Evidence map›Paper›PMID 40176953›Full record

ArticleBiochemistry and biophysics reports2025

An association study of

Samira Abdollahi, Abbas Sahebghadam Lotfi, Ramin Saravani, Hamed Taheri

Abstract read
In one paragraph

Article in Biochemistry and biophysics reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Article
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

4 authors.

Samira AbdollahiDepartment of Clinical Biochemistry, Faculty of Medical Science, Tarbiat Modares University, Tehran, Iran.
Abbas Sahebghadam LotfiDepartment of Clinical Biochemistry, Faculty of Medical Science, Tarbiat Modares University, Tehran, Iran.
Ramin SaravaniCellular and Molecular Research Center, Research Institute of Cellular and Molecular Sciences in Infectious Diseases, Zahedan University of Medical Sciences, Zahedan, Iran.
Hamed TaheriCellular and Molecular Research Center, Research Institute of Cellular and Molecular Sciences in Infectious Diseases, Zahedan University of Medical Sciences, Zahedan, Iran.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Metabolic dysfunction-associated steatotic liver disease (MASLD) is a type of fat accumulation in the liver that can lead to cirrhosis and chronic liver disease. MASLD is recognized as the most frequent of liver-associated deaths worldwide. The Methods: In this case-control study, 120 patients affected by MASLD and 120 healthy subjects participated. The Nephelometry system measured serum levels of α1-antitrypsin (A1AT). Biochemical tests were conducted to assess serum levels of blood parameters using commercially available kits. DNA extraction was performed using the salting-out method, followed by the amplification refractory mutation system-polymerase chain reaction (ARMS-PCR) method for genotyping. Statistical analysis was performed by SPSS v16.0. Results: The findings showed that the rs6647 G allele significantly increased the risk of MASLD. The G allele in codominant, dominant, and over-dominant models caused an increase in the risk of MASLD. Additionally, the rs709932 T allele was more frequent among patients compared to healthy subjects and significantly enhanced the risk of MASLD. The T allele in the codominant and recessive models indicated a high risk for MASLD in our population. The G allele of rs1303 caused an enhancement in the mean serum levels of A1AT in the MASLD group. Conclusions: Our results show an association between

Indexed as

A1ATMASLDSERPINA1Single nucleotide polymorphisms (SNPs)

Identifiers

PMID40176953
PMCPMC11964567

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Registered trials

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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.