Evidence map›Paper›PMID 38491451›Full record

ArticleBMC gastroenterology2024

Using new indices to predict metabolism dysfunction-associated fatty liver disease (MAFLD): analysis of the national health and nutrition examination survey database.

Xu Ming Li, Song Lian Liu, Ya Jun He, Jian Chang Shu

Abstract read
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Article in BMC gastroenterology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers.

0numbers the graph read from it
0cells of the map it votes in
26citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

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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

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3 · Its place in the literature

Who cites it

26 citing papers in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

4 authors.

Xu Ming Li *Department of Gastroenterology, Guangzhou Red Cross Hospital(Guangzhou Red Cross Hospital of Jinan University), Jinan University, Guangzhou, China.
Song Lian Liu *Department of Hepatology, Guangzhou Eighth People's Hospital, Guangzhou Medical University, Guangzhou, China.
Ya Jun HeDepartment of Gastroenterology, Guangzhou Red Cross Hospital(Guangzhou Red Cross Hospital of Jinan University), Jinan University, Guangzhou, China.
Jian Chang ShuDepartment of Gastroenterology, Guangzhou Red Cross Hospital(Guangzhou Red Cross Hospital of Jinan University), Jinan University, Guangzhou, China. shujc0328@163.com.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundMetabolism dysfunction-associated fatty liver disease (MAFLD), is the most common chronic liver disease. Few MAFLD predictions are simple and accurate. We examined the predictive performance of the albumin-to-glutamyl transpeptidase ratio (AGTR), plasma atherogenicity index (AIP), and serum uric acid to high-density lipoprotein cholesterol ratio (UHR) for MAFLD to design practical, inexpensive, and reliable models.

methodsThe National Health and Nutrition Examination Survey (NHANES) 2007-2016 cycle dataset, which contained 12,654 participants, was filtered and randomly separated into internal validation and training sets. This study examined the relationships of the AGTR and AIP with MAFLD using binary multifactor logistic regression. We then created a MAFLD predictive model using the training dataset and validated the predictive model performance with the 2017-2018 NHANES and internal datasets.

resultsIn the total population, the predictive ability (AUC) of the AIP, AGTR, UHR, and the combination of all three for MAFLD showed in the following order: 0.749, 0.773, 0.728 and 0.824. Further subgroup analysis showed that the AGTR (AUC1 = 0.796; AUC2 = 0.690) and the combination of the three measures (AUC1 = 0.863; AUC2 = 0.766) better predicted MAFLD in nondiabetic patients. Joint prediction outperformed the individual measures in predicting MAFLD in the subgroups. Additionally, the model better predicted female MAFLD. Adding waist circumference and or BMI to this model improves predictive performance.

conclusionOur study showed that the AGTR, AIP, and UHR had strong MAFLD predictive value, and their combination can increase MAFLD predictive performance. They also performed better in females.

Indexed as

Non-alcoholic Fatty Liver DiseaseUric AcidAlbuminsCholesterol, HDLFemalegamma-GlutamyltransferaseHumansNutrition SurveysAlbuminsCholesterol, HDLgamma-GlutamyltransferaseUric AcidAIPALB/GGTMetabolic dysfunction-associated fatty liverNHANESPredictive modelsUHR

Identifiers

PMID38491451
PMCPMC10943835

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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.