ArticleBMC gastroenterology2026
Development and validation of an online dynamic nomogram for nonobese metabolic dysfunction-associated steatotic liver disease based on body composition analysis.
Article in BMC gastroenterology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
Abstract
backgroundThe factors influencing nonobese Metabolic dysfunction-associated steatotic liver disease are discussed, and an online dynamic nomogram model is constructed.
methodsA retrospective analysis was conducted on the clinical data of 216 non-obese patients with MASLD and 322 non-obese healthy controls from the Second Affiliated Hospital of Soochow University. The data were randomly divided into a training set and a validation set in a 7:3 ratio. Potential variables were initially screened through univariate analysis, correlation analysis, and LASSO regression (including cross-validation). Subsequently, binary logistic regression and SHAP methods were employed to identify independent risk factors, and an online dynamic ROC curve was constructed. Model performance was evaluated using the area under the curve (AUC), sensitivity, specificity, calibration curves, and recalibration methods.
resultsLASSO regression initially screened 11 potential variables. Binary logistic regression ultimately identified six independent risk factors which is past history (odds ratio (OR): 2.399, P = 0.0008), triglyceride(TG) (OR: 1.176, P = 0.008), high density lipoprotein cholesterol(HDL-C) (OR: 0.173, P = 0.014), low density lipoprotein cholesterol(LDL-C) (OR: 3.916, P = 0.001), fat mass(Fat) (OR: 4.299, P = 0.0009) and dPhaseAngle(OR: 3.174, P = 0.022). SHAP analysis revealed that TG was the most influential feature, while HDL-C showed a negative contribution. All variance inflation factor (VIF) values were below 1.5, indicating no significant multicollinearity. The AUC values were 0.869 in the training cohort and 0.802 in the validation cohort. The calibration curve demonstrates that the prediction results of the line graph for the outcome closely approximate the ideal curve, aligning with the actual outcomes.
conclusionAn online dynamic nomogram for nonobese MASLD patients with good identified performance was constructed, which can be used as a practical approach for personalized early screening and auxiliary diagnosis of potential risk factors and can assist physicians in making personalized diagnoses and treatments for patients.
Indexed as
Identifiers
What OpenQuestion holds
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.