Development and Validation of a nomogram model for Predicting Postprandial Hypertriglyceridemia

Wei Gu^1*Liwei Shi¹Xiaolong Li¹Kunjie Zheng¹Guangyao Song²

• ¹Hengshui people hospital, hengshui, China
• ²Hebei Provincial People's Hospital, Shijiazhuang, China

Objective This study aims to identify risk factors associated with postprandial hypertriglyceridemia (PHTG) and develop a validated predictive model for its assessment. Methods We recruited 346 volunteers from the outpatient clinic of Hebei Provincial People's Hospital between January and December 2019. Participants were divided into a model group (January –September 2019, n=256) and an external validation group (October–December 2019, n=90). The model group was further categorized into a normal lipotolerance group (NFT, n=164) and a PHTG group (n=92) based on fasting triglyceride levels and 4-hour postprandial triglyceride measurements. Univariate analysis was performed on general information and auxiliary test results. Predictors were selected using LASSO regression, and a nomogram model of PHTG risk was constructed via logistic regression. The model's discriminatory ability was evaluated using the area under the curve (AUC). Calibration was assessed using the GiViTI calibration curves and the Hosmer-Lemeshow (H-L) test, while clinical utility was examined through decision curve analysis (DCA). Internal validation was performed using the Bootstrap method. The model's predictive accuracy was validated in the external group. Results Age, fasting glucose, plasma atherogenic index (AIP), and triglyceride-glucose index (TyG) were identified as independent predictors of PHTG. The developed nomogram model demonstrated strong discriminatory power, with an AUC of 0.894(95%CI:0.856-0.931)in the model group and 0.903(95%CI: 0.842-0.964)in the validation group. The H-L test, DCA, and GiViTI calibration curves confirmed excellent model calibration, demonstrating a robust agreement between predicted and observed outcomes, thus supporting the model ' s clinical utility. Conclusion The prediction model developed in this study can serve as an effective tool for predicting PHTG and help identify the high-risk population of PHTG at an early stage.