ArticleFrontiers in endocrinology2023
Identifying advanced MAFLD in a cohort of T2DM and clinical features.
Article in Frontiers in endocrinology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Machine Learning-Based Comparative Analysis of Blood Cell-Derived Inflammatory Indices for Predicting MAFLD and Liver Fibrosis: Evidence From NHANES 2017-2020.Journal of clinical laboratory analysis · 2026Article
- Efficacy and safety of lower-carbohydrate dietary patterns for metabolic associated fatty liver disease: evidence from randomized controlled trials with grade analysis.Nutrition & metabolism · 2026Article
- HEPNET Position Statement-I, Case Definition, Classification, Screening & Diagnosis of Metabolic Dysfunction-Associated Steatotic Liver Disease (MASLD) in Pakistan: A Resource for Primary and Secondary Care Physicians.Pakistan journal of medical sciences · 2025Article
- Metabolic dysfunction associated fatty liver disease and type 2 diabetes: pathophysiological links, epidemiological trends, and clinical implications.Frontiers in endocrinology · 2025Review
- Associations of sex-related and thyroid-related hormones with risk of metabolic dysfunction-associated fatty liver disease in T2DM patients.BMC endocrine disorders · 2024Article
- Associations of metabolic dysfunction-associated fatty liver disease and hepatic fibrosis with bone mineral density and risk of osteopenia/osteoporosis in T2DM patients.Frontiers in endocrinology · 2023Article
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5 authors.
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Abstract
Background: MAFLD is the most common cause of chronic liver disease, affecting 25% of the global population. Patients with T2DM have an increased risk of developing MAFLD. In addition, patients with T2DM have a higher risk of advanced forms of steatohepatitis and fibrosis. Identifying those patients is critical in order to refer them to specialist and appropriate management of their disease. Aims and Objectives: To estimate advanced fibrosis prevalence in a cohort of patients with T2DM and to identify possible predictors. Methods: subjects with T2DM during regular health check-up were enrolled. Demographic and general characteristics were measured, including metabolic parameters and homeostasis model assessment of insulin resistance (HOMA2-IR). Four non-invasive fibrosis scores (NAFLD fibrosis scores, FIB-4, APRI, Hepamet fibrosis score) were measure and compared with transient elastography (TE). Results: 96 patients (21%) presented risk of significant fibrosis (≥F2) measured by TE and 45 patients (10%) presented with risk of advanced fibrosis F3-F4. Liver fibrosis was related to BMI, AC, HOMA2-IR. The results of the non-invasive fibrosis scores have been validated with the results obtained in the TE. It is observed that the index with the greatest area under the curve (AUC) is APRI (AUC=0.729), with a sensitivity of 62.2% and a specificity of 76.1%. However, the test with better positive likelihood ratio (LR+) in our study is NAFLD fibrosis score. Conclusions: Our results show that in a general T2DM follow up, 10% of patients were at risk of advanced fibrosis. We found a positive correlation between liver fibrosis and BMI, AC and HOMA2-IR. Non-invasive fibrosis markers can be useful for screening, showing NAFLD Fibrosis score a better LHR+ compared to TE. Further studies are needed to validate these results and elucidate the best screening approach to identify those patients at risk of advanced MAFLD.
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