U-Shaped Association between Serum Uric Acid and In-hospital Mortality in Severe Ischemic Stroke: A MIMIC-IV Retrospective Cohort Study

U-Shaped Association between Serum Uric Acid and In-hospital Mortality in Severe Ischemic Stroke: A MIMIC-IV Retrospective Cohort Study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article U-Shaped Association between Serum Uric Acid and In-hospital Mortality in Severe Ischemic Stroke: A MIMIC-IV Retrospective Cohort Study Yiting Guo, Chenzi Hu, Junshan Zhou, Jie Gao, Zhihui Huang, Yijia Fu, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9473205/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 8 You are reading this latest preprint version Abstract Background Serum uric acid has been proposed to play a complex role in ischemic stroke because of its dual antioxidant and pro-oxidative properties. However, existing evidence regarding its prognostic value remains inconsistent, and the shape of its association with mortality has not been well characterized in critically ill patients with severe ischemic stroke. We aimed to investigate the association between baseline serum uric acid and in-hospital mortality in this population, with particular emphasis on potential non-linear relationships. Methods This retrospective cohort study included adult patients with severe ischemic stroke identified from the MIMIC-IV database. Logistic regression models were used to evaluate the association between serum uric acid quartiles and in-hospital mortality. Restricted cubic spline analysis was used to examine the non-linear relationship between serum uric acid and in-hospital mortality. Prespecified subgroup analyses were conducted according to age, sex, hypertension, and diabetes. Results A total of 336 patients were included, of whom 58 (17.3%) died during hospitalization. In quartile-based analyses, patients in the middle serum uric acid categories tended to have lower mortality than those in the lowest and highest categories. However, these associations were not statistically significant. In contrast, restricted cubic spline analysis revealed a significant non-linear association between baseline serum uric acid and in-hospital mortality (P for overall association < 0.001; P for non-linearity < 0.001). Mortality risk decreased with increasing serum uric acid levels at the lower end of the distribution, reached its lowest point at approximately 391 µmol/L, and increased again at higher concentrations, indicating a U-shaped relationship. An intermediate serum uric acid range of approximately 379.2-408.7 µmol/L was associated with the lowest estimated risk. In subgroup analyses, the non-linear association appeared broadly consistent across major clinical subgroups, although a significant interaction by sex was observed. Conclusions Baseline serum uric acid was associated with in-hospital mortality in a U-shaped non-linear manner among critically ill patients with severe ischemic stroke. This finding suggests that both lower and higher serum uric acid levels were associated with adverse biological states. Health sciences/Biomarkers Health sciences/Cardiology Health sciences/Diseases Health sciences/Medical research Health sciences/Neurology Health sciences/Risk factors ischemic stroke biomarkers serum uric acid in-hospital mortality restricted cubic spline MIMIC-IV Figures Figure 1 Figure 2 Figure 3 Figure 4 Background Ischemic stroke remains a leading cause of death and long-term disability worldwide and continues to impose a substantial burden on patients, families, and healthcare systems[ 1 ]. Early identification of prognostic markers is important for risk stratification and individualized management after ischemic stroke. Serum uric acid, the end product of purine metabolism in humans, has attracted considerable interest in cerebrovascular research because of its complex biological effects[ 2 , 3 ]. On the one hand, serum uric acid is a major endogenous antioxidant in plasma and may help neutralize reactive oxygen and nitrogen species generated during cerebral ischemia-reperfusion[ 4 , 5 ]. Experimental and translational studies have supported its potential cerebroprotective effects[ 6 ]. On the other hand, serum uric acid has also been linked to metabolic dysfunction, vascular injury, inflammation, and endothelial impairment, suggesting that its relationship with stroke prognosis may be complex rather than uniformly protective[ 2 ]. However, the clinical significance of serum uric acid in ischemic stroke remains unsettled[ 7 , 8 ]. In reperfusion-treated populations, several observational studies have linked higher baseline serum uric acid levels to better functional recovery, smaller infarct volume, and lower rates of malignant edema or hemorrhagic transformation, supporting a potential protective role under conditions of marked oxidative stress[ 6 , 9 ]. In contrast, other studies have associated elevated serum uric acid with worse short-term outcome or early mortality[ 3 , 10 ], and recent integrated evidence from cohort analysis, meta-analysis, and Mendelian randomization did not support a clear linear or causal relationship between serum uric acid and stroke prognosis[ 7 ]. Notably, some prior studies have reported U-shaped or J-shaped associations, suggesting that both low and high serum uric acid levels may be unfavorable and that the prognostic effect of serum uric acid may vary across concentration ranges rather than follow a simple monotonic pattern[ 11 , 12 ]. In addition, the URICO-ICTUS randomized trial showed that adjunctive serum uric acid therapy was safe but did not significantly improve the primary 90-day functional outcome in the overall study population[ 13 ]. Taken together, these findings suggest that the association between serum uric acid and stroke prognosis is likely non-linear and context-dependent and insufficiently captured by conventional linear or categorical analyses, particularly in critically ill patients with severe ischemic stroke. Although the prognostic role of serum uric acid in ischemic stroke has received increasing attention, the precise form of this association has not been fully clarified. In particular, potential non-linear associations have not been adequately examined in critically ill patients with severe ischemic stroke. Therefore, using the MIMIC-IV database, we investigated the association between baseline serum uric acid and in-hospital mortality in severe ischemic stroke, with particular focus on potential non-linear dose-response patterns and effect modification across key clinical subgroups. Methods This was a single-center retrospective observational cohort study. All clinical data were extracted from the Medical Information Mart for Intensive Care IV (MIMIC-IV, version 3.1) database, using structured query language (SQL)[ 14 ]. This database contains comprehensive,, de-identified clinical data of inpatients at Beth Israel Deaconess Medical Center from 2008 to 2019. This study strictly followed the Declaration of Helsinki. The patient selection process is presented in Fig. 1 . Patients were eligible for inclusion if they met all of the following criteria: 1) age ≥ 18 years; 2) a primary or secondary discharge diagnosis of ischemic stroke; and 3) availability of a baseline serum uric acid measurement obtained during the predefined admission window, from 48 hours before emergency admission to 48 hours after admission. For patients with multiple serum uric acid measurements during this period, the value closest to the time of admission was defined as the baseline level. Patients were excluded if they: 1) had a prior diagnosis of gout; 2) had end-stage renal disease or were dependent on dialysis; or 3) lacked baseline serum uric acid data. A total of 336 patients were ultimately included in the analysis. Baseline clinical data extracted from the electronic medical records included demographic characteristics, comorbidities, and laboratory parameters. Demographic variables included age and sex. Comorbidities included hypertension, diabetes mellitus, atrial fibrillation, hyperlipidemia, and chronic kidney disease. Laboratory variables included serum creatinine, blood glucose, white blood cell count, neutrophil percentage, and lymphocyte percentage. For these laboratory variables, the first available measurement closest to the time of admission within 48 hours after admission was used. In addition, estimated glomerular filtration rate (eGFR) was calculated using the 2021 Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) equation as an integrated indicator of renal function[ 15 ]. The primary outcome of this study was in-hospital all-cause mortality. For categorical analyses, serum uric acid levels were categorized into quartiles: Q1, 83.27-303.35 µmol/L; Q2, 303.35-410.41 µmol/L; Q3, 410.41-536.81 µmol/L; and Q4, 536.81-2789.61 µmol/L. Statistical analyses were performed using R software version 3.5.2 (Institute for Statistics and Mathematics, Vienna, Austria). Continuous data were presented as mean ± standard deviation or median (interquartile range) and categorical data as number (percentage). Metric and ordinal variables were analyzed by Student t-test and Kruskal-Wallis test, respectively, while frequencies were compared using the chi-square test. To preliminarily evaluate the association between baseline serum uric acid and in-hospital mortality, univariable and multivariable logistic regression analyses were first performed using serum uric acid quartiles, with the lowest quartile as the reference. The multivariable model was adjusted for sex, age, hypertension, diabetes mellitus, atrial fibrillation, hyperlipidemia, chronic kidney disease history, blood glucose, white blood cell count, neutrophil percentage, lymphocyte percentage, and eGFR. To evaluate the potential non-linear association between baseline serum uric acid and in-hospital mortality, restricted cubic spline (RCS) regression within a multivariable logistic framework was used as the primary analytic approach. Knot locations were selected according to the minimum Akaike information criterion (AIC), corresponding to the 5th, 35th, 65th, and 95th percentiles. The overall association and non-linearity were assessed using Wald tests. The nadir of risk and the serum uric acid range associated with relatively lower estimated risk were further derived from the spline model. Subgroup analyses were performed according to age (< 65 vs. ≥65 years), sex, hypertension, and diabetes mellitus. Given the U-shaped association observed in the overall cohort and the limited sample size within subgroups, subgroup-specific RCS models were used to evaluate the continuous dose–response relationship rather than conventional categorical subgroup analyses. Interaction was assessed for both the overall association and the non-linear component. To assess the stability of the multivariable models, collinearity diagnostics were performed among candidate covariates. Variance inflation factors (VIFs) were calculated, and pairwise correlations were visualized using a correlation heatmap. A two-sided P value < 0.05 was considered statistically significant. During manuscript preparation, the authors used ChatGPT solely for language polishing and grammatical revision. All scientific content, interpretation, and final approval of the manuscript were performed by the authors. Results A total of 336 patients with severe ischemic stroke were included, of whom 58 (17.3%) died during hospitalization. According to baseline serum uric acid quartiles, significant differences between groups were observed for sex distribution (P = 0.041), hyperlipidemia (P = 0.040), serum creatinine (P < 0.001), eGFR (P < 0.001), and in-hospital mortality (P = 0.032). The proportion of female patients was highest in the top serum uric acid quartile (Q4), whereas hyperlipidemia was most common in Q3. Renal function progressively worsened across quartiles, as reflected by higher creatinine and lower eGFR at higher serum uric acid levels. In contrast, age, hypertension, diabetes, atrial fibrillation, chronic kidney disease history, blood glucose, inflammatory indices, and hospital length of stay were comparable across groups. In-hospital mortality showed a U-shaped distribution across quartiles, being lower in Q2 (11.1%) and Q3 (11.9%) than in Q1 (19.5%) and Q4 (26.2%). More details of baseline information were shown in Table 1 . Table 1 Baseline Characteristics and In-Hospital Mortality by Serum Uric Acid Quartiles Characteristic Overall (n = 336) Q1 (n = 87) Q2 (n = 81) Q3 (n = 84) Q4 (n = 84) P value Age, years (mean (SD)) 66.52 (15.32) 67.09 (15.67) 64.63 (15.98) 66.62 (15.29) 67.65 (14.44) 0.613 Sex (%), Female 186 (55.4) 43 (49.4) 39 (48.1) 47 (56.0) 57 (67.9) 0.041 Hypertension (%), No 170 (50.6) 43 (49.4) 44 (54.3) 43 (51.2) 40 (47.6) 0.848 DM (%), No 237 (70.5) 61 (70.1) 57 (70.4) 58 (69.0) 61 (72.6) 0.965 AF (%), NO 287 (85.4) 79 (90.8) 72 (88.9) 67 (79.8) 69 (82.1) 0.128 Hyperlipidemia (%), No 156 (46.4) 50 (57.5) 36 (44.4) 30 (35.7) 40 (47.6) 0.04 CKD (%), No 283 (84.2) 77 (88.5) 71 (87.7) 70 (83.3) 65 (77.4) 0.177 Scr, mg/dL (mean (SD)) 1.20 (0.81) 0.96 (0.59) 1.00 (0.33) 1.25 (0.65) 1.58 (1.22) < 0.001 BG, mg/dL (mean (SD)) 136.80 (62.35) 131.91 (49.78) 137.38 (67.46) 140.69 (72.47) 137.40 (58.67) 0.832 WBC, ×10⁹/L (median [IQR]) 8.75 [6.30, 12.83] 8.60 [6.00, 12.65] 8.40 [6.20, 12.40] 9.65 [6.38, 13.22] 8.35 [6.47, 13.33] 0.799 Neu, % (mean (SD)) 68.04 (20.91) 64.56 (23.58) 67.29 (22.17) 71.77 (16.75) 68.64 (20.18) 0.154 Lym, % (mean (SD)) 18.79 (15.51) 18.33 (15.75) 18.94 (14.51) 17.77 (14.26) 20.14 (17.44) 0.781 eGFR, mL/min/1.73 m² (mean (SD)) 71.78 (30.46) 84.52 (29.31) 78.54 (26.36) 66.20 (29.03) 57.64 (29.89) < 0.001 IHM (%), Died 58 (17.3) 17 (19.5) 9 (11.1) 10 (11.9) 22 (26.2) 0.032 Note: Values are presented as n (%), mean ± SD, or median [interquartile range] (IQR) as appropriate. Abbreviations: AF, atrial fibrillation; BG, blood glucose; CKD, chronic kidney disease; DM, diabetes mellitus; eGFR, estimated Glomerular Filtration Rate; IHM, in-hospital mortality; Lym, lymphocyte; Neu, neutrophil; Q, Quartile; Scr, serum creatinine; WBC, white blood cell count. Note: P < 0.05 indicates statistical significance. Quartile-based univariable and multivariable logistic regression results are presented in Table 2 . In quartile-based logistic regression analyses, no statistically significant association was observed between baseline serum uric acid quartiles and in-hospital mortality in either the univariable or multivariable models. Nevertheless, the adjusted effect estimates suggested a possible U-shaped association, with lower mortality risk observed in the intermediate quartiles. Restricted cubic spline analysis was therefore subsequently performed to further assess and characterize the potential non-linear relationship between serum uric acid and in-hospital mortality. Table 2 Logistic regression analysis of serum uric acid quartiles and in-hospital mortality Quartile Univariable OR (95% CI) P Multivariable OR (95% CI) P Q1 Reference - Reference - Q2 0.693 (0.291–1.604) 0.395 0.561 (0.216–1.414) 0.224 Q3 0.622 (0.255–1.461) 0.281 0.376 (0.130–1.037) 0.063 Q4 1.632 (0.784–3.476) 0.195 1.255 (0.500–3.190) 0.629 Note: P < 0.05 indicates statistical significance. Restricted cubic spline analysis demonstrated a significant non-linear association between baseline serum uric acid and in-hospital mortality (Fig. 2 ). Both the overall association and the test for non-linearity were statistically significant (P overall < 0.001; P for non-linearity < 0.001). The exposure-response curve showed a clear U-shaped pattern, with the estimated risk of in-hospital mortality decreasing as serum uric acid increased from lower levels, reaching its nadir at approximately 391 µmol/L, and then rising again at higher concentrations. The spline model suggested that the estimated odds ratio remained below the reference level within an intermediate serum uric acid range of approximately 379.2-408.7 µmol/L (OR < 1). These findings suggested that both lower and higher serum uric acid levels were associated with increased mortality risk, whereas the intermediate range was associated with relatively lower estimated risk. Subgroup RCS analyses are shown in Fig. 3 . They were performed according to age, sex, hypertension, and diabetes status. In the sex-stratified analysis, significant interaction by sex was observed for the association between serum uric acid and in-hospital mortality. Both the overall interaction and the non-linear interaction reached statistical significance (P overall interaction = 0.046; P non-linear interaction = 0.026), indicating sex-related heterogeneity in the exposure–response pattern. In the age-stratified analysis, the shape of the association appeared to differ between patients aged < 65 years and those aged ≥ 65 years, with a possibly steeper increase in risk at higher serum uric acid levels among older patients. However, neither the overall interaction nor the non-linear interaction reached statistical significance (P overall interaction = 0.090; P non-linear interaction = 0.121). The dose-response patterns were broadly similar across hypertension and diabetes subgroups, with no evidence of significant interaction for hypertension (P overall interaction = 0.419; P non-linear interaction = 0.514) or diabetes (P overall interaction = 0.456; P non-linear interaction = 0.767). Collinearity diagnostics (Table 3 ) showed no evidence of severe multicollinearity among the covariates included in the multivariable models. All variance inflation factors were below commonly accepted thresholds, supporting the stability of the regression estimates. Given that serum creatinine is incorporated into the calculation of eGFR, eGFR was selected as the renal function indicator in the multivariable logistic regression and RCS models, whereas serum creatinine was not entered simultaneously to avoid redundancy. Table 3 Variance inflation factors for candidate covariates Variable Sex Age SUA HTN DM AF HLP CKD Scr BG WBC Neu Lym eGFR VIF 1.21 1.69 1.11 1.29 1.39 1.15 1.27 1.38 3.34 1.33 1.67 2.69 2.08 3.97 Abbreviations: AF, atrial fibrillation; BG, blood glucose; CKD, chronic kidney disease; DM, diabetes mellitus; eGFR, estimated Glomerular Filtration Rate; HLP, hyperlipidemia; HTN, hypertension; IHM, in-hospital mortality; Lym, lymphocyte; Neu, neutrophil; Scr, serum creatinine; SUA, serum uric acid; VIF, variance inflation factor; WBC, white blood cell count. Discussion In this retrospective cohort of critically ill patients with severe ischemic stroke, we found a significant U-shaped association between baseline serum uric acid and in-hospital mortality, with the lowest estimated risk observed at an intermediate serum uric acid level. Both lower and higher serum uric acid concentrations were associated with increased mortality, suggesting that the prognostic role of serum uric acid in severe ischemic stroke is non-linear rather than simply protective or harmful. This U-shaped association is biologically plausible. Serum uric acid is a major endogenous antioxidant in human plasma and may attenuate oxidative and nitrosative injury during cerebral ischemia-reperfusion[ 2 , 4 ]. Experimental studies have shown that serum uric acid can scavenge free radicals, reduce peroxynitrite-mediated damage, and protect neurons against ischemic and excitotoxic insults[ 16 , 17 ]. In this context, lower serum uric acid levels may indicate insufficient antioxidant reserve during the acute phase of severe ischemic injury, thereby contributing to worse early outcomes. At the other end of the curve, however, higher serum uric acid levels were also associated with increased mortality. In critically ill stroke patients, elevated serum uric acid may reflect more than redox status alone and may instead represent the combined influence of renal dysfunction, metabolic stress, and overall systemic vulnerability[ 2 , 9 ]. This interpretation is supported by our baseline data, in which higher serum uric acid quartiles were accompanied by higher creatinine levels and lower eGFR. Therefore, the adverse association observed at higher serum uric acid levels should not be interpreted as evidence of a direct toxic effect alone, but rather as a marker of a broader high-risk physiological state. Our findings may help explain the heterogeneous results reported in previous studies. In reperfusion-treated populations, several studies have linked higher serum uric acid levels to better functional recovery, smaller infarct volume, and lower rates of malignant edema or hemorrhagic transformation, suggesting that serum uric acid may be beneficial in settings characterized by pronounced oxidative stress[ 18 , 19 ]. In contrast, more general ischemic stroke cohorts have often shown neutral associations, and a recent triangulation study integrating cohort analysis, meta-analysis, and Mendelian randomization did not support a clear linear or causal relationship between serum uric acid and 3-month functional outcome[ 7 , 8 ]. Importantly, one prior clinical study has suggested a U-shaped relationship between serum uric acid and stroke outcome, which is more consistent with our findings than a simple monotonic model[ 20 ]. The URICO-ICTUS trial further supports this interpretation: adjunctive serum uric acid therapy was safe but did not improve the primary outcome in the overall population, although exploratory analyses suggested potential benefit in selected subgroups such as patients with hyperglycemia[ 13 ]. Taken together, the available evidence suggests that the prognostic relevance of serum uric acid is likely context-dependent and non-linear, and may vary according to the underlying biological milieu, outcome definition, and analytic approach[ 21 ]. The subgroup analyses suggest that the U-shaped association between serum uric acid and in-hospital mortality was not confined to a single clinical subgroup, supporting the robustness of the overall finding. Significant overall and non-linear interactions were observed in the sex-stratified analysis, suggesting that the exposure-response relationship may differ between men and women. This finding is consistent with prior evidence suggesting sex-related differences in serum uric acid metabolism and in the clinical implications of hyperuricemia[ 22 ]. Moreover, previous studies have also reported sex-specific associations between serum uric acid and stroke outcomes[ 23 ]. Although the age-stratified curves showed some visual separation, particularly in the higher serum uric acid range, the interaction tests by age were not statistically significant. Accordingly, the age-specific pattern should be interpreted with caution and requires confirmation in larger studies. Several limitations should be acknowledged. First, the estimated nadir and lower-risk interval were derived from a spline-based model and should be interpreted cautiously until externally validated. Second, because of the structure of the MIMIC-IV database, key clinical scales for initial stroke severity (like the National Institutes of Health Stroke Scale (NIHSS) score) and acute-phase specific reperfusion treatments (intravenous thrombolysis or mechanical thrombectomy) are mostly recorded in unstructured free-text medical records. Therefore, these variables could not be robustly incorporated into the primary adjusted analyses. Third, we attempted to extract the Glasgow Coma Scale (GCS) scores to serve as a surrogate marker for initial stroke severity. However, due to the substantial proportion of missing data, this variable was excluded from the multivariable models. Fourth, serum uric acid was measured only at baseline, and dynamic changes during hospitalization could not be assessed. Fifth, the sample size was modest for subgroup analyses, which limited the precision of interaction estimates. Finally, external validation is needed before the present findings can be generalized to other cohorts. Despite these limitations, the study has several strengths, including the use of a large critical-care database, a spline-based modeling strategy that captured non-linearity, and consistent findings across major subgroup analyses. Conclusions Baseline serum uric acid was associated with in-hospital mortality in a U-shaped non-linear manner among critically ill patients with severe ischemic stroke. An intermediate serum uric acid range was associated with relatively lower estimated mortality risk, whereas both lower and higher levels may mark adverse biological states. Further prospective studies are needed to validate this non-linear association and clarify its clinical and mechanistic significance. List of abbreviations AIC, Akaike information criterion; CKD-EPI, Chronic Kidney Disease Epidemiology Collaboration; eGFR, estimated glomerular filtration rate; GCS, Glasgow Coma Scale; MIMIC-IV, Medical Information Mart for Intensive Care IV; NIHSS, National Institutes of Health Stroke Scale; RCS, restricted cubic spline; SQL, structured query language; VIFs, variance inflation factors. Declarations Ethics approval and consent to participate This study was based on the MIMIC-IV database, a publicly available deidentified critical care database. The establishment of the database was approved by the Institutional Review Boards of Beth Israel Deaconess Medical Center, and the requirement for individual informed consent was waived because all data were deidentified (Institutional Review Board approval number: 65083321). Consent for publication Not applicable. Availability of data and materials The data used in this study were obtained from the MIMIC-IV database, which is available on PhysioNet to credentialed users who complete the required training and data use agreement. The analytic code is available from the corresponding author upon reasonable request. Competing interests The authors declare that they have no competing interests. Funding This study was funded by National Natural Science Foundation of China with grant 82102011 to Yukai Liu. Authors’ contributions GY performed the software analysis, formal analysis, investigation, data curation, visualization, and drafted the manuscript. YL and DL conceived and designed the study. YL developed the methodology, provided resources, administered the project, and acquired funding. HC, GJ, and HZ contributed to validation of the analyses. FY contributed to formal data analysis. ZJ supervised the study. YL and DL critically revised the manuscript. 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The Lancet Neurology [Internet]. 2016 [cited 2026 Mar 2];15:869–81. https://doi.org/10.1016/S1474-4422(16)00114-9 Zhu, B. et al. A New Perspective on the Prediction and Treatment of Stroke: The Role of Uric Acid. Neurosci. Bull. [Internet] . 41 , 486–500. https://doi.org/10.1007/s12264-024-01301-3 (2025). [cited 2026 Mar 2];. Leira, E. C., Planas, A. M., Chauhan, A. K., Chamorro, A. & Uric Acid A Translational Journey in Cerebroprotection That Spanned Preclinical and Human Data. Neurology [Internet]. 2023 [cited 2026 Mar 2];101:1068–1074. https://doi.org/10.1212/WNL.0000000000207825 Halperin Kuhns, V. L. & Woodward, O. M. Sex Differences in Urate Handling. Int. J. Mol. Sci. 21 , 4269. https://doi.org/10.3390/ijms21124269 (2020). Llull, L. et al. Uric Acid Therapy Improves Clinical Outcome in Women With Acute Ischemic Stroke. Stroke 46 , 2162–2167. https://doi.org/10.1161/STROKEAHA.115.009960 (2015). Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Reviewers agreed at journal 12 May, 2026 Reviews received at journal 10 May, 2026 Reviewers agreed at journal 09 May, 2026 Reviewers invited by journal 29 Apr, 2026 Editor invited by journal 27 Apr, 2026 Editor assigned by journal 21 Apr, 2026 Submission checks completed at journal 21 Apr, 2026 First submitted to journal 20 Apr, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9473205","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":635392107,"identity":"14a41d78-800e-4cf8-b64b-7143f37064e7","order_by":0,"name":"Yiting Guo","email":"","orcid":"","institution":"Nanjing Medical University","correspondingAuthor":false,"prefix":"","firstName":"Yiting","middleName":"","lastName":"Guo","suffix":""},{"id":635392109,"identity":"32285206-b80f-4d8b-a488-521530153785","order_by":1,"name":"Chenzi Hu","email":"","orcid":"","institution":"Nanjing Medical University","correspondingAuthor":false,"prefix":"","firstName":"Chenzi","middleName":"","lastName":"Hu","suffix":""},{"id":635392112,"identity":"1896b4c6-f3ab-41f4-9532-440fe0a77c48","order_by":2,"name":"Junshan Zhou","email":"","orcid":"","institution":"Nanjing Medical University","correspondingAuthor":false,"prefix":"","firstName":"Junshan","middleName":"","lastName":"Zhou","suffix":""},{"id":635392115,"identity":"0182c243-3a67-447a-af70-99c779bf580c","order_by":3,"name":"Jie Gao","email":"","orcid":"","institution":"Nanjing Medical University","correspondingAuthor":false,"prefix":"","firstName":"Jie","middleName":"","lastName":"Gao","suffix":""},{"id":635392117,"identity":"d463a583-d7be-4c1c-a35a-819819a5cac5","order_by":4,"name":"Zhihui Huang","email":"","orcid":"","institution":"Nanjing Medical University","correspondingAuthor":false,"prefix":"","firstName":"Zhihui","middleName":"","lastName":"Huang","suffix":""},{"id":635392118,"identity":"a1575758-6b07-41c2-a09b-01c1ab54e09a","order_by":5,"name":"Yijia Fu","email":"","orcid":"","institution":"Nanjing Medical University","correspondingAuthor":false,"prefix":"","firstName":"Yijia","middleName":"","lastName":"Fu","suffix":""},{"id":635392119,"identity":"50b2192f-4513-4248-afed-67c749bd25a1","order_by":6,"name":"Yukai Liu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAuUlEQVRIie3PsQrCMBCA4QuB6xKrY1zUR0jpC0UK7dLuWaVwk/gI+hbOydIpPoFLoeAzdBAUJ9cbBfNPN9zHcQCp1A+WA4rp6V4bzHrPIwgojYqyzNVg2QT1guT+rNsdk+i6MjpiQ9ACzO7KImE0TnUENy+O8c4hTW9s1B2Jk5WCeIR0INOgVIZL6mF9IGsR2UQ9qhKiL0ihDaxfllldTOD8dnuZwjg7BoGV/c6esf85w9xLpVKpP+4NNcQ3xo6XRzcAAAAASUVORK5CYII=","orcid":"","institution":"Nanjing Medical University","correspondingAuthor":true,"prefix":"","firstName":"Yukai","middleName":"","lastName":"Liu","suffix":""},{"id":635392120,"identity":"1f155d74-090d-4ff8-8006-11df8c461c64","order_by":7,"name":"Linzhe Du","email":"","orcid":"","institution":"Nanjing Medical University","correspondingAuthor":false,"prefix":"","firstName":"Linzhe","middleName":"","lastName":"Du","suffix":""}],"badges":[],"createdAt":"2026-04-20 13:54:26","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9473205/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9473205/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":108946389,"identity":"1da49ac0-4d6b-40e0-86a7-e81fabe7459f","added_by":"auto","created_at":"2026-05-11 06:22:52","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":1418312,"visible":true,"origin":"","legend":"\u003cp\u003eFlowchart of patient selection. Adult patients with severe ischemic stroke were screened from the MIMIC-IV database. Patients were excluded if they lacked a primary or secondary discharge diagnosis of ischemic stroke, had no baseline serum uric acid measurement within the predefined admission window, or had gout, end-stage renal disease, or dialysis dependence. The final analytic cohort comprised 336 patients.\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-9473205/v1/6e2c90b921b73ec148cc4401.png"},{"id":108946388,"identity":"c011f984-07f5-431b-a0a0-e14e3d225e3d","added_by":"auto","created_at":"2026-05-11 06:22:52","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":135389,"visible":true,"origin":"","legend":"\u003cp\u003eNonlinear association between serum uric acid and in-hospital mortality. Restricted cubic spline analysis showing the adjusted odds ratio for in-hospital mortality across serum uric acid levels. The solid blue line indicates the estimated odds ratio, and the shaded area represents the 95% confidence interval. The horizontal dashed line indicates an odds ratio of 1.0, and the vertical dash-dotted line indicates the reference serum uric acid value. The overall association and nonlinearity were both statistically significant (P overall \u0026lt; 0.001; P for nonlinearity \u0026lt; 0.001).\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-9473205/v1/909635ea76d3718bed76ee90.png"},{"id":108977807,"identity":"234d2f9a-8ad2-4ecc-a0cc-1538fe37f079","added_by":"auto","created_at":"2026-05-11 11:33:00","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":430784,"visible":true,"origin":"","legend":"\u003cp\u003eNonlinear association between serum uric acid and in-hospital mortality across subgroups. Restricted cubic spline analyses stratified by age (\u0026lt;65 vs ≥65 years) (A), sex (female vs male) (B), hypertension status (no vs yes) (C), and diabetes status (no vs yes) (D). Solid lines indicate the estimated odds ratios, and shaded areas represent the 95% confidence intervals. The horizontal dashed line indicates an odds ratio of 1.0. P values for overall interaction and nonlinear interaction are shown in each panel.\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-9473205/v1/247c14409a712f5f8e88ca8f.png"},{"id":108977921,"identity":"4bafdffd-5928-4440-a2b6-7e1f895e978c","added_by":"auto","created_at":"2026-05-11 11:33:28","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":209110,"visible":true,"origin":"","legend":"\u003cp\u003eCorrelation matrix of covariates included in the multivariable analysis. Heatmap showing pairwise correlations among covariates included in the multivariable model. Color intensity indicates the strength and direction of the correlation, with red representing positive correlations and blue representing negative correlations. Abbreviations: AF, atrial fibrillation; BG, blood glucose; CKD, chronic kidney disease; DM, diabetes mellitus; eGFR, estimated Glomerular Filtration Rate; HLP, hyperlipidemia; HTN, hypertension; Lym, lymphocyte; Neu, neutrophil; Scr, serum creatinine; SUA, serum uric acid; WBC, white blood cellcount.\u003c/p\u003e","description":"","filename":"Figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-9473205/v1/a093912263d5c76c3ce84a7b.png"},{"id":108979858,"identity":"51e05bd2-102b-4ead-846c-9c0e4ab5ba3e","added_by":"auto","created_at":"2026-05-11 12:02:01","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":4304946,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9473205/v1/b78505d9-e063-40f5-9b7c-a8d55d479cf2.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"U-Shaped Association between Serum Uric Acid and In-hospital Mortality in Severe Ischemic Stroke: A MIMIC-IV Retrospective Cohort Study","fulltext":[{"header":"Background","content":"\u003cp\u003eIschemic stroke remains a leading cause of death and long-term disability worldwide and continues to impose a substantial burden on patients, families, and healthcare systems[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Early identification of prognostic markers is important for risk stratification and individualized management after ischemic stroke.\u003c/p\u003e \u003cp\u003eSerum uric acid, the end product of purine metabolism in humans, has attracted considerable interest in cerebrovascular research because of its complex biological effects[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. On the one hand, serum uric acid is a major endogenous antioxidant in plasma and may help neutralize reactive oxygen and nitrogen species generated during cerebral ischemia-reperfusion[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Experimental and translational studies have supported its potential cerebroprotective effects[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. On the other hand, serum uric acid has also been linked to metabolic dysfunction, vascular injury, inflammation, and endothelial impairment, suggesting that its relationship with stroke prognosis may be complex rather than uniformly protective[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eHowever, the clinical significance of serum uric acid in ischemic stroke remains unsettled[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. In reperfusion-treated populations, several observational studies have linked higher baseline serum uric acid levels to better functional recovery, smaller infarct volume, and lower rates of malignant edema or hemorrhagic transformation, supporting a potential protective role under conditions of marked oxidative stress[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. In contrast, other studies have associated elevated serum uric acid with worse short-term outcome or early mortality[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e], and recent integrated evidence from cohort analysis, meta-analysis, and Mendelian randomization did not support a clear linear or causal relationship between serum uric acid and stroke prognosis[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Notably, some prior studies have reported U-shaped or J-shaped associations, suggesting that both low and high serum uric acid levels may be unfavorable and that the prognostic effect of serum uric acid may vary across concentration ranges rather than follow a simple monotonic pattern[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. In addition, the URICO-ICTUS randomized trial showed that adjunctive serum uric acid therapy was safe but did not significantly improve the primary 90-day functional outcome in the overall study population[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Taken together, these findings suggest that the association between serum uric acid and stroke prognosis is likely non-linear and context-dependent and insufficiently captured by conventional linear or categorical analyses, particularly in critically ill patients with severe ischemic stroke.\u003c/p\u003e \u003cp\u003eAlthough the prognostic role of serum uric acid in ischemic stroke has received increasing attention, the precise form of this association has not been fully clarified. In particular, potential non-linear associations have not been adequately examined in critically ill patients with severe ischemic stroke. Therefore, using the MIMIC-IV database, we investigated the association between baseline serum uric acid and in-hospital mortality in severe ischemic stroke, with particular focus on potential non-linear dose-response patterns and effect modification across key clinical subgroups.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003eThis was a single-center retrospective observational cohort study. All clinical data were extracted from the Medical Information Mart for Intensive Care IV (MIMIC-IV, version 3.1) database, using structured query language (SQL)[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. This database contains comprehensive,, de-identified clinical data of inpatients at Beth Israel Deaconess Medical Center from 2008 to 2019. This study strictly followed the Declaration of Helsinki.\u003c/p\u003e \u003cp\u003eThe patient selection process is presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. Patients were eligible for inclusion if they met all of the following criteria: 1) age\u0026thinsp;\u0026ge;\u0026thinsp;18 years; 2) a primary or secondary discharge diagnosis of ischemic stroke; and 3) availability of a baseline serum uric acid measurement obtained during the predefined admission window, from 48 hours before emergency admission to 48 hours after admission. For patients with multiple serum uric acid measurements during this period, the value closest to the time of admission was defined as the baseline level. Patients were excluded if they: 1) had a prior diagnosis of gout; 2) had end-stage renal disease or were dependent on dialysis; or 3) lacked baseline serum uric acid data. A total of 336 patients were ultimately included in the analysis.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eBaseline clinical data extracted from the electronic medical records included demographic characteristics, comorbidities, and laboratory parameters. Demographic variables included age and sex. Comorbidities included hypertension, diabetes mellitus, atrial fibrillation, hyperlipidemia, and chronic kidney disease. Laboratory variables included serum creatinine, blood glucose, white blood cell count, neutrophil percentage, and lymphocyte percentage. For these laboratory variables, the first available measurement closest to the time of admission within 48 hours after admission was used. In addition, estimated glomerular filtration rate (eGFR) was calculated using the 2021 Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) equation as an integrated indicator of renal function[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. The primary outcome of this study was in-hospital all-cause mortality. For categorical analyses, serum uric acid levels were categorized into quartiles: Q1, 83.27-303.35 \u0026micro;mol/L; Q2, 303.35-410.41 \u0026micro;mol/L; Q3, 410.41-536.81 \u0026micro;mol/L; and Q4, 536.81-2789.61 \u0026micro;mol/L.\u003c/p\u003e \u003cp\u003eStatistical analyses were performed using R software version 3.5.2 (Institute for Statistics and Mathematics, Vienna, Austria). Continuous data were presented as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation or median (interquartile range) and categorical data as number (percentage). Metric and ordinal variables were analyzed by Student t-test and Kruskal-Wallis test, respectively, while frequencies were compared using the chi-square test.\u003c/p\u003e \u003cp\u003eTo preliminarily evaluate the association between baseline serum uric acid and in-hospital mortality, univariable and multivariable logistic regression analyses were first performed using serum uric acid quartiles, with the lowest quartile as the reference. The multivariable model was adjusted for sex, age, hypertension, diabetes mellitus, atrial fibrillation, hyperlipidemia, chronic kidney disease history, blood glucose, white blood cell count, neutrophil percentage, lymphocyte percentage, and eGFR.\u003c/p\u003e \u003cp\u003eTo evaluate the potential non-linear association between baseline serum uric acid and in-hospital mortality, restricted cubic spline (RCS) regression within a multivariable logistic framework was used as the primary analytic approach. Knot locations were selected according to the minimum Akaike information criterion (AIC), corresponding to the 5th, 35th, 65th, and 95th percentiles. The overall association and non-linearity were assessed using Wald tests. The nadir of risk and the serum uric acid range associated with relatively lower estimated risk were further derived from the spline model. Subgroup analyses were performed according to age (\u0026lt;\u0026thinsp;65 vs. \u0026ge;65 years), sex, hypertension, and diabetes mellitus. Given the U-shaped association observed in the overall cohort and the limited sample size within subgroups, subgroup-specific RCS models were used to evaluate the continuous dose\u0026ndash;response relationship rather than conventional categorical subgroup analyses. Interaction was assessed for both the overall association and the non-linear component.\u003c/p\u003e \u003cp\u003eTo assess the stability of the multivariable models, collinearity diagnostics were performed among candidate covariates. Variance inflation factors (VIFs) were calculated, and pairwise correlations were visualized using a correlation heatmap. A two-sided P value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant.\u003c/p\u003e \u003cp\u003eDuring manuscript preparation, the authors used ChatGPT solely for language polishing and grammatical revision. All scientific content, interpretation, and final approval of the manuscript were performed by the authors.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eA total of 336 patients with severe ischemic stroke were included, of whom 58 (17.3%) died during hospitalization. According to baseline serum uric acid quartiles, significant differences between groups were observed for sex distribution (P\u0026thinsp;=\u0026thinsp;0.041), hyperlipidemia (P\u0026thinsp;=\u0026thinsp;0.040), serum creatinine (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), eGFR (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and in-hospital mortality (P\u0026thinsp;=\u0026thinsp;0.032). The proportion of female patients was highest in the top serum uric acid quartile (Q4), whereas hyperlipidemia was most common in Q3. Renal function progressively worsened across quartiles, as reflected by higher creatinine and lower eGFR at higher serum uric acid levels. In contrast, age, hypertension, diabetes, atrial fibrillation, chronic kidney disease history, blood glucose, inflammatory indices, and hospital length of stay were comparable across groups. In-hospital mortality showed a U-shaped distribution across quartiles, being lower in Q2 (11.1%) and Q3 (11.9%) than in Q1 (19.5%) and Q4 (26.2%). More details of baseline information were shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eBaseline Characteristics and In-Hospital Mortality by Serum Uric Acid Quartiles\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"7\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eCharacteristic\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eOverall\u003c/p\u003e\n \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;336)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003eQ1\u003c/p\u003e\n \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;87)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003eQ2\u003c/p\u003e\n \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;81)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003eQ3\u003c/p\u003e\n \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;84)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003eQ4\u003c/p\u003e\n \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;84)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e\u003cem\u003eP value\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eAge, years (mean (SD))\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\n \u003cp\u003e66.52 (15.32)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e67.09 (15.67)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e64.63 (15.98)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\n \u003cp\u003e66.62 (15.29)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\n \u003cp\u003e67.65 (14.44)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\n \u003cp\u003e0.613\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eSex (%), Female\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\n \u003cp\u003e186 (55.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e43 (49.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e39 (48.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\n \u003cp\u003e47 (56.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\n \u003cp\u003e57 (67.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\n \u003cp\u003e0.041\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eHypertension (%), No\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\n \u003cp\u003e170 (50.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e43 (49.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e44 (54.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\n \u003cp\u003e43 (51.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\n \u003cp\u003e40 (47.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\n \u003cp\u003e0.848\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eDM (%), No\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\n \u003cp\u003e237 (70.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e61 (70.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e57 (70.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\n \u003cp\u003e58 (69.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\n \u003cp\u003e61 (72.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\n \u003cp\u003e0.965\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eAF (%), NO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\n \u003cp\u003e287 (85.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e79 (90.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e72 (88.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\n \u003cp\u003e67 (79.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\n \u003cp\u003e69 (82.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\n \u003cp\u003e0.128\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eHyperlipidemia (%), No\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\n \u003cp\u003e156 (46.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e50 (57.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e36 (44.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\n \u003cp\u003e30 (35.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\n \u003cp\u003e40 (47.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\n \u003cp\u003e0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eCKD (%), No\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\n \u003cp\u003e283 (84.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e77 (88.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e71 (87.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\n \u003cp\u003e70 (83.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\n \u003cp\u003e65 (77.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\n \u003cp\u003e0.177\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eScr, mg/dL (mean (SD))\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\n \u003cp\u003e1.20 (0.81)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e0.96 (0.59)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e1.00 (0.33)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\n \u003cp\u003e1.25 (0.65)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\n \u003cp\u003e1.58 (1.22)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eBG, mg/dL (mean (SD))\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\n \u003cp\u003e136.80 (62.35)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e131.91 (49.78)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e137.38 (67.46)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\n \u003cp\u003e140.69 (72.47)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\n \u003cp\u003e137.40 (58.67)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\n \u003cp\u003e0.832\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eWBC, \u0026times;10⁹/L (median [IQR])\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\n \u003cp\u003e8.75 [6.30, 12.83]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e8.60 [6.00, 12.65]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e8.40 [6.20, 12.40]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\n \u003cp\u003e9.65 [6.38, 13.22]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\n \u003cp\u003e8.35 [6.47, 13.33]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\n \u003cp\u003e0.799\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eNeu, % (mean (SD))\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\n \u003cp\u003e68.04 (20.91)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e64.56 (23.58)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e67.29 (22.17)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\n \u003cp\u003e71.77 (16.75)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\n \u003cp\u003e68.64 (20.18)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\n \u003cp\u003e0.154\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eLym, % (mean (SD))\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\n \u003cp\u003e18.79 (15.51)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e18.33 (15.75)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e18.94 (14.51)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\n \u003cp\u003e17.77 (14.26)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\n \u003cp\u003e20.14 (17.44)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\n \u003cp\u003e0.781\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eeGFR, mL/min/1.73 m\u0026sup2; (mean (SD))\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\n \u003cp\u003e71.78 (30.46)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e84.52 (29.31)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e78.54 (26.36)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\n \u003cp\u003e66.20 (29.03)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\n \u003cp\u003e57.64 (29.89)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eIHM (%), Died\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\n \u003cp\u003e58 (17.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e17 (19.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e9 (11.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\n \u003cp\u003e10 (11.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\n \u003cp\u003e22 (26.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\n \u003cp\u003e0.032\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003eNote:\u0026nbsp;\u003c/strong\u003eValues are presented as n (%), mean \u0026plusmn; SD, or median [interquartile range] (IQR) as appropriate.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAbbreviations:\u0026nbsp;\u003c/strong\u003eAF, atrial fibrillation;\u0026nbsp;BG, blood glucose;\u0026nbsp;CKD, chronic kidney disease;\u0026nbsp;DM, diabetes mellitus;\u0026nbsp;eGFR,\u0026nbsp;estimated Glomerular Filtration Rate; IHM, in-hospital mortality; Lym, lymphocyte; Neu, neutrophil; Q, Quartile; Scr, serum\u0026nbsp;creatinine;\u0026nbsp;WBC, white blood cell count.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eNote:\u0026nbsp;\u003c/strong\u003eP \u0026lt; 0.05 indicates statistical significance.\u003c/p\u003e\n\u003cp\u003eQuartile-based univariable and multivariable logistic regression results are presented in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. In quartile-based logistic regression analyses, no statistically significant association was observed between baseline serum uric acid quartiles and in-hospital mortality in either the univariable or multivariable models. Nevertheless, the adjusted effect estimates suggested a possible U-shaped association, with lower mortality risk observed in the intermediate quartiles. Restricted cubic spline analysis was therefore subsequently performed to further assess and characterize the potential non-linear relationship between serum uric acid and in-hospital mortality.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eLogistic regression analysis of serum uric acid quartiles and in-hospital mortality\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"5\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eQuartile\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eUnivariable OR (95% CI)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003eMultivariable OR (95% CI)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eQ1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eQ2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e0.693 (0.291\u0026ndash;1.604)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e0.395\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e0.561 (0.216\u0026ndash;1.414)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e0.224\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eQ3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e0.622 (0.255\u0026ndash;1.461)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e0.281\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e0.376 (0.130\u0026ndash;1.037)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e0.063\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eQ4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e1.632 (0.784\u0026ndash;3.476)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e0.195\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e1.255 (0.500\u0026ndash;3.190)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e0.629\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eNote: P \u0026lt; 0.05 indicates statistical significance.\u003c/p\u003e\n\u003cp\u003eRestricted cubic spline analysis demonstrated a significant non-linear association between baseline serum uric acid and in-hospital mortality (Fig. \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Both the overall association and the test for non-linearity were statistically significant (P overall\u0026thinsp;\u0026lt;\u0026thinsp;0.001; P for non-linearity\u0026thinsp;\u0026lt;\u0026thinsp;0.001). The exposure-response curve showed a clear U-shaped pattern, with the estimated risk of in-hospital mortality decreasing as serum uric acid increased from lower levels, reaching its nadir at approximately 391 \u0026micro;mol/L, and then rising again at higher concentrations. The spline model suggested that the estimated odds ratio remained below the reference level within an intermediate serum uric acid range of approximately 379.2-408.7 \u0026micro;mol/L (OR\u0026thinsp;\u0026lt;\u0026thinsp;1). These findings suggested that both lower and higher serum uric acid levels were associated with increased mortality risk, whereas the intermediate range was associated with relatively lower estimated risk.\u003c/p\u003e\n\u003cp\u003eSubgroup RCS analyses are shown in Fig. \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. They were performed according to age, sex, hypertension, and diabetes status. In the sex-stratified analysis, significant interaction by sex was observed for the association between serum uric acid and in-hospital mortality. Both the overall interaction and the non-linear interaction reached statistical significance (P overall interaction\u0026thinsp;=\u0026thinsp;0.046; P non-linear interaction\u0026thinsp;=\u0026thinsp;0.026), indicating sex-related heterogeneity in the exposure\u0026ndash;response pattern. In the age-stratified analysis, the shape of the association appeared to differ between patients aged\u0026thinsp;\u0026lt;\u0026thinsp;65 years and those aged\u0026thinsp;\u0026ge;\u0026thinsp;65 years, with a possibly steeper increase in risk at higher serum uric acid levels among older patients. However, neither the overall interaction nor the non-linear interaction reached statistical significance (P overall interaction\u0026thinsp;=\u0026thinsp;0.090; P non-linear interaction\u0026thinsp;=\u0026thinsp;0.121). The dose-response patterns were broadly similar across hypertension and diabetes subgroups, with no evidence of significant interaction for hypertension (P overall interaction\u0026thinsp;=\u0026thinsp;0.419; P non-linear interaction\u0026thinsp;=\u0026thinsp;0.514) or diabetes (P overall interaction\u0026thinsp;=\u0026thinsp;0.456; P non-linear interaction\u0026thinsp;=\u0026thinsp;0.767).\u003c/p\u003e\n\u003cp\u003eCollinearity diagnostics (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e) showed no evidence of severe multicollinearity among the covariates included in the multivariable models. All variance inflation factors were below commonly accepted thresholds, supporting the stability of the regression estimates. Given that serum creatinine is incorporated into the calculation of eGFR, eGFR was selected as the renal function indicator in the multivariable logistic regression and RCS models, whereas serum creatinine was not entered simultaneously to avoid redundancy.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eVariance inflation factors for candidate covariates\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"15\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eVariable\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eSex\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003eAge\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003eSUA\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003eHTN\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003eDM\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003eAF\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003eHLP\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c9\"\u003e\n \u003cp\u003eCKD\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c10\"\u003e\n \u003cp\u003eScr\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c11\"\u003e\n \u003cp\u003eBG\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c12\"\u003e\n \u003cp\u003eWBC\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c13\"\u003e\n \u003cp\u003eNeu\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c14\"\u003e\n \u003cp\u003eLym\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c15\"\u003e\n \u003cp\u003eeGFR\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eVIF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e1.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e1.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e1.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e1.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e1.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e1.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003e1.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c9\"\u003e\n \u003cp\u003e1.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c10\"\u003e\n \u003cp\u003e3.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c11\"\u003e\n \u003cp\u003e1.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c12\"\u003e\n \u003cp\u003e1.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c13\"\u003e\n \u003cp\u003e2.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c14\"\u003e\n \u003cp\u003e2.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c15\"\u003e\n \u003cp\u003e3.97\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003eAbbreviations:\u0026nbsp;\u003c/strong\u003eAF, atrial fibrillation; BG, blood glucose; CKD, chronic kidney disease; DM, diabetes mellitus; eGFR, estimated Glomerular Filtration Rate; HLP, hyperlipidemia; HTN, hypertension; IHM, in-hospital mortality; Lym, lymphocyte; Neu, neutrophil; Scr, serum creatinine; SUA, serum uric acid; VIF, variance inflation factor; WBC, white blood cell count.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this retrospective cohort of critically ill patients with severe ischemic stroke, we found a significant U-shaped association between baseline serum uric acid and in-hospital mortality, with the lowest estimated risk observed at an intermediate serum uric acid level. Both lower and higher serum uric acid concentrations were associated with increased mortality, suggesting that the prognostic role of serum uric acid in severe ischemic stroke is non-linear rather than simply protective or harmful.\u003c/p\u003e \u003cp\u003eThis U-shaped association is biologically plausible. Serum uric acid is a major endogenous antioxidant in human plasma and may attenuate oxidative and nitrosative injury during cerebral ischemia-reperfusion[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Experimental studies have shown that serum uric acid can scavenge free radicals, reduce peroxynitrite-mediated damage, and protect neurons against ischemic and excitotoxic insults[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. In this context, lower serum uric acid levels may indicate insufficient antioxidant reserve during the acute phase of severe ischemic injury, thereby contributing to worse early outcomes.\u003c/p\u003e \u003cp\u003eAt the other end of the curve, however, higher serum uric acid levels were also associated with increased mortality. In critically ill stroke patients, elevated serum uric acid may reflect more than redox status alone and may instead represent the combined influence of renal dysfunction, metabolic stress, and overall systemic vulnerability[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. This interpretation is supported by our baseline data, in which higher serum uric acid quartiles were accompanied by higher creatinine levels and lower eGFR. Therefore, the adverse association observed at higher serum uric acid levels should not be interpreted as evidence of a direct toxic effect alone, but rather as a marker of a broader high-risk physiological state.\u003c/p\u003e \u003cp\u003eOur findings may help explain the heterogeneous results reported in previous studies. In reperfusion-treated populations, several studies have linked higher serum uric acid levels to better functional recovery, smaller infarct volume, and lower rates of malignant edema or hemorrhagic transformation, suggesting that serum uric acid may be beneficial in settings characterized by pronounced oxidative stress[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. In contrast, more general ischemic stroke cohorts have often shown neutral associations, and a recent triangulation study integrating cohort analysis, meta-analysis, and Mendelian randomization did not support a clear linear or causal relationship between serum uric acid and 3-month functional outcome[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Importantly, one prior clinical study has suggested a U-shaped relationship between serum uric acid and stroke outcome, which is more consistent with our findings than a simple monotonic model[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. The URICO-ICTUS trial further supports this interpretation: adjunctive serum uric acid therapy was safe but did not improve the primary outcome in the overall population, although exploratory analyses suggested potential benefit in selected subgroups such as patients with hyperglycemia[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Taken together, the available evidence suggests that the prognostic relevance of serum uric acid is likely context-dependent and non-linear, and may vary according to the underlying biological milieu, outcome definition, and analytic approach[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe subgroup analyses suggest that the U-shaped association between serum uric acid and in-hospital mortality was not confined to a single clinical subgroup, supporting the robustness of the overall finding. Significant overall and non-linear interactions were observed in the sex-stratified analysis, suggesting that the exposure-response relationship may differ between men and women. This finding is consistent with prior evidence suggesting sex-related differences in serum uric acid metabolism and in the clinical implications of hyperuricemia[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Moreover, previous studies have also reported sex-specific associations between serum uric acid and stroke outcomes[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Although the age-stratified curves showed some visual separation, particularly in the higher serum uric acid range, the interaction tests by age were not statistically significant. Accordingly, the age-specific pattern should be interpreted with caution and requires confirmation in larger studies.\u003c/p\u003e \u003cp\u003eSeveral limitations should be acknowledged. First, the estimated nadir and lower-risk interval were derived from a spline-based model and should be interpreted cautiously until externally validated. Second, because of the structure of the MIMIC-IV database, key clinical scales for initial stroke severity (like the National Institutes of Health Stroke Scale (NIHSS) score) and acute-phase specific reperfusion treatments (intravenous thrombolysis or mechanical thrombectomy) are mostly recorded in unstructured free-text medical records. Therefore, these variables could not be robustly incorporated into the primary adjusted analyses. Third, we attempted to extract the Glasgow Coma Scale (GCS) scores to serve as a surrogate marker for initial stroke severity. However, due to the substantial proportion of missing data, this variable was excluded from the multivariable models. Fourth, serum uric acid was measured only at baseline, and dynamic changes during hospitalization could not be assessed. Fifth, the sample size was modest for subgroup analyses, which limited the precision of interaction estimates. Finally, external validation is needed before the present findings can be generalized to other cohorts. Despite these limitations, the study has several strengths, including the use of a large critical-care database, a spline-based modeling strategy that captured non-linearity, and consistent findings across major subgroup analyses.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eBaseline serum uric acid was associated with in-hospital mortality in a U-shaped non-linear manner among critically ill patients with severe ischemic stroke. An intermediate serum uric acid range was associated with relatively lower estimated mortality risk, whereas both lower and higher levels may mark adverse biological states. Further prospective studies are needed to validate this non-linear association and clarify its clinical and mechanistic significance.\u003c/p\u003e"},{"header":"List of abbreviations","content":"\u003cp\u003eAIC, Akaike information criterion;\u003c/p\u003e\n\u003cp\u003eCKD-EPI, Chronic Kidney Disease Epidemiology Collaboration;\u003c/p\u003e\n\u003cp\u003eeGFR, estimated glomerular filtration rate;\u003c/p\u003e\n\u003cp\u003eGCS, Glasgow Coma Scale;\u003c/p\u003e\n\u003cp\u003eMIMIC-IV, Medical Information Mart for Intensive Care IV;\u003c/p\u003e\n\u003cp\u003eNIHSS, National Institutes of Health Stroke Scale;\u003c/p\u003e\n\u003cp\u003eRCS, restricted cubic spline;\u003c/p\u003e\n\u003cp\u003eSQL, structured query language;\u003c/p\u003e\n\u003cp\u003eVIFs, variance inflation factors.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was based on the MIMIC-IV database, a publicly available deidentified critical care database. The establishment of the database was approved by the Institutional Review Boards of Beth Israel Deaconess Medical Center, and the requirement for individual informed consent was waived because all data were deidentified (Institutional Review Board approval number: 65083321).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data used in this study were obtained from the MIMIC-IV database, which is available on PhysioNet to credentialed users who complete the required training and data use agreement. The analytic code is available from the corresponding author upon reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was funded by National Natural Science Foundation of China with grant 82102011 to Yukai Liu.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eGY performed the software analysis, formal analysis, investigation, data curation, visualization, and drafted the manuscript. YL and DL conceived and designed the study. YL developed the methodology, provided resources, administered the project, and acquired funding. HC, GJ, and HZ contributed to validation of the analyses. FY contributed to formal data analysis. ZJ supervised the study. YL and DL critically revised the manuscript. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors would like to thank the PhysioNet community for providing access to the Medical Information Mart for Intensive Care IV (MIMIC-IV) database.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eFeigin, V. L. et al. Global, regional, and national burden of stroke and its risk factors, 1990\u0026ndash;2021: a systematic analysis for the Global Burden of Disease Study 2021. The Lancet Neurology [Internet]. Elsevier; [cited 2026 Mar 18];\u003cb\u003e23\u003c/b\u003e:973\u0026ndash;1003. 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Sci.\u003c/em\u003e \u003cb\u003e21\u003c/b\u003e, 4269. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/ijms21124269\u003c/span\u003e\u003cspan address=\"10.3390/ijms21124269\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2020).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLlull, L. et al. Uric Acid Therapy Improves Clinical Outcome in Women With Acute Ischemic Stroke. \u003cem\u003eStroke\u003c/em\u003e \u003cb\u003e46\u003c/b\u003e, 2162\u0026ndash;2167. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1161/STROKEAHA.115.009960\u003c/span\u003e\u003cspan address=\"10.1161/STROKEAHA.115.009960\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2015).\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"ischemic stroke, biomarkers, serum uric acid, in-hospital mortality, restricted cubic spline, MIMIC-IV","lastPublishedDoi":"10.21203/rs.3.rs-9473205/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9473205/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eSerum uric acid has been proposed to play a complex role in ischemic stroke because of its dual antioxidant and pro-oxidative properties. However, existing evidence regarding its prognostic value remains inconsistent, and the shape of its association with mortality has not been well characterized in critically ill patients with severe ischemic stroke. We aimed to investigate the association between baseline serum uric acid and in-hospital mortality in this population, with particular emphasis on potential non-linear relationships.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eThis retrospective cohort study included adult patients with severe ischemic stroke identified from the MIMIC-IV database. Logistic regression models were used to evaluate the association between serum uric acid quartiles and in-hospital mortality. Restricted cubic spline analysis was used to examine the non-linear relationship between serum uric acid and in-hospital mortality. Prespecified subgroup analyses were conducted according to age, sex, hypertension, and diabetes.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eA total of 336 patients were included, of whom 58 (17.3%) died during hospitalization. In quartile-based analyses, patients in the middle serum uric acid categories tended to have lower mortality than those in the lowest and highest categories. However, these associations were not statistically significant. In contrast, restricted cubic spline analysis revealed a significant non-linear association between baseline serum uric acid and in-hospital mortality (P for overall association\u0026thinsp;\u0026lt;\u0026thinsp;0.001; P for non-linearity\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Mortality risk decreased with increasing serum uric acid levels at the lower end of the distribution, reached its lowest point at approximately 391 \u0026micro;mol/L, and increased again at higher concentrations, indicating a U-shaped relationship. An intermediate serum uric acid range of approximately 379.2-408.7 \u0026micro;mol/L was associated with the lowest estimated risk. In subgroup analyses, the non-linear association appeared broadly consistent across major clinical subgroups, although a significant interaction by sex was observed.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eBaseline serum uric acid was associated with in-hospital mortality in a U-shaped non-linear manner among critically ill patients with severe ischemic stroke. This finding suggests that both lower and higher serum uric acid levels were associated with adverse biological states.\u003c/p\u003e","manuscriptTitle":"U-Shaped Association between Serum Uric Acid and In-hospital Mortality in Severe Ischemic Stroke: A MIMIC-IV Retrospective Cohort Study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-05-11 06:22:43","doi":"10.21203/rs.3.rs-9473205/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"179729760314977770370525360610582476666","date":"2026-05-12T12:22:28+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-10T11:07:31+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"257084679977525040729981487096405330707","date":"2026-05-09T21:01:02+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-04-29T10:21:13+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-04-27T09:08:26+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-04-21T12:03:28+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-04-21T12:02:49+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2026-04-20T13:39:58+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"9b274c7b-2f74-4e1b-b106-ff63d69fefa9","owner":[],"postedDate":"May 11th, 2026","published":true,"recentEditorialEvents":[{"type":"reviewerAgreed","content":"179729760314977770370525360610582476666","date":"2026-05-12T12:22:28+00:00","index":70,"fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-10T11:07:31+00:00","index":62,"fulltext":""},{"type":"reviewerAgreed","content":"257084679977525040729981487096405330707","date":"2026-05-09T21:01:02+00:00","index":59,"fulltext":""},{"type":"reviewersInvited","content":"33","date":"2026-04-29T10:21:13+00:00","index":"","fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":67622554,"name":"Health sciences/Biomarkers"},{"id":67622555,"name":"Health sciences/Cardiology"},{"id":67622556,"name":"Health sciences/Diseases"},{"id":67622557,"name":"Health sciences/Medical research"},{"id":67622558,"name":"Health sciences/Neurology"},{"id":67622559,"name":"Health sciences/Risk factors"}],"tags":[],"updatedAt":"2026-05-11T06:22:43+00:00","versionOfRecord":[],"versionCreatedAt":"2026-05-11 06:22:43","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9473205","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9473205","identity":"rs-9473205","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}