Evidence map›Paper›PMID 35829866›Full record

ArticleHepatology international2022

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

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

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In one paragraph

Article in Hepatology international, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
9citing papers in PubMed, 1 pooled it
1.7field-weighted citation impact, top 16% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

9 citing papers in PubMed, 1 synthesis or guideline pooled it, 11 citations in OpenAlex.

  1. Pooled it
  2. Review
  3. Review
  4. Article
  5. Article
  6. Impact of PNPLA3 I148M on Clinical Outcomes in Patients With MASLD.Liver international : official journal of the International Association for the Study of the Liver · 2025
    Review
  7. Article
  8. Article
  9. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

10 authors at 1 institution in 1 country.

Aruhan YangPhase I Clinical Trial Unit, First Hospital of Jilin University, No. 71 Xinmin Street, Changchun, 130021, Jilin, China.
Xiaoxue ZhuPhase I Clinical Trial Unit, First Hospital of Jilin University, No. 71 Xinmin Street, Changchun, 130021, Jilin, China.
Lei ZhangDepartment of Radiology, First Hospital of Jilin University, Changchun, China.
Yingwen ZhangDepartment of Hepatology, First Hospital of Jilin University, Changchun, China.
Dezhi ZhangDepartment of Pathology, First Hospital of Jilin University, Changchun, China.
Meishan JinDepartment of Pathology, First Hospital of Jilin University, Changchun, China.
Junqi NiuDepartment of Hepatology, First Hospital of Jilin University, Changchun, China.
Huimao ZhangDepartment of Radiology, First Hospital of Jilin University, Changchun, China.
Yanhua DingPhase I Clinical Trial Unit, First Hospital of Jilin University, No. 71 Xinmin Street, Changchun, 130021, Jilin, China. dingyanh@jlu.edu.cn.ORCID http://orcid.org/0000-0002-0498-8939
Guoyue LvDepartment of Hepatobiliary and Pancreatic Surgery, First Hospital of Jilin University, No. 71 Xinmin Street, Changchun, 130021, Jilin, China. lvgy@jlu.edu.cn.
Jilin University · CN

Funding

This study was supported by the National Natural Science Foundation of China 11415
6 · The paper itself

Abstract

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

designPatients 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.

resultsApproximately, 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%).

conclusionsBased 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.

Indexed as

Non-alcoholic Fatty Liver DiseaseBiomarkersCross-Sectional StudiesHumansLiverMagnetic Resonance ImagingObesityBiomarkersDigestive system diseaseHepatobiliary disordersLiver dysfunctionLiver fat contentLiver steatosisMetabolic fatty liver disease (MAFLD)MRI-proton-density-fat-fraction (PDFF)Non-alcoholic fatty liver disease (NAFLD)Non-alcoholic steatohepatitisOdds ratiosSingle-nucleotide polymorphisms (SNP)

Identifiers

PMID35829866
OpenAlexW4285090500

What OpenQuestion holds

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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.