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Related Experiment Video

Updated: Jan 14, 2026

Establishing a Competing Risk Regression Nomogram Model for Survival Data
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Development and validation of a nomogram model for predicting postprandial hypertriglyceridemia.

Wei Gu1, Liwei Shi1, Xiaolong Li1

  • 1Department of Endocrinology, Hengshui People's Hospital, Hengshui, China.

Frontiers in Nutrition
|October 17, 2025
PubMed
Summary

This study identifies key risk factors for postprandial hypertriglyceridemia (PHTG) and develops a validated predictive model. The model accurately identifies individuals at high risk for PHTG, enabling early intervention.

Keywords:
hypertriglyceridemiapostprandialpredictive modellingrisk factorrisk prediction model

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Area of Science:

  • Metabolic Health
  • Cardiovascular Risk Assessment
  • Clinical Prediction Modeling

Background:

  • Postprandial hypertriglyceridemia (PHTG) is a significant risk factor for cardiovascular disease.
  • Identifying individuals at risk for PHTG is crucial for timely intervention.
  • Current methods for PHTG risk assessment may lack comprehensive predictive power.

Purpose of the Study:

  • To identify independent risk factors associated with postprandial hypertriglyceridemia (PHTG).
  • To develop and validate a predictive model for assessing PHTG risk.
  • To provide an early identification tool for high-risk populations.

Main Methods:

  • Recruited 346 volunteers, dividing them into model (n=256) and external validation (n=90) groups.
  • Utilized LASSO regression to select predictors and logistic regression to construct a nomogram model for PHTG risk.
  • Evaluated model performance using AUC, Hosmer-Lemeshow test, decision curve analysis, and GiViTI calibration curves.

Main Results:

  • Identified age, fasting glucose, plasma atherogenic index (AIP), and triglyceride-glucose index (TyG) as independent predictors of PHTG.
  • The nomogram model showed strong discriminatory power with AUCs of 0.894 (model group) and 0.903 (validation group).
  • The model demonstrated excellent calibration and clinical utility, validated both internally and externally.

Conclusions:

  • The developed prediction model is an effective tool for predicting PHTG.
  • The model facilitates early identification of individuals at high risk for PHTG.
  • This tool can aid in proactive management and prevention strategies for PHTG.