Serum Uric Acid Is Highly Associated with Epilepsy Secondary to Cerebral Infarction

Wang, Dongxing; Hu, Bo; Dai, Yong-Ping; Sun, Jing; Liu, Zhaoxia; Yu, Feng; Cheng, Feng; Zhang, Xia

doi:10.1007/s12640-018-9930-2

Cited by 22 publications

(17 citation statements)

References 35 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…What is more, many laboratory findings like urine leukocytes, uric acid, alanine aminotransferase, creatine kinase and lactate dehydrogenase were also found to be associated with PSE. Uric acid is now believed to be inflammatory and has been confirmed by previous clinical and basic researches that both increase and decrease of uric acid level could lead to an increased incidence of PSE [25,26]. But the association between other laboratory findings and PSE need further research.…”

Section: Discussionmentioning

confidence: 92%

Prediction of post-stroke epilepsy using machine learning method

Peng¹,

Yang²,

Wen³

et al. 2020

Preprint

View full text Add to dashboard Cite

Background : Stroke is one of the most important causes of epilepsy and we aimed to find if it is possible to predict patients with high risk of developing post-stroke epilepsy (PSE) at the time of discharge using machine learning methods. Methods : Patients with stroke were enrolled and followed at least one year. Machine learning methods including support vector machine (SVM), random forest (RF) and logistic regression (LR) were used to learn the data. Results : A total of 2730 patients with cerebral infarction and 844 patients with cerebral hemorrhage were enrolled and the risk of PSE was 2.8% after cerebral infarction and 7.8% after cerebral hemorrhage in one year. Machine learning methods showed good performance in predicting PSE. The area under the receiver operating characteristic curve (AUC) for SVM and RF in predicting PSE after cerebral infarction was close to 1 and it was 0.92 for LR. When predicting PSE after cerebral hemorrhage, the performance of SVM was best with AUC being close to 1, followed by RF ( AUC = 0.99) and LR (AUC = 0.85) . Conclusion : Machine learning methods could be used to predict patients with high risk of developing PSE, which will help to stratify patients with high risk and start treatment earlier. Nevertheless, more work is needed before the application of thus intelligent predictive model in clinical practice.

show abstract

Section: Discussionmentioning

confidence: 92%

Prediction of post-stroke epilepsy using machine learning method

Peng¹,

Yang²,

Wen³

et al. 2020

Preprint

View full text Add to dashboard Cite

show abstract

“…A complex relationship has been reported between SUA and epilepsy. SUA levels greater than 322 μmol/L or less than 248 μmol/L have been associated with an increased risk of developing epileptic seizures after cerebral infarction compared to SUA levels of 248-322 μmol/L, indicating a U-shaped dose-effect relationship between the SUA level and epilepsy after cerebral infarction [19]. A previous study reported that treatment with carbamazepine and phenytoin was related with low SUA levels, and phenobarbitone and sodium valproate were associated with elevated SUA [20].…”

Section: Discussionmentioning

confidence: 99%

“…These results may explain the link between SUA levels and depressive symptoms in PWE. Considering these findings, epileptic seizure and SUA affect each other, and the interaction is responsible for the development of epilepsy [19]. Given that lowered SUA levels have also been observed in depression and anxiety disorders, there may be a complex relationship between SUA, mental illness and epilepsy.…”

Section: Discussionmentioning

confidence: 99%

See 1 more Smart Citation

A cross-sectional study on the association of serum uric acid levels with depressive and anxiety symptoms in people with epilepsy

Zhong

Chen

et al. 2021

BMC Psychiatry

View full text Add to dashboard Cite

Background High serum uric acid (SUA) levels may provide protection against depression and anxiety through its defensive role in oxidative damage. The aim of this study was to test the hypothesis of the independent associations of lower SUA levels with depressive and anxiety symptoms among patients with epilepsy (PWE). Methods A cross-sectional study was performed among 320 PWE aged ≥18 years old in Northeast China. The Neurological Disorders Depression Inventory for Epilepsy (NDDI-E; Chinese version) and the Generalized Anxiety Disorder-7 scale (GAD-7; Chinese version) were used as screening tools for depressive and anxiety symptoms for PWE. Serum uric acid levels were measured. The associations of SUA levels with depressive and anxiety symptoms were assessed by using binary logistic regression models, with adjustment for the related risk factors (P< 0.05). Results Lower SUA tertiles were significantly associated with higher C-NDDI-E and GAD-7 scores compared with the higher two tertiles (p=0.001, and p= 0.002). Patients with depressive symptoms exhibited significantly lower SUA levels compared to those without depressive symptoms (p< 0.001). SUA levels of patients with anxiety symptoms were significantly lower than those of patients without anxiety symptoms (p< 0.001). The first and second SUA tertiles were associated with depressive symptoms, with the third tertile group as the reference group, after adjusting for confounders (first tertile: OR = 4.694, 95% CI = 1.643~ 13.413, P = 0.004; second tertile: OR = 3.440, 95% CI = 1.278~9.256, P = 0.014). However, The first and second SUA tertiles were not associated with the risk of anxiety symptoms compared with the third tertile in the adjusted logistic regression model (First tertile: OR = 1.556, 95% CI = 0.699~3.464, P = 0.279; second tertile: OR = 1.265, 95% CI = 0.607~2.635, P = 0.530). Conclusion We found that lower SUA levels were independently associated with depressive symptoms but not with anxiety symptoms among PWE. Further well-designed prospective cohort studies are required to determine the causality of the associations and to further clarify the mechanisms of SUA in depressive symptoms.

show abstract

“…(3) Previous studies have shown that UA may be related to the size, type, and location of cerebral infarction [ 5 , 27 ]. Studies have suggested that UA levels may be associated with secondary epilepsy following cerebral infarction [ 28 ], but these data were not collected in this study. (4) Patients with cerebral infarction may also have Parkinson's disease, decreased cognitive function, or other chronic diseases, which may also be related to UA levels.…”

Section: Discussionmentioning

confidence: 99%

Relationship between Uric Acid Level and Severity of Acute Primary Cerebral Infarction: A Cross-Sectional Study

Ru-ying

Zhong

Zhou

et al. 2020

BioMed Research International

View full text Add to dashboard Cite

Numerous studies have shown that uric acid (UA) is associated with cerebrovascular disease, but whether UA is a protective factor or worsens the risk of developing cerebrovascular disease remains controversial. This study investigated the relationship between UA levels detected at admission and the severity of acute primary cerebral infarction. This cross-sectional study enrolled patients with acute primary cerebral infarction (N=238, 157 men). We designated the levels of serum UA measured at the time of admission as the independent variable and the degree of neurological impairment at admission as the dependent variable. The National Institutes of Health Stroke Scale (NIHSS) was used to assess the extent of neurological dysfunction: NIHSS≤5, minor stroke; NIHSS>5, moderate to severe stroke. There was a statistically significant difference in UA levels between patients with mild cerebral infarctions (NIHSS≤5) and those with moderate or severe cerebral infarctions (NIHSS>5) (P<0.0001). After adjusting for confounding factors, the serum UA level was found to be nonlinearly related to NIHSS, and the inflection point was 372 μmol/L. The extent of the influence and confidence interval was 0.99 (0.98, 0.99) on the left side of the inflection point and 1.00 (1.00, 1.01) on the right side. There was a nonlinear relationship between the UA level measured on admission and the degree of neurological impairment in patients with acute primary cerebral infarction. When UA was <372 μmol/L, it was negatively correlated with the degree of neurological impairment in patients with acute cerebral infarction, but when UA was ≥372 μmol/L, the protective effect of UA disappeared.

show abstract

Serum Uric Acid Is Highly Associated with Epilepsy Secondary to Cerebral Infarction

Cited by 22 publications

References 35 publications

Prediction of post-stroke epilepsy using machine learning method

Prediction of post-stroke epilepsy using machine learning method

A cross-sectional study on the association of serum uric acid levels with depressive and anxiety symptoms in people with epilepsy

Relationship between Uric Acid Level and Severity of Acute Primary Cerebral Infarction: A Cross-Sectional Study

Contact Info

Product

Resources

About