SAMM50- rs2073082, - rs738491 and - rs3761472 Interactions Enhancement of Susceptibility to Non-Alcoholic Fatty Liver Disease

Abstract

Background and aim: Several studies have identified that three SAMM50 polymorphisms (rs2073082, rs738491, rs3761472) are associated with an increased risk of non-alcoholic fatty liver disease (NAFLD). However, the clinical significance of the SAMM50 SNP in relation to NAFLD remains largely unknown. Therefore, we conducted a clinical study and SNP-SNP interaction analysis to further elucidate the effect of the SAMM50 SNP on the progression of NAFLD in the elderly.

Methods: A total of 1053 patients over the age of 65 years were recruited. Liver fat and fibrosis were detected by abdominal ultrasound or FibroScan, respectively. Genomic DNA was extracted and then genotyped by Fluidigm 96.96 Dynamic Array. Multivariable logistic regression was used to evaluate the association between NAFLD and SNP. SNP-SNP interactions were analyzed using generalized multivariate dimensionality reduction (GMDR).

Results: The risk of NAFLD was substantially higher in people who carried SAMM50-rs2073082 G and -rs738491 T alleles (OR, 1.962; 95% CI, 1.448-2.659; p < 0.001; OR, 1.532; 95% CI, 1.246-1.884; p = 0.021, respectively) compared to noncarriers. Carriers of the rs738491 T and rs3761472 G alleles in the cohort showed a significant increase in liver stiffness measurements (LSM). The combination of the three SNPs showed the highest predictive power for NAFLD. The rs2073082 G allele, rs738491 T allele and rs3761472 G carriers had a two-fold higher risk of NAFLD compared to noncarriers.

Conclusions: Our research has demonstrated a strong correlation between the genetic polymorphism of SAMM50 and NAFLD in the elderly, which will contribute to a better understanding of the impact of age and genetics on this condition. Additionally, this study provides a potential predictive model for the early clinical warning of NAFLD.

Keywords: SAMM50; SNP–SNP interactions; aging; nonalcoholic fatty liver disease; single nucleotide polymorphism.

Figure 1

Flow chart of patients’ selection. A total of 1423 patients were initially collected in the cohort, 143 were excluded according to the exclusion criteria, and after exclusion of 227 patients with incomplete data or unqualified genotyping, 1053 patients were finally enrolled in the study.

Figure 2

SNP−SNP interactions among SAMM50 rs2073082, rs738491 and rs3761472 loci in NAFLD and Control subjects. Inside each square, the left bar represents NAFLD subjects (positive score), and the right bar represents control subjects (negative score). The number at the top of each bar is the scoring statistic, which is the product of the affiliation coefficient and the residuals. The scoring statistic categorizes data into high and low risk by calculating whether an individual’s scoring mean exceeds a set threshold (e.g., ≥1). High-risk combinations of genotypes are indicated in dark squares; low-risk genotype combinations are indicated in gray squares; empty squares present the absence of identified genotype combination.

Figure 3

SNP−SNP Interaction Dendrogram. Different types of SNP–SNP effects on NAFLD risk. Orange (synergy); Blue (redundancy or antagonism). Short lines represent stronger interactions, and long lines represent weaker interactions.

Figure 4

Fruchterman−Rheingold. This interaction model describes the percent of the entropy that is explained by each factor. Each SNP is shown in a box with the percent of entropy below the label. Interactions between SNPs are depicted as lines in different colors. Synergy is depicted as an orange line between SNPs accompanied by a positive percent of entropy, while redundancy is indicated as a blue line accompanied by a negative percent of entropy. Rs3761472 synergized with both rs738491 and rs2073082, resulting in positive information gain values of 0.12% and 0.04% in NAFLD, while rs2073082 and rs738491 were antagonistic with negative IG values (−0.76%).