Background: Non-alcoholic fatty liver disease (NAFLD) is a hepatic manifestation of metabolic syndrome that is closely associated with multiple factors such as obesity, hyperlipidemia and type 2 diabetes mellitus. However, other risk factors for the development of NAFLD are unclear. With the association between periodontal disease and the development of systemic diseases receiving increasing attention recently, we conducted this study to investigate the relationship between NAFLD and infection withPorphyromonas gingivalis(P. gingivalis), a major causative agent of periodontitis.Methods: The detection frequencies of periodontal bacteria in oral samples collected from 150 biopsy-proven NAFLD patients (102 with non-alcoholic steatohepatitis (NASH) and 48 with non-alcoholic fatty liver (NAFL) patients) and 60 non-NAFLD control subjects were determined. Detection ofP. gingivalisand other periodontopathic bacteria were detected by PCR assay. In addition, effect ofP. gingivalis-infection on mouse NAFLD model was investigated. To clarify the exact contribution ofP. gingivalis-induced periodontitis, non-surgical periodontal treatments were also undertaken for 3 months in 10 NAFLD patients with periodontitis.Results: The detection frequency ofP. gingivalisin NAFLD patients was significantly higher than that in the non-NAFLD control subjects (46.7% vs. 21.7%, odds ratio: 3.16). In addition, the detection frequency ofP. gingivalisin NASH patients was markedly higher than that in the non-NAFLD subjects (52.0%, odds ratio: 3.91). Most of theP. gingivalisfimbria detected in the NAFLD patients was of invasive genotypes, especially type II (50.0%). Infection of type IIP. gingivalison NAFLD model of mice accelerated the NAFLD progression. The non-surgical periodontal treatments on NAFLD patients carried out for 3 months ameliorated the liver function parameters, such as the serum levels of AST and ALT.Conclusions: Infection with high-virulenceP. gingivalismight be an additional risk factor for the development/progression of NAFLD/NASH.

The genetic factors affecting the natural history of nonalcoholic fatty liver disease (NAFLD), including the development of nonalcoholic steatohepatitis (NASH) and NASH-derived hepatocellular carcinoma (NASH-HCC), are still unknown. In the current study, we sought to identify genetic factors related to the development of NAFLD, NASH, and NASH-HCC, and to establish risk-estimation models for them. For these purposes, 936 histologically proven NAFLD patients were recruited, and genome-wide association (GWA) studies were conducted for 902, including 476 NASH and 58 NASH-HCC patients, against 7,672 general-population controls. Risk estimations for NAFLD and NASH were then performed using the SNPs identified as having significant associations in the GWA studies. We found that rs2896019 in PNPLA3 [p = 2.3x10^-31, OR (95%CI) = 1.85 (1.67–2.05)], rs1260326 in GCKR [p = 9.6x10^-10, OR (95%CI) = 1.38(1.25–1.53)], and rs4808199 in GATAD2A [p = 2.3x10^-8, OR (95%CI) = 1.37 (1.23–1.53)] were significantly associated with NAFLD. Notably, the number of risk alleles in PNPLA3 and GATAD2A was much higher in Matteoni type 4 (NASH) patients than in type 1, type 2, and type 3 NAFLD patients. In addition, we newly identified rs17007417 in DYSF [p = 5.2x10^-7, OR (95%CI) = 2.74 (1.84–4.06)] as a SNP associated with NASH-HCC. Rs641738 in TMC4, which showed association with NAFLD in patients of European descent, was not replicated in our study (p = 0.73), although the complicated LD pattern in the region suggests the necessity for further investigation. The genetic variants of PNPLA3, GCKR, and GATAD2A were then used to estimate the risk for NAFLD. The obtained Polygenic Risk Scores showed that the risk for NAFLD increased with the accumulation of risk alleles [AUC (95%CI) = 0.65 (0.63–0.67)]. Conclusions: We demonstrated that NASH is genetically and clinically different from the other NAFLD subgroups. We also established risk-estimation models for NAFLD and NASH using multiple genetic markers. These models can be used to improve the accuracy of NAFLD diagnosis and to guide treatment decisions for patients.

Aim: We aimed to develop a novel noninvasive test using an artificial intelligence (AI)/neural network (NN) system (named nonalcoholic steatohepatitis [NASH]‐Scope) to screen nonalcoholic fatty liver disease (NAFLD) and NASH.Methods: We enrolled 324 and 74 patients histologically diagnosed with NAFLD for training and validation studies, respectively. Two independent pathologists histologically diagnosed patients with NAFLD for validation study. Additionally, 48 subjects who underwent a medical health checkup and did not show fatty liver ultrasonographically and had normal serum aminotransferase levels were categorized as the non‐NAFLD group. NASH‐Scope was based on 11 clinical values: age, sex, height, weight, waist circumference, aspartate aminotransferase, alanine aminotransferase, γ‐glutamyl transferase, cholesterol, triglyceride, and platelet count.Results: The sensitivity, specificity, positive predictive value, negative predictive value, and area under the receiver operator characteristic curve of NASH‐Scope for distinguishing NAFLD from non‐NAFLD in the training study and validation study were 99.7% versus 97.2%, 97.8% versus 97.8%, 99.7% versus 98.6%, 97.8% versus 95.7%, and 0.999 versus 0.950, respectively. Those for distinguishing NASH with fibrosis from NAFLD without fibrosis were 99.5% versus 90.7%, 84.3% versus 93.3%, 94.2% versus 98.0%, 98.6% versus 73.7%, and 0.960 versus 0.950. These results were excellent, even when the output data were divided into two categories without any gray zone.Conclusions: The AI/NN system, termed as NASH‐Scope, is practical and can accurately differentially diagnose between NAFLD and non‐NAFLD and between NAFLD without fibrosis and NASH with fibrosis. Thus, NASH‐Scope is useful for screening nonalcoholic fatty liver and NASH.