Evidence map›Paper›PMID 39872312›Full record

ArticleFrontiers in endocrinology2024

Golgi protein 73: charting new territories in diagnosing significant fibrosis in MASLD: a prospective cross-sectional study.

Shan Hong, Ziyu Liu, Ping Li, Jing Zhang, Hongshan Wei

Abstract read
In one paragraph

Article in Frontiers in endocrinology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–field-weighted citation impact
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

3 citing papers in PubMed.

  1. Article
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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

5 authors.

Shan HongDepartment of Gastroenterology, Beijing Ditan Hospital, Capital Medical University, Beijing, China.
Ziyu LiuDepartment of Gastroenterology, Beijing Xuanwu Hospital, Capital Medical University, Beijing, China.
Ping LiDepartment of Gastroenterology, Beijing Ditan Hospital, Capital Medical University, Beijing, China.
Jing ZhangDepartment of Liver Diseases, Beijing Youan Hospital, Capital Medical University, Beijing, China.
Hongshan WeiDepartment of Gastroenterology, Beijing Ditan Hospital, Capital Medical University, Beijing, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Objectives: To explore the correlation between serum Golgi protein 73 (GP73) levels and the degree of fibrosis in Metabolic dysfunction associated steatotic liver disease (MASLD); to establish a non-invasive diagnostic algorithm based on serum GP73 and liver elasticity. Methods: This is a prospective cross-sectional study, including 228 patients diagnosed with MASLD from May 2018 to January 2024 at two tertiary hospitals. Clinical data and hepatic pathological features and the correlation between serum GP73 and liver fibrosis were assessed. A new algorithm was conducted after logistic regression. Receiver Operating Characteristic (ROC) curve was used to compare its diagnostic performance with traditional models. Results: Significant fibrosis was diagnosed in 37.2% (85/228) patients. Serum GP73 levels were markedly higher in patients with significant fibrosis than in those without (128 ng/mL v.s 46 ng/mL, p< 0.001). Serum GP73 levels independently predicted significant liver fibrosis (adjusted odds ratio, aOR 1.028, p< 0.001). A new algorithm based on GP73 was developed with a higher area under ROC (AUC) of 0.840 than that of Fibrosis index-4 (p< 0.001). Conclusions: Serum GP73 is an independent risk factor for significant liver fibrosis in MASLD, and the GFA (GP73-Fibroscan-Age) model has good diagnostic efficacy for significant liver fibrosis.

Indexed as

Fatty LiverLiver CirrhosisMembrane ProteinsAdultAgedAlgorithmsBiomarkersCross-Sectional StudiesFemaleHumansMaleMiddle AgedProspective StudiesROC CurveBiomarkersGOLM1 protein, humanMembrane ProteinsdiagnosisGP73liver biopsyMASLDmetabolic diseases

Identifiers

PMID39872312
PMCPMC11769827

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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.