ArticleAnnals of medicine2024
LASSO-derived model for the prediction of lean-non-alcoholic fatty liver disease in examinees attending a routine health check-up.
Article in Annals of medicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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14 citing papers in PubMed, 12 citations in OpenAlex.
- Estimated Body Fat Percentage and Triglyceride-Glucose Index for Identifying MASLD in Lean Asian Adults: A Cross-Sectional Analysis.The Kaohsiung journal of medical sciences · 2026Article
- Mean arterial pressure is not associated with type 2 diabetes mellitus in Japan: a secondary retrospective analysis.BMC endocrine disorders · 2026Article
- Machine learning-based risk prediction model for sepsis development in patients with multidrug-resistantFrontiers in cellular and infection microbiology · 2026Article
- Association Between Nonalcoholic Fatty Liver Disease and the Dietary Index for Gut Microbiota: A Cross-Sectional Study.Food science & nutrition · 2026Article
- Development and validation of a high-performance clinical predictive model for early identification of non-alcoholic fatty liver disease.Frontiers in physiology · 2026Article
- A competing risk-based prognostic model for cancer-specific survival in non-metastatic head and neck adenoid cystic carcinoma.Frontiers in oncology · 2026Article
- A nomogram for assessing the risk of MAFLD based on the albumin-to-gamma-glutamyl transferase ratio.European journal of medical research · 2025Article
- Development and validation of a nomogram-based risk prediction model for non-alcoholic fatty liver disease (NAFLD): a logistic regression analysis in a physical examination population.BMC gastroenterology · 2025Article
- Identification of Diagnostic Biomarkers for Colorectal Polyps Based on Noninvasive Urinary Metabolite Screening and Construction of a Nomogram.Cancer medicine · 2025Article
- Integrated machine learning and single-cell RNA sequencing reveal COL4A2 and CXCL6 as oxidative stress-associated biomarkers in periodontitis.Frontiers in immunology · 2025Article
- Regarding: LASSO-derived model for the prediction of lean-non-alcoholic fatty liver disease in examinees attending a routine health check-up.Annals of medicine · 2024Article
- Response to letter regarding "LASSO-derived model for the prediction of lean-non-alcoholic fatty liver disease in examinees attending a routine health check-up".Annals of medicine · 2024Article
- Key genes and immune pathways in T-cell mediated rejection post-liver transplantation identified via integrated RNA-seq and machine learning.Scientific reports · 2024Article
- Nomogram for predicting 5-year metabolic dysfunction-associated steatotic liver disease risk: retrospective cohort study.Endocrine connections · 2024Article
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Authors and funding
4 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundLean individuals with non-alcohol fatty liver disease (NAFLD) often have normal body size but abnormal visceral fat. Therefore, an alternative to body mass index should be considered for prediction of lean-NAFLD. This study aimed to use representative visceral fat links with other laboratory parameters using the least absolute shrinkage and selection operator (LASSO) method to construct a predictive model for lean-NAFLD.
methodsThis retrospective cross-sectional analysis enrolled 2325 subjects with BMI < 24 kg/m
resultsThe LASSO-derived model included four variables-visceral fat, triglyceride levels, HDL-C-C levels, and waist hip ratio-and demonstrated superior performance in predicting lean-NAFLD with high discriminatory ability (AUC, 0.8416; 95% CI: 0.811-0.872) that was comparable with the FLI model. Using a cut-off of 0.1484, moderate sensitivity (75.69%) and specificity (79.86%), as well as high negative predictive value (95.9%), were achieved in the LASSO model. In addition, with normal WC subgroup analysis, the LASSO model exhibits a trend of higher accuracy compared to FLI (cut-off 15.45).
conclusionsWe developed a LASSO-derived predictive model with the potential for use as an alternative tool for predicting lean-NAFLD in clinical settings.
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