Evidence map›Paper›PMID 42669412›Full record

ReviewClinical and molecular hepatology2026

Risk assessment in metabolic dysfunction-associated steatotic liver disease and metabolic dysfunction and alcohol-related liver disease: current approaches and future directions.

Huapeng Lin, Haixia Wang, Kangsen Yang, Min Li, Yanting Wang, Terry Cheuk-Fung Yip, Seung Up Kim

Abstract readReview
In one paragraph

Review in Clinical and molecular hepatology, 2026. 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

7 authors.

Huapeng Lin *State Key Laboratory of Organ Failure Research, Guangdong Provincial Key Laboratory of Viral Hepatitis Research, Department of Infectious Diseases, Nanfang Hospital, Southern Medical University, Guangzhou, China.
Haixia Wang *Department of Colorectal Medicine, Zhejiang Cancer Hospital, Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou, China.
Kangsen Yang *State Key Laboratory of Organ Failure Research, Guangdong Provincial Key Laboratory of Viral Hepatitis Research, Department of Infectious Diseases, Nanfang Hospital, Southern Medical University, Guangzhou, China.
Min Li *Department of Emergency Medicine, The Affiliated JiangNing Hospital of Nanjing Medical University, Nanjing, China.
Yanting WangDepartment of Medicine, University of California San Diego, La Jolla, CA, USA.
Terry Cheuk-Fung YipState Key Laboratory of Digestive Disease, The Chinese University of Hong Kong, Hong Kong. tcfyip@cuhk.edu.hk.
Seung Up KimDepartment of Internal Medicine, Yonsei University College of Medicine, Seoul, Korea. KSUKOREA@yuhs.ac.

Funding

AbbiveBristol-Myers SquibbGilead Sciences
6 · The paper itself

Abstract

Metabolic dysfunction-associated steatotic liver disease (MASLD) and metabolic dysfunction and alcohol-related liver disease (MetALD) exist on a continuous spectrum in which fibrosis stage remains the dominant predictor of liver‑related complications. Contemporary non‑invasive risk stratification uses simple serum scores (e.g., Fibrosis‑4 index [FIB‑4]) as a first step, followed by vibration‑controlled transient elastography (VCTE) or liver‑specific blood tests (enhanced liver fibrosis [ELF], procollagen III N‑terminal propeptide [PRO‑C3]) to refine risk and triage referrals. Magnetic resonance elastography (MRE) provides higher accuracy than ultrasound‑based elastography, though limited by its availability. Composite models (Agile 3+/4, steatosis‑associated fibrosis estimator [SAFE]) can reduce indeterminate classifications but may require additional calibration for alcohol exposure for use in MetALD. Prognosis should incorporate dynamic, longitudinal measurement of non‑invasive tests, and integrate extrahepatic risks, particularly cardiovascular and kidney disease. In MetALD, quantitative alcohol assessment can be improved by incorporating objective biomarkers such as phosphatidylethanol (PEth) as part of a triangulation approach that integrates self‑reported alcohol intake, the Alcohol Use Disorders Identification Test‑Consumption (AUDIT‑C), and clinical history. Challenges remain, particularly for MetALD, and future research will focus on integrating dynamic biomarkers, predicting treatment response, and utilizing artificial intelligence (AI) for personalized risk and treatment prediction, combining genetic modifiers and polygenic risk scores with clinical and imaging data, with validation across diverse populations.

Indexed as

Fatty LiverLiver Diseases, AlcoholicMetabolic DiseasesBiomarkersElasticity Imaging TechniquesHumansLiver CirrhosisPrognosisRisk AssessmentRisk FactorsBiomarkersAlcohol exposureArtificial intelligenceFibrosisMASLDMetALD

Identifiers

PMID42669412
PMCPMC13641674

What OpenQuestion holds

Textmetadata
Read underepoch 390

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.