ArticleFrontiers in medicine2022
Screening New Blood Indicators for Non-alcoholic Fatty Liver Disease (NAFLD) Diagnosis of Chinese Based on Machine Learning.
Article in Frontiers in medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers, 1 of them a synthesis that pooled it.
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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.
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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.
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Who cites it
5 citing papers in PubMed, 1 synthesis or guideline pooled it, 8 citations in OpenAlex.
- An updated systematic review and meta-analysis: correlation between circulating plasminogen activator inhibitor-1 levels and metabolic dysfunction-associated steatotic liver disease.BMC gastroenterology · 2025Pooled it
- AI-driven diagnostic and prognostic models for metabolic dysfunction-associated steatotic liver disease: insights from clinical, imaging, and multi-omics studies-a scoping review.Frontiers in medicine · 2026Review
- Investigation of predictive factors for fatty liver in children and adolescents using artificial intelligence.Frontiers in pediatrics · 2025Article
- Clinical significance of serum FGF21 levels in diagnosing nonalcoholic fatty liver disease early.Scientific reports · 2024Article
- Identification of Drug-Induced Liver Injury Biomarkers from Multiple Microarrays Based on Machine Learning and Bioinformatics Analysis.International journal of molecular sciences · 2022Article
Corrections and comments
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Authors and funding
7 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: The prevalence of NAFLD is increasing annually. The early diagnosis and control are crucial for the disease. Currently, metabolic indicators are always used clinically as an auxiliary diagnosis of NAFLD. However, the prevalence of NAFLD is not only increased in obese/metabolic-disordered populations. NAFLD patients with thin body are also increasing. Only using metabolic indicators to assist in the diagnosis of NAFLD may have some deficiencies. Continue to develop more clinical auxiliary diagnostic indicators is pressing. Methods: Machine learning methods are applied to capture risk factors for NAFLD in 365 adults from Zhejiang Province. Predictive models are constructed for NAFLD using fibrinolytic indicators and metabolic indicators as predictors respectively. Then the predictive effects are compared; ELISA kits were used to detect the blood indicators of non-NAFLD and NAFLD patients and compare the differences. Results: The prediction accuracy for NAFLD based on fibrinolytic indicators [Tissue Plasminogen Activator (TPA), Plasminogen Activator Inhibitor-1 (PAI-1)] is higher than that based on metabolic indicators. TPA and PAI-1 are more suitable than metabolic indicators to be selected to predict NAFLD. Conclusions: The fibrinolytic indicators have a stronger association with NAFLD than metabolic indicators. We should attach more importance to TPA and PAI-1, in addition to TC, HDL-C, LDL-C, and ALT/AST, when conducting blood tests to assess NAFLD.
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Registered trials
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.