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

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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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Data availability

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

Abbreviations

AC:

Abdominal circumference

ALT:

Alanine transaminase

AST:

Aspartate aminotransferase

AUC:

Area under curve

BMI:

Body mass index

CAP:

Controlled attenuation parameter

CK18:

Cytokeratin 18

DBP:

Diastolic blood pressure

FAST:

FibroScan-AST

FGF21:

Fibroblast growth factor 21

FLI:

Fatty liver index

GGT:

Gamma-glutamyl transferase

HC:

Hip circumference

HCC:

Hepatocellular carcinoma

HDL-C:

High-density lipoprotein cholesterol

HSI:

Hepatocyte steatosis index

IGF-1:

Insulin-like growth factor-1

IL-6:

Interleukin-6

IP-10:

Interferon-inducible protein 10

IR:

Insulin resistance

LDL-C:

Low-density lipoprotein cholesterol

LFC:

Liver fat content

LSM:

Liver stiffness measurement

MAFLD:

Metabolic associated fatty liver disease

MetS:

Metabolic syndrome

MRI-PDFF:

Magnetic resonance imaging-derived proton density fat fraction

NAFLD:

Non-alcoholic fatty liver disease

NASH:

Non-alcoholic steatohepatitis

OR:

Odds ratio

SBP:

Systolic blood pressure

T2DM:

Diabetes mellitus type 2

TB:

Total bilirubin

TBA:

Total bile acid

TC:

Total cholesterol

TG:

Triglyceride

TIMP1:

Tissue inhibitor matrix metalloproteinase 1

WC:

Waist circumference

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Acknowledgements

We would like to show our deepest gratitude to every author.

Funding

This study was supported by the National Natural Science Foundation of China.

Author information

Authors and Affiliations

  1. Phase I Clinical Trial Unit, First Hospital of Jilin University, No. 71 Xinmin Street, Changchun, 130021, Jilin, China

    Aruhan Yang, Xiaoxue Zhu & Yanhua Ding

  2. Department of Radiology, First Hospital of Jilin University, Changchun, China

    Lei Zhang & Huimao Zhang

  3. Department of Hepatology, First Hospital of Jilin University, Changchun, China

    Yingwen Zhang & Junqi Niu

  4. Department of Pathology, First Hospital of Jilin University, Changchun, China

    Dezhi Zhang & Meishan Jin

  5. Department of Hepatobiliary and Pancreatic Surgery, First Hospital of Jilin University, No. 71 Xinmin Street, Changchun, 130021, Jilin, China

    Guoyue Lv

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  1. Aruhan Yang
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Contributions

YA, DY, LG, and JN designed the study. LZ, XZ, YZ, HUZ, DZ, QD, HJ, HC, LX, HOZ, MJ, LC, ZW, and DW performed the imaging or biopsy examination. YA performed the experiments and analyzed the data. YA and DY wrote the manuscript.

Corresponding authors

Correspondence to Yanhua Ding or Guoyue Lv.

Ethics declarations

Conflict of interest

Aruhan Yang, Xiaoxue Zhu, Lei Zhang, Yingwen Zhang, Dezhi Zhang, Meishan Jin, Junqi Niu and Huimao Zhang declare that they have no conflict of interest.

Animal research

Not applicable.

Consent to participate

This study was approved by the Ethics Committee of the First Hospital of Jilin University (Ethical Approval Number: 19K096001), and written informed consent was obtained from all the participants.

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Every author consented to publish.

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Not applicable.

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Supplementary Information

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Supplementary file1 (DOC 45 KB)

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12072_2022_10355_MOESM6_ESM.pdf

Supplementary file6 Supplementary Fig. 1. Cross validation of LASSO for the penalty term. The λ values ranged from 0.00006 to 0.158323 with a minimal binomial deviance achieved at 0.0081 and a more stringent value of 0.0224. (PDF 416 KB)

12072_2022_10355_MOESM7_ESM.pdf

Supplementary file7 Supplementary Fig. 2. Correlation analysis between imaging results and histologic results. Spearman’s correlation analysis showed a stronger correlation of the LFC with steatosis and NAS score than FibroScan. (PDF 455 KB)

12072_2022_10355_MOESM8_ESM.pdf

Supplementary file8 Supplementary Fig. 3. (A) Selected variables in the classification tree models to differentiate NAFLD and healthy subjects. Supplementary Fig. 3. (B) Selected variables in classification tree models to differentiate mild NAFLD and moderate/severe NAFLD.The importance of variables in the classification tree models is shown, and only the top five most important variables are shown. (PDF 397 KB)

12072_2022_10355_MOESM9_ESM.pdf

Supplementary file9 Supplementary Fig. 4. (A) Selected variables in the classification tree models to differentiate NAFLD and healthy subjects among nonobese populations. Supplementary Fig. 4. (B) Selected variables in the classification tree models to differentiate mild NAFLD and moderate/severe NAFLD among the nonobese population. The importance of variables in the classification tree models among the non-obese group is shown. (PDF 400 KB)

12072_2022_10355_MOESM10_ESM.pdf

Supplementary file10 Supplementary Fig. 5. (A) Selected variables in the regression tree models to predict LFC. Supplementary Fig. 5. (B) Selected variables in the regression tree models to predict LFC among the nonobese population. The importance of variables in the regression tree models is shown. (PDF 398 KB)

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Yang, A., Zhu, X., Zhang, L. et al. Non-invasive evaluation of NAFLD and the contribution of genes: an MRI-PDFF-based cross-sectional study. Hepatol Int 16, 1035–1051 (2022). https://doi.org/10.1007/s12072-022-10355-2

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