Evidence map›Paper›PMID 36566480›Full record

ArticleJournal of diabetes investigation2023

Diagnostic ability using fatty liver and metabolic markers for metabolic-associated fatty liver disease stratified by metabolic/glycemic abnormalities.

Akira Okada, Gen Yamada, Takeshi Kimura, Yasuhiro Hagiwara, Satoko Yamaguchi, Kayo Ikeda Kurakawa, Masaomi Nangaku, Toshimasa Yamauchi, Yutaka Matsuyama, Takashi Kadowaki

Open access · goldAbstract read
In one paragraph

Article in Journal of diabetes investigation, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.

0numbers the graph read from it
0cells of the map it votes in
12citing papers in PubMed
1.9field-weighted citation impact, top 14% 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

12 citing papers in PubMed, 14 citations in OpenAlex.

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  5. [Predictive factors for MASLD in type 2 diabetes: 1 year follow-up study].Revista medica del Instituto Mexicano del Seguro Social · 2024
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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

10 authors at 3 institutions in 1 country.

Akira OkadaDepartment of Prevention of Diabetes and Lifestyle-Related Diseases, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan.ORCID https://orcid.org/0000-0001-6480-3388
Gen YamadaDepartment of Biostatistics, School of Public Health, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan.
Takeshi KimuraCenter for Preventive Medicine, St Luke's International Hospital, Tokyo, Japan.
Yasuhiro HagiwaraDepartment of Biostatistics, School of Public Health, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan.
Satoko YamaguchiDepartment of Prevention of Diabetes and Lifestyle-Related Diseases, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan.
Kayo Ikeda KurakawaDepartment of Prevention of Diabetes and Lifestyle-Related Diseases, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan.
Masaomi NangakuDivision of Nephrology and Endocrinology, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan.
Toshimasa YamauchiDepartment of Diabetes and Metabolism, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan.
Yutaka MatsuyamaDepartment of Biostatistics, School of Public Health, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan.
Takashi KadowakiDepartment of Prevention of Diabetes and Lifestyle-Related Diseases, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan.
The University of Tokyo · JPSt. Luke's International Hospital · JPToranomon Hospital · JP

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

AIMS/

introductionAlthough several noninvasive predictive markers for fatty liver and metabolic markers have been used for fatty liver prediction, whether such markers can also predict metabolic-associated fatty liver disease (MAFLD) remains unclear. We aimed to examine the ability of existing fatty liver or metabolic markers to predict MAFLD. MATERIALS AND

methodsParticipants in a high-volume center in Tokyo were classified into groups with and without MAFLD, based on the presence of metabolic abnormalities and fatty liver diagnosed through abdominal ultrasonography, between 2008 and 2018. The diagnostic abilities of three fatty liver markers: fatty liver index (FLI), hepatic steatosis index (HSI), and lipid accumulation product (LAP), and three common metabolic markers: waist-to-height ratio (WHR), body mass index (BMI), and waist circumference (WC), for predicting MAFLD, were evaluated. Analyses stratified by MAFLD subtypes were performed.

resultsOf 92,374 individuals, 19,392 (36.1%) had MAFLD. The diagnostic performances for MAFLD prediction, measured as c-statistics, for FLI, HSI, LAP, WHR, BMI, and WC were 0.906, 0.892, 0.878, 0.844, 0.877, and 0.878, respectively. Optimal cutoff values for diagnosing MAFLD for FLI, HSI, LAP, WHR, BMI, and WC were 20.3, 32.7, 20.0, 0.49, 22.9, and 82.1, respectively. Analyses stratified by MAFLD subtypes, based on BMI and metabolic/glycemic abnormalities, suggested that FLI and HSI had acceptable (c-statistics >0.700) diagnostic abilities throughout all the analyses.

conclusionsAll six markers were excellent predictors of MAFLD in diagnosing among the general population, with FLI and HSI particularly useful among all sub-populations.

Indexed as

Non-alcoholic Fatty Liver DiseaseAbdomenBiomarkersHumansLiver Function TestsTriglyceridesBiomarkersTriglyceridesDiagnostic markerMetabolic-associated fatty liver diseaseNonalcoholic fatty liver disease

Identifiers

PMID36566480
PMCPMC9951571
OpenAlexW4312141183

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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.