Pre-treatment risk predictors of valproic acid-induced dyslipidemia in pediatric patients with epilepsy
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 dyslipidem...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2024-04-01
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| Series: | Frontiers in Pharmacology |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fphar.2024.1349043/full |
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| author | Tiantian Liang Tiantian Liang Chenquan Lin Chenquan Lin Hong Ning Fuli Qin Bikui Zhang Bikui Zhang Bikui Zhang Yichang Zhao Yichang Zhao Ting Cao Ting Cao Shimeng Jiao Shimeng Jiao Hui Chen Hui Chen Yifang He Yifang He Hualin Cai Hualin Cai Hualin Cai |
| author_facet | Tiantian Liang Tiantian Liang Chenquan Lin Chenquan Lin Hong Ning Fuli Qin Bikui Zhang Bikui Zhang Bikui Zhang Yichang Zhao Yichang Zhao Ting Cao Ting Cao Shimeng Jiao Shimeng Jiao Hui Chen Hui Chen Yifang He Yifang He Hualin Cai Hualin Cai Hualin Cai |
| author_sort | Tiantian Liang |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-24T15:27:40Z |
| format | Article |
| id | doaj.art-5bb155f1edc8478aa1a57301278206d6 |
| institution | Directory Open Access Journal |
| issn | 1663-9812 |
| language | English |
| last_indexed | 2024-04-24T15:27:40Z |
| publishDate | 2024-04-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Pharmacology |
| spelling | doaj.art-5bb155f1edc8478aa1a57301278206d62024-04-02T05:25:40ZengFrontiers Media S.A.Frontiers in Pharmacology1663-98122024-04-011510.3389/fphar.2024.13490431349043Pre-treatment risk predictors of valproic acid-induced dyslipidemia in pediatric patients with epilepsyTiantian Liang0Tiantian Liang1Chenquan Lin2Chenquan Lin3Hong Ning4Fuli Qin5Bikui Zhang6Bikui Zhang7Bikui Zhang8Yichang Zhao9Yichang Zhao10Ting Cao11Ting Cao12Shimeng Jiao13Shimeng Jiao14Hui Chen15Hui Chen16Yifang He17Yifang He18Hualin Cai19Hualin Cai20Hualin Cai21Department of Pharmacy, The Second Xiangya Hospital of Central South University, Institute of Clinical Pharmacy, Central South University, Changsha, ChinaDepartment of Pharmacy, Mianyang Central Hospital, School of Medicine, University of Electronic Science and Technology of China, Mianyang, ChinaDepartment of Pharmacy, The Second Xiangya Hospital of Central South University, Institute of Clinical Pharmacy, Central South University, Changsha, ChinaInstitute of Clinical Pharmacy, Central South University, Changsha, ChinaDepartment of Pharmacy, Mianyang Central Hospital, School of Medicine, University of Electronic Science and Technology of China, Mianyang, ChinaDepartment of Pharmacy, The First Affiliated Hospital of Guangxi Medical University, Nanning, ChinaDepartment of Pharmacy, The Second Xiangya Hospital of Central South University, Institute of Clinical Pharmacy, Central South University, Changsha, ChinaInstitute of Clinical Pharmacy, Central South University, Changsha, ChinaInternational Research Center for Precision Medicine, Transformative Technology and Software Services, Hunan, ChinaDepartment of Pharmacy, The Second Xiangya Hospital of Central South University, Institute of Clinical Pharmacy, Central South University, Changsha, ChinaInstitute of Clinical Pharmacy, Central South University, Changsha, ChinaDepartment of Pharmacy, The Second Xiangya Hospital of Central South University, Institute of Clinical Pharmacy, Central South University, Changsha, ChinaInstitute of Clinical Pharmacy, Central South University, Changsha, ChinaDepartment of Pharmacy, The Second Xiangya Hospital of Central South University, Institute of Clinical Pharmacy, Central South University, Changsha, ChinaInstitute of Clinical Pharmacy, Central South University, Changsha, ChinaDepartment of Pharmacy, The Second Xiangya Hospital of Central South University, Institute of Clinical Pharmacy, Central South University, Changsha, ChinaInstitute of Clinical Pharmacy, Central South University, Changsha, ChinaDepartment of Pharmacy, The Second Xiangya Hospital of Central South University, Institute of Clinical Pharmacy, Central South University, Changsha, ChinaInstitute of Clinical Pharmacy, Central South University, Changsha, ChinaDepartment of Pharmacy, The Second Xiangya Hospital of Central South University, Institute of Clinical Pharmacy, Central South University, Changsha, ChinaInstitute of Clinical Pharmacy, Central South University, Changsha, ChinaInternational Research Center for Precision Medicine, Transformative Technology and Software Services, Hunan, ChinaBackground: 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.https://www.frontiersin.org/articles/10.3389/fphar.2024.1349043/fullvalproic aciddyslipidemiaepilepsypediatricsrisk predictors |
| spellingShingle | Tiantian Liang Tiantian Liang Chenquan Lin Chenquan Lin Hong Ning Fuli Qin Bikui Zhang Bikui Zhang Bikui Zhang Yichang Zhao Yichang Zhao Ting Cao Ting Cao Shimeng Jiao Shimeng Jiao Hui Chen Hui Chen Yifang He Yifang He Hualin Cai Hualin Cai Hualin Cai Pre-treatment risk predictors of valproic acid-induced dyslipidemia in pediatric patients with epilepsy Frontiers in Pharmacology valproic acid dyslipidemia epilepsy pediatrics risk predictors |
| title | Pre-treatment risk predictors of valproic acid-induced dyslipidemia in pediatric patients with epilepsy |
| title_full | Pre-treatment risk predictors of valproic acid-induced dyslipidemia in pediatric patients with epilepsy |
| title_fullStr | Pre-treatment risk predictors of valproic acid-induced dyslipidemia in pediatric patients with epilepsy |
| title_full_unstemmed | Pre-treatment risk predictors of valproic acid-induced dyslipidemia in pediatric patients with epilepsy |
| title_short | Pre-treatment risk predictors of valproic acid-induced dyslipidemia in pediatric patients with epilepsy |
| title_sort | pre treatment risk predictors of valproic acid induced dyslipidemia in pediatric patients with epilepsy |
| topic | valproic acid dyslipidemia epilepsy pediatrics risk predictors |
| url | https://www.frontiersin.org/articles/10.3389/fphar.2024.1349043/full |
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