Data Management And Sharing System of China National Clinical Research Center for Neurological Diseases

Association Between Serum Uric Acid Levels and Cognitive Functions in Patients with ischemic stroke/TIA: a follow-up study

First author:Qi Liu

Corresponding author:Yumei Zhang,Yongjun Wang

uric acid cognitive impairment stroke transient ischemic attack U-shaped curve

essential_information

  • Person in charge:Qi Liu
  • Data Sources: ICONS
  • Number of papers:CNSR-Ⅲ20200201
  • Published time:2021-03-31

2021-03-31

Published articles

Journal:Neuropsychiatr Dis Treat

Publication type: Original Articles

IF(IF): 2.157

PMID: 33833515 Link To Pubmed Full text Download

Background And Purpose

Uric acid (UA), as an end product of purine metabolism in the human body, is an important antioxidant and oxygen free radical scavenger, while it also can play a role in the process of oxidative stress under other settings. Previous studies have assessed that serum UA is an independent predictive factor of post-stroke cognitive impairment (PSCI), but results have been controversial on the effect of uric acid level on PSCI. Our study aimed to evaluate whether UA level contributes to the incidence of PSCI.

Method

Patients were selected from the cognition and sleep subgroup (ICONS) of China National Stroke Registry-3 (CNSR-3). Serum UA concentration was assessed at baseline. Global cognitive status was evaluated using the Montreal Cognitive Assessment (MoCA). Patients in this study were categorized into 4 groups by the serum UA quartile and were grouped by sex due to the gender differences in UA levels. Patients were followed up for 3 months and 1 year after ischemic stroke/TIA for clinical outcomes of PSCI. Patients were assessed the association between UA status and risk of PSCI by multivariable Cox regression models adjusted for potential covariates. Main steps: 1. We tested the incidence of PSCI in the quartiles of serum UA levels at baseline to examine their interaction effect (Figure 2). 2. We assessed the association between UA status and risk of PSCI by multivariable Cox regression models adjusted for potential covariates. We performed 2 models. In the first model, we adjusted only age and sex. In the second model, we included all the potential confounders listed in table 1 (Table 2). 3. We used the Cox proportional hazards regression model with GAM and smooth curve fitting (penalized spline method) to address the nonlinearity of the relation between UA and the incidence of PSCI, then we can find the inflection point (when UA was lower than this point, increased SUA was significantly associated with reduced risk of PSCI while when SUA was upper than this point, increased SUA was significantly associated with increased risk of PSCI) (Figure 3). 4. We conducted a threshold effect analysis of serum UA on the incidence of PSCI (Table 3).

Conclusion

The association between SUA and PSCI differed between males and females. In males, both low and high SUA levels were associated with relatively higher incidences of PSCI, supporting a U-shaped association between SUA levels and PSCI

Main results