Evidence map›Paper›PMID 36686469›Full record

ArticleFrontiers in endocrinology2022

Sequential algorithm to stratify liver fibrosis risk in overweight/obese metabolic dysfunction-associated fatty liver disease.

Chi-Ho Lee, David Tak-Wai Lui, Raymond Hang-Wun Li, Michele Mae-Ann Yuen, Carol Ho-Yi Fong, Ambrose Pak-Wah Leung, Justin Chiu-Man Chu, Loey Lung-Yi Mak, Tai-Hing Lam, Jean Woo and 7 more

Open access · goldAbstract read
In one paragraph

Article in Frontiers in endocrinology, 2022. 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
1.1field-weighted citation impact, top 23% 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

3 citing papers in PubMed, 5 citations in OpenAlex.

  1. Observational
  2. Review
  3. 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

17 authors at 3 institutions in 2 countries.

Chi-Ho LeeDepartment of Medicine, School of Clinical Medicine, University of Hong Kong, Queen Mary Hospital, Hong Kong, Hong Kong SAR, China.
David Tak-Wai LuiDepartment of Medicine, School of Clinical Medicine, University of Hong Kong, Queen Mary Hospital, Hong Kong, Hong Kong SAR, China.
Raymond Hang-Wun LiDepartment of Obstetrics and Gynaecology, University of Hong Kong, Queen Mary Hospital, Hong Kong, Hong Kong SAR, China.
Michele Mae-Ann YuenDepartment of Medicine, School of Clinical Medicine, University of Hong Kong, Queen Mary Hospital, Hong Kong, Hong Kong SAR, China.
Carol Ho-Yi FongDepartment of Medicine, School of Clinical Medicine, University of Hong Kong, Queen Mary Hospital, Hong Kong, Hong Kong SAR, China.
Ambrose Pak-Wah LeungDepartment of Medicine, School of Clinical Medicine, University of Hong Kong, Queen Mary Hospital, Hong Kong, Hong Kong SAR, China.
Justin Chiu-Man ChuDepartment of Medicine, School of Clinical Medicine, University of Hong Kong, Queen Mary Hospital, Hong Kong, Hong Kong SAR, China.
Loey Lung-Yi MakDepartment of Medicine, School of Clinical Medicine, University of Hong Kong, Queen Mary Hospital, Hong Kong, Hong Kong SAR, China.
Tai-Hing LamThe School of Public Health, University of Hong Kong, Queen Mary Hospital, Hong Kong, Hong Kong SAR, China.
Jean WooDepartment of Medicine and Therapeutics, The Chinese University of Hong Kong, Hong Kong, Hong Kong SAR, China.
Yu-Cho WooDepartment of Medicine, School of Clinical Medicine, University of Hong Kong, Queen Mary Hospital, Hong Kong, Hong Kong SAR, China.
Aimin XuDepartment of Medicine, School of Clinical Medicine, University of Hong Kong, Queen Mary Hospital, Hong Kong, Hong Kong SAR, China.
Hung-Fat TseDepartment of Medicine, School of Clinical Medicine, University of Hong Kong, Queen Mary Hospital, Hong Kong, Hong Kong SAR, China.
Kathryn Choon-Beng TanDepartment of Medicine, School of Clinical Medicine, University of Hong Kong, Queen Mary Hospital, Hong Kong, Hong Kong SAR, China.
Bernard Man-Yung CheungDepartment of Medicine, School of Clinical Medicine, University of Hong Kong, Queen Mary Hospital, Hong Kong, Hong Kong SAR, China.
Man-Fung YuenDepartment of Medicine, School of Clinical Medicine, University of Hong Kong, Queen Mary Hospital, Hong Kong, Hong Kong SAR, China.
Karen Siu-Ling LamDepartment of Medicine, School of Clinical Medicine, University of Hong Kong, Queen Mary Hospital, Hong Kong, Hong Kong SAR, China.
University of Hong Kong · HKQueen Mary Hospital · CNChinese University of Hong Kong · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Non-diabetic overweight/obese metabolic dysfunction-associated fatty liver disease (MAFLD) represents the largest subgroup with heterogeneous liver fibrosis risk. Metabolic dysfunction promotes liver fibrosis. Here, we investigated whether incorporating additional metabolic risk factors into clinical evaluation improved liver fibrosis risk stratification among individuals with non-diabetic overweight/obese MAFLD. Materials and methods: Comprehensive metabolic evaluation including 75-gram oral glucose tolerance test was performed in over 1000 participants from the New Hong Kong Cardiovascular Risk Factor Prevalence Study (HK-NCRISPS), a contemporary population-based study of HK Chinese. Hepatic steatosis and fibrosis were evaluated based on controlled attenuation parameter and liver stiffness (LS) measured using vibration-controlled transient elastography, respectively. Clinically significant liver fibrosis was defined as LS ≥8.0 kPa. Our findings were validated in an independent pooled cohort comprising individuals with obesity and/or polycystic ovarian syndrome. Results: Of the 1020 recruited community-dwelling individuals, 312 (30.6%) had non-diabetic overweight/obese MAFLD. Among them, 6.4% had LS ≥8.0 kPa. In multivariable stepwise logistic regression analysis, abnormal serum aspartate aminotransferase (AST) (OR 7.95, p<0.001) and homeostasis model assessment of insulin resistance (HOMA-IR) ≥2.5 (OR 5.01, p=0.008) were independently associated with LS ≥8.0 kPa, in a model also consisting of other metabolic risk factors including central adiposity, hypertension, dyslipidaemia and prediabetes. A sequential screening algorithm using abnormal AST, followed by elevated HOMA-IR, was developed to identify individuals with LS ≥8.0 kPa, and externally validated with satisfactory sensitivity (>80%) and negative predictive value (>90%). Conclusion: A sequential algorithm incorporating AST and HOMA-IR levels improves fibrosis risk stratification among non-diabetic overweight/obese MAFLD individuals.

Indexed as

Non-alcoholic Fatty Liver DiseaseOverweightHumansLiver CirrhosisObesityRisk Factorsfatty liver diseaseMAFLD (metabolic associated fatty liver disease)obesityoverweightpopulation based study

Identifiers

PMID36686469
PMCPMC9853017
OpenAlexW4313641482

What OpenQuestion holds

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LicenceCC BY
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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.