Serum uric acid and prognosis in acute ischemic stroke: a dose–response meta-analysis of cohort studies

Zhang, Wenyuan; Cheng, Zicheng; Fu, Fangwang; Zhan, Zhenxiang

doi:10.3389/fnagi.2023.1223015

Cited by 3 publications

(7 citation statements)

References 44 publications

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“…Excluding the outlier studies and then conducting the forest plot analysis again showed that there was no heterogeneity in the remaining six studies. (I 2 = 45.8%, P = 0.100) ( Fig. 5 ).…”

Section: Resultsmentioning

confidence: 90%

“…2 ). However, the high degree of heterogeneity (I 2 = 89.0%, P < 0.001) was observed; thus, the random-effects model was used to pooled SMDs.…”

Section: Resultsmentioning

confidence: 99%

“…There was shown to be considerable heterogeneity among the trials (I 2 = 89.0%, P < 0.001), which prompted an investigation using sensitivity analysis ( Fig. 3 ) and the Galbraith plot ( Fig.…”

Section: Resultsmentioning

confidence: 99%

“…The forest plot of pooled SMDs suggested that, compared with high UA degrees, low UA degrees were associated with HT after AIS (SMD = -0.34, 95% CI = -0.60–-0.08, P = 0.010). Meanwhile, the high heterogeneity was discovered among the studies (I 2 = 89.0%, P < 0.001). Initially, we performed subgroup analyses on language (Chinese or English), sample size of participants (≤250 or more), age (≤65 or older), treatment (IVT or EVT), HT time points, SUA time points, and adjusted OR (YES or NO) to find articles that may cause high heterogeneity, but there was still high heterogeneity among groups (figures not shown) .…”

Section: Discussionmentioning

confidence: 96%

“…The Galbraith plot analysis identified six studies as potential sources of heterogeneity. When deleting the outlier studies, the further forest plot of the remaining studies showed the I 2 value decreased dramatically (I 2 = 45.8%, P = 0.100), and the repeat pooled SMDs (SMD = -0.487, 95% CI = -0.632–-0.342, P = 0.000) still suggested that UA degrees are lower in the HT group. Moreover, Begg’s funnel plot and Egger’s test also showed no existence of publication bias in the Meta-analysis.…”

Section: Discussionmentioning

confidence: 99%

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Association Between Uric Acid and Hemorrhagic Transformation: A Systematic Review and Meta-Analysis

Qian,

Li,

et al. 2023

Preprint

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BackgroundThe relationship between uric acid (UA) and hemorrhagic transformation (HT) remained controversial. The purpose of this study was to investigate the relationship between UA degrees and the risk of HT after acute ischemic stroke (AIS).Methods and methodsElectronic databases were sought for studies on UA and HT from inception to October 31, 2023. Two researchers independently reviewed the studies for inclusion. STATA Software 16.0 was used to compute the standardized mean difference (SMD) and 95% confidence interval (CI) of the pooled and post-outlier outcomes. The heterogeneity was evaluated using the I2statistic and the Galbraith plot, and sensitivity analysis was also performed. Begg’s funnel plot and Egger’s test were used to assess publication bias.Results12 trials were included in the meta-analysis, with a total of 4,708 individuals. Low UA degrees were linked to higher HT post-AIS patients following the pooled SMDs’ forest plot (SMD = -0.34, 95% CI = -0.60–0.08,P= 0.010). The high heterogeneity (I2= 89.0%,P<0.001) was found in the studies. Six papers were outside the Galbraith plot regression line and there was no heterogeneity when they were excluded (I2= 45.8%,P= 0.100). Meanwhile, the repeated SMDs (SMD = -0.487, 95% CI = -0.632–0.342,P= 0.000) still meant that the HT group had lower UA degrees. There was no publication bias in our meta-analysis following Begg’s funnel plot or Egger’s test.ConclusionsThe meta-analysis illustrated a substantial positive connection between UA degrees and HT, with lower UA separately linked with higher HT post-AIS. It provided a certain theoretical basis for the later related research.

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“…Excluding the outlier studies and then conducting the forest plot analysis again showed that there was no heterogeneity in the remaining six studies. (I 2 = 45.8%, P = 0.100) ( Fig. 5 ).…”

Section: Resultsmentioning

confidence: 90%

“…2 ). However, the high degree of heterogeneity (I 2 = 89.0%, P < 0.001) was observed; thus, the random-effects model was used to pooled SMDs.…”

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 96%

Section: Discussionmentioning

confidence: 99%

See 3 more Smart Citations

Association Between Uric Acid and Hemorrhagic Transformation: A Systematic Review and Meta-Analysis

Qian,

Li,

et al. 2023

Preprint

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Predictive models for secondary epilepsy within 1 year in patients with acute ischemic stroke: a multicenter retrospective study

Liu,

He,

Wang

et al. 2024

Preprint

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ObjectivePost-stroke epilepsy (PSE) is a significant complication that has a negative impact on the prognosis and quality of life of ischemic stroke patients. We collected medical records from multiple hospitals and created an interpretable machine learning model for prediction.MethodsWe collected medical records, imaging reports, and laboratory tests from 21459 patients with a history of ischemic stroke in several hospitals. We conducted traditional univariable and multivariable statistics analyses to compare and identify important features. Then the data was divided into a 70% training set and a 30% testing set. We employed the Synthetic Minority Oversampling Technique method to augment the positive class in the training set. Nine commonly used methods were used to build machine learning models, and relevant prediction metrics were compared to select the best-performing model. Finally, we used SHAP(SHapley Additive exPlanations) for model interpretability analysis, assessing the contribution and clinical significance of different features to the prediction.ResultsIn the traditional regression analysis, complications such as hydrocephalus, cerebral hernia, uremia, deep vein thrombosis; significant brain regions included the involvement of the cortical regions including frontal lobe, parietal lobe, occipital lobe, temporal lobe, subcortical region of basal ganglia, thalamus and so on contributed to PSE. General features such as age, gender, and NIHSS(the National Institutes of Health Stroke Scale) score, as well as laboratory indicators including WBC count, D-dimer, lactate, HbA1c and so on were associated with a higher likelihood of PSE. Patients with conditions such as fatty liver, coronary heart disease, hyperlipidemia, and low HDL had a higher likelihood of developing PSE. The machine learning models, particularly tree models such as Random Forest, XGBoost, and LightGBM, demonstrated good predictive performance with an AUC of 0.99.ConclusionThe model built on a large dataset can effectively predict the likelihood of PSE, with tree-based models performing the best. The NIHSS score, WBC count and d-dimer were found to have the greatest impact.

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Dual effects of serum urate on stroke risk and prognosis: insights from Mendelian randomization

Chen,

et al. 2024

Front. Neurol.

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BackgroundTo investigate the causal associations of serum urate (SUA) with stroke risk and prognosis using Mendelian randomization (MR) and the potential mediating role of stroke risk factors in the causal pathways.MethodsWe used the random-effects inverse variance weighting (IVW) as our primary method. We initially performed two-sample univariable MR (UVMR) to identify the causal associations of SUA (n = 437,354) with any stroke (AS, FinnGen: n = 311,635; MEGASTROKE: n = 446,696), ischemic stroke (IS, FinnGen: n = 212,774; MEGASTROKE: n = 440,328), intracranial hemorrhage (ICH, FinnGen: n = 343,663; ISGC: n = 3,026), functional outcome after ischemic stroke at 90d (n = 4,363), and motor recovery within 24 months after stroke (n = 488), and then multivariable MR (MVMR) to estimate the direct causal effects of SUA on these outcomes, adjusting for potential confounders. Finally, we further conducted a two-step MR to investigate the potential mediating role of body mass index (BMI), systolic blood pressure (SBP), diastolic blood pressure (DBP), and estimated glomerular filtration rate (eGFR) in the identified causal pathways.ResultsGenetically predicted elevated SUA levels were significantly associated with increased risk of AS (meta-analysis: OR = 1.09, 95% CI [1.04–1.13], p = 3.69e-05) and IS (meta-analysis: OR = 1.10, 95% CI [1.01–1.19], p = 0.021) and with improved poor functional outcome after ischemic stroke at 90d (OR = 0.81, 95% CI [0.72–0.90], p = 1.79e-04) and motor recovery within 24 months after stroke (OR = 1.42, 95% CI [1.23–1.64], p = 2.15e-06). In MVMR, SBP and DBP significantly attenuated the causal effects of SUA on AS, IS, and functional outcome after ischemic stroke at 90d and motor recovery within 24 months after stroke. Further mediation analyses showed that SBP mediated 52.4 and 34.5% of the effects of SUA on AS and IS, while DBP mediated 28.5 and 23.4% of the causal effects, respectively.ConclusionThis study supports the dual role of genetically predicted SUA in increasing stroke risk, especially ischemic stroke risk, and in improving functional outcome and motor recovery. SBP and DBP are key mediators lying on the causal pathways of SUA with AS and IS.

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Serum uric acid and prognosis in acute ischemic stroke: a dose–response meta-analysis of cohort studies

Cited by 3 publications

References 44 publications

Association Between Uric Acid and Hemorrhagic Transformation: A Systematic Review and Meta-Analysis

Association Between Uric Acid and Hemorrhagic Transformation: A Systematic Review and Meta-Analysis

Predictive models for secondary epilepsy within 1 year in patients with acute ischemic stroke: a multicenter retrospective study

Dual effects of serum urate on stroke risk and prognosis: insights from Mendelian randomization

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