Biomarker-Based Prediction of Ischemic Stroke in Patients With H-type Hypertension

Biomarker-Based Prediction of Ischemic Stroke in Patients With H-type Hypertension | 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 Biomarker-Based Prediction of Ischemic Stroke in Patients With H-type Hypertension Ke Chen, Jianxun He, Lan Fu, Xiaohua Song, Ning Cao, Hui Yuan This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5223664/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 07 Jan, 2025 Read the published version in Scientific Reports → Version 1 posted 10 You are reading this latest preprint version Abstract Hypertension combined with hyperhomocysteinemia significantly raises the risk of ischemic stroke. Our study aimed to develop and validate a biomarker-based prediction model for ischemic stroke in H-type hypertension patients. We retrospectively included 3,305 patients in the development cohort, and externally validated in 103 patients from another cohort. Logistic regression, LASSO regression, and best subset selection analysis were used to assess the contribution of variables to ischemic stroke, and models were derived using four machine learning algorithms. Area Under Curve (AUC), calibration plot and decision-curve analysis (DCA) respectively evaluated the discrimination and calibration of four models, then external validation and visualization of the best-performing model. There were 1,415 and 42 patients with ischemic stroke in the development and validation cohorts. The final model included 8 predictors: age, antihypertensive therapy, biomarkers (serum magnesium, serum potassium, proteinuria and hypersensitive C-reactive protein), and comorbidities (atrial fibrillation and hyperlipidemia). The optimal model, named A 2 BC ischemic stroke model, showed good discrimination and calibration ability for ischemic stroke with AUC of 0.91 and 0.87 in the internal and external validation cohorts. The A 2 BC ischemic stroke model had satisfactory predictive performances to assist clinicians in accurately identifying the risk of ischemic stroke for patients with H-type hypertension. Health sciences/Diseases/Cardiovascular diseases Biological sciences/Biochemistry H-type hypertension ischemic stroke predicted model Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Introduction Due to its high incidence, recurrence, mortality, and disability rates, stroke continues to be the second leading cause of death worldwide, according to the Global Burden of Disease Study (GBD) 2019 1 . Intravenous thrombolysis and endovascular thrombectomy are the main treatments for ischemic stroke at the moment 2 . A limited therapeutic window presents a significant challenge for nations with insufficient or unbalanced medical resources 3 . Therefore, the best way to lessen the burden of stroke is early prevention. More than half of stroke patients worldwide are attributed to hypertension, making it one of the most significant modifiable risk factors for stroke 4 . Additionally, there is a causal relationship between homocysteine (Hcy) concentration and stroke 5 . The prevalence of hyperhomocysteinemia (HHcy) is about 3 / 4 of the hypertension population in China. H-type hypertension is defined as hypertension combined with elevated Hcy level 6 . Surprisingly, vascular damage is worsened by a synergistic effect between Hcy and hypertension 7 . The above suggests that H-type hypertension patients should be focused on monitoring the risk of ischemic stroke. The Framingham stroke risk profile (FSP) and CHA2DS2-VASc score are widely used to assess the risk of stroke in general population and and nonvalvular atrial fibrillation (AF) patients 8 , 9 . However, there are few validated tools available for assessing the risk of ischemic stroke in patients with H-type hypertension. Clinicians typically manage high-risk population in the cardiovascular field using a combination of demographics characteristics and medical history, along with some laboratory indicators. By employing this strategy, our study aims to screen out high-risk groups for ischemic stroke, develop and validate a high-performance prediction model for ischemic stroke in patients with H-type hypertension, and facilitate further risk stratification management by clinicians for patients. Results Baseline characteristics According to the inclusion and exclusion criteria, among the 11,631 patients diagnosed with H-type hypertension at Beijing Anzhen Hospital from January 2022 and December 2023, 4,632 suffered an ischemic stroke. A total of 3,305 had medical records in same hospital between January 2018 and December 2021, and 2,340 were assigned to the training set and 965 to the testing set (Supplementary Fig. 1). Another 103 H-type hypertension patients, including 61 patients without ischemic stroke and 42 patients with ischemic stroke, were enrolled as an external validation cohort from the China-Japan Friendship Hospital (Supplementary Fig. 2). Detailed information about the characteristics of patients in the total cohort, training, and internal validation sets are shown in Table 1 and Supplementary Table 1, respectively. As shown in Table 1 , patients with ischemic stroke were older with higher SBP and had a higher proportion of smokers and a history of cardiovascular disease (all P < 0.05) as compared to non-stroke patients. Table 1 Baseline clinical and biochemical characteristics of all patients Variable Total (n = 3408) non-Stroke (n = 1951) Ischemic stroke (n = 1457) P value Age, years, median (IQR) 56 (42–66) 46 (37–58) 65 (57–74) < 0.001 Male, % 2435 (71.4%) 1382 (70.8%) 1053 (72.3%) 0.358 BMI, median (IQR) 26.26 (24.00-28.98) 26.84 (24.38–29.59) 25.61 (23.44–28.04) < 0.001 SBP, mmHg, median (IQR) 142 (130–155) 140 (130–152) 145 (132–159) < 0.001 DBP, mmHg, median (IQR) 86 (78–97) 90 (80–100) 82 (74–92) < 0.001 Smoke, % 1605 (47.1%) 882 (45.2%) 723 (49.6%) 0.011 Drink, % 1482 (43.5%) 873 (44.7%) 609 (41.8%) 0.086 Diabetes mellitus, % 1043 (30.6%) 405 (20.8%) 638 (43.8%) < 0.001 Hyperlipidemia, % 2796 (82.0%) 1413 (72.4%) 1383 (94.9%) < 0.001 Coronary heart disease, % 729 (21.4%) 315 (16.1%) 414 (28.4%) < 0.001 Atrial fibrillation, % 178 (5.2%) 16 (0.8%) 162 (11.1%) < 0.001 Antihypertensive drugs, % 2287 (67.1%) 1366 (70.0%) 921 (63.2%) < 0.001 Antiplatelet drugs, % 218 (6.4%) 123 (6.3%) 95 (6.5%) 0.799 Family history of cerebral infarction, % 216 (6.3%) 118 (6.0%) 98 (6.7%) 0.422 hs-CRP, mg/L, median (IQR) 1.34 (0.65–3.18) 1.17 (0.60–2.65) 1.72 (0.78–4.83) < 0.001 Na, mmol/L, median (IQR) 140.6 (139.0-142.0) 140.4 (138.9-141.7) 140.9 (139.2-142.4) < 0.001 K, mmol/L, median (IQR) 4.05 (3.80–4.27) 4.13 (3.92–4.33) 3.90 (3.68–4.15) < 0.001 Mg, mmol/L, median (IQR) 0.91 (0.86–0.95) 0.92 (0.87–0.96) 0.89 (0.84–0.94) < 0.001 TBil, µmol/L, median (IQR) 13.00 (10.05–16.82) 13.20 (10.52-17.00) 12.51 (9.40–16.60) < 0.001 DBil, µmol/L, median (IQR) 4.20 (3.05–5.57) 4.21 (3.06–5.47) 4.20 (3.03–5.71) 0.202 Urinary protein, % < 0.001 - 2831 (83.1%) 1804 (92.5%) 1027 (70.5%) + 425 (12.5%) 95 (4.9%) 330 (22.6%) ++ 108 (3.3%) 38 (1.9%) 70 (5.0%) +++ 40 (1.2%) 13 (0.7%) 27 (1.9%) Hcy, µmol/L, % < 0.001 30 265 (7.8%) 150 (7.7%) 115 (7.9%) Carotid artery stenosis, % 282 (8.3%) 144 (7.4%) 138 (9.5%) 0.028 IQR, interquartile range; BMI, body mass index; SBP, systolic blood pressure; DBP, diastolic blood pressure; hs-CRP, hypersensitive C-reactive protein; Na, Sodium; K, Potassium; Mg, magnesium; TBil, total bilirubin; DBil, direct bilirubin; Hcy, homocysteine. Predictor selections There were 16 variables with P < 0.05 by univariate logistic regression (Supplementary Table 2). After stepwise regression, 13 variables were ultimately retained, namely, age, antihypertensive therapy, hyperlipidemia, atrial fibrillation (AF), diabetes mellitus (DM), BMI, SBP, DBP, hs-CRP, K, Mg, Hcy and proteinuria. In best subset selection regression, when the model included eight variables, the BIC of the model reached its minimum. These eight variables were age, antihypertensive therapy, hyperlipidemia, AF, hs-CRP, K, Mg, and proteinuria, respectively (Fig. 1 A and B). In LASSO regression, 17 variables were selected with a lambda that is within 1 standard error (SE), namely age, gender, antihypertensive therapy, antiplatelet therapy, hyperlipidemia, AF, DM, coronary artery disease (CAD), BMI, SBP, DBP, hs-CRP, K, Mg, Hcy, proteinuria and carotid artery stenosis (Fig. 1 C and D). Eventually, eight variables were included to develop models: age, antihypertensive therapy, hyperlipidemia, AF, hs-CRP, K, Mg, and proteinuria (Fig. 2 ). Model development and validation Eight variables were entered into a multivariable logistic regression model, linear kernel SVM model, random forest model, and XGBoost model, respectively. Four models yielded the AUC of 0.905 (95% CI: 0.887–0.924), 0.896 (95% CI: 0.876–0.915), 0.893 (95% CI: 0.872–0.914), 0.909 (95% CI: 0.890–0.927) for the risk of ischemic stroke (Fig. 3 and Table 2 ). The difference of AUC between logistic regression model and XGBoost model was not significant (DeLong test, P = 0.406). Based on the maximal Youden’s index, the threshold of four models were 55%, 46%, 37%, and 43% in order. The XGBoost model had the highest sensitivity, 0.825, with a specificity of 0.860. Table 2 Predict performances of four models on the testing set AUC (95%CI) Sensitivity Specificity Accuracy PPV NPV Logistic model 0.905 (0.887–0.924) # 0.745 0.905 0.833 0.860 0.816 SVM model 0.896 (0.876–0.915) * 0.778 0.851 0.819 0.806 0.828 Random forest model 0.893 (0.872–0.914) 0.820 0.840 0.831 0.803 0.854 XGBoost model 0.909 (0.890–0.927) 0.825 0.860 0.845 0.825 0.860 # : There was no significant difference in AUC between the logistic model and the XGBoost model by Delong test; * : There was no significant difference in AUC between the SVM model and the random forest model by Delong test; AUC: area under curve; CI: confidence interval; PPV: positive predictive value; NPV: negative predictive value; SVM: support vector machine. Calibration plots were used to assess the calibration of models. As shown in Fig. 4 , four models had a good calibration. Among them, the predicted odds of the outcome of the logistic regression model and XGBoost model were close to the actual probability (Fig. 4 A and D). Four models resulted in a high net benefit, especially the logistic regression model and XGBoost model (Fig. 5 ). Conclusively, the logistic regression model and XGBoost model exhibited excellent discrimination and calibration performance. Considering the visualization and scalability of the prediction model, we ultimately chose the logical regression model as the optimal model. The weight coefficients of eight variables in the logistic regression model was shown in Supplementary Fig. 3. Serum magnesium, serum potassium, AF, and hyperlipidemia have a higher weight in the optimal model. In the external cohort, the logistic regression model achieved an AUC of 0.872 (95% CI: 0.805–0.939) showing good discrimination capacity (Supplementary Fig. 4 and Supplementary Table 3). The logistic regression model also was well-calibrated and had a high net benefit in the external cohort (Supplementary Fig. 5 and Supplementary Fig. 6). Model Visualization The eight variables: age (A), antihypertensive therapy (A), biomarkers (B) (serum magnesium, serum potassium, proteinuria, and hypersensitive C-reactive protein), comorbidities (C) (atrial fibrillation and hyperlipidemia) were fitted a logistic regression model to predict the risk of ischemic stroke in H-type hypertension patients was termed the A 2 BC ischemic stroke model and presented as a nomogram (Fig. 6 ). The variables were listed separately, and the cumulative score is matched to a risk score. Discussion Based on two independent retrospective cohorts with a large sample size, our study developed and internally and externally validated a model to predict the risk of ischemic stroke. This model included 8 variables: age (A), antihypertensive therapy (A), biomarkers (B) (serum magnesium, serum potassium, proteinuria, and hypersensitive C-reactive protein), comorbidities (C) (atrial fibrillation and hyperlipidemia), which termed the A 2 BC ischemic stroke model. The A 2 BC ischemic stroke model showed great discrimination and calibration for the risk of ischemic stroke, with similar findings when externally validated. At present, the most effective treatment of acute ischemic stroke (AIS) is reperfusion therapy in therapeutic time window, including intravenous thrombolysis (IVT) and endovascular therapy (EVT), but about 3/4 patients present over 4.5 hours after stroke onset or with an unknown time of onset 10 . Besides, there are numerous contraindications associated with IVT that must be carefully considered 11 . The rates of IVT and EVT were 5·64% and 1·45% between 2019 and 2020 in China 12 . Recurrent ischemic stroke is another challenge even with improved secondary prevention, recurrence rates of ischemic stroke seem unchanged over time 13 . Because of above all, primary prevention of high-risk population may be another effective way to improve the burden of ischemic stroke. However, there is an unmet need for accurate and validated models for estimating risk of ischemic stroke. Some guidelines propose FSP as a reliable tool for 10-year stroke risk estimates 14 . Despite its widespread application, the validity of the FSP has not been sufficiently studied in populations with different age range or ethnicity. A prospective study showed that FSP overestimates the risk of stroke in Chinese 15 . In the same way, both the CV risk calculator and Stroke Riskometer need to be validated and adapted in the Chinese population 16 , 17 . Also, although most risk factors have an independent effect on ischemic stroke, interactions may exist between these factors when considering predicting overall risk. A combined analysis of hypertension and Hcy showed they act additively to increase the risk of stroke 22 . Therefore, it is necessary to establish a prediction model for ischemic stroke specific to the H-type hypertension subset. One of the purposes of risk assessment is to guide an appropriate primary prevention program. Additional folic acid significantly reduces the risk of first stroke in hypertension patients, compared with antihypertensive therapy alone 18 . Serum magnesium, an inorganic ion, is given the most weight in our model, and serum potassium is also significant. Magnesium and potassium are crucial trace elements for organisms, as we all know. Magnesium helps to prevent ischemic stroke. Through various mechanisms, it lowers blood pressure more effectively than potassium 19 . Inflammation, endothelial dysfunction, and platelet dysfunction have all been linked to low magnesium levels 20 . Stroke risk was 2.5 times higher for diuretic users with low serum potassium than for those with high serum potassium 21 . When compared to adults receiving antihypertensive therapy, hypokalemia is independently associated with an increased risk of ischemic stroke and is unrelated to diuretics. Dyslipidemia is an independent risk factor for stroke 22 . The risk of an ischemic stroke can be decreased by lowering atherogenic lipoproteins 23 , 24 . New lipid-lowering medications have made it possible to lower LDL-C to extremely low levels, but doing so will raise the risk of hemorrhagic stroke 25 . A significant risk factor for stroke is AF. A thrombus from the left atrial (LA) cavity, particularly the left atrial appendage (LAA), is primarily responsible for ischemic stroke associated with AF 26 . Plasma Hcy levels were found to be associated with LA/LAA thrombus and could be used to predict the risk of LA/LAA thrombus in non-valvular AF patients with low CHA2DS2-VASc scores 27 . Numerous studies have demonstrated that hypertension can cause cerebrovascular diseases through a variety of mechanisms, including adapting automatic regulation of cerebral blood flow (CBF) to hypertension 28 , endothelial dysfunction, reduction of nitric oxide (NO) 29 , elevated levels of angiotensin II (Ang II) leading to cerebral artery hypertrophy and inward remodeling 30 . Fortunately, the negative effects of hypertension can be offset by a variety of antihypertensive medications. Using long-lasting dihydropyridine-Ca 2+ channel blocker attributes to the normalization of autoregulation of CBF 31 . Similarly, other types of antihypertensive drugs also have this effect 32 . Additionally, combining antihypertensive medications in suboptimal doses can establish tolerance and effectively treat the remodeling of cerebral arteries brought on by hypertension 33 . Proteinuria, the other factor in our model, is a common sign of renal damage and has a particularly strong association with stroke 34 , 35 . Researchers have proposed the term "cerebro-renal interaction" because kidney disease and cerebrovascular disease are closely related 36 . The above can be explained by the idea that increased urinary protein excretion rate may be connected to significant vascular damage 37 . According to epidemiological studies, people over 65 account for the majority of stroke cases, and the risk rises with age 38 , 39 . Variety in circulation factors in the systemic environment, cellular senescence, and hypertension during human aging can all increase the risk of stroke 40 . The A 2 BC ischemic stroke model consists of 8 general variables, which are simple to collect in clinical practice, there is no need to take into account specialized examination equipment and technical personnel, allowing community hospitals to conduct rapid screening and significantly saving medical resources. Our study had some limitations as well. First, certain H-type hypertension-specific risk factors for ischemic stroke, like MTHFR polymorphism, have not been studied. However, not many community hospitals in China offer MTHFR polymorphism detection services. Second, since our research was a retrospective study and the prediction model created by the machine learning algorithm was just a reflection of mathematical logic, there was no causal relationship. Therefore, even though we have demonstrated the model's good performance on an external validation cohort, more clinical data and prospective queues were required to improve the model's performance in specific clinical application scenarios. Finally, we excluded secondary hypertension in patients with H-type hypertension, as the causes of secondary hypertension are diverse, and the predictive factors are complex. Therefore, our model cannot be used for secondary hypertension patients with elevated Hcy levels. Materials and methods Study population This retrospective cohort study consecutively included inpatients diagnosed with H-type hypertension, whether or not they suffered first ischemic stroke, at Beijing Anzhen Hospital, Capital Medical University from January 2022 to December 2023. Patients with secondary hypertension or a history of ischemic stroke would be excluded. These patients would also be excluded if they lack data in Beijing Anzhen Hospital from January 2018 to December 2021. Meanwhile, we extracted an external validation cohort from the China-Japan Friendship Hospital between January 2023 and June 2023. Patients with hypertension were diagnosed according to the International Society of Hypertension recommendations 41 . Systolic blood pressure (SBP) in the office or clinic was ≥ 140 mmHg and/or diastolic blood pressure (DBP) was ≥ 90 mmHg following repeated examinations were considered as hypertension. In addition, the guideline also suggested that blood pressure < 140 / 90 mmHg in patients with a history of hypertension and currently using antihypertensive drugs were still diagnosed as hypertension. Patients with essential hypertension were identified when secondary hypertension was excluded. Hypertension patients, together with serum Hcy concentrations ≥ 10 µmol/L, were identified as H-type hypertension 42 . Ischemic stroke was confirmed via computed tomography (CT) or brain magnetic resonance imaging (MRI) combined with clinical symptoms and signs. Our study was conducted according to the Declaration of Helsinki and was approved by the hospital’s ethical review board (Beijing Anzhen Hospital, Capital Medical University, Beijing, China). The need to obtain informed consent was waived by the hospital’s ethical review board. Declarations Additional Information The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Author Contribution Study concept and design: Hui Yuan, Ke Chen. Data collection: Ke Chen, Jianxun He, Lan Fu, Xiaohua Song, Ning Cao. Data analysis and interpretation: Ke Chen, Jianxun He, Lan Fu, Xiaohua Song, Ning Cao. Drafting of the manuscript: Ke Chen, Hui Yuan. Critical revision of the manuscript: all authors. Final approval: all authors. Acknowledgement This work was supported by the National Key Research and Development Program (2022YFC2009600) (2022YFC2009602). Data Availability Some or all data sets generated and/or analyzed during the present study are not publicly available but are available from the corresponding author upon reasonable request. References Collaborators, G. B. D. S. Global, regional, and national burden of stroke and its risk factors, 1990–2019: a systematic analysis for the Global Burden of Disease Study 2019. Lancet Neurol. 20 , 795–820. https://doi.org:10.1016/S1474-4422(21)00252-0 (2021). Campbell, B. C. V. et al. Ischaemic stroke. Nat. Rev. Dis. Primers . 5 , 70. https://doi.org:10.1038/s41572-019-0118-8 (2019). Barthels, D. & Das, H. Current advances in ischemic stroke research and therapies. Biochim. Biophys. Acta Mol. Basis Dis. 1866 , 165260. https://doi.org:10.1016/j.bbadis.2018.09.012 (2020). Caprio, F. Z. & Sorond, F. A. 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Stroke epidemiology: a review of population-based studies of incidence, prevalence, and case-fatality in the late 20th century. Lancet Neurol. 2 , 43–53. https://doi.org:10.1016/s1474-4422(03)00266-7 (2003). Wang, X. et al. Exosomes and Exosomal microRNAs in Age-associated Stroke. Curr. Vasc Pharmacol. 19 , 587–600. https://doi.org:10.2174/1570161119666210208202621 (2021). Unger, T. et al. International Society of Hypertension Global Hypertension Practice Guidelines. Hypertension 75, 1334–1357 (2020). (2020). https://doi.org:10.1161/HYPERTENSIONAHA.120.15026 Tan, Y. et al. Impact of H-Type Hypertension on Intraplaque Neovascularization Assessed by Contrast-Enhanced Ultrasound. J. Atheroscler Thromb. 29 , 492–501. https://doi.org:10.5551/jat.61275 (2022). Additional Declarations No competing interests reported. Supplementary Files SupplementalMaterial.docx Cite Share Download PDF Status: Published Journal Publication published 07 Jan, 2025 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 21 Nov, 2024 Reviews received at journal 19 Nov, 2024 Reviews received at journal 30 Oct, 2024 Reviewers agreed at journal 29 Oct, 2024 Reviewers agreed at journal 29 Oct, 2024 Reviewers invited by journal 29 Oct, 2024 Editor assigned by journal 29 Oct, 2024 Editor invited by journal 11 Oct, 2024 Submission checks completed at journal 11 Oct, 2024 First submitted to journal 08 Oct, 2024 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. 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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-5223664","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":364940858,"identity":"021854e5-037a-43ba-b43b-10c191549c0b","order_by":0,"name":"Ke Chen","email":"","orcid":"","institution":"Beijing Anzhen Hospital","correspondingAuthor":false,"prefix":"","firstName":"Ke","middleName":"","lastName":"Chen","suffix":""},{"id":364940859,"identity":"73dd1047-cb12-46d4-a287-2fbb1991af4d","order_by":1,"name":"Jianxun He","email":"","orcid":"","institution":"Beijing Anzhen Hospital","correspondingAuthor":false,"prefix":"","firstName":"Jianxun","middleName":"","lastName":"He","suffix":""},{"id":364940860,"identity":"1b06d371-244c-426d-8529-ddd942ad5524","order_by":2,"name":"Lan Fu","email":"","orcid":"","institution":"Beijing Anzhen Hospital","correspondingAuthor":false,"prefix":"","firstName":"Lan","middleName":"","lastName":"Fu","suffix":""},{"id":364940861,"identity":"b4a57536-eb9b-4922-981b-a332bb33bef2","order_by":3,"name":"Xiaohua Song","email":"","orcid":"","institution":"Beijing Anzhen Hospital","correspondingAuthor":false,"prefix":"","firstName":"Xiaohua","middleName":"","lastName":"Song","suffix":""},{"id":364940862,"identity":"d1208e02-4d14-46a8-8f58-d53727ea68d2","order_by":4,"name":"Ning Cao","email":"","orcid":"","institution":"China-Japan Friendship Hospital","correspondingAuthor":false,"prefix":"","firstName":"Ning","middleName":"","lastName":"Cao","suffix":""},{"id":364940863,"identity":"ddd0eaa3-94cf-4d14-80c8-d60346c1fa50","order_by":5,"name":"Hui Yuan","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA20lEQVRIie2PoQ7CMBCGS5aAaVZkZ8YrLJkEx4u0CdkMYrKOLSOdxC7hJSaRN5pM9QEmEMzgcUgKFpIOh+gn/vzivtwdQg7HHzKdlQBMrEJCiuJqil3xccfhqpM4qFUZmWJXQrqN22F/5g2kMhikGnEY1hHwHCYNtFIwrRA55rZfZAb8dPECVcieiRTRC9i2qAa4vk19s6VneokiyiwKZeYwqTAymXHpjVE2b4XOTSIu1yMU3DFgOomCui2pKZj2FmVRler+EKvdgVTDq4Sktigf4B/nHQ6Hw/GNJ1wCVSqtNGf+AAAAAElFTkSuQmCC","orcid":"","institution":"Beijing Anzhen Hospital","correspondingAuthor":true,"prefix":"","firstName":"Hui","middleName":"","lastName":"Yuan","suffix":""}],"badges":[],"createdAt":"2024-10-08 08:53:54","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5223664/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5223664/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41598-024-83662-3","type":"published","date":"2025-01-07T15:56:56+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":67264527,"identity":"ebcf2d98-d13d-4917-b3da-c9a8bf1e8d89","added_by":"auto","created_at":"2024-10-23 06:35:50","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":327288,"visible":true,"origin":"","legend":"\u003cp\u003eBest subset selection regression and LASSO regression for the selection of variables. (A) Variation of BCI with the change of model size. (B) Features included when BIC reaches its minimum value. (C) Coeffificient of each variable in LASSO regression with the change of log lambda. First vertical dotted line: l value (lambda min) when binomial deviance was minimum. Second vertical dotted line: lambda min + 1se (lambda 1se). (D) Variation of binomial deviance with the change of log lambda in LASSO regression. First vertical dotted line: l value (lambda min) when binomial deviance was minimum. Second vertical dotted line: lambda min + 1se (lambda 1se).\u003c/p\u003e","description":"","filename":"OnlineFigure1.png","url":"https://assets-eu.researchsquare.com/files/rs-5223664/v1/000cfb253e536506057bf9a2.png"},{"id":67264529,"identity":"3199608d-7f03-4fa0-a891-0f1e3ae9a2e1","added_by":"auto","created_at":"2024-10-23 06:35:50","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":154674,"visible":true,"origin":"","legend":"\u003cp\u003eThe common variables were confirmed by all three feature selection methods: stepwise regression, LASSO regression, and best subset selection regression.\u003c/p\u003e","description":"","filename":"OnlineFigure2.png","url":"https://assets-eu.researchsquare.com/files/rs-5223664/v1/93516e30731aeeb0cc3222e5.png"},{"id":67265914,"identity":"8880044f-b73f-4af1-829d-dc8972261a69","added_by":"auto","created_at":"2024-10-23 06:43:50","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":432844,"visible":true,"origin":"","legend":"\u003cp\u003eDiscrimination of the four models. Receiver operating characteristic (ROC) curves of the four models with AUC and 95% CI.\u003c/p\u003e","description":"","filename":"OnlineFigure3.png","url":"https://assets-eu.researchsquare.com/files/rs-5223664/v1/e89ef1eefbe8e484e6143c38.png"},{"id":67264530,"identity":"883e0e38-dcd4-4b78-ba8e-c4a927a9af93","added_by":"auto","created_at":"2024-10-23 06:35:50","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":519185,"visible":true,"origin":"","legend":"\u003cp\u003eCalibration of the four models. (A) Logistic model. (B) SVM model. (C) random forest model. (D) XGBoost model.\u003c/p\u003e","description":"","filename":"OnlineFigure4.png","url":"https://assets-eu.researchsquare.com/files/rs-5223664/v1/22ba52e5d18db13d7627303a.png"},{"id":67264523,"identity":"a729207c-bb2b-41de-b95c-cfc290408cf1","added_by":"auto","created_at":"2024-10-23 06:35:50","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":23473,"visible":true,"origin":"","legend":"\u003cp\u003eDecision-curve analysis of the four models. Decision curves of the four models showing the net benefit of using each model according to different threshold probabilities in the internal validation cohort.\u003c/p\u003e","description":"","filename":"OnlineFigure5.png","url":"https://assets-eu.researchsquare.com/files/rs-5223664/v1/390e00aaf757e6e79e7b9aff.png"},{"id":67266155,"identity":"56c8034e-d46d-402d-be17-4595170d37a1","added_by":"auto","created_at":"2024-10-23 06:51:50","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":29450,"visible":true,"origin":"","legend":"\u003cp\u003eNomogram of the optimal model. The probability of ischemic stroke in patients with H-type hypertension. The clinical indicators were placed on each variable axis, and the vertical line was drawn from that value to the top points scale for calculating the score for each predictor. The total scores from each variable value represent the possibility of ischemic stroke in patients with H-type hypertension.\u003c/p\u003e","description":"","filename":"OnlineFigure6.png","url":"https://assets-eu.researchsquare.com/files/rs-5223664/v1/72c9892aaeed5801bd9d4d1a.png"},{"id":73693778,"identity":"2925a9e2-a99e-4ba3-8418-e64d629395e3","added_by":"auto","created_at":"2025-01-13 16:06:07","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1155755,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5223664/v1/603d82a5-57df-4a71-bfd6-b733fc15d51f.pdf"},{"id":67265916,"identity":"1e4306f4-eb16-4ae5-9f1d-683980c400ae","added_by":"auto","created_at":"2024-10-23 06:43:50","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":1693593,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementalMaterial.docx","url":"https://assets-eu.researchsquare.com/files/rs-5223664/v1/1a1708fb05b2b01c1aa5f184.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Biomarker-Based Prediction of Ischemic Stroke in Patients With H-type Hypertension","fulltext":[{"header":"Introduction","content":"\u003cp\u003eDue to its high incidence, recurrence, mortality, and disability rates, stroke continues to be the second leading cause of death worldwide, according to the Global Burden of Disease Study (GBD) 2019\u003csup\u003e1\u003c/sup\u003e. Intravenous thrombolysis and endovascular thrombectomy are the main treatments for ischemic stroke at the moment\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e. A limited therapeutic window presents a significant challenge for nations with insufficient or unbalanced medical resources\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e. Therefore, the best way to lessen the burden of stroke is early prevention.\u003c/p\u003e \u003cp\u003eMore than half of stroke patients worldwide are attributed to hypertension, making it one of the most significant modifiable risk factors for stroke\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e. Additionally, there is a causal relationship between homocysteine (Hcy) concentration and stroke\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e. The prevalence of hyperhomocysteinemia (HHcy) is about 3 / 4 of the hypertension population in China. H-type hypertension is defined as hypertension combined with elevated Hcy level\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e. Surprisingly, vascular damage is worsened by a synergistic effect between Hcy and hypertension\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e. The above suggests that H-type hypertension patients should be focused on monitoring the risk of ischemic stroke.\u003c/p\u003e \u003cp\u003eThe Framingham stroke risk profile (FSP) and CHA2DS2-VASc score are widely used to assess the risk of stroke in general population and and nonvalvular atrial fibrillation (AF) patients\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e,\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e. However, there are few validated tools available for assessing the risk of ischemic stroke in patients with H-type hypertension. Clinicians typically manage high-risk population in the cardiovascular field using a combination of demographics characteristics and medical history, along with some laboratory indicators. By employing this strategy, our study aims to screen out high-risk groups for ischemic stroke, develop and validate a high-performance prediction model for ischemic stroke in patients with H-type hypertension, and facilitate further risk stratification management by clinicians for patients.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eBaseline characteristics\u003c/h2\u003e \u003cp\u003eAccording to the inclusion and exclusion criteria, among the 11,631 patients diagnosed with H-type hypertension at Beijing Anzhen Hospital from January 2022 and December 2023, 4,632 suffered an ischemic stroke. A total of 3,305 had medical records in same hospital between January 2018 and December 2021, and 2,340 were assigned to the training set and 965 to the testing set (Supplementary Fig.\u0026nbsp;1). Another 103 H-type hypertension patients, including 61 patients without ischemic stroke and 42 patients with ischemic stroke, were enrolled as an external validation cohort from the China-Japan Friendship Hospital (Supplementary Fig.\u0026nbsp;2). Detailed information about the characteristics of patients in the total cohort, training, and internal validation sets are shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e and Supplementary Table\u0026nbsp;1, respectively. As shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, patients with ischemic stroke were older with higher SBP and had a higher proportion of smokers and a history of cardiovascular disease (all P\u0026thinsp;\u0026lt;\u0026thinsp;0.05) as compared to non-stroke patients.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eBaseline clinical and biochemical characteristics of all patients\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;3408)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003enon-Stroke\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;1951)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eIschemic stroke\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;1457)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge, years, median (IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e56 (42\u0026ndash;66)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e46 (37\u0026ndash;58)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e65 (57\u0026ndash;74)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale, %\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2435 (71.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1382 (70.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1053 (72.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.358\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI, median (IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e26.26 (24.00-28.98)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e26.84 (24.38\u0026ndash;29.59)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e25.61 (23.44\u0026ndash;28.04)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSBP, mmHg, median (IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e142 (130\u0026ndash;155)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e140 (130\u0026ndash;152)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e145 (132\u0026ndash;159)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDBP, mmHg, median (IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e86 (78\u0026ndash;97)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e90 (80\u0026ndash;100)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e82 (74\u0026ndash;92)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSmoke, %\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1605 (47.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e882 (45.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e723 (49.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.011\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDrink, %\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1482 (43.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e873 (44.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e609 (41.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.086\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiabetes mellitus, %\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1043 (30.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e405 (20.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e638 (43.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHyperlipidemia, %\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2796 (82.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1413 (72.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1383 (94.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCoronary heart disease, %\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e729 (21.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e315 (16.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e414 (28.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAtrial fibrillation, %\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e178 (5.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16 (0.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e162 (11.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAntihypertensive drugs, %\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2287 (67.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1366 (70.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e921 (63.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAntiplatelet drugs, %\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e218 (6.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e123 (6.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e95 (6.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.799\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFamily history of cerebral infarction, %\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e216 (6.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e118 (6.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e98 (6.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.422\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ehs-CRP, mg/L, median (IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.34 (0.65\u0026ndash;3.18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.17 (0.60\u0026ndash;2.65)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.72 (0.78\u0026ndash;4.83)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNa, mmol/L, median (IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e140.6 (139.0-142.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e140.4 (138.9-141.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e140.9 (139.2-142.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eK, mmol/L, median (IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.05 (3.80\u0026ndash;4.27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.13 (3.92\u0026ndash;4.33)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.90 (3.68\u0026ndash;4.15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMg, mmol/L, median (IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.91 (0.86\u0026ndash;0.95)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.92 (0.87\u0026ndash;0.96)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.89 (0.84\u0026ndash;0.94)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTBil, \u0026micro;mol/L, median (IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13.00 (10.05\u0026ndash;16.82)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13.20 (10.52-17.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e12.51 (9.40\u0026ndash;16.60)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDBil, \u0026micro;mol/L, median (IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.20 (3.05\u0026ndash;5.57)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.21 (3.06\u0026ndash;5.47)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.20 (3.03\u0026ndash;5.71)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.202\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUrinary protein, %\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2831 (83.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1804 (92.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1027 (70.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e425 (12.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e95 (4.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e330 (22.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e++\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e108 (3.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e38 (1.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e70 (5.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e+++\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e40 (1.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13 (0.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e27 (1.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHcy, \u0026micro;mol/L, %\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2092 (61.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1260 (64.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e832 (57.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e15\u0026ndash;30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1051 (30.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e541 (27.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e510 (35.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e265 (7.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e150 (7.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e115 (7.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCarotid artery stenosis, %\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e282 (8.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e144 (7.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e138 (9.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.028\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eIQR, interquartile range; BMI, body mass index; SBP, systolic blood pressure; DBP, diastolic blood pressure; hs-CRP, hypersensitive C-reactive protein; Na, Sodium; K, Potassium; Mg, magnesium; TBil, total bilirubin; DBil, direct bilirubin; Hcy, homocysteine.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003ePredictor selections\u003c/h3\u003e\n\u003cp\u003eThere were 16 variables with \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 by univariate logistic regression (Supplementary Table\u0026nbsp;2). After stepwise regression, 13 variables were ultimately retained, namely, age, antihypertensive therapy, hyperlipidemia, atrial fibrillation (AF), diabetes mellitus (DM), BMI, SBP, DBP, hs-CRP, K, Mg, Hcy and proteinuria. In best subset selection regression, when the model included eight variables, the BIC of the model reached its minimum. These eight variables were age, antihypertensive therapy, hyperlipidemia, AF, hs-CRP, K, Mg, and proteinuria, respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA and B). In LASSO regression, 17 variables were selected with a lambda that is within 1 standard error (SE), namely age, gender, antihypertensive therapy, antiplatelet therapy, hyperlipidemia, AF, DM, coronary artery disease (CAD), BMI, SBP, DBP, hs-CRP, K, Mg, Hcy, proteinuria and carotid artery stenosis (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC and D). Eventually, eight variables were included to develop models: age, antihypertensive therapy, hyperlipidemia, AF, hs-CRP, K, Mg, and proteinuria (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \n\u003ch3\u003eModel development and validation\u003c/h3\u003e\n\u003cp\u003eEight variables were entered into a multivariable logistic regression model, linear kernel SVM model, random forest model, and XGBoost model, respectively. Four models yielded the AUC of 0.905 (95% CI: 0.887\u0026ndash;0.924), 0.896 (95% CI: 0.876\u0026ndash;0.915), 0.893 (95% CI: 0.872\u0026ndash;0.914), 0.909 (95% CI: 0.890\u0026ndash;0.927) for the risk of ischemic stroke (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e and Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The difference of AUC between logistic regression model and XGBoost model was not significant (DeLong test, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.406). Based on the maximal Youden\u0026rsquo;s index, the threshold of four models were 55%, 46%, 37%, and 43% in order. The XGBoost model had the highest sensitivity, 0.825, with a specificity of 0.860.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePredict performances of four models on the testing set\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAUC (95%CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSensitivity\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSpecificity\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAccuracy\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003ePPV\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eNPV\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLogistic model\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.905 (0.887\u0026ndash;0.924)\u003csup\u003e#\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.745\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.905\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.833\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.860\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.816\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSVM model\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.896 (0.876\u0026ndash;0.915)\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.778\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.851\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.819\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.806\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.828\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRandom forest model\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.893 (0.872\u0026ndash;0.914)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.820\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.840\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.831\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.803\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.854\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eXGBoost model\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.909 (0.890\u0026ndash;0.927)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.825\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.860\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.845\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.825\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.860\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003e\u003csup\u003e#\u003c/sup\u003e: There was no significant difference in AUC between the logistic model and the XGBoost model by Delong test; \u003csup\u003e*\u003c/sup\u003e: There was no significant difference in AUC between the SVM model and the random forest model by Delong test; AUC: area under curve; CI: confidence interval; PPV: positive predictive value; NPV: negative predictive value; SVM: support vector machine.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eCalibration plots were used to assess the calibration of models. As shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e, four models had a good calibration. Among them, the predicted odds of the outcome of the logistic regression model and XGBoost model were close to the actual probability (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA and D). Four models resulted in a high net benefit, especially the logistic regression model and XGBoost model (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). Conclusively, the logistic regression model and XGBoost model exhibited excellent discrimination and calibration performance. Considering the visualization and scalability of the prediction model, we ultimately chose the logical regression model as the optimal model. The weight coefficients of eight variables in the logistic regression model was shown in Supplementary Fig.\u0026nbsp;3. Serum magnesium, serum potassium, AF, and hyperlipidemia have a higher weight in the optimal model.\u003c/p\u003e \u003cp\u003eIn the external cohort, the logistic regression model achieved an AUC of 0.872 (95% CI: 0.805\u0026ndash;0.939) showing good discrimination capacity (Supplementary Fig.\u0026nbsp;4 and Supplementary Table\u0026nbsp;3). The logistic regression model also was well-calibrated and had a high net benefit in the external cohort (Supplementary Fig.\u0026nbsp;5 and Supplementary Fig.\u0026nbsp;6).\u003c/p\u003e\n\u003ch3\u003eModel Visualization\u003c/h3\u003e\n\u003cp\u003eThe eight variables: age (A), antihypertensive therapy (A), biomarkers (B) (serum magnesium, serum potassium, proteinuria, and hypersensitive C-reactive protein), comorbidities (C) (atrial fibrillation and hyperlipidemia) were fitted a logistic regression model to predict the risk of ischemic stroke in H-type hypertension patients was termed the A\u003csub\u003e2\u003c/sub\u003eBC ischemic stroke model and presented as a nomogram (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). The variables were listed separately, and the cumulative score is matched to a risk score.\u003c/p\u003e "},{"header":"Discussion","content":"\u003cp\u003eBased on two independent retrospective cohorts with a large sample size, our study developed and internally and externally validated a model to predict the risk of ischemic stroke. This model included 8 variables: age (A), antihypertensive therapy (A), biomarkers (B) (serum magnesium, serum potassium, proteinuria, and hypersensitive C-reactive protein), comorbidities (C) (atrial fibrillation and hyperlipidemia), which termed the A\u003csub\u003e2\u003c/sub\u003eBC ischemic stroke model. The A\u003csub\u003e2\u003c/sub\u003eBC ischemic stroke model showed great discrimination and calibration for the risk of ischemic stroke, with similar findings when externally validated.\u003c/p\u003e \u003cp\u003eAt present, the most effective treatment of acute ischemic stroke (AIS) is reperfusion therapy in therapeutic time window, including intravenous thrombolysis (IVT) and endovascular therapy (EVT), but about 3/4 patients present over 4.5 hours after stroke onset or with an unknown time of onset\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e. Besides, there are numerous contraindications associated with IVT that must be carefully considered\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e. The rates of IVT and EVT were 5\u0026middot;64% and 1\u0026middot;45% between 2019 and 2020 in China\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e. Recurrent ischemic stroke is another challenge even with improved secondary prevention, recurrence rates of ischemic stroke seem unchanged over time\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e. Because of above all, primary prevention of high-risk population may be another effective way to improve the burden of ischemic stroke. However, there is an unmet need for accurate and validated models for estimating risk of ischemic stroke.\u003c/p\u003e \u003cp\u003eSome guidelines propose FSP as a reliable tool for 10-year stroke risk estimates\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e. Despite its widespread application, the validity of the FSP has not been sufficiently studied in populations with different age range or ethnicity. A prospective study showed that FSP overestimates the risk of stroke in Chinese\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e. In the same way, both the CV risk calculator and Stroke Riskometer need to be validated and adapted in the Chinese population\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e,\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e. Also, although most risk factors have an independent effect on ischemic stroke, interactions may exist between these factors when considering predicting overall risk. A combined analysis of hypertension and Hcy showed they act additively to increase the risk of stroke\u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e. Therefore, it is necessary to establish a prediction model for ischemic stroke specific to the H-type hypertension subset. One of the purposes of risk assessment is to guide an appropriate primary prevention program. Additional folic acid significantly reduces the risk of first stroke in hypertension patients, compared with antihypertensive therapy alone\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eSerum magnesium, an inorganic ion, is given the most weight in our model, and serum potassium is also significant. Magnesium and potassium are crucial trace elements for organisms, as we all know. Magnesium helps to prevent ischemic stroke. Through various mechanisms, it lowers blood pressure more effectively than potassium\u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e. Inflammation, endothelial dysfunction, and platelet dysfunction have all been linked to low magnesium levels\u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e. Stroke risk was 2.5 times higher for diuretic users with low serum potassium than for those with high serum potassium\u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e. When compared to adults receiving antihypertensive therapy, hypokalemia is independently associated with an increased risk of ischemic stroke and is unrelated to diuretics.\u003c/p\u003e \u003cp\u003eDyslipidemia is an independent risk factor for stroke\u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e. The risk of an ischemic stroke can be decreased by lowering atherogenic lipoproteins\u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e,\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e. New lipid-lowering medications have made it possible to lower LDL-C to extremely low levels, but doing so will raise the risk of hemorrhagic stroke\u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e. A significant risk factor for stroke is AF. A thrombus from the left atrial (LA) cavity, particularly the left atrial appendage (LAA), is primarily responsible for ischemic stroke associated with AF\u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e. Plasma Hcy levels were found to be associated with LA/LAA thrombus and could be used to predict the risk of LA/LAA thrombus in non-valvular AF patients with low CHA2DS2-VASc scores\u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eNumerous studies have demonstrated that hypertension can cause cerebrovascular diseases through a variety of mechanisms, including adapting automatic regulation of cerebral blood flow (CBF) to hypertension\u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e, endothelial dysfunction, reduction of nitric oxide (NO)\u003csup\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e, elevated levels of angiotensin II (Ang II) leading to cerebral artery hypertrophy and inward remodeling\u003csup\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e. Fortunately, the negative effects of hypertension can be offset by a variety of antihypertensive medications. Using long-lasting dihydropyridine-Ca\u003csup\u003e2+\u003c/sup\u003e channel blocker attributes to the normalization of autoregulation of CBF\u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e. Similarly, other types of antihypertensive drugs also have this effect\u003csup\u003e\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e. Additionally, combining antihypertensive medications in suboptimal doses can establish tolerance and effectively treat the remodeling of cerebral arteries brought on by hypertension\u003csup\u003e\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eProteinuria, the other factor in our model, is a common sign of renal damage and has a particularly strong association with stroke\u003csup\u003e\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e,\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u003c/sup\u003e. Researchers have proposed the term \"cerebro-renal interaction\" because kidney disease and cerebrovascular disease are closely related\u003csup\u003e\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003e. The above can be explained by the idea that increased urinary protein excretion rate may be connected to significant vascular damage\u003csup\u003e\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u003c/sup\u003e. According to epidemiological studies, people over 65 account for the majority of stroke cases, and the risk rises with age\u003csup\u003e\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e,\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u003c/sup\u003e. Variety in circulation factors in the systemic environment, cellular senescence, and hypertension during human aging can all increase the risk of stroke\u003csup\u003e\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThe A\u003csub\u003e2\u003c/sub\u003eBC ischemic stroke model consists of 8 general variables, which are simple to collect in clinical practice, there is no need to take into account specialized examination equipment and technical personnel, allowing community hospitals to conduct rapid screening and significantly saving medical resources. Our study had some limitations as well. First, certain H-type hypertension-specific risk factors for ischemic stroke, like MTHFR polymorphism, have not been studied. However, not many community hospitals in China offer MTHFR polymorphism detection services. Second, since our research was a retrospective study and the prediction model created by the machine learning algorithm was just a reflection of mathematical logic, there was no causal relationship. Therefore, even though we have demonstrated the model's good performance on an external validation cohort, more clinical data and prospective queues were required to improve the model's performance in specific clinical application scenarios. Finally, we excluded secondary hypertension in patients with H-type hypertension, as the causes of secondary hypertension are diverse, and the predictive factors are complex. Therefore, our model cannot be used for secondary hypertension patients with elevated Hcy levels.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eStudy population\u003c/h2\u003e \u003cp\u003eThis retrospective cohort study consecutively included inpatients diagnosed with H-type hypertension, whether or not they suffered first ischemic stroke, at Beijing Anzhen Hospital, Capital Medical University from January 2022 to December 2023. Patients with secondary hypertension or a history of ischemic stroke would be excluded. These patients would also be excluded if they lack data in Beijing Anzhen Hospital from January 2018 to December 2021. Meanwhile, we extracted an external validation cohort from the China-Japan Friendship Hospital between January 2023 and June 2023.\u003c/p\u003e \u003cp\u003ePatients with hypertension were diagnosed according to the International Society of Hypertension recommendations\u003csup\u003e\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u003c/sup\u003e. Systolic blood pressure (SBP) in the office or clinic was \u0026ge;\u0026thinsp;140 mmHg and/or diastolic blood pressure (DBP) was \u0026ge;\u0026thinsp;90 mmHg following repeated examinations were considered as hypertension. In addition, the guideline also suggested that blood pressure\u0026thinsp;\u0026lt;\u0026thinsp;140 / 90 mmHg in patients with a history of hypertension and currently using antihypertensive drugs were still diagnosed as hypertension. Patients with essential hypertension were identified when secondary hypertension was excluded. Hypertension patients, together with serum Hcy concentrations\u0026thinsp;\u0026ge;\u0026thinsp;10 \u0026micro;mol/L, were identified as H-type hypertension\u003csup\u003e\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e\u003c/sup\u003e. Ischemic stroke was confirmed via computed tomography (CT) or brain magnetic resonance imaging (MRI) combined with clinical symptoms and signs. Our study was conducted according to the Declaration of Helsinki and was approved by the hospital\u0026rsquo;s ethical review board (Beijing Anzhen Hospital, Capital Medical University, Beijing, China). The need to obtain informed consent was waived by the hospital\u0026rsquo;s ethical review board.\u003c/p\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003ch2\u003eAdditional Information\u003c/h2\u003e \u003cp\u003eThe authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eStudy concept and design: Hui Yuan, Ke Chen. Data collection: Ke Chen, Jianxun He, Lan Fu, Xiaohua Song, Ning Cao. Data analysis and interpretation: Ke Chen, Jianxun He, Lan Fu, Xiaohua Song, Ning Cao. Drafting of the manuscript: Ke Chen, Hui Yuan. Critical revision of the manuscript: all authors. Final approval: all authors.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eThis work was supported by the National Key Research and Development Program (2022YFC2009600) (2022YFC2009602).\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eSome or all data sets generated and/or analyzed during the present study are not publicly available but are available from the corresponding author upon reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eCollaborators, G. B. D. S. 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Atheroscler Thromb.\u003c/em\u003e \u003cb\u003e29\u003c/b\u003e, 492\u0026ndash;501. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org:10.5551/jat.61275\u003c/span\u003e\u003cspan address=\"https://doi.org:10.5551/jat.61275\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2022).\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":true,"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":"H-type hypertension, ischemic stroke, predicted model","lastPublishedDoi":"10.21203/rs.3.rs-5223664/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5223664/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eHypertension combined with hyperhomocysteinemia significantly raises the risk of ischemic stroke. Our study aimed to develop and validate a biomarker-based prediction model for ischemic stroke in H-type hypertension patients. We retrospectively included 3,305 patients in the development cohort, and externally validated in 103 patients from another cohort. Logistic regression, LASSO regression, and best subset selection analysis were used to assess the contribution of variables to ischemic stroke, and models were derived using four machine learning algorithms. Area Under Curve (AUC), calibration plot and decision-curve analysis (DCA) respectively evaluated the discrimination and calibration of four models, then external validation and visualization of the best-performing model. There were 1,415 and 42 patients with ischemic stroke in the development and validation cohorts. The final model included 8 predictors: age, antihypertensive therapy, biomarkers (serum magnesium, serum potassium, proteinuria and hypersensitive C-reactive protein), and comorbidities (atrial fibrillation and hyperlipidemia). The optimal model, named A\u003csub\u003e2\u003c/sub\u003eBC ischemic stroke model, showed good discrimination and calibration ability for ischemic stroke with AUC of 0.91 and 0.87 in the internal and external validation cohorts. The A\u003csub\u003e2\u003c/sub\u003eBC ischemic stroke model had satisfactory predictive performances to assist clinicians in accurately identifying the risk of ischemic stroke for patients with H-type hypertension.\u003c/p\u003e","manuscriptTitle":"Biomarker-Based Prediction of Ischemic Stroke in Patients With H-type Hypertension","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-10-23 06:35:45","doi":"10.21203/rs.3.rs-5223664/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-11-21T08:10:16+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-11-20T04:33:29+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-10-30T10:49:01+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"278824689091580763185089323305752590749","date":"2024-10-29T18:33:51+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"10339784112629053518904135029687047492","date":"2024-10-29T14:10:39+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-10-29T13:37:39+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-10-29T13:08:57+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2024-10-11T11:32:45+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-10-11T06:56:24+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2024-10-08T08:44:16+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":"17d32098-b0e5-46ef-a41f-98bf37a148ba","owner":[],"postedDate":"October 23rd, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":38830771,"name":"Health sciences/Diseases/Cardiovascular diseases"},{"id":38830772,"name":"Biological sciences/Biochemistry"}],"tags":[],"updatedAt":"2025-01-13T15:58:55+00:00","versionOfRecord":{"articleIdentity":"rs-5223664","link":"https://doi.org/10.1038/s41598-024-83662-3","journal":{"identity":"scientific-reports","isVorOnly":false,"title":"Scientific Reports"},"publishedOn":"2025-01-07 15:56:56","publishedOnDateReadable":"January 7th, 2025"},"versionCreatedAt":"2024-10-23 06:35:45","video":"","vorDoi":"10.1038/s41598-024-83662-3","vorDoiUrl":"https://doi.org/10.1038/s41598-024-83662-3","workflowStages":[]},"version":"v1","identity":"rs-5223664","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5223664","identity":"rs-5223664","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}