Trial reportScientific reports2022
Risk factors and prediction model for nonalcoholic fatty liver disease in northwest China.
Trial report in Scientific reports, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers, 2 of them syntheses 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.
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.
Who cites it
24 citing papers in PubMed, 2 syntheses or guidelines pooled it, 27 citations in OpenAlex.
- Inappropriate Diet Exacerbates Metabolic Dysfunction-Associated Steatotic Liver Disease via Abdominal Obesity.Nutrients · 2024Pooled it
- Application and impact of Lasso regression in gastroenterology: A systematic review.Indian journal of gastroenterology : official journal of the Indian Society of Gastroenterology · 2023Pooled it
- Multi-target modulation of traditional Chinese medicine based on the liver-gut axis: a new therapeutic strategy for NAFLD.American journal of translational research · 2026Review
- Development and validation of a high-performance clinical predictive model for early identification of non-alcoholic fatty liver disease.Frontiers in physiology · 2026Article
- Analysis of risk factors associated with metabolic-associated fatty liver disease from Suzhong Region, China: a cross-sectional study.BMJ open · 2025Article
- Development of a novel deep learning method that transforms tabular input variables into images for the prediction of SLD.Scientific reports · 2025Article
- Machine learning survival models for Non-alcoholic fatty liver disease based on a health checkup cohort.BMC gastroenterology · 2025Article
- Development and validation of a new diagnostic prediction model for NAFLD based on machine learning algorithms in NHANES 2017-2020.3.Hormones (Athens, Greece) · 2025Article
- Causal relationship analysis of MRI measurements of major human internal organs and liver disease.European journal of medical research · 2025Article
- AI-Based Platelet-Independent Noninvasive Test for Liver Fibrosis in MASLD Patients.JGH open : an open access journal of gastroenterology and hepatology · 2025Article
- Establishing of a risk prediction model for metabolic dysfunction-associated steatotic liver disease: a retrospective cohort study.BMC gastroenterology · 2025Article
- Development of a machine learning model for hepatic steatosis screening using non-invasive Traditional Chinese Medicine diagnostics and clinical variables: a health checkup study with community screening potential.Frontiers in medicine · 2025Article
- Analysis and study of risk factors related to the progression of non-alcoholic fatty liver disease: A retrospective cohort study.PloS one · 2025Article
- Predictive Value of TyG and TyG-BMI Indices for Non-Alcoholic Fatty Liver Disease in High-Altitude Regions of China: A Cross-Sectional Study.Journal of clinical medicine · 2024Article
- Metabolic dysfunction-associated steatotic liver disease: The question of long-term high-normal alanine aminotransferase as a screening test.World journal of gastroenterology · 2024Article
- Machine-Learning Application for Predicting Metabolic Dysfunction-Associated Steatotic Liver Disease Using Laboratory and Body Composition Indicators.Archives of Iranian medicine · 2024Article
- Nomogram for predicting the risk of nonalcoholic fatty liver disease in older adults in Qingdao, China: A cross-sectional study.Asia Pacific journal of clinical nutrition · 2024Article
- Causal relationship between air pollution, lung function, gastroesophageal reflux disease, and non-alcoholic fatty liver disease: univariate and multivariate Mendelian randomization study.Frontiers in public health · 2024Article
- Development and Validation of a Risk Prediction Model for NAFLD: A Study Based on a Physical Examination Population.Diabetes, metabolic syndrome and obesity : targets and therapy · 2024Article
- Gastrointestinal and Hepatological Manifestations in Severe Acute Respiratory Syndrome Coronavirus 2 Infection: Results from the Major COVID Hospital in Serbia.Microorganisms · 2023Article
Corrections and comments
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Authors and funding
5 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
In recent years, nonalcoholic fatty liver disease (NAFLD) has become the most important chronic liver disease worldwide. The prevalence of NAFLD in China has also increased year by year. This study aimed to detect NAFLD early by developing a nomogram model in Chinese individuals. A total of 8861 subjects who underwent physical examination in Karamay and were 18 to 62 years old were enrolled. Clinical information, laboratory results and ultrasound findings were retrieved. The participants were randomly assigned to the development set (n = 6203) and the validation set (n = 2658). Significant variables independently associated with NAFLD were identified by least absolute shrinkage and selection operator (LASSO) regression and the multiple logistic regression model. Six variables were selected to construct the nomogram: age, sex, waist circumference (WC), body mass index (BMI), alanine aminotransferase (ALT), triglycerides and glucose index (TyG). The area under the receiver operating characteristic curve (AUROC) of the development set and validation set was 0.886 and 0.894, respectively. The calibration curves showed excellent accuracy of the nomogram model. This physical examination and laboratory test-based nomogram can predict the risk of NAFLD intuitively and individually.
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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.