Pre-treatment risk predictors of valproic acid-induced dyslipidemia in pediatric patients with epilepsy

Tiantian Liang; Chenquan Lin; Hong Ning; Fuli Qin; Bikui Zhang; Yichang Zhao; Ting Cao; Shimeng Jiao; Hui Chen; Yifang He; Hualin Cai

doi:10.3389/fphar.2024.1349043

Pre-treatment risk predictors of valproic acid-induced dyslipidemia in pediatric patients with epilepsy

Front Pharmacol. 2024 Apr 2:15:1349043. doi: 10.3389/fphar.2024.1349043. eCollection 2024.

Authors

Tiantian Liang^{1

2}, Chenquan Lin^{1

3}, Hong Ning², Fuli Qin⁴, Bikui Zhang^{1

3

5}, Yichang Zhao^{1

3}, Ting Cao^{1

3}, Shimeng Jiao^{1

3}, Hui Chen^{1

3}, Yifang He^{1

3}, Hualin Cai^{1

3

5}

Affiliations

¹ Department of Pharmacy, The Second Xiangya Hospital of Central South University, Institute of Clinical Pharmacy, Central South University, Changsha, China.
² Department of Pharmacy, Mianyang Central Hospital, School of Medicine, University of Electronic Science and Technology of China, Mianyang, China.
³ Institute of Clinical Pharmacy, Central South University, Changsha, China.
⁴ Department of Pharmacy, The First Affiliated Hospital of Guangxi Medical University, Nanning, China.
⁵ International Research Center for Precision Medicine, Transformative Technology and Software Services, Hunan, China.

Abstract

Background: Valproic acid (VPA) stands as one of the most frequently prescribed medications in children with newly diagnosed epilepsy. Despite its infrequent adverse effects within therapeutic range, prolonged VPA usage may result in metabolic disturbances including insulin resistance and dyslipidemia. These metabolic dysregulations in childhood are notably linked to heightened cardiovascular risk in adulthood. Therefore, identification and effective management of dyslipidemia in children hold paramount significance. Methods: In this retrospective cohort study, we explored the potential associations between physiological factors, medication situation, biochemical parameters before the first dose of VPA (baseline) and VPA-induced dyslipidemia (VID) in pediatric patients. Binary logistic regression was utilized to construct a predictive model for blood lipid disorders, aiming to identify independent pre-treatment risk factors. Additionally, The Receiver Operating Characteristic (ROC) curve was used to evaluate the performance of the model. Results: Through binary logistic regression analysis, we identified for the first time that direct bilirubin (DBIL) (odds ratios (OR) = 0.511, p = 0.01), duration of medication (OR = 0.357, p = 0.009), serum albumin (ALB) (OR = 0.913, p = 0.043), BMI (OR = 1.140, p = 0.045), and aspartate aminotransferase (AST) (OR = 1.038, p = 0.026) at baseline were independent risk factors for VID in pediatric patients with epilepsy. Notably, the predictive ability of DBIL (AUC = 0.690, p < 0.0001) surpassed that of other individual factors. Furthermore, when combined into a predictive model, incorporating all five risk factors, the predictive capacity significantly increased (AUC = 0.777, p < 0.0001), enabling the forecast of 77.7% of dyslipidemia events. Conclusion: DBIL emerges as the most potent predictor, and in conjunction with the other four factors, can effectively forecast VID in pediatric patients with epilepsy. This insight can guide the formulation of individualized strategies for the clinical administration of VPA in children.

Keywords: dyslipidemia; epilepsy; pediatrics; risk predictors; valproic acid.

Grants and funding

The author(s) declare financial support was received for the research, authorship, and/or publication of this article. This study was supported in part by the grants from project of Mianyang Central Hospital, School of Medicine, University of Electronic Science and Technology of China (2023YJ014) and New Clinical Medical Technology Project of the Second Xiangya Hospital of Central South University ([2021]94).