with, and without, NASH. In addition, Table 3 shows that genotypes and alleles in each SNP had a strong link with the presence of NASH. TT in SAMM50-rs738491, AA in PARVB-rs5764455 and GG in PNPLA3-rs738409 were significantly associated with an increased risk of having NASH, even after adjusting for age, sex and BMI (adjusted OR: 2.26, 95% CI: 1.24 to 4.13; adjusted OR: 3.27, 95% CI: 1.73 to 6.18; adjusted OR: 2.89, 95% CI: 1.59 to 5.26, respectively). Besides, Table 3 also shows rs738491-T, rs5764455-A and rs738409-G as the risk alleles significantly associated with NASH (adjusted OR: 1.54, 95% CI: 1.15 to 2.08; adjusted OR: 1.74, 95% CI: 1.29 to 2.35; adjusted OR 1.73, 95% CI: 1.29 to 2.33, respectively).

Table 2

Distribution of genotypes and allele frequencies of genetic variants in SAMM50-rs738491, PARVB-rs5764455 and PNPLA3-rs738409, stratified by NASH status

Genotypes and allelesNon-NASHNASHχ2p
SAMM50-rs738491
  Genotypes12.620.002
    CC43 (25.4)43 (17.5)
    CT88 (52.1)108 (43.9)
    TT38 (22.5)95 (38.6)
  Alleles11.790.001
    C174 (51.5)194 (39.4)
    T164 (48.5)298 (60.6)
PARVB-rs5764455
  Genotypes16.04<0.0001
    GG64 (37.9)57 (23.2)
    GA83 (49.1)124 (50.4)
    AA22 (13.0)65 (26.4)
  Alleles15.93<0.0001
    G211 (62.4)238 (48.4)
    A127 (37.6)254 (51.6)
PNPLA3-rs738409
  Genotypes14.750.001
    CC65 (38.5)61 (24.8)
    CG78 (46.2)112 (45.5)
    GG26 (15.4)73 (29.7)
  Alleles15.72<0.0001
    C208 (61.5)234 (47.6)
    G130 (38.5)258 (52.4)

Data are expressed as number (percentage) and tested by the Pearson χ2 test or the Fisher’s exact test as appropriate. Nash, nonalcoholic steatohepatitis.

Table 3

OR for NASH according to the genotypes and alleles of SAMM50-rs738491, PARVB-rs5764455 or PNPLA3-rs738409

Genotypes and allelesUnadjusted OR (95% CI)pAdjusted OR (95% CI)ap
SAMM50-rs738491
  Genotypes
    CC11
    CT1.227 (0.739, 2.039)0.4291.148 (0.667, 1.975)0.618
    TT2.500 (1.420, 4.402)0.0022.262 (1.240, 4.127)0.008
  Alleles
    C11
    T1.630 (1.232, 2.156)0.0011.544 (1.148, 2.078)0.004
PARVB-rs5764455
  Genotypes
    GG11
    GA1.677 (1.067, 2.637)0.0251.610 (0.993, 2.611)0.053
    AA3.317 (1.819, 6.050)<0.00013.265 (1.725, 6.177)<0.0001
  Alleles
    G11
    A1.773 (1.337, 2.352)<0.00011.744 (1.293, 2.352)<0.0001
PNPLA3-rs738409
  Genotypes
    CC11
    CG1.530 (0.972, 2.408)0.0661.415 (0.872, 2.297)0.16
    GG2.992 (1.696, 5.279)<0.00012.894 (1.593, 5.258)<0.0001
  Alleles
    C11
    G1.764 (1.331, 2.338)<0.00011.732 (1.285, 2.333)<0.0001

aMultivariate logistic regression model adjusted for age, sex and BMI. BMI, body mass index; NASH, nonalcoholic steatohepatitis; OR, odds ratio.

We also compared the genotype distribution of these three variants in NASH patients, stratified by presence of overweight/obesity (BMI <25 kg/m2 vs. ≥25 kg/m2). Table S1 shows that there were no significant differences in distribution of these genetic variants between the two groups of patients. In addition, as shown in Supplementary Table S2, no significant differences were found in the distribution of the three genetic variants in NASH patients stratified by presence of T2DM.

Association between genotypes of rs738491, rs5764455 and rs738409 and clinical features

Due to the fact that TT in SAMM50-rs738491, AA in PARVB-rs5764455 and GG in PNPLA3-rs738409 increased the susceptibility to NASH, we divided patients into high-risk and low-risk groups of developing NASH based on their genetic polymorphisms (TT vs. CC+CT according to rs738491; AA vs. GG+GA according to rs5764455; and GG vs. CC+CG according to rs738409, respectively). Table 4 shows that the proportion of women was greater in the risk genotype group of each of the three SNPs. For SAMM50-rs738491, the TT group had higher plasma total cholesterol, LDL-C and HDL-C levels compared to the CC+CT group. As to PARVB-rs5764455, the AA group had a higher plasma LDL-C level compared to the GG+GA group. The histological severity of NAFLD (i.e. NAS ≥4) differed significantly between the two genotype groups in each of the three SNPs (Table 4). Furthermore, after adjusting for age, sex and BMI, all three aforementioned SNPs remained statistically associated with NAS ≥4, mostly with higher steatosis grade (Table 5). Fibrosis stage, HOMA-estimated insulin resistance and serum liver enzyme levels did not show any difference between the two patient groups for each of the three SNPs. Supplementary Table S3 shows baseline characteristics of participants, stratified by SAMM50-rs738491, PARVB-rs5764455 or PNPLA3-rs738409 genotypes. Apart from differences in liver histology features (as reflected by NAS ≥4), there were no significant differences in many clinical and biochemical parameters among the different patient groups, except for a slight difference in plasma LDL-C levels.

Table 4

Baseline characteristics of participants stratified by SAMM50-rs738491, PARVB-rs5764455 and PNPLA3-rs738409 genotypes

SAMM50-rs738491
PARVB-rs5764455
PNPLA3-rs738409
CC+CTTTpGG+GAAApCC+CGGGp
n (%)282 (67.95)133 (32.05)328 (79.04)87 (20.96)316 (76.14)99 (23.86)
Female sex (%)58 (20.6)42 (31.6)0.01470 (21.30)30 (34.50)0.01170 (22.2)30 (30.3)0.098
Age, years43 (32–50)41 (32–49)0.65542 (32–50)42 (31–52)0.84142 (31–50)42 (33–48)0.977
BMI, kg/m226.57 (24.22–28.68)26.95 (24.63–28.80)0.33926.72 (24.22–28.67)26.53 (24.65–28.73)0.71126.69 (24.22–28.83)26.63 (24.62–28.67)0.893
Hypertension0.9570.3440.913
  Yes101 (35.8)48 (36.1)114 (34.8)35 (40.2)113 (35.8)36 (36.4)
  No181 (64.2)85 (63.9)214 (65.2)52 (59.8)203 (64.2)63 (63.6)
Type 2 diabetes0.3370.4700.959
  Yes67 (23.8)26 (19.5)76 (23.2)17 (19.5)71 (22.5)22 (22.2)
  No215 (76.2)107 (80.5)252 (76.8)70 (80.5)245 (77.5)77 (77.8)
HOMA-IR3.61 (2.43–5.60)3.27 (2.19–5.15)0.1653.61 (2.42–5.44)3.20 (2.11–5.29)0.2463.45 (2.35–5.43)3.54 (2.22–5.42)0.504
ALT, U/L49 (29.7–88)55 (36–99)0.16849 (30–88.7)60 (37–101)0.10049.5 (30–89.7)56 (36–91)0.204
AST, U/L33.5 (25–52)36 (26–60)0.20232.5 (25–52)38 (26–61)0.05133 (25.5–52)37 (26–59)0.165
GGT, U/L51 (33–84.2)52 (32–80.5)0.75052 (33–84.7)51 (31–77)0.38351 (33–83)51 (33–80)0.709
TG, mmol/L1.96 (1.33–2.92)1.82 (1.44–2.54)0.3031.96 (1.40–2.91)1.82 (1.33–2.50)0.1111.95 (1.35–2.92)1.87 (1.45–2.41)0.282
TC, mmol/L4.94±1.125.20±1.120.0274.97±1.115.22±1.150.0594.99±1.125.13±1.120.263
HDL-C, mmol/L0.96 (0.85–1.12)1.01 (0.89–1.16)0.0310.96 (0.85–1.13)1.03 (0.89–1.15)0.0700.96 (0.85–1.13)1.02 (0.89–1.19)0.107
LDL-C, mmol/L2.96±0.893.23±0.890.0042.98±0.873.30±0.960.0033.00±0.893.18±0.920.074
UA, mmol/L389 (325–470.2)380 (327–452)0.741389 (325–466)378 (334–456)0.788389.5 (325.5–469)377 (325–441)0.370
Liver histology features
NAS ≥40.0010.0020.001
  Yes154 (54.6)95 (71.4)184 (56.1)65 (74.7)176 (55.7)73 (73.7)
  No128 (45.4)38 (28.6)144 (43.9)22 (25.3)140 (44.3)26 (26.3)
Fibrosis stage0.8930.8470.730
  F≥261 (21.6)28 (21.1)71 (21.6)18 (20.7)69 (21.8)20 (20.2)
  F<2221 (78.4)105 (78.9)399 (78.4)68 (79.3)247 (78.2)79 (79.8)

Sample size n=415. Data are expressed as number (percentage) for categorical variables, as mean±SD for normally distributed continuous variables and median (interquartile range) for skewed distributed continuous variables. Differences between the groups were determined using the Student’s t-test or the Mann-Whitney U test for continuous variables, as well as the Pearson χ2 test or the Fisher’s exact test for categorical variables. ALB, albumin; ALP, alkaline phosphatase.

Table 5

Associations between SNPs and individual histologic features of NAFLD

Steatosis gradeaHepatocyte ballooningaLobular inflammationaNAS ≥4bSignificant fibrosis (F≥2)b
β (SE)pβ (SE)pβ (SE)pβ (SE)pβ (SE)p
SAMM50-rs738491
  CC+CTRef.Ref.Ref.Ref.Ref.
  TT0.896 (0.202)<0.0001−0.113 (0.211)0.5910.327 (0.241)0.1750.676 (0.241)0.005−0.115 (0.262)0.662
PARVB-rs5764455
  GG+GARef.Ref.Ref.Ref.Ref.
  AA0.952 (0.233)<0.0001−0.064 (0.242)0.7920.152 (0.275)0.5810.859 (0.289)0.003−0.144 (0.302)0.634
PNPLA3-rs738409
  CC+CGRef.Ref.Ref.Ref.Ref.
  GG0.920 (0.220)<0.0001−0.139 (0.230)0.5450.344 (0.261)0.1880.818 (0.268)0.002-0.144 (0.289)0.618

Data are expressed as beta coefficient and standers error (SE). aMultivariate linear regression model adjusted for age, sex and BMI. bMultivariate logistic regression model adjusted for age, sex and BMI. BMI, body mass index; NAS, nonalcoholic fatty liver disease activity score; NASH, nonalcoholic steatohepatitis; SNPs, single nucleotide polymorphisms.

Analysis of gene-gene interactions

We performed a GMDR analysis to examine the gene-gene interactions. This analysis showed that the PARVB-rs5764455 was the best single-locus model that predicted the presence of NASH. The best two-locus model was the combination of the SAMM50-rs738491 and PNPLA3-rs738409. The three-locus model had perfect cross-validation consistency (CVC: 10/10) and high testing accuracy (57.23%) (p=0.011), appearing to be the best predictive model (Table 6). All these models were adjusted by age, sex and BMI. When we analyzed the strength of the association between each genetic model and NASH after adjusting for age, sex and BMI, we found that the OR values increased progressively with the increasing number of loci included (adjusted OR: 2.431, 95% CI: 1.384 to 4.269 for a one-locus model; adjusted OR: 2.453, 95% CI: 1.413 to 4.259 for a two-locus model; and adjusted OR: 2.751, 95% CI: 1.452 to 5.211 for a three-locus model, respectively). Importantly, the OR of the haplotype T-A-G for predicting the risk of NASH was nearly three times higher compared to that of the haplotype C-G-C. Additionally, the OR of the haplotype T-A-G to C-G-C was nearly twice compared to the haplotype T-G to C-C or A to G, suggesting that the combination of these three risk alleles increases the susceptibility to NASH (Table 7 and Fig. 1).

Table 6

Best models to predict the presence of NASH by GMDR analysisa

GMDR modelTraining accuracy (%)Testing accuracy (%)Sign test (p)CVC
PARVB-rs576445557.7152.058 (0.055)4/10
SAMM50-rs738491, PNPLA3-rs73840959.9057.808 (0.055)10/10
SAMM50-rs738491, PARVB-rs5764455, PNPLA3-rs73840960.5957.239 (0.011)10/10
a

Data adjusted for age, sex and BMI. BMI, body mass index; CVC, cross-validation consistency; GMDR, generalized multifactor dimensionality reduction; NASH, nonalcoholic steatohepatitis.

Table 7

Interaction analysis between each best genetic model and risk of having NASH

Genotypes and allelesn (%)
Adjusted OR (95% CI)ap
Non-NASHNASH
PARVB-rs5764455
  Genotypes
    GG+GA147 (86.98)181 (73.58)1
    AA22 (13.02)65 (26.42)2.431(1.384, 4.269)0.002
  Alleles
    G211 (62.43)238 (48.37)1
    A127 (37.57)254 (51.63)1.744 (1.293, 2.352)<0.0001
SAMM50-rs738491, PNPLA3-rs738409
  Genotypes
    CC+CT, CC+CG129 (84.31)147 (68.06)1
    TT, GG24 (15.69)69 (31.94)2.453 (1.413, 4.259)0.001
  Alleles
    C-C172 (57.33)185 (42.63)1
    T-G128 (42.67)249 (57.37)1.733 (1.261, 2.381)0.001
SAMM50-rs738491, PARVB-rs5764455, PNPLA3-rs738409
  Genotypes
    CC+CT, GG+GA, CC+CG129 (88.97)144 (73.85)1
    TT, AA, GG16 (11.03)51 (26.15)2.751 (1.452, 5.211)0.002
  Alleles
    C-G-C105 (70.00)94 (41.23)1
    T-A-G45 (30.00)134 (58.77)3.157 (1.988, 5.012)<0.0001

aMultivariate logistic regression model adjusted for age, sex and BMI. BMI, body mass index; CI, confidence interval; NASH, nonalcoholic steatohepatitis; OR, odds ratio.

Interaction analysis between each best genetic model and risk of having NASH.
Fig. 1  Interaction analysis between each best genetic model and risk of having NASH.

aOR (95% CI) adjusted for age, sex and BMI. BMI, body mass index; CI, confidence interval; NASH, nonalcoholic steatohepatitis; OR, odds ratio.

Discussion

Recent studies have shown that various genes and SNPs play important roles in the development and progression of NAFLD.3 In addition, it is known that the magnitude of the NAFLD-related SNP effects vary markedly in different ethnic populations.31 Based on GWAS results, polymorphisms in SAMM50-rs738491, PARVB-rs5764455 and PNPLA3-rs738409 genes were found to be associated with the development and progression of NAFLD, while the relationship between these SNPs and the presence of NASH was not obvious.13 With regard to the Han Chinese population, two prior studies have compared genetic variants in the SAMM50 and PARVB genes with the presence of NAFLD as detected by ultrasonography.15,32 In those two studies, the authors reported that both the rs738491 T allele in the SAMM50 gene and the rs5764455 A allele in the PARVB gene were significantly associated with presence of ultrasound-defined NAFLD. Interestingly, in our study that involved patients with biopsy-confirmed NAFLD, we showed for the first time that patients carrying TT in SAMM50-rs738491, AA in PARVB-rs5764455 or GG in PNPLA3-rs738409 were more likely to have NASH. In addition, we found that the rs738491-T, rs5764455-A and rs738409-G risk alleles remained significantly associated with the risk of NASH, even after adjusting for age, sex and BMI. We also undertook a subgroup analysis comparing these three genetic variants in patients with NASH, stratified by presence of overweight/obesity or T2DM. These data indicate that the three genetic variants may play a role in the development of NASH, regardless of the presence or absence of overweight/obesity and T2DM. However, larger studies are needed to further validate these findings.

From a pathophysiological point of view, previous studies have shown that the PNPLA3, SAMM50 and PARVB genes may be implicated in regulating lipid metabolism,33 maintaining mitochondrial morphology,34 and activating Akt/protein kinase B (Akt/PKB) signaling pathways,35 respectively. Specifically, the PNPLA3 protein is an enzyme with triacylglycerol lipase and acylglycerol O-acyltransferase activity, which promotes remodeling of lipid droplets in hepatocytes.33 The PNPLA3-rs738409 C>G genotype reduces this enzymatic activity, leading to the entire histopathological spectrum of NAFLD from simple steatosis to cirrhosis and hepatocellular carcinoma.3 In our study, the analysis of liver histology features showed that the GG genotype of rs738409 was associated with a higher proportion of NAS ≥4 (especially with a higher steatosis grade), but not with a more atherogenic lipid profile. This finding is also in line with previous data from Speliotes et al.36 showing that the PNPLA3-rs738409 variant specifically conferred a higher risk of histologic hepatic fat accumulation, but not NAFLD-related metabolic traits. The SAMM50 gene encodes the protein Sam50 that is part of the sorting and assembly machinery, which is necessary for assembling β-barrel proteins located in the outer membrane of mitochondria.34 β-barrel proteins play a crucial role in maintaining mitochondrial shape, morphology of mitochondrial cristae, and in assembling the respiratory chain complexes.34,37 Abnormalities of β-barrel proteins lead to mitochondrial dysfunction, which in turn reduces the removal of reactive oxygen species (ROS).13 ROS accumulation leads to destruction of organelles oxidizing fatty acids and promotes lipotoxicity, thereby inducing hepatocyte damage.38 Based on the observation that loss of mitochondrial cristae has been found in the livers of patients with NASH after depletion of the protein Sam50,39 there is a close link between SAMM50-rs738491 and proinflammatory factors in hepatocytes.

Parvin-β, encoded by the PARVB gene, takes part in forming the integrin-linked kinase-pinch-parvin complex, which transmits signals from integrin to Akt/PKB.35 Kinases of Akt/PKB modulate basic cell activation to produce proinflammatory factors.40 Over-expression of parvin-β not only leads to a concomitant increase in lipogenic gene expression41 but also promotes apoptosis.42 Increased hepatic lipogenesis and apoptosis are considered key biological mechanisms occurring with NAFLD progression from simple steatosis to NASH and fibrosis.43 Polymorphisms of both rs5764455 and rs6006473 in the PARVB gene have also been reported to be associated with the development and progression of NAFLD in the Han Chinese population.32 Unfortunately, we did not examine PARVB rs6006473 in this study. However, previous studies have shown that the rs5764455 in the PARVB gene showed the strongest association with NASH.13 Similarly, in our study the rs5764455 in the PARVB gene showed the highest OR for NASH amongst all the examined SNPs, comparing the A allele to the G allele.

The progression from simple steatosis to NASH is recognized to have two consecutive steps, which are fat accumulation followed by necroinflammation in the liver.44 Our study suggests that the PNPLA3-rs738409 and SAMM50-rs738491 genetic variants are involved, respectively, in the first and second steps of NAFLD progression, while the PARVB-rs5764455 variant seems to be involved in both of these steps.

All the findings mentioned above support the existence of an association between these three SNPs and NASH and also suggest possible underlying mechanisms (as schematically shown in Fig. 2). However, it has been shown that the NAFLD-related SNPs identified by GWAS only explain a small part of disease etiology, because the relatedness between complex diseases and multiple genes and/or their interactions are ignored.45 Therefore, analyses emphasizing gene-gene interactions have been one of the new approaches to gaining a better understanding of the etiology of common complex traits. The GMDR methodology is widely used in detecting gene-gene and gene-environment interactions in various diseases.46 Using the GMDR method, our results have identified the PARVB-rs5764455 as the best single-locus model and the combination of SAMM50-rs738491 and PNPLA3-rs738409 as the best double-locus model for predicting the presence of NASH. These data further confirm the correctness of the hypothesis we mentioned above, i.e. the PNPLA3 and SAMM50 genes are, respectively, involved in the first and second stages of progression from simple steatosis to NASH, while the PARVB gene seems to be implicated in both of these stages. Our study showed that the risk of NASH among patients carrying the haplotype T-A-G was three-times higher than among those carrying the haplotype C-G-C. In addition, the OR of the haplotype T-A-G to C-G-C for predicting the risk of NASH was nearly twice compared to that of haplotype T-G to C-C or A to G, thereby suggesting a synergistic interaction between these three SNPs.

Putative mechanisms by which these three polymorphisms may affect susceptibility to NASH.
Fig. 2  Putative mechanisms by which these three polymorphisms may affect susceptibility to NASH.

NASH, nonalcoholic steatohepatitis.

Our study has some important limitations that should be considered. First, we did not include control subjects without NAFLD to examine the allele frequency of SAMM50, PARVB and PNPLA3 polymorphisms in NAFLD vs. non-NAFLD populations. Second, only one SNP in each of the SAMM50, PARVB and PNPLA3 genes was chosen. The limited number of SNPs was therefore not likely to capture most of the genetic information conveyed by these three genes. Third, our results suggesting the existence of a synergistic interaction between the three aforementioned SNPs need further validation in larger cohorts of NAFLD patients of different ethnicity. Fourth, our case-finding strategy could have contributed to the high prevalence of NASH (59.3%) observed in this cohort of subjects with biopsy-proven NAFLD. Therefore, these results need to be further confirmed in populations at lower risk of NASH. Lastly, the underlying mechanisms that may explain the interactions linking these three SNPs to the pathophysiology of NASH need to be better elucidated.

Conclusions

Our results show for the first time that genetic variants of SAMM50-rs738491, PARVB-rs5764455 and PNPLA3-rs738409 are associated with the presence of NASH in Chinese individuals with biopsy-proven NAFLD, independent of age, sex and BMI. Furthermore, we have also shown that SAMM50-rs738491, PARVB-rs5764455 and PNPLA3-rs738409 genetic variants may interact synergistically to increase the susceptibility to NASH.

Supporting information

Supplementary Table S1

Distribution of genotypes of genetic variants in SAMM50-rs738491, PARVB-rs5764455 and PNPLA3-rs738409 in NASH patients, stratified by BMI.

(DOCX)

Click here for additional data file.

Supplementary Table S2

Distribution of genotypes of genetic variants in SAMM50-rs738491, PARVB-rs5764455 and PNPLA3-rs738409 in NASH patients, stratified by T2DM.

(DOCX)

Click here for additional data file.

Supplementary Table S3

Baseline characteristics of participants stratified by SAMM50-rs738491, PARVB-rs5764455 and PNPLA3-rs738409 genotypes.

(DOCX)

Click here for additional data file.

Supplementary Fig. S1

Representative hematoxylin-eosin staining images of liver biopsy in NASH and non-NASH patients (magnification ×100). NASH, nonalcoholic steatohepatitis.

(PDF)

Click here for additional data file.

Abbreviations

BMI: 

body mass index

CI: 

confidence interval

CVC: 

cross-validation consistency

GMDR: 

generalized multifactor dimensionality reduction

GWAS: 

genome-wide association studies

HDL-C: 

high-density lipoprotein cholesterol

LDL-C: 

low-density lipoprotein cholesterol

MALDI-TOF: 

Matrix-Assisted Laser Desorption/Ionization-Time of Flight

NAFLD: 

nonalcoholic fatty liver disease

NAS: 

nonalcoholic fatty liver disease activity score

NASH: 

nonalcoholic steatohepatitis

OR: 

odds ratio

PCR: 

polymerase chain reaction

PERSONS: 

Prospective Epidemic Research Specifically Of NASH

PKB: 

protein kinase B

PNPLA3: 

patatin-like phospholipase domain containing 3

ROS: 

reactive oxygen species

SAM: 

sorting and assembly machinery

SD: 

standard deviation

SNPs: 

single nucleotide polymorphisms

T2DM: 

type 2 diabetes

Declarations

Ethical statement

Ethical approval was obtained from the First Affiliated Hospital of Wenzhou Medical University Ethics Committee and the study protocol was registered at the Chinese Clinical Trial Registry (ChiCTR-EOC-17013562). Written informed consent was obtained from all participants included in the study.

Data sharing statement

The clinical and histological data used to support the findings of this study are available from the corresponding author upon request.

Funding

This work was supported by grants from the National Natural Science Foundation of China (82070588) and the High Level Creative Talents from Department of Public Health in Zhejiang Province (S2032102600032) and Project of New Century 551 Talent Nurturing in Wenzhou. GT was supported in part by grants from the School of Medicine, University of Verona, Verona, Italy. CDB was supported in part by the Southampton NIHR Biomedical Research Centre (IS-BRC-20004), UK.

Conflict of interest

MHZ has been an associate editor of Journal of Clinical and Translational Hepatology since 2013.The other authors have no conflicts of interest related to this publication.

Authors’ contributions

Study concept and design (KX, MHZ), acquisition of data (PWZ, WYL, HLM, GL, LJT, RSR), pathological analyses (XDW), drafting of the manuscript (KX, KIZ), critical revision (GT, CDB), statistical analyses (KX, KIZ), study supervision (YPC, MHZ), and guarantee of the article (MHZ). All authors contributed to the manuscript for important intellectual content and approved the submission.

References

  1. Younossi Z, Anstee QM, Marietti M, Hardy T, Henry L, Eslam M, et al. Global burden of NAFLD and NASH: trends, predictions, risk factors and prevention. Nat Rev Gastroenterol Hepatol 2018;15(1):11-20 View Article
  2. 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
  3. Eslam M, George J. Genetic contributions to NAFLD: leveraging shared genetics to uncover systems biology. Nat Rev Gastroenterol Hepatol 2020;17(1):40-52 View Article
  4. Angulo P. Long-term mortality in nonalcoholic fatty liver disease: is liver histology of any prognostic significance?. Hepatology 2010;51(2):373-375 View Article
  5. Targher G, Byrne CD, Tilg H. NAFLD and increased risk of cardiovascular disease: clinical associations, pathophysiological mechanisms and pharmacological implications. Gut 2020;69(9):1691-1705 View Article
  6. Mantovani A, Petracca G, Beatrice G, Tilg H, Byrne CD, Targher G. Non-alcoholic fatty liver disease and risk of incident diabetes mellitus: an updated meta-analysis of 501 022 adult individuals. Gut 2021;70(5):962-969 View Article
  7. Mantovani A, Petracca G, Beatrice G, Csermely A, Tilg H, Byrne CD, et al. Non-alcoholic fatty liver disease and increased risk of incident extrahepatic cancers: a meta-analysis of observational cohort studies. Gut 2021:gutjnl-2021-324191 View Article
  8. Estes C, Anstee QM, Arias-Loste MT, Bantel H, Bellentani S, Caballeria J, et al. Modeling NAFLD disease burden in China, France, Germany, Italy, Japan, Spain, United Kingdom, and United States for the period 2016-2030. J Hepatol 2018;69(4):896-904 View Article
  9. Byrne CD, Patel J, Scorletti E, Targher G. Tests for diagnosing and monitoring non-alcoholic fatty liver disease in adults. Bmj 2018;362:k2734 View Article
  10. Molina-Molina E, Krawczyk M, Stachowska E, Lammert F, Portincasa P. Non-alcoholic fatty liver disease in non-obese individuals: prevalence, pathogenesis and treatment. Clin Res Hepatol Gastroenterol 2019;43(6):638-645 View Article
  11. Chalasani N, Guo X, Loomba R, Goodarzi MO, Haritunians T, Kwon S, et al. Genome-wide association study identifies variants associated with histologic features of nonalcoholic fatty liver disease. Gastroenterology 2010;139(5):1567-1576 View Article
  12. Speliotes EK, Yerges-Armstrong LM, Wu J, Hernaez R, Kim LJ, Palmer CD, et al. Genome-wide association analysis identifies variants associated with nonalcoholic fatty liver disease that have distinct effects on metabolic traits. PLoS Genet 2011;7(3):e1001324 View Article
  13. 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
  14. Sookoian S, Pirola CJ. Meta-analysis of the influence of I148M variant of patatin-like phospholipase domain containing 3 gene (PNPLA3) on the susceptibility and histological severity of nonalcoholic fatty liver disease. Hepatology 2011;53(6):1883-1894 View Article
  15. Chen L, Lin Z, Jiang M, Lu L, Zhang H, Xin Y, et al. Genetic variants in the SAMM50 gene create susceptibility to nonalcoholic fatty liver disease in a Chinese Han population. Hepat Mon 2015;15(10):e31076 View Article
  16. Timpson NJ, Greenwood CMT, Soranzo N, Lawson DJ, Richards JB. Genetic architecture: the shape of the genetic contribution to human traits and disease. Nat Rev Genet 2018;19(2):110-124 View Article
  17. Liu WY, Zheng KI, Pan XY, Ma HL, Zhu PW, Wu XX, et al. Effect of PNPLA3 polymorphism on diagnostic performance of various noninvasive markers for diagnosing and staging nonalcoholic fatty liver disease. J Gastroenterol Hepatol 2020;35(6):1057-1064 View Article
  18. Sun DQ, Zheng KI, Xu G, Ma HL, Zhang HY, Pan XY, et al. PNPLA3 rs738409 is associated with renal glomerular and tubular injury in NAFLD patients with persistently normal ALT levels. Liver Int 2020;40(1):107-119 View Article
  19. EASL-EORTC clinical practice guidelines: management of hepatocellular carcinoma. Eur J Cancer 2012;48(5):599-641 View Article
  20. 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
  21. Harrison SA, Bashir MR, Guy CD, Zhou R, Moylan CA, Frias JP, et al. Resmetirom (MGL-3196) for the treatment of non-alcoholic steatohepatitis: a multicentre, randomised, double-blind, placebo-controlled, phase 2 trial. Lancet 2019;394(10213):2012-2024 View Article
  22. Friedman SL, Ratziu V, Harrison SA, Abdelmalek MF, Aithal GP, Caballeria J, et al. A randomized, placebo-controlled trial of cenicriviroc for treatment of nonalcoholic steatohepatitis with fibrosis. Hepatology 2018;67(5):1754-1767 View Article
  23. Neuschwander-Tetri BA, Loomba R, Sanyal AJ, Lavine JE, Van Natta ML, Abdelmalek MF, et al. Farnesoid X nuclear receptor ligand obeticholic acid for non-cirrhotic, non-alcoholic steatohepatitis (FLINT): a multicentre, randomised, placebo-controlled trial. Lancet 2015;385(9972):956-965 View Article
  24. Brunt EM, Janney CG, Di Bisceglie AM, Neuschwander-Tetri BA, Bacon BR. Nonalcoholic steatohepatitis: a proposal for grading and staging the histological lesions. Am J Gastroenterol 1999;94(9):2467-2474 View Article
  25. Shi YY, He L. SHEsis, a powerful software platform for analyses of linkage disequilibrium, haplotype construction, and genetic association at polymorphism loci. Cell Res 2005;15(2):97-98 View Article
  26. Li Z, Zhang Z, He Z, Tang W, Li T, Zeng Z, et al. A partition-ligation-combination-subdivision EM algorithm for haplotype inference with multiallelic markers: update of the SHEsis (http://analysis.bio-x.cn). Cell Res 2009;19(4):519-523 View Article
  27. Lou XY, Chen GB, Yan L, Ma JZ, Zhu J, Elston RC, et al. A generalized combinatorial approach for detecting gene-by-gene and gene-by-environment interactions with application to nicotine dependence. Am J Hum Genet 2007;80(6):1125-1137 View Article
  28. Tunesi S, Ferrante D, Mirabelli D, Andorno S, Betti M, Fiorito G, et al. Gene-asbestos interaction in malignant pleural mesothelioma susceptibility. Carcinogenesis 2015;36(10):1129-1135 View Article
  29. Skol AD, Scott LJ, Abecasis GR, Boehnke M. Joint analysis is more efficient than replication-based analysis for two-stage genome-wide association studies. Nat Genet 2006;38(2):209-213 View Article
  30. Younossi Z, Tacke F, Arrese M, Chander Sharma B, Mostafa I, Bugianesi E, et al. Global perspectives on nonalcoholic fatty liver disease and nonalcoholic steatohepatitis. Hepatology 2019;69(6):2672-2682 View Article
  31. Li YY. Genetic and epigenetic variants influencing the development of nonalcoholic fatty liver disease. World J Gastroenterol 2012;18(45):6546-6551 View Article
  32. Wu G, Wang K, Xue Y, Song G, Wang Y, Sun X, et al. Association of rs5764455 and rs6006473 polymorphisms in PARVB with liver damage of nonalcoholic fatty liver disease in Han Chinese population. Gene 2016;575(2 Pt 1):270-275 View Article
  33. Jenkins CM, Mancuso DJ, Yan W, Sims HF, Gibson B, Gross RW. Identification, cloning, expression, and purification of three novel human calcium-independent phospholipase A2 family members possessing triacylglycerol lipase and acylglycerol transacylase activities. J Biol Chem 2004;279(47):48968-48975 View Article
  34. Ott C, Ross K, Straub S, Thiede B, Götz M, Goosmann C, et al. Sam50 functions in mitochondrial intermembrane space bridging and biogenesis of respiratory complexes. Mol Cell Biol 2012;32(6):1173-1188 View Article
  35. Kimura M, Murakami T, Kizaka-Kondoh S, Itoh M, Yamamoto K, Hojo Y, et al. Functional molecular imaging of ILK-mediated Akt/PKB signaling cascades and the associated role of beta-parvin. J Cell Sci 2010;123(Pt 5):747-755 View Article
  36. Speliotes EK, Butler JL, Palmer CD, Voight BF, Hirschhorn JN. PNPLA3 variants specifically confer increased risk for histologic nonalcoholic fatty liver disease but not metabolic disease. Hepatology 2010;52(3):904-912 View Article
  37. Takeda H, Tsutsumi A, Nishizawa T, Lindau C, Busto JV, Wenz LS, et al. Mitochondrial sorting and assembly machinery operates by β-barrel switching. Nature 2021;590(7844):163-169 View Article
  38. Suzuki A, Diehl AM. Nonalcoholic steatohepatitis. Annu Rev Med 2017;68:85-98 View Article
  39. Sanyal AJ, Campbell-Sargent C, Mirshahi F, Rizzo WB, Contos MJ, Sterling RK, et al. Nonalcoholic steatohepatitis: association of insulin resistance and mitochondrial abnormalities. Gastroenterology 2001;120(5):1183-1192 View Article
  40. Abraham E. Akt/protein kinase B. Crit Care Med 2005;33(12 Suppl):S420-422 View Article
  41. Johnstone CN, Mongroo PS, Rich AS, Schupp M, Bowser MJ, Delemos AS, et al. Parvin-beta inhibits breast cancer tumorigenicity and promotes CDK9-mediated peroxisome proliferator-activated receptor gamma 1 phosphorylation. Mol Cell Biol 2008;28(2):687-704 View Article
  42. Zhang Y, Chen K, Tu Y, Wu C. Distinct roles of two structurally closely related focal adhesion proteins, alpha-parvins and beta-parvins, in regulation of cell morphology and survival. J Biol Chem 2004;279(40):41695-41705 View Article
  43. Fujii H, Kawada N. Inflammation and fibrogenesis in steatohepatitis. J Gastroenterol 2012;47(3):215-225 View Article
  44. Day CP, James OF. Steatohepatitis: a tale of two “hits”?. Gastroenterology 1998;114(4):842-845 View Article
  45. Eslam M, Valenti L, Romeo S. Genetics and epigenetics of NAFLD and NASH: clinical impact. J Hepatol 2018;68(2):268-279 View Article
  46. Xu HM, Xu LF, Hou TT, Luo LF, Chen GB, Sun XW, et al. GMDR: versatile software for detecting gene-gene and gene-environ-ment interactions underlying complex traits. Curr Genomics 2016;17(5):396-402 View Article
Copyright © 2022 Authors. This is an Open Access article distributed under the terms of the Creative Commons Attribution-Noncommercial 4.0 License (CC BY-NC 4.0), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
  • Journal of Clinical and Translational Hepatology
  • pISSN 2225-0719
  • eISSN 2310-8819

Article Options

Metrics

Cite this Article

  • © 2024 Xia & He Publishing Inc.
  • Total visits : 1964333
  • Visits today : 2203
Back to Top

Interaction of SAMM50-rs738491, PARVB-rs5764455 and PNPLA3-rs738409 Increases Susceptibility to Nonalcoholic Steatohepatitis

Ke Xu, Kenneth I. Zheng, Pei-Wu Zhu, Wen-Yue Liu, Hong-Lei Ma, Gang Li, Liang-Jie Tang, Rafael S. Rios, Giovanni Targher, Christopher D. Byrne, Xiao-Dong Wang, Yong-Ping Chen, Ming-Hua Zheng
  • Reset Zoom
  • Download TIFF