Discover Community Create You

Loading publication...

Noodle always counts identifier-free feature events for service operation. With your permission, Google Analytics can also estimate audience and engagement. Search text, publication identifiers, cookies, IP addresses and browser fingerprints are never included in Noodle's first-party event counters.

Privacy details

Article

Clinical and biochemical phenotypes of Lean MAFLD: machine learning-based biomarker identification and statistical association analysis in a cross-sectional study.

Frontiers in public health - 1 Jan 2026

Zhang Hui, Ji Yansong, Chen Guilian, Tian Zhen

Abstract excerpt

Background: Metabolic dysfunction-associated fatty liver disease (MAFLD) presents a significant public health issue in China. The "lean" subtype (L-MAFLD) accounts for a certain proportion of cases but is frequently underrecognized in primary care due to the absence of visible obesity. Its distinct clinical and biochemical phenotype remains unclear. This study aimed to identify routine biomarker features and...

Read the complete abstract on PubMed

Topics

Share this publication in a Topic to start or enrich a Post.

Share to Biomarkers
Share to Body Mass Index

Methodology Data sources Editorial policy Privacy About Contact Helena Bioinformatics

Share to Cross-Sectional Studies

Share to Machine Learning

Share to Non-alcoholic Fatty Liver Disease

Share to Phenotype

Source

Open in PubMed Open full text

Identifiers

PMID: 42602026
DOI: 10.3389/fpubh.2026.1835144
PMCID: PMC13473254

Provenance

PubMed metadata and Literature Corpus relationships. Corpus snapshot updated 8 Sept 2026.

Related research

Semantic proximity does not establish scientific evidence.

Research trail→

Click a neighbor to travelStep 1 · 12 closest

Clinical and biochemical phenotypes of Lean MAFLD: machine learning-based biomarker identification and statistical association analysis in a cross-sectional study.PMID 42602026Read ↗

ML-Guided GWAS Reveals Genetic Architectures for MASLD for Overweight and Lean Individuals in the <i>All of Us</i> CohortSemantic proximity Read ↗

[Precise clinical classification of metabolic associated fatty liver disease].Semantic proximity Read ↗

Estimation of fatty liver disease clinical role on glucose metabolic remodelling phenotypes and T2DM onset.Semantic proximity Read ↗

Machine Learning–Based Prediction of Ultrasound-Detected Metabolic Dysfunction–Associated Steatotic Liver Disease Using Routine Clinical and Biochemical ParametersSemantic proximity Read ↗

Machine Learning Enables Single-Score Assessment of MASLD Presence and SeveritySemantic proximity Read ↗

Utility of Six Novel Anthropometric Indicators for Assessing Metabolic Dysfunction-Associated Steatotic Liver Disease in US Reproductive-Aged Women: An NHANES Cross-Sectional StudySemantic proximity Read ↗

Machine learning across multiple imaging and biomarker modalities in the UK Biobank improves genetic discovery for liver fat accumulationSemantic proximity Read ↗

Clinical and Genetic Predictors of Non-Alcoholic Steatotic Liver Disease and Fibrosis in Lean Individuals.Semantic proximity Read ↗

Clinical correlates of CT imaging-derived phenotypes among lean and overweight patients with hepatic steatosis.Semantic proximity Read ↗

Machine learning enables new insights into clinical significance of and genetic contributions to liver fat accumulationSemantic proximity Read ↗

Varied Relationship of Lipid and Lipoprotein Profiles to Liver Fat Content in Phenotypes of Metabolic Associated Fatty Liver Disease.Semantic proximity Read ↗

Predicting and elucidating the etiology of fatty liver disease using a machine learning-based approach: an IMI DIRECT studySemantic proximity Read ↗

⌁Select a neighboring publication to make it the new centre.