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Hepatology International
Published in: Hepatology International 5/2022

12-07-2022 | Fatty Liver | Original Article

Non-invasive evaluation of NAFLD and the contribution of genes: an MRI-PDFF-based cross-sectional study

Authors: Aruhan Yang, Xiaoxue Zhu, Lei Zhang, Yingwen Zhang, Dezhi Zhang, Meishan Jin, Junqi Niu, Huimao Zhang, Yanhua Ding, Guoyue Lv

Published in: Hepatology International | Issue 5/2022

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Abstract

Objective

To investigate the clinical, laboratory and genetic features of NAFLD patients based on MRI-PDFF in China.

Design

Patients with high ALT and with a diagnosis of fatty liver were included in this cross-sectional study. Fasting blood was collected to test biomarkers and SNPs. A total of 266 patients underwent MRI-PDFF and FibroScan examinations, and 38 underwent liver biopsy. Diagnostic models (decision tree, LASSO, and elastic net) were developed based on the diagnosis from MRI-PDFF reports.

Results

Approximately, 1/3 of the patients were found to have NASH and fibrosis. After quantifying liver steatosis by MRI-PDFF (healthy: n = 47; mild NAFLD: n = 136; moderate/severe NAFLD: n = 83; liver fat content (LFC): 3.6% vs. 8.7% vs. 19.0%), most biomarkers showed significant differences among the three groups, and patients without obesity were found to have a similar LFC as those with obesity (11.1% vs. 12.3%). Models including biomarkers showed strong diagnostic ability (accuracy: 0.80–0.91). Variant alleles of PNPLA3, HSD17B13 and MBOAT7 were identified as genetic risk factors causing higher LFC (8.7% vs. 12.3%; 11.0% vs. 14.5%; 8.5% vs. 10.2%, p < 0.05); those with the UQCC1 rs878639 variant allele showed lower LFC (10.4% vs. 8.4%; OR = 0.58, p < 0.05). Patients with more risk alleles had higher LFCs (8.1% vs. 10.7% vs. 11.6% vs. 14.5%).

Conclusions

Based on MRI-PDFF, a combination of several specific biomarkers can accurately predict disease status. When the effects of genes on liver steatosis were first quantified by MRI-PDFF, the UQCC1 rs878639 G allele was identified as a protective factor, and the MBOAT7 T allele was identified as a risk only among nonobese individuals.
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Literature
1.
Paik JM, Golabi P, Younossi Y, et al. Changes in the global burden of chronic liver diseases from 2012 to 2017: the growing impact of NAFLD. Hepatology. 2020;72:1605–1616PubMedCrossRef
2.
Eslam M, Fan JG, Mendez-Sanchez N. Non-alcoholic fatty liver disease in non-obese individuals: the impact of metabolic health. Lancet Gastroenterol Hepatol. 2020;5:713–715PubMedCrossRef
3.
Eslam M, Sanyal AJ, George J, et al. MAFLD: a consensus-driven proposed nomenclature for metabolic associated fatty liver disease. Gastroenterology. 2020;158:1999-2014.e1PubMedCrossRef
4.
Nan Y, An J, Bao J, et al. The Chinese society of hepatology position statement on the redefinition of fatty liver disease. J Hepatol. 2021;75:454–461PubMedCrossRef
5.
Bessone F, Razori MV, Roma MG. Molecular pathways of nonalcoholic fatty liver disease development and progression. Cell Mol Life Sci. 2019;76:99–128PubMedCrossRef
6.
Ekstedt M, Hagstrom H, Nasr P, et al. Fibrosis stage is the strongest predictor for disease-specific mortality in NAFLD after up to 33 years of follow-up. Hepatology. 2015;61:1547–1554PubMedCrossRef
7.
Leung JC, Loong TC, Wei JL, et al. Histological severity and clinical outcomes of nonalcoholic fatty liver disease in nonobese patients. Hepatology. 2017;65:54–64PubMedCrossRef
8.
Simon TG, Roelstraete B, Khalili H, et al. Mortality in biopsy-confirmed nonalcoholic fatty liver disease: results from a nationwide cohort. Gut. 2021;70:1375–1382PubMedCrossRef
9.
Davison BA, Harrison SA, Cotter G, et al. Suboptimal reliability of liver biopsy evaluation has implications for randomized clinical trials. J Hepatol. 2020;73:1322–1332PubMedCrossRef
10.
Tamaki N, Ajmera V, Loomba R. Non-invasive methods for imaging hepatic steatosis and their clinical importance in NAFLD. Nat Rev Endocrinol. 2022;18:55–66PubMedCrossRef
11.
Chen J, Duan S, Ma J, et al. MRI-determined liver fat correlates with risk of metabolic syndrome in patients with nonalcoholic fatty liver disease. Eur J Gastroenterol Hepatol. 2020;32:754–761PubMedCrossRef
12.
Shao C, Ye J, Li F, et al. Different predictors of steatosis and fibrosis severity among lean, overweight and obese patients with nonalcoholic fatty liver disease. Dig Liver Dis. 2019;51:1392–1399PubMedCrossRef
13.
Chen H, Zeng WK, Shi GZ, et al. Liver fat accumulation measured by high-speed T2-corrected multi-echo magnetic resonance spectroscopy can predict risk of cholelithiasis. World J Gastroenterol. 2020;26:4996–5007PubMedPubMedCentralCrossRef
14.
Lee SW, Lee TY, Yang SS, et al. The association of non-alcoholic fatty liver disease and metabolic syndrome in a Chinese population. Hepatobiliary Pancreat Dis Int. 2017;16:176–180PubMedCrossRef
15.
16.
Sun L, Wang Q, Liu M, et al. Albumin binding function is a novel biomarker for early liver damage and disease progression in non-alcoholic fatty liver disease. Endocrine. 2020;69:294–302PubMedCrossRef
17.
Dai J, Yi J, Zhang S, et al. Serum 3-carboxy-4-methyl-5-propyl-2-furanpropanoic acid is associated with lipid profiles and might protect against non-alcoholic fatty liver disease in Chinese individuals. J Diabetes Investig. 2019;10:793–800PubMedCrossRef
18.
Castera L, Friedrich-Rust M, Loomba R. Noninvasive assessment of liver disease in patients with nonalcoholic fatty liver disease. Gastroenterology. 2019;156:1264–81.e4PubMedCrossRef
19.
Alvarez-Sola G, Uriarte I, Latasa MU, et al. Fibroblast growth factor 15/19 (FGF15/19) protects from diet-induced hepatic steatosis: development of an FGF19-based chimeric molecule to promote fatty liver regeneration. Gut. 2017;66:1818–1828PubMedCrossRef
20.
Bedogni G, Bellentani S, Miglioli L, et al. The Fatty Liver Index: a simple and accurate predictor of hepatic steatosis in the general population. BMC Gastroenterol. 2006;6:33PubMedPubMedCentralCrossRef
21.
Lee JH, Kim D, Kim HJ, et al. Hepatic steatosis index: a simple screening tool reflecting nonalcoholic fatty liver disease. Dig Liver Dis. 2010;42:503–508PubMedCrossRef
22.
Eslam M, George J. Genetic contributions to NAFLD: leveraging shared genetics to uncover systems biology. Nat Rev Gastroenterol Hepatol. 2020;17:40–52PubMedCrossRef
23.
Ji F, Liu Y, Hao JG, et al. KLB gene polymorphism is associated with obesity and non-alcoholic fatty liver disease in the Han Chinese. Aging (Albany NY). 2019;11:7847–7858CrossRef
24.
Liu Q, Liu SS, Zhao ZZ, et al. TRIB1 rs17321515 gene polymorphism increases the risk of coronary heart disease in general population and non-alcoholic fatty liver disease patients in Chinese Han population. Lipids Health Dis. 2019;18:165PubMedPubMedCentralCrossRef
25.
Wang X, Liu Z, Peng Z, et al. The TM6SF2 rs58542926 T allele is significantly associated with non-alcoholic fatty liver disease in Chinese. J Hepatol. 2015;62:1438–1439PubMedCrossRef
26.
Meisner J, Albrechtsen A. Testing for Hardy-Weinberg equilibrium in structured populations using genotype or low-depth next generation sequencing data. Mol Ecol Resour. 2019;19:1144–1152PubMedCrossRef
27.
Kouvari M, Chrysohoou C, Skoumas J, et al. The presence of NAFLD influences the transition of metabolically healthy to metabolically unhealthy obesity and the ten-year cardiovascular disease risk: a population-based cohort study. Metabolism. 2021;128: 154893PubMedCrossRef
28.
Zhang J, Xu Q, Lai F, et al. Joint associations of metabolically healthy abdominal obesity and non-alcoholic fatty liver disease with prediabetes and diabetes in Chinese adults. BMJ Open Diabetes Res Care. 2021;9: e002362PubMedPubMedCentralCrossRef
29.
Garcia-Carretero R, Vigil-Medina L, Barquero-Perez O, et al. Logistic LASSO and elastic net to characterize vitamin D deficiency in a hypertensive obese population. Metab Syndr Relat Disord. 2020;18:79–85PubMedCrossRef
30.
McEligot AJ, Poynor V, Sharma R, et al. Logistic LASSO regression for dietary intakes and breast cancer. Nutrients. 2020;12:2652PubMedCentralCrossRef
31.
Zhou F, Zhou J, Wang W, et al. Unexpected rapid increase in the burden of NAFLD in China from 2008 to 2018: a systematic review and meta-analysis. Hepatology. 2019;70:1119–1133PubMedCrossRef
32.
Wieckowska A, Zein NN, Yerian LM, et al. In vivo assessment of liver cell apoptosis as a novel biomarker of disease severity in nonalcoholic fatty liver disease. Hepatology. 2006;44:27–33PubMedCrossRef
33.
Kliewer SA, Mangelsdorf DJ. Bile acids as hormones: the FXR-FGF15/19 pathway. Dig Dis. 2015;33:327–331PubMedCrossRef
34.
Wojcik M, Janus D, Dolezal-Oltarzewska K, et al. A decrease in fasting FGF19 levels is associated with the development of non-alcoholic fatty liver disease in obese adolescents. J Pediatr Endocrinol Metab. 2012;25:1089–1093PubMedCrossRef
35.
Schuster S, Cabrera D, Arrese M, et al. Triggering and resolution of inflammation in NASH. Nat Rev Gastroenterol Hepatol. 2018;15:349–364PubMedCrossRef
36.
Newsome PN, Sasso M, Deeks JJ, et al. FibroScan-AST (FAST) score for the non-invasive identification of patients with non-alcoholic steatohepatitis with significant activity and fibrosis: a prospective derivation and global validation study. Lancet Gastroenterol Hepatol. 2020;5:362–373PubMedPubMedCentralCrossRef
37.
38.
Mi WF, Chen XM, Fan TT, et al. Identifying modifiable risk factors for relapse in patients with schizophrenia in China. Front Psychiatry. 2020;11: 574763PubMedPubMedCentralCrossRef
39.
Ye Q, Zou B, Yeo YH, 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;5:739–752PubMedCrossRef
40.
Wong VW, Wong GL, Chan RS, et al. Beneficial effects of lifestyle intervention in non-obese patients with non-alcoholic fatty liver disease. J Hepatol. 2018;69:1349–1356PubMedCrossRef
41.
Eslam M, Valenti L, Romeo S. Genetics and epigenetics of NAFLD and NASH: clinical impact. J Hepatol. 2018;68:268–279PubMedCrossRef
42.
Trepo E, Valenti L. Update on NAFLD genetics: from new variants to the clinic. J Hepatol. 2020;72:1196–1209PubMedCrossRef
43.
Tucker EJ, Wanschers BF, Szklarczyk R, et al. Mutations in the UQCC1-interacting protein, UQCC2, cause human complex III deficiency associated with perturbed cytochrome b protein expression. PLoS Genet. 2013;9: e1004034PubMedPubMedCentralCrossRef
44.
Vetter K, Wurst W. Expression of a novel mouse gene “mbFZb” in distinct regions of the developing nervous system and the adult brain. Mech Dev. 2001;100:123–125PubMedCrossRef
45.
46.
Sanna S, Jackson AU, Nagaraja R, et al. Common variants in the GDF5-UQCC region are associated with variation in human height. Nat Genet. 2008;40:198–203PubMedPubMedCentralCrossRef
47.
Brunt EM, Wong VW, Nobili V, et al. Nonalcoholic fatty liver disease. Nat Rev Dis Primers. 2015;1:15080PubMedCrossRef
Metadata
Title
Non-invasive evaluation of NAFLD and the contribution of genes: an MRI-PDFF-based cross-sectional study
Authors
Aruhan Yang
Xiaoxue Zhu
Lei Zhang
Yingwen Zhang
Dezhi Zhang
Meishan Jin
Junqi Niu
Huimao Zhang
Yanhua Ding
Guoyue Lv
Publication date
12-07-2022
Publisher
Springer India
Published in
Hepatology International / Issue 5/2022
Print ISSN: 1936-0533
Electronic ISSN: 1936-0541
DOI
https://doi.org/10.1007/s12072-022-10355-2

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